diff --git a/.Rhistory b/.Rhistory
index 5bb5b6edfd0467a2548107e48d9335f20e7bf051..d0bc336f7bb93c2ded73cb353d9573672bdd2351 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,14 +1,11 @@
-}
-test_tab <- tab2[1:20,]
-tab3 <- mark_prop(test_tab,cross="F2",pval=0.05)
-mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
-#calculate total number of individuals genotyped for each marker
 tab <- tab %>% mutate(n_geno = (n_HM1 + n_HM2 + n_HT))
 #calculate proportion of each genotype
 tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
 tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
 tab <- tab %>% mutate(p_HT = n_HT/n_geno)
 tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
+#stock colnames to join
+names <- colnames(tab)
 #stop if cross != "F2" or "N2"
 if(!cross %in% c("F2","N2")){
 stop("Cross must be F2 or N2")
@@ -21,7 +18,7 @@ stop("Arguments homo and hetero or argument pval must be specified")
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 T ~ 0))
-#stop with prop of homo/hetero
+#exclude with prop of homo/hetero
 if(is.na(pval)==TRUE){
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
@@ -30,34 +27,22 @@ cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1,
 T ~ exclude_prop
 ))
 }
-#stop with pval chisq.test
+#exclude with pval chisq.test
+## NEED TO ADD THIS FILTER IF CROSS = N2
 if(is.na(pval)==FALSE){
 tab <- tab %>% filter(p_NA != 1) %>% rowwise() %>%
 mutate(.,chi_pval = tibble(n_HM1,n_HM2,n_HT) %>%
 chisq.test(p=c(0.25,0.25,0.5)) %>% .$p.value) %>%
-full_join(.,tab)
+full_join(.,tab,by=all_of(names))
+tab <- tab %>% mutate(exclude_prop=case_when(chi_pval < pval ~ 1,
+T ~ exclude_prop))
 }
 print(tab)
-# #stop if no value for "homo" for F2 cross
-# if(cross=="F2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for F2 crosses")
-# }
-#
-# #stop if no value for "homo" and "hetero" for N2 cross
-# if(cross=="N2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for N2 crosses")
-# }
-# #exclude markers according to proportion of na
-# tab <- tab %>% mutate(exclude_prop=case_when(p_NA > na ~ 1, #exclude markers according to proportion of na
-#                                              cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous individuals for F2 cross
-#                                              cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous and heterozygous individuals for N2 cross
-#                                              T ~ 0))
-# tab <- tab %>% select(-c(p_HM1,p_HM2,p_HT,p_NA,n_geno))
-# return(tab)
-}
-test_tab <- tab2[1:20,]
-tab3 <- mark_prop(test_tab,cross="F2",pval=0.05)
-View(tab3)
+tab <- tab %>% select(all_of(names))
+print(tab)
+}
+tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
+head(tab2)
 mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
 #calculate total number of individuals genotyped for each marker
 tab <- tab %>% mutate(n_geno = (n_HM1 + n_HM2 + n_HT))
@@ -66,6 +51,8 @@ tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
 tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
 tab <- tab %>% mutate(p_HT = n_HT/n_geno)
 tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
+#stock colnames to join
+names <- colnames(tab)
 #stop if cross != "F2" or "N2"
 if(!cross %in% c("F2","N2")){
 stop("Cross must be F2 or N2")
@@ -78,7 +65,7 @@ stop("Arguments homo and hetero or argument pval must be specified")
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 T ~ 0))
-#stop with prop of homo/hetero
+#exclude with prop of homo/hetero
 if(is.na(pval)==TRUE){
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
@@ -87,43 +74,26 @@ cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1,
 T ~ exclude_prop
 ))
 }
-#stop with pval chisq.test
+#exclude with pval chisq.test
+## NEED TO ADD THIS FILTER IF CROSS = N2
 if(is.na(pval)==FALSE){
-#stock colnames to join
-names <- colnames(tab)
 tab <- tab %>% filter(p_NA != 1) %>% rowwise() %>%
 mutate(.,chi_pval = tibble(n_HM1,n_HM2,n_HT) %>%
 chisq.test(p=c(0.25,0.25,0.5)) %>% .$p.value) %>%
-full_join(.,tab,by=names)
+full_join(.,tab,by=all_of(names))
+tab <- tab %>% mutate(exclude_prop=case_when(chi_pval < pval ~ 1,
+T ~ exclude_prop))
 }
 print(tab)
-# #stop if no value for "homo" for F2 cross
-# if(cross=="F2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for F2 crosses")
-# }
-#
-# #stop if no value for "homo" and "hetero" for N2 cross
-# if(cross=="N2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for N2 crosses")
-# }
-# #exclude markers according to proportion of na
-# tab <- tab %>% mutate(exclude_prop=case_when(p_NA > na ~ 1, #exclude markers according to proportion of na
-#                                              cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous individuals for F2 cross
-#                                              cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous and heterozygous individuals for N2 cross
-#                                              T ~ 0))
-# tab <- tab %>% select(-c(p_HM1,p_HM2,p_HT,p_NA,n_geno))
-# return(tab)
-}
-test_tab <- tab2[1:20,]
-tab3 <- mark_prop(test_tab,cross="F2",pval=0.05)
+tab <- tab %>% select(all_of(names),exclude_prop)
+print(tab)
+}
+tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
 mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
 #calculate total number of individuals genotyped for each marker
 tab <- tab %>% mutate(n_geno = (n_HM1 + n_HM2 + n_HT))
-#calculate proportion of each genotype
-tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
-tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
-tab <- tab %>% mutate(p_HT = n_HT/n_geno)
-tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
+#stock colnames to join
+names <- colnames(tab)
 #stop if cross != "F2" or "N2"
 if(!cross %in% c("F2","N2")){
 stop("Cross must be F2 or N2")
@@ -136,8 +106,13 @@ stop("Arguments homo and hetero or argument pval must be specified")
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 T ~ 0))
-#stop with prop of homo/hetero
+#exclude with prop of homo/hetero
 if(is.na(pval)==TRUE){
+#calculate proportion of each genotype
+tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
+tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
+tab <- tab %>% mutate(p_HT = n_HT/n_geno)
+tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1,
@@ -145,43 +120,27 @@ cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1,
 T ~ exclude_prop
 ))
 }
-#stop with pval chisq.test
+#exclude with pval chisq.test
+## NEED TO ADD THIS FILTER IF CROSS = N2
 if(is.na(pval)==FALSE){
-#stock colnames to join
-names <- colnames(tab)
 tab <- tab %>% filter(p_NA != 1) %>% rowwise() %>%
 mutate(.,chi_pval = tibble(n_HM1,n_HM2,n_HT) %>%
 chisq.test(p=c(0.25,0.25,0.5)) %>% .$p.value) %>%
-full_join(.,tab,by=names)
+full_join(.,tab,by=all_of(names))
+tab <- tab %>% mutate(exclude_prop=case_when(chi_pval < pval ~ 1,
+T ~ exclude_prop))
 }
 print(tab)
-# #stop if no value for "homo" for F2 cross
-# if(cross=="F2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for F2 crosses")
-# }
-#
-# #stop if no value for "homo" and "hetero" for N2 cross
-# if(cross=="N2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for N2 crosses")
-# }
-# #exclude markers according to proportion of na
-# tab <- tab %>% mutate(exclude_prop=case_when(p_NA > na ~ 1, #exclude markers according to proportion of na
-#                                              cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous individuals for F2 cross
-#                                              cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous and heterozygous individuals for N2 cross
-#                                              T ~ 0))
-tab <- tab %>% select(-c(p_HM1,p_HM2,p_HT,p_NA,n_geno))
-return(tab)
-}
-test_tab <- tab2[1:20,]
-tab3 <- mark_prop(test_tab,cross="F2",pval=0.05)
+tab <- tab %>% select(all_of(names),exclude_prop)
+print(tab)
+}
+tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
 mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
 #calculate total number of individuals genotyped for each marker
 tab <- tab %>% mutate(n_geno = (n_HM1 + n_HM2 + n_HT))
-#calculate proportion of each genotype
-tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
-tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
-tab <- tab %>% mutate(p_HT = n_HT/n_geno)
-tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
+#stock colnames to join
+names <- colnames(tab)
+print(names)
 #stop if cross != "F2" or "N2"
 if(!cross %in% c("F2","N2")){
 stop("Cross must be F2 or N2")
@@ -194,8 +153,13 @@ stop("Arguments homo and hetero or argument pval must be specified")
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 T ~ 0))
-#stop with prop of homo/hetero
+#exclude with prop of homo/hetero
 if(is.na(pval)==TRUE){
+#calculate proportion of each genotype
+tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
+tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
+tab <- tab %>% mutate(p_HT = n_HT/n_geno)
+tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1,
@@ -203,43 +167,31 @@ cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1,
 T ~ exclude_prop
 ))
 }
-#stop with pval chisq.test
+#exclude with pval chisq.test
+## NEED TO ADD THIS FILTER IF CROSS = N2
 if(is.na(pval)==FALSE){
-#stock colnames to join
-names <- colnames(tab)
 tab <- tab %>% filter(p_NA != 1) %>% rowwise() %>%
 mutate(.,chi_pval = tibble(n_HM1,n_HM2,n_HT) %>%
 chisq.test(p=c(0.25,0.25,0.5)) %>% .$p.value) %>%
-full_join(.,tab,by=names)
-}
-# #stop if no value for "homo" for F2 cross
-# if(cross=="F2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for F2 crosses")
-# }
-#
-# #stop if no value for "homo" and "hetero" for N2 cross
-# if(cross=="N2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for N2 crosses")
-# }
-# #exclude markers according to proportion of na
-# tab <- tab %>% mutate(exclude_prop=case_when(p_NA > na ~ 1, #exclude markers according to proportion of na
-#                                              cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous individuals for F2 cross
-#                                              cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous and heterozygous individuals for N2 cross
-#                                              T ~ 0))
-tab <- tab %>% select(-c(p_HM1,p_HM2,p_HT,p_NA,n_geno))
-return(tab)
-}
-test_tab <- tab2[1:20,]
-tab3 <- mark_prop(test_tab,cross="F2",pval=0.05)
-tab3 <- mark_prop(tab2,cross="F2",pval=0.05)
+full_join(.,tab,by=all_of(names))
+tab <- tab %>% mutate(exclude_prop=case_when(chi_pval < pval ~ 1,
+T ~ exclude_prop))
+}
+print(tab)
+tab <- tab %>% select(all_of(names),exclude_prop)
+print(tab)
+}
+tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
+tab2 <- mark_match(stuart_tab,ref=strains)
+tab2 %>% filter(exclude_match==1)
+tab2 <- mark_poly(tab2)
+head(tab2)
 mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
 #calculate total number of individuals genotyped for each marker
 tab <- tab %>% mutate(n_geno = (n_HM1 + n_HM2 + n_HT))
-#calculate proportion of each genotype
-tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
-tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
-tab <- tab %>% mutate(p_HT = n_HT/n_geno)
-tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
+#stock colnames to join
+names <- colnames(tab)
+print(names)
 #stop if cross != "F2" or "N2"
 if(!cross %in% c("F2","N2")){
 stop("Cross must be F2 or N2")
@@ -252,8 +204,13 @@ stop("Arguments homo and hetero or argument pval must be specified")
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 T ~ 0))
-#stop with prop of homo/hetero
+#exclude with prop of homo/hetero
 if(is.na(pval)==TRUE){
+#calculate proportion of each genotype
+tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
+tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
+tab <- tab %>% mutate(p_HT = n_HT/n_geno)
+tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1,
@@ -261,43 +218,27 @@ cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1,
 T ~ exclude_prop
 ))
 }
-#stop with pval chisq.test
+#exclude with pval chisq.test
+## NEED TO ADD THIS FILTER IF CROSS = N2
 if(is.na(pval)==FALSE){
-#stock colnames to join
-names <- colnames(tab)
 tab <- tab %>% filter(p_NA != 1) %>% rowwise() %>%
 mutate(.,chi_pval = tibble(n_HM1,n_HM2,n_HT) %>%
 chisq.test(p=c(0.25,0.25,0.5)) %>% .$p.value) %>%
-full_join(.,tab,by=names)
+full_join(.,tab,by=all_of(names))
 tab <- tab %>% mutate(exclude_prop=case_when(chi_pval < pval ~ 1,
 T ~ exclude_prop))
 }
-# #stop if no value for "homo" for F2 cross
-# if(cross=="F2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for F2 crosses")
-# }
-#
-# #stop if no value for "homo" and "hetero" for N2 cross
-# if(cross=="N2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for N2 crosses")
-# }
-# #exclude markers according to proportion of na
-# tab <- tab %>% mutate(exclude_prop=case_when(p_NA > na ~ 1, #exclude markers according to proportion of na
-#                                              cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous individuals for F2 cross
-#                                              cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous and heterozygous individuals for N2 cross
-#                                              T ~ 0))
-tab <- tab %>% select(-c(p_HM1,p_HM2,p_HT,p_NA,n_geno))
-return(tab)
-}
-tab3 <- mark_prop(tab2,cross="F2",pval=0.05)
+print(tab)
+tab <- tab %>% select(all_of(names),exclude_prop)
+print(tab)
+}
+tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
 mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
 #calculate total number of individuals genotyped for each marker
 tab <- tab %>% mutate(n_geno = (n_HM1 + n_HM2 + n_HT))
-#calculate proportion of each genotype
-tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
-tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
-tab <- tab %>% mutate(p_HT = n_HT/n_geno)
-tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
+#stock colnames to join
+names <- colnames(tab)
+print(names)
 #stop if cross != "F2" or "N2"
 if(!cross %in% c("F2","N2")){
 stop("Cross must be F2 or N2")
@@ -307,11 +248,17 @@ if((is.na(homo)==TRUE | is.na(hetero)==TRUE) & is.na(pval)==TRUE){
 stop("Arguments homo and hetero or argument pval must be specified")
 }
 #stop with prop of na
+#calculate proportion
+tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 T ~ 0))
-#stop with prop of homo/hetero
+#exclude with prop of homo/hetero
 if(is.na(pval)==TRUE){
+#calculate proportion of each genotype
+tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
+tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
+tab <- tab %>% mutate(p_HT = n_HT/n_geno)
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1,
@@ -319,43 +266,27 @@ cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1,
 T ~ exclude_prop
 ))
 }
-#stop with pval chisq.test
+#exclude with pval chisq.test
+## NEED TO ADD THIS FILTER IF CROSS = N2
 if(is.na(pval)==FALSE){
-#stock colnames to join
-names <- colnames(tab)
 tab <- tab %>% filter(p_NA != 1) %>% rowwise() %>%
 mutate(.,chi_pval = tibble(n_HM1,n_HM2,n_HT) %>%
 chisq.test(p=c(0.25,0.25,0.5)) %>% .$p.value) %>%
-full_join(.,tab,by=names)
+full_join(.,tab,by=all_of(names))
 tab <- tab %>% mutate(exclude_prop=case_when(chi_pval < pval ~ 1,
 T ~ exclude_prop))
 }
-# #stop if no value for "homo" for F2 cross
-# if(cross=="F2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for F2 crosses")
-# }
-#
-# #stop if no value for "homo" and "hetero" for N2 cross
-# if(cross=="N2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-#   stop("Arguments homo and hetero must be specified for N2 crosses")
-# }
-# #exclude markers according to proportion of na
-# tab <- tab %>% mutate(exclude_prop=case_when(p_NA > na ~ 1, #exclude markers according to proportion of na
-#                                              cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous individuals for F2 cross
-#                                              cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous and heterozygous individuals for N2 cross
-#                                              T ~ 0))
-tab <- tab %>% select(-c(p_HM1,p_HM2,p_HT,p_NA,n_geno,chi_pval))
-return(tab)
-}
-tab3 <- mark_prop(tab2,cross="F2",pval=0.05)
+print(tab)
+tab <- tab %>% select(all_of(names),exclude_prop)
+print(tab)
+}
+tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
 mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
+#stock colnames to join
+names <- colnames(tab)
+print(names)
 #calculate total number of individuals genotyped for each marker
 tab <- tab %>% mutate(n_geno = (n_HM1 + n_HM2 + n_HT))
-#calculate proportion of each genotype
-tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
-tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
-tab <- tab %>% mutate(p_HT = n_HT/n_geno)
-tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
 #stop if cross != "F2" or "N2"
 if(!cross %in% c("F2","N2")){
 stop("Cross must be F2 or N2")
@@ -365,11 +296,17 @@ if((is.na(homo)==TRUE | is.na(hetero)==TRUE) & is.na(pval)==TRUE){
 stop("Arguments homo and hetero or argument pval must be specified")
 }
 #stop with prop of na
+#calculate proportion
+tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 T ~ 0))
-#stop with prop of homo/hetero
+#exclude with prop of homo/hetero
 if(is.na(pval)==TRUE){
+#calculate proportion of each genotype
+tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
+tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
+tab <- tab %>% mutate(p_HT = n_HT/n_geno)
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1,
@@ -377,26 +314,30 @@ cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1,
 T ~ exclude_prop
 ))
 }
-#stop with pval chisq.test
+#exclude with pval chisq.test
+## NEED TO ADD THIS FILTER IF CROSS = N2
 if(is.na(pval)==FALSE){
-#stock colnames to join
-names <- colnames(tab)
 tab <- tab %>% filter(p_NA != 1) %>% rowwise() %>%
 mutate(.,chi_pval = tibble(n_HM1,n_HM2,n_HT) %>%
 chisq.test(p=c(0.25,0.25,0.5)) %>% .$p.value) %>%
-full_join(.,tab,by=names)
+full_join(.,tab,by=all_of(names))
 tab <- tab %>% mutate(exclude_prop=case_when(chi_pval < pval ~ 1,
 T ~ exclude_prop))
 }
-T ~ 0))
+print(tab)
+tab <- tab %>% select(all_of(names),exclude_prop)
+print(tab)
+}
+tab2 <- mark_match(stuart_tab,ref=strains)
+tab2 %>% filter(exclude_match==1)
+tab2 <- mark_poly(tab2)
+head(tab2)
 mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
+#stock colnames to join
+names <- colnames(tab)
+print(names)
 #calculate total number of individuals genotyped for each marker
 tab <- tab %>% mutate(n_geno = (n_HM1 + n_HM2 + n_HT))
-#calculate proportion of each genotype
-tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
-tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
-tab <- tab %>% mutate(p_HT = n_HT/n_geno)
-tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
 #stop if cross != "F2" or "N2"
 if(!cross %in% c("F2","N2")){
 stop("Cross must be F2 or N2")
@@ -406,11 +347,17 @@ if((is.na(homo)==TRUE | is.na(hetero)==TRUE) & is.na(pval)==TRUE){
 stop("Arguments homo and hetero or argument pval must be specified")
 }
 #stop with prop of na
+#calculate proportion
+tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 T ~ 0))
-#stop with prop of homo/hetero
+#exclude with prop of homo/hetero
 if(is.na(pval)==TRUE){
+#calculate proportion of each genotype
+tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
+tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
+tab <- tab %>% mutate(p_HT = n_HT/n_geno)
 tab <- tab %>%
 mutate(exclude_prop=case_when(p_NA > na ~ 1,
 cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1,
@@ -418,21 +365,22 @@ cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1,
 T ~ exclude_prop
 ))
 }
-#stop with pval chisq.test
+#exclude with pval chisq.test
+## NEED TO ADD THIS FILTER IF CROSS = N2
 if(is.na(pval)==FALSE){
-#stock colnames to join
-names <- colnames(tab)
 tab <- tab %>% filter(p_NA != 1) %>% rowwise() %>%
 mutate(.,chi_pval = tibble(n_HM1,n_HM2,n_HT) %>%
 chisq.test(p=c(0.25,0.25,0.5)) %>% .$p.value) %>%
-full_join(.,tab,by=names)
+full_join(.,tab,by=all_of(names))
 tab <- tab %>% mutate(exclude_prop=case_when(chi_pval < pval ~ 1,
 T ~ exclude_prop))
 }
-tab <- tab %>% select(-c(p_HM1,p_HM2,p_HT,p_NA,n_geno,chi_pval))
-return(tab)
+print(tab)
+tab <- tab %>% select(all_of(names),exclude_prop)
+print(tab)
 }
-tab2 <- mark_prop(tab2,cross="F2",pval=0.05)
+tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
+devtools::build(path=".",vignettes=FALSE)
 knitr::opts_chunk$set(
 collapse = TRUE,
 comment = "#>"
@@ -453,20 +401,40 @@ tab2 <- mark_match(stuart_tab,ref=strains)
 tab2 %>% filter(exclude_match==1)
 tab2 <- mark_poly(tab2)
 head(tab2)
-tab2 <- mark_prop(tab2,cross="F2",pval=0.05)
-devtools::build(path=".",vignettes = FALSE)
-devtools::build_vignettes()
-devtools::build_vignettes()
-devtools::build(path=".",vignettes = FALSE)
-devtools::build_vignettes()
-devtools::build(path=".",vignettes = FALSE)
-devtools::build_vignettes()
+tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
+head(tab2)
+knitr::opts_chunk$set(
+collapse = TRUE,
+comment = "#>"
+)
 library(dplyr)
 library(stuart)
+annot_mini <- read.csv(url("https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv"))
+data(genos)
+summary(genos)
+data(phenos)
+summary(phenos)
+strains <- geno_strains(ref=annot_mini,geno=genos,par1=c("StrainsA_1","StrainsA_2"),par2=c("StrainsB_1","StrainsB_2"),name1="parent1",name2="parent2")
+head(strains)
+genos <- genos %>% filter(!Sample.ID %in% c("StrainsA_1", "StrainsA_2", "StrainsB_1","StrainsB_2"))
+data(stuart_tab)
+summary(stuart_tab)
+View(stuart_tab)
+tab2 <- mark_match(stuart_tab,ref=strains)
+tab2 %>% filter(exclude_match==1)
+View(tab2)
+tab2 <- mark_poly(tab2)
+head(tab2)
+View(tab2)
+tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
+View(tab2)
+library(stuart)
 knitr::opts_chunk$set(
 collapse = TRUE,
 comment = "#>"
 )
+library(dplyr)
+library(stuart)
 annot_mini <- read.csv(url("https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv"))
 data(genos)
 summary(genos)
@@ -483,30 +451,62 @@ tab2 <- mark_poly(tab2)
 head(tab2)
 tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
 head(tab2)
+View(tab2)
 tab2 <- mark_allele(tab=tab2,ref=strains,par1="parent1",par2="parent2")
 tab2 %>% arrange(desc(exclude_allele)) %>% head()
 strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354","gUNC15530876","gUNC21555204","gUNC21596600")) %>% arrange(marker) %>% select(marker,parent1,parent2)
+View(tab2)
+strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354","gUNC15530876","gUNC21555204","gUNC21596600")) %>% arrange(marker) %>% select(marker,parent1,parent2)
 rqtl_file <- write_rqtl(geno=genos,pheno=phenos,tab=tab2,ref=strains,par1="parent1",par2="parent2",prefix="ind_",pos="cM_cox")
 rqtl_file[1:10,1:7]
 devtools::build_vignettes()
+devtools::build_manual()
+build_manual
+devtools::build_manual
+Rd2pdf
+tools::texi2pdf
+devtools::build_manual()
+devtools::build(path=".",vignettes=FALSE,manual=TRUE)
+devtools::build_manual()
+Rd2pdf()
+Rd2pdf
+install.packages("RdUtils")
+RdUtils
+RdUtils()
+system("R CMD Rd2pdf stuart")
+system("R CMD Rd2pdf stuaRt")
+getwd()
+cd ..
+setwd("~/Documents/stuart_package/stuart")
+setwd("~/Documents/stuart_package/stuart")
+setwd("~/Documents/stuart_package")
+system("R CMD Rd2pdf stuaRt")
+system("R CMD Rd2pdf stuart")
+system("R CMD Rd2pdf stuart")
+setwd("~/Documents/stuart_package/stuart")
+devtools::build_manual()
+getwd()
+devtools::build_manual(path=".")
+devtools::build_manual(path=".")
+View(genos)
+devtools::build(path=".",vignettes=FALSE)
 devtools::build_vignettes()
-devtools::build_vignettes()
-devtools::build_vignettes()
-devtools::build_vignettes()
-devtools::build_vignettes()
-demo()
-install.packages("devtools")
-install.packages("usethat")
-install.packages("raportools")
-install.packages("rapportools")
-install.packages("roxygen2")
-install.packages("testthat")
-library(stuart)
-library(dplyr)
-library(stuart)
-knitr::opts_chunk$set(
-collapse = TRUE,
-comment = "#>"
-)
+devtools::build_manual(path=".")
+devtools::build_manual(path=".")
+devtools::build(path=".",vignettes=FALSE)
+devtools::build_manual(path=".")
+roxygen2::roxygenise()
+devtools::build_manual(path=".")
 library(dplyr)
 library(stuart)
+test <- tibble(x=c("A","T"),y=c("A","A"),z=c("A","A"))
+View(test)
+View(test)
+View(test)
+View(test)
+View(rqtl_file)
+View(annot_mini)
+View(genos)
+View(genos)
+View(genos)
+View(test)
diff --git a/.Rproj.user/shared/notebooks/paths b/.Rproj.user/shared/notebooks/paths
index 8274802bed17cf345bd9ae5194f830b2d0379fe6..f7981f85816e8bfeabadadc7943a9d617abaeca3 100644
--- a/.Rproj.user/shared/notebooks/paths
+++ b/.Rproj.user/shared/notebooks/paths
@@ -1,5 +1,18 @@
+/home/marie/Documents/stuart_package/stuart/.Rbuildignore="739A4511"
+/home/marie/Documents/stuart_package/stuart/.Rhistory="77548AEE"
+/home/marie/Documents/stuart_package/stuart/.gitignore="05FA27A5"
+/home/marie/Documents/stuart_package/stuart/DESCRIPTION="E286725B"
+/home/marie/Documents/stuart_package/stuart/R/geno_strains.R="DCEFD103"
+/home/marie/Documents/stuart_package/stuart/R/genos-data.R="33820B87"
+/home/marie/Documents/stuart_package/stuart/R/mark_allele.R="64630E28"
+/home/marie/Documents/stuart_package/stuart/R/mark_match.R="CBA3F514"
+/home/marie/Documents/stuart_package/stuart/R/mark_poly.R="63E868BF"
 /home/marie/Documents/stuart_package/stuart/R/mark_prop.R="19C6446D"
+/home/marie/Documents/stuart_package/stuart/R/phenos-data.R="75BCF577"
+/home/marie/Documents/stuart_package/stuart/R/stuart_tab-data.R="9C18AF59"
+/home/marie/Documents/stuart_package/stuart/R/tab_mark.R="60E85DC0"
 /home/marie/Documents/stuart_package/stuart/R/write_rqtl.R="D25FAC55"
 /home/marie/Documents/stuart_package/stuart/README.Rmd="C395B1B3"
 /home/marie/Documents/stuart_package/stuart/README.md="8BBA9900"
+/home/marie/Documents/stuart_package/stuart/man/genos.Rd="383A8DC0"
 /home/marie/Documents/stuart_package/stuart/vignettes/stuaRt.Rmd="007031F6"
diff --git a/R/geno_strains.R b/R/geno_strains.R
index d4104d0f3af4780745e4939ce649d7e90e8c995e..3ebdc122afa068df6278a01e843b77cf66df0fa5 100755
--- a/R/geno_strains.R
+++ b/R/geno_strains.R
@@ -34,16 +34,18 @@ geno_strains <- function(ref,geno,par1,par2,name1,name2){
 
