cute_little_R_functions.R 869 KB
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# $same.row.nb: logical. Are number of rows identical ?
# $row.nb: nb of rows of the 2 datasets if identical (NULL otherwise)
# $same.col.nb: logical. Are number of columns identical ?
# $col.nb: nb of columns of the 2 datasets if identical (NULL otherwise)
# $same.row.name: logical. Are row names identical ? NULL if no row names in the two 2D datasets
# $row.name: name of rows of the 2 datasets if identical (NULL otherwise)
# $any.id.row.name: logical. Is there any row names identical ? NULL if no row names in the two 2D datasets
# $same.row.name.pos1: position, in data1, of the row names identical in data2
# $same.row.name.pos2: position, in data2, of the row names identical in data1
# $common.row.names: common row names between data1 and data2 (can be a subset of $name or not). NULL if no common row names
# $same.col.name: logical. Are column names identical ? NULL if no col names in the two 2D datasets
# $col.name: name of columns of the 2 datasets if identical (NULL otherwise)
# $any.id.col.name: logical. Is there any column names identical ? NULL if no col names in the two 2D datasets
# $same.col.name.pos1: position, in data1, of the column names identical in data2
# $same.col.name.pos2: position, in data2, of the column names identical in data1
# $common.col.names: common column names between data1 and data2 (can be a subset of $name or not). NULL if no common column names
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# $any.id.row: logical. is there identical rows (not considering row names)? NULL if nrow(data1) * nrow(data2) > 1e10
# $same.row.pos1: position, in data1, of the rows identical in data2 (not considering row names). Return "TOO BIG FOR EVALUATION" if nrow(data1) * nrow(data2) > 1e10
# $same.row.pos2: position, in data2, of the rows identical in data1 (not considering row names). Return "TOO BIG FOR EVALUATION" if nrow(data1) * nrow(data2) > 1e10
# $any.id.col: logical. is there identical columns (not considering column names)? NULL if ncol(data1) * ncol(data2) > 1e10
# $same.col.pos1: position in data1 of the cols identical in data2 (not considering column names). Return "TOO BIG FOR EVALUATION" if ncol(data1) * ncol(data2) > 1e10
# $same.col.pos2: position in data2 of the cols identical in data1 (not considering column names). Return "TOO BIG FOR EVALUATION" if ncol(data1) * ncol(data2) > 1e10
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# $identical.object: logical. Are objects identical (including row & column names)?
# $identical.content: logical. Are content objects identical (identical excluding row & column names)?
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# REQUIRED PACKAGES
# none
# REQUIRED FUNCTIONS FROM CUTE_LITTLE_R_FUNCTION
# none
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# EXAMPLES
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# obs1 = matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) ; obs2 = as.data.frame(matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])), stringsAsFactors = TRUE) ; obs1 ; obs2 ; fun_comp_2d(obs1, obs2)
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# obs1 = matrix(101:110, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) ; obs2 = matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) ; obs1 ; obs2 ; fun_comp_2d(obs1, obs2)
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# large matrices
# obs1 = matrix(1:1e6, ncol = 5, dimnames = list(NULL, LETTERS[1:5])) ; obs2 = matrix(as.integer((1:1e6)+1e6/5), ncol = 5, dimnames = list(NULL, LETTERS[1:5])) ; head(obs1) ; head(obs2) ; fun_comp_2d(obs1, obs2)
# WARNING: when comparing content (rows, columns, or total), double and integer data are considered as different -> double(1) != integer(1)
# obs1 = matrix(1:1e6, ncol = 5, dimnames = list(NULL, LETTERS[1:5])) ; obs2 = matrix((1:1e6)+1e6/5, ncol = 5, dimnames = list(NULL, LETTERS[1:5])) ; head(obs1) ; head(obs2) ; fun_comp_2d(obs1, obs2)
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# obs1 = matrix(1:10, byrow = TRUE, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) ; obs2 = matrix(c(1:5, 101:105, 6:10), byrow = TRUE, ncol = 5, dimnames = list(c("a", "z", "b"), c(LETTERS[1:2], "k", LETTERS[5:4]))) ; obs1 ; obs2 ; fun_comp_2d(obs1, obs2)
# obs1 = t(matrix(1:10, byrow = TRUE, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5]))) ; obs2 = t(matrix(c(1:5, 101:105, 6:10), byrow = TRUE, ncol = 5, dimnames = list(c("a", "z", "b"), c(LETTERS[1:2], "k", LETTERS[5:4])))) ; obs1 ; obs2 ; fun_comp_2d(obs1, obs2)
# DEBUGGING
# data1 = matrix(1:10, ncol = 5) ; data2 = matrix(1:10, ncol = 5) # for function debugging
# data1 = matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) ; data2 = matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) # for function debugging
# data1 = matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) ; data2 = matrix(1:10, ncol = 5) # for function debugging
# data1 = matrix(1:15, byrow = TRUE, ncol = 5, dimnames = list(letters[1:3], LETTERS[1:5])) ; data2 = matrix(1:10, byrow = TRUE, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) # for function debugging
# data1 = matrix(1:15, ncol = 5, dimnames = list(letters[1:3], LETTERS[1:5])) ; data2 = matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) # for function debugging
# data1 = matrix(1:15, ncol = 5, dimnames = list(paste0("A", letters[1:3]), LETTERS[1:5])) ; data2 = matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) # for function debugging
# data1 = matrix(1:15, ncol = 5, dimnames = list(letters[1:3], LETTERS[1:5])) ; data2 = matrix(1:12, ncol = 4, dimnames = list(letters[1:3], LETTERS[1:4])) # for function debugging
# data1 = matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) ; data2 = matrix(101:110, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) # for function debugging
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# data1 = data.frame(a = 1:3, b= letters[1:3], row.names = LETTERS[1:3], stringsAsFactors = TRUE) ; data2 = data.frame(A = 1:3, B= letters[1:3], stringsAsFactors = TRUE) # for function debugging
# data1 = matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) ; data2 = as.data.frame(matrix(1:10, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])), stringsAsFactors = TRUE) # for function debugging
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# data1 = matrix(1:10, byrow = TRUE, ncol = 5, dimnames = list(letters[1:2], LETTERS[1:5])) ; data2 = matrix(c(1:5, 101:105, 6:10), byrow = TRUE, ncol = 5, dimnames = list(c("a", "z", "b"), c(LETTERS[1:2], "k", LETTERS[5:4]))) # for function debugging
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# data1 = table(Exp1 = c("A", "A", "A", "B", "B", "B"), Exp2 = c("A1", "B1", "A1", "C1", "C1", "B1")) ; data2 = data.frame(A = 1:3, B= letters[1:3], stringsAsFactors = TRUE) # for function debugging
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# data1 = matrix(1:1e6, ncol = 5, dimnames = list(NULL, LETTERS[1:5])) ; data2 = matrix((1:1e6)+1e6/5, ncol = 5, dimnames = list(NULL, LETTERS[1:5]))
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# function name
