Guidelines for specifying variables in a function

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Consider dat1 created here:

set.seed(123)
dat1 <- data.frame(Region = rep(c("r1","r2"), each = 100),
                   State = rep(c("NY","MA","FL","GA"), each = 10),
                   Loc = rep(c("a","b","c","d","e","f","g","h"),each = 5),
                   ID = rep(c(1:10), each = 2),
                   var1 = rnorm(200),
                   var2 = rnorm(200),
                   var3 = rnorm(200),
                   var4 = rnorm(200),
                   var5 = rnorm(200))

dat1 has measurements for 5 variables, and observations (IDs) can be grouped according to 3 grouping variables: Loc, State, and Region I am having to perform various tasks on each response variable/grouping variable combination, so I have been writing functions to make it easier, and keep my analysis tidy. I am using the rstatix package to do several operations. The following function will conduct a Kruskal Wallis test on the data I specify, calculate the effect size efsz and return the results in a single data frame res:

library(rstatix)
KruskTest <- function(dat, groupvar, var){
  kt <- dat%>%kruskal_test(get(var) ~ get(groupvar))
  efsz <- dat%>%kruskal_effsize(get(var) ~ get(groupvar))
  res <<- cbind(kt, efsz[,3:5])
  res[1,1] <<- var
  res$groupvar <<- groupvar 
  res <<- res[,c(10,1:9)]
}
KruskTest(dat=dat1, groupvar = "Region", var = "var1")

Now I can use that function to loop over each response variable and get the results for a grouping variable (example shows it for Region) in a single data frame, which is what I need:

vars <- paste(names(dat1[,5:9]))
a <- data.frame()
for(i in vars){
  KruskTest(dat=dat1, groupvar="Region", var= i)
  a <- rbind(a, res)
}

That works great for the Kruskal Wallis test, now I want to make a very similar function that will do a duns test, but watch what happens: 

dunn <- function(dat, groupvar, var){
  res <<- dat%>%rstatix::dunn_test(get(var) ~ get(groupvar), p.adjust.method = "bonferroni")
}
dunn(dat=dat1, groupvar="Region", var = "var1")

r:Error: Can't extract columns that don't exist. x The column `get(groupvar)` doesn't exist.

Outside of a user-written function, you specify data for the dunn_test() and kruskal_test() the exact same way. So what is the difference between specifying variables in these two funcitons, and why does the first one work but not the second?

kruskal-wallis r function loops functional-programming
1 Answers

Taking into account @Gregor 's comments about not writing to the environment and trying to clean up some other rough edges's I have a proposed improvement although Gregor is correct your biggest problem was nothing but a typo.

library(rstatix)
library(purrr)

# rewritten to avoid writing to environment

NewKruskTest <- function(dat, groupvar, var) {
  kt <- dat %>% kruskal_test(as.formula(paste(var, "~", groupvar)))
  efsz <- dat %>% kruskal_effsize(as.formula(paste(var, "~", groupvar)))
  results <- cbind(kt, efsz[,3:5])
  results$groupvar <- groupvar 
  results <- results[,c(10,1:9)]
  return(results)
}

# works on a single if you want to test
# NewKruskTest(dat = dat1, groupvar = "Region", var = "var1") 

# No paste needed
vars <- names(dat1[,5:9])

# NewKruskTest will work in your existing for loop but you 
# may find `purrr:map_dfr` cleaner

map_dfr(vars, ~ NewKruskTest(dat = dat1, groupvar = "Region", var = .))
#>   groupvar  .y.   n statistic df      p         method      effsize method.1
#> 1   Region var1 200 3.0520896  1 0.0806 Kruskal-Wallis  0.010364089  eta2[H]
#> 2   Region var2 200 0.5961552  1 0.4400 Kruskal-Wallis -0.002039620  eta2[H]
#> 3   Region var3 200 1.6330090  1 0.2010 Kruskal-Wallis  0.003197015  eta2[H]
#> 4   Region var4 200 3.4031343  1 0.0651 Kruskal-Wallis  0.012137042  eta2[H]
#> 5   Region var5 200 0.7230090  1 0.3950 Kruskal-Wallis -0.001398945  eta2[H]
#>   magnitude
#> 1     small
#> 2     small
#> 3     small
#> 4     small
#> 5     small

# NewDunn rewritten

NewDunn <- function(dat, groupvar, var) {
  results <- dat %>% rstatix::dunn_test(as.formula(paste(var, "~", groupvar)), 
                        p.adjust.method = "bonferroni")
  results$groupvar <- groupvar 
  results <- results[,c(10,1:9)]
  return(results)
}

# works on a single if you want to test
# NewDunn(dat=dat1, groupvar ="Region", var = "var1")

map_dfr(vars, ~ NewDunn(dat = dat1, groupvar = "Region", var = .))
#> # A tibble: 5 x 10
#>   groupvar .y.   group1 group2    n1    n2 statistic      p  p.adj p.adj.signif
#>   <chr>    <chr> <chr>  <chr>  <int> <int>     <dbl>  <dbl>  <dbl> <chr>       
#> 1 Region   var1  r1     r2       100   100    -1.75  0.0806 0.0806 ns          
#> 2 Region   var2  r1     r2       100   100    -0.772 0.440  0.440  ns          
#> 3 Region   var3  r1     r2       100   100    -1.28  0.201  0.201  ns          
#> 4 Region   var4  r1     r2       100   100     1.84  0.0651 0.0651 ns          
#> 5 Region   var5  r1     r2       100   100    -0.850 0.395  0.395  ns

based on your data


set.seed(123)
dat1 <- data.frame(Region = rep(c("r1","r2"), each = 100),
                   State = rep(c("NY","MA","FL","GA"), each = 10),
                   Loc = rep(c("a","b","c","d","e","f","g","h"),each = 5),
                   ID = rep(c(1:10), each = 2),
                   var1 = rnorm(200),
                   var2 = rnorm(200),
                   var3 = rnorm(200),
                   var4 = rnorm(200),
                   var5 = rnorm(200))
user_91900340
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