替换列 r 中的多个值

Question

I am trying to create a function that takes in two variables, the continent and the column that would like to be worked with from a dataframe. I am then trying to calculate the mean value of the column for that particular continent to replace the NAs that are in that column for that continent. However, I seem to be having trouble when it comes to the actual replacement of the values, I keep running into errors. I have tried multiple ways such as replace, replace_na and mutate but I keep getting errors that I cannot seem to get away from. This code works when it is not in a function, but the minute I add it to the function I seem to get this error.

df<-structure(list(location = c("Algeria", "Angola", "Benin", "Botswana", 
"Burkina Faso", "Burundi"), iso_code = c("DZA", "AGO", "BEN", 
"BWA", "BFA", "BDI"), continent = c("Africa", "Africa", "Africa", 
"Africa", "Africa", "Africa"), date = c("2020-09-02", "2020-09-02", 
"2020-09-02", "2020-09-02", "2020-09-02", "2020-09-02"), total_cases = c(44833, 
2654, 2145, 1733, 1375, 445), new_cases = c(339, 30, 0, 9, 5, 
0), new_cases_smoothed = c(372.143, 53, 4.286, 24.429, 3.286, 
2.143), total_deaths = c(1518, 108, 40, 6, 55, 1), new_deaths = c(8, 
1, 0, 0, 0, 0), new_deaths_smoothed = c(8.857, 0.857, 0.143, 
0.429, 0, 0), total_cases_per_million = c(1022.393, 80.751, 176.934, 
736.937, 65.779, 37.424), new_cases_per_million = c(7.731, 0.913, 
0, 3.827, 0.239, 0), new_cases_smoothed_per_million = c(8.487, 
1.613, 0.354, 10.388, 0.157, 0.18), total_deaths_per_million = c(34.617, 
3.286, 3.299, 2.551, 2.631, 0.084), new_deaths_per_million = c(0.182, 
0.03, 0, 0, 0, 0), new_deaths_smoothed_per_million = c(0.202, 
0.026, 0.012, 0.182, 0, 0), population = c(43851043, 32866268, 
12123198, 2351625, 20903278, 11890781), population_density = c(17.348, 
23.89, 99.11, 4.044, 70.151, 423.062), median_age = c(29.1, 16.8, 
18.8, 25.8, 17.6, 17.5), aged_65_older = c(6.211, 2.405, 3.244, 
3.941, 2.409, 2.562), aged_70_older = c(3.857, 1.362, 1.942, 
2.242, 1.358, 1.504), gdp_per_capita = c(13913.839, 5819.495, 
2064.236, 15807.374, 1703.102, 702.225), extreme_poverty = c(0.5, 
NA, 49.6, NA, 43.7, 71.7), cardiovasc_death_rate = c(278.364, 
276.045, 235.848, 237.372, 269.048, 293.068), diabetes_prevalence = c(6.73, 
3.94, 0.99, 4.81, 2.42, 6.05), female_smokers = c(0.7, NA, 0.6, 
5.7, 1.6, NA), male_smokers = c(30.4, NA, 12.3, 34.4, 23.9, NA
), handwashing_facilities = c(83.741, 26.664, 11.035, NA, 11.877, 
6.144), hospital_beds_per_thousand = c(1.9, NA, 0.5, 1.8, 0.4, 
0.8), life_expectancy = c(76.88, 61.15, 61.77, 69.59, 61.58, 
61.58)), row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"
))


fun1 <- function(cont, column)
{
  countries<-df%>%
    filter(continent == cont)
  
  m<-mean(countries[[column]],na.rm=T)

    df[,column]<-ifelse(is.na(df[,column]) & df$continent==cont,m,(df[,column]=df[,column]))
}

fun1("Europe","median_age")

Error: Error during wrapup: Can't recycle input of size 208 to size 1. Error: no more error handlers available (recursive errors?); invoking 'abort' restart

Answer 1

You have a number of problems here. The first is that you seem to have made an error copying your dput over, so your example code doesn't run. Secondly, you are using the name mean as a variable name in the function, which is very likely to cause debugging confusion later. The third is that your function doesn't return anything. Lastly, your spacing makes the code very difficult to read. You have lots of vertical spaces with new lines, but don't separate out your variable names and operators with spaces. Again this makes things harder to debug.

If you are using dplyr functions, you can take advantage of quasiquotation to make your code simpler and more intuitive to use. For example, you can write it to pass bare column names without having to wrap them in "double quotes"

fun1 <- function(cont, col)
{
  col <- enquo(col)
  
  filter(df, continent == cont) %>%
    mutate(!!col := replace(!!col, is.na(!!col), mean(!!col, na.rm = TRUE)))
}

So you can write:

fun1("Africa", new_cases)
#>       location iso_code continent       date total_cases new_cases new_cases_smoothed
#> 1      Algeria      DZA    Africa 2020-09-02       44833       339            372.143
#> 2       Angola      AGO    Africa 2020-09-02        2654        30             53.000
#> 3        Benin      BEN    Africa 2020-09-02        2145         0              4.286
#> 4     Botswana      BWA    Africa 2020-09-02        1733         9             24.429
#> 5 Burkina Faso      BFA    Africa 2020-09-02        1375         5              3.286
#> 6      Burundi      BDI    Africa 2020-09-02         445         0              2.143
#>   total_deaths new_deaths
#> 1         1518          8
#> 2          108          1
#> 3           40          0
#> 4            6          0
#> 5           55          0
#> 6            1          0

If you just want to replace all NA values in the numeric columns by the mean of the other countries in that continent, then you don't need a function at all. You can just use:

df <- df %>% 
        group_by(continent) %>%
        mutate(across(total_cases:life_expectancy,
               function(x) replace(x, is.na(x), mean(x, na.rm = TRUE))))

To transform the entire data frame.