CODEWITHSUNDEEP
rdataframedplyrrowwise
Thomas Philips
Thomas Philips

Reputation: 1089

Problem using rowwise() to count the number of NA's in each row of a dataframe

I'm having trouble using rowwise() to count the number of NAs in each row. My minimal example:

df <- data.frame(Q1 = c(rep(1, 1), rep(NA, 9)),
                 Q2 = c(rep(2, 2), rep(NA, 8)),
                 Q3 = c(rep(3, 3), rep(NA, 7))
)
df
   Q1 Q2 Q3
1   1  2  3
2  NA  2  3
3  NA NA  3
4  NA NA NA
5  NA NA NA
6  NA NA NA
7  NA NA NA
8  NA NA NA
9  NA NA NA
10 NA NA NA

I would like to create a new column that counts the number of NAs in each row. I can do this very simply by writing

 df$Count_NA <- rowSums(is.na(df))
 df
   Q1 Q2 Q3 Count_NA
1   1  2  3        0
2  NA  2  3        1
3  NA NA  3        2
4  NA NA NA        3
5  NA NA NA        3
6  NA NA NA        3
7  NA NA NA        3
8  NA NA NA        3
9  NA NA NA        3
10 NA NA NA        3

But if I try and do this via dplyr using rowwise(), I get the wrong answer - the column Count_NA has the same number in each row:

df %>%
   rowwise() %>%
   mutate(Count_NA = sum(is.na(.)))
# A tibble: 10 x 4
# Rowwise: 
      Q1    Q2    Q3 Count_NA
   <dbl> <dbl> <dbl>    <int>
 1     1     2     3       24
 2    NA     2     3       24
 3    NA    NA     3       24
 4    NA    NA    NA       24
 5    NA    NA    NA       24
 6    NA    NA    NA       24
 7    NA    NA    NA       24
 8    NA    NA    NA       24
 9    NA    NA    NA       24
10    NA    NA    NA       24

what am I doing wrong, and how do i fix this?

Many thanks in advance

Thomas Philips

Upvotes: 8

Views: 2590

Answers (7)

Sandy
Sandy

Reputation: 1148

This might be late, however, an alternate way to address your problem where, for example, you needed to calculate NAs over different sets of columns would be like this:

# You can specify different column sets
cols_to_check1 <- c("Q1", "Q2", "Q3")
cols_to_check2 <- c("Q1", "Q2")

Then, you apply the rowSums function with mutate()

df %>%
 mutate(count_NA1 = rowSums(across(cols_to_check1, ~ is.na(.))),
        count_NA2 = rowSums(across(cols_to_check2, ~ is.na(.))))

The solution for this would be as below:

   Q1 Q2 Q3 count_NA1  count_NA2
1   1  2  3        0           0
2  NA  2  3        1           1
3  NA NA  3        2           2
4  NA NA NA        3           2
5  NA NA NA        3           2
6  NA NA NA        3           2
7  NA NA NA        3           2
8  NA NA NA        3           2
9  NA NA NA        3           2
10 NA NA NA        3           2

Upvotes: 1

akrun
akrun

Reputation: 886948

Using dapply

library(collapse)
dapply(df, function(x) sum(is.na(x)), MARGIN = 1)
#[1] 0 1 2 3 3 3 3 3 3 3

Upvotes: 3

Waldi
Waldi

Reputation: 41210

rowSums works directly with mutate without rowwise:

df %>% mutate(count_NA = rowSums(is.na(.)))

   Q1 Q2 Q3 count_NA
1   1  2  3        0
2  NA  2  3        1
3  NA NA  3        2
4  NA NA NA        3
5  NA NA NA        3
6  NA NA NA        3
7  NA NA NA        3
8  NA NA NA        3
9  NA NA NA        3
10 NA NA NA        3

Note that your initial solution is by far the fastest one:

microbenchmark::microbenchmark(
  df$Count_NA <- rowSums(is.na(df)),
  df$Count_NA <- apply(df, 1, function(x) sum(is.na(x))),
  df %>% mutate(count_NA = rowSums(is.na(.))),
  df %>%
    mutate(Count_NA = purrr::pmap(., ~ sum(is.na(c(...))))),
  df %>%
    rowwise() %>%
    mutate(a=sum(is.na(c_across(everything())))),
  df %>%
  rowwise() %>%
  mutate(Count_NA = sum(is.na(cur_data()))) %>%
  ungroup
)

