Remove row on group depending on multiple criteria r

Question

I have a dataset with some repeated values on Date variable, so I would like to filter this rows based on several conditions. As an example, the dataframe looks like:

df <- read.table(text = 
          "Date       column_A   column_B    column_C   Column_D
1        2020-01-01     10          15         15          20
2        2020-01-02     10          15         15          20
3        2020-01-03     10          13         15          20
4        2020-01-04     10          15         15          20
5        2020-01-05     NA          14         15          20
6        2020-01-05     7           NA         NA          28
7        2020-01-06     10          15         15          20
8        2020-01-07     10          15         15          20
9        2020-01-07     10          NA         NA          20
10       2020-01-08     10          15         15          20", header=TRUE)

df$Date <- as.Date(df$Date)

The different conditions to filter should be, ONLY on duplicated rows:

• If "column A" is NA and the other numeric, select the numeric row
• If both values are similar(both NA or both numeric), select row with less NAs.

My best approach, after several options is:

df$cnt_na <- apply(df[,2:5], 1, function(x) sum(is.na(x)))
df <- df %>%  group_by(Date) %>% slice(which.min(all_of(cnt_na))) %>% select(-cnt_na)

Although in my case, it doesn't do the first condition. The main problem is that if I filter by.is,na(Date). I also remove other not duplicated rows.

Thanks in advance

Answer 1

I would sort your table based on your conditions and then pick the first row for every group:

library(dplyr)

df %>% 
  rowwise() %>% 
  mutate(cnt_na = sum(across(-Date, ~ sum(is.na(.))))) %>% 
  arrange(Date, is.na(column_A), cnt_na) %>% 
  group_by(Date) %>% 
  slice_head() %>% 
  ungroup()

which gives

# A tibble: 8 x 6
  Date       column_A column_B column_C Column_D cnt_na
  <date>        <int>    <int>    <int>    <int>  <int>
1 2020-01-01       10       15       15       20      0
2 2020-01-02       10       15       15       20      0
3 2020-01-03       10       13       15       20      0
4 2020-01-04       10       15       15       20      0
5 2020-01-05        7       NA       NA       28      2
6 2020-01-06       10       15       15       20      0
7 2020-01-07       10       15       15       20      0
8 2020-01-08       10       15       15       20      0