Need to create a variable based on the equality of other variables - r

Question

I have a dataset called CSES (Comparative Study of Electoral Systems) where each row corresponds to an individual (one interview in a public opinion survey), from many countries, in many different years .
I need to create a variable which identifies the ideology of the party each person voted, as perceived by this same person.
However, the dataset identifies this perceived ideology of each party (as many other variables) by letters A, B, C, etc. Then, when it comes to identify WHICH PARTY each person voted for, it has a UNIQUE CODE NUMBER, that does not correspond to these letters across different years (i.e., the same party can have a different letter in different years – and, of course, it is never the same party across different countries, since each country has its own political parties).
Fictitious data to help clarify, reproduce and create a code:
Let’s say:
country = c(1,1,1,1,2,2,2,2,3,3,3,3)
year = c (2000,2000,2004,2004, 2002,2002,2004,2008,2000,2000,2000,2000)
party_A_number = c(11,11,12,12,21,21,22,23,31,31,31,31)
party_B_number = c(12, 12, 11, 11, 22,22,21,22,32,32,32,32)
party_C_number = c(13,13,13,13,23,23,23,21,33,33,33,33)
party_voted = c(12,13,12,11,21,24,23,22,31,32,33,31)
ideology_party_A <- floor(runif (12, min=1, max=10))
ideology_party_B <- floor(runif (12, min=1, max=10))
ideology_party_C <- floor(runif (12, min=1, max=10))
Let’s call the variable I want to create “ideology_voted”:
I need something like:
IF party_A_number == party_voted THEN ideology_voted = ideology_party_A
IF party_B_number == party_voted, THEN ideology_voted == ideology_party_B
IF party_C_number == party_voted, THEN ideology_voted == ideology_party_C
The real dataset has 9 letters for (up to) 9 main parties in each country , dozens of countries and election-years. Therefore, it would be great to have a code where I could iterate through letters A-I instead of “if voted party A, then …; if voted party B then….”
Nevertheless, I am having trouble even when I try longer, repetitive codes (one transformation for each party letter - which would give me 8 lines of code)

Answer 1

library(tidyverse)
df <- tibble(
country = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3),
year = c(2000, 2000, 2004, 2004, 2002, 2002, 2004, 2008, 2000, 2000, 2000, 2000),
party_A_number = c(11, 11, 12, 12, 21, 21, 22, 23, 31, 31, 31, 31),
party_B_number = c(12, 12, 11, 11, 22, 22, 21, 22, 32, 32, 32, 32),
party_C_number = c(13, 13, 13, 13, 23, 23, 23, 21, 33, 33, 33, 33),
party_voted = c(12, 13, 12, 11, 21, 24, 23, 22, 31, 32, 33, 31),
ideology_party_A = floor(runif (12, min = 1, max = 10)),
ideology_party_B = floor(runif (12, min = 1, max = 10)),
ideology_party_C = floor(runif (12, min = 1, max = 10))
)
> df
# A tibble: 12 x 9
country year party_A_number party_B_number party_C_number party_voted ideology_party_A ideology_party_B
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 2000 11 12 13 12 9 3
2 1 2000 11 12 13 13 2 6
3 1 2004 12 11 13 12 3 8
4 1 2004 12 11 13 11 7 8
5 2 2002 21 22 23 21 2 7
6 2 2002 21 22 23 24 8 2
7 2 2004 22 21 23 23 1 7
8 2 2008 23 22 21 22 7 7
9 3 2000 31 32 33 31 4 3
10 3 2000 31 32 33 32 7 5
11 3 2000 31 32 33 33 1 6
12 3 2000 31 32 33 31 2 1
# ... with 1 more variable: ideology_party_C <dbl>
It seems you're after conditioning using case_when:
ideology_voted <- df %>% transmute(
ideology_voted = case_when(
party_A_number == party_voted ~ ideology_party_A,
party_B_number == party_voted ~ ideology_party_B,
party_C_number == party_voted ~ ideology_party_C,
TRUE ~ party_voted
)
)
> ideology_voted
# A tibble: 12 x 1
ideology_voted
<dbl>
1 3
2 7
3 3
4 8
5 2
6 24
7 8
8 7
9 4
10 5
11 6
12 2
Note that the evaluation of case_when is lazy, so the first true condition is used (if it happens that more than one is actually true, say).