Column manipulation in R - matching correct names - r

Question

I have a data.frame composed of multiple columns and thousands of rows. Below I attempt to display its (head):
|year |state_name|idealPoint| vote_no| vote_yes|
|:--------------|---------:|---------:|---------:|---------:|
|1971 | China | -25.0000| 31.0000| 45.4209|
|1972 | China | -26.2550| 38.2974| 45.4209|
|1973 | China | 28.2550| 35.2974| 45.4209|
|1994 | Czech | 27.2550| 34.2974| 45.4209|
As you can see. Not all countries [there are 196 of them] joined voting at the UN in the same year.
What I want to do is to create a new column in my data.frame (votes) that consists of the absolute difference between ChinaIdealpoints to Czech Ideal points (for given year...). I know how to create the new column with dplyr but how do I multiply correct countries from the list of 196 countries? (the difference between the year of joining can be then deleted manually I think).
The final Output should be new data.frame (or new columns in votes) looking like this: China ideal point in 1994 was, for instance, 2.2550
|year |state_name|idealPoint|Abs.Difference China_Czech
|:--------------|---------:|---------:|-------------------------:|
|1971 | China | -25.0000| NA |
|1972 | China | -26.2550| NA |
|1973 | China | 28.2550| NA |
|1994 | Czech | 27.2550| 25.0000 |

Answer 1

Codes:
df1 <- data.frame(year = c(1994,1995,1996,1997,1994,1995,1996,1997),
state_name = c("China","China","China","China","Czech_Republic","Czech_Republic","Czech_Republic","Czech_Republic"),
idealpoints = c(-25.0000,-26.2550,28.2550,27.2550,-27.0000,-28.2550,29.2550,22.2550),
vote_no = c(31.0000,38.2974,35.2974,34.2974,33.0000,36.2974,37.2974,38.2974),
vote_yes = c(45.4209,45.4209,45.4209,45.4209,45.4209,45.4209,45.4209,45.4209))
china_df <- df1[df1$state_name == "China",]
czech_df <- df1[df1$state_name == "Czech_Republic",]
china_czech_merge <- merge(china_df,czech_df,by = "year")
china_czech_merge$Abs_diff <- abs(china_czech_merge$idealpoints.x - china_czech_merge$idealpoints.y)
Output:
year state_name.x idealpoints.x vote_no.x vote_yes.x state_name.y idealpoints.y vote_no.y vote_yes.y Abs_diff
1 1994 China -25.000 31.0000 45.4209 Czech_Republic -27.000 33.0000 45.4209 2
2 1995 China -26.255 38.2974 45.4209 Czech_Republic -28.255 36.2974 45.4209 2
3 1996 China 28.255 35.2974 45.4209 Czech_Republic 29.255 37.2974 45.4209 1
4 1997 China 27.255 34.2974 45.4209 Czech_Republic 22.255 38.2974 45.4209 5
I think this will work for you.
Thanks

Answer 2

Does this perhaps solve your problem?
library(tibble)
library(dplyr)
a <- tribble(
~year, ~ctry, ~vote,
1994, "China", 5,
1995, "China", 100,
1996, "China", 600,
1997, "China", 45,
1998, "China", 9,
1994, "Czech_Republic", 1,
1995, "Czech_Republic", 5,
1996, "Czech_Republic", 100,
1997, "Czech_Republic", 40,
1998, "Czech_Republic", 6,
)
a %>%
group_by(year) %>%
mutate(foo = abs(lag(lead(vote) - vote)))
Output:
# A tibble: 10 x 4
# Groups: year [5]
year ctry vote foo
<dbl> <chr> <dbl> <dbl>
1 1994 China 5 NA
2 1995 China 100 NA
3 1996 China 600 NA
4 1997 China 45 NA
5 1998 China 9 NA
6 1994 Czech_Republic 1 4
7 1995 Czech_Republic 5 95
8 1996 Czech_Republic 100 500
9 1997 Czech_Republic 40 5
10 1998 Czech_Republic 6 3
You'll have to filter down the data to fit your needs, e.g. by country.