Unit: microseconds
                                                                            expr     min       lq
                                               df$Count_NA <- rowSums(is.na(df))    39.8    64.30
                          df$Count_NA <- apply(df, 1, function(x) sum(is.na(x)))  1661.6  1868.40
                                     df %>% mutate(count_NA = rowSums(is.na(.)))  1181.7  1572.80
                   df %>% mutate(Count_NA = purrr::pmap(., ~sum(is.na(c(...)))))  4749.9  5190.35
             df %>% rowwise() %>% mutate(a = sum(is.na(c_across(everything())))) 29124.1 31148.50
 df %>% rowwise() %>% mutate(Count_NA = sum(is.na(cur_data()))) %>%      ungroup 70473.0 73659.70
      mean   median       uq     max neval   cld
    79.033    76.25    88.75   174.0   100 a    
  2082.960  1966.50  2075.75  8777.3   100  b   
  1722.178  1676.20  1791.60  3112.9   100  b   
  5726.549  5396.40  5745.25 28592.1   100   c  
 33567.825 31983.05 33637.00 54676.9   100    d 
 77902.342 76492.85 81199.15 98942.1   100     e
Unit: microseconds
                                                                            expr     min       lq
                                               df$Count_NA <- rowSums(is.na(df))    38.2    44.95
                          df$Count_NA <- apply(df, 1, function(x) sum(is.na(x)))  1584.8  1765.30
                                     df %>% mutate(count_NA = rowSums(is.na(.)))  1247.9  1496.95
                   df %>% mutate(Count_NA = purrr::pmap(., ~sum(is.na(c(...)))))  4614.0  5110.50
 df %>% rowwise() %>% mutate(Count_NA = sum(is.na(cur_data()))) %>%      ungroup 67413.5 70865.45
      mean   median       uq      max neval cld
    71.159    65.85    84.40    162.2   100 a  
  1967.629  1894.45  2093.30   3436.6   100 ab 
  1814.193  1666.25  1895.35   9031.0   100 a  
  5796.483  5380.70  5665.10  15309.7   100  b 
 78309.807 75275.30 79776.40 286964.3   100   c

Upvotes: 8

G. Grothendieck
G. Grothendieck

Reputation: 269451

Use cur_data() rather than dot. .[cur_group_id(), ], c(Q1, Q2, Q3), across() or c_across() (or c_across with the argument as per other answer) would also work.

Note that it is best to use ungroup afterwards or else it will retain the memory of the rowwise and you might get unexpected results later on.

df %>%
   rowwise() %>%
   mutate(Count_NA = sum(is.na(cur_data()))) %>%
   ungroup

giving:

# A tibble: 10 x 4
      Q1    Q2    Q3 Count_NA
   <dbl> <dbl> <dbl>    <int>
 1     1     2     3        0
 2    NA     2     3        1
 3    NA    NA     3        2
 4    NA    NA    NA        3
 5    NA    NA    NA        3
 6    NA    NA    NA        3
 7    NA    NA    NA        3
 8    NA    NA    NA        3
 9    NA    NA    NA        3
10    NA    NA    NA        3

Upvotes: 9

AnilGoyal
AnilGoyal

Reputation: 26218

baseR answer

df$Count_NA <- apply(df, 1, function(x) sum(is.na(x)))                 

df
   Q1 Q2 Q3 Count_NA
1   1  2  3        0
2  NA  2  3        1
3  NA NA  3        2
4  NA NA NA        3
5  NA NA NA        3
6  NA NA NA        3
7  NA NA NA        3
8  NA NA NA        3
9  NA NA NA        3
10 NA NA NA        3

So can be integrated into dplyr pipe

df %>% mutate(count_NA = apply(., 1, function(x) sum(is.na(x))))

   Q1 Q2 Q3 count_NA
1   1  2  3        0
2  NA  2  3        1
3  NA NA  3        2
4  NA NA NA        3
5  NA NA NA        3
6  NA NA NA        3
7  NA NA NA        3
8  NA NA NA        3
9  NA NA NA        3
10 NA NA NA        3

Upvotes: 5

Anoushiravan R
Anoushiravan R

Reputation: 21908

In case future you were interested in a row-wise solution with purrr package functions:

library(purrr)

df %>%
  mutate(Count_NA = pmap(., ~ sum(is.na(c(...)))))


   Q1 Q2 Q3 Count_NA
1   1  2  3        0
2  NA  2  3        1
3  NA NA  3        2
4  NA NA NA        3
5  NA NA NA        3
6  NA NA NA        3
7  NA NA NA        3
8  NA NA NA        3
9  NA NA NA        3
10 NA NA NA        3

Upvotes: 4

r2evans
r2evans

Reputation: 160407

One issue is that . here resolves to the whole frame, not just the whole row. Another dplyr method, using c_across:

df %>%
    rowwise() %>%
    mutate(a=sum(is.na(c_across(everything()))))
# # A tibble: 10 x 4
# # Rowwise: 
#       Q1    Q2    Q3     a
#    <dbl> <dbl> <dbl> <int>
#  1     1     2     3     0
#  2    NA     2     3     1
#  3    NA    NA     3     2
#  4    NA    NA    NA     3
#  5    NA    NA    NA     3
#  6    NA    NA    NA     3
#  7    NA    NA    NA     3
#  8    NA    NA    NA     3
#  9    NA    NA    NA     3
# 10    NA    NA    NA     3

The biggest difference I can see between using this and cur_data() is that c_across allows for variable-selection a little more directly, as in c_across(starts_with("Q")). Granted, one could always select(cur_data(),...), so this is a weak argument.

Upvotes: 5

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