Different ways of selecting columns inside function resulting in different results, why? - r

Question

I have written a short function to clean some dataframes that I have in a list. When selecting columns using the df[,1] method, my function doesn't work. However when I select using df$Column it does. Why is this?
columns_1 <- function(x) {
x[,1] <- dmy_hm(x[,1])
x[,2] <- NULL
x[,3] <- as.numeric(x[,3])
x[,4] <- NULL
return(x)
}
MS_ <- lapply(MS_, columns_1)
columns_2 <- function(x) {
x$DateTime <- dmy_hm(x$DateTime)
x$LogSeconds <- NULL
x$Pressure <- as.numeric(x$Pressure)
x$Temperature <- NULL
return(x)
}
MS_ <- lapply(MS_, columns_2)
The function columns_2 produces the desired results (all dataframes in list are cleaned). columns_1 returns the error message:
Error in FUN(X[[i]], ...) :
(list) object cannot be coerced to type 'double'
In addition: Warning message:
All formats failed to parse. No formats found.

Answer 1

The issue would be that the assignment was carried out after the first run and here some columns were lost.
library(lubridate)
MS_ <- lapply(MS_, columns_1)
Instead, it can be done by assigning to a different object
MS2_ <- lapply(MS_, columns_1)
data
set.seed(24)
df1 <- data.frame(DateTime = format(Sys.Date() + 1:5, "%d-%m-%Y %H:%M"),
LogSeconds = 1:5,
Pressure = rnorm(5), Temperature = rnorm(5, 25),
stringsAsFactors = FALSE)
MS_ <- list(df1, df1)