How to print the number of observations dropped by tidyverse's functions like filter or drop_na?

Question

For different analyses, I use different samples, but I need to make it clear how the samples came about.

Stata shows me "XX observations dropped" after each drop command. Is there a way to get R to print the number of dropped observations by a "tidyverse styled" sample selection (see below)?

In this example I would like to see in the console how many observations were dropped by the filter and drop_na functions. I tried: summarise_all(~sum(is.na(.))) but it was unsuccessful.

capmkt_df <- stata_df %>%
  filter(change != 1 & reg_mkt == 1) %>% 
  select(any_of(capmkt_vars)) %>%
  mutate_at(vars(country, year), factor) %>%
  drop_na()

Answer 1

Since you're using tidyverse packages, a good resource is tidylog, a package that provides additional information for a lot of tidyverse (including dplyr and tidyr) functions.

For example, using drop_na, you'll get a message drop_na: removed X rows. An illustration with the base R airquality dataset:

library(tidyverse)
library(tidylog, warn.conflicts = F)

airquality %>% 
  drop_na()

# drop_na: removed 42 rows (27%), 111 rows remaining
#     Ozone Solar.R Wind Temp Month Day
# 1      41     190  7.4   67     5   1
# 2      36     118  8.0   72     5   2
# 3      12     149 12.6   74     5   3
# 4      18     313 11.5   62     5   4
# 5      23     299  8.6   65     5   7
# 6      19      99 13.8   59     5   8
# 7       8      19 20.1   61     5   9
# 8      16     256  9.7   69     5  12
# 9      11     290  9.2   66     5  13
# 10     14     274 10.9   68     5  14
# ...

Answer 2

One option is to print a sum of not complete.cases before dropping the NA values. Here, we can use the tee pipe (%T>%) from magrittr to print the results along the way.

library(tidyverse)

df %>%
  filter(x %in% c(1, 2, NA)) %T>%
  {print(sum(!complete.cases(.)))} %>%
  drop_na()

Output

So, you will see that 2 rows were dropped, as they both had NAs.

[1] 2
# A tibble: 1 × 2
      x y    
  <dbl> <chr>
1     1 a    

So, for your code, you could write:

capmkt_df <- stata_df %>%
  filter(change != 1 & reg_mkt == 1) %>% 
  select(any_of(capmkt_vars)) %>%
  mutate_at(vars(country, year), factor) %T>%
  {print(sum(!complete.cases(.)))} %>%
  drop_na()

Data

df <- structure(list(x = c(1, 2, NA), y = c("a", NA, "b")), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -3L))