 
   #create consensus
+  geno <- geno %>% mutate(parent1 = !!sym(par1[1]))
   if(length(par1)!=1){
-    geno <- geno %>% mutate(parent1 = ifelse(!!sym(par1[1])==!!sym(par1[2]),!!sym(par1[1]),"N"))
-  } else {
-    geno <- geno %>% rename(parent1=!!sym(par1[1]))
+    for(i in 2:length(par1)){
+      geno <- geno %>% mutate(parent1 = ifelse(parent1==!!sym(par1[i]),parent1,"N"))
+    }
   }
 
+  geno <- geno %>% mutate(parent2 = !!sym(par2[1]))
   if(length(par2)!=1){
-    geno <- geno %>% mutate(parent2 = ifelse(!!sym(par2[1])==!!sym(par2[2]),!!sym(par2[1]),"N"))
-  } else {
-    geno <- geno %>% rename(parent2=!!sym(par2[1]))
+    for(i in 2:length(par2)){
+      geno <- geno %>% mutate(parent2 = ifelse(parent2==!!sym(par2[i]),parent2,"N"))
+    }
   }
 
   geno <- geno %>% select(marker,parent1,parent2)
diff --git a/R/mark_allele.R b/R/mark_allele.R
index 1646ed5b30ec06af760e4cc03ae537448fb46770..83f4a70e835e097ce9c54f0e256860c043a4fabe 100755
--- a/R/mark_allele.R
+++ b/R/mark_allele.R
@@ -24,7 +24,6 @@ mark_allele <- function(tab,ref,par1,par2,parNH=TRUE){
   ref <- ref %>% select(marker,!!sym(par1),!!sym(par2))
   tab <- full_join(tab,ref,by=c("marker"="marker"))
 
-  print(parNH)
   #function core
   tab <- tab %>% mutate(exclude_allele = case_when(parNH==FALSE &
                                                      (!!sym(par1) == "N" | !!sym(par2) == "N" | !!sym(par1) == "H" | !!sym(par2) == "H") ~ 1,
@@ -43,8 +42,6 @@ mark_allele <- function(tab,ref,par1,par2,parNH=TRUE){
                                                    T ~ 0)
   )
 
-  print(tab)
-
   tab <- tab %>% select(-c(!!sym(par1),!!sym(par2)))
 
   return(tab)
diff --git a/R/mark_prop.R b/R/mark_prop.R
index 499ed92b1ca36c27647d2e77a5ec14b0195be7ec..3b0ff5d96bf7ef30104d589b40786ff2825f24ab 100755
--- a/R/mark_prop.R
+++ b/R/mark_prop.R
@@ -17,7 +17,6 @@
 mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
   #stock colnames to join
   names <- colnames(tab)
-  print(names)
 
