CODEWITHSUNDEEP
rdataframedplyr
sickrun
sickrun

Reputation: 5

How to fix: "no applicable method for 'pull'" (dplyr)

I have a list of several NUTS2 codes for the years 1984-1986. I am trying to get the average GDP for each NUTS2 region over the course of these 3 years. Below is the code I tried to achieve this with - unfortunately, I always retrieve an error with my last line, saying that "pull" is not an applicable method for this class.

The initial dataset looks as follows:

NUTS_CODE NUTS_LEVEL SCENARIO_ID REF_YEAR IND_VALUE NUTS_C
837      BE10          2           1     1984 2.307e+10     BE
838      BE21          2           1     1984 2.195e+10     BE
839      BE22          2           1     1984 6.330e+09     BE
840      BE23          2           1     1984 1.340e+10     BE
841      BE24          2           1     1984 9.430e+09     BE
842      BE25          2           1     1984 1.093e+10     BE

My code:

regions <- unique(as.character(gdp_nuts2_member_1984to1986$NUTS_CODE))
data84_86 <- regions
data84_86 <-cbind(data84_86, rep(as.numeric(NA), length(regions)))

colnames(data84_86) <- c("regions","values")


for(i in 1:nrow(data84_86))
{
  data84_86[i,2]<-mean(pull((gdp_nuts2_member_1984to1986%>%filter(NUTS_CODE == regions[i]))[,"IND_VALUE"]))
}

Error in UseMethod("pull") : no applicable method for 'pull' applied to an object of class "c('double', 'numeric')"

Upvotes: 0

Views: 8452

Answers (1)

Cettt
Cettt

Reputation: 11981

the pull function works a little bit differently:

library(dplyr)
 data84_86[i,2] <- mean(pull(gdp_nuts2_member_1984to1986 %>%
             filter(NUTS_CODE == regions[i]), IND_VALUE))

Your code becomes even more readable if you write it like this:

data84_86[i,2] <- gdp_nuts2_member_1984to1986 %>%
                 filter(NUTS_CODE == regions[i]) %>%
                 pull(IND_VALUE) %>%
                 mean()

Also, the same result can be obtained like this:

gdp_nuts2_member_1984to1986 %>%
  group_by(NUTS_CODE) %>%
  summarise(values = mean(IND_VALUE))

This way is even easier to read and computationally more efficient.

Upvotes: 1

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