Commit 2388d08c authored by Gael  MILLOT's avatar Gael MILLOT
Browse files

tempo saving

parent c023b091
......@@ -39,37 +39,36 @@
######## fun_mat_inv() #### return the inverse of a square matrix 41
######## fun_mat_fill() #### fill the empty half part of a symmetric square matrix 42
######## fun_permut() #### progressively breaks a vector order 45
######## fun_permut_consec() #### as fun permut() but permuting consecutive positions 51
################ Graphics management 60
######## fun_width() #### window width depending on classes to plot 60
######## fun_open() #### open a GUI or pdf graphic window 61
######## fun_prior_plot() #### set graph param before plotting (erase axes for instance) 65
######## fun_scale() #### select nice label numbers when setting number of ticks on an axis 69
######## fun_post_plot() #### set graph param after plotting (axes redesign for instance) 73
######## fun_close() #### close specific graphic windows 85
################ Standard graphics 86
######## fun_empty_graph() #### text to display for empty graphs 86
################ gg graphics 88
######## fun_gg_palette() #### ggplot2 default color palette 88
######## fun_gg_just() #### ggplot2 justification of the axis labeling, depending on angle 89
######## fun_gg_point_rast() #### ggplot2 raster scatterplot layer 92
######## fun_gg_scatter() #### ggplot2 scatterplot + lines (up to 6 overlays totally) 95
######## fun_gg_bar_mean() #### ggplot2 mean barplot + overlaid dots if required 131
######## fun_gg_boxplot() #### ggplot2 boxplot + background dots if required 165
######## fun_gg_bar_prop() #### ggplot2 proportion barplot 170
######## fun_gg_strip() #### ggplot2 stripchart + mean/median 171
######## fun_gg_violin() #### ggplot2 violins 171
######## fun_gg_line() #### ggplot2 lines + background dots and error bars 171
######## fun_gg_heatmap() #### ggplot2 heatmap + overlaid mask if required 173
######## fun_gg_empty_graph() #### text to display for empty graphs 187
################ Graphic extraction 188
######## fun_trim() #### display values from a quantitative variable and trim according to defined cut-offs 188
######## fun_segmentation() #### segment a dot cloud on a scatterplot and define the dots from another cloud outside the segmentation 197
################ Import 229
######## fun_pack() #### check if R packages are present and import into the working environment 229
######## fun_python_pack() #### check if python packages are present 230
################ Exporting results (text & tables) 232
######## fun_report() #### print string or data object into output file 232
################ Graphics management 55
######## fun_width() #### window width depending on classes to plot 56
######## fun_open() #### open a GUI or pdf graphic window 57
######## fun_prior_plot() #### set graph param before plotting (erase axes for instance) 60
######## fun_scale() #### select nice label numbers when setting number of ticks on an axis 64
######## fun_post_plot() #### set graph param after plotting (axes redesign for instance) 69
######## fun_close() #### close specific graphic windows 80
################ Standard graphics 81
######## fun_empty_graph() #### text to display for empty graphs 82
################ gg graphics 83
######## fun_gg_palette() #### ggplot2 default color palette 83
######## fun_gg_just() #### ggplot2 justification of the axis labeling, depending on angle 84
######## fun_gg_point_rast() #### ggplot2 raster scatterplot layer 87
######## fun_gg_scatter() #### ggplot2 scatterplot + lines (up to 6 overlays totally) 90
######## fun_gg_bar_mean() #### ggplot2 mean barplot + overlaid dots if required 126
######## fun_gg_boxplot() #### ggplot2 boxplot + background dots if required 161
######## fun_gg_bar_prop() #### ggplot2 proportion barplot 166
######## fun_gg_strip() #### ggplot2 stripchart + mean/median 166
######## fun_gg_violin() #### ggplot2 violins 166
######## fun_gg_line() #### ggplot2 lines + background dots and error bars 166
######## fun_gg_heatmap() #### ggplot2 heatmap + overlaid mask if required 168
######## fun_gg_empty_graph() #### text to display for empty graphs 182
################ Graphic extraction 184
######## fun_trim() #### display values from a quantitative variable and trim according to defined cut-offs 184
######## fun_segmentation() #### segment a dot cloud on a scatterplot and define the dots from another cloud outside the segmentation 192
################ Import 224
######## fun_pack() #### check if R packages are present and import into the working environment 224
######## fun_python_pack() #### check if python packages are present 226
################ Exporting results (text & tables) 227
######## fun_report() #### print string or data object into output file 227
 
 
################################ FUNCTIONS ################################
......@@ -2158,10 +2157,10 @@ return(list(mat = mat, warnings = warning))
}
 