   #calculate total number of individuals genotyped for each marker
   tab <- tab %>% mutate(n_geno = (n_HM1 + n_HM2 + n_HT))
@@ -71,7 +70,5 @@ mark_prop <- function(tab,cross,homo=NA,hetero=NA,pval=NA,na=0.5){
     tab <- tab %>% mutate(exclude_prop=case_when(chi_pval < pval ~ 1,
                                                  T ~ exclude_prop))
   }
-  print(tab)
   tab <- tab %>% select(all_of(names),exclude_prop)
-  print(tab)
 }
diff --git a/stuart.Rcheck/00_pkg_src/stuart/DESCRIPTION b/stuart.Rcheck/00_pkg_src/stuart/DESCRIPTION
deleted file mode 100755
index 2b3476283fbba4a5b44e24460ae2db69c04a27f5..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/DESCRIPTION
+++ /dev/null
@@ -1,23 +0,0 @@
-Package: stuart
-Title: stuaRt
-Version: 0.1.0
-Authors@R: 
-    person(given = "Marie",
-           family = "Bourdon",
-           role = c("aut", "cre"),
-           email = "mariefbourdon@gmail.com",
-           comment = c(ORCID = "YOUR-ORCID-ID"))
-Description: Sorts markers of miniMUGA genotyping for F2 or N2 individuals, for Rqtl analysis. 
-License: GPL-3
-Encoding: UTF-8
-LazyData: true
-Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.1.1
-Imports: dplyr, tidyr, utils, stringr, rapportools
-Suggests: knitr, rmarkdown
-VignetteBuilder: knitr
-Depends: R (>= 3.5.0)
-NeedsCompilation: no
-Packaged: 2021-06-01 08:06:30 UTC; mariebourdon
-Author: Marie Bourdon [aut, cre] (YOUR-ORCID-ID)
-Maintainer: Marie Bourdon <mariefbourdon@gmail.com>
diff --git a/stuart.Rcheck/00_pkg_src/stuart/NAMESPACE b/stuart.Rcheck/00_pkg_src/stuart/NAMESPACE
deleted file mode 100755
index 902e04361e27306cd36fac0de688e5a6ab80da2c..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/NAMESPACE
+++ /dev/null
@@ -1,13 +0,0 @@
-# Generated by roxygen2: do not edit by hand
-
-export(geno_strains)
-export(mark_allele)
-export(mark_match)
-export(mark_poly)
-export(mark_prop)
-export(tab_mark)
-export(write_rqtl)
-import(dplyr)
-import(stringr)
-import(tidyr)
-import(utils)
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/geno_strains.R b/stuart.Rcheck/00_pkg_src/stuart/R/geno_strains.R
deleted file mode 100755
index a0ac113461536b3410adb9bc4e176c06600024a6..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/geno_strains.R
+++ /dev/null
@@ -1,48 +0,0 @@
-#' @title Create haplotype for a new mouse strain into a reference dataframe
-#'
-#' @description This functions adds columns for parental strains used in the cross in the annotation data frame, from the genotype data frame in which one or several animal of the parental strains were genotyped.
-#' If several animals of one strain were genotyped, a consensus is created from these animals.
-#' The consensus is created as follow : if the indivuals carry the same allele, this allele is kept, otherwise, the allele is noted as "N". If individuals show residual heterozygosity, it is encoded as "H".
-#' @param ref data frame with the reference genotypes of mouse lines
-#' @param geno data frame with the genotyping results for your cross from miniMUGA array
-#' @param par1 first parental strain used in the cross, the name must be written as in the geno data frame
-#' @param par2 second parental strain used in the cross, the name must be written as in the geno data frame
-#' @param name1 name of the first parental strain to use as the column name in the ref data frame
-#' @param name2 name of the second parental strain to use as the column name in the ref data frame
-#'
-#' @import dplyr
-#' @import tidyr
-#'
-#' @export
-#'
-geno_strains <- function(ref,geno,par1,par2,name1,name2){
-  #recode genotypes from 2 alleles to 1
-  geno <- geno %>% mutate_all(as.character)
-  geno <- geno %>% filter(Sample.ID %in% c(par1,par2))
-  geno <- geno %>% mutate(Geno=case_when(Allele1...Forward == "-" | Allele2...Forward == "-" ~ "N",
-                                         Allele1...Forward == Allele2...Forward ~ Allele1...Forward,
-                                         Allele1...Forward %in% c("A","T","G","C") & Allele2...Forward %in% c("A","T","G","C") ~ "H"))
-
-  geno <- geno %>% select(SNP.Name,Sample.ID,Geno) %>% pivot_wider(names_from = Sample.ID, values_from = Geno)
-
-
-  #create consensus
-  if(length(par1)!=1){
-    geno <- geno %>% mutate(parent1 = ifelse(!!sym(par1[1])==!!sym(par1[2]),!!sym(par1[1]),"N"))
-  } else {
-    geno <- geno %>% rename(parent1=!!sym(par1[1]))
-  }
-
-  if(length(par2)!=1){
-    geno <- geno %>% mutate(parent2 = ifelse(!!sym(par2[1])==!!sym(par2[2]),!!sym(par2[1]),"N"))
-  } else {
-    geno <- geno %>% rename(parent2=!!sym(par2[1]))
-  }
-
-  geno <- geno %>% select(SNP.Name,parent1,parent2)
-  colnames(geno) <- c("SNP.Name",name1,name2)
-
-  #merge with ref file
-  ref <- full_join(ref,geno,by=c("marker"="SNP.Name"))
-  return(ref)
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/genos-data.R b/stuart.Rcheck/00_pkg_src/stuart/R/genos-data.R
deleted file mode 100755
index fd50eb0ea6a1188c1f619aa7510b6b276c9c2895..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/genos-data.R
+++ /dev/null
@@ -1,8 +0,0 @@
-#' Data frame with miniMUGA genotyping of F2 individuals and parental strains
-#'
-#' A dataset containing the genotypes of 176 F2 individuals
-#'
-#' @format A data frame with 2002493 observations of 11 variables
-
-
-"genos"
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/mark_allele.R b/stuart.Rcheck/00_pkg_src/stuart/R/mark_allele.R
deleted file mode 100755
index b679916993328de1dfd7b34b3325bf8066424b8e..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/mark_allele.R
+++ /dev/null
@@ -1,46 +0,0 @@
-#' @title Exclude markers that have different alleles in the individuals of the cross and in parental strains
-#'
-#' @description This functions uses the dataframe produced by the tab_mark function and fills the "exclude" column for all the markers which have alleles observed in the individuals of the cross that do not correspond to the alleles observed in the parental strains. For example, a marker which is not polymorphic between the two parental strains but which has two alleles in the cross individuals will be excluded.
-#' @param tab data frame obtained with tab_mark function
-#' @param ref data frame with the reference genotypes of mouse lines
-#' @param par1 first parental strain used in the cross, the name must be written as in the "ref" data frame
-#' @param par2 second parental strain used in the cross, the name must be written as in the "ref" data frame
-#'
-#' @import dplyr
-#'
-#' @export
-#'
-mark_allele <- function(tab,ref,par1,par2){
-
-  #markers of ref df as characters
-  ref$marker <- as.character(ref$marker)
-  colnames(ref) <- make.names(colnames(ref))
-
-  #recode parents' names to match column names nomenclature
-  par1 <- make.names(par1)
-  par2 <- make.names(par2)
-
-  #join tab and ref genotypes
-  ref <- ref %>% select(marker,!!sym(par1),!!sym(par2))
-  tab <- full_join(tab,ref,by=c("SNP.Name"="marker"))
-
-  #function core
-  tab <- tab %>% mutate(exclude_allele = case_when(is.na(Allele_2)==FALSE &
-                                                     !!sym(par1) != "N" & !!sym(par2) != "N" & !!sym(par1) != "H" & !!sym(par2) != "H" &
-                                                     ((Allele_1!=!!sym(par1) & Allele_1!=!!sym(par2)) | (Allele_2!=!!sym(par1) & Allele_2!=!!sym(par2))) ~ 1,
-                                                   is.na(Allele_2)==FALSE &
-                                                     (!!sym(par1)=="N" | !!sym(par2)=="N" | !!sym(par1)=="H" | !!sym(par2)=="H") &
-                                                     ((Allele_1!=!!sym(par1) & Allele_1!=!!sym(par2)) & (Allele_2!=!!sym(par1) & Allele_2!=!!sym(par2))) ~ 1,
-                                                   is.na(Allele_2)==TRUE &
-                                                     !!sym(par1) != "N" & !!sym(par2) != "N" & !!sym(par1) != "H" & !!sym(par2) != "H" &
-                                                     (Allele_1!=!!sym(par1) | Allele_1!=!!sym(par2)) ~ 1,
-                                                   is.na(Allele_2)==TRUE &
-                                                     (!!sym(par1)=="N" | !!sym(par2)=="N" | !!sym(par1)=="H" | !!sym(par2)=="H") &
-                                                     Allele_1!=!!sym(par1) & Allele_1!=!!sym(par2) ~ 1,
-                                                   T ~ 0)
-  )
-
-  tab <- tab %>% select(-c(!!sym(par1),!!sym(par2)))
-
-  return(tab)
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/mark_match.R b/stuart.Rcheck/00_pkg_src/stuart/R/mark_match.R
deleted file mode 100755
index 8781242a7568eb4081bcd0170f5ff17a049f2358..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/mark_match.R
+++ /dev/null
@@ -1,25 +0,0 @@
-#' @title Exclude markers that were not genotyped in the reference strains
-#'
-#' @description This functions uses the dataframe produced by the tab_mark function and fills the "exclude" column for all the markers that were genotyped in the individuals of the cross but not in the reference strains. This is useful if the parental strains of the cross were not genotyped with the individuals and a previous genotyping result is used. Indeed, changes in the markers of the array may have happened. We recommend always using this function in order to avoid errors.
-#' @param tab data frame obtained with tab_mark function
-#' @param ref data frame with the reference genotypes of mouse lines
-#'
-#' @import dplyr
-#'
-#' @export
-#'
-mark_match <- function(tab,  #tab_mark df
-                       ref){  #strain ref geno file
-
-  #finds SNPs that are in both files:
-  snp_strains <- as.character(ref$marker) #extracts SNPs in strains ref geno file
-  snp_genfile <- as.character(tab$SNP.Name) #extracts SNPs in cross geno file
-  snp <- intersect(snp_strains,snp_genfile) #take intercept
-
-
-  #add results in exclude column
-  return(tab %>% mutate(exclude_match=ifelse(!SNP.Name %in% snp,
-                                             1,
-                                             0)))
-
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/mark_poly.R b/stuart.Rcheck/00_pkg_src/stuart/R/mark_poly.R
deleted file mode 100755
index 168ccf3c35f28348477d6700238dc487666aae6a..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/mark_poly.R
+++ /dev/null
@@ -1,13 +0,0 @@
-#' @title Exclude markers that are not polymorphic
-#'
-#' @description This functions uses the dataframe produced by the tab_mark function and fills the "exclude" column for all the markers that are not polymorphic.
-#' @param tab data frame obtained with tab_mark function
-#'
-#' @import dplyr
-#'
-#' @export
-mark_poly <- function(tab){
-  return(tab %>% mutate(exclude_poly=ifelse(is.na(Allele_2)==TRUE,
-                                            1,
-                                            0)))
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/mark_prop.R b/stuart.Rcheck/00_pkg_src/stuart/R/mark_prop.R
deleted file mode 100755
index a0827e8c27b02c9d65a6cc3af34cf595f50a5840..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/mark_prop.R
+++ /dev/null
@@ -1,50 +0,0 @@
-#' @title Exclude markers depending on proportions of homo/hetorozygous
-#'
-#' @description uses the dataframe produced by the tab_mark function and fills the "exclude" column for all the markers that present odd proportions of each genotype. You can define these proportions thanks to the arguments of the function.
-#' @param tab data frame obtained with tab_mark function.
-#' @param cross F2 or N2.
-#' @param homo proportion of homozygous individuals under which the marker is excluded. Will apply on both homozygous genotypes for a F2, but only on one for N2.
-#' @param hetero proportion of heterozygous individuals under which the marker is excluded.
-#' @param na proportion of non-genotyped individuals above which the marker is excluded.
-#'
-#' @import dplyr
-#'
-#' @export
-#'
-
-#### mark_prop ####
-## excludes markers depending on proportions of homo/hetorozygous
-mark_prop <- function(tab,cross,homo=NA,hetero=NA,na=0.5){
-  #calculate total number of individuals genotyped for each marker
-  tab <- tab %>% mutate(n_geno = tab %>% select(n_HM1,n_HM2,n_HT) %>% rowSums(na.rm=TRUE))
-
-  #calculate proportion of each genotype
-  tab <- tab %>% mutate(p_HM1 = n_HM1/n_geno)
-  tab <- tab %>% mutate(p_HM2 = n_HM2/n_geno)
-  tab <- tab %>% mutate(p_HT = n_HT/n_geno)
-  tab <- tab %>% mutate(p_NA = n_NA/(n_geno+n_NA))
-
-  #stop if cross != "F2" or "N2"
-  if(!cross %in% c("F2","N2")){
-    stop("Cross must be F2 or N2")
-  }
-
-  #stop if no value for "homo" for F2 cross
-  if(cross=="F2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-    stop("Arguments homo and hetero must be specified for F2 crosses")
-  }
-
-  #stop if no value for "homo" and "hetero" for N2 cross
-  if(cross=="N2" & (is.na(homo)==TRUE | is.na(hetero)==TRUE)){
-    stop("Arguments homo and hetero must be specified for N2 crosses")
-  }
-
-  #exclude markers according to proportion of na
-  tab <- tab %>% mutate(exclude_prop=case_when(p_NA > na ~ 1, #exclude markers according to proportion of na
-                                               cross=="F2" & (p_HM1 < homo | p_HM2 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous individuals for F2 cross
-                                               cross=="N2" & (p_HM1 < homo | p_HT < hetero) ~ 1, #exclude markers according to proportion of homozygous and heterozygous individuals for N2 cross
-                                               T ~ 0))
-
-  tab <- tab %>% select(-c(p_HM1,p_HM2,p_HT,p_NA,n_geno))
-  return(tab)
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/phenos-data.R b/stuart.Rcheck/00_pkg_src/stuart/R/phenos-data.R
deleted file mode 100755
index ae4ba0006a4c4eda85b75c15c796816894a06f79..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/phenos-data.R
+++ /dev/null
@@ -1,8 +0,0 @@
-#' Data frame with phenotype of F2 individuals
-#'
-#' A dataset containing the phenotype of 176 F2 individuals
-#'
-#' @format A data frame with 176 observations of 4 variables
-
-
-"phenos"
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/ref_strains_mini-data.R b/stuart.Rcheck/00_pkg_src/stuart/R/ref_strains_mini-data.R
deleted file mode 100755
index 3fdffa9ee1a5665666e7c7b9c25aa92d3d144cbf..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/ref_strains_mini-data.R
+++ /dev/null
@@ -1,29 +0,0 @@
-#' Data frame with miniMUGA genotyping of classical lab strains.
-#'
-#' A dataset containing the genotypes of 10 mouse strains of the Institut pasteur. Markers positions and other information are from by Karl Broman (https://kbroman.org/MUGAarrays/mini_revisited.html). Strains genotyped from Institut Pasteur.
-#'
-#' @format A data frame with 11299 rows and 18 variables
-#' \describe{
-#'   \item{CC001}{CC001 mouse strain}
-#'   \item{CC005}{CC005 mouse strain}
-#'   \item{CC042}{CC042 mouse strain}
-#'   \item{CC071}{CC071 mouse strain}
-#'   \item{Ifnar.KO.129}{Ifnar KO 129 mouse strain}
-#'   \item{Ifnar.KO.B6}{Ifnar KO B6 mouse strain}
-#'   \item{Rvfs2.1}{Rvfs2-1 mouse strain}
-#'   \item{Rvfs2.2}{Rvfs2-2 mouse strain}
-#'   \item{Rvfs2.6}{Rvfs2-6 mouse strain}
-#'   \item{Rvfs2.7}{Rvfs2-7 mouse strain}
-#'   \item{marker}{name of the marker}
-#'   \item{chr}{chromosome}
-#'   \item{bp_mm10}{localisation on chromosome in bp (mm10 assembly)}
-#'   \item{cM_cox}{localisation on chromosome in cM (from Cox et al.)}
-#'   \item{cM_g2f1}{localisation on chromosome in cM (from Liu et al.)}
-#'   \item{snp}{marker alleles}
-#'   \item{unique}{indicates if the marker maps uniquely on mm10}
-#'   \item{multi}{indicates if the marker maps more than one time on mm10}
-#'   \item{unmapped}{indicates if the marker does not map perfectly on mm10}
-#' }
-
-
-"ref_strains_mini"
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/stuart_tab-data.R b/stuart.Rcheck/00_pkg_src/stuart/R/stuart_tab-data.R
deleted file mode 100644
index a5e0d4fb13d058c8b8147662389812fa7c300f28..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/stuart_tab-data.R
+++ /dev/null
@@ -1,17 +0,0 @@
-#' Output of tab_mark function
-#'
-#' A dataset with the output of tab_mark() function.
-#'
-#' @format A data frame with 11125 rows and 7 variables
-#' \describe{
-#'   \item{SNP.Name}{name of the marker}
-#'   \item{Allele_1}{first allele of the marker}
-#'   \item{Allele_2}{second allele of the marker}
-#'   \item{n_HM1}{number of homozygous individuals for the first allele}
-#'   \item{n_HM2}{number of homozygous individuals for the second allele}
-#'   \item{n_HT}{number of heterozygous individuals}
-#'   \item{n_NA}{number of non genotyped individuals}
-#' }
-
-
-"stuart_tab"
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/tab_mark.R b/stuart.Rcheck/00_pkg_src/stuart/R/tab_mark.R
deleted file mode 100755
index 612c27db930cb54d481e09a9a7ab5efd4912420f..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/tab_mark.R
+++ /dev/null
@@ -1,137 +0,0 @@
-#' @title Create of the summary table for all markers from the genotype data frame
-#'
-#' @description This function creates a table with all the markers that were genotyped in the array, the alleles for these markers, the number of homozygous and heterozygous animals, as well as the number of non genotyped animals.
-#' @param geno data frame with the genotyping results for your cross
-#'
-#' @import dplyr
-#' @import tidyr
-#'
-#' @export
-#'
-
-#### tab_mark function ####
-## create table with markers and counts
-tab_mark <- function(geno){
-  #create geno column in geno df
-  geno <- geno %>% unite(Geno,c("Allele1...Forward","Allele2...Forward"),sep="",remove=FALSE)
-
-  #recode genotypes to have all heterozygous encoded the same way (ex: only "AT", no "TA")
-  geno <- geno %>% mutate(Geno=recode(Geno,
-                                      "TA" = "AT",
-                                      "GA" = "AG",
-                                      "CA" = "AC",
-                                      "GT" = "TG",
-                                      "CT" = "TC",
-                                      "GC" = "CG"))
-
-
-  #create df with counts for each genotype
-  df_count <- tibble(SNP.Name = as.character(unique(geno$SNP.Name)),
-                     Allele_1 = NA,
-                     Allele_2 = NA,
-                     n_HM1 = NA,
-                     n_HM2 = NA,
-                     n_HT = NA,
-                     n_NA = NA)
-
-
-  ## loop to count genotype
-  for(i in df_count$SNP.Name){
-    #extract alleles for each marker
-    Alleles <- geno %>% filter(SNP.Name==i) %>%
-      select(c(SNP.Name,Sample.ID,Geno,Allele1...Forward,Allele2...Forward)) %>%
-      pivot_longer(c(Allele1...Forward,Allele2...Forward),names_to="Allele_name",values_to="Allele") %>%
-      distinct(Allele) %>% filter(Allele != "-")
-    Alleles <- as.factor(paste(Alleles$Allele))
-
-    #sort alleles
-    Alleles <- factor(Alleles,levels=c("A","T","C","G"))
-    Alleles <- sort(Alleles)
-
-    #add alleles and counts, only for markers with alleles (not markers with no genotyped ind)
-    if(all(rapportools::is.empty(Alleles))==FALSE){
-
-      #add alleles to df_count
-      df_count <- df_count %>% mutate(Allele_1 = ifelse(SNP.Name == i,
-                                                        paste(Alleles[1]), Allele_1))
-
-
-
-      #count for homozygous for allele 1
-      n1 <- geno %>% filter(SNP.Name==i) %>%
-        filter(Geno == paste(Alleles[1],Alleles[1],sep="")) %>%
-        summarise(n=n())
-
-
-      #add count for homozygous for allele 1 to df_count
-      df_count <- df_count %>% mutate(n_HM1 = ifelse(SNP.Name == i,
-                                                     n1$n, n_HM1))
-
-
-    }
-
-    #if marker not polymorphic
-    if(is.na(Alleles[2])==TRUE){
-      #NA as Allele_2
-      df_count <- df_count %>% mutate(Allele_2 = ifelse(SNP.Name == i,
-                                                        NA, Allele_2))
-
-      #NA as n_HM2
-      df_count <- df_count %>% mutate(n_HM2 = ifelse(SNP.Name == i,
-                                                     NA, n_HM2))
-
-      #NA as n_HT
-      df_count <- df_count %>% mutate(n_HT = ifelse(SNP.Name == i,
-                                                    NA, n_HT))
-    } else {
-      #add alleles to df_count
-      df_count <- df_count %>% mutate(Allele_2 = ifelse(SNP.Name == i,
-                                                        paste(Alleles[2]), Allele_2))
-
-
-      #count for homozygous for allele 2
-      n2 <- geno %>% filter(SNP.Name==i) %>%
-        filter(Geno == paste(Alleles[2],Alleles[2],sep="")) %>%
-        summarise(n=n())
-
-      #add count for homozygous for allele 1 to df_count
-      df_count <- df_count %>% mutate(n_HM2 = ifelse(SNP.Name == i,
-                                                     n2$n, n_HM2))
-
-
-      #count for heterozygous
-      n3 <- geno %>% filter(SNP.Name==i) %>%
-        filter(Geno == paste(Alleles[1],Alleles[2],sep="")) %>%
-        summarise(n=n())
-
-
-      #add count for homozygous for allele 1 to df_count
-      df_count <- df_count %>% mutate(n_HT = ifelse(SNP.Name == i,
-                                                    n3$n, n_HT))
-
-
-    }
-
-    #count for NA
-    n4 <- geno %>% filter(SNP.Name==i) %>%
-      filter(Geno == "--" |
-               Geno == paste(Alleles[1],"-",sep="") | Geno == paste(Alleles[2],"-",sep="") |
-               Geno == paste("-",Alleles[1],sep="") | Geno == paste("-",Alleles[2],sep="")) %>%
-      summarise(n=n())
-
-    #add count for NA to df_count
-    df_count <- df_count %>% mutate(n_NA = ifelse(SNP.Name == i,
-                                                  n4$n, n_NA))
-  }
-  #change class of counts as numeric :
-  df_count$n_HM1 <- df_count$n_HM1 %>% as.numeric()
-  df_count$n_HM2 <- df_count$n_HM2 %>% as.numeric()
-  df_count$n_HT <- df_count$n_HT %>% as.numeric()
-  df_count$n_NA <- df_count$n_NA %>% as.numeric()
-
-  #add 0 for null counts
-  df_count <- df_count %>% mutate_at(.vars=vars(n_HM1,n_HM2,n_HT,n_NA),~replace(., is.na(.), 0))
-
-  #return
-  return(df_count)
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/R/write_rqtl.R b/stuart.Rcheck/00_pkg_src/stuart/R/write_rqtl.R
deleted file mode 100755
index b02eb333df6c67886b4a8245fe181dab8e923155..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/R/write_rqtl.R
+++ /dev/null
@@ -1,137 +0,0 @@
-#' @title Create data frame in Rqtl CSV format
-#'
-#' @description This function uses the table produced by tab_mark function filled by all the mark_* functions in order to create a data frame in the right format for Rqtl read.cross function. Only the non-excluded markers will be kept and genotypeds will be encoded in "0", "1" and "2", "0" being homozygous for the first parental strain, "1" heterozygous and "2" homozygous for the second parental strain. Caution, this file create a data frame and a CSV file in the path of your choice if indicated by the "path" argument. This function does not create a "cross" object in your environment that can be directly used for QTL mapping. You will need to load the CSV file with qtl::read.cross.
-#' @param geno data frame with the genotyping results for your cross
-#' @param pheno data frame with phenotypes of the individuals (individuals must have the same ID in the geno data frame and in the pheno data frame)
-#' @param prefix potential prefix present in the names of the individuals in the geno data frame to be removed in ordere to have the same names as in the pheno file
-#' @param tab data frame obtained with tab_mark function
-#' @param ref data frame with the reference genotypes of mouse lines
-#' @param par1 first parental strain used in the cross, the name must be written as in the "ref" data frame
-#' @param par2 second parental strain used in the cross, the name must be written as in the "ref" data frame
-#' @param method method of calculation of cM position, can be "cM_cox" of "cM_g2f1"
-#' @param path if indicated, the data frame will be exported in this path
-#'
-#' @import dplyr
-#' @import tidyr
-#' @import utils
-#' @import stringr
-#'
-#' @export
-#'
-#### write_rqtl ####
-## write data frame in rqtl format (csv), if path != NA writes the file in the path indicated
-write_rqtl <- function(geno,pheno,tab,ref,par1,par2,prefix,method="cM_cox",path=NA){
-  #extract snps non excluded
-  if("exclude_match" %in% colnames(tab)){
-    tab <- tab %>% filter(exclude_match==0)
-  }
-
-  if("exclude_poly" %in% colnames(tab)){
-    tab <- tab %>% filter(exclude_poly==0)
-  }
-
-  if("exclude_prop" %in% colnames(tab)){
-    tab <- tab %>% filter(exclude_prop==0)
-  }
-
-  if("exclude_allele" %in% colnames(tab)){
-    tab <- tab %>% filter(exclude_allele==0)
-  }
-
-
-  #filter genotypes for non excluded markers in geno file
-  geno <- geno %>% select(c(SNP.Name,Sample.ID,Allele1...Forward,Allele2...Forward)) %>% filter(SNP.Name %in% tab$SNP.Name)
-
-  #recode parents' names to match column names nomenclature
-  par1 <- make.names(par1)
-  par2 <- make.names(par2)
-
-  #keep parental lines genotypes
-  colnames(ref) <- make.names(colnames(ref))
-  ref <- ref %>% select(marker,chr,bp_mm10,cM_cox,cM_g2f1,!!sym(par1),!!sym(par2))
-
-  #merge genotypes with parents
-  geno <- left_join(geno,ref,by=c("SNP.Name"="marker"))
-
-  #recode "-" in "N" in geno file
-  geno <- geno %>% mutate(Allele1...Forward = recode(Allele1...Forward,
-                                                     "-" = "N"))
-
-  geno <- geno %>% mutate(Allele2...Forward = recode(Allele2...Forward,
-                                                     "-" = "N"))
-
-  #recode geno in factors with same levels
-  geno <- geno %>% mutate(Allele1...Forward = factor(Allele1...Forward,levels=c("A","C","G","H","N","T")))
-  geno <- geno %>% mutate(Allele2...Forward = factor(Allele2...Forward,levels=c("A","C","G","H","N","T")))
-
-
-
-  #recode genotypes depending on parents' genotypes
-  geno <- geno %>% mutate(Geno = case_when(
-    #if one allele not genotyped:
-    Allele1...Forward=="N" | Allele2...Forward=="N" ~ "NA",
-
-    #if both alleles genotyped
-    ##homozygous 0
-    Allele1...Forward==Allele2...Forward & Allele1...Forward==!!sym(par1) ~ "0",
-    ##homozygous 2
-    Allele1...Forward==Allele2...Forward & Allele1...Forward==!!sym(par2) ~ "2",
-    ##heterozygous
-    Allele1...Forward!=Allele2...Forward ~ "1",
-
-    #if parental strains are N/H
-    ##homozygous for parent that is N/H
-    ###homozygous 0
-    (!!sym(par1)%in%c("H","N") | !!sym(par2)%in%c("H","N")) &
-      !!sym(par1)%in%c("H","N") ~ "0",
-    ###homozygous 2
-    (!!sym(par1)%in%c("H","N") | !!sym(par2)%in%c("H","N")) &
-      !!sym(par2)%in%c("H","N") ~ "2",
-  )
-  )
-
-
-  #keep positions of markers
-  markers <- geno %>% select(SNP.Name,chr,bp_mm10,cM_cox,cM_g2f1) %>% distinct()
-  markers <- markers %>% arrange(chr,bp_mm10)
-
-
-  #keep only interesting columns in geno file
-  geno <- geno %>% arrange(chr,bp_mm10)
-  geno <- geno %>% select(SNP.Name,Sample.ID,Geno)
-
-  #remove prefix
-  geno <- geno %>% mutate(Sample.ID=str_remove(Sample.ID,prefix))
-
-  #keep only non excluded markers and merge with positions
-  markers <- markers %>% mutate(SNP.Name=as.character(SNP.Name))
-  markers <- markers %>% mutate(chr=as.character(chr))
-  geno <- markers %>% select(SNP.Name,chr,!!sym(method)) %>% full_join(.,geno,by="SNP.Name")
-
-
-  #pivoting
-  geno <- geno %>% pivot_wider(names_from = c(SNP.Name,chr,!!sym(method)),values_from = Geno,names_sep=",")
-  geno <- geno %>% mutate(Sample.ID=as.character(Sample.ID))
-  geno <- geno %>% rename("Sample.ID,,"=Sample.ID)
-
-
-  #merge with phenotype file
-  pheno <- pheno %>% mutate_all(as.character)
-  colnames(pheno) <- str_c(colnames(pheno),",,")
-  qtl_file <- right_join(pheno,geno,by=c("Ind,,"="Sample.ID,,"))
-
-  #prepare file
-  qtl_file <- rbind(colnames(qtl_file),qtl_file)
-  qtl_file <- separate_rows(qtl_file,everything(),sep=",")
-  colnames(qtl_file) <- qtl_file[1,]
-  qtl_file <- qtl_file %>% slice(-1)
-
-  if(is.na(path)==FALSE){
-    write.csv(qtl_file,file=path,quote=FALSE,row.names = FALSE)
-  }
-
-
-
-
-  return(qtl_file)
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/README.md b/stuart.Rcheck/00_pkg_src/stuart/README.md
deleted file mode 100644
index 2497f6e0824b3a3c72b4a108b6adfe55cce49a03..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
-
-<!-- README.md is generated from README.Rmd. Please edit that file -->
-
-# stuart
-
-<!-- badges: start -->
-
-<!-- badges: end -->
-
-stuart is a R package used to analyze whole genome genotyping results of
-animals used by crossing laboratory strains. It is particularly useful
-for F2 or N2 individuals as it allows to filter the markers in the
-arrays that can or cannot be used for further analysis from a genetic
-point of view. Mrkers will be selected depending on their proportion of
-each genotype, correspondance between F2 or N2 individuals alleles and
-parental strains alleles, etc.
-
-## Installation
-
-You can install the released version of stuart from GitLab
-
-``` r
-```
-
-## Example
-
-To see an example of the use of stuart package with miniMUGA array,
-consult the package vignette.
diff --git a/stuart.Rcheck/00_pkg_src/stuart/build/vignette.rds b/stuart.Rcheck/00_pkg_src/stuart/build/vignette.rds
deleted file mode 100644
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-## ---- include = FALSE---------------------------------------------------------
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-
-## ----setup--------------------------------------------------------------------
-library(dplyr)
-library(stuart)
-
-## ----annot--------------------------------------------------------------------
-annot_mini <- read.csv(url("https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv"))
-
-## ----load---------------------------------------------------------------------
-data(genos)
-summary(genos)
-data(phenos)
-summary(phenos)
-
-## ----strains------------------------------------------------------------------
-strains <- geno_strains(ref=annot_mini,geno=genos,par1=c("StrainsA_1","StrainsA_2"),par2=c("StrainsB_1","StrainsB_2"),name1="parent1",name2="parent2")
-head(strains)
-
-## ----no_parent----------------------------------------------------------------
-genos <- genos %>% filter(!Sample.ID %in% c("StrainsA_1", "StrainsA_2", "StrainsB_1","StrainsB_2"))
-
-## ----tab_mark-----------------------------------------------------------------
-data(stuart_tab)
-summary(stuart_tab)
-
-## ----mark_match---------------------------------------------------------------
-tab2 <- mark_match(stuart_tab,ref=strains)
-
-
-tab2 %>% filter(exclude_match==1)
-
-## ----mark_poly ex-------------------------------------------------------------
-tab2 <- mark_poly(tab2)
-head(tab2)
-
-## ----mark_prop ex-------------------------------------------------------------
-tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
-head(tab2)
-
-## ----mark_allele--------------------------------------------------------------
-tab2 <- mark_allele(tab=tab2,ref=strains,par1="parent1",par2="parent2")
-tab2 %>% arrange(desc(exclude_allele)) %>% head()
-
-## ----mark_allele-strains------------------------------------------------------
-strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354","gUNC15530876","gUNC21555204","gUNC21596600")) %>% arrange(marker) %>% select(marker,parent1,parent2)
-
-## ----write_qtl----------------------------------------------------------------
-rqtl_file <- (write_rqtl(geno=genos,pheno=phenos,tab=tab2,ref=strains,par1="parent1",par2="parent2",prefix="ind_"))
-
-rqtl_file[1:10,1:7]
-
diff --git a/stuart.Rcheck/00_pkg_src/stuart/inst/doc/stuaRt.Rmd b/stuart.Rcheck/00_pkg_src/stuart/inst/doc/stuaRt.Rmd
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----
-title: "stuaRt"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{stuaRt}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-```
-
-Marie Bourdon
-
-April 2021
-
-## Goal
-
-stuaRt is a R package which formats the genotyping data from MUGA arrays (Neogen) to use it in Rqtl, for backcross or F2 crosses. It allows to filter the markers in MUGA arrays that can or cannot be used for Rqtl analysis, from a genetic point of view. Indeed, markers will be selected depending on their proportion of each genotype, correspondance between F2 or N2 individuals alleles and parental strains alleles, etc.
-
-The examples shown here require the use of dplyr package.
-
-
-```{r setup}
-library(dplyr)
-library(stuart)
-```
-
-
-## Annotation files
-
-The developer of Rqtl and Rqtl2 packages, Karl Broman, realised that the annotation of the MUGA arrays was not correct for some markers. Thus, he produced new annotation files for MUGA, miniMUGA, megaMUGA and gigaMUGA arrays. These files contain some informations about the markers including the chromosome and position where the probe of the marker matchs on the genome, wether the marker maps uniquely or not, etc. These files also contains the genetic position of the markers calculated with two methods : "cM_cox" and "cM_g2f1" (see https://kbroman.org/MUGAarrays/mini_revisited.html for more informations).
-
-We recommand to use these annotation files to reconstruct the file use for Rqtl analysis. You can load the datasets with these annotations from GitHub (https://github.com/kbroman/MUGAarrays/tree/master/UWisc). Choose the file corresponding to the MUGA array that you used and use the URL to load the dataset in R.
-
-Here, we will present an example of the use of stuaRt with results of a F2 cross genotyped with miniMUGA. We load the result of Neogen genotyping: `genos` and thephenotype dataset produced by the lab: `phenos`. All these datasets are available for example in stuaRt package.
-
-```{r annot}
-annot_mini <- read.csv(url("https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv"))
-```
-
-
-
-```{r load}
-data(genos)
-summary(genos)
-data(phenos)
-summary(phenos)
-```
-
-### Genotyping of parental strains
-
-To use genotyping result for Rqtl analysis, we need to recode the genotypes of the individuals (originally encoded in A, T, G, C) depending on the genotype of the parental strains: homozygous for the first parental strain (0), heterozygous (1) or homozygous for the second parental strain (2).
-
-We recommend to always genotype the parental strains of the cross. Here, their genotypes are in the `genos` file and correspond to the Sample.ID "StrainsA_1", "StrainsA_2", "StrainsB_1" and "StrainsB_2". Two individuals were genotyped for each parental strain. The first step will be to create a consensus genotype for each strain from the two genotyped individuals. The consensus genotype will be added to the annotation dataset in order to obtain a dataset with both annotation and reference genotype of the parental strains that will be used for recoding the genotypes or the F2 individuals. 
-
-This is done with the `geno_strains` function. 
-
-```{r strains}
-strains <- geno_strains(ref=annot_mini,geno=genos,par1=c("StrainsA_1","StrainsA_2"),par2=c("StrainsB_1","StrainsB_2"),name1="parent1",name2="parent2")
-head(strains)
-```
-
-After this step, we need to remove the genotyping result for these individuals from the `genos` dataset.
-```{r no_parent}
-genos <- genos %>% filter(!Sample.ID %in% c("StrainsA_1", "StrainsA_2", "StrainsB_1","StrainsB_2"))
-```
-
-
-## Markers sorting
-
-### Marker tab
-
-The first step of the markers sorting is to create the marker dataframe with the tab_mark() function. This dataframe contains for each marker the two alleles that can be found in the F2/N2 population (`Allele_1` and `Allele_2`), the number of individuals for each genotype (homozygous for each allele (`n_HM1` and `n_HM2`) and heterozygous (`n_HT`)), and the number of non genotyped individuals (`n_NA`) This step can take several minutes. You can also load the output of this function.
-
-
-```{r tab_mark}
-data(stuart_tab)
-summary(stuart_tab)
-```
-
-Then we will use the different mark_* functions in order to filter the markers. First, we can use mark_match() function. This function excludes markers that are in your genotype file but not in the reference genotype dataset. We recomend using this function as the chip used for genotyping may change.
-
-```{r mark_match}
-tab2 <- mark_match(stuart_tab,ref=strains)
-
-
-tab2 %>% filter(exclude_match==1)
-```
-
-Here the reference strains were genotyped with the same version of the chip as the F2 individuals so no marker was excluded.
-
-Then, we can use the mark_poly() function, which will exclude the markers that are not polymorphic.
-
-```{r mark_poly ex}
-tab2 <- mark_poly(tab2)
-head(tab2)
-```
-
-The mark_prop() function can be used to filter markers depending on the proportion of each genotype. Here, we have a F2 so we can use the "homo" argument in order to filter depending on the proportion of both homozygous genotype. If we have a N2, we can filter with the proportion of homozygous individuals with the "homo" argument and of heterozygous individuals with the hetero" argument. Moreover, this function allows to filter marker depending on the proportion on non genotyped animals. By defaults, markers for which more than 50% of individuals were not genotyped.
-
-```{r mark_prop ex}
-tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
-head(tab2)
-```
-
-Last, we can use the mark_allele(). This very helpful function excludes markers for which the alleles found in the F2/N2 individuals do not correspond to the alleles found in the parental strains. For example, if for a marker is not polymorphic in the parental strains but we found two alleles in the F2/N2 individuals, it will be excluded.
-
-```{r mark_allele}
-tab2 <- mark_allele(tab=tab2,ref=strains,par1="parent1",par2="parent2")
-tab2 %>% arrange(desc(exclude_allele)) %>% head()
-```
-
-Indeed, we can see that the markers excluded with mark_allele() have different alleles in the parental strains.
-
-```{r mark_allele-strains}
-strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354","gUNC15530876","gUNC21555204","gUNC21596600")) %>% arrange(marker) %>% select(marker,parent1,parent2)
-```
-
-# Creation of the R/qtl file
-
-After excluding the problematic markers, we can create the R/qtl file. The individuals must have the same ID in the geno and in the pheno file. If there is a prefix in the geno file that must be removed in order to acheive this, you can use the "prefix" argument. The "path" argument can be used in order to create a CSV file that you can laod with qtl::read.cross. 
-
-```{r write_qtl}
-rqtl_file <- (write_rqtl(geno=genos,pheno=phenos,tab=tab2,ref=strains,par1="parent1",par2="parent2",prefix="ind_"))
-
-rqtl_file[1:10,1:7]
-```
-
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-<h1 class="title toc-ignore">stuaRt</h1>
-
-
-
-<p>Marie Bourdon</p>
-<p>April 2021</p>
-<div id="goal" class="section level2">
-<h2>Goal</h2>
-<p>stuaRt is a R package which formats the genotyping data from MUGA arrays (Neogen) to use it in Rqtl, for backcross or F2 crosses. It allows to filter the markers in MUGA arrays that can or cannot be used for Rqtl analysis, from a genetic point of view. Indeed, markers will be selected depending on their proportion of each genotype, correspondance between F2 or N2 individuals alleles and parental strains alleles, etc.</p>
-<p>The examples shown here require the use of dplyr package.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(dplyr)</span>
-<span id="cb1-2"><a href="#cb1-2"></a><span class="co">#&gt; </span></span>
-<span id="cb1-3"><a href="#cb1-3"></a><span class="co">#&gt; Attaching package: &#39;dplyr&#39;</span></span>
-<span id="cb1-4"><a href="#cb1-4"></a><span class="co">#&gt; The following objects are masked from &#39;package:stats&#39;:</span></span>
-<span id="cb1-5"><a href="#cb1-5"></a><span class="co">#&gt; </span></span>
-<span id="cb1-6"><a href="#cb1-6"></a><span class="co">#&gt;     filter, lag</span></span>
-<span id="cb1-7"><a href="#cb1-7"></a><span class="co">#&gt; The following objects are masked from &#39;package:base&#39;:</span></span>
-<span id="cb1-8"><a href="#cb1-8"></a><span class="co">#&gt; </span></span>
-<span id="cb1-9"><a href="#cb1-9"></a><span class="co">#&gt;     intersect, setdiff, setequal, union</span></span>
-<span id="cb1-10"><a href="#cb1-10"></a><span class="kw">library</span>(stuart)</span></code></pre></div>