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function.name <- paste0(as.list(match.call(expand.dots = FALSE))[[1]], "()")
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# end function name
# argument checking
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if( ! (any(class(data1) %in% c("data.frame", "table")) | all(class(data1) %in% c("matrix", "array")))){ # before R4.0.0, it was  ! any(class(data1) %in% c("matrix", "data.frame", "table"))
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tempo.cat <- paste0("ERROR IN ", function.name, ": THE data1 ARGUMENT MUST BE A MATRIX, DATA FRAME OR TABLE")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
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if( ! (any(class(data2) %in% c("data.frame", "table")) | all(class(data2) %in% c("matrix", "array")))){ # before R4.0.0, it was  ! any(class(data2) %in% c("matrix", "data.frame", "table"))
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tempo.cat <- paste0("ERROR IN ", function.name, ": THE data2 ARGUMENT MUST BE A MATRIX, DATA FRAME OR TABLE")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
# source("C:/Users/Gael/Documents/Git_versions_to_use/debugging_tools_for_r_dev-v1.2/r_debugging_tools-v1.2.R") ; eval(parse(text = str_basic_arg_check_dev)) # activate this line and use the function to check arguments status
# end argument checking
# main code
same.class <- NULL
class <- NULL
same.dim <- NULL
dim <- NULL
same.row.nb <- NULL
row.nb <- NULL
same.col.nb <- NULL
col.nb <- NULL
same.row.name <- NULL
row.name <- NULL
any.id.row.name <- NULL
same.row.name.pos1 <- NULL
same.row.name.pos2 <- NULL
common.row.names <- NULL
same.col.name <- NULL
any.id.col.name <- NULL
same.col.name.pos1 <- NULL
same.col.name.pos2 <- NULL
common.col.names <- NULL
col.name <- NULL
any.id.row <- NULL
same.row.pos1 <- NULL
same.row.pos2 <- NULL
any.id.col <- NULL
same.col.pos1 <- NULL
same.col.pos2 <- NULL
identical.object <- NULL
identical.content <- NULL
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if(identical(data1, data2) & (any(class(data1) %in% c("data.frame", "table")) | all(class(data1) %in% c("matrix", "array")))){ # before R4.0.0, it was  ! any(class(data1) %in% c("matrix", "data.frame", "table"))
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same.class <- TRUE
class <- class(data1)
same.dim <- TRUE
dim <- dim(data1)
same.row.nb <- TRUE
row.nb <- nrow(data1)
same.col.nb <- TRUE
col.nb <- ncol(data1)
same.row.name <- TRUE
row.name <- dimnames(data1)[[1]]
any.id.row.name <- TRUE
same.row.name.pos1 <- 1:row.nb
same.row.name.pos2 <- 1:row.nb
common.row.names <- dimnames(data1)[[1]]
same.col.name <- TRUE
col.name <- dimnames(data1)[[2]]
any.id.col.name <- TRUE
same.col.name.pos1 <- 1:col.nb
same.col.name.pos2 <- 1:col.nb
common.col.names <- dimnames(data1)[[2]]
any.id.row <- TRUE
same.row.pos1 <- 1:row.nb
same.row.pos2 <- 1:row.nb
any.id.col <- TRUE
same.col.pos1 <- 1:col.nb
same.col.pos2 <- 1:col.nb
identical.object <- TRUE
identical.content <- TRUE
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}else{
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identical.object <- FALSE
if(all(class(data1) == "table") & length(dim(data1)) == 1){
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tempo.cat <- paste0("ERROR IN ", function.name, ": THE data1 ARGUMENT IS A 1D TABLE. USE THE fun_comp_1d FUNCTION")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
if(all(class(data2) == "table") & length(dim(data2)) == 1){
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tempo.cat <- paste0("ERROR IN ", function.name, ": THE data2 ARGUMENT IS A 1D TABLE. USE THE fun_comp_1d FUNCTION")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
if( ! identical(class(data1), class(data2))){
same.class <- FALSE
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}else if( ! (any(class(data1) %in% c("data.frame", "table")) | all(class(data1) %in% c("matrix", "array")))){ # before R4.0.0, it was  ! any(class(data1) %in% c("matrix", "data.frame", "table"))
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tempo.cat <- paste0("ERROR IN ", function.name, ": THE data1 AND data2 ARGUMENTS MUST BE EITHER MATRIX, DATA FRAME OR TABLE")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}else{
same.class <- TRUE
class <- class(data1)
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}
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if( ! identical(dim(data1), dim(data2))){
same.dim <- FALSE
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}else{
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same.dim <- TRUE
dim <- dim(data1)
}
if( ! identical(nrow(data1), nrow(data2))){
same.row.nb <- FALSE
}else{
same.row.nb <- TRUE
row.nb <- nrow(data1)
}
if( ! identical(ncol(data1), ncol(data2))){
same.col.nb <- FALSE
}else{
same.col.nb <- TRUE
col.nb <- ncol(data1)
}
# row and col names
if(is.null(dimnames(data1)) & is.null(dimnames(data2))){
same.row.name <- NULL
same.col.name <- NULL
# row and col names remain NULL
}else if((is.null(dimnames(data1)) & ! is.null(dimnames(data2))) | ( ! is.null(dimnames(data1)) & is.null(dimnames(data2)))){
same.row.name <- FALSE
same.col.name <- FALSE
# row and col names remain NULL
}else{
if( ! identical(dimnames(data1)[[1]], dimnames(data2)[[1]])){
same.row.name <- FALSE
# row names remain NULL
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}else{
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same.row.name <- TRUE
row.name <- dimnames(data1)[[1]]
}
# row names
any.id.row.name <- FALSE
if(any(dimnames(data1)[[1]] %in% dimnames(data2)[[1]])){
any.id.row.name <- TRUE
same.row.name.pos1 <- which(dimnames(data1)[[1]] %in% dimnames(data2)[[1]])
}
if(any(dimnames(data2)[[1]] %in% dimnames(data1)[[1]])){
any.id.row.name <- TRUE
same.row.name.pos2 <- which(dimnames(data2)[[1]] %in% dimnames(data1)[[1]])
}
if(any.id.row.name == TRUE){
common.row.names <- unique(c(dimnames(data1)[[1]][same.row.name.pos1], dimnames(data2)[[1]][same.row.name.pos2]))
}
# col names
any.id.col.name <- FALSE
if(any(dimnames(data1)[[2]] %in% dimnames(data2)[[2]])){
any.id.col.name <- TRUE
same.col.name.pos1 <- which(dimnames(data1)[[2]] %in% dimnames(data2)[[2]])
}
if(any(dimnames(data2)[[2]] %in% dimnames(data1)[[2]])){
any.id.col.name <- TRUE
same.col.name.pos2 <- which(dimnames(data2)[[2]] %in% dimnames(data1)[[2]])
}
if(any.id.col.name == TRUE){
common.col.names <- unique(c(dimnames(data1)[[2]][same.col.name.pos1], dimnames(data2)[[2]][same.col.name.pos2]))
}
if( ! identical(dimnames(data1)[[2]], dimnames(data2)[[2]])){
same.col.name <- FALSE
# col names remain NULL
}else{
same.col.name <- TRUE
col.name <- dimnames(data1)[[2]]
}
}
# identical row and col content
if(all(class(data1) == "table")){
as.data.frame(matrix(data1, ncol = ncol(data1)), stringsAsFactors = FALSE)
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}else if(all(class(data1) %in% c("matrix", "array"))){
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data1 <- as.data.frame(data1, stringsAsFactors = FALSE)
}else if(all(class(data1) == "data.frame")){
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data1 <- data.frame(lapply(data1, as.character), stringsAsFactors = FALSE)
