Count Number of Values in Row Using Dplyr

Count number of values in row using dplyr

Try rowSums:

> set.seed(1)
> ID <- LETTERS[1:5]
> X1 <- sample(1:5, 5,T)
> X2 <- sample(1:5, 5,T)
> X3 <- sample(1:5, 5,T)
> df <- data.frame(ID,X1,X2,X3)
> df
  ID X1 X2 X3
1  A  2  5  2
2  B  2  5  1
3  C  3  4  4
4  D  5  4  2
5  E  2  1  4
> rowSums(df == 2)
[1] 2 1 0 1 1

Alternatively, with dplyr:

> df %>% mutate(numtwos = rowSums(. == 2))
  ID X1 X2 X3 numtwos
1  A  2  5  2       2
2  B  2  5  1       1
3  C  3  4  4       0
4  D  5  4  2       1
5  E  2  1  4       1

Count the number of times a value appears in a column using dplyr

Using the n() function:

x %>%
  group_by(Code) %>%
  mutate(Code_frequency = n()) %>%
  ungroup()

counting the number of observations row wise using dplyr

Using base R. First line checks all columns, second one checks columns by name, third might not work as good if the number of columns is substantial.

sample$z1 <- rowSums(!is.na(sample))
sample$z2 <- rowSums(!is.na(sample[c("x", "y")]))
sample$z3 <- is.finite(sample$x) + is.finite(sample$y)

> sample
# A tibble: 4 x 5
      x     y    z1    z2    z3
  <dbl> <dbl> <dbl> <dbl> <int>
1     1     5     2     2     2
2     2    NA     1     1     1
3     3     2     2     2     2
4    NA    NA     0     0     0

Count occurrence of string values per row in dataframe in R (dplyr)

You can use across with rowSums -

library(dplyr)

df %>% mutate(d9 = rowSums(across(all_of(cols), `%in%`, bcde)))

#  d1    d2    d3    d4    d5    d6    d7    d8       d9
#  <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#1 b     a     a     a     a     a     a     a         0
#2 a     a     a     a     c     a     a     a         1
#3 a     b     a     a     a     a     a     a         1
#4 a     a     c     a     a     b     a     a         2
#5 a     a     a     a     a     a     a     a         0
#6 a     a     b     a     a     a     a     a         1
#7 a     a     a     a     a     d     a     a         1
#8 a     a     a     d     a     a     a     a         1

This can also be written in base R -

df$d9 <- rowSums(sapply(df[cols], `%in%`, bcde))

How to use R dplyr's summarize to count the number of rows that match a criteria?

You can use sum on logical vectors - it will automatically convert them into numeric values (TRUE being equal to 1 and FALSE being equal to 0), so you need only do:

test %>%
  group_by(location) %>%
  summarize(total_score = sum(score),
            n_outliers  = sum(more_than_300))
#> # A tibble: 2 x 3
#>   location total_score n_outliers
#>   <chr>          <dbl>      <int>
#> 1 away             927          2
#> 2 home             552          0

Or, if these are your only 3 columns, an equivalent would be:

test %>%
  group_by(location) %>%
  summarize(across(everything(), sum))

In fact, you don't need to make the more_than_300 column - it would suffice to do:

test %>%
  group_by(location) %>%
  summarize(total_score = sum(score),
            n_outliers  = sum(score > 300))

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

How can I count a number of conditional rows within r dplyr mutate?

Here is a dplyr only solution:

The trick is to substract the grouping number of X (e.g. cumsum(Product=="X") from the sum of X (e.g. sum(Product=="X") in each Customer group:

library(dplyr)

  df %>%
    arrange(Customer, Date) %>%
    group_by(Customer) %>%
    mutate(nSubsqX1 = sum(Product=="X") - cumsum(Product=="X"))  

   Date       Customer Product nSubsqX1
   <date>     <chr>    <chr>      <int>
 1 2020-05-18 A        X              0
 2 2020-02-10 B        X              5
 3 2020-02-12 B        Y              5
 4 2020-03-04 B        Z              5
 5 2020-03-29 B        X              4
 6 2020-04-08 B        X              3
 7 2020-04-30 B        X              2
 8 2020-05-13 B        X              1
 9 2020-05-23 B        Y              1
10 2020-07-02 B        Y              1
11 2020-08-26 B        Y              1
12 2020-12-06 B        X              0
13 2020-01-31 C        X              3
14 2020-09-19 C        X              2
15 2020-10-13 C        X              1
16 2020-11-11 C        X              0
17 2020-12-26 C        Y              0

---

Related Topics