-</div>
-<div id="annotation-files" class="section level2">
-<h2>Annotation files</h2>
-<p>The developer of Rqtl and Rqtl2 packages, Karl Broman, realised that the annotation of the MUGA arrays was not correct for some markers. Thus, he produced new annotation files for MUGA, miniMUGA, megaMUGA and gigaMUGA arrays. These files contain some informations about the markers including the chromosome and position where the probe of the marker matchs on the genome, wether the marker maps uniquely or not, etc. These files also contains the genetic position of the markers calculated with two methods : “cM_cox” and “cM_g2f1” (see <a href="https://kbroman.org/MUGAarrays/mini_revisited.html" class="uri">https://kbroman.org/MUGAarrays/mini_revisited.html</a> for more informations).</p>
-<p>We recommand to use these annotation files to reconstruct the file use for Rqtl analysis. You can load the datasets with these annotations from GitHub (<a href="https://github.com/kbroman/MUGAarrays/tree/master/UWisc" class="uri">https://github.com/kbroman/MUGAarrays/tree/master/UWisc</a>). Choose the file corresponding to the MUGA array that you used and use the URL to load the dataset in R.</p>
-<p>Here, we will present an example of the use of stuaRt with results of a F2 cross genotyped with miniMUGA. We load the result of Neogen genotyping: <code>genos</code> and thephenotype dataset produced by the lab: <code>phenos</code>. All these datasets are available for example in stuaRt package.</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a>annot_mini &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="kw">url</span>(<span class="st">&quot;https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv&quot;</span>))</span></code></pre></div>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="kw">data</span>(genos)</span>
-<span id="cb3-2"><a href="#cb3-2"></a><span class="kw">summary</span>(genos)</span>
-<span id="cb3-3"><a href="#cb3-3"></a><span class="co">#&gt;    SNP.Name          Sample.ID         Allele1...Forward  Allele2...Forward </span></span>
-<span id="cb3-4"><a href="#cb3-4"></a><span class="co">#&gt;  Length:2002493     Length:2002493     Length:2002493     Length:2002493    </span></span>
-<span id="cb3-5"><a href="#cb3-5"></a><span class="co">#&gt;  Class :character   Class :character   Class :character   Class :character  </span></span>
-<span id="cb3-6"><a href="#cb3-6"></a><span class="co">#&gt;  Mode  :character   Mode  :character   Mode  :character   Mode  :character  </span></span>
-<span id="cb3-7"><a href="#cb3-7"></a><span class="co">#&gt;                                                                             </span></span>
-<span id="cb3-8"><a href="#cb3-8"></a><span class="co">#&gt;                                                                             </span></span>
-<span id="cb3-9"><a href="#cb3-9"></a><span class="co">#&gt;                                                                             </span></span>
-<span id="cb3-10"><a href="#cb3-10"></a><span class="co">#&gt;                                                                             </span></span>
-<span id="cb3-11"><a href="#cb3-11"></a><span class="co">#&gt;        X                 Y              GC.Score          Theta       </span></span>
-<span id="cb3-12"><a href="#cb3-12"></a><span class="co">#&gt;  Min.   : 0.0000   Min.   : 0.0000   Min.   :0.0000   Min.   :0.0000  </span></span>
-<span id="cb3-13"><a href="#cb3-13"></a><span class="co">#&gt;  1st Qu.: 0.0260   1st Qu.: 0.0480   1st Qu.:0.6747   1st Qu.:0.0420  </span></span>
-<span id="cb3-14"><a href="#cb3-14"></a><span class="co">#&gt;  Median : 0.1750   Median : 0.5010   Median :0.8550   Median :0.6620  </span></span>
-<span id="cb3-15"><a href="#cb3-15"></a><span class="co">#&gt;  Mean   : 0.5716   Mean   : 0.6564   Mean   :0.7276   Mean   :0.5606  </span></span>
-<span id="cb3-16"><a href="#cb3-16"></a><span class="co">#&gt;  3rd Qu.: 0.8560   3rd Qu.: 1.0470   3rd Qu.:0.9245   3rd Qu.:0.9800  </span></span>
-<span id="cb3-17"><a href="#cb3-17"></a><span class="co">#&gt;  Max.   :25.1610   Max.   :20.7940   Max.   :0.9889   Max.   :1.0000  </span></span>
-<span id="cb3-18"><a href="#cb3-18"></a><span class="co">#&gt;  NA&#39;s   :87        NA&#39;s   :87        NA&#39;s   :326      NA&#39;s   :413     </span></span>
-<span id="cb3-19"><a href="#cb3-19"></a><span class="co">#&gt;      X.Raw           Y.Raw             R         </span></span>
-<span id="cb3-20"><a href="#cb3-20"></a><span class="co">#&gt;  Min.   :    0   Min.   :    0   Min.   : 0.000  </span></span>
-<span id="cb3-21"><a href="#cb3-21"></a><span class="co">#&gt;  1st Qu.:  546   1st Qu.:  678   1st Qu.: 0.605  </span></span>
-<span id="cb3-22"><a href="#cb3-22"></a><span class="co">#&gt;  Median : 1611   Median : 3908   Median : 1.027  </span></span>
-<span id="cb3-23"><a href="#cb3-23"></a><span class="co">#&gt;  Mean   : 5265   Mean   : 5578   Mean   : 1.228  </span></span>
-<span id="cb3-24"><a href="#cb3-24"></a><span class="co">#&gt;  3rd Qu.: 7924   3rd Qu.: 9232   3rd Qu.: 1.553  </span></span>
-<span id="cb3-25"><a href="#cb3-25"></a><span class="co">#&gt;  Max.   :49906   Max.   :43935   Max.   :26.041  </span></span>
-<span id="cb3-26"><a href="#cb3-26"></a><span class="co">#&gt;                                  NA&#39;s   :413</span></span>
-<span id="cb3-27"><a href="#cb3-27"></a><span class="kw">data</span>(phenos)</span>
-<span id="cb3-28"><a href="#cb3-28"></a><span class="kw">summary</span>(phenos)</span>
-<span id="cb3-29"><a href="#cb3-29"></a><span class="co">#&gt;       Ind          Sex                 Age            Pheno       </span></span>
-<span id="cb3-30"><a href="#cb3-30"></a><span class="co">#&gt;  201    :  1   Length:176         Min.   :5.000   Min.   : 8.609  </span></span>
-<span id="cb3-31"><a href="#cb3-31"></a><span class="co">#&gt;  202    :  1   Class :character   1st Qu.:7.000   1st Qu.:10.420  </span></span>
-<span id="cb3-32"><a href="#cb3-32"></a><span class="co">#&gt;  203    :  1   Mode  :character   Median :7.000   Median :10.628  </span></span>
-<span id="cb3-33"><a href="#cb3-33"></a><span class="co">#&gt;  204    :  1                      Mean   :7.176   Mean   :10.526  </span></span>
-<span id="cb3-34"><a href="#cb3-34"></a><span class="co">#&gt;  205    :  1                      3rd Qu.:8.000   3rd Qu.:10.793  </span></span>
-<span id="cb3-35"><a href="#cb3-35"></a><span class="co">#&gt;  206    :  1                      Max.   :9.000   Max.   :11.147  </span></span>
-<span id="cb3-36"><a href="#cb3-36"></a><span class="co">#&gt;  (Other):170</span></span></code></pre></div>
-<div id="genotyping-of-parental-strains" class="section level3">
-<h3>Genotyping of parental strains</h3>
-<p>To use genotyping result for Rqtl analysis, we need to recode the genotypes of the individuals (originally encoded in A, T, G, C) depending on the genotype of the parental strains: homozygous for the first parental strain (0), heterozygous (1) or homozygous for the second parental strain (2).</p>
-<p>We recommend to always genotype the parental strains of the cross. Here, their genotypes are in the <code>genos</code> file and correspond to the Sample.ID “StrainsA_1”, “StrainsA_2”, “StrainsB_1” and “StrainsB_2”. Two individuals were genotyped for each parental strain. The first step will be to create a consensus genotype for each strain from the two genotyped individuals. The consensus genotype will be added to the annotation dataset in order to obtain a dataset with both annotation and reference genotype of the parental strains that will be used for recoding the genotypes or the F2 individuals.</p>
-<p>This is done with the <code>geno_strains</code> function.</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>strains &lt;-<span class="st"> </span><span class="kw">geno_strains</span>(<span class="dt">ref=</span>annot_mini,<span class="dt">geno=</span>genos,<span class="dt">par1=</span><span class="kw">c</span>(<span class="st">&quot;StrainsA_1&quot;</span>,<span class="st">&quot;StrainsA_2&quot;</span>),<span class="dt">par2=</span><span class="kw">c</span>(<span class="st">&quot;StrainsB_1&quot;</span>,<span class="st">&quot;StrainsB_2&quot;</span>),<span class="dt">name1=</span><span class="st">&quot;parent1&quot;</span>,<span class="dt">name2=</span><span class="st">&quot;parent2&quot;</span>)</span>
-<span id="cb4-2"><a href="#cb4-2"></a><span class="kw">head</span>(strains)</span>
-<span id="cb4-3"><a href="#cb4-3"></a><span class="co">#&gt;          marker chr bp_mm10 cM_cox cM_g2f1 strand snp unique unmapped</span></span>
-<span id="cb4-4"><a href="#cb4-4"></a><span class="co">#&gt; 1  S3J010123784   1 3094603  1.542   0.046   plus  CA   TRUE    FALSE</span></span>
-<span id="cb4-5"><a href="#cb4-5"></a><span class="co">#&gt; 2  SAH010136363   1 3409090  1.608   0.175  minus  CA   TRUE    FALSE</span></span>
-<span id="cb4-6"><a href="#cb4-6"></a><span class="co">#&gt; 3  S2H010137098   1 3427467  1.609   0.177  minus  AG   TRUE    FALSE</span></span>
-<span id="cb4-7"><a href="#cb4-7"></a><span class="co">#&gt; 4   UNCHS000006   1 3439034  1.610   0.178   plus  CT   TRUE    FALSE</span></span>
-<span id="cb4-8"><a href="#cb4-8"></a><span class="co">#&gt; 5 mUNC010515443   1 3668628  1.627   0.202  minus  GA   TRUE    FALSE</span></span>
-<span id="cb4-9"><a href="#cb4-9"></a><span class="co">#&gt; 6  S2J010178507   1 4462692  1.733   0.234  minus  GA   TRUE    FALSE</span></span>
-<span id="cb4-10"><a href="#cb4-10"></a><span class="co">#&gt;                                                probe chr_unc  bp_unc parent1</span></span>
-<span id="cb4-11"><a href="#cb4-11"></a><span class="co">#&gt; 1 ATAAATTCTACAGCCACAGAAGTCACATTTTAGCACTGCTGTGTTTCCAG       1 3094603       C</span></span>
-<span id="cb4-12"><a href="#cb4-12"></a><span class="co">#&gt; 2 CCACCTTTGTACTCTGTATGCTACACAGAAGCTATGAGTATTCTTTTCCC       1 3409090       C</span></span>
-<span id="cb4-13"><a href="#cb4-13"></a><span class="co">#&gt; 3 ATTGAAAATGATCTAAGGGAGTCATGAGTACAAGGAGAAATGGGCATATT       1 3427467       A</span></span>
-<span id="cb4-14"><a href="#cb4-14"></a><span class="co">#&gt; 4 AATTTCTACCAGATCTCTTTGTCCTCCTAGAAGCATTGTGATACTCAGGA       1 3439034       C</span></span>
-<span id="cb4-15"><a href="#cb4-15"></a><span class="co">#&gt; 5 CAGGAAATGATGCTGAGAAAGTGAGAAGTAGGAAAACGTGGAGAAAAATA       1 3668628       G</span></span>
-<span id="cb4-16"><a href="#cb4-16"></a><span class="co">#&gt; 6 GACCTATGGTTAAAAGTCAGGCATTTCTTGTGTCTTCTTGTATTATTGGT       1 4462692       G</span></span>
-<span id="cb4-17"><a href="#cb4-17"></a><span class="co">#&gt;   parent2</span></span>
-<span id="cb4-18"><a href="#cb4-18"></a><span class="co">#&gt; 1       C</span></span>
-<span id="cb4-19"><a href="#cb4-19"></a><span class="co">#&gt; 2       C</span></span>
-<span id="cb4-20"><a href="#cb4-20"></a><span class="co">#&gt; 3       A</span></span>
-<span id="cb4-21"><a href="#cb4-21"></a><span class="co">#&gt; 4       C</span></span>
-<span id="cb4-22"><a href="#cb4-22"></a><span class="co">#&gt; 5       G</span></span>
-<span id="cb4-23"><a href="#cb4-23"></a><span class="co">#&gt; 6       G</span></span></code></pre></div>
-<p>After this step, we need to remove the genotyping result for these individuals from the <code>genos</code> dataset.</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a>genos &lt;-<span class="st"> </span>genos <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(<span class="op">!</span>Sample.ID <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;StrainsA_1&quot;</span>, <span class="st">&quot;StrainsA_2&quot;</span>, <span class="st">&quot;StrainsB_1&quot;</span>,<span class="st">&quot;StrainsB_2&quot;</span>))</span></code></pre></div>
-</div>
-</div>
-<div id="markers-sorting" class="section level2">
-<h2>Markers sorting</h2>
-<div id="marker-tab" class="section level3">
-<h3>Marker tab</h3>
-<p>The first step of the markers sorting is to create the marker dataframe with the tab_mark() function. This dataframe contains for each marker the two alleles that can be found in the F2/N2 population (<code>Allele_1</code> and <code>Allele_2</code>), the number of individuals for each genotype (homozygous for each allele (<code>n_HM1</code> and <code>n_HM2</code>) and heterozygous (<code>n_HT</code>)), and the number of non genotyped individuals (<code>n_NA</code>) This step can take several minutes. You can also load the output of this function.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="kw">data</span>(stuart_tab)</span>
-<span id="cb6-2"><a href="#cb6-2"></a><span class="kw">summary</span>(stuart_tab)</span>
-<span id="cb6-3"><a href="#cb6-3"></a><span class="co">#&gt;    SNP.Name           Allele_1           Allele_2             n_HM1      </span></span>
-<span id="cb6-4"><a href="#cb6-4"></a><span class="co">#&gt;  Length:11125       Length:11125       Length:11125       Min.   :  0.0  </span></span>
-<span id="cb6-5"><a href="#cb6-5"></a><span class="co">#&gt;  Class :character   Class :character   Class :character   1st Qu.: 44.0  </span></span>
-<span id="cb6-6"><a href="#cb6-6"></a><span class="co">#&gt;  Mode  :character   Mode  :character   Mode  :character   Median :174.0  </span></span>
-<span id="cb6-7"><a href="#cb6-7"></a><span class="co">#&gt;                                                           Mean   :123.9  </span></span>
-<span id="cb6-8"><a href="#cb6-8"></a><span class="co">#&gt;                                                           3rd Qu.:176.0  </span></span>
-<span id="cb6-9"><a href="#cb6-9"></a><span class="co">#&gt;                                                           Max.   :176.0  </span></span>
-<span id="cb6-10"><a href="#cb6-10"></a><span class="co">#&gt;      n_HM2             n_HT             n_NA       </span></span>
-<span id="cb6-11"><a href="#cb6-11"></a><span class="co">#&gt;  Min.   :  0.00   Min.   :  0.00   Min.   :  0.00  </span></span>
-<span id="cb6-12"><a href="#cb6-12"></a><span class="co">#&gt;  1st Qu.:  0.00   1st Qu.:  0.00   1st Qu.:  0.00  </span></span>
-<span id="cb6-13"><a href="#cb6-13"></a><span class="co">#&gt;  Median :  0.00   Median :  0.00   Median :  1.00  </span></span>
-<span id="cb6-14"><a href="#cb6-14"></a><span class="co">#&gt;  Mean   : 19.92   Mean   : 19.24   Mean   : 12.91  </span></span>
-<span id="cb6-15"><a href="#cb6-15"></a><span class="co">#&gt;  3rd Qu.: 34.00   3rd Qu.:  5.00   3rd Qu.:  5.00  </span></span>
-<span id="cb6-16"><a href="#cb6-16"></a><span class="co">#&gt;  Max.   :175.00   Max.   :176.00   Max.   :176.00</span></span></code></pre></div>
-<p>Then we will use the different mark_* functions in order to filter the markers. First, we can use mark_match() function. This function excludes markers that are in your genotype file but not in the reference genotype dataset. We recomend using this function as the chip used for genotyping may change.</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>tab2 &lt;-<span class="st"> </span><span class="kw">mark_match</span>(stuart_tab,<span class="dt">ref=</span>strains)</span>
-<span id="cb7-2"><a href="#cb7-2"></a></span>
-<span id="cb7-3"><a href="#cb7-3"></a></span>
-<span id="cb7-4"><a href="#cb7-4"></a>tab2 <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(exclude_match<span class="op">==</span><span class="dv">1</span>)</span>
-<span id="cb7-5"><a href="#cb7-5"></a><span class="co">#&gt; # A tibble: 0 x 8</span></span>
-<span id="cb7-6"><a href="#cb7-6"></a><span class="co">#&gt; # … with 8 variables: SNP.Name &lt;chr&gt;, Allele_1 &lt;chr&gt;, Allele_2 &lt;chr&gt;,</span></span>
-<span id="cb7-7"><a href="#cb7-7"></a><span class="co">#&gt; #   n_HM1 &lt;dbl&gt;, n_HM2 &lt;dbl&gt;, n_HT &lt;dbl&gt;, n_NA &lt;dbl&gt;, exclude_match &lt;dbl&gt;</span></span></code></pre></div>
-<p>Here the reference strains were genotyped with the same version of the chip as the F2 individuals so no marker was excluded.</p>
-<p>Then, we can use the mark_poly() function, which will exclude the markers that are not polymorphic.</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>tab2 &lt;-<span class="st"> </span><span class="kw">mark_poly</span>(tab2)</span>
-<span id="cb8-2"><a href="#cb8-2"></a><span class="kw">head</span>(tab2)</span>
-<span id="cb8-3"><a href="#cb8-3"></a><span class="co">#&gt; # A tibble: 6 x 9</span></span>
-<span id="cb8-4"><a href="#cb8-4"></a><span class="co">#&gt;   SNP.Name  Allele_1 Allele_2 n_HM1 n_HM2  n_HT  n_NA exclude_match exclude_poly</span></span>
-<span id="cb8-5"><a href="#cb8-5"></a><span class="co">#&gt;   &lt;chr&gt;     &lt;chr&gt;    &lt;chr&gt;    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;         &lt;dbl&gt;        &lt;dbl&gt;</span></span>
-<span id="cb8-6"><a href="#cb8-6"></a><span class="co">#&gt; 1 B1001000… C        &lt;NA&gt;       175     0     0     1             0            1</span></span>
-<span id="cb8-7"><a href="#cb8-7"></a><span class="co">#&gt; 2 B1001001… &lt;NA&gt;     &lt;NA&gt;         0     0     0   176             0            1</span></span>
-<span id="cb8-8"><a href="#cb8-8"></a><span class="co">#&gt; 3 B1001001… A        &lt;NA&gt;       175     0     0     1             0            1</span></span>
-<span id="cb8-9"><a href="#cb8-9"></a><span class="co">#&gt; 4 B1001002… A        G          171     0     2     3             0            0</span></span>
-<span id="cb8-10"><a href="#cb8-10"></a><span class="co">#&gt; 5 B1001002… C        &lt;NA&gt;       176     0     0     0             0            1</span></span>
-<span id="cb8-11"><a href="#cb8-11"></a><span class="co">#&gt; 6 B1001003… A        &lt;NA&gt;       176     0     0     0             0            1</span></span></code></pre></div>
-<p>The mark_prop() function can be used to filter markers depending on the proportion of each genotype. Here, we have a F2 so we can use the “homo” argument in order to filter depending on the proportion of both homozygous genotype. If we have a N2, we can filter with the proportion of homozygous individuals with the “homo” argument and of heterozygous individuals with the hetero&quot; argument. Moreover, this function allows to filter marker depending on the proportion on non genotyped animals. By defaults, markers for which more than 50% of individuals were not genotyped.</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a>tab2 &lt;-<span class="st"> </span><span class="kw">mark_prop</span>(tab2,<span class="dt">cross=</span><span class="st">&quot;F2&quot;</span>,<span class="dt">homo=</span><span class="fl">0.1</span>,<span class="dt">hetero=</span><span class="fl">0.1</span>)</span>
-<span id="cb9-2"><a href="#cb9-2"></a><span class="kw">head</span>(tab2)</span>
-<span id="cb9-3"><a href="#cb9-3"></a><span class="co">#&gt; # A tibble: 6 x 10</span></span>
-<span id="cb9-4"><a href="#cb9-4"></a><span class="co">#&gt;   SNP.Name  Allele_1 Allele_2 n_HM1 n_HM2  n_HT  n_NA exclude_match exclude_poly</span></span>
-<span id="cb9-5"><a href="#cb9-5"></a><span class="co">#&gt;   &lt;chr&gt;     &lt;chr&gt;    &lt;chr&gt;    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;         &lt;dbl&gt;        &lt;dbl&gt;</span></span>
-<span id="cb9-6"><a href="#cb9-6"></a><span class="co">#&gt; 1 B1001000… C        &lt;NA&gt;       175     0     0     1             0            1</span></span>
-<span id="cb9-7"><a href="#cb9-7"></a><span class="co">#&gt; 2 B1001001… &lt;NA&gt;     &lt;NA&gt;         0     0     0   176             0            1</span></span>
-<span id="cb9-8"><a href="#cb9-8"></a><span class="co">#&gt; 3 B1001001… A        &lt;NA&gt;       175     0     0     1             0            1</span></span>
-<span id="cb9-9"><a href="#cb9-9"></a><span class="co">#&gt; 4 B1001002… A        G          171     0     2     3             0            0</span></span>
-<span id="cb9-10"><a href="#cb9-10"></a><span class="co">#&gt; 5 B1001002… C        &lt;NA&gt;       176     0     0     0             0            1</span></span>
-<span id="cb9-11"><a href="#cb9-11"></a><span class="co">#&gt; 6 B1001003… A        &lt;NA&gt;       176     0     0     0             0            1</span></span>
-<span id="cb9-12"><a href="#cb9-12"></a><span class="co">#&gt; # … with 1 more variable: exclude_prop &lt;dbl&gt;</span></span></code></pre></div>
-<p>Last, we can use the mark_allele(). This very helpful function excludes markers for which the alleles found in the F2/N2 individuals do not correspond to the alleles found in the parental strains. For example, if for a marker is not polymorphic in the parental strains but we found two alleles in the F2/N2 individuals, it will be excluded.</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a>tab2 &lt;-<span class="st"> </span><span class="kw">mark_allele</span>(<span class="dt">tab=</span>tab2,<span class="dt">ref=</span>strains,<span class="dt">par1=</span><span class="st">&quot;parent1&quot;</span>,<span class="dt">par2=</span><span class="st">&quot;parent2&quot;</span>)</span>
-<span id="cb10-2"><a href="#cb10-2"></a>tab2 <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(exclude_allele)) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">head</span>()</span>
-<span id="cb10-3"><a href="#cb10-3"></a><span class="co">#&gt; # A tibble: 6 x 11</span></span>
-<span id="cb10-4"><a href="#cb10-4"></a><span class="co">#&gt;   SNP.Name  Allele_1 Allele_2 n_HM1 n_HM2  n_HT  n_NA exclude_match exclude_poly</span></span>
-<span id="cb10-5"><a href="#cb10-5"></a><span class="co">#&gt;   &lt;chr&gt;     &lt;chr&gt;    &lt;chr&gt;    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;         &lt;dbl&gt;        &lt;dbl&gt;</span></span>
-<span id="cb10-6"><a href="#cb10-6"></a><span class="co">#&gt; 1 B1002001… T        C            0   172     1     3             0            0</span></span>
-<span id="cb10-7"><a href="#cb10-7"></a><span class="co">#&gt; 2 B1002009… A        G            0   170     2     4             0            0</span></span>
-<span id="cb10-8"><a href="#cb10-8"></a><span class="co">#&gt; 3 B1003003… T        C            0   162     2    12             0            0</span></span>
-<span id="cb10-9"><a href="#cb10-9"></a><span class="co">#&gt; 4 B1004002… T        C            0   172     2     2             0            0</span></span>
-<span id="cb10-10"><a href="#cb10-10"></a><span class="co">#&gt; 5 B1004002… A        C            0   168     1     7             0            0</span></span>
-<span id="cb10-11"><a href="#cb10-11"></a><span class="co">#&gt; 6 B1004005… T        C            0    95    46    35             0            0</span></span>
-<span id="cb10-12"><a href="#cb10-12"></a><span class="co">#&gt; # … with 2 more variables: exclude_prop &lt;dbl&gt;, exclude_allele &lt;dbl&gt;</span></span></code></pre></div>
-<p>Indeed, we can see that the markers excluded with mark_allele() have different alleles in the parental strains.</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>strains <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(marker <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;gJAX00038569&quot;</span>,<span class="st">&quot;gJAX00425031&quot;</span>,<span class="st">&quot;gUNC12245354&quot;</span>,<span class="st">&quot;gUNC15530876&quot;</span>,<span class="st">&quot;gUNC21555204&quot;</span>,<span class="st">&quot;gUNC21596600&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">arrange</span>(marker) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(marker,parent1,parent2)</span>
-<span id="cb11-2"><a href="#cb11-2"></a><span class="co">#&gt;         marker parent1 parent2</span></span>
-<span id="cb11-3"><a href="#cb11-3"></a><span class="co">#&gt; 1 gJAX00038569       C       C</span></span>
-<span id="cb11-4"><a href="#cb11-4"></a><span class="co">#&gt; 2 gJAX00425031       C       C</span></span>
-<span id="cb11-5"><a href="#cb11-5"></a><span class="co">#&gt; 3 gUNC12245354       N       N</span></span>
-<span id="cb11-6"><a href="#cb11-6"></a><span class="co">#&gt; 4 gUNC15530876       N       N</span></span>
-<span id="cb11-7"><a href="#cb11-7"></a><span class="co">#&gt; 5 gUNC21555204       T       T</span></span>
-<span id="cb11-8"><a href="#cb11-8"></a><span class="co">#&gt; 6 gUNC21596600       A       A</span></span></code></pre></div>
-</div>
-</div>
-<div id="creation-of-the-rqtl-file" class="section level1">
-<h1>Creation of the R/qtl file</h1>
-<p>After excluding the problematic markers, we can create the R/qtl file. The individuals must have the same ID in the geno and in the pheno file. If there is a prefix in the geno file that must be removed in order to acheive this, you can use the “prefix” argument. The “path” argument can be used in order to create a CSV file that you can laod with qtl::read.cross.</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a>rqtl_file &lt;-<span class="st"> </span>(<span class="kw">write_rqtl</span>(<span class="dt">geno=</span>genos,<span class="dt">pheno=</span>phenos,<span class="dt">tab=</span>tab2,<span class="dt">ref=</span>strains,<span class="dt">par1=</span><span class="st">&quot;parent1&quot;</span>,<span class="dt">par2=</span><span class="st">&quot;parent2&quot;</span>,<span class="dt">prefix=</span><span class="st">&quot;ind_&quot;</span>))</span>
-<span id="cb12-2"><a href="#cb12-2"></a></span>
-<span id="cb12-3"><a href="#cb12-3"></a>rqtl_file[<span class="dv">1</span><span class="op">:</span><span class="dv">10</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">7</span>]</span>
-<span id="cb12-4"><a href="#cb12-4"></a><span class="co">#&gt; # A tibble: 10 x 7</span></span>
-<span id="cb12-5"><a href="#cb12-5"></a><span class="co">#&gt;    Ind   Sex   Age   Pheno              S6J010381992 S6J011498219 S6J011558924</span></span>
-<span id="cb12-6"><a href="#cb12-6"></a><span class="co">#&gt;    &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt;              &lt;chr&gt;        &lt;chr&gt;        &lt;chr&gt;       </span></span>
-<span id="cb12-7"><a href="#cb12-7"></a><span class="co">#&gt;  1 &quot;&quot;    &quot;&quot;    &quot;&quot;    &quot;&quot;                 1            1            1           </span></span>
-<span id="cb12-8"><a href="#cb12-8"></a><span class="co">#&gt;  2 &quot;&quot;    &quot;&quot;    &quot;&quot;    &quot;&quot;                 2.188        15.746       17.347      </span></span>
-<span id="cb12-9"><a href="#cb12-9"></a><span class="co">#&gt;  3 &quot;201&quot; &quot;M&quot;   &quot;7&quot;   &quot;10.5296676084955&quot; 1            1            1           </span></span>
-<span id="cb12-10"><a href="#cb12-10"></a><span class="co">#&gt;  4 &quot;210&quot; &quot;M&quot;   &quot;7&quot;   &quot;10.4877403515456&quot; 2            0            0           </span></span>
-<span id="cb12-11"><a href="#cb12-11"></a><span class="co">#&gt;  5 &quot;308&quot; &quot;F&quot;   &quot;7&quot;   &quot;10.9714806769608&quot; 1            0            0           </span></span>
-<span id="cb12-12"><a href="#cb12-12"></a><span class="co">#&gt;  6 &quot;309&quot; &quot;M&quot;   &quot;7&quot;   &quot;10.8463976841841&quot; 0            1            1           </span></span>
-<span id="cb12-13"><a href="#cb12-13"></a><span class="co">#&gt;  7 &quot;310&quot; &quot;M&quot;   &quot;7&quot;   &quot;11.0680508131391&quot; NA           1            1           </span></span>
-<span id="cb12-14"><a href="#cb12-14"></a><span class="co">#&gt;  8 &quot;311&quot; &quot;F&quot;   &quot;9&quot;   &quot;10.5753482053348&quot; 0            2            2           </span></span>
-<span id="cb12-15"><a href="#cb12-15"></a><span class="co">#&gt;  9 &quot;312&quot; &quot;M&quot;   &quot;7&quot;   &quot;10.7137055438312&quot; 0            2            2           </span></span>
-<span id="cb12-16"><a href="#cb12-16"></a><span class="co">#&gt; 10 &quot;313&quot; &quot;M&quot;   &quot;8&quot;   &quot;10.7375743556446&quot; 0            1            1</span></span></code></pre></div>
-</div>
-
-
-
-<!-- code folding -->
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
-  (function () {
-    var script = document.createElement("script");
-    script.type = "text/javascript";
-    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
-    document.getElementsByTagName("head")[0].appendChild(script);
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diff --git a/stuart.Rcheck/00_pkg_src/stuart/inst/extdata/genos.txt b/stuart.Rcheck/00_pkg_src/stuart/inst/extdata/genos.txt
deleted file mode 100755
index 49a9e33f41b395a716aaf3df972513b9058f5bf4..0000000000000000000000000000000000000000
Binary files a/stuart.Rcheck/00_pkg_src/stuart/inst/extdata/genos.txt and /dev/null differ
diff --git a/stuart.Rcheck/00_pkg_src/stuart/inst/extdata/phenos.txt b/stuart.Rcheck/00_pkg_src/stuart/inst/extdata/phenos.txt
deleted file mode 100755
index 1d25e9ab2a19f593f1c7809e71eeb16337aa3820..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/inst/extdata/phenos.txt
+++ /dev/null
@@ -1,177 +0,0 @@
-Ind	Sex	Exp	Age	Death	Day.death	Pheno
-201	M	36	7	1	14	10.5296676084955
-210	M	38	7	0	14	10.4877403515456
-308	F	53	7	1	8	10.9714806769608
-309	M	53	7	1	7	10.8463976841841
-310	M	53	7	1	8	11.0680508131391
-311	F	53	9	1	7	10.5753482053348
-312	M	56	7	1	7	10.7137055438312
-313	M	56	8	1	7	10.7375743556446
-315	M	56	7	0	14	10.8719419391157
-318	F	56	6	1	6	10.8396273596649
-319	F	56	6	0	14	10.7668793836238
-320	M	58	8	0	14	10.7325547648656
-211	M	38	7	0	14	10.8695302496376
-321	M	58	6	1	7	10.8283736794678
-322	M	58	6	1	7	10.8915635045684
-323	M	58	6	1	7	10.5356077157252
-324	F	58	6	1	7	10.6280255935391
-325	F	58	8	0	14	10.7480587696075
-326	F	58	6	1	7	10.723232575067
-327	F	58	6	1	7	10.561378054744
-328	M	58	8	1	8	10.678328733254
-329	M	58	7	1	8	10.5553266906605
-330	M	58	7	1	8	9.80206867803321
-212	M	38	7	0	14	10.6357450501074
-331	M	58	7	1	7	10.5982605721032
-332	M	58	7	1	7	10.8473250099944
-333	F	58	6	1	7	10.3764348734637
-334	F	58	6	1	7	10.5460358865664
-336	F	58	5	1	7	10.5222855493513
-337	F	67	8	1	9	10.5327174182859
-338	F	67	6	0	14	10.5473443040275
-339	F	67	8	1	8	9.87194179754362
-340	F	67	6	1	8	10.7090598750034
-341	M	67	6	1	8	10.2209997596409
-213	M	38	7	1	8	11.0270167306899
-342	M	67	8	1	8	10.5431290182179
-343	M	68	8	0	14	10.4981076961986
-344	M	68	8	0	14	10.3735386730113
-345	F	68	8	1	14	10.4320215351478
-346	F	68	8	0	14	8.60947058208406
-347	F	73	7	0	14	10.4168301170408
-348	F	73	7	1	6	11.0628524075666
-349	F	73	7	1	7	10.4330268533001
-350	M	73	7	1	7	10.4800637563932
-351	M	73	6	1	7	10.6324192103035
-214	M	38	7	1	8	10.8218250314825
-352	M	73	6	1	8	10.4725870266356
-353	M	73	6	1	8	10.463431310619
-354	F	73	8	1	9	10.2353468625069
-355	F	73	8	1	8	10.4784947722463
-356	M	73	8	0	14	10.2302554084309
-357	M	73	7	1	7	10.3331446827217
-358	M	75	7	1	8	10.4392178227394
-359	M	75	7	1	9	10.2749993364651
-360	M	75	7	1	9	10.5896956543931
-361	M	75	7	1	9	10.4102656461775
-215	M	38	7	1	7	10.98405240378
-362	M	75	7	0	14	9.25536232298768
-363	M	75	9	1	9	10.3492941307168
-364	M	75	9	1	9	10.3620959742092
-365	M	75	9	1	12	10.4744976511434
-366	M	75	9	1	12	11.030867369515
-369	F	75	9	0	14	10.628477999175
-370	F	75	9	0	14	10.4670786491756
-373	F	75	7	0	14	10.9179711868264
-374	F	75	9	1	8	10.4953620125703
-375	F	75	7	1	7	10.1874531724974
-216	M	38	7	0	14	10.5655205985375
-316	M	78	9	0	14	10.766442313453
-371	M	78	9	1	12	10.4195168879626
-372	M	78	9	1	9	9.95272129347567
-376	M	78	8	0	14	10.4195168879626
-377	F	78	9	0	14	9.95272129347567
-379	F	78	8	0	14	10.0823979142355
-380	F	78	8	1	8	10.9095058001147
-381	M	78	8	1	12	10.8801492251438
-383	F	78	7	1	8	10.9965190456509
-384	F	78	7	0	14	11.0509121188558
-217	M	38	7	1	7	10.4994444268072
-385	F	78	7	1	8	11.0429066559961
-386	F	78	7	0	14	10.8109357224842
-387	M	78	8	0	14	10.6074095323141
-388	M	78	7	0	14	10.7086232219316
-389	M	78	7	1	8	10.8244554978178
-390	M	78	7	1	8	10.5135622767937
-391	M	78	7	1	8	10.6549253863367
-219	F	38	7	1	9	10.9538292220917
-220	M	40	7	1	7	10.7769347175043
-202	M	36	7	1	7	10.6939691207128
-221	M	40	7	0	14	10.6933765093072
-222	F	43	6	1	7	10.6767026645835
-223	F	43	6	0	14	10.5003906942331
-224	F	43	6	0	14	10.5149649858964
-225	F	43	6	0	14	10.8903239014372
-226	M	43	6	0	14	10.505656838062
-227	M	43	6	1	7	10.9661802289593
-228	M	43	6	1	7	10.7323373517824
-229	M	45	8	1	7	10.7312300131548
-230	M	45	8	0	14	10.5748054422932
-203	M	36	7	0	14	10.6753018614348
-231	M	45	8	0	14	10.6367921020912
-232	M	45	8	1	7	11.0739366525724
-233	F	45	8	0	14	10.1491227173623
-234	F	45	8	1	8	10.6667311696339
-235	F	45	8	0	14	8.7941942405191
-236	F	45	8	0	14	10.9093248127322
-237	M	47	7	0	14	10.9324949209267
-238	M	47	7	0	14	10.6947153326779
-239	M	47	7	0	14	10.6120985065664
-240	M	47	7	1	8	10.8327443879201
-204	M	36	7	1	7	10.7791570102755
-241	M	47	7	1	9	10.5125518260128
-242	M	47	7	1	7	10.6894120122994
-243	M	47	7	1	7	11.0691241089673
-245	M	47	7	0	14	10.4819031965733
-246	F	47	7	1	8	9.96762433427887
-247	F	47	7	1	8	10.7245450774586
-248	F	47	7	0	14	10.4912425829631
-249	F	47	7	0	14	10.7474083506145
-250	M	48	8	0	14	10.2720876656636
-251	M	48	8	0	14	9.31399254303701
-205	M	38	7	0	14	10.4428500375673
-252	M	48	8	1	7	10.2862132021395
-253	M	48	7	1	8	10.5048761731899
-254	F	48	8	0	14	10.228672834882
-255	F	48	7	1	7	9.62470062290951
-256	F	48	7	0	14	10.6406621644657
-257	M	48	7	0	14	9.94948396721433
-258	M	48	7	1	7	9.30736764204129
-259	M	48	7	0	14	9.88913119725402
-260	M	48	7	0	14	10.2380596666633
-261	M	48	7	1	7	10.2540669844702
-206	M	38	7	0	14	10.7606379038488
-262	M	48	7	0	14	9.76042991401441
-263	M	48	7	0	14	10.6091919117065
-264	F	50	8	0	14	10.6851731967701
-266	F	50	7	1	8	10.7673408387505
-267	M	50	8	1	7	10.0482542502632
-269	M	50	8	1	8	11.0733835864953
-270	M	50	8	0	14	10.4484942670828
-271	M	50	7	0	14	10.1718549349538
-272	M	50	6	1	7	10.3653629747907
-274	M	50	8	1	7	10.7987817154853
-207	M	38	7	0	14	9.28000922051108
-265	F	51	8	1	8	10.6396558357786
-268	F	51	8	1	8	10.38209131129
-273	F	51	7	0	14	10.668205277928
-277	F	51	7	0	14	10.3487626209106
-278	M	51	7	0	14	8.79404815627125
-279	M	51	6	0	14	10.983879782484
-284	M	52	7	1	7	10.9273290008382
-285	M	52	7	0	14	10.9386568661041
-286	M	52	7	0	14	10.8807485243696
-287	M	52	7	1	8	10.9037194625542
-208	M	38	7	0	14	10.7753050361387
-288	M	52	7	0	14	10.7677108901204
-289	F	52	7	1	7	10.9101761509059
-290	F	52	7	1	7	10.9405884955895
-291	F	52	7	0	14	8.61409649857943
-292	F	52	7	0	14	10.7908109750272
-293	F	52	7	1	7	10.9585494660728
-283	M	52	7	0	14	10.7347416773051
-294	M	52	7	0	14	10.5163444828732
-295	M	52	7	0	14	10.7527016128882
-296	M	52	6	0	14	10.7427219571178
-209	M	38	7	1	7	10.6341153687419
-297	M	52	6	1	8	10.6603294779049
-298	M	52	6	1	7	10.952768506441
-299	F	52	6	1	7	11.1474990509139
-300	F	52	6	0	14	9.35603884473677
-301	F	52	6	1	8	11.1056293961659
-303	M	53	9	0	14	10.1069851602543
-304	F	53	7	1	8	10.7275476145336
-305	F	53	7	0	14	10.9381137134461
-306	F	53	7	0	14	10.5449698000374
-307	F	53	7	0	14	10.6311447764499
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/geno_strains.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/geno_strains.Rd
deleted file mode 100755
index c7be59d453890a7cfa5929321dbbb2b8d99f9418..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/geno_strains.Rd
+++ /dev/null
@@ -1,26 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/geno_strains.R
-\name{geno_strains}
-\alias{geno_strains}
-\title{Create haplotype for a new mouse strain into a reference dataframe}
-\usage{
-geno_strains(ref, geno, par1, par2, name1, name2)
-}
-\arguments{
-\item{ref}{data frame with the reference genotypes of mouse lines}
-
-\item{geno}{data frame with the genotyping results for your cross from miniMUGA array}
-
-\item{par1}{first parental strain used in the cross, the name must be written as in the geno data frame}
-
-\item{par2}{second parental strain used in the cross, the name must be written as in the geno data frame}
-
-\item{name1}{name of the first parental strain to use as the column name in the ref data frame}
-
-\item{name2}{name of the second parental strain to use as the column name in the ref data frame}
-}
-\description{
-This functions adds columns for parental strains used in the cross in the annotation data frame, from the genotype data frame in which one or several animal of the parental strains were genotyped.
-If several animals of one strain were genotyped, a consensus is created from these animals.
-The consensus is created as follow : if the indivuals carry the same allele, this allele is kept, otherwise, the allele is noted as "N". If individuals show residual heterozygosity, it is encoded as "H".
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/genos.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/genos.Rd
deleted file mode 100755
index 1aad5938b7e0d7fd09e31b1ca35a07c887f09449..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/genos.Rd
+++ /dev/null
@@ -1,16 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/genos-data.R
-\docType{data}
-\name{genos}
-\alias{genos}
-\title{Data frame with miniMUGA genotyping of F2 individuals and parental strains}
-\format{
-A data frame with 2002493 observations of 11 variables
-}
-\usage{
-genos
-}
-\description{
-A dataset containing the genotypes of 176 F2 individuals
-}
-\keyword{datasets}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/mark_allele.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/mark_allele.Rd
deleted file mode 100755
index 6bb2a189321bcc7d4990373d4ff75b65d3950546..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/mark_allele.Rd
+++ /dev/null
@@ -1,20 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/mark_allele.R
-\name{mark_allele}
-\alias{mark_allele}
-\title{Exclude markers that have different alleles in the individuals of the cross and in parental strains}
-\usage{
-mark_allele(tab, ref, par1, par2)
-}
-\arguments{
-\item{tab}{data frame obtained with tab_mark function}
-
-\item{ref}{data frame with the reference genotypes of mouse lines}
-
-\item{par1}{first parental strain used in the cross, the name must be written as in the "ref" data frame}
-
-\item{par2}{second parental strain used in the cross, the name must be written as in the "ref" data frame}
-}
-\description{
-This functions uses the dataframe produced by the tab_mark function and fills the "exclude" column for all the markers which have alleles observed in the individuals of the cross that do not correspond to the alleles observed in the parental strains. For example, a marker which is not polymorphic between the two parental strains but which has two alleles in the cross individuals will be excluded.
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/mark_match.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/mark_match.Rd
deleted file mode 100755
index 15791a97b75b92476cf5a33d9a3720a849a4d1a0..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/mark_match.Rd
+++ /dev/null
@@ -1,16 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/mark_match.R
-\name{mark_match}
-\alias{mark_match}
-\title{Exclude markers that were not genotyped in the reference strains}
-\usage{
-mark_match(tab, ref)
-}
-\arguments{
-\item{tab}{data frame obtained with tab_mark function}
-
-\item{ref}{data frame with the reference genotypes of mouse lines}
-}
-\description{
-This functions uses the dataframe produced by the tab_mark function and fills the "exclude" column for all the markers that were genotyped in the individuals of the cross but not in the reference strains. This is useful if the parental strains of the cross were not genotyped with the individuals and a previous genotyping result is used. Indeed, changes in the markers of the array may have happened. We recommend always using this function in order to avoid errors.
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/mark_poly.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/mark_poly.Rd
deleted file mode 100755
index d68157afc68e62187a31bf8862ea3fdaf16585eb..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/mark_poly.Rd
+++ /dev/null
@@ -1,14 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/mark_poly.R
-\name{mark_poly}
-\alias{mark_poly}
-\title{Exclude markers that are not polymorphic}
-\usage{
-mark_poly(tab)
-}
-\arguments{
-\item{tab}{data frame obtained with tab_mark function}
-}
-\description{
-This functions uses the dataframe produced by the tab_mark function and fills the "exclude" column for all the markers that are not polymorphic.
