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I have data which looks like this:
library(dplyr)
set.seed(123)
df <- data_frame(X1 = rep(LETTERS[1:4], 6),
X2 = rep(1:2, 12),
ref = sample(1:50, 24),
sampl1 = sample(1:50, 24),
var2 = sample(1:50, 24),
meas3 = sample(1:50, 24))
With dplyr's scooped commands I can edit and create multiple columns at once e.g:
df %>% mutate_if(is.numeric, funs(new = . - ref))
and if I want to do this to only a subset of columns I can use the select helpers like so:
df %>% mutate_at(vars(one_of(c("X2", "ref"))), funs(new = . - ref))
However in my case I know that my data will always contain the columns X1, X2 and ref but would like to subset the data in such a way to mutate only the columns which are NOT X1, X2 and ref. These other columns will be variable in number and name but always numeric. I thought I could do something like this:
df %>% mutate_at(vars(!one_of(c("X1", "X2", "ref"))), funs(new = . - ref))
or maybe
df %>% mutate_at(vars(one_of(!names %in% c("X1", "X2", "ref"))), funs(new = . - ref))
But neither work. How do you do negative dplyr select helpers?
915
If applied on a grouped tibble, these operations are not applied to the grouping variables. The behaviour depends on whether the selection is implicit ( all and if selections) or explicit ( at selections). Grouping variables covered by explicit selections in mutate_at () and transmute_at () are always an error.
The mutate()function adds new variables to a data frame while preserving any existing variables. The basic synax for mutate() is as follows: data <- mutate(new_variable = existing_variable/3)
Grouping variables covered by implicit selections are ignored by mutate_all (), transmute_all (), mutate_if (), and transmute_if (). The names of the new columns are derived from the names of the input variables and the names of the functions.
So when you use mutate (), you’ll call the function by name. Then the first argument is the dataframe that you want to manipulate. For example, if you had a dataframe named df, that would be the first item inside of the parenthasis (i.e., the first “argument” to the mutate function):
The one_of requires - and not !
df %>%
mutate_at(vars(-one_of(c("X1", "X2", "ref"))), funs(new = . - ref))
# A tibble: 24 x 9
# X1 X2 ref sampl1 var2 meas3 sampl1_new var2_new meas3_new
# <chr> <int> <int> <int> <int> <int> <int> <int> <int>
# 1 A 1 15 33 14 36 18 -1 21
# 2 B 2 39 35 43 1 -4 4 -38
# 3 C 1 20 27 3 23 7 -17 3
# 4 D 2 42 28 21 11 -14 -21 -31
# 5 A 1 44 14 37 18 -30 -7 -26
# 6 B 2 3 7 6 28 4 3 25
# 7 C 1 24 43 25 16 19 1 -8
# 8 D 2 49 39 9 5 -10 -40 -44
# 9 A 1 46 30 45 47 -16 -1 1
#10 B 2 19 50 31 45 31 12 26
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