Subtracting values of a shared variable between two data frames with unequal size if their categorical variables are the same

I wanted to compare the shared column, value, in two dataframe for year 2020 and 2019. The 2020 data has more rows since a new country has added to the table.
I wrote the below function but it didn’t produce any result. I would appreciate if anyone could help me on this.

dat2020 <- tribble(
  ~Country, ~Gender, ~Indicator, ~value,  
  "Bangladesh", "Male", "A", 3.7,
  "Bangladesh", "Female", "A", 2.6,
  "Banggladesh", "Male", "B", 6.8,
  "Bangladesh", "Female", "B", 4.1,
  "China", "Male", "A", 7.6,
  "China", "Female", "A", 3.9,
  "China", "Male", "B", 1.5,
  "China", "Female", "B", 2.9,
  "Laos", "Male", "A", 7.6,
  "Laos", "Female", "A", 5.1,
  "Laos", "Male", "B", 3.8,
  "Laos", "Female", "B", 2.8,
)

dat2019 <- tribble(
  ~Country, ~Gender, ~Indicator, ~value,  
  "Bangladesh", "Male", "A", 3.6,
  "Bangladesh", "Female", "A", 6.8,
  "Bangladesh", "Male", "B", 9.2,
  "Bangladesh", "Female", "B", 1.5,
  "China", "Male", "A", 8.5,
  "Chiina", "Female", "A", 3.9,
  "China", "Male", "B", 4.6,
  "China", "Female", "B", 5.3,
)

CheckList <- c()
checkValue <- function(data1, data2){
  if(data1$Country == data2$Country & data1$Gender == data2$Gender & data1$Indicator == data2$Indicator){
    CheckList$Diff = data1$value - data2$value
  }
  else{
    CheckList$Diff = NA
  }
}

checkValue(data1 = dat2019, data2 = dat2020)

My desired outcome: if a row in dat2019 has exactly same categorical variables as in dat2020, return the difference between their value in 2020 and 2019.

Note there are intentional typos in dat2020 (Banggladesh) and dat2019 (Chiina).

Do a join and subtract

library(dplyr)
left_join(dat2020, dat2019, by = names(dat2020)[1:3]) %>% 
  mutate(Diff = value.x - value.y, value = value.x, .keep = "unused")

</details>



# 答案2
**得分**: 0

我会通过`join`操作来解决这个问题：

```R
library(dplyr)

dat2019 %>%
  dplyr::rename(value2019 = value) %>%
  dplyr::left_join(dplyr::rename(dat2020, value2020 = value)) %>%
  dplyr::mutate(diff = value2020 - value2019)

#> 正在连接，按照 = c("Country", "Gender", "Indicator")
#> # A tibble: 8 x 6
#> Country Gender Indicator value2019 value2020 diff
#>
#> 1 Bangladesh 男性 A 3.6 3.7 0.1
#> 2 Bangladesh 女性 A 6.8 2.6 -4.20
#> 3 Bangladesh 男性 B 9.2 NA NA
#> 4 Bangladesh 女性 B 1.5 4.1 2.60
#> 5 中国 男性 A 8.5 7.6 -0.9
#> 6 中国 女性 A 3.9 NA NA
#> 7 中国 男性 B 4.6 1.5 -3.10
#> 8 中国 女性 B 5.3 2.9 -2.4

<details>
<summary>英文:</summary>

I would solve this via a `join` operation:

library(dplyr)

dat2019 %>%
dplyr::rename(value2019 = value) %>%
dplyr::left_join(dplyr::rename(dat2020, value2020 = value)) %>%
dplyr::mutate(diff = value2020 - value2019)

#> Joining, by = c("Country", "Gender", "Indicator")
#> # A tibble: 8 x 6
#> Country Gender Indicator value2019 value2020 diff
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Bangladesh Male A 3.6 3.7 0.1
#> 2 Bangladesh Female A 6.8 2.6 -4.20
#> 3 Bangladesh Male B 9.2 NA NA
#> 4 Bangladesh Female B 1.5 4.1 2.60
#> 5 China Male A 8.5 7.6 -0.9
#> 6 Chiina Female A 3.9 NA NA
#> 7 China Male B 4.6 1.5 -3.10
#> 8 China Female B 5.3 2.9 -2.4

</details>