Counting observations by group using collapse R package

user_707145 0
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I want to translate the following R code from tidyverse to collapse. The following code count observations by group and append as a column to the data.frame.

library(tidyverse)
library(collapse)
head(wlddev)

wlddev %>% 
  group_by(income) %>% 
  add_count(., name = "Size") %>% 
  select(country, income, Size) %>% 
  distinct()
# A tibble: 216 x 3
# Groups:   income [4]
   country             income               Size
   <chr>               <fct>               <int>
 1 Afghanistan         Low income           1830
 2 Albania             Upper middle income  3660
 3 Algeria             Upper middle income  3660
 4 American Samoa      Upper middle income  3660
 5 Andorra             High income          4819
 6 Angola              Lower middle income  2867
 7 Antigua and Barbuda High income          4819
 8 Argentina           Upper middle income  3660
 9 Armenia             Upper middle income  3660
10 Aruba               High income          4819
# ... with 206 more rows

Now want to accomplish the same task with collapse R package.

The following code works as expected.

wlddev %>%
  fgroup_by(income) %>%
  fselect(country) %>% 
  fnobs()

               income country
1         High income    4819
2          Low income    1830
3 Lower middle income    2867
4 Upper middle income    3660

However, not able to append the column to original data.frame.

wlddev %>%
  fgroup_by(income) %>%
  fselect(country) %>% 
  fnobs() %>% 
  ftransform(.data = wlddev, Size = .)

Error in ftransform_core(.data, e) : 
  Lengths of replacements must be equal to nrow(.data) or 1, or NULL to delete columns

Any hints, please.

collapse tidyverse r
3 Answers

Unlike add_count which creates a column in the original data, the fnobs is a summarised data, which we can join

library(collapse)
 wlddev %>% 
    fgroup_by(income) %>%
    fselect(country) %>%   
    fnobs() %>% 
    rename(size = country) %>% 
   left_join(wlddev %>% 
      slt(country, income), .) %>% 
   distinct
user_37322710

Found a very simple solution:

wlddev %>% 
  fmutate(Size = fnobs(income, income, TRA = "replace_fill"))  %>% 
  fselect(country, income, Size) %>% 
  funique()
user_7071450

So in principle fnobs counts the number of non-missing values, an option to add the group count is not really afforded (I also wonder why that would be necessary, I have never required it). Nevertheless, the count is there in the grouping object which can be retrieved using GRP(.). So you could create a function:

gcount <- function(x) {
   # Just turning some unnecessary things off in case we pass a plain vector
   g <- GRP(x, sort = FALSE, return.groups = FALSE, call = FALSE) 
   g$group.sizes[g$group.id]
}

Then we can do

wlddev %>% 
  ftransform(Size = gcount(income)) %>%
  fselect(country, income, Size) %>% 
  funique(cols = 1) # Observations are uniquely identified by country

# or 

wlddev %>% 
  fgroup_by(income) %>%
  ftransform(Size = gcount(.)) %>%
  fselect(country, income, Size) %>% 
  fungroup() %>%
  funique(cols = 1)

Of course we can also use fnobs:

wlddev %>% 
  fgroup_by(income) %>%
  fmutate(Size = fnobs(income)) %>%
  fselect(country, income, Size) %>% 
  fungroup() %>%
  funique(cols = 1)

but that could be misleading if incomecontained missing values. Note (as stated in the documentation) that ftransform is a faster version of base::transform that ignores groupings and fmutate is a faster dplyr::mutate which respects groupings.

If you tell me why the group count would be required as a variable in a data frame, I can think of adding gcount to the next collapse release.

user_110017510
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