I am trying to write some functions that take a dataframe and check whether certain variables fulfill certain criteria. For each check I would like to create a new variable "check_" giving the result of the check. Unfortunately, I still struggle to get it right. Can someone help me?
# Some sample data
dat <- data.frame(Q1_1 = c(1, 1, 2, 5, 2, 1),
Q1_2 = c(1, 2, 3, 5, 1, 3),
Q1_3 = c(4, 3, 3, 5, 1, 3),
Q1_4 = c(4, 2, 2, 5, 1, 2),
Q1_5 = c(2, 2, 1, 5, 5, 4),
Q2_1 = c(1, 2, 1, 2, 1, 2),
Q2_2 = c(2, 1, 1, 1, 2, 1),
Q2_3 = c(1, 1, 1, 2, 2, 1),
age = c(22,36,20,27,13, 9))
# Some checker-functions
check_age <- function(.df, agevar = "age"){
#' Function should check if the age value is within a certain range
#' and create a new variable "check_age" giving the result of the check
.df %>% mutate(check_age = ifelse(age > 100, FALSE, TRUE),
check_age = ifelse(age < 4, FALSE, TRUE))
???
}
check_sameAnswers <- function(.df, varname = "Q1_"){
#' Function should check whether all sub Of a question (e.g. Q1_1 to Q1_5) have the
#' same values and create a new variable "check_sameAnswers" giving the result of the check.
#' It should be TRUE if Q1_1, Q1_2, ... have the value 5 for example, otherwise FALSE
???
}
# Apply checker functions to dataframe in "dplyr-style"
dat <- dat %>%
check_age(agevar = "age") %>%
check_sameAnswers(varname = "Q1_")
CodePudding user response：
As @Bloxx already mentioned, your ifelse is the first problem. Those conditions are evaluated sequentially (same would happen if you would use dplyr::case_when). Its not a problem with the functions, but the way you declare the conditions, it should be one condition, not two. But you also have a second problem in your first function - you call age variable directly from the data frame (by data masking), not the column corresponding to the column name stored in argument agevar. This is why I used deparse(substitute()) to get the column corresponding to the column name in agevar.
I changed the first values in the age column so you can see the results of the function.
In your second function you want to check if different items have the same values across colums. In the tidyverse you generally use rowwise for this. contains was used to give you more flexibility when specifying column prefixes that define your grouppings.
library(tidyverse)
# Some sample data
dat <- data.frame(Q1_1 = c(1, 1, 2, 5, 2, 1),
Q1_2 = c(1, 2, 3, 5, 1, 3),
Q1_3 = c(4, 3, 3, 5, 1, 3),
Q1_4 = c(4, 2, 2, 5, 1, 2),
Q1_5 = c(2, 2, 1, 5, 5, 4),
Q2_1 = c(1, 2, 1, 2, 1, 2),
Q2_2 = c(2, 1, 1, 1, 2, 1),
Q2_3 = c(1, 1, 1, 2, 2, 1),
age = c(1,101,20,27,13, 9)) # here changed first 2 values
# Some checker-functions
check_age <- function(.df, agevar = "age"){
#' Function should check if the age value is within a certain range
#' and create a new variable "check_age" giving the result of the check
age <- deparse(substitute(agevar))
.df %>% mutate(check_age = ifelse(age > 100 | age < 4, FALSE, TRUE))
}
check_sameAnswers <- function(.df, varname = "Q1_"){
#' Function should check whether all sub Of a question (e.g. Q1_1 to Q1_5) have the
#' same values and create a new variable "check_sameAnswers" giving the result of the check.
#' It should be TRUE if Q1_1, Q1_2, ... have the value 5 for example, otherwise FALSE
.df %>%
rowwise() %>%
mutate(check_sameAnswers = length(unique(c_across(contains(varname)))) == 1)
}
# Apply checker functions to dataframe in "dplyr-style"
dat %>%
check_age(agevar = "age") %>%
check_sameAnswers(varname = "Q1_") %>%
dplyr::select(-contains("Q2_")) # just for uncluttered printing
#> # A tibble: 6 x 8
#> # Rowwise:
#> Q1_1 Q1_2 Q1_3 Q1_4 Q1_5 age check_age check_sameAnswers
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl>
#> 1 1 1 4 4 2 1 FALSE FALSE
#> 2 1 2 3 2 2 101 FALSE FALSE
#> 3 2 3 3 2 1 20 TRUE FALSE
#> 4 5 5 5 5 5 27 TRUE TRUE
#> 5 2 1 1 1 5 13 TRUE FALSE
#> 6 1 3 3 2 4 9 TRUE FALSE
Created on 2022-02-16 by the reprex package (v2.0.1)
CodePudding user response：
I think the problem is in your ifelse statement. Try this:
check_age <- function(.df, agevar = "age"){
#' Function should check if the age value is within a certain range
#' and create a new variable "check_age" giving the result of the check
.df %>% mutate(check_age = ifelse(age > 100 | age < 4, FALSE, TRUE))
}
check_sameAnswers <- function(.df, varname = "Q1_"){
#' Function should check whether all sub Of a question (e.g. Q1_1 to Q1_5) have the
#' same values and create a new variable "check_sameAnswers" giving the result of the check.
#' It should be TRUE if Q1_1, Q1_2, ... have the value 5 for example, otherwise FALSE
.df %>% mutate(sameAnswers = ifelse(length(unique(dat$Q1_2)) == 1, TRUE, FALSE))
}
# Apply checker functions to dataframe in "dplyr-style"
dat <- dat %>%
check_age(agevar = "age") %>%
check_sameAnswers(varname = "Q1_")
dat
CodePudding user response：
You can embrace the argument to use variables (from data masking) in your function
Functions
library(dplyr)
check_age <- function(data, age_var, start = 0, end = 0){
data %>%
mutate(between = ifelse({{age_var}} >= start & {{age_var}} <= end,T,F))
}
check_sameAnswers <- function(data, cols){
data %>%
rowwise() %>%
mutate(same = length(unique(c_across(starts_with(cols)))) == 1) %>%
ungroup()
}
Use
dat %>%
check_age(age, 30, 40) %>%
check_sameAnswers(cols="Q1")
# A tibble: 6 × 11
Q1_1 Q1_2 Q1_3 Q1_4 Q1_5 Q2_1 Q2_2 Q2_3 age between same
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl>
1 1 1 4 4 2 1 2 1 22 FALSE FALSE
2 1 2 3 2 2 2 1 1 36 TRUE FALSE
3 2 3 3 2 1 1 1 1 20 FALSE FALSE
4 5 5 5 5 5 2 1 2 27 FALSE TRUE
5 2 1 1 1 5 1 2 2 13 FALSE FALSE
6 1 3 3 2 4 2 1 1 9 FALSE FALSE