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/mark_prop.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/mark_prop.Rd
deleted file mode 100755
index fa0163af9fb00460925f56115d4688185f175841..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/mark_prop.Rd
+++ /dev/null
@@ -1,22 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/mark_prop.R
-\name{mark_prop}
-\alias{mark_prop}
-\title{Exclude markers depending on proportions of homo/hetorozygous}
-\usage{
-mark_prop(tab, cross, homo = NA, hetero = NA, na = 0.5)
-}
-\arguments{
-\item{tab}{data frame obtained with tab_mark function.}
-
-\item{cross}{F2 or N2.}
-
-\item{homo}{proportion of homozygous individuals under which the marker is excluded. Will apply on both homozygous genotypes for a F2, but only on one for N2.}
-
-\item{hetero}{proportion of heterozygous individuals under which the marker is excluded.}
-
-\item{na}{proportion of non-genotyped individuals above which the marker is excluded.}
-}
-\description{
-uses the dataframe produced by the tab_mark function and fills the "exclude" column for all the markers that present odd proportions of each genotype. You can define these proportions thanks to the arguments of the function.
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/phenos.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/phenos.Rd
deleted file mode 100755
index bd7b403cc69888e78b28149e4a30c0ded16367a2..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/phenos.Rd
+++ /dev/null
@@ -1,16 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/phenos-data.R
-\docType{data}
-\name{phenos}
-\alias{phenos}
-\title{Data frame with phenotype of F2 individuals}
-\format{
-A data frame with 176 observations of 4 variables
-}
-\usage{
-phenos
-}
-\description{
-A dataset containing the phenotype of 176 F2 individuals
-}
-\keyword{datasets}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/ref_strains_mini.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/ref_strains_mini.Rd
deleted file mode 100755
index e9117d1075cc3823b7511e4b219a5656a5cf348a..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/ref_strains_mini.Rd
+++ /dev/null
@@ -1,37 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/ref_strains_mini-data.R
-\docType{data}
-\name{ref_strains_mini}
-\alias{ref_strains_mini}
-\title{Data frame with miniMUGA genotyping of classical lab strains.}
-\format{
-A data frame with 11299 rows and 18 variables
-\describe{
-\item{CC001}{CC001 mouse strain}
-\item{CC005}{CC005 mouse strain}
-\item{CC042}{CC042 mouse strain}
-\item{CC071}{CC071 mouse strain}
-\item{Ifnar.KO.129}{Ifnar KO 129 mouse strain}
-\item{Ifnar.KO.B6}{Ifnar KO B6 mouse strain}
-\item{Rvfs2.1}{Rvfs2-1 mouse strain}
-\item{Rvfs2.2}{Rvfs2-2 mouse strain}
-\item{Rvfs2.6}{Rvfs2-6 mouse strain}
-\item{Rvfs2.7}{Rvfs2-7 mouse strain}
-\item{marker}{name of the marker}
-\item{chr}{chromosome}
-\item{bp_mm10}{localisation on chromosome in bp (mm10 assembly)}
-\item{cM_cox}{localisation on chromosome in cM (from Cox et al.)}
-\item{cM_g2f1}{localisation on chromosome in cM (from Liu et al.)}
-\item{snp}{marker alleles}
-\item{unique}{indicates if the marker maps uniquely on mm10}
-\item{multi}{indicates if the marker maps more than one time on mm10}
-\item{unmapped}{indicates if the marker does not map perfectly on mm10}
-}
-}
-\usage{
-ref_strains_mini
-}
-\description{
-A dataset containing the genotypes of 10 mouse strains of the Institut pasteur. Markers positions and other information are from by Karl Broman (https://kbroman.org/MUGAarrays/mini_revisited.html). Strains genotyped from Institut Pasteur.
-}
-\keyword{datasets}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/stuart_tab.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/stuart_tab.Rd
deleted file mode 100644
index ebe357ac22fecfe7fca149db36df30412e4d55ea..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/stuart_tab.Rd
+++ /dev/null
@@ -1,25 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/stuart_tab-data.R
-\docType{data}
-\name{stuart_tab}
-\alias{stuart_tab}
-\title{Output of tab_mark function}
-\format{
-A data frame with 11125 rows and 7 variables
-\describe{
-\item{SNP.Name}{name of the marker}
-\item{Allele_1}{first allele of the marker}
-\item{Allele_2}{second allele of the marker}
-\item{n_HM1}{number of homozygous individuals for the first allele}
-\item{n_HM2}{number of homozygous individuals for the second allele}
-\item{n_HT}{number of heterozygous individuals}
-\item{n_NA}{number of non genotyped individuals}
-}
-}
-\usage{
-stuart_tab
-}
-\description{
-A dataset with the output of tab_mark() function.
-}
-\keyword{datasets}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/tab_mark.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/tab_mark.Rd
deleted file mode 100755
index fb6ef3952d5a3b8147940e428aaba0dfb728e905..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/tab_mark.Rd
+++ /dev/null
@@ -1,14 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/tab_mark.R
-\name{tab_mark}
-\alias{tab_mark}
-\title{Create of the summary table for all markers from the genotype data frame}
-\usage{
-tab_mark(geno)
-}
-\arguments{
-\item{geno}{data frame with the genotyping results for your cross}
-}
-\description{
-This function creates a table with all the markers that were genotyped in the array, the alleles for these markers, the number of homozygous and heterozygous animals, as well as the number of non genotyped animals.
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/man/write_rqtl.Rd b/stuart.Rcheck/00_pkg_src/stuart/man/write_rqtl.Rd
deleted file mode 100755
index 77fb9302cf40dd2bff55569500ef452f85e9214b..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/man/write_rqtl.Rd
+++ /dev/null
@@ -1,40 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/write_rqtl.R
-\name{write_rqtl}
-\alias{write_rqtl}
-\title{Create data frame in Rqtl CSV format}
-\usage{
-write_rqtl(
-  geno,
-  pheno,
-  tab,
-  ref,
-  par1,
-  par2,
-  prefix,
-  method = "cM_cox",
-  path = NA
-)
-}
-\arguments{
-\item{geno}{data frame with the genotyping results for your cross}
-
-\item{pheno}{data frame with phenotypes of the individuals (individuals must have the same ID in the geno data frame and in the pheno data frame)}
-
-\item{tab}{data frame obtained with tab_mark function}
-
-\item{ref}{data frame with the reference genotypes of mouse lines}
-
-\item{par1}{first parental strain used in the cross, the name must be written as in the "ref" data frame}
-
-\item{par2}{second parental strain used in the cross, the name must be written as in the "ref" data frame}
-
-\item{prefix}{potential prefix present in the names of the individuals in the geno data frame to be removed in ordere to have the same names as in the pheno file}
-
-\item{method}{method of calculation of cM position, can be "cM_cox" of "cM_g2f1"}
-
-\item{path}{if indicated, the data frame will be exported in this path}
-}
-\description{
-This function uses the table produced by tab_mark function filled by all the mark_* functions in order to create a data frame in the right format for Rqtl read.cross function. Only the non-excluded markers will be kept and genotypeds will be encoded in "0", "1" and "2", "0" being homozygous for the first parental strain, "1" heterozygous and "2" homozygous for the second parental strain. Caution, this file create a data frame and a CSV file in the path of your choice if indicated by the "path" argument. This function does not create a "cross" object in your environment that can be directly used for QTL mapping. You will need to load the CSV file with qtl::read.cross.
-}
diff --git a/stuart.Rcheck/00_pkg_src/stuart/stuart_0.1.0.tar.gz b/stuart.Rcheck/00_pkg_src/stuart/stuart_0.1.0.tar.gz
deleted file mode 100644
index ffbb4d84106e8cdf3040091f9393e381e2dc6d90..0000000000000000000000000000000000000000
Binary files a/stuart.Rcheck/00_pkg_src/stuart/stuart_0.1.0.tar.gz and /dev/null differ
diff --git a/stuart.Rcheck/00_pkg_src/stuart/vignettes/stuaRt.Rmd b/stuart.Rcheck/00_pkg_src/stuart/vignettes/stuaRt.Rmd
deleted file mode 100755
index 4e294ab0cc3558b6b84acf6a660e790a6c6522f4..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00_pkg_src/stuart/vignettes/stuaRt.Rmd
+++ /dev/null
@@ -1,134 +0,0 @@
----
-title: "stuaRt"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{stuaRt}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-```
-
-Marie Bourdon
-
-April 2021
-
-## Goal
-
-stuaRt is a R package which formats the genotyping data from MUGA arrays (Neogen) to use it in Rqtl, for backcross or F2 crosses. It allows to filter the markers in MUGA arrays that can or cannot be used for Rqtl analysis, from a genetic point of view. Indeed, markers will be selected depending on their proportion of each genotype, correspondance between F2 or N2 individuals alleles and parental strains alleles, etc.
-
-The examples shown here require the use of dplyr package.
-
-
-```{r setup}
-library(dplyr)
-library(stuart)
-```
-
-
-## Annotation files
-
-The developer of Rqtl and Rqtl2 packages, Karl Broman, realised that the annotation of the MUGA arrays was not correct for some markers. Thus, he produced new annotation files for MUGA, miniMUGA, megaMUGA and gigaMUGA arrays. These files contain some informations about the markers including the chromosome and position where the probe of the marker matchs on the genome, wether the marker maps uniquely or not, etc. These files also contains the genetic position of the markers calculated with two methods : "cM_cox" and "cM_g2f1" (see https://kbroman.org/MUGAarrays/mini_revisited.html for more informations).
-
-We recommand to use these annotation files to reconstruct the file use for Rqtl analysis. You can load the datasets with these annotations from GitHub (https://github.com/kbroman/MUGAarrays/tree/master/UWisc). Choose the file corresponding to the MUGA array that you used and use the URL to load the dataset in R.
-
-Here, we will present an example of the use of stuaRt with results of a F2 cross genotyped with miniMUGA. We load the result of Neogen genotyping: `genos` and thephenotype dataset produced by the lab: `phenos`. All these datasets are available for example in stuaRt package.
-
-```{r annot}
-annot_mini <- read.csv(url("https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv"))
-```
-
-
-
-```{r load}
-data(genos)
-summary(genos)
-data(phenos)
-summary(phenos)
-```
-
-### Genotyping of parental strains
-
-To use genotyping result for Rqtl analysis, we need to recode the genotypes of the individuals (originally encoded in A, T, G, C) depending on the genotype of the parental strains: homozygous for the first parental strain (0), heterozygous (1) or homozygous for the second parental strain (2).
-
-We recommend to always genotype the parental strains of the cross. Here, their genotypes are in the `genos` file and correspond to the Sample.ID "StrainsA_1", "StrainsA_2", "StrainsB_1" and "StrainsB_2". Two individuals were genotyped for each parental strain. The first step will be to create a consensus genotype for each strain from the two genotyped individuals. The consensus genotype will be added to the annotation dataset in order to obtain a dataset with both annotation and reference genotype of the parental strains that will be used for recoding the genotypes or the F2 individuals. 
-
-This is done with the `geno_strains` function. 
-
-```{r strains}
-strains <- geno_strains(ref=annot_mini,geno=genos,par1=c("StrainsA_1","StrainsA_2"),par2=c("StrainsB_1","StrainsB_2"),name1="parent1",name2="parent2")
-head(strains)
-```
-
-After this step, we need to remove the genotyping result for these individuals from the `genos` dataset.
-```{r no_parent}
-genos <- genos %>% filter(!Sample.ID %in% c("StrainsA_1", "StrainsA_2", "StrainsB_1","StrainsB_2"))
-```
-
-
-## Markers sorting
-
-### Marker tab
-
-The first step of the markers sorting is to create the marker dataframe with the tab_mark() function. This dataframe contains for each marker the two alleles that can be found in the F2/N2 population (`Allele_1` and `Allele_2`), the number of individuals for each genotype (homozygous for each allele (`n_HM1` and `n_HM2`) and heterozygous (`n_HT`)), and the number of non genotyped individuals (`n_NA`) This step can take several minutes. You can also load the output of this function.
-
-
-```{r tab_mark}
-data(stuart_tab)
-summary(stuart_tab)
-```
-
-Then we will use the different mark_* functions in order to filter the markers. First, we can use mark_match() function. This function excludes markers that are in your genotype file but not in the reference genotype dataset. We recomend using this function as the chip used for genotyping may change.
-
-```{r mark_match}
-tab2 <- mark_match(stuart_tab,ref=strains)
-
-
-tab2 %>% filter(exclude_match==1)
-```
-
-Here the reference strains were genotyped with the same version of the chip as the F2 individuals so no marker was excluded.
-
-Then, we can use the mark_poly() function, which will exclude the markers that are not polymorphic.
-
-```{r mark_poly ex}
-tab2 <- mark_poly(tab2)
-head(tab2)
-```
-
-The mark_prop() function can be used to filter markers depending on the proportion of each genotype. Here, we have a F2 so we can use the "homo" argument in order to filter depending on the proportion of both homozygous genotype. If we have a N2, we can filter with the proportion of homozygous individuals with the "homo" argument and of heterozygous individuals with the hetero" argument. Moreover, this function allows to filter marker depending on the proportion on non genotyped animals. By defaults, markers for which more than 50% of individuals were not genotyped.
-
-```{r mark_prop ex}
-tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
-head(tab2)
-```
-
-Last, we can use the mark_allele(). This very helpful function excludes markers for which the alleles found in the F2/N2 individuals do not correspond to the alleles found in the parental strains. For example, if for a marker is not polymorphic in the parental strains but we found two alleles in the F2/N2 individuals, it will be excluded.
-
-```{r mark_allele}
-tab2 <- mark_allele(tab=tab2,ref=strains,par1="parent1",par2="parent2")
-tab2 %>% arrange(desc(exclude_allele)) %>% head()
-```
-
-Indeed, we can see that the markers excluded with mark_allele() have different alleles in the parental strains.
-
-```{r mark_allele-strains}
-strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354","gUNC15530876","gUNC21555204","gUNC21596600")) %>% arrange(marker) %>% select(marker,parent1,parent2)
-```
-
-# Creation of the R/qtl file
-
-After excluding the problematic markers, we can create the R/qtl file. The individuals must have the same ID in the geno and in the pheno file. If there is a prefix in the geno file that must be removed in order to acheive this, you can use the "prefix" argument. The "path" argument can be used in order to create a CSV file that you can laod with qtl::read.cross. 
-
-```{r write_qtl}
-rqtl_file <- (write_rqtl(geno=genos,pheno=phenos,tab=tab2,ref=strains,par1="parent1",par2="parent2",prefix="ind_"))
-
-rqtl_file[1:10,1:7]
-```
-
diff --git a/stuart.Rcheck/00check.log b/stuart.Rcheck/00check.log
deleted file mode 100644
index ce5827b5840a1d1881cc62e551b30cbc0bb42e59..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00check.log
+++ /dev/null
@@ -1,124 +0,0 @@
-* using log directory ‘/Users/mariebourdon/stuart_package/stuart/stuart/stuart.Rcheck’
-* using R version 4.0.4 (2021-02-15)
-* using platform: x86_64-apple-darwin17.0 (64-bit)
-* using session charset: UTF-8
-* using options ‘--no-manual --as-cran’
-* checking for file ‘stuart/DESCRIPTION’ ... OK
-* this is package ‘stuart’ version ‘0.1.0’
-* package encoding: UTF-8
-* checking package namespace information ... OK
-* checking package dependencies ... OK
-* checking if this is a source package ... OK
-* checking if there is a namespace ... OK
-* checking for executable files ... OK
-* checking for hidden files and directories ... OK
-* checking for portable file names ... OK
-* checking for sufficient/correct file permissions ... OK
-* checking whether package ‘stuart’ can be installed ... OK
-* checking installed package size ... NOTE
-  installed size is 166.5Mb
-  sub-directories of 1Mb or more:
-    data      41.4Mb
-    extdata  124.9Mb
-* checking package directory ... OK
-* checking for future file timestamps ... OK
-* checking ‘build’ directory ... OK
-* checking DESCRIPTION meta-information ... OK
-* checking top-level files ... NOTE
-Non-standard file/directory found at top level:
-  ‘stuart_0.1.0.tar.gz’
-* checking for left-over files ... OK
-* checking index information ... OK
-* checking package subdirectories ... OK
-* checking R files for non-ASCII characters ... OK
-* checking R files for syntax errors ... OK
-* checking whether the package can be loaded ... OK
-* checking whether the package can be loaded with stated dependencies ... OK
-* checking whether the package can be unloaded cleanly ... OK
-* checking whether the namespace can be loaded with stated dependencies ... OK
-* checking whether the namespace can be unloaded cleanly ... OK
-* checking dependencies in R code ... OK
-* checking S3 generic/method consistency ... OK
-* checking replacement functions ... OK
-* checking foreign function calls ... OK
-* checking R code for possible problems ... NOTE
-geno_strains: no visible binding for global variable ‘Sample.ID’
-geno_strains: no visible binding for global variable ‘SNP.Name’
-geno_strains: no visible binding for global variable ‘Geno’
-geno_strains: no visible binding for global variable ‘parent1’
-geno_strains: no visible binding for global variable ‘parent2’
-mark_allele: no visible binding for global variable ‘marker’
-mark_match: no visible binding for global variable ‘SNP.Name’
-mark_poly: no visible binding for global variable ‘Allele_2’
-mark_prop: no visible binding for global variable ‘n_HM1’
-mark_prop: no visible binding for global variable ‘n_HM2’
-mark_prop: no visible binding for global variable ‘n_HT’
-mark_prop: no visible binding for global variable ‘n_geno’
-mark_prop: no visible binding for global variable ‘n_NA’
-mark_prop: no visible binding for global variable ‘p_HM1’
-mark_prop: no visible binding for global variable ‘p_HM2’
-mark_prop: no visible binding for global variable ‘p_HT’
-mark_prop: no visible binding for global variable ‘p_NA’
-tab_mark: no visible binding for global variable ‘Geno’
-tab_mark: no visible binding for global variable ‘SNP.Name’
-tab_mark: no visible binding for global variable ‘Sample.ID’
-tab_mark: no visible binding for global variable ‘Allele1...Forward’
-tab_mark: no visible binding for global variable ‘Allele2...Forward’
-tab_mark: no visible binding for global variable ‘Allele’
-tab_mark: no visible binding for global variable ‘Allele_1’
-tab_mark: no visible binding for global variable ‘n_HM1’
-tab_mark: no visible binding for global variable ‘Allele_2’
-tab_mark: no visible binding for global variable ‘n_HM2’
-tab_mark: no visible binding for global variable ‘n_HT’
-tab_mark: no visible binding for global variable ‘n_NA’
-write_rqtl: no visible binding for global variable ‘exclude_match’
-write_rqtl: no visible binding for global variable ‘exclude_poly’
-write_rqtl: no visible binding for global variable ‘exclude_prop’
-write_rqtl: no visible binding for global variable ‘exclude_allele’
-write_rqtl: no visible binding for global variable ‘SNP.Name’
-write_rqtl: no visible binding for global variable ‘Sample.ID’
-write_rqtl: no visible binding for global variable ‘Allele1...Forward’
-write_rqtl: no visible binding for global variable ‘Allele2...Forward’
-write_rqtl: no visible binding for global variable ‘marker’
-write_rqtl: no visible binding for global variable ‘chr’
-write_rqtl: no visible binding for global variable ‘bp_mm10’
-write_rqtl: no visible binding for global variable ‘cM_cox’
-write_rqtl: no visible binding for global variable ‘cM_g2f1’
-write_rqtl: no visible binding for global variable ‘Geno’
-write_rqtl: no visible binding for global variable ‘.’
-Undefined global functions or variables:
-  . Allele Allele1...Forward Allele2...Forward Allele_1 Allele_2 Geno
-  SNP.Name Sample.ID bp_mm10 cM_cox cM_g2f1 chr exclude_allele
-  exclude_match exclude_poly exclude_prop marker n_HM1 n_HM2 n_HT n_NA
-  n_geno p_HM1 p_HM2 p_HT p_NA parent1 parent2
-* checking Rd files ... OK
-* checking Rd metadata ... OK
-* checking Rd line widths ... OK
-* checking Rd cross-references ... OK
-* checking for missing documentation entries ... OK
-* checking for code/documentation mismatches ... WARNING
-Data codoc mismatches from documentation object 'ref_strains_mini':
-Variables in data frame 'ref_strains_mini'
-  Code: CC001 CC005 CC042 CC071 Ifnar.KO.129 Ifnar.KO.B6 Rvfs2.1
-        Rvfs2.2 Rvfs2.6 Rvfs2.7 bp_mm10 cM_cox cM_g2f1 chr marker snp
-        unique unmapped
-  Docs: CC001 CC005 CC042 CC071 Ifnar.KO.129 Ifnar.KO.B6 Rvfs2.1
-        Rvfs2.2 Rvfs2.6 Rvfs2.7 bp_mm10 cM_cox cM_g2f1 chr marker multi
-        snp unique unmapped
-
-* checking Rd \usage sections ... OK
-* checking Rd contents ... OK
-* checking for unstated dependencies in examples ... OK
-* checking contents of ‘data’ directory ... OK
-* checking data for non-ASCII characters ... OK
-* checking data for ASCII and uncompressed saves ... OK
-* checking installed files from ‘inst/doc’ ... OK
-* checking files in ‘vignettes’ ... OK
-* checking examples ... NONE
-* checking for unstated dependencies in vignettes ... OK
-* checking package vignettes in ‘inst/doc’ ... OK
-* checking re-building of vignette outputs ... OK
-* checking for non-standard things in the check directory ... OK
-* checking for detritus in the temp directory ... OK
-* DONE
-Status: 1 WARNING, 3 NOTEs
diff --git a/stuart.Rcheck/00install.out b/stuart.Rcheck/00install.out
deleted file mode 100644
index 85123f6e14dff9c876627ded1d7669e3af45c77c..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/00install.out
+++ /dev/null
@@ -1,15 +0,0 @@
-* installing *source* package ‘stuart’ ...
-** using staged installation
-** R
-** data
-*** moving datasets to lazyload DB
-** inst
-** byte-compile and prepare package for lazy loading
-** help
-*** installing help indices
-** building package indices
-** installing vignettes
-** testing if installed package can be loaded from temporary location
-** testing if installed package can be loaded from final location
-** testing if installed package keeps a record of temporary installation path
-* DONE (stuart)
diff --git a/stuart.Rcheck/R_check_bin/R b/stuart.Rcheck/R_check_bin/R
deleted file mode 100755
index 3e289bc21d3f8a3f979596483c640411ca6e8b2f..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/R_check_bin/R
+++ /dev/null
@@ -1,2 +0,0 @@
-echo "'R' should not be used without a path -- see par. 1.6 of the manual"
-exit 1
diff --git a/stuart.Rcheck/R_check_bin/Rscript b/stuart.Rcheck/R_check_bin/Rscript
deleted file mode 100755
index 6fead74173b543aa25ff26146b3d59659136a0c1..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/R_check_bin/Rscript
+++ /dev/null
@@ -1,2 +0,0 @@
-echo "'Rscript' should not be used without a path -- see par. 1.6 of the manual"
-exit 1
diff --git a/stuart.Rcheck/stuart/DESCRIPTION b/stuart.Rcheck/stuart/DESCRIPTION
deleted file mode 100644
index 821f2e19414e18c62f4b35fa51e754bd13dee7cd..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/stuart/DESCRIPTION
+++ /dev/null
@@ -1,24 +0,0 @@
-Package: stuart
-Title: stuaRt
-Version: 0.1.0
-Authors@R: 
-    person(given = "Marie",
-           family = "Bourdon",
-           role = c("aut", "cre"),
-           email = "mariefbourdon@gmail.com",
-           comment = c(ORCID = "YOUR-ORCID-ID"))
-Description: Sorts markers of miniMUGA genotyping for F2 or N2 individuals, for Rqtl analysis. 
-License: GPL-3
-Encoding: UTF-8
-LazyData: true
-Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.1.1
-Imports: dplyr, tidyr, utils, stringr, rapportools
-Suggests: knitr, rmarkdown
-VignetteBuilder: knitr
-Depends: R (>= 3.5.0)
-NeedsCompilation: no
-Packaged: 2021-06-01 08:06:30 UTC; mariebourdon
-Author: Marie Bourdon [aut, cre] (YOUR-ORCID-ID)
-Maintainer: Marie Bourdon <mariefbourdon@gmail.com>
-Built: R 4.0.4; ; 2021-06-01 08:07:48 UTC; unix
diff --git a/stuart.Rcheck/stuart/INDEX b/stuart.Rcheck/stuart/INDEX
deleted file mode 100644
index 224235f8b427f4a688db3615f2a05028b216892a..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/stuart/INDEX
+++ /dev/null
@@ -1,19 +0,0 @@
-geno_strains            Create haplotype for a new mouse strain into a
-                        reference dataframe
-genos                   Data frame with miniMUGA genotyping of F2
-                        individuals and parental strains
-mark_allele             Exclude markers that have different alleles in
-                        the individuals of the cross and in parental
-                        strains
-mark_match              Exclude markers that were not genotyped in the
-                        reference strains
-mark_poly               Exclude markers that are not polymorphic
-mark_prop               Exclude markers depending on proportions of
-                        homo/hetorozygous
-phenos                  Data frame with phenotype of F2 individuals
-ref_strains_mini        Data frame with miniMUGA genotyping of
-                        classical lab strains.
-stuart_tab              Output of tab_mark function
-tab_mark                Create of the summary table for all markers
-                        from the genotype data frame
-write_rqtl              Create data frame in Rqtl CSV format
diff --git a/stuart.Rcheck/stuart/Meta/Rd.rds b/stuart.Rcheck/stuart/Meta/Rd.rds
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diff --git a/stuart.Rcheck/stuart/Meta/links.rds b/stuart.Rcheck/stuart/Meta/links.rds
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diff --git a/stuart.Rcheck/stuart/Meta/nsInfo.rds b/stuart.Rcheck/stuart/Meta/nsInfo.rds
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diff --git a/stuart.Rcheck/stuart/Meta/package.rds b/stuart.Rcheck/stuart/Meta/package.rds
deleted file mode 100644
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diff --git a/stuart.Rcheck/stuart/Meta/vignette.rds b/stuart.Rcheck/stuart/Meta/vignette.rds
deleted file mode 100644
index 87a9d6290223156fd3752ff37a7c170f33892ee0..0000000000000000000000000000000000000000
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diff --git a/stuart.Rcheck/stuart/NAMESPACE b/stuart.Rcheck/stuart/NAMESPACE
deleted file mode 100644
index 902e04361e27306cd36fac0de688e5a6ab80da2c..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/stuart/NAMESPACE
+++ /dev/null
@@ -1,13 +0,0 @@
-# Generated by roxygen2: do not edit by hand
-
-export(geno_strains)
-export(mark_allele)
-export(mark_match)
-export(mark_poly)
-export(mark_prop)
-export(tab_mark)
-export(write_rqtl)
-import(dplyr)
-import(stringr)
-import(tidyr)
-import(utils)
diff --git a/stuart.Rcheck/stuart/R/stuart b/stuart.Rcheck/stuart/R/stuart
deleted file mode 100644
index 6686156321de4dbdb52db7e32cd99961007f56e2..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/stuart/R/stuart
+++ /dev/null
@@ -1,27 +0,0 @@
-#  File share/R/nspackloader.R
-#  Part of the R package, https://www.R-project.org
-#
-#  Copyright (C) 1995-2012 The R Core Team
-#
-#  This program is free software; you can redistribute it and/or modify
-#  it under the terms of the GNU General Public License as published by
-#  the Free Software Foundation; either version 2 of the License, or
-#  (at your option) any later version.
-#
-#  This program is distributed in the hope that it will be useful,
-#  but WITHOUT ANY WARRANTY; without even the implied warranty of
-#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#  GNU General Public License for more details.
-#
-#  A copy of the GNU General Public License is available at
-#  https://www.r-project.org/Licenses/
-
-local({
-    info <- loadingNamespaceInfo()
-    pkg <- info$pkgname
-    ns <- .getNamespace(as.name(pkg))
-    if (is.null(ns))
-        stop("cannot find namespace environment for ", pkg, domain = NA);
-    dbbase <- file.path(info$libname, pkg, "R", pkg)
-    lazyLoad(dbbase, ns, filter = function(n) n != ".__NAMESPACE__.")
-})
diff --git a/stuart.Rcheck/stuart/R/stuart.rdb b/stuart.Rcheck/stuart/R/stuart.rdb
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diff --git a/stuart.Rcheck/stuart/doc/index.html b/stuart.Rcheck/stuart/doc/index.html
deleted file mode 100644
index 1946e82821cd67a4f6c75ec171142c3113c81912..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/stuart/doc/index.html
+++ /dev/null
@@ -1,28 +0,0 @@
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml">
-<head><title>R: Vignettes and other documentation</title>
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<link rel="stylesheet" type="text/css" href="/doc/html/R.css" />
-</head><body>
-<h1> Vignettes and other documentation
-<img class="toplogo" src="/doc/html/Rlogo.svg" alt="[R logo]" />
-</h1>
-<hr/>
-<div style="text-align: center;">
-<a href="/doc/html/index.html"><img class="arrow" src="/doc/html/up.jpg" alt="[Top]" /></a>
-</div>
-<h2>Vignettes from package 'stuart'</h2>
-<table width="100%">
-<col style="width: 22%;" />
-<col style="width:  2%;" />
-<col style="width: 50%;" />
-<col style="width:  8%;" />
-<col style="width:  8%;" />
-<col style="width:  8%;" />
-<tr><td style="text-align: right; vertical-align: top;"><a href="../../../library/stuart/doc/stuaRt.html">stuart::stuaRt</a></td>
-<td></td><td valign="top">stuaRt</td>
-<td valign="top"><a href="../../../library/stuart/doc/stuaRt.html">HTML</a></td>
-<td valign="top"><a href="../../../library/stuart/doc/stuaRt.Rmd">source</a></td>
-<td valign="top" style="white-space: nowrap"><a href="../../../library/stuart/doc/stuaRt.R">R code</a></td></tr>
-</table>
-</body></html>
diff --git a/stuart.Rcheck/stuart/doc/stuaRt.R b/stuart.Rcheck/stuart/doc/stuaRt.R
deleted file mode 100644
index 71e57691d5f643cb3edb7e751d2c7641ee3b5f96..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/stuart/doc/stuaRt.R
+++ /dev/null
@@ -1,56 +0,0 @@
-## ---- include = FALSE---------------------------------------------------------
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-
-## ----setup--------------------------------------------------------------------
-library(dplyr)
-library(stuart)
-
-## ----annot--------------------------------------------------------------------
-annot_mini <- read.csv(url("https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv"))
-
-## ----load---------------------------------------------------------------------
-data(genos)
-summary(genos)
-data(phenos)
-summary(phenos)
-
-## ----strains------------------------------------------------------------------
-strains <- geno_strains(ref=annot_mini,geno=genos,par1=c("StrainsA_1","StrainsA_2"),par2=c("StrainsB_1","StrainsB_2"),name1="parent1",name2="parent2")
-head(strains)
-
-## ----no_parent----------------------------------------------------------------
-genos <- genos %>% filter(!Sample.ID %in% c("StrainsA_1", "StrainsA_2", "StrainsB_1","StrainsB_2"))
-
-## ----tab_mark-----------------------------------------------------------------
-data(stuart_tab)
-summary(stuart_tab)
-
-## ----mark_match---------------------------------------------------------------
-tab2 <- mark_match(stuart_tab,ref=strains)
-
-
-tab2 %>% filter(exclude_match==1)
-
-## ----mark_poly ex-------------------------------------------------------------
-tab2 <- mark_poly(tab2)
-head(tab2)
-
-## ----mark_prop ex-------------------------------------------------------------
-tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
-head(tab2)
-
-## ----mark_allele--------------------------------------------------------------
-tab2 <- mark_allele(tab=tab2,ref=strains,par1="parent1",par2="parent2")
-tab2 %>% arrange(desc(exclude_allele)) %>% head()
-
-## ----mark_allele-strains------------------------------------------------------
-strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354","gUNC15530876","gUNC21555204","gUNC21596600")) %>% arrange(marker) %>% select(marker,parent1,parent2)
-
-## ----write_qtl----------------------------------------------------------------
-rqtl_file <- (write_rqtl(geno=genos,pheno=phenos,tab=tab2,ref=strains,par1="parent1",par2="parent2",prefix="ind_"))
-
-rqtl_file[1:10,1:7]
-
diff --git a/stuart.Rcheck/stuart/doc/stuaRt.Rmd b/stuart.Rcheck/stuart/doc/stuaRt.Rmd
deleted file mode 100755
index 4e294ab0cc3558b6b84acf6a660e790a6c6522f4..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/stuart/doc/stuaRt.Rmd
+++ /dev/null
@@ -1,134 +0,0 @@
----
-title: "stuaRt"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{stuaRt}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-```
-
-Marie Bourdon
-
-April 2021
-
-## Goal
-
-stuaRt is a R package which formats the genotyping data from MUGA arrays (Neogen) to use it in Rqtl, for backcross or F2 crosses. It allows to filter the markers in MUGA arrays that can or cannot be used for Rqtl analysis, from a genetic point of view. Indeed, markers will be selected depending on their proportion of each genotype, correspondance between F2 or N2 individuals alleles and parental strains alleles, etc.
-
-The examples shown here require the use of dplyr package.
-
-
-```{r setup}
-library(dplyr)
-library(stuart)
-```
-
-
-## Annotation files
-
-The developer of Rqtl and Rqtl2 packages, Karl Broman, realised that the annotation of the MUGA arrays was not correct for some markers. Thus, he produced new annotation files for MUGA, miniMUGA, megaMUGA and gigaMUGA arrays. These files contain some informations about the markers including the chromosome and position where the probe of the marker matchs on the genome, wether the marker maps uniquely or not, etc. These files also contains the genetic position of the markers calculated with two methods : "cM_cox" and "cM_g2f1" (see https://kbroman.org/MUGAarrays/mini_revisited.html for more informations).
-
-We recommand to use these annotation files to reconstruct the file use for Rqtl analysis. You can load the datasets with these annotations from GitHub (https://github.com/kbroman/MUGAarrays/tree/master/UWisc). Choose the file corresponding to the MUGA array that you used and use the URL to load the dataset in R.
-
-Here, we will present an example of the use of stuaRt with results of a F2 cross genotyped with miniMUGA. We load the result of Neogen genotyping: `genos` and thephenotype dataset produced by the lab: `phenos`. All these datasets are available for example in stuaRt package.
-
-```{r annot}
-annot_mini <- read.csv(url("https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv"))
-```
-
-
-
-```{r load}
-data(genos)
-summary(genos)
-data(phenos)
-summary(phenos)
-```
-
-### Genotyping of parental strains
-
-To use genotyping result for Rqtl analysis, we need to recode the genotypes of the individuals (originally encoded in A, T, G, C) depending on the genotype of the parental strains: homozygous for the first parental strain (0), heterozygous (1) or homozygous for the second parental strain (2).
-
-We recommend to always genotype the parental strains of the cross. Here, their genotypes are in the `genos` file and correspond to the Sample.ID "StrainsA_1", "StrainsA_2", "StrainsB_1" and "StrainsB_2". Two individuals were genotyped for each parental strain. The first step will be to create a consensus genotype for each strain from the two genotyped individuals. The consensus genotype will be added to the annotation dataset in order to obtain a dataset with both annotation and reference genotype of the parental strains that will be used for recoding the genotypes or the F2 individuals. 
-
-This is done with the `geno_strains` function. 
-
-```{r strains}
-strains <- geno_strains(ref=annot_mini,geno=genos,par1=c("StrainsA_1","StrainsA_2"),par2=c("StrainsB_1","StrainsB_2"),name1="parent1",name2="parent2")
-head(strains)
-```
-
-After this step, we need to remove the genotyping result for these individuals from the `genos` dataset.
-```{r no_parent}
-genos <- genos %>% filter(!Sample.ID %in% c("StrainsA_1", "StrainsA_2", "StrainsB_1","StrainsB_2"))
-```
-
-
-## Markers sorting
-
-### Marker tab
-
-The first step of the markers sorting is to create the marker dataframe with the tab_mark() function. This dataframe contains for each marker the two alleles that can be found in the F2/N2 population (`Allele_1` and `Allele_2`), the number of individuals for each genotype (homozygous for each allele (`n_HM1` and `n_HM2`) and heterozygous (`n_HT`)), and the number of non genotyped individuals (`n_NA`) This step can take several minutes. You can also load the output of this function.
-
-
-```{r tab_mark}
-data(stuart_tab)
-summary(stuart_tab)
-```
-
-Then we will use the different mark_* functions in order to filter the markers. First, we can use mark_match() function. This function excludes markers that are in your genotype file but not in the reference genotype dataset. We recomend using this function as the chip used for genotyping may change.
-
-```{r mark_match}
-tab2 <- mark_match(stuart_tab,ref=strains)
-
-
-tab2 %>% filter(exclude_match==1)
-```
-
-Here the reference strains were genotyped with the same version of the chip as the F2 individuals so no marker was excluded.
-
-Then, we can use the mark_poly() function, which will exclude the markers that are not polymorphic.
-
-```{r mark_poly ex}
-tab2 <- mark_poly(tab2)
-head(tab2)
-```
-
-The mark_prop() function can be used to filter markers depending on the proportion of each genotype. Here, we have a F2 so we can use the "homo" argument in order to filter depending on the proportion of both homozygous genotype. If we have a N2, we can filter with the proportion of homozygous individuals with the "homo" argument and of heterozygous individuals with the hetero" argument. Moreover, this function allows to filter marker depending on the proportion on non genotyped animals. By defaults, markers for which more than 50% of individuals were not genotyped.
-
-```{r mark_prop ex}
-tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
-head(tab2)
-```
-
-Last, we can use the mark_allele(). This very helpful function excludes markers for which the alleles found in the F2/N2 individuals do not correspond to the alleles found in the parental strains. For example, if for a marker is not polymorphic in the parental strains but we found two alleles in the F2/N2 individuals, it will be excluded.
-
-```{r mark_allele}
-tab2 <- mark_allele(tab=tab2,ref=strains,par1="parent1",par2="parent2")
-tab2 %>% arrange(desc(exclude_allele)) %>% head()
-```
-
-Indeed, we can see that the markers excluded with mark_allele() have different alleles in the parental strains.
-
-```{r mark_allele-strains}
-strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354","gUNC15530876","gUNC21555204","gUNC21596600")) %>% arrange(marker) %>% select(marker,parent1,parent2)
-```
-
-# Creation of the R/qtl file
-
-After excluding the problematic markers, we can create the R/qtl file. The individuals must have the same ID in the geno and in the pheno file. If there is a prefix in the geno file that must be removed in order to acheive this, you can use the "prefix" argument. The "path" argument can be used in order to create a CSV file that you can laod with qtl::read.cross. 
-
-```{r write_qtl}
-rqtl_file <- (write_rqtl(geno=genos,pheno=phenos,tab=tab2,ref=strains,par1="parent1",par2="parent2",prefix="ind_"))
-
-rqtl_file[1:10,1:7]
-```
-
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-<h1 class="title toc-ignore">stuaRt</h1>
-
-
-
-<p>Marie Bourdon</p>
-<p>April 2021</p>
-<div id="goal" class="section level2">
-<h2>Goal</h2>
-<p>stuaRt is a R package which formats the genotyping data from MUGA arrays (Neogen) to use it in Rqtl, for backcross or F2 crosses. It allows to filter the markers in MUGA arrays that can or cannot be used for Rqtl analysis, from a genetic point of view. Indeed, markers will be selected depending on their proportion of each genotype, correspondance between F2 or N2 individuals alleles and parental strains alleles, etc.</p>
-<p>The examples shown here require the use of dplyr package.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(dplyr)</span>
