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I want to create several columns with a ifelse()-condition for multiple dataframes. Here is my example code:

df1 <- tibble( 
date = lubridate::today() +0:9,
return= runif(n = 10, min = 0, max = 5))

df2 <- tibble( 
date = lubridate::today() +0:9,
return= runif(n = 10, min = 0, max = 5))

df3 <- tibble( 
date = lubridate::today() +0:9,
return= runif(n = 10, min = 0, max = 5))

And now I want to add new columns with ascending conditions (from 1 to 5). The first column should only contain values from the "return"-column, which is higher than 1, the second column should only contain values, which is higher than 1.5, the third column should only contain values, which is higher than 2, and so on...

For example, I can do it with a for-loop. But that works only for one dataframe:

for(i in seq(1, 5, 0.5))
varname =paste0("return>",i)
df1[[varname]] <- with(df1, ifelse(return > i, return, NA))


> head(df1)
# A tibble: 6 x 12
date return `return > 0.5 s~ `return > 1 sd` `return > 1.5 s~ `return > 2 sd` `return > 2.5 s~
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2019-03-09 4.94 4.94 4.94 4.94 4.94 4.94
2 2019-03-10 0.936 0.936 NA NA NA NA 
3 2019-03-11 0.770 0.770 NA NA NA NA 
4 2019-03-12 1.03 1.03 1.03 NA NA NA 
5 2019-03-13 3.34 3.34 3.34 3.34 3.34 3.34
6 2019-03-14 0.983 0.983 NA NA NA NA 
# ... with 5 more variables: `return > 3 sd` <dbl>, `return > 3.5 sd` <dbl>, `return > 4 sd` <dbl>,
# `return > 4.5 sd` <dbl>, `return > 5 sd` <dbl>

Here is another code to get the desired output for one dataframe:

n <- seq(0.5, 5, 0.5)
df1[paste0("return > ", n, " sd")] <- lapply(n, function(x) 
replace(df1$return, df1$return <= x, NA))

My question is, how can I put this code in a map-function (or in another function) to run this code for all 3 dataframes?

Put all the dataframes in a list and create a function to return new columns

df_list <- list(df1, df2, df3)

return_new_cols <- function(df1) 
 n <- seq(0.5, 5, 0.5)
 df1[paste0("return > ", n, " sd")] <- lapply(n, function(x) 
 replace(df1$return, df1$return <= x, NA))
 df1

Now apply return_new_cols function to each dataframe in the list

lapply(df_list, return_new_cols)

You can achieve the same with purrr::map instead of lapply

purrr::map(df_list, return_new_cols)