tests duplicateCol using columns' names 🛡️

R/duplicateRow.R

@@ -1,39 +1,43 @@
-#' Duplicate element of a dataframe.
+#' Duplicates elements of a dataframe.
 #'
 #' Duplicates rows and colmns of a given a dataframe.
 #'
 #' @author
 #' Kevin Cazelles
+#'
 #' @param x a data.frame.
 #' @param id.el identity of the elements to be duplicated.
 #' @param times number of times elements are duplicated. Could be a vector of the same length as id.el.
 #' @param append A logical. If \code{TRUE}, duplicated elements will be appended to the dataframe otherwise duplicated elements remain next to their parent. Non-existing columns cannot be duplicated while non-existing rows can and produce \code{NA}.
-#' @importFrom magrittr %>%
-#' @importFrom magrittr %<>%
+#'
+#' @importFrom magrittr %>% %<>%
 #' @export
 #' @examples
 #' data(iris, package='datasets')
 #' iris2 <- duplicateRow(iris, id.el=1:50, times=2)
 #' iris3 <- duplicateCol(iris, id.el=c('Petal.Length', 'Petal.Width'), times=c(1,2), append=TRUE)
 
 
-#' @describeIn duplicateRow A dataframe with duplicated rows.
+#' @describeIn duplicateRow returns a dataframe with duplicated rows.
 duplicateRow <- function(x, id.el = 1, times = 1, append = FALSE) {
     pos <- rep(id.el, times) %>% sort
     if (class(pos) == "character") 
         ord <- c(rownames(x), pos) else ord <- c(1:nrow(x), pos)
     if (!append) 
         ord %<>% sort
-    return(x[ord, ])
+
+    x[ord, ]
 }
 
-#' @describeIn duplicateRow A dataframe with duplicated columns.
+#' @describeIn duplicateRow returns a dataframe with duplicated columns.
 #' @export
+
 duplicateCol <- function(x, id.el = 1, times = 1, append = FALSE) {
     pos <- rep(id.el, times) %>% sort
     if (class(pos) == "character") 
         ord <- c(colnames(x), pos) else ord <- c(1:ncol(x), pos)
     if (!append) 
         ord %<>% sort
-    return(x[, ord])
+
+    x[, ord]
 }

README.Rmd

@@ -7,21 +7,23 @@ output:
 
 ```{r echo=FALSE, message = FALSE}
 knitr::opts_chunk$set(
-  fig.path = "inst/assets/img/",
+  fig.path = "inst/assets/fig/",
   comment = "R>> ",
   collapse = TRUE,
   warning = FALSE,
   message = FALSE,
-  fig.width = 4,
-  fig.height = 4
+  fig.width = 5,
+  fig.height = 5
 )
-devtools::load_all(".")
 ```
 
 
-## letiRmisc
+# letiRmisc
 
-### Short description
+
+## About
+
+### Description
 
 The *inSilecoMisc* package is a set of useful R functions created to ease some
 operations we often do.

README.md

@@ -1,7 +1,10 @@
 letiRmisc
----------
+=========
+
+About
+-----
 
-### Short description
+### Description
 
 The *inSilecoMisc* package is a set of useful R functions created to
 ease some operations we often do. Functions here are written to be used
@@ -53,16 +56,16 @@ Main features
 ### Assign a category
 
       (seqv <- stats::runif(40))
-    R>>   [1] 0.13780573 0.17708009 0.77330267 0.30710906 0.67434486 0.55012283
-    R>>   [7] 0.63048250 0.79462166 0.23519578 0.78091285 0.32436219 0.45307328
-    R>>  [13] 0.35748185 0.24681598 0.76751976 0.66642766 0.96600211 0.04952655
-    R>>  [19] 0.03451996 0.34324260 0.14052181 0.85171006 0.98864700 0.88616456
-    R>>  [25] 0.96326465 0.12279069 0.85347725 0.39668267 0.65347731 0.86984038
-    R>>  [31] 0.09091806 0.02286843 0.27840141 0.70495423 0.55510063 0.03851234
-    R>>  [37] 0.84837195 0.33887783 0.35538841 0.88467788
+    R>>   [1] 0.99214337 0.31171799 0.56070470 0.54466872 0.29966394 0.32109119
+    R>>   [7] 0.28128818 0.59589856 0.81216719 0.29817815 0.26570390 0.67276404
+    R>>  [13] 0.94476824 0.36332951 0.92936313 0.23618996 0.50067110 0.29241868
+    R>>  [19] 0.21714723 0.44384743 0.22142397 0.77767900 0.67439501 0.45173673
+    R>>  [25] 0.27326727 0.66848496 0.80267346 0.56277968 0.96802984 0.10215451
+    R>>  [31] 0.56928679 0.97048877 0.43552671 0.09509066 0.67638032 0.06425101
+    R>>  [37] 0.06244420 0.55870936 0.60233759 0.32699378
       categorize(seqv, categ=seq(0.1,0.9, 0.1))
-    R>>   [1]  2  2  8  4  7  6  7  8  3  8  4  5  4  3  8  7 10  1  1  4  2  9 10
-    R>>  [24]  9 10  2  9  4  7  9  1  1  3  8  6  1  9  4  4  9
+    R>>   [1] 10  4  6  6  3  4  3  6  9  3  3  7 10  4 10  3  6  3  3  5  3  8  7
+    R>>  [24]  5  3  7  9  6 10  2  6 10  5  1  7  1  1  6  7  4
 