-<span id="cb1-2"><a href="#cb1-2"></a><span class="co">#&gt; </span></span>
-<span id="cb1-3"><a href="#cb1-3"></a><span class="co">#&gt; Attaching package: &#39;dplyr&#39;</span></span>
-<span id="cb1-4"><a href="#cb1-4"></a><span class="co">#&gt; The following objects are masked from &#39;package:stats&#39;:</span></span>
-<span id="cb1-5"><a href="#cb1-5"></a><span class="co">#&gt; </span></span>
-<span id="cb1-6"><a href="#cb1-6"></a><span class="co">#&gt;     filter, lag</span></span>
-<span id="cb1-7"><a href="#cb1-7"></a><span class="co">#&gt; The following objects are masked from &#39;package:base&#39;:</span></span>
-<span id="cb1-8"><a href="#cb1-8"></a><span class="co">#&gt; </span></span>
-<span id="cb1-9"><a href="#cb1-9"></a><span class="co">#&gt;     intersect, setdiff, setequal, union</span></span>
-<span id="cb1-10"><a href="#cb1-10"></a><span class="kw">library</span>(stuart)</span></code></pre></div>
-</div>
-<div id="annotation-files" class="section level2">
-<h2>Annotation files</h2>
-<p>The developer of Rqtl and Rqtl2 packages, Karl Broman, realised that the annotation of the MUGA arrays was not correct for some markers. Thus, he produced new annotation files for MUGA, miniMUGA, megaMUGA and gigaMUGA arrays. These files contain some informations about the markers including the chromosome and position where the probe of the marker matchs on the genome, wether the marker maps uniquely or not, etc. These files also contains the genetic position of the markers calculated with two methods : “cM_cox” and “cM_g2f1” (see <a href="https://kbroman.org/MUGAarrays/mini_revisited.html" class="uri">https://kbroman.org/MUGAarrays/mini_revisited.html</a> for more informations).</p>
-<p>We recommand to use these annotation files to reconstruct the file use for Rqtl analysis. You can load the datasets with these annotations from GitHub (<a href="https://github.com/kbroman/MUGAarrays/tree/master/UWisc" class="uri">https://github.com/kbroman/MUGAarrays/tree/master/UWisc</a>). Choose the file corresponding to the MUGA array that you used and use the URL to load the dataset in R.</p>
-<p>Here, we will present an example of the use of stuaRt with results of a F2 cross genotyped with miniMUGA. We load the result of Neogen genotyping: <code>genos</code> and thephenotype dataset produced by the lab: <code>phenos</code>. All these datasets are available for example in stuaRt package.</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a>annot_mini &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="kw">url</span>(<span class="st">&quot;https://raw.githubusercontent.com/kbroman/MUGAarrays/master/UWisc/mini_uwisc_v2.csv&quot;</span>))</span></code></pre></div>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="kw">data</span>(genos)</span>
-<span id="cb3-2"><a href="#cb3-2"></a><span class="kw">summary</span>(genos)</span>
-<span id="cb3-3"><a href="#cb3-3"></a><span class="co">#&gt;    SNP.Name          Sample.ID         Allele1...Forward  Allele2...Forward </span></span>
-<span id="cb3-4"><a href="#cb3-4"></a><span class="co">#&gt;  Length:2002493     Length:2002493     Length:2002493     Length:2002493    </span></span>
-<span id="cb3-5"><a href="#cb3-5"></a><span class="co">#&gt;  Class :character   Class :character   Class :character   Class :character  </span></span>
-<span id="cb3-6"><a href="#cb3-6"></a><span class="co">#&gt;  Mode  :character   Mode  :character   Mode  :character   Mode  :character  </span></span>
-<span id="cb3-7"><a href="#cb3-7"></a><span class="co">#&gt;                                                                             </span></span>
-<span id="cb3-8"><a href="#cb3-8"></a><span class="co">#&gt;                                                                             </span></span>
-<span id="cb3-9"><a href="#cb3-9"></a><span class="co">#&gt;                                                                             </span></span>
-<span id="cb3-10"><a href="#cb3-10"></a><span class="co">#&gt;                                                                             </span></span>
-<span id="cb3-11"><a href="#cb3-11"></a><span class="co">#&gt;        X                 Y              GC.Score          Theta       </span></span>
-<span id="cb3-12"><a href="#cb3-12"></a><span class="co">#&gt;  Min.   : 0.0000   Min.   : 0.0000   Min.   :0.0000   Min.   :0.0000  </span></span>
-<span id="cb3-13"><a href="#cb3-13"></a><span class="co">#&gt;  1st Qu.: 0.0260   1st Qu.: 0.0480   1st Qu.:0.6747   1st Qu.:0.0420  </span></span>
-<span id="cb3-14"><a href="#cb3-14"></a><span class="co">#&gt;  Median : 0.1750   Median : 0.5010   Median :0.8550   Median :0.6620  </span></span>
-<span id="cb3-15"><a href="#cb3-15"></a><span class="co">#&gt;  Mean   : 0.5716   Mean   : 0.6564   Mean   :0.7276   Mean   :0.5606  </span></span>
-<span id="cb3-16"><a href="#cb3-16"></a><span class="co">#&gt;  3rd Qu.: 0.8560   3rd Qu.: 1.0470   3rd Qu.:0.9245   3rd Qu.:0.9800  </span></span>
-<span id="cb3-17"><a href="#cb3-17"></a><span class="co">#&gt;  Max.   :25.1610   Max.   :20.7940   Max.   :0.9889   Max.   :1.0000  </span></span>
-<span id="cb3-18"><a href="#cb3-18"></a><span class="co">#&gt;  NA&#39;s   :87        NA&#39;s   :87        NA&#39;s   :326      NA&#39;s   :413     </span></span>
-<span id="cb3-19"><a href="#cb3-19"></a><span class="co">#&gt;      X.Raw           Y.Raw             R         </span></span>
-<span id="cb3-20"><a href="#cb3-20"></a><span class="co">#&gt;  Min.   :    0   Min.   :    0   Min.   : 0.000  </span></span>
-<span id="cb3-21"><a href="#cb3-21"></a><span class="co">#&gt;  1st Qu.:  546   1st Qu.:  678   1st Qu.: 0.605  </span></span>
-<span id="cb3-22"><a href="#cb3-22"></a><span class="co">#&gt;  Median : 1611   Median : 3908   Median : 1.027  </span></span>
-<span id="cb3-23"><a href="#cb3-23"></a><span class="co">#&gt;  Mean   : 5265   Mean   : 5578   Mean   : 1.228  </span></span>
-<span id="cb3-24"><a href="#cb3-24"></a><span class="co">#&gt;  3rd Qu.: 7924   3rd Qu.: 9232   3rd Qu.: 1.553  </span></span>
-<span id="cb3-25"><a href="#cb3-25"></a><span class="co">#&gt;  Max.   :49906   Max.   :43935   Max.   :26.041  </span></span>
-<span id="cb3-26"><a href="#cb3-26"></a><span class="co">#&gt;                                  NA&#39;s   :413</span></span>
-<span id="cb3-27"><a href="#cb3-27"></a><span class="kw">data</span>(phenos)</span>
-<span id="cb3-28"><a href="#cb3-28"></a><span class="kw">summary</span>(phenos)</span>
-<span id="cb3-29"><a href="#cb3-29"></a><span class="co">#&gt;       Ind          Sex                 Age            Pheno       </span></span>
-<span id="cb3-30"><a href="#cb3-30"></a><span class="co">#&gt;  201    :  1   Length:176         Min.   :5.000   Min.   : 8.609  </span></span>
-<span id="cb3-31"><a href="#cb3-31"></a><span class="co">#&gt;  202    :  1   Class :character   1st Qu.:7.000   1st Qu.:10.420  </span></span>
-<span id="cb3-32"><a href="#cb3-32"></a><span class="co">#&gt;  203    :  1   Mode  :character   Median :7.000   Median :10.628  </span></span>
-<span id="cb3-33"><a href="#cb3-33"></a><span class="co">#&gt;  204    :  1                      Mean   :7.176   Mean   :10.526  </span></span>
-<span id="cb3-34"><a href="#cb3-34"></a><span class="co">#&gt;  205    :  1                      3rd Qu.:8.000   3rd Qu.:10.793  </span></span>
-<span id="cb3-35"><a href="#cb3-35"></a><span class="co">#&gt;  206    :  1                      Max.   :9.000   Max.   :11.147  </span></span>
-<span id="cb3-36"><a href="#cb3-36"></a><span class="co">#&gt;  (Other):170</span></span></code></pre></div>
-<div id="genotyping-of-parental-strains" class="section level3">
-<h3>Genotyping of parental strains</h3>
-<p>To use genotyping result for Rqtl analysis, we need to recode the genotypes of the individuals (originally encoded in A, T, G, C) depending on the genotype of the parental strains: homozygous for the first parental strain (0), heterozygous (1) or homozygous for the second parental strain (2).</p>
-<p>We recommend to always genotype the parental strains of the cross. Here, their genotypes are in the <code>genos</code> file and correspond to the Sample.ID “StrainsA_1”, “StrainsA_2”, “StrainsB_1” and “StrainsB_2”. Two individuals were genotyped for each parental strain. The first step will be to create a consensus genotype for each strain from the two genotyped individuals. The consensus genotype will be added to the annotation dataset in order to obtain a dataset with both annotation and reference genotype of the parental strains that will be used for recoding the genotypes or the F2 individuals.</p>
-<p>This is done with the <code>geno_strains</code> function.</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>strains &lt;-<span class="st"> </span><span class="kw">geno_strains</span>(<span class="dt">ref=</span>annot_mini,<span class="dt">geno=</span>genos,<span class="dt">par1=</span><span class="kw">c</span>(<span class="st">&quot;StrainsA_1&quot;</span>,<span class="st">&quot;StrainsA_2&quot;</span>),<span class="dt">par2=</span><span class="kw">c</span>(<span class="st">&quot;StrainsB_1&quot;</span>,<span class="st">&quot;StrainsB_2&quot;</span>),<span class="dt">name1=</span><span class="st">&quot;parent1&quot;</span>,<span class="dt">name2=</span><span class="st">&quot;parent2&quot;</span>)</span>
-<span id="cb4-2"><a href="#cb4-2"></a><span class="kw">head</span>(strains)</span>
-<span id="cb4-3"><a href="#cb4-3"></a><span class="co">#&gt;          marker chr bp_mm10 cM_cox cM_g2f1 strand snp unique unmapped</span></span>
-<span id="cb4-4"><a href="#cb4-4"></a><span class="co">#&gt; 1  S3J010123784   1 3094603  1.542   0.046   plus  CA   TRUE    FALSE</span></span>
-<span id="cb4-5"><a href="#cb4-5"></a><span class="co">#&gt; 2  SAH010136363   1 3409090  1.608   0.175  minus  CA   TRUE    FALSE</span></span>
-<span id="cb4-6"><a href="#cb4-6"></a><span class="co">#&gt; 3  S2H010137098   1 3427467  1.609   0.177  minus  AG   TRUE    FALSE</span></span>
-<span id="cb4-7"><a href="#cb4-7"></a><span class="co">#&gt; 4   UNCHS000006   1 3439034  1.610   0.178   plus  CT   TRUE    FALSE</span></span>
-<span id="cb4-8"><a href="#cb4-8"></a><span class="co">#&gt; 5 mUNC010515443   1 3668628  1.627   0.202  minus  GA   TRUE    FALSE</span></span>
-<span id="cb4-9"><a href="#cb4-9"></a><span class="co">#&gt; 6  S2J010178507   1 4462692  1.733   0.234  minus  GA   TRUE    FALSE</span></span>
-<span id="cb4-10"><a href="#cb4-10"></a><span class="co">#&gt;                                                probe chr_unc  bp_unc parent1</span></span>
-<span id="cb4-11"><a href="#cb4-11"></a><span class="co">#&gt; 1 ATAAATTCTACAGCCACAGAAGTCACATTTTAGCACTGCTGTGTTTCCAG       1 3094603       C</span></span>
-<span id="cb4-12"><a href="#cb4-12"></a><span class="co">#&gt; 2 CCACCTTTGTACTCTGTATGCTACACAGAAGCTATGAGTATTCTTTTCCC       1 3409090       C</span></span>
-<span id="cb4-13"><a href="#cb4-13"></a><span class="co">#&gt; 3 ATTGAAAATGATCTAAGGGAGTCATGAGTACAAGGAGAAATGGGCATATT       1 3427467       A</span></span>
-<span id="cb4-14"><a href="#cb4-14"></a><span class="co">#&gt; 4 AATTTCTACCAGATCTCTTTGTCCTCCTAGAAGCATTGTGATACTCAGGA       1 3439034       C</span></span>
-<span id="cb4-15"><a href="#cb4-15"></a><span class="co">#&gt; 5 CAGGAAATGATGCTGAGAAAGTGAGAAGTAGGAAAACGTGGAGAAAAATA       1 3668628       G</span></span>
-<span id="cb4-16"><a href="#cb4-16"></a><span class="co">#&gt; 6 GACCTATGGTTAAAAGTCAGGCATTTCTTGTGTCTTCTTGTATTATTGGT       1 4462692       G</span></span>
-<span id="cb4-17"><a href="#cb4-17"></a><span class="co">#&gt;   parent2</span></span>
-<span id="cb4-18"><a href="#cb4-18"></a><span class="co">#&gt; 1       C</span></span>
-<span id="cb4-19"><a href="#cb4-19"></a><span class="co">#&gt; 2       C</span></span>
-<span id="cb4-20"><a href="#cb4-20"></a><span class="co">#&gt; 3       A</span></span>
-<span id="cb4-21"><a href="#cb4-21"></a><span class="co">#&gt; 4       C</span></span>
-<span id="cb4-22"><a href="#cb4-22"></a><span class="co">#&gt; 5       G</span></span>
-<span id="cb4-23"><a href="#cb4-23"></a><span class="co">#&gt; 6       G</span></span></code></pre></div>
-<p>After this step, we need to remove the genotyping result for these individuals from the <code>genos</code> dataset.</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a>genos &lt;-<span class="st"> </span>genos <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(<span class="op">!</span>Sample.ID <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;StrainsA_1&quot;</span>, <span class="st">&quot;StrainsA_2&quot;</span>, <span class="st">&quot;StrainsB_1&quot;</span>,<span class="st">&quot;StrainsB_2&quot;</span>))</span></code></pre></div>
-</div>
-</div>
-<div id="markers-sorting" class="section level2">
-<h2>Markers sorting</h2>
-<div id="marker-tab" class="section level3">
-<h3>Marker tab</h3>
-<p>The first step of the markers sorting is to create the marker dataframe with the tab_mark() function. This dataframe contains for each marker the two alleles that can be found in the F2/N2 population (<code>Allele_1</code> and <code>Allele_2</code>), the number of individuals for each genotype (homozygous for each allele (<code>n_HM1</code> and <code>n_HM2</code>) and heterozygous (<code>n_HT</code>)), and the number of non genotyped individuals (<code>n_NA</code>) This step can take several minutes. You can also load the output of this function.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="kw">data</span>(stuart_tab)</span>
-<span id="cb6-2"><a href="#cb6-2"></a><span class="kw">summary</span>(stuart_tab)</span>
-<span id="cb6-3"><a href="#cb6-3"></a><span class="co">#&gt;    SNP.Name           Allele_1           Allele_2             n_HM1      </span></span>
-<span id="cb6-4"><a href="#cb6-4"></a><span class="co">#&gt;  Length:11125       Length:11125       Length:11125       Min.   :  0.0  </span></span>
-<span id="cb6-5"><a href="#cb6-5"></a><span class="co">#&gt;  Class :character   Class :character   Class :character   1st Qu.: 44.0  </span></span>
-<span id="cb6-6"><a href="#cb6-6"></a><span class="co">#&gt;  Mode  :character   Mode  :character   Mode  :character   Median :174.0  </span></span>
-<span id="cb6-7"><a href="#cb6-7"></a><span class="co">#&gt;                                                           Mean   :123.9  </span></span>
-<span id="cb6-8"><a href="#cb6-8"></a><span class="co">#&gt;                                                           3rd Qu.:176.0  </span></span>
-<span id="cb6-9"><a href="#cb6-9"></a><span class="co">#&gt;                                                           Max.   :176.0  </span></span>
-<span id="cb6-10"><a href="#cb6-10"></a><span class="co">#&gt;      n_HM2             n_HT             n_NA       </span></span>
-<span id="cb6-11"><a href="#cb6-11"></a><span class="co">#&gt;  Min.   :  0.00   Min.   :  0.00   Min.   :  0.00  </span></span>
-<span id="cb6-12"><a href="#cb6-12"></a><span class="co">#&gt;  1st Qu.:  0.00   1st Qu.:  0.00   1st Qu.:  0.00  </span></span>
-<span id="cb6-13"><a href="#cb6-13"></a><span class="co">#&gt;  Median :  0.00   Median :  0.00   Median :  1.00  </span></span>
-<span id="cb6-14"><a href="#cb6-14"></a><span class="co">#&gt;  Mean   : 19.92   Mean   : 19.24   Mean   : 12.91  </span></span>
-<span id="cb6-15"><a href="#cb6-15"></a><span class="co">#&gt;  3rd Qu.: 34.00   3rd Qu.:  5.00   3rd Qu.:  5.00  </span></span>
-<span id="cb6-16"><a href="#cb6-16"></a><span class="co">#&gt;  Max.   :175.00   Max.   :176.00   Max.   :176.00</span></span></code></pre></div>
-<p>Then we will use the different mark_* functions in order to filter the markers. First, we can use mark_match() function. This function excludes markers that are in your genotype file but not in the reference genotype dataset. We recomend using this function as the chip used for genotyping may change.</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>tab2 &lt;-<span class="st"> </span><span class="kw">mark_match</span>(stuart_tab,<span class="dt">ref=</span>strains)</span>
-<span id="cb7-2"><a href="#cb7-2"></a></span>
-<span id="cb7-3"><a href="#cb7-3"></a></span>
-<span id="cb7-4"><a href="#cb7-4"></a>tab2 <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(exclude_match<span class="op">==</span><span class="dv">1</span>)</span>
-<span id="cb7-5"><a href="#cb7-5"></a><span class="co">#&gt; # A tibble: 0 x 8</span></span>
-<span id="cb7-6"><a href="#cb7-6"></a><span class="co">#&gt; # … with 8 variables: SNP.Name &lt;chr&gt;, Allele_1 &lt;chr&gt;, Allele_2 &lt;chr&gt;,</span></span>
-<span id="cb7-7"><a href="#cb7-7"></a><span class="co">#&gt; #   n_HM1 &lt;dbl&gt;, n_HM2 &lt;dbl&gt;, n_HT &lt;dbl&gt;, n_NA &lt;dbl&gt;, exclude_match &lt;dbl&gt;</span></span></code></pre></div>
-<p>Here the reference strains were genotyped with the same version of the chip as the F2 individuals so no marker was excluded.</p>
-<p>Then, we can use the mark_poly() function, which will exclude the markers that are not polymorphic.</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>tab2 &lt;-<span class="st"> </span><span class="kw">mark_poly</span>(tab2)</span>
-<span id="cb8-2"><a href="#cb8-2"></a><span class="kw">head</span>(tab2)</span>
-<span id="cb8-3"><a href="#cb8-3"></a><span class="co">#&gt; # A tibble: 6 x 9</span></span>
-<span id="cb8-4"><a href="#cb8-4"></a><span class="co">#&gt;   SNP.Name  Allele_1 Allele_2 n_HM1 n_HM2  n_HT  n_NA exclude_match exclude_poly</span></span>
-<span id="cb8-5"><a href="#cb8-5"></a><span class="co">#&gt;   &lt;chr&gt;     &lt;chr&gt;    &lt;chr&gt;    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;         &lt;dbl&gt;        &lt;dbl&gt;</span></span>
-<span id="cb8-6"><a href="#cb8-6"></a><span class="co">#&gt; 1 B1001000… C        &lt;NA&gt;       175     0     0     1             0            1</span></span>
-<span id="cb8-7"><a href="#cb8-7"></a><span class="co">#&gt; 2 B1001001… &lt;NA&gt;     &lt;NA&gt;         0     0     0   176             0            1</span></span>
-<span id="cb8-8"><a href="#cb8-8"></a><span class="co">#&gt; 3 B1001001… A        &lt;NA&gt;       175     0     0     1             0            1</span></span>
-<span id="cb8-9"><a href="#cb8-9"></a><span class="co">#&gt; 4 B1001002… A        G          171     0     2     3             0            0</span></span>
-<span id="cb8-10"><a href="#cb8-10"></a><span class="co">#&gt; 5 B1001002… C        &lt;NA&gt;       176     0     0     0             0            1</span></span>
-<span id="cb8-11"><a href="#cb8-11"></a><span class="co">#&gt; 6 B1001003… A        &lt;NA&gt;       176     0     0     0             0            1</span></span></code></pre></div>
-<p>The mark_prop() function can be used to filter markers depending on the proportion of each genotype. Here, we have a F2 so we can use the “homo” argument in order to filter depending on the proportion of both homozygous genotype. If we have a N2, we can filter with the proportion of homozygous individuals with the “homo” argument and of heterozygous individuals with the hetero&quot; argument. Moreover, this function allows to filter marker depending on the proportion on non genotyped animals. By defaults, markers for which more than 50% of individuals were not genotyped.</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a>tab2 &lt;-<span class="st"> </span><span class="kw">mark_prop</span>(tab2,<span class="dt">cross=</span><span class="st">&quot;F2&quot;</span>,<span class="dt">homo=</span><span class="fl">0.1</span>,<span class="dt">hetero=</span><span class="fl">0.1</span>)</span>
-<span id="cb9-2"><a href="#cb9-2"></a><span class="kw">head</span>(tab2)</span>
-<span id="cb9-3"><a href="#cb9-3"></a><span class="co">#&gt; # A tibble: 6 x 10</span></span>
-<span id="cb9-4"><a href="#cb9-4"></a><span class="co">#&gt;   SNP.Name  Allele_1 Allele_2 n_HM1 n_HM2  n_HT  n_NA exclude_match exclude_poly</span></span>
-<span id="cb9-5"><a href="#cb9-5"></a><span class="co">#&gt;   &lt;chr&gt;     &lt;chr&gt;    &lt;chr&gt;    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;         &lt;dbl&gt;        &lt;dbl&gt;</span></span>
-<span id="cb9-6"><a href="#cb9-6"></a><span class="co">#&gt; 1 B1001000… C        &lt;NA&gt;       175     0     0     1             0            1</span></span>
-<span id="cb9-7"><a href="#cb9-7"></a><span class="co">#&gt; 2 B1001001… &lt;NA&gt;     &lt;NA&gt;         0     0     0   176             0            1</span></span>
-<span id="cb9-8"><a href="#cb9-8"></a><span class="co">#&gt; 3 B1001001… A        &lt;NA&gt;       175     0     0     1             0            1</span></span>
-<span id="cb9-9"><a href="#cb9-9"></a><span class="co">#&gt; 4 B1001002… A        G          171     0     2     3             0            0</span></span>
-<span id="cb9-10"><a href="#cb9-10"></a><span class="co">#&gt; 5 B1001002… C        &lt;NA&gt;       176     0     0     0             0            1</span></span>
-<span id="cb9-11"><a href="#cb9-11"></a><span class="co">#&gt; 6 B1001003… A        &lt;NA&gt;       176     0     0     0             0            1</span></span>
-<span id="cb9-12"><a href="#cb9-12"></a><span class="co">#&gt; # … with 1 more variable: exclude_prop &lt;dbl&gt;</span></span></code></pre></div>
-<p>Last, we can use the mark_allele(). This very helpful function excludes markers for which the alleles found in the F2/N2 individuals do not correspond to the alleles found in the parental strains. For example, if for a marker is not polymorphic in the parental strains but we found two alleles in the F2/N2 individuals, it will be excluded.</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a>tab2 &lt;-<span class="st"> </span><span class="kw">mark_allele</span>(<span class="dt">tab=</span>tab2,<span class="dt">ref=</span>strains,<span class="dt">par1=</span><span class="st">&quot;parent1&quot;</span>,<span class="dt">par2=</span><span class="st">&quot;parent2&quot;</span>)</span>
-<span id="cb10-2"><a href="#cb10-2"></a>tab2 <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(exclude_allele)) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">head</span>()</span>
-<span id="cb10-3"><a href="#cb10-3"></a><span class="co">#&gt; # A tibble: 6 x 11</span></span>
-<span id="cb10-4"><a href="#cb10-4"></a><span class="co">#&gt;   SNP.Name  Allele_1 Allele_2 n_HM1 n_HM2  n_HT  n_NA exclude_match exclude_poly</span></span>
-<span id="cb10-5"><a href="#cb10-5"></a><span class="co">#&gt;   &lt;chr&gt;     &lt;chr&gt;    &lt;chr&gt;    &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;         &lt;dbl&gt;        &lt;dbl&gt;</span></span>
-<span id="cb10-6"><a href="#cb10-6"></a><span class="co">#&gt; 1 B1002001… T        C            0   172     1     3             0            0</span></span>
-<span id="cb10-7"><a href="#cb10-7"></a><span class="co">#&gt; 2 B1002009… A        G            0   170     2     4             0            0</span></span>
-<span id="cb10-8"><a href="#cb10-8"></a><span class="co">#&gt; 3 B1003003… T        C            0   162     2    12             0            0</span></span>
-<span id="cb10-9"><a href="#cb10-9"></a><span class="co">#&gt; 4 B1004002… T        C            0   172     2     2             0            0</span></span>
-<span id="cb10-10"><a href="#cb10-10"></a><span class="co">#&gt; 5 B1004002… A        C            0   168     1     7             0            0</span></span>
-<span id="cb10-11"><a href="#cb10-11"></a><span class="co">#&gt; 6 B1004005… T        C            0    95    46    35             0            0</span></span>
-<span id="cb10-12"><a href="#cb10-12"></a><span class="co">#&gt; # … with 2 more variables: exclude_prop &lt;dbl&gt;, exclude_allele &lt;dbl&gt;</span></span></code></pre></div>
-<p>Indeed, we can see that the markers excluded with mark_allele() have different alleles in the parental strains.</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>strains <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">filter</span>(marker <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">&quot;gJAX00038569&quot;</span>,<span class="st">&quot;gJAX00425031&quot;</span>,<span class="st">&quot;gUNC12245354&quot;</span>,<span class="st">&quot;gUNC15530876&quot;</span>,<span class="st">&quot;gUNC21555204&quot;</span>,<span class="st">&quot;gUNC21596600&quot;</span>)) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">arrange</span>(marker) <span class="op">%&gt;%</span><span class="st"> </span><span class="kw">select</span>(marker,parent1,parent2)</span>
-<span id="cb11-2"><a href="#cb11-2"></a><span class="co">#&gt;         marker parent1 parent2</span></span>
-<span id="cb11-3"><a href="#cb11-3"></a><span class="co">#&gt; 1 gJAX00038569       C       C</span></span>
-<span id="cb11-4"><a href="#cb11-4"></a><span class="co">#&gt; 2 gJAX00425031       C       C</span></span>
-<span id="cb11-5"><a href="#cb11-5"></a><span class="co">#&gt; 3 gUNC12245354       N       N</span></span>
-<span id="cb11-6"><a href="#cb11-6"></a><span class="co">#&gt; 4 gUNC15530876       N       N</span></span>
-<span id="cb11-7"><a href="#cb11-7"></a><span class="co">#&gt; 5 gUNC21555204       T       T</span></span>
-<span id="cb11-8"><a href="#cb11-8"></a><span class="co">#&gt; 6 gUNC21596600       A       A</span></span></code></pre></div>
-</div>
-</div>
-<div id="creation-of-the-rqtl-file" class="section level1">
-<h1>Creation of the R/qtl file</h1>
-<p>After excluding the problematic markers, we can create the R/qtl file. The individuals must have the same ID in the geno and in the pheno file. If there is a prefix in the geno file that must be removed in order to acheive this, you can use the “prefix” argument. The “path” argument can be used in order to create a CSV file that you can laod with qtl::read.cross.</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a>rqtl_file &lt;-<span class="st"> </span>(<span class="kw">write_rqtl</span>(<span class="dt">geno=</span>genos,<span class="dt">pheno=</span>phenos,<span class="dt">tab=</span>tab2,<span class="dt">ref=</span>strains,<span class="dt">par1=</span><span class="st">&quot;parent1&quot;</span>,<span class="dt">par2=</span><span class="st">&quot;parent2&quot;</span>,<span class="dt">prefix=</span><span class="st">&quot;ind_&quot;</span>))</span>
-<span id="cb12-2"><a href="#cb12-2"></a></span>
-<span id="cb12-3"><a href="#cb12-3"></a>rqtl_file[<span class="dv">1</span><span class="op">:</span><span class="dv">10</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">7</span>]</span>
-<span id="cb12-4"><a href="#cb12-4"></a><span class="co">#&gt; # A tibble: 10 x 7</span></span>
-<span id="cb12-5"><a href="#cb12-5"></a><span class="co">#&gt;    Ind   Sex   Age   Pheno              S6J010381992 S6J011498219 S6J011558924</span></span>
-<span id="cb12-6"><a href="#cb12-6"></a><span class="co">#&gt;    &lt;chr&gt; &lt;chr&gt; &lt;chr&gt; &lt;chr&gt;              &lt;chr&gt;        &lt;chr&gt;        &lt;chr&gt;       </span></span>
-<span id="cb12-7"><a href="#cb12-7"></a><span class="co">#&gt;  1 &quot;&quot;    &quot;&quot;    &quot;&quot;    &quot;&quot;                 1            1            1           </span></span>
-<span id="cb12-8"><a href="#cb12-8"></a><span class="co">#&gt;  2 &quot;&quot;    &quot;&quot;    &quot;&quot;    &quot;&quot;                 2.188        15.746       17.347      </span></span>
-<span id="cb12-9"><a href="#cb12-9"></a><span class="co">#&gt;  3 &quot;201&quot; &quot;M&quot;   &quot;7&quot;   &quot;10.5296676084955&quot; 1            1            1           </span></span>
-<span id="cb12-10"><a href="#cb12-10"></a><span class="co">#&gt;  4 &quot;210&quot; &quot;M&quot;   &quot;7&quot;   &quot;10.4877403515456&quot; 2            0            0           </span></span>
-<span id="cb12-11"><a href="#cb12-11"></a><span class="co">#&gt;  5 &quot;308&quot; &quot;F&quot;   &quot;7&quot;   &quot;10.9714806769608&quot; 1            0            0           </span></span>
-<span id="cb12-12"><a href="#cb12-12"></a><span class="co">#&gt;  6 &quot;309&quot; &quot;M&quot;   &quot;7&quot;   &quot;10.8463976841841&quot; 0            1            1           </span></span>
-<span id="cb12-13"><a href="#cb12-13"></a><span class="co">#&gt;  7 &quot;310&quot; &quot;M&quot;   &quot;7&quot;   &quot;11.0680508131391&quot; NA           1            1           </span></span>
-<span id="cb12-14"><a href="#cb12-14"></a><span class="co">#&gt;  8 &quot;311&quot; &quot;F&quot;   &quot;9&quot;   &quot;10.5753482053348&quot; 0            2            2           </span></span>
-<span id="cb12-15"><a href="#cb12-15"></a><span class="co">#&gt;  9 &quot;312&quot; &quot;M&quot;   &quot;7&quot;   &quot;10.7137055438312&quot; 0            2            2           </span></span>
-<span id="cb12-16"><a href="#cb12-16"></a><span class="co">#&gt; 10 &quot;313&quot; &quot;M&quot;   &quot;8&quot;   &quot;10.7375743556446&quot; 0            1            1</span></span></code></pre></div>
-</div>
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diff --git a/stuart.Rcheck/stuart/extdata/genos.txt b/stuart.Rcheck/stuart/extdata/genos.txt
deleted file mode 100755
index 49a9e33f41b395a716aaf3df972513b9058f5bf4..0000000000000000000000000000000000000000
Binary files a/stuart.Rcheck/stuart/extdata/genos.txt and /dev/null differ
diff --git a/stuart.Rcheck/stuart/extdata/phenos.txt b/stuart.Rcheck/stuart/extdata/phenos.txt
deleted file mode 100755
index 1d25e9ab2a19f593f1c7809e71eeb16337aa3820..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/stuart/extdata/phenos.txt
+++ /dev/null
@@ -1,177 +0,0 @@
-Ind	Sex	Exp	Age	Death	Day.death	Pheno
-201	M	36	7	1	14	10.5296676084955
-210	M	38	7	0	14	10.4877403515456
-308	F	53	7	1	8	10.9714806769608
-309	M	53	7	1	7	10.8463976841841
-310	M	53	7	1	8	11.0680508131391
-311	F	53	9	1	7	10.5753482053348
-312	M	56	7	1	7	10.7137055438312
-313	M	56	8	1	7	10.7375743556446
-315	M	56	7	0	14	10.8719419391157
-318	F	56	6	1	6	10.8396273596649
-319	F	56	6	0	14	10.7668793836238
-320	M	58	8	0	14	10.7325547648656
-211	M	38	7	0	14	10.8695302496376
-321	M	58	6	1	7	10.8283736794678
-322	M	58	6	1	7	10.8915635045684
-323	M	58	6	1	7	10.5356077157252
-324	F	58	6	1	7	10.6280255935391
-325	F	58	8	0	14	10.7480587696075
-326	F	58	6	1	7	10.723232575067
-327	F	58	6	1	7	10.561378054744
-328	M	58	8	1	8	10.678328733254
-329	M	58	7	1	8	10.5553266906605
-330	M	58	7	1	8	9.80206867803321
-212	M	38	7	0	14	10.6357450501074
-331	M	58	7	1	7	10.5982605721032
-332	M	58	7	1	7	10.8473250099944
-333	F	58	6	1	7	10.3764348734637
-334	F	58	6	1	7	10.5460358865664
-336	F	58	5	1	7	10.5222855493513
-337	F	67	8	1	9	10.5327174182859
-338	F	67	6	0	14	10.5473443040275
-339	F	67	8	1	8	9.87194179754362
-340	F	67	6	1	8	10.7090598750034
-341	M	67	6	1	8	10.2209997596409
-213	M	38	7	1	8	11.0270167306899
-342	M	67	8	1	8	10.5431290182179
-343	M	68	8	0	14	10.4981076961986
-344	M	68	8	0	14	10.3735386730113
-345	F	68	8	1	14	10.4320215351478
-346	F	68	8	0	14	8.60947058208406
-347	F	73	7	0	14	10.4168301170408
-348	F	73	7	1	6	11.0628524075666
-349	F	73	7	1	7	10.4330268533001
-350	M	73	7	1	7	10.4800637563932
-351	M	73	6	1	7	10.6324192103035
-214	M	38	7	1	8	10.8218250314825
-352	M	73	6	1	8	10.4725870266356
-353	M	73	6	1	8	10.463431310619
-354	F	73	8	1	9	10.2353468625069
-355	F	73	8	1	8	10.4784947722463
-356	M	73	8	0	14	10.2302554084309
-357	M	73	7	1	7	10.3331446827217
-358	M	75	7	1	8	10.4392178227394
-359	M	75	7	1	9	10.2749993364651
-360	M	75	7	1	9	10.5896956543931
-361	M	75	7	1	9	10.4102656461775
-215	M	38	7	1	7	10.98405240378
-362	M	75	7	0	14	9.25536232298768
-363	M	75	9	1	9	10.3492941307168
-364	M	75	9	1	9	10.3620959742092
-365	M	75	9	1	12	10.4744976511434
-366	M	75	9	1	12	11.030867369515
-369	F	75	9	0	14	10.628477999175
-370	F	75	9	0	14	10.4670786491756
-373	F	75	7	0	14	10.9179711868264
-374	F	75	9	1	8	10.4953620125703
-375	F	75	7	1	7	10.1874531724974
-216	M	38	7	0	14	10.5655205985375
-316	M	78	9	0	14	10.766442313453
-371	M	78	9	1	12	10.4195168879626
-372	M	78	9	1	9	9.95272129347567
-376	M	78	8	0	14	10.4195168879626
-377	F	78	9	0	14	9.95272129347567
-379	F	78	8	0	14	10.0823979142355
-380	F	78	8	1	8	10.9095058001147
-381	M	78	8	1	12	10.8801492251438
-383	F	78	7	1	8	10.9965190456509
-384	F	78	7	0	14	11.0509121188558
-217	M	38	7	1	7	10.4994444268072
-385	F	78	7	1	8	11.0429066559961
-386	F	78	7	0	14	10.8109357224842
-387	M	78	8	0	14	10.6074095323141
-388	M	78	7	0	14	10.7086232219316
-389	M	78	7	1	8	10.8244554978178
-390	M	78	7	1	8	10.5135622767937
-391	M	78	7	1	8	10.6549253863367
-219	F	38	7	1	9	10.9538292220917
-220	M	40	7	1	7	10.7769347175043
-202	M	36	7	1	7	10.6939691207128
-221	M	40	7	0	14	10.6933765093072
-222	F	43	6	1	7	10.6767026645835
-223	F	43	6	0	14	10.5003906942331
-224	F	43	6	0	14	10.5149649858964
-225	F	43	6	0	14	10.8903239014372
-226	M	43	6	0	14	10.505656838062
-227	M	43	6	1	7	10.9661802289593
-228	M	43	6	1	7	10.7323373517824
-229	M	45	8	1	7	10.7312300131548
-230	M	45	8	0	14	10.5748054422932
-203	M	36	7	0	14	10.6753018614348
-231	M	45	8	0	14	10.6367921020912
-232	M	45	8	1	7	11.0739366525724
-233	F	45	8	0	14	10.1491227173623
-234	F	45	8	1	8	10.6667311696339
-235	F	45	8	0	14	8.7941942405191
-236	F	45	8	0	14	10.9093248127322
-237	M	47	7	0	14	10.9324949209267
-238	M	47	7	0	14	10.6947153326779
-239	M	47	7	0	14	10.6120985065664
-240	M	47	7	1	8	10.8327443879201
-204	M	36	7	1	7	10.7791570102755
-241	M	47	7	1	9	10.5125518260128
-242	M	47	7	1	7	10.6894120122994
-243	M	47	7	1	7	11.0691241089673
-245	M	47	7	0	14	10.4819031965733
-246	F	47	7	1	8	9.96762433427887
-247	F	47	7	1	8	10.7245450774586
-248	F	47	7	0	14	10.4912425829631
-249	F	47	7	0	14	10.7474083506145
-250	M	48	8	0	14	10.2720876656636
-251	M	48	8	0	14	9.31399254303701
-205	M	38	7	0	14	10.4428500375673
-252	M	48	8	1	7	10.2862132021395
-253	M	48	7	1	8	10.5048761731899
-254	F	48	8	0	14	10.228672834882
-255	F	48	7	1	7	9.62470062290951
-256	F	48	7	0	14	10.6406621644657
-257	M	48	7	0	14	9.94948396721433
-258	M	48	7	1	7	9.30736764204129
-259	M	48	7	0	14	9.88913119725402
-260	M	48	7	0	14	10.2380596666633
-261	M	48	7	1	7	10.2540669844702
-206	M	38	7	0	14	10.7606379038488
-262	M	48	7	0	14	9.76042991401441
-263	M	48	7	0	14	10.6091919117065
-264	F	50	8	0	14	10.6851731967701
-266	F	50	7	1	8	10.7673408387505
-267	M	50	8	1	7	10.0482542502632
-269	M	50	8	1	8	11.0733835864953
-270	M	50	8	0	14	10.4484942670828
-271	M	50	7	0	14	10.1718549349538
-272	M	50	6	1	7	10.3653629747907
-274	M	50	8	1	7	10.7987817154853
-207	M	38	7	0	14	9.28000922051108
-265	F	51	8	1	8	10.6396558357786
-268	F	51	8	1	8	10.38209131129
-273	F	51	7	0	14	10.668205277928
-277	F	51	7	0	14	10.3487626209106
-278	M	51	7	0	14	8.79404815627125
-279	M	51	6	0	14	10.983879782484
-284	M	52	7	1	7	10.9273290008382
-285	M	52	7	0	14	10.9386568661041
-286	M	52	7	0	14	10.8807485243696
-287	M	52	7	1	8	10.9037194625542
-208	M	38	7	0	14	10.7753050361387
-288	M	52	7	0	14	10.7677108901204
-289	F	52	7	1	7	10.9101761509059
-290	F	52	7	1	7	10.9405884955895
-291	F	52	7	0	14	8.61409649857943
-292	F	52	7	0	14	10.7908109750272
-293	F	52	7	1	7	10.9585494660728
-283	M	52	7	0	14	10.7347416773051
-294	M	52	7	0	14	10.5163444828732
-295	M	52	7	0	14	10.7527016128882
-296	M	52	6	0	14	10.7427219571178
-209	M	38	7	1	7	10.6341153687419
-297	M	52	6	1	8	10.6603294779049
-298	M	52	6	1	7	10.952768506441
-299	F	52	6	1	7	11.1474990509139
-300	F	52	6	0	14	9.35603884473677
-301	F	52	6	1	8	11.1056293961659
-303	M	53	9	0	14	10.1069851602543
-304	F	53	7	1	8	10.7275476145336
-305	F	53	7	0	14	10.9381137134461
-306	F	53	7	0	14	10.5449698000374
-307	F	53	7	0	14	10.6311447764499
diff --git a/stuart.Rcheck/stuart/help/AnIndex b/stuart.Rcheck/stuart/help/AnIndex
deleted file mode 100644
index 2d740c1843ddacadc6225e3c243ceb9b8cf1bcf5..0000000000000000000000000000000000000000
--- a/stuart.Rcheck/stuart/help/AnIndex
+++ /dev/null
@@ -1,11 +0,0 @@
-genos	genos
-geno_strains	geno_strains
-mark_allele	mark_allele
-mark_match	mark_match
-mark_poly	mark_poly
-mark_prop	mark_prop
-phenos	phenos
-ref_strains_mini	ref_strains_mini
-stuart_tab	stuart_tab
-tab_mark	tab_mark
-write_rqtl	write_rqtl
diff --git a/stuart.Rcheck/stuart/help/aliases.rds b/stuart.Rcheck/stuart/help/aliases.rds
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diff --git a/stuart.Rcheck/stuart/html/00Index.html b/stuart.Rcheck/stuart/html/00Index.html
deleted file mode 100644
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+++ /dev/null
@@ -1,47 +0,0 @@
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
-<html xmlns="http://www.w3.org/1999/xhtml">
-<head><title>R: stuaRt</title>
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<link rel="stylesheet" type="text/css" href="R.css" />
-</head><body>
-<h1> stuaRt
-<img class="toplogo" src="../../../doc/html/Rlogo.svg" alt="[R logo]" />
-</h1>
-<hr/>
-<div style="text-align: center;">
-<a href="../../../doc/html/packages.html"><img class="arrow" src="../../../doc/html/left.jpg" alt="[Up]" /></a>
-<a href="../../../doc/html/index.html"><img class="arrow" src="../../../doc/html/up.jpg" alt="[Top]" /></a>
-</div><h2>Documentation for package &lsquo;stuart&rsquo; version 0.1.0</h2>
-
-<ul><li><a href="../DESCRIPTION">DESCRIPTION file</a>.</li>
-<li><a href="../doc/index.html">User guides, package vignettes and other documentation.</a></li>
-</ul>
-
-<h2>Help Pages</h2>
-
-
-<table width="100%">
-<tr><td style="width: 25%;"><a href="genos.html">genos</a></td>
-<td>Data frame with miniMUGA genotyping of F2 individuals and parental strains</td></tr>
-<tr><td style="width: 25%;"><a href="geno_strains.html">geno_strains</a></td>
-<td>Create haplotype for a new mouse strain into a reference dataframe</td></tr>
-<tr><td style="width: 25%;"><a href="mark_allele.html">mark_allele</a></td>
-<td>Exclude markers that have different alleles in the individuals of the cross and in parental strains</td></tr>
-<tr><td style="width: 25%;"><a href="mark_match.html">mark_match</a></td>
-<td>Exclude markers that were not genotyped in the reference strains</td></tr>
-<tr><td style="width: 25%;"><a href="mark_poly.html">mark_poly</a></td>
-<td>Exclude markers that are not polymorphic</td></tr>
-<tr><td style="width: 25%;"><a href="mark_prop.html">mark_prop</a></td>
-<td>Exclude markers depending on proportions of homo/hetorozygous</td></tr>
-<tr><td style="width: 25%;"><a href="phenos.html">phenos</a></td>
-<td>Data frame with phenotype of F2 individuals</td></tr>
-<tr><td style="width: 25%;"><a href="ref_strains_mini.html">ref_strains_mini</a></td>
-<td>Data frame with miniMUGA genotyping of classical lab strains.</td></tr>
-<tr><td style="width: 25%;"><a href="stuart_tab.html">stuart_tab</a></td>
-<td>Output of tab_mark function</td></tr>
-<tr><td style="width: 25%;"><a href="tab_mark.html">tab_mark</a></td>
-<td>Create of the summary table for all markers from the genotype data frame</td></tr>
-<tr><td style="width: 25%;"><a href="write_rqtl.html">write_rqtl</a></td>
-<td>Create data frame in Rqtl CSV format</td></tr>
-</table>
-</body></html>
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-body {
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-span.samp{
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-div.vignettes a:hover {
-    background: rgb(85%, 85%, 85%);
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diff --git a/stuart_1.0.2.pdf b/stuart_1.0.2.pdf
index ba8a6d728314b0933827e229787f7b8d912ac958..c562000e015a9cf6a5899dc22fe8c2cf84b0f264 100644
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diff --git a/stuart_1.0.2.tar.gz b/stuart_1.0.2.tar.gz
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diff --git a/vignettes/stuaRt.Rmd b/vignettes/stuaRt.Rmd
index b4af84f39711829855378cf4d6021838f3cc450f..48568f25bde86d655292635f2cf02bd343f03ae5 100755
--- a/vignettes/stuaRt.Rmd
+++ b/vignettes/stuaRt.Rmd
@@ -69,13 +69,15 @@ We recommend to always genotype the parental strains of the cross. Here, their g
 This is done with the `geno_strains` function. 
 
 ```{r strains}
-strains <- geno_strains(ref=annot_mini,geno=genos,par1=c("StrainsA_1","StrainsA_2"),par2=c("StrainsB_1","StrainsB_2"),name1="parent1",name2="parent2")
-head(strains)
+strains <- geno_strains(ref=annot_mini,geno=genos,par1=c("StrainsA_1","StrainsA_2"),
+                        par2=c("StrainsB_1","StrainsB_2"),name1="parent1",name2="parent2")
+head(strains) %>% print.data.frame()
 ```
 