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}
if(all(class(data2) == "table")){
as.data.frame(matrix(data2, ncol = ncol(data2)), stringsAsFactors = FALSE)
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}else if(all(class(data2) %in% c("matrix", "array"))){
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data2 <- as.data.frame(data2, stringsAsFactors = FALSE)
}else if(all(class(data2) == "data.frame")){
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data2 <- data.frame(lapply(data2, as.character), stringsAsFactors = FALSE)
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}
row.names(data1) <- paste0("A", 1:nrow(data1))
row.names(data2) <- paste0("A", 1:nrow(data2))
if(same.col.nb == TRUE){ # because if not the same col nb, the row cannot be identical
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if(all(sapply(data1, FUN = typeof) == "integer") & all(sapply(data2, FUN = typeof) == "integer") & as.double(nrow(data1)) * nrow(data2) <= 1e10){ # as.double(nrow(data1)) to prevent integer overflow because R is 32 bits for integers
same.row.pos1 <- which(c(as.data.frame(t(data1), stringsAsFactors = FALSE)) %in% c(as.data.frame(t(data2), stringsAsFactors = FALSE))) # this work fast with only integers (because 32 bits)
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same.row.pos2 <- which(c(as.data.frame(t(data2), stringsAsFactors = FALSE)) %in% c(as.data.frame(t(data1), stringsAsFactors = FALSE)))
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}else if(as.double(nrow(data1)) * nrow(data2) <= 1e6){ # as.double(nrow(data1)) to prevent integer overflow because R is 32 bits for integers
same.row.pos1 <- logical(length = nrow(data1)) # FALSE by default
same.row.pos1[] <- FALSE # security
for(i3 in 1:nrow(data1)){
for(i4 in 1:nrow(data2)){
same.row.pos1[i3] <- identical(data1[i3, ], data2[i4, ])
}
}
same.row.pos1 <- which(same.row.pos1)

same.row.pos2 <- logical(length = nrow(data2)) # FALSE by default
same.row.pos2[] <- FALSE # security
for(i3 in 1:nrow(data2)){
for(i4 in 1:nrow(data1)){
same.row.pos2[i3] <- identical(data2[i3, ], data1[i4, ])
}
}
same.row.pos2 <- which(same.row.pos2)
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}else{
same.row.pos1 <- "TOO BIG FOR EVALUATION"
same.row.pos2 <- "TOO BIG FOR EVALUATION"
}
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names(same.row.pos1) <- NULL
names(same.row.pos2) <- NULL
if(all(is.na(same.row.pos1))){
same.row.pos1 <- NULL
}else{
same.row.pos1 <- same.row.pos1[ ! is.na(same.row.pos1)]
any.id.row <- TRUE
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}
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if(all(is.na(same.row.pos2))){
same.row.pos2 <- NULL
}else{
same.row.pos2 <- same.row.pos2[ ! is.na(same.row.pos2)]
any.id.row <- TRUE
}
if(is.null(same.row.pos1) & is.null(same.row.pos2)){
any.id.row <- FALSE
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}else if(length(same.row.pos1) == 0 & length(same.row.pos2) == 0){
any.id.row <- FALSE
}else if(all(same.row.pos1 == "TOO BIG FOR EVALUATION") & all(same.row.pos2 == "TOO BIG FOR EVALUATION")){
any.id.row <- NULL
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}
}else{
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any.id.row <- FALSE
# same.row.pos1 and 2 remain NULL
}
if(same.row.nb == TRUE){ # because if not the same row nb, the col cannot be identical
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if(all(sapply(data1, FUN = typeof) == "integer") & all(sapply(data2, FUN = typeof) == "integer") & as.double(ncol(data1)) * ncol(data2) <= 1e10){ # as.double(ncol(data1)) to prevent integer overflow because R is 32 bits for integers
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same.col.pos1 <- which(c(data1) %in% c(data2))
same.col.pos2 <- which(c(data2) %in% c(data1))
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}else if(as.double(ncol(data1)) * ncol(data2) <= 1e6){ # as.double(ncol(data1)) to prevent integer overflow because R is 32 bits for integers
same.col.pos1 <- logical(length = ncol(data1)) # FALSE by default
same.col.pos1[] <- FALSE # security
for(i3 in 1:ncol(data1)){
for(i4 in 1:ncol(data2)){
same.col.pos1[i3] <- identical(data1[ , i3], data2[ ,i4])
}
}
same.col.pos1 <- which(same.col.pos1)

same.col.pos2 <- logical(length = ncol(data2)) # FALSE by default
same.col.pos2[] <- FALSE # security
for(i3 in 1:ncol(data2)){
for(i4 in 1:ncol(data1)){
same.col.pos2[i3] <- identical(data2[ , i3], data1[ , i4])
}
}
same.col.pos2 <- which(same.col.pos2)
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}else{
same.col.pos1 <- "TOO BIG FOR EVALUATION"
same.col.pos2 <- "TOO BIG FOR EVALUATION"
}
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names(same.col.pos1) <- NULL
names(same.col.pos2) <- NULL
if(all(is.na(same.col.pos1))){
same.col.pos1 <- NULL
}else{
same.col.pos1 <- same.col.pos1[ ! is.na(same.col.pos1)]
any.id.col <- TRUE
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}
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if(all(is.na(same.col.pos2))){
same.col.pos2 <- NULL
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}else{
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same.col.pos2 <- same.col.pos2[ ! is.na(same.col.pos2)]
any.id.col <- TRUE
}
if(is.null(same.col.pos1) & is.null(same.col.pos2)){
any.id.col <- FALSE
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}else if(length(same.col.pos1) == 0 & length(same.col.pos2) == 0){
any.id.col <- FALSE
}else if(all(same.col.pos1 == "TOO BIG FOR EVALUATION") & all(same.col.pos2 == "TOO BIG FOR EVALUATION")){
any.id.col <- NULL
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}
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}else{
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any.id.col <- FALSE
# same.col.pos1 and 2 remain NULL
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}
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if(same.dim == TRUE){
names(data1) <- NULL
row.names(data1) <- NULL
names(data2) <- NULL
row.names(data2) <- NULL
if(identical(data1, data2)){
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identical.content <- TRUE
}else{
identical.content <- FALSE
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}
}else{
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identical.content <- FALSE
}
}
output <- list(same.class = same.class, class = class, same.dim = same.dim, dim = dim, same.row.nb = same.row.nb, row.nb = row.nb, same.col.nb = same.col.nb , col.nb = col.nb, same.row.name = same.row.name, row.name = row.name, any.id.row.name = any.id.row.name, same.row.name.pos1 = same.row.name.pos1, same.row.name.pos2 = same.row.name.pos2, common.row.names = common.row.names, same.col.name = same.col.name, col.name = col.name,any.id.col.name = any.id.col.name, same.col.name.pos1 = same.col.name.pos1, same.col.name.pos2 = same.col.name.pos2, common.col.names = common.col.names, any.id.row = any.id.row, same.row.pos1 = same.row.pos1, same.row.pos2 = same.row.pos2, any.id.col = any.id.col, same.col.pos1 = same.col.pos1, same.col.pos2 = same.col.pos2, identical.object = identical.object, identical.content = identical.content)
return(output)
}


######## fun_comp_list() #### comparison of two lists