 ### Turn a matrix or a data frame into a squared matrix
 
@@ -90,11 +93,11 @@ Main features
       df1 <- matrix(signif(runif(20),4), ncol=2)
       df2 <- assignClass2df(df1, 2, 'character')
       str(df1)
-    R>>   num [1:10, 1:2] 0.183 0.128 0.652 0.228 0.919 ...
+    R>>   num [1:10, 1:2] 0.341 0.786 0.639 0.343 0.919 ...
       str(df2)
     R>>  'data.frame':  10 obs. of  2 variables:
-    R>>   $ V1: num  0.183 0.128 0.652 0.228 0.919 ...
-    R>>   $ V2: chr  "0.01379" "0.09421" "0.3086" "0.4628" ...
+    R>>   $ V1: num  0.341 0.786 0.639 0.343 0.919 ...
+    R>>   $ V2: chr  "0.03802" "0.9461" "0.6638" "0.9869" ...
 
 ### Assign a symbol to a p-value
 

tests/testthat/test-duplicate.R

@@ -1,9 +1,9 @@
 context("Duplicate rows and columns")
 
 mydf <- data.frame(
-  var1=1:10,
-  var2=LETTERS[1:10],
-  var3=LETTERS[1:10]
+  var1 = 1:10,
+  var2 = LETTERS[1:10],
+  var3 = LETTERS[1:10]
 )
 rownames(mydf) <- letters[1:nrow(mydf)]
 
@@ -13,38 +13,40 @@ mydf$var3 <- as.character(mydf$var3)
 mydf1a <- duplicateRow(mydf)
 mydf1b <- duplicateCol(mydf)
 
-mydf2a <- duplicateRow(mydf, id.el=11)
+mydf2a <- duplicateRow(mydf, id.el = 11)
 
-mydf3a <- duplicateRow(mydf, id.el=c(2,6), times=c(1,2))
-mydf3b <- duplicateRow(mydf, id.el=c("b", "f"), times=c(1,2))
-mydf3c <- duplicateCol(mydf, id.el=c(2,3), times=c(1,2))
+mydf3a <- duplicateRow(mydf, id.el = c(2, 6), times = c(1,2))
+mydf3b <- duplicateRow(mydf, id.el = c("b", "f"), times = c(1,2))
+mydf3c <- duplicateCol(mydf, id.el = c(2, 3), times =c(1,2))
+mydf3d <- duplicateCol(mydf, id.el = c("var2", "var3"), times =c(1,2))
 #
 mydf4a <- duplicateRow(mydf, append = TRUE)
 mydf4b <- duplicateCol(mydf, append = TRUE)
 
 
 test_that("duplicates by default", {
   expect_equal(nrow(mydf1a), nrow(mydf)+1)
-  expect_equal(all(mydf1a[1,]==mydf1a[2,]), TRUE)
+  expect_equal(all(mydf1a[1,] == mydf1a[2,]), TRUE)
   expect_equal(ncol(mydf1b), ncol(mydf)+1)
 })
 
 test_that("duplicates missings", {
-  expect_equal(mydf2a[11,1], NA_integer_)
-  expect_equal(as.numeric(mydf2a[11,2]), NA_integer_)
-  expect_equal(mydf2a[11,3], NA_character_)
-  expect_error(duplicateCol(mydf, id.el=4))
+  expect_equal(mydf2a[11, 1], NA_integer_)
+  expect_equal(as.numeric(mydf2a[11, 2]), NA_integer_)
+  expect_equal(mydf2a[11, 3], NA_character_)
+  expect_error(duplicateCol(mydf, id.el = 4))
 })
 
 test_that("duplicates multi elements", {
   expect_equal(nrow(mydf3a), nrow(mydf)+3)
   expect_true(identical(mydf3b, mydf3a))
   expect_equal(ncol(mydf3c), ncol(mydf)+3)
+  expect_true(identical(mydf3c, mydf3d))
 })
 
 test_that("duplicates and appends", {
   expect_equal(nrow(mydf4a), nrow(mydf)+1)
   expect_equal(ncol(mydf4b), ncol(mydf)+1)
-  expect_equal(all(mydf4a[1,]==mydf4a[11,]), TRUE)
-  expect_equal(all(mydf4b[,1]==mydf4b[,4]), TRUE)
+  expect_equal(all(mydf4a[1,] == mydf4a[11,]), TRUE)
+  expect_equal(all(mydf4b[, 1] == mydf4b[, 4]), TRUE)
 })