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Mutate value only for numeric columns AND only in the first and last row of a data frame using dplyr

Given a data frame like:

library(dplyr)
library(lubridate)

df <- data.frame(
date = seq(ymd('2018-01-01'), ymd('2018-01-10'), by = 'days'),
location = "AMS",
V1 = seq(1:10),
V2 = seq(11:20)
)


I would like to use dplyr to change the value of the first and last row, and only in numeric columns.

dplyr

I can do it for one column, as in:

df %>%
mutate(V1 = ifelse(row_number()==1, mean(V1)*100, V1)) %>%
mutate(V1 = ifelse(row_number()==nrow(.), mean(V1)*100, V1))


However I cannot manage to find a way to use mutate_at or mutate_if to do that for all numeric columns at once. Could you help me with that?

mutate_at

mutate_if

seq()

V1

V2

:

1 Answer
1

I think this addresses your issue:

df <- data.frame(
date = seq(ymd('2018-01-01'), ymd('2018-01-10'), by = 'days'),
location = "AMS",
V1 = 1:10,
V2 = 11:20
)

df %>% mutate_at(vars(V1, V2),
funs(ifelse(row_number() %in% c(1, n()), mean(.)*100, .)))

date location V1 V2
1 2018-01-01 AMS 550 1550
2 2018-01-02 AMS 2 12
3 2018-01-03 AMS 3 13
4 2018-01-04 AMS 4 14
5 2018-01-05 AMS 5 15
6 2018-01-06 AMS 6 16
7 2018-01-07 AMS 7 17
8 2018-01-08 AMS 8 18
9 2018-01-09 AMS 9 19
10 2018-01-10 AMS 550 1550


If you'd like to do it on all numeric columns, you can just use mutate_if with is.numeric as the .predicate argument.

mutate_if

is.numeric

.predicate

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