 
######## fun_permut_consec() #### progressively breaks a vector order
######## fun_permut() #### progressively breaks a vector order
 
 
fun_permut_consec <- function(data1, data2 = NULL, n = NULL, seed = NULL, count.print = 10, text.print = "", cor.method = "spearman", cor.limit = 0.2, warn.print = FALSE, path.lib = NULL){
fun_permut <- function(data1, data2 = NULL, n = NULL, seed = NULL, count.print = 10, text.print = "", cor.method = "spearman", cor.limit = 0.2, warn.print = FALSE, path.lib = NULL){
# AIM
# reorder the elements of the data1 vector by flipping 2 randomly selected consecutive positions either:
# 1) n times (when n is precised) or
......@@ -2194,17 +2193,17 @@ fun_permut_consec <- function(data1, data2 = NULL, n = NULL, seed = NULL, count.
# $cor: a spearman correlation between the initial positions (1:length(data1) and the final positions if data2 is not specified and the final correlation between data1 and data2 otherwise, according to cor.method
# $count: the number of loops used
# EXAMPLES
# example (1) showing that for loop, used in fun_permut_consec(), is faster than while loop
# example (1) showing that for loop, used in fun_permut(), is faster than while loop
# ini.time <- as.numeric(Sys.time()) ; count <- 0 ; for(i0 in 1:1e9){count <- count + 1} ; tempo.time <- as.numeric(Sys.time()) ; tempo.lapse <- round(lubridate::seconds_to_period(tempo.time - ini.time)) ; tempo.lapse
# example (2) showing that for loop, used in fun_permut_consec(), is faster than while loop
# example (2) showing that for loop, used in fun_permut(), is faster than while loop
# ini.time <- as.numeric(Sys.time()) ; count <- 0 ; while(count < 1e9){count <- count + 1} ; tempo.time <- as.numeric(Sys.time()) ; tempo.lapse <- round(lubridate::seconds_to_period(tempo.time - ini.time)) ; tempo.lapse
# fun_permut_consec(data1 = LETTERS[1:5], data2 = NULL, n = 100, seed = 1, count.print = 10, text.print = "CPU NB 4")
# fun_permut_consec(data1 = 101:110, data2 = 21:30, seed = 1, count.print = 1e4, text.print = "", cor.method = "spearman", cor.limit = 0.2)
# fun_permut(data1 = LETTERS[1:5], data2 = NULL, n = 100, seed = 1, count.print = 10, text.print = "CPU NB 4")
# fun_permut(data1 = 101:110, data2 = 21:30, seed = 1, count.print = 1e4, text.print = "", cor.method = "spearman", cor.limit = 0.2)
# a way to use the cor.limit argument just considering data1
# obs1 <- 101:110 ; fun_permut_consec(data1 = obs1, data2 = obs1, seed = 1, count.print = 10, cor.method = "spearman", cor.limit = 0.2)
# fun_permut_consec(data1 = 1:1e3, data2 = 1e3:1, seed = 1, count.print = 1e6, text.print = "", cor.method = "spearman", cor.limit = 0.7)
# fun_permut_consec(data1 = 1:1e2, data2 = 1e2:1, seed = 1, count.print = 1e3, cor.limit = 0.5)
# fun_permut_consec(data1 = c(0,0,0,0,0), n = 5, data2 = NULL, seed = 1, count.print = 1e3, cor.limit = 0.5)
# obs1 <- 101:110 ; fun_permut(data1 = obs1, data2 = obs1, seed = 1, count.print = 10, cor.method = "spearman", cor.limit = 0.2)
# fun_permut(data1 = 1:1e3, data2 = 1e3:1, seed = 1, count.print = 1e6, text.print = "", cor.method = "spearman", cor.limit = 0.7)
# fun_permut(data1 = 1:1e2, data2 = 1e2:1, seed = 1, count.print = 1e3, cor.limit = 0.5)
# fun_permut(data1 = c(0,0,0,0,0), n = 5, data2 = NULL, seed = 1, count.print = 1e3, cor.limit = 0.5)
# DEBUGGING
# data1 = LETTERS[1:5] ; data2 = NULL ; n = 1e6 ; seed = NULL ; count.print = 1e3 ; text.print = "" ; cor.method = "spearman" ; cor.limit = 0.2 ; warn.print = TRUE ; path.lib = NULL
# data1 = LETTERS[1:5] ; data2 = NULL ; n = 10 ; seed = 22 ; count.print = 10 ; text.print = "" ; cor.method = "spearman" ; cor.limit = 0.2 ; warn.print = TRUE ; path.lib = NULL
......
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