# Check OK: clear to go Apollo
fun_comp_list <- function(data1, data2){
# AIM
# compare two lists. Check and report in a list if the 2 datasets have:
# same length
# common names
# common compartments
# ARGUMENTS
# data1: list
# data2: list
# RETURN
# a list containing:
# $same.length: logical. Are number of elements identical?
# $length: number of elements in the 2 datasets (NULL otherwise)
# $same.name: logical. Are element names identical ?
# $name: name of elements of the 2 datasets if identical (NULL otherwise)
# $any.id.name: logical. Is there any element names identical ?
# $same.name.pos1: position, in data1, of the element names identical in data2
# $same.name.pos2: position, in data2, of the compartment names identical in data1
# $any.id.compartment: logical. is there any identical compartments ?
# $same.compartment.pos1: position, in data1, of the compartments identical in data2
# $same.compartment.pos2: position, in data2, of the compartments identical in data1
# $identical.object: logical. Are objects identical (kind of object, compartment names and content)?
# $identical.content: logical. Are content objects identical (identical compartments excluding compartment names)?
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# REQUIRED PACKAGES
# none
# REQUIRED FUNCTIONS FROM CUTE_LITTLE_R_FUNCTION
# none
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# EXAMPLES
# obs1 = list(a = 1:5, b = LETTERS[1:2], d = matrix(1:6)) ; obs2 = list(a = 1:5, b = LETTERS[1:2], d = matrix(1:6)) ; fun_comp_list(obs1, obs2)
# obs1 = list(1:5, LETTERS[1:2]) ; obs2 = list(a = 1:5, b = LETTERS[1:2]) ; fun_comp_list(obs1, obs2)
# obs1 = list(b = 1:5, c = LETTERS[1:2]) ; obs2 = list(a = 1:5, b = LETTERS[1:2], d = matrix(1:6)) ; fun_comp_list(obs1, obs2)
# obs1 = list(b = 1:5, c = LETTERS[1:2]) ; obs2 = list(LETTERS[5:9], matrix(1:6), 1:5) ; fun_comp_list(obs1, obs2)
# DEBUGGING
# data1 = list(a = 1:5, b = LETTERS[1:2], d = matrix(1:6)) ; data2 = list(a = 1:5, b = LETTERS[1:2], d = matrix(1:6)) # for function debugging
# data1 = list(a = 1:5, b = LETTERS[1:2]) ; data2 = list(a = 1:5, b = LETTERS[1:2], d = matrix(1:6)) # for function debugging
# function name
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function.name <- paste0(as.list(match.call(expand.dots = FALSE))[[1]], "()")
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# end function name
# argument checking
if( ! any(class(data1) %in% "list")){
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tempo.cat <- paste0("ERROR IN ", function.name, ": THE data1 ARGUMENT MUST BE A LIST")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
if( ! any(class(data2) %in% "list")){
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tempo.cat <- paste0("ERROR IN ", function.name, ": THE data2 ARGUMENT MUST BE A LIST")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
# source("C:/Users/Gael/Documents/Git_versions_to_use/debugging_tools_for_r_dev-v1.2/r_debugging_tools-v1.2.R") ; eval(parse(text = str_basic_arg_check_dev)) # activate this line and use the function to check arguments status
# end argument checking
# main code
same.length <- NULL
length <- NULL
same.name <- NULL
name <- NULL
any.id.name <- NULL
same.name.pos1 <- NULL
same.name.pos2 <- NULL
any.id.compartment <- NULL
same.compartment.pos1 <- NULL
same.compartment.pos2 <- NULL
identical.object <- NULL
identical.content <- NULL
if(identical(data1, data2)){
same.length <- TRUE
length <- length(data1)
if( ! is.null(names(data1))){
same.name <- TRUE
name <- names(data1)
any.id.name <- TRUE
same.name.pos1 <- 1:length(data1)
same.name.pos2 <- 1:length(data2)
}
any.id.compartment <- TRUE
same.compartment.pos1 <- 1:length(data1)
same.compartment.pos2 <- 1:length(data2)
identical.object <- TRUE
identical.content <- TRUE
}else{
identical.object <- FALSE
if( ! identical(length(data1), length(data2))){
same.length<- FALSE
}else{
same.length<- TRUE
length <- length(data1)
}
if( ! (is.null(names(data1)) & is.null(names(data2)))){
if( ! identical(names(data1), names(data2))){
same.name <- FALSE
}else{
same.name <- TRUE
name <- names(data1)
}
any.id.name <- FALSE
if(any(names(data1) %in% names(data2))){
any.id.name <- TRUE
same.name.pos1 <- which(names(data1) %in% names(data2))
}
if(any(names(data2) %in% names(data1))){
any.id.name <- TRUE
same.name.pos2 <- which(names(data2) %in% names(data1))
}
}
names(data1) <- NULL
names(data2) <- NULL
any.id.compartment <- FALSE
if(any(data1 %in% data2)){
any.id.compartment <- TRUE
same.compartment.pos1 <- which(data1 %in% data2)
}
if(any(data2 %in% data1)){
any.id.compartment <- TRUE
same.compartment.pos2 <- which(data2 %in% data1)
}
if(same.length == TRUE & ! all(is.null(same.compartment.pos1), is.null(same.compartment.pos2))){
if(identical(same.compartment.pos1, same.compartment.pos2)){
identical.content <- TRUE
}else{
identical.content <- FALSE
}
}else{
identical.content <- FALSE
}
}
output <- list(same.length = same.length, length = length, same.name = same.name, name = name, any.id.name = any.id.name, same.name.pos1 = same.name.pos1, same.name.pos2 = same.name.pos2, any.id.compartment = any.id.compartment, same.compartment.pos1 = same.compartment.pos1, same.compartment.pos2 = same.compartment.pos2, identical.object = identical.object, identical.content = identical.content)
return(output)
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}


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######## fun_test() #### test combinations of argument values of a function and return errors (and graphs)
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# add traceback https://stackoverflow.com/questions/47414119/how-to-read-a-traceback-in-r

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# Check OK: clear to go Apollo
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fun_test <- function(
fun, 
arg, 
val, 
expect.error = NULL, 
thread.nb = NULL, 
print.count = 10, 
plot.fun = FALSE, 
export = FALSE, 
res.path = NULL, 
lib.path = NULL, 
cute.path = "C:\\Users\\Gael\\Documents\\Git_projects\\cute_little_R_functions\\cute_little_R_functions.R"
){
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# AIM
# test combinations of argument values of a function
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# WARNINGS
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# Limited to 43 arguments with at least 2 values each. The total number of arguments tested can be more if the additional arguments have a single value. The limit is due to nested "for" loops (https://stat.ethz.ch/pipermail/r-help/2008-March/157341.html), but it should not be a problem since the number of tests would be 2^43 > 8e12
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# ARGUMENTS
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# fun: character string indicating the name of the function tested (without brackets)
# arg: vector of character strings of arguments of fun. At least arguments that do not have default values must be present in this vector
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# val: list with number of compartments equal to length of arg, each compartment containing values of the corresponding argument in arg. Each different value must be in a list or in a vector. For instance, argument 3 in arg is a logical argument (values accepted TRUE, FALSE, NA). Thus, compartment 3 of val can be either list(TRUE, FALSE, NA), or c(TRUE, FALSE, NA)
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# expect.error: list of exactly the same structure as val argument, but containing FALSE or TRUE, depending on whether error is expected (TRUE) or not (FALSE) for each corresponding value of val. A message is returned depending on discrepancies between the expected and observed errors. BEWARE: not always possible to write the expected errors for all the combination of argument values. Ignored if NULL
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# thread.nb: numeric value indicating the number of available threads. Write NULL if no parallelization wanted
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# print.count: interger value. Print a working progress message every print.count during loops. BEWARE: can increase substentially the time to complete the process using a small value, like 10 for instance. Use Inf is no loop message desired
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# plot.fun: logical. Plot the plotting function tested for each test?
