CODEWITHSUNDEEP
rfunctiondataframe
user2899944
user2899944

Reputation: 269

Variable names as Input in an R Function

I have a dataframe with several numeric variables along with factors. I wish to run over the numeric variables and replace the negative values to missing. I couldn't do that.

My alternative idea was to write a function that gets a dataframe and a variable, and does it. It didn't work either.

My code is:

NegativeToMissing = function(df,var)
{
  df$var[df$var < 0] = NA
}

Error in $<-.data.frame(`*tmp*`, "var", value = logical(0)) : replacement has 0 rows, data has 40

what am I doing wrong ?

Thank you.

Upvotes: 0

Views: 1505

Answers (3)

RLave
RLave

Reputation: 8374

If you still want to change only one selected variable a solution withdplyr would be to use non-standard evaluation:

library(dplyr)
NegativeToMissing <- function(df, var) {
  quo_var = quo_name(var)
  df %>% 
    mutate(!!quo_var := ifelse(!!var < 0, NA, !!var))

}

NegativeToMissing(data, var=quo(val1)) # use quo() function without ""
#   val1 val2
# 1    1    1
# 2   NA    2
# 3    2    3

Data used:

data <- data.frame(val1 = c(1, -1, 2),
                   val2 = 1:3)
data
#   val1 val2
# 1    1    1
# 2   -1    2
# 3    2    3

Upvotes: 0

akrun
akrun

Reputation: 887671

With tidyverse, we can make use of mutate_if

library(tidyverse)
df1 %>%
    mutate_if(is.numeric, funs(replace(., . < 0, NA)))

Upvotes: 0

markus
markus

Reputation: 26353

Here is an example with some dummy data.

df1 <- data.frame(col1 = c(-1, 1, 2, 0, -3),
                  col2 = 1:5,
                  col3 = LETTERS[1:5])
df1
#  col1 col2 col3
#1   -1    1    A
#2    1    2    B
#3    2    3    C
#4    0    4    D
#5   -3    5    E

Now find columns that are numeric

numeric_cols <- sapply(df1, is.numeric)

And replace negative values

df1[numeric_cols] <- lapply(df1[numeric_cols], function(x) replace(x, x < 0 , NA))
df1
#  col1 col2 col3
#1   NA    1    A
#2    1    2    B
#3    2    3    C
#4    0    4    D
#5   NA    5    E

You could also do

df1[df1 < 0] <- NA

Upvotes: 1

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