Given the following dataframe I am trying to mutate all but c and d columns using dplyr::mutate_at with lambda function but without luck:
structure(list(a = c(1, 2, 3), b = c(43, 2, -1), c = c(234242,
-223, 1), d = c(1, 1, 2)), .Names = c("a", "b", "c", "d"), row.names = c(NA,
-3L), class = c("tbl_df", "tbl", "data.frame"))
df %>% mutate_at(vars(-c("c", "d"), funs(x = x + rnorm(1, mean = mean(x), sd = sd(x))
I want to overwrite the existing a and b columns without the need to mutate each other manually.
Please advise.
NOTE: My real dataset is similar but it has 50 columns to mutate and add this normal distribution random variable value. I want to automate it except for specific columns.
You were nearly there. Two little things to change:
- Change
x to .
- Access the column names without quote marks:
-c(c, d) instead of -c("c", "d").
This leads to
df %>% mutate_at(vars(-c(c, d)), funs(. + rnorm(1, mean = mean(.), sd = sd(.))))
which works fine. Output is:
# A tibble: 3 x 4
a b c d
<dbl> <dbl> <dbl> <dbl>
1 3.05 56.6 234242 1
2 4.05 15.6 -223 1
3 5.05 12.6 1 2
Just as a side note:
You could use: mutate_if
df1 %>% mutate_if(names(.)%in%c("a","b"), funs(. + rnorm(1, mean = mean(.), sd = sd(.))))
There, of course, you have to use " because you're comparing strings with each other.
If all the variables you want to change are in order, you can index them in vars. And you'll want to change x to "."
df %>% mutate_at(vars(a:b), funs(. + rnorm(1, mean = mean(.), sd = sd(.))))
# a b c d
# 1 3.20 34.2 234242 1
# 2 4.20 -6.85 -223 1
# 3 5.20 -9.85 1 2