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# export: logical. Export the results into a .RData file and into a .txt file? If FALSE, return a list into the console (see below). BEWARE: will be automatically set to TRUE if thread.nb is not NULL. This means that when using parallelization, the results are systematically exported, not returned into the console
# res.path: character string indicating the absolute pathway of folder where the txt results and pdfs, containing all the plots, will be saved. Several txt and pdf, one per thread, if parallelization. Ignored if export is FALSE. Must be specified if thread.nb is not NULL or if export is TRUE
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# lib.path: character vector specifying the absolute pathways of the directories containing the required packages if not in the default directories. Ignored if NULL
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# cute.path: character string indicating the absolute path of the cute.R file. Will be remove when cute will be a package. Not considered if thread.nb is NULL
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# REQUIRED PACKAGES
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# lubridate
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# parallel if thread.nb argument is not NULL (included in the R installation packages)
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# if the tested function is in a package, this package must be imported first (no parallelization) or must be in the classical R package folder indicated by the lib.path argument (parallelization)
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# RETURN
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# if export is FALSE a list containing:
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# $fun: the tested function
# $data: a data frame of all the combination tested, containing the following columns:
# the different values tested, named by arguments
# $kind: a vector of character strings indicating the kind of test result: either "ERROR", or "WARNING", or "OK"
# $problem: a logical vector indicating if error or not
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# $expected.error: optional logical vector indicating the expected error specified in the expect.error argument
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# $message: either NULL if $kind is always "OK", or the messages
# $instruction: the initial instruction
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# $sys.info: system and packages info
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# if export is TRUE 1) the same list object into a .RData file, 2) also the $data data frame into a .txt file, and 3) if expect.error is non NULL and if any discrepancy, the $data data frame into a .txt file but containing only the rows with discrepancies between expected and observed errors
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# one or several pdf if a plotting function is tested and if the plot.fun argument is TRUE
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# REQUIRED PACKAGES
# none
# REQUIRED FUNCTIONS FROM CUTE_LITTLE_R_FUNCTION
# fun_check()
# fun_get_message()
# fun_pack()
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# EXAMPLES
# fun_test(fun = "unique", arg = c("x", "incomparables"), val = list(x = list(1:10, c(1,1,2,8), NA), incomparable = c(TRUE, FALSE, NA)))
# fun_test(fun = "fun_round", arg = c("data", "dec.nb", "after.lead.zero"), val = list(L1 = list(c(1, 1.0002256, 1.23568), "a", NA), L2 = list(2, c(1,3), NA), L3 = c(TRUE, FALSE, NA)))
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# fun_test(fun = "plot", arg = c("x", "y"), val = list(x = list(1:10, 12:13, NA, (1:10)^2), y = list(1:10, NA, NA)),  expect.error = list(x = list(FALSE, TRUE, TRUE, FALSE), y = list(FALSE, TRUE, TRUE)), thread.nb = NULL, plot.fun = TRUE, res.path = "C:\\Users\\Gael\\Desktop\\", lib.path = NULL)
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# fun_test(fun = "plot", arg = c("x", "y"), val = list(x = list(1:10, 12:13, NA, (1:10)^2), y = list(1:10, NA, NA)), thread.nb = 4, plot.fun = TRUE, res.path = "C:\\Users\\Gael\\Desktop\\", lib.path = "C:\\Program Files\\R\\R-4.0.2\\library\\")
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# set.seed(1) ; obs1 <- data.frame(Time = c(rnorm(10), rnorm(10) + 2), Group1 = rep(c("G", "H"), each = 10), stringsAsFactors = TRUE) ; fun_test(fun = "fun_gg_boxplot", arg = c("data1", "y", "categ"), val = list(L1 = list(L1 = obs1), L2 = list(L1 = "Time"), L3 = list(L1 = "Group1")))
# set.seed(1) ; obs1 <- data.frame(Time = c(rnorm(10), rnorm(10) + 2), Group1 = rep(c("G", "H"), each = 10), stringsAsFactors = TRUE) ; fun_test(fun = "fun_gg_boxplot", arg = c("data1", "y", "categ"), val = list(L1 = list(obs1), L2 = "Time", L3 = "Group1"), thread.nb = NULL, plot.fun = TRUE, res.path = "C:\\Users\\Gael\\Desktop\\", lib.path = "C:\\Program Files\\R\\R-4.0.2\\library\\")
# library(ggplot2) ; fun_test(fun = "geom_histogram", arg = c("data", "mapping"), val = list(x = list(data.frame(X = "a", stringsAsFactors = TRUE)), y = list(ggplot2::aes(x = X))), thread.nb = NULL, plot.fun = TRUE, res.path = "C:\\Users\\Gael\\Desktop\\", lib.path = "C:\\Program Files\\R\\R-4.0.2\\library\\") # BEWARE: ggplot2::geom_histogram does not work
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# DEBUGGING
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# fun = "unique" ; arg = "x" ; val = list(x = list(1:10, c(1,1,2,8), NA)) ; expect.error = list(x = list(FALSE, FALSE, TRUE)) ; thread.nb = NULL ; plot.fun = FALSE ; export = FALSE ; res.path = "C:\\Users\\Gael\\Desktop\\" ; lib.path = NULL ; print.count = 1 ; cute.path = "C:\\Users\\Gael\\Documents\\Git_projects\\cute_little_R_functions\\cute_little_R_functions.R" # for function debugging
# fun = "unique" ; arg = c("x", "incomparables") ; val = list(x = list(1:10, c(1,1,2,8), NA), incomparable = c(TRUE, FALSE, NA)) ; expect.error = NULL ; thread.nb = 2 ; plot.fun = FALSE ; export = TRUE ; res.path = "C:\\Users\\Gael\\Desktop\\" ; lib.path = NULL ; print.count = 10 ; cute.path = "C:\\Users\\Gael\\Documents\\Git_projects\\cute_little_R_functions\\cute_little_R_functions.R" # for function debugging
# fun = "plot" ; arg = c("x", "y") ; val = list(x = list(1:10, 12:13, NA), y = list(1:10, NA, NA)) ; expect.error = list(x = list(FALSE, FALSE, TRUE, FALSE), y = list(FALSE, TRUE, TRUE)) ; print.count = 10 ; thread.nb = NULL ; plot.fun = TRUE ; export = TRUE ; res.path = "C:\\Users\\Gael\\Desktop\\" ; lib.path = NULL # for function debugging
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# set.seed(1) ; obs1 <- data.frame(Time = c(rnorm(10), rnorm(10) + 2), Group1 = rep(c("G", "H"), each = 10), stringsAsFactors = TRUE) ; fun = "fun_gg_boxplot" ; arg = c("data1", "y", "categ") ; val = list(L1 = list(L1 = obs1), L2 = list(L1 = "Time"), L3 = list(L1 = "Group1")) ; expect.error = NULL ; print.count = 10 ; thread.nb = NULL ; plot.fun = TRUE ; export = TRUE ; res.path = "C:\\Users\\Gael\\Desktop\\" ; lib.path = NULL # for function debugging
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# function name
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function.name <- paste0(as.list(match.call(expand.dots = FALSE))[[1]], "()")
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instruction <- match.call()
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# end function name
# required function checking
req.function <- c(
"fun_check", 
"fun_get_message", 
"fun_pack"
)
for(i1 in req.function){
if(length(find(i1, mode = "function")) == 0){
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tempo.cat <- paste0("ERROR IN ", function.name, ": REQUIRED ", i1, "() FUNCTION IS MISSING IN THE R ENVIRONMENT")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
}
# end required function checking
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# argument primary checking
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# arg with no default values
if(any(missing(fun) | missing(arg) | missing(val))){
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tempo.cat <- paste0("ERROR IN ", function.name, ": ARGUMENTS fun, arg AND val HAVE NO DEFAULT VALUE AND REQUIRE ONE")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
