Change the class from factor to numeric of many columns in a data frame

user_155406 0
Viewed 231331

What is the quickest/best way to change a large number of columns to numeric from factor?

I used the following code but it appears to have re-ordered my data.

> head(stats[,1:2])
  rk                 team
1  1 Washington Capitals*
2  2     San Jose Sharks*
3  3  Chicago Blackhawks*
4  4     Phoenix Coyotes*
5  5   New Jersey Devils*
6  6   Vancouver Canucks*

for(i in c(1,3:ncol(stats))) {
    stats[,i] <- as.numeric(stats[,i])
}

> head(stats[,1:2])
  rk                 team
1  2 Washington Capitals*
2 13     San Jose Sharks*
3 24  Chicago Blackhawks*
4 26     Phoenix Coyotes*
5 27   New Jersey Devils*
6 28   Vancouver Canucks*

What is the best way, short of naming every column as in:

df$colname <- as.numeric(ds$colname)
r
16 Answers

Here are some dplyr options:

# by column type:
df %>% 
  mutate_if(is.factor, ~as.numeric(as.character(.)))

# by specific columns:
df %>% 
  mutate_at(vars(x, y, z), ~as.numeric(as.character(.))) 

# all columns:
df %>% 
  mutate_all(~as.numeric(as.character(.)))
user_30581230

you can use unfactor() function from "varhandle" package form CRAN:

library("varhandle")

my_iris <- data.frame(Sepal.Length = factor(iris$Sepal.Length),
                      sample_id = factor(1:nrow(iris)))

my_iris <- unfactor(my_iris)
user_16130050

I like this code because it's pretty handy:

  data[] <- lapply(data, function(x) type.convert(as.character(x), as.is = TRUE)) #change all vars to their best fitting data type

It is not exactly what was asked for (convert to numeric), but in many cases even more appropriate.

user_57948370

Based on @SDahm's answer, this was an "optimal" solution for my tibble:

data %<>% lapply(type.convert) %>% as.data.table()

This requires dplyr and magrittr.

user_13496730

I tried a bunch of these on a similar problem and kept getting NAs. Base R has some really irritating coercion behaviors, which are generally fixed in Tidyverse packages. I used to avoid them because I didn't want to create dependencies, but they make life so much easier that now I don't even bother trying to figure out the Base R solution most of the time.

Here's the Tidyverse solution, which is extremely simple and elegant:

library(purrr)

mydf <- data.frame(
  x1 = factor(c(3, 5, 4, 2, 1)),
  x2 = factor(c("A", "C", "B", "D", "E")),
  x3 = c(10, 8, 6, 4, 2))

map_df(mydf, as.numeric)
user_79444600

df$colname <- as.numeric(df$colname)

I tried this way for changing one column type and I think it is better than many other versions, if you are not going to change all column types

df$colname <- as.character(df$colname)

for the vice versa.

user_113401050
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