 After this step, we need to remove the genotyping result for these individuals from the `genos` dataset.
 ```{r no_parent}
-genos <- genos %>% filter(!Sample.ID %in% c("StrainsA_1", "StrainsA_2", "StrainsB_1","StrainsB_2"))
+genos <- genos %>% filter(!Sample.ID %in% c("StrainsA_1", "StrainsA_2", 
+                                            "StrainsB_1","StrainsB_2"))
 ```
 
 
@@ -95,7 +97,7 @@ Then we will use the different mark_* functions in order to filter the markers.
 
 ```{r mark_match}
 tab2 <- mark_match(stuart_tab,ref=strains)
-tab2 %>% filter(exclude_match==1)
+tab2 %>% filter(exclude_match==1) %>% print.data.frame()
 ```
 
 Here the reference strains were genotyped with the same version of the chip as the F2 individuals so no marker was excluded.
@@ -104,27 +106,29 @@ Then, we can use the `mark_poly()` function, which will exclude the markers that
 
 ```{r mark_poly ex}
 tab2 <- mark_poly(tab2)
-head(tab2)
+head(tab2) %>% print.data.frame()
 ```
 
 The `mark_prop()` function can be used to filter markers depending on the proportion of each genotype. Here, we have a F2 so we can use the "homo" argument in order to filter depending on the proportion of both homozygous genotype. If we have a N2, we can filter with the proportion of homozygous individuals with the "homo" argument and of heterozygous individuals with the "hetero" argument. Moreover, this function allows to filter marker depending on the proportion on non genotyped animals. By defaults, markers for which more than 50% of individuals were not genotyped.
 
 ```{r mark_prop ex}
 tab2 <- mark_prop(tab2,cross="F2",homo=0.1,hetero=0.1)
-head(tab2)
+head(tab2) %>% print.data.frame()
 ```
 