# end arg with no default values
# using fun_check()
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arg.check <- NULL #
text.check <- NULL #
checked.arg.names <- NULL # for function debbuging: used by r_debugging_tools
ee <- expression(arg.check <- c(arg.check, tempo$problem) , text.check <- c(text.check, tempo$text) , checked.arg.names <- c(checked.arg.names, tempo$fun.name))
tempo <- fun_check(data = fun, class = "vector", mode = "character", length = 1, fun.name = function.name) ; eval(ee)
if(tempo$problem == FALSE){
if(grepl(x = fun, pattern = "()$")){ # remove ()
fun <- sub(x = fun, pattern = "()$", replacement = "")
}
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if( ! exists(fun)){
tempo.cat <- paste0("ERROR IN ", function.name, ": CHARACTER STRING IN fun ARGUMENT DOES NOT EXIST IN THE R WORKING ENVIRONMENT: ", paste(fun, collapse = "\n"))
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text.check <- c(text.check, tempo.cat)
arg.check <- c(arg.check, TRUE)
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}else if( ! all(class(get(fun)) == "function")){ # here no env = sys.nframe(), inherit = FALSE for get() because fun is a function in the classical scope
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tempo.cat <- paste0("ERROR IN ", function.name, ": fun ARGUMENT IS NOT CLASS \"function\" BUT: ", paste(class(get(fun)), collapse = "\n"), "\nCHECK IF ANY CREATED OBJECT WOULD HAVE THE NAME OF THE TESTED FUNCTION")
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text.check <- c(text.check, tempo.cat)
arg.check <- c(arg.check, TRUE)
}
}
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tempo <- fun_check(data = arg, class = "vector", mode = "character", fun.name = function.name) ; eval(ee)
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if(tempo$problem == FALSE & length(arg) == 0){
tempo.cat <- paste0("ERROR IN ", function.name, ": arg ARGUMENT CANNOT BE LENGTH 0")
text.check <- c(text.check, tempo.cat)
arg.check <- c(arg.check, TRUE)
}
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tempo <- fun_check(data = val, class = "list", fun.name = function.name) ; eval(ee)
if(tempo$problem == FALSE){
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for(i2 in 1:length(val)){
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tempo1 <- fun_check(data = val[[i2]], class = "vector", na.contain = TRUE, fun.name = function.name)
tempo2 <- fun_check(data = val[[i2]], class = "list", na.contain = TRUE, fun.name = function.name)
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if(tempo1$problem == TRUE & tempo2$problem == TRUE){
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tempo.cat <- paste0("ERROR IN ", function.name, ": COMPARTMENT ", i2, " OF val ARGUMENT MUST BE A VECTOR OR A LIST")
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text.check <- c(text.check, tempo.cat)
arg.check <- c(arg.check, TRUE)
}else if(tempo1$problem == FALSE){ # vector split into list compartments
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val[[i2]] <- split(x = val[[i2]], f = 1:length(val[[i2]]))
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}
}
}
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if( ! is.null(expect.error)){
tempo <- fun_check(data = expect.error, class = "list", fun.name = function.name) ; eval(ee)
if(tempo$problem == FALSE){
for(i3 in 1:length(expect.error)){
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tempo1 <- fun_check(data = expect.error[[i3]], class = "vector",  mode = "logical", fun.name = function.name)
tempo2 <- fun_check(data =  expect.error[[i3]], class = "list", fun.name = function.name)
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if(tempo1$problem == TRUE & tempo2$problem == TRUE){
tempo.cat <- paste0("ERROR IN ", function.name, ": COMPARTMENT ", i3, " OF expect.error ARGUMENT MUST BE TRUE OR FALSE")
text.check <- c(text.check, tempo.cat)
arg.check <- c(arg.check, TRUE)
}else if(tempo1$problem == FALSE){ # vector split into list compartments
expect.error[[i3]] <- split(x = expect.error[[i3]], f = 1:length(expect.error[[i3]]))
}
}
}
}
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if( ! is.null(thread.nb)){
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tempo <- fun_check(data = thread.nb, typeof = "integer", double.as.integer.allowed = TRUE, neg.values = FALSE, length = 1, fun.name = function.name) ; eval(ee)
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if(tempo$problem == FALSE & thread.nb < 1){
tempo.cat <- paste0("ERROR IN ", function.name, ": thread.nb PARAMETER MUST EQUAL OR GREATER THAN 1: ", thread.nb)
text.check <- c(text.check, tempo.cat)
arg.check <- c(arg.check, TRUE)
}
}
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tempo <- fun_check(data = print.count, class = "vector", typeof = "integer", length = 1, double.as.integer.allowed = TRUE, neg.values = FALSE, fun.name = function.name) ; eval(ee)
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tempo <- fun_check(data = plot.fun, class = "vector", mode = "logical", length = 1, fun.name = function.name) ; eval(ee)
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tempo <- fun_check(data = export, class = "vector", mode = "logical", length = 1, fun.name = function.name) ; eval(ee)
if( ! is.null(res.path)){
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tempo <- fun_check(data = res.path, class = "vector", mode = "character", fun.name = function.name) ; eval(ee)
if(tempo$problem == FALSE){
if( ! all(dir.exists(res.path))){ # separation to avoid the problem of tempo$problem == FALSE and res.path == NA
tempo.cat <- paste0("ERROR IN ", function.name, ": DIRECTORY PATH INDICATED IN THE res.path ARGUMENT DOES NOT EXISTS:\n", paste(res.path, collapse = "\n"))
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text.check <- c(text.check, tempo.cat)
arg.check <- c(arg.check, TRUE)
}
}
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}
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if( ! is.null(lib.path)){
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tempo <- fun_check(data = lib.path, class = "vector", mode = "character", fun.name = function.name) ; eval(ee)
if(tempo$problem == FALSE){
if( ! all(dir.exists(lib.path))){ # separation to avoid the problem of tempo$problem == FALSE and lib.path == NA
tempo.cat <- paste0("ERROR IN ", function.name, ": DIRECTORY PATH INDICATED IN THE lib.path ARGUMENT DOES NOT EXISTS:\n", paste(lib.path, collapse = "\n"))
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text.check <- c(text.check, tempo.cat)
arg.check <- c(arg.check, TRUE)
}
}
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}
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if( ! is.null(thread.nb)){
tempo <- fun_check(data = cute.path, class = "vector", typeof = "character", length = 1, fun.name = function.name) ; eval(ee)
if(tempo$problem == FALSE){
if( ! file.exists(cute.path)){
tempo.cat <- paste0("ERROR IN ", function.name, ": FILE PATH INDICATED IN THE cute.path PARAMETER DOES NOT EXISTS:\n", cute.path)
text.check <- c(text.check, tempo.cat)
arg.check <- c(arg.check, TRUE)
}
}
}
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if(any(arg.check) == TRUE){
stop(paste0("\n\n================\n\n", paste(text.check[arg.check], collapse = "\n"), "\n\n================\n\n"), call. = FALSE) #
}
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# end using fun_check()
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# source("C:/Users/Gael/Documents/Git_versions_to_use/debugging_tools_for_r_dev-v1.2/r_debugging_tools-v1.2.R") ; eval(parse(text = str_basic_arg_check_dev)) ; eval(parse(text = str_arg_check_with_fun_check_dev)) # activate this line and use the function (with no arguments left as NULL) to check arguments status and if they have been checked using fun_check()
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# end argument primary checking
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# second round of checking and data preparation
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# dealing with NA
if(any(is.na(fun)) | any(is.na(arg)) | any(is.na(expect.error)) | any(is.na(thread.nb)) | any(is.na(print.count)) | any(is.na(plot.fun)) | any(is.na(export)) | any(is.na(res.path)) | any(is.na(lib.path))){
