update vignettes

vignettes/a_usage_guide.Rmd

@@ -58,9 +58,9 @@ dataset for instructive purposes:
 library(dplyr, warn.conflicts = FALSE)
 library(forcats)
 set.seed(123)
-dat <- iris[, c("Species", "Sepal.Length")] %>%
-  mutate(animal = c("Mammal", "Fish") %>% rep(75) %>% factor()) %>%
-  mutate(food = c("fries", "wedges") %>% sample(150, TRUE) %>% factor())
+dat <- iris[, c("Species", "Sepal.Length")] |>
+  mutate(animal = c("Mammal", "Fish") |> rep(75) |> factor()) |>
+  mutate(food = c("fries", "wedges") |> sample(150, TRUE) |> factor())
 head(dat)
 ```
 
@@ -91,7 +91,7 @@ Rstudios autocomplete suggestions are very helpful when navigating this list.
 The `print` function returns a formatted version of this list, which you can also save and access using the same syntax.
 
 ```{r}
-my_table <- descr(dat) %>% print(silent=TRUE)
+my_table <- descr(dat) |> print(silent=TRUE)
 ```
 
 
@@ -130,22 +130,22 @@ There are a lot of different tests available. Check out the test_choice vignette
 Here are some different tests in action:
 
 ```{r}
-descr(dat %>% select(-"Species"), "animal", test_options = list(exact=TRUE, nonparametric=TRUE))
+descr(dat |> select(-"Species"), "animal", test_options = list(exact=TRUE, nonparametric=TRUE))
 ```
 
 
 ```{r}
-descr(dat %>% select(c("Species", "Sepal.Length")), "Species", test_options = list(nonparametric=TRUE))
+descr(dat |> select(c("Species", "Sepal.Length")), "Species", test_options = list(nonparametric=TRUE))
 ```
 
 ## Paired observations
 
 In situations with paired data, the `group` variable usually denotes the timing of the measurement (e.g. "before" and "after" or "time 1", "time 2", etc.). In these scenarios, you need an additional index variable that specifies which observations from the different timepoints should be paired. The `test_options =list(paired=TRUE, indices = <Character name of index variable name or vector of indices>)` option can be used to specify the pairing indices, see the example below. DescrTab2 only works with data in "long format", see e.g. `?reshape` or `?tidyr::pivot_longer` for information on how to transoform your data from wide to long format.
 
 ```{r}
-descr(dat %>% mutate(animal = fct_recode(animal, Before="Fish", After="Mammal")) %>% select(-"Species"), "animal", test_options = list(paired=TRUE, indices=rep(1:75, each=2)))
+descr(dat |> mutate(animal = fct_recode(animal, Before="Fish", After="Mammal")) |> select(-"Species"), "animal", test_options = list(paired=TRUE, indices=rep(1:75, each=2)))
 
-descr(dat %>% mutate(animal = fct_recode(animal, Before="Fish", After="Mammal"), idx = rep(1:75, each=2)) %>% select(-"Species"), "animal", test_options = list(paired=TRUE, indices="idx" ))
+descr(dat |> mutate(animal = fct_recode(animal, Before="Fish", After="Mammal"), idx = rep(1:75, each=2)) |> select(-"Species"), "animal", test_options = list(paired=TRUE, indices="idx" ))
 ```
 
 ## Significant digits
@@ -173,7 +173,7 @@ Let's say you have a categorical variable, but for some reason it's levels are n
 ```{r}
 # Create example dataset
 dat2 <- iris
-dat2$cat_var <- c(1,2) %>% sample(150, TRUE) %>% factor()
+dat2$cat_var <- c(1,2) |> sample(150, TRUE) |> factor()
 dat2 <- dat2[, c("Species", "cat_var")]
 
 descr(dat2, "Species", summary_stats_cat=list(mean=DescrTab2:::.factormean))
@@ -234,7 +234,7 @@ custom_ttest <- list(
   }
 )
 
-descr(iris %>% select(-Species), test_options = list(test_override = custom_ttest))
+descr(iris |> select(-Species), test_options = list(test_override = custom_ttest))
 ```
 
 ## Supress the last factor level

vignettes/c_other_software_comparison.Rmd

@@ -14,7 +14,6 @@ knitr::opts_chunk$set(
   comment = "#>"
 )
 library(DescrTab2)
-library(tidyverse)
 options(print_format="html")
 ```
 
@@ -48,7 +47,7 @@ Dataset origin: `?wilcox.test` (accessed on R-version 4.0.3).
 
