Transform for loop to map-CodePudding

I have a script written in explicit for loop and I would like to transform it so I can use the map family of functions or the apply family. I would like to do so is to improve on time taken for the script to run. Any leads?

library(dplyr)
library(stringr)

mydata <- tibble(
  a_1 = c(20,22, 13,14,44),
  a_2 = c(42, 13, 32, 31, 14),
  b = c(1, 2, 1, 1, 2),
  c = c(1, 2, 1, 3, 1)
)


dictionary <- tibble(
  variable = c("a", "b", "c"),
  label = c("Age", "Gender", "Education"),
  type = c("mselect", "select", "select"),
  values = c(NA, "1, 2", "1, 2,3" ),
  valuelabel = c(NA, "Male, Female", "Primary, Secondary, Tertiary")

)


factor_vars <- dictionary %>%
  filter(type == "select") %>% pull(variable)


for (var in factor_vars){

  fct_levels <-  dictionary %>% filter(variable == var) %>%
    pull(values)
  fct_levels <- str_trim(unlist(strsplit(fct_levels, split = ",")))


  fct_labels <-  dictionary %>% filter(variable == var) %>%
    pull(valuelabel)
  fct_labels <- unlist(strsplit(fct_labels, split = ","))


  mydata[[var]] <- factor(mydata[[var]],
                          levels = fct_levels,
                          labels = fct_labels)

}

CodePudding user response：

You could do something like this. Better provide a clean dictionary (i.e. spaces after commas) instead of using trimws, str_trim, or the like.

v <- c("b", "c")

u <- lapply(v, function(x) {
  d <- dictionary[dictionary$variable == x, c('values', 'valuelabel')] |>
    unlist() |>
    strsplit(', ') |>
    as.data.frame()
  factor(mydata[[x]], levels=d$values, labels=d$valuelabel)
  }) |>
  setNames(v) |>
  as.data.frame()

Result

res <- cbind(mydata[!names(mydata) %in% v], u)
#   a_1 a_2      b         c
# 1  20  42   Male   Primary
# 2  22  13 Female Secondary
# 3  13  32   Male   Primary
# 4  14  31   Male  Tertiary
# 5  44  14 Female   Primary

Where:

str(res)
# 'data.frame': 5 obs. of  4 variables:
# $ a_1: num  20 22 13 14 44
# $ a_2: num  42 13 32 31 14
# $ b  : Factor w/ 2 levels "Male","Female": 1 2 1 1 2
# $ c  : Factor w/ 3 levels "Primary","Secondary",..: 1 2 1 3 1

Note:

> R.version.string
[1] "R version 4.1.2 (2021-11-01)"