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tempo.cat <- paste0("ERROR IN ", function.name, ": NO ARGUMENT EXCEPT val CAN HAVE NA VALUES")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
# end dealing with NA
# dealing with NULL
if(is.null(fun) | is.null(arg) | is.null(val) | is.null(print.count) | is.null(plot.fun) | is.null(export)){
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tempo.cat <- paste0("ERROR IN ", function.name, ": THESE ARGUMENTS\nfun\narg\nval\nprint.count\nplot.fun\nexport\nCANNOT BE NULL") #problematic arguments are -> put everywhere
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
# end dealing with NULL
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if(length(arg) != length(val)){
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tempo.cat <- paste0("ERROR IN ", function.name, ": LENGTH OF arg ARGUMENT MUST BE IDENTICAL TO LENGTH OF val ARGUMENT:\nHERE IT IS: ", length(arg), " VERSUS ", length(val))
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stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE)
}
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args <- names(formals(get(fun))) # here no env = sys.nframe(), inherit = FALSE for get() because fun is a function in the classical scope
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if( ! all(arg %in% args)){
tempo.cat <- paste0("ERROR IN ", function.name, ": SOME OF THE STRINGS IN arg ARE NOT ARGUMENTS OF fun\nfun ARGUMENTS: ", paste(args, collapse = " "),"\nPROBLEMATIC STRINGS IN arg: ", paste(arg[ ! arg %in% args], collapse = " "))
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stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE)
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}
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if(sum(sapply(val, FUN = length) > 1) > 43){
tempo.cat <- paste0("ERROR IN ", function.name, ": CANNOT TEST MORE THAN 43 ARGUMENTS IF THEY ALL HAVE AT LEAST 2 VALUES EACH\nHERE THE NUMBER IS: ", sum(sapply(val, FUN = length) > 1))
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stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE)
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}
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if( ! is.null(expect.error)){
if(length(val) != length(expect.error)){
tempo.cat <- paste0("ERROR IN ", function.name, ": LENGTH OF val ARGUMENT MUST BE IDENTICAL TO LENGTH OF expect.error ARGUMENT:\nHERE IT IS: ", length(val), " VERSUS ", length(expect.error))
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE)
}
}
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if( ! is.null(thread.nb) & is.null(res.path)){
tempo.cat <- paste0("ERROR IN ", function.name, ": res.path ARGUMENT MUST BE SPECIFIED IF thread.nb ARGUMENT IS NOT NULL")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE)
}
if(is.null(res.path) & export == TRUE){
tempo.cat <- paste0("ERROR IN ", function.name, ": res.path ARGUMENT MUST BE SPECIFIED IF export ARGUMENT TRUE")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE)
}
if( ! is.null(thread.nb) & export == FALSE){
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export <- TRUE
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tempo.cat <- paste0("WARNING FROM ", function.name, ": export ARGUMENT CONVERTED TO TRUE BECAUSE thread.nb ARGUMENT IS NOT NULL")
warning(paste0("\n", tempo.cat, "\n"), call. = FALSE)
}
# end second round of checking and data preparation
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# package checking
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fun_pack(req.package = c("lubridate"), lib.path = lib.path)
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if( ! is.null(thread.nb)){
fun_pack(req.package = c("parallel"), lib.path = lib.path)
}
# end package checking
# declaration of special plot functions
sp.plot.fun <- c("fun_gg_scatter", "fun_gg_bar", "fun_gg_boxplot")
# end declaration of special plot functions
# main code
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ini.warning.length <- options()$warning.length
options(warning.length = 8170)
warn <- NULL
warn.count <- 0
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cat("\nfun_test JOB IGNITION\n")
ini.date <- Sys.time()
ini.time <- as.numeric(ini.date) # time of process begin, converted into seconds
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if(export == TRUE){
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res.path <- paste0(res.path, "/fun_test_res_", trunc(ini.time))
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if(dir.exists(res.path)){
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tempo.cat <- paste0("ERROR IN ", function.name, ": FOLDER ALREADY EXISTS\n", res.path, "\nPLEASE RERUN ONCE")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}else{
dir.create(res.path)
}
}
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total.comp.nb <- prod(sapply(val, FUN = "length"))
cat(paste0("\nTHE TOTAL NUMBER OF TESTS IS: ", total.comp.nb, "\n"))
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# creation of the txt instruction that includes several loops
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loop.string <- NULL
end.loop.string <- NULL
fun.args <- NULL
fun.args2 <- NULL
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error.values <- NULL
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arg.values <- "list("
for(i1 in 1:length(arg)){
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if(is.null(thread.nb)){
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if(length(val[[i1]]) > 1){ # loop only if more than one value in length(val[[i1]])
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loop.string <- paste0(loop.string, "for(i", i1, " in 1:", length(val[[i1]]), "){")
end.loop.string <- paste0(end.loop.string, "}")
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}
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}else{
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loop.string <- "for(i in x){"
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end.loop.string <- "}"
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}
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fun.args <- paste0(
fun.args, 
ifelse(i1 == 1, "", ", "), 
arg[i1], 
" = val[[", 
i1, 
"]][[", 
if(is.null(thread.nb)){
if(length(val[[i1]]) > 1){
paste0("i", i1)
}else{
"1" # a unique element in val[[i1]]
}
}else{
paste0("i.list[[", i1, "]][i]")
}, 
"]]"
)
fun.args2 <- paste0(
fun.args2, 
ifelse(i1 == 1, "", ", "), 
arg[i1], 
" = val[[", 
i1, 
"]][[', ", 
if(is.null(thread.nb)){
if(length(val[[i1]]) > 1){
paste0("i", i1)
}else{
"1" # a unique element in val[[i1]]
}
}else{
paste0("i.list[[", i1, "]][i]")
}, 
", ']]"
)
arg.values <- paste0(
arg.values, 
"val[[", i1, "]][[", 
if(is.null(thread.nb)){
if(length(val[[i1]]) > 1){
paste0("i", i1)
}else{
"1" # a unique element in val[[i1]]
}
}else{
paste0("i.list[[", i1, "]][i]")
}, 
"]]", 
ifelse(i1 == length(arg), "", ", ")
)
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error.values <- paste0(
error.values, 
ifelse(i1 == 1, "", " | "), 
"expect.error[[", i1, "]][[", 
if(is.null(thread.nb)){
if(length(expect.error[[i1]]) > 1){
paste0("i", i1)
}else{
"1" # a unique element in expect.error[[i1]]
}
}else{
paste0("i.list[[", i1, "]][i]")
}, 
"]]"
)
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}
arg.values <- paste0(arg.values, ")")
fun.test <- paste0(fun, "(", fun.args, ")")
fun.test2 <- paste0("paste0('", fun, "(", fun.args2, ")')")
# plot title for special plot functions
if(plot.fun == TRUE){
plot.kind <- "classic"
if(fun %in% sp.plot.fun){
plot.kind <- "special"
if(any(arg %in% "title")){ # this is for the special functions
tempo.match <- regmatches(x = fun.test, m = regexpr(text = fun.test, pattern = "title = .+[,)]"))
tempo.match <- substring(tempo.match , 1, nchar(tempo.match) - 1)
fun.test <- sub(x = fun.test, pattern = tempo.match, replacement = paste0(tempo.match, "\ntempo.title"))
}else{
fun.test <- sub(x = fun.test, pattern = ")$", replacement = ", title = tempo.title)")
}
}
}
# end plot title for special plot functions
kind <- character()
problem <- logical()
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expected.error <- logical()
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res <- character()
count <- 0
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print.count.loop <- 0
