I'm working on a questionnaire and I need to turn some observations to NA when two arguments are true. In this case, I want my values to be NA when the variable name starts with "Q" and when the token is equals to the obs I no longer want.
So far I came up with this, which does not work.
Fusion_2_nom <- Fusion_2_nom %>%
mutate_at(vars(starts_with("Q")),
replace(token %in% c("3d4faa","214c61", "a786b6", "52a719", "1f9887", "9b6347"), NA))
How should I go about it?
Thanks!
You need to use a lambda function with replace, and the current column .xas imput.
I suggest you use updated dplyr synthax( mutate(across(...)), instead of the deprecated mutate_at(vars(...))).
library(dplyr)
Fusion_2_nom <- Fusion_2_nom %>%
mutate(across(starts_with("Q"),
~replace(.x, token %in% c("3d4faa","214c61", "a786b6", "52a719", "1f9887", "9b6347"), NA))
A similar example with the mtcars dataset:
mtcars %>% mutate(across(vs:carb, ~replace(.x, mpg %in% c(21.0, 10.4, 15.0), NA)))
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 NA NA NA NA
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 NA NA NA NA
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 NA NA NA NA
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 NA NA NA NA
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 NA NA NA NA
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
My approach would be similar, but I prefer to use across and if_else. Also for a better "readability" I created a separated vector for the tokens.
token_na <- c("3d4faa","214c61", "a786b6", "52a719", "1f9887", "9b6347")
df %>%
mutate(
across(
.cols = starts_with("Q"),
.fns = function(x)if_else(x %in% token_na,NA_character_,x))
)
Using case_when
Fusion_2_nom %>%
mutate(across(starts_with("Q")),
case_when(!token %in% c("3d4faa","214c61", "a786b6", "52a719", "1f9887", "9b6347")~ .)))