I have a set of survey data that uses Likert scale responses, coded 1-5 ('strongly disagree' to 'strongly agree'). I am trying to re-centre the scores around 0, such that -2 is 'strongly disagree' and +2 is 'strongly agree'.
An obvious way of getting there is to subtract all columns by 3, but I do not know to subtract the same number from multiple columns in one line of code, I am sure there's a way...
Example data:
likert_data <- data.frame(id=c(1:10),
a=sample(x = 1:5, size = 10,replace=T),
b=sample(x = 1:5, size = 10,replace=T),
c=sample(x = 1:5, size = 10,replace=T)
)
I could of course do something like this...
likert_data %<>%
mutate(across(c(a:c), ~case_when(. == 1 ~ as.numeric(-2),
. == 2 ~ as.numeric(-1),
. == 3 ~ as.numeric(0),
. == 4 ~ as.numeric(1),
. == 5 ~ as.numeric(2))))
... but I don't think it's very elegant.
Is there a way of subtracting columns a:c
by 3? Doesn't have to be using dplyr
, but would really appreciate a dplyr
solution if one exists! :)
Since R is vectorized, simply substract 3 from the columns:
cars <- mtcars
cars[1:3] <- cars[1:3] - 3
cars
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 18.0 3 157.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 18.0 3 157.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 19.8 1 105.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 18.4 3 255.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 15.7 5 357.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 15.1 3 222.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 11.3 5 357.0 245 3.21 3.570 15.84 0 0 3 4
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