how to use pipes in condition matching in r? - r

Question

I am getting an error while trying to filter data in 1st df based on countries available in 2nd df by using pipe operator in conditional statement.
Referencing countries df
Overall_top5
########### output ###########
continent country gdpPercap
Africa Botswana 8090
Africa Equatorial Guinea 20500
Africa Gabon 19600
Africa Libya 12100
Africa Mauritius 10900
Americas Canada 51600
Americas Chile 15100
Americas Trinidad and Tobago 17100
main df
gap_longer
########### output #############
country year gdpPercap continent
Australia 2019 57100 Oceania
Botswana 2019 8090 Africa
Canada 2019 51600 Americas
Chile 2019 15100 Americas
Denmark 2019 65100 Europe
Error: When I try below code it gives me errors:
gap_longer %>%
filter(year == 2019,
country %in% Overall_top5 %>% select(country) )
Error: Problem with `filter()` input `..1`. x no applicable method for 'select_' applied to an object of class "logical" i Input `..1` is `country %in% Overall_top5 %>% select(country)`. Run `rlang::last_error()` to see where the error occurred.
How can I run this using pipes ? I am able to run this using base R but don't know how to fix it using pipes .
gap_longer %>%
filter(year == 2019,
country %in% Overall_top5$country )
Raw data
Overall_top5 <- structure(list(continent = c("Africa", "Africa", "Africa", "Africa", "Africa", "Americas", "Americas", "Americas"), country = c("Botswana", "Equatorial Guinea", "Gabon", "Libya", "Mauritius", "Canada", "Chile", "Trinidad and Tobago"), gdpPercap = c(8090L, 20500L, 19600L, 12100L, 10900L, 51600L, 15100L, 17100L)), row.names = c(NA, -8L), class = "data.frame")
gap_longer <- structure(list(country = c("Australia", "Botswana", "Canada", "Chile", "Denmark"), year = c(2019L, 2019L, 2019L, 2019L, 2019L), gdpPercap = c(57100L, 8090L, 51600L, 15100L, 65100L), continent = c("Oceania", "Africa", "Americas", "Americas", "Europe")), class = "data.frame", row.names = c(NA, -5L))

Answer 1

First, you want to use pull rather than select as select will return a data frame rather than a vector (but that doesn't solve your problem).
Your problem comes from precedence. In your example, %in% is evaluated first, then %>%. To fix this, use parentheses.
gap_longer %>%
filter(
year == 2019,
country %in% (Overall_top5 %>% pull(country))
)
#> # A tibble: 3 x 4
#> country year gdpPercap continent
#> <chr> <dbl> <dbl> <chr>
#> 1 Botswana 2019 8090 Africa
#> 2 Canada 2019 51600 Americas
#> 3 Chile 2019 15100 Americas

Answer 2

Parenthesis problem. Try:
gap_longer %>%
filter(year == 2019, country %in% Overall_top5$country) %>%
select(country)
or, if you want a vector of country names, not a data frame:
gap_longer %>%
filter(year == 2019, country %in% Overall_top5$country) %>%
pull(country)