 x <- c(1.83,  0.50,  1.62,  2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
 y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
-dat_wilcox.test_1_sample <- tibble(diff = x-y)
+dat_wilcox.test_1_sample <- tibble::tibble(diff = x-y)
 
 descr(dat_wilcox.test_1_sample, test_options = c(nonparametric=TRUE))
 ```
@@ -77,7 +76,7 @@ Dataset origin: `?wilcox.test` (accessed on R-version 4.0.3).
 x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46)
 y <- c(1.15, 0.88, 0.90, 0.74, 1.21)
 group <- c(rep("Trt", length(x)), rep("Ctrl", length(y)))
-dat_wilcox.test_2_sample <- tibble(var=c(x,y), group=group)
+dat_wilcox.test_2_sample <- tibble::tibble(var=c(x,y), group=group)
 
 descr(dat_wilcox.test_2_sample, "group", test_options = c(nonparametric=TRUE))
 ```
@@ -108,7 +107,7 @@ x <- c(2.9, 3.0, 2.5, 2.6, 3.2) # normal subjects
 y <- c(3.8, 2.7, 4.0, 2.4)      # with obstructive airway disease
 z <- c(2.8, 3.4, 3.7, 2.2, 2.0) # with asbestosis
 group <- c(rep("Trt", length(x)), rep("Ctrl", length(y)), rep("Placebo", length(z)))
-dat_kruskal.test <- tibble(var=c(x,y,z), group=group)
+dat_kruskal.test <- tibble::tibble(var=c(x,y,z), group=group)
 
 descr(dat_kruskal.test, "group", test_options = c(nonparametric=TRUE)) 
 ```
@@ -165,7 +164,7 @@ matrix(c(5.40, 5.50, 5.55,
                        c("Round Out", "Narrow Angle", "Wide Angle")))
 
 idx <- rep(1:22, 3)
-dat <- tibble(var = c(RoundingTimes[,1], RoundingTimes[,2], RoundingTimes[,3]),
+dat <- tibble::tibble(var = c(RoundingTimes[,1], RoundingTimes[,2], RoundingTimes[,3]),
               group = c(rep("Round Out", 22), rep("Narrow Angle", 22), rep("Wide Angle", 22)))
 
 
@@ -203,7 +202,7 @@ d.frm <- DescTools::Untable(xtabs(c(6,2,2,6,16,4,4,6) ~ .,
 d.long <- reshape(d.frm, varying=1:3, times=names(d.frm)[c(1:3)], 
                   v.names="resp", direction="long")
 idx <- d.long$id
-dat <- d.long[, 1:2] %>% mutate(time=as.character(time), resp=as.character(resp))
+dat <- d.long[, 1:2] |> dplyr::mutate(time=as.character(time), resp=as.character(resp))
 
 descr(dat, "time", test_options = list(indices=idx, paired=TRUE))
 
@@ -281,7 +280,7 @@ Dataset origin: "Gender-Party dataset":`?chisq.test` (accessed on R-version 4.0.
 
 ```{r chisq.test1, results='asis'}
 
-dat <- tibble(gender=c(rep("F",sum(c(762, 327, 468)) ), rep("M", sum( c(484, 239, 477)))),
+dat <- tibble::tibble(gender=c(rep("F",sum(c(762, 327, 468)) ), rep("M", sum( c(484, 239, 477)))),
               party=c(rep("Democrat", 762), rep("Independent", 327), rep("Republican", 468),
                       rep("Democrat", 484), rep("Independent", 239), rep("Republican", 477)))
               
@@ -296,7 +295,7 @@ chisq.test(table(dat$party))
 ```
 
 ```{r chisq.test3, results='asis'}
-dat <- tibble(
+dat <- tibble::tibble(
   
   a = factor(c(0,
                0,
@@ -419,9 +418,9 @@ dat <- nlme::Orthodont
 dat2 <- nlme::Orthodont[1:64,]
 dat2$Sex <- "Divers"
 dat2$distance <- dat2$distance + c(rep(0.1*c(1,4,3,2), 10), 0.1*rep(c(0.4,2,1.5, 2.3), 6) )
-dat2$Subject <- str_replace_all(dat2$Subject, "M", "D")
-dat <- bind_rows(dat, dat2)
-dat <- as_tibble(dat)
+dat2$Subject <- stringr::str_replace_all(dat2$Subject, "M", "D")
+dat <- dplyr::bind_rows(dat, dat2)
+dat <- tibble::as_tibble(dat)
 
 
 descr(dat[, c("Sex", "distance")], "Sex", test_options = list(paired=TRUE, indices=dat$Subject))
@@ -450,7 +449,7 @@ Dataset origin: selfmade.
 
 DescrTab2 uses the exact2x2::boschloo with option `tsmethod=central`  to calculate p-values. There is no comparison for this option readily available.
 ```{r boschloo1, results='asis'}
-dat <- tibble(gender=factor(c("M", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F")),
+dat <- tibble::tibble(gender=factor(c("M", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F")),
               party=factor(c("A", "A", "B", "B", "B", "B", "A", "A", "A", "B", "B")))
 descr(dat, "gender", test_options = c(exact=TRUE))
 ```