Count How Many Values in Some Cells of a Row Are Not Na (In R)

Count how many values in some cells of a row are not NA (in R)

You can use is.na() over the selected columns, then rowSums() the result:

library(stringr)
df <- data_frame(
  id = 1:10
  , name = fruit[1:10]
  , word1 = c(words[1:5],NA,words[7:10])
  , word2 = words[11:20]
  , word3 = c(NA,NA,NA,words[25],NA,NA,words[32],NA,NA,words[65]))

df$word_count <- rowSums( !is.na( df [,3:5]))

df
      id         name    word1     word2   word3 n_words
   <int>        <chr>    <chr>     <chr>   <chr>   <dbl>
1      1        apple        a    actual    <NA>       2
2      2      apricot     able       add    <NA>       2
3      3      avocado    about   address    <NA>       2
4      4       banana absolute     admit   agree       3
5      5  bell pepper   accept advertise    <NA>       2
6      6     bilberry     <NA>    affect    <NA>       1
7      7   blackberry  achieve    afford alright       3
8      8 blackcurrant   across     after    <NA>       2
9      9 blood orange      act afternoon    <NA>       2
10    10    blueberry   active     again   awful       3

Edit

Using dplyr you could do this:

df %>% 
    select(3:5) %>% 
    is.na %>% 
    `!` %>% 
    rowSums

Count the number of non-NA numeric values of each row in dplyr

Use select + is.na + rowSums, select(., -id) returns the original data frame (.) with id excluded, and then count number of non-NA values with rowSums(!is.na(...)):

df %>% mutate(var4 = rowSums(!is.na(select(., -id))))

#   id var1 var2 var3 var4
#1   1   10   NA    4    2
#2   2   11    1   NA    2
#3   3   12    2    5    3
#4   4   13    2   NA    2
#5   5   14    1   NA    2
#6   6   15    1   NA    2
#7   7   16    1    5    3
#8   8   17   NA    4    2
#9   9   18   NA    4    2
#10 10   19   NA   NA    1

How to 'count' number of non-empty values in a single row across multiple columns in a dataframe

If you are talking about missing values in R, it's represented in capital letter NA instead of na, otherwise, R will treat it as a string, which is not empty.

Also, I have artificially included some Name in your df to act like each row represents one Name, and a artificial Comp5 which includes some NAs but will not be included in the calculation.

rowSums() as its name suggests, calculates the sum of the row.

is.na(df[, 2:4]) makes it only counts the NA in df from column 2 to column 4.

df <-read.table(header = T, 
                text = 
                  "Name Comp1  Comp2  Comp3  Comp4  Comp5
                A 0.5      0.4    NA     0.6  NA
                B 0.6      NA     NA      0.7 1
                C NA       0.4    NA      1.1 NA")

df$Count_NA <- rowSums(is.na(df[, 2:4]))

Output

  Name Comp1 Comp2 Comp3 Comp4 Comp5 Count_NA
1    A   0.5   0.4    NA   0.6    NA        1
2    B   0.6    NA    NA   0.7     1        2
3    C    NA   0.4    NA   1.1    NA        2

Count number of non-NA values for every column in a dataframe

You can also call is.na on the entire data frame (implicitly coercing to a logical matrix) and call colSums on the inverted response:

# make sample data
set.seed(47)
df <- as.data.frame(matrix(sample(c(0:1, NA), 100*5, TRUE), 100))

str(df)
#> 'data.frame':    100 obs. of  5 variables:
#>  $ V1: int  NA 1 NA NA 1 NA 1 1 1 NA ...
#>  $ V2: int  NA NA NA 1 NA 1 0 1 0 NA ...
#>  $ V3: int  1 1 0 1 1 NA NA 1 NA NA ...
#>  $ V4: int  NA 0 NA 0 0 NA 1 1 NA NA ...
#>  $ V5: int  NA NA NA 0 0 0 0 0 NA NA ...

colSums(!is.na(df))
#> V1 V2 V3 V4 V5 
#> 69 55 62 60 70

Count non-NA values by group

You can use this

mydf %>% group_by(col_1) %>% summarise(non_na_count = sum(!is.na(col_2)))

# A tibble: 2 x 2
   col_1 non_na_count
  <fctr>        <int>
1      A            1
2      B            2

Count number of NA's in a Row in Specified Columns R

df$na_count <- rowSums(is.na(df[c('first', 'last', 'address', 'phone', 'state')])) 

df
   first m_initial     last         address    phone state customer na_count
1    Bob         L   Turner 123 Turner Lane 410-3141  Iowa     <NA>        0
2   Will         P Williams 456 Williams Rd 491-2359  <NA>        Y        1
3 Amanda         C    Jones    789 Haggerty     <NA>  <NA>        Y        2
4   Lisa      <NA>    Evans            <NA>     <NA>  <NA>        N        3

Create new column based on counting non-NA values across multiple columns

df$column_non_NA= rowSums(!is.na(df[-1]))
df
   Q1  Q1a  Q1b  Q1c column_non_NA
1 Yes  AAA  BBB <NA>             2
2  No <NA> <NA> <NA>             0
3 Yes  AAA <NA> <NA>             1
4  No <NA> <NA> <NA>             0
5 Yes  ABC  BCD  EFG             3
6 Yes  DDD <NA> <NA>             1
7 Yes  EEE  AAA  AAA             3

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