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plot.count <- 0
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if(length(arg) == 1){
data <- data.frame()
}else{ # length(arg) == 0 already tested above
data <- data.frame(t(vector("character", length(arg))), stringsAsFactors = FALSE)[-1, ] # -1 to remove the single row created and to have an empty data frame with length(arg) columns
}
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code <- paste(
loop.string, '
count <- count + 1
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print.count.loop <- print.count.loop + 1
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data <- rbind(data, as.character(sapply(eval(parse(text = arg.values)), FUN = "paste", collapse = " ")), stringsAsFactors = FALSE) # each colum is a test
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tempo.try.error <- fun_get_message(data = eval(parse(text = fun.test2)), kind = "error", header = FALSE, env = get(env.name, env = sys.nframe(), inherit = FALSE)) # data argument needs a character string but eval(parse(text = fun.test2)) provides it (eval parse replace the i1, i2, etc., by the correct values, meaning that only val is required in the env.name environment)
tempo.try.warning <- fun_get_message(data = eval(parse(text = fun.test2)), kind = "warning", header = FALSE, env = get(env.name, env = sys.nframe(), inherit = FALSE), print.no = TRUE) # data argument needs a character string but eval(parse(text = fun.test2)) provides it (eval parse replace the i1, i2, etc., by the correct values, meaning that only val is required in the env.name environment)
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if( ! is.null(expect.error)){
expected.error <- c(expected.error, eval(parse(text = error.values)))
}
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if( ! is.null(tempo.try.error)){
kind <- c(kind, "ERROR")
problem <- c(problem, TRUE)
res <- c(res, tempo.try.error)
}else{
if( ! is.null(tempo.try.warning)){
kind <- c(kind, "WARNING")
problem <- c(problem, FALSE)
res <- c(res, tempo.try.warning)
}else{
kind <- c(kind, "OK")
problem <- c(problem, FALSE)
res <- c(res, "")
}
if(plot.fun == TRUE){
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invisible(dev.set(window.nb))
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plot.count <- plot.count + 1
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tempo.title <- paste0("test_", sprintf(paste0("%0", nchar(total.comp.nb), "d"), ifelse(is.null(thread.nb), count, x[count])))
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if(plot.kind == "classic"){
eval(parse(text = fun.test))
tempo <- fun_post_plot(corner.text = tempo.title)
}else if(plot.kind == "special"){
eval(parse(text = fun.test))
}else{
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tempo.cat <- paste0("INTERNAL CODE ERROR 1 IN ", function.name, ": CODE HAS TO BE MODIFIED")
stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between ==
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}
}
}
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if(print.count.loop == print.count){
print.count.loop <- 0
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tempo.time <- as.numeric(Sys.time())
tempo.lapse <- round(lubridate::seconds_to_period(tempo.time - ini.time))
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final.loop <- (tempo.time - ini.time) / count * ifelse(is.null(thread.nb), total.comp.nb, length(x)) # expected duration in seconds # intra nb.compar loop lapse: time lapse / cycles done * cycles remaining
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final.exp <- as.POSIXct(final.loop, origin = ini.date)
cat(paste0(ifelse(is.null(thread.nb), "\n", paste0("\nIN PROCESS ", process.id, " | ")), "LOOP ", format(count, big.mark=","), " / ", format(ifelse(is.null(thread.nb), total.comp.nb, length(x)), big.mark=","), " | TIME SPENT: ", tempo.lapse, " | EXPECTED END: ", final.exp))
}
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if(count == ifelse(is.null(thread.nb), total.comp.nb, length(x))){
tempo.time <- as.numeric(Sys.time())
tempo.lapse <- round(lubridate::seconds_to_period(tempo.time - ini.time))
cat(paste0(ifelse(is.null(thread.nb), "\nLOOP PROCESS ENDED | ", paste0("\nPROCESS ", process.id, " ENDED | ")), "LOOP ", format(count, big.mark=","), " / ", format(ifelse(is.null(thread.nb), total.comp.nb, length(x)), big.mark=","), " | TIME SPENT: ", tempo.lapse, "\n\n"))
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}
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', 
end.loop.string
)
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# end creation of the txt instruction that includes several loops
if( ! is.null(thread.nb)){
# list of i numbers that will be split
i.list <- vector("list", length(val)) # positions to split in parallel jobs
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for(i2 in 1:length(arg)){
if(i2 == 1){
tempo.divisor <- total.comp.nb / length(val[[i2]])
i.list[[i2]] <- rep(1:length(val[[i2]]), each = as.integer(tempo.divisor))
tempo.multi <- length(val[[i2]])
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}else{
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tempo.divisor <- tempo.divisor / length(val[[i2]])
i.list[[i2]] <- rep(rep(1:length(val[[i2]]), each = as.integer(tempo.divisor)), time = as.integer(tempo.multi))
tempo.multi <- tempo.multi * length(val[[i2]])
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}
}
# end list of i numbers that will be split
tempo.cat <- paste0("PARALLELIZATION INITIATED AT: ", ini.date)
cat(paste0("\n", tempo.cat, "\n"))
tempo.thread.nb = parallel::detectCores(all.tests = FALSE, logical = TRUE) # detect the number of threads
if(tempo.thread.nb < thread.nb){
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thread.nb <- tempo.thread.nb
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}
tempo.cat <- paste0("NUMBER OF THREADS USED: ", thread.nb)
cat(paste0("\n    ", tempo.cat, "\n"))
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Clust <- parallel::makeCluster(thread.nb, outfile = paste0(res.path, "/fun_test_parall_log.txt")) # outfile to print or cat during parallelization (only possible in a file, outfile = "" do not work on windows)
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tempo.cat <- paste0("SPLIT OF TEST NUMBERS IN PARALLELISATION:")
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cat(paste0("\n    ", tempo.cat, "\n"))
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cluster.list <- parallel::clusterSplit(Clust, 1:total.comp.nb) # split according to the number of cluster
str(cluster.list) # using print(str()) add a NULL below the result
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cat("\n")
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paral.output.list <- parallel::clusterApply( # paral.output.list is a list made of thread.nb compartments, each made of n / thread.nb (mat theo column number) compartment. Each compartment receive the corresponding results of fun_permut(), i.e., data (permuted mat1.perm), warning message, cor (final correlation) and count (number of permutations)
cl = Clust,
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x = cluster.list,
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function.name = function.name, 
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instruction = instruction, 
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thread.nb = thread.nb, 
print.count = print.count, 
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total.comp.nb = total.comp.nb, 
sp.plot.fun = sp.plot.fun,
i.list = i.list, 
fun.tested = fun,
arg.values = arg.values,
fun.test = fun.test,
fun.test2 = fun.test2,
kind = kind,
problem = problem,
res = res,
count = count,
plot.count = plot.count,
data = data,
code = code,
plot.fun = plot.fun, 
res.path = res.path, 
lib.path = lib.path, 
cute.path = cute.path, 
fun = function(
x, 
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function.name, 
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instruction, 
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thread.nb, 
print.count, 
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total.comp.nb, 
sp.plot.fun, 
i.list, 
fun.tested, 
arg.values, 
fun.test, 
fun.test2, 
kind, 
problem, 
res, 
count, 
plot.count, 
data, 
code, 
plot.fun, 
res.path,