 Last, we can use the `mark_allele()` function. This very helpful function excludes markers for which the alleles found in the F2/N2 individuals do not correspond to the alleles found in the parental strains. For example, if for a marker is not polymorphic in the parental strains but we found two alleles in the F2/N2 individuals, it will be excluded.
 
 ```{r mark_allele}
 tab2 <- mark_allele(tab=tab2,ref=strains,par1="parent1",par2="parent2")
-tab2 %>% arrange(desc(exclude_allele)) %>% head()
+tab2 %>% arrange(desc(exclude_allele)) %>% head() %>% print.data.frame()
 ```
 
 Indeed, we can see that the markers excluded with `mark_allele()` have different alleles in the parental strains.
 
 ```{r mark_allele-strains}
-strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354","gUNC15530876","gUNC21555204","gUNC21596600")) %>% arrange(marker) %>% select(marker,parent1,parent2)
+strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354",
+                                 "gUNC15530876","gUNC21555204","gUNC21596600")) %>% arrange(marker) %>% 
+  select(marker,parent1,parent2) %>% print.data.frame()
 ```
 
 # Creation of the R/qtl file
@@ -132,8 +136,11 @@ strains %>% filter(marker %in% c("gJAX00038569","gJAX00425031","gUNC12245354","g
 After excluding the problematic markers, we can create the R/qtl file. The individuals must have the same ID in the geno and in the pheno file. If there is a prefix in the geno file that must be removed in order to acheive this, you can use the "prefix" argument. The "path" argument can be used in order to create a CSV file that you can laod with `qtl::read.cross`. 
 
 ```{r write_qtl}
-rqtl_file <- write_rqtl(geno=genos,pheno=phenos,tab=tab2,ref=strains,par1="parent1",par2="parent2",prefix="ind_",pos="cM_cox")
+rqtl_file <- write_rqtl(geno=genos,pheno=phenos,tab=tab2,
+                        ref=strains,par1="parent1",par2="parent2",prefix="ind_",pos="cM_cox")
 
-rqtl_file[1:10,1:7]
+rqtl_file[1:10,1:7] %>% print.data.frame()
 ```
 
+
+