1 Load packages
library(tidyverse) # data wrangling2 Sample data
data("mtcars")Create some NA:
mtcars$mpg[c(1,2,3)] <- NA
mtcars$hp[c(1,2,3)] <- NA3 Count NA rowwise
What we would like to achieve is to comfortable count the missing values per row.
Define helper function:
sum_isna <- function(x) sum(is.na(x))4 Way 1: rowwise sum with mutate and c_across
mtcars %>%
rowwise() %>%
mutate(Na_n = sum_isna(c_across(everything()))) %>%
ungroup()
#> # A tibble: 32 × 12
#> mpg cyl disp hp drat wt qsec vs am gear carb Na_n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 NA 6 160 NA 3.9 2.62 16.5 0 1 4 4 2
#> 2 NA 6 160 NA 3.9 2.88 17.0 0 1 4 4 2
#> 3 NA 4 108 NA 3.85 2.32 18.6 1 1 4 1 2
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 0
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 0
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 0
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 0
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 0
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 0
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 0
#> # … with 22 more rowsA more in-depth treatment can be found here.
5 Way 2: apply() with margin 1
Margin 1 means rowwise:
mtcars %>%
mutate(Na_n = apply(mtcars, 1, sum_isna))
#> mpg cyl disp hp drat wt qsec vs am gear carb Na_n
#> Mazda RX4 NA 6 160.0 NA 3.90 2.620 16.46 0 1 4 4 2
#> Mazda RX4 Wag NA 6 160.0 NA 3.90 2.875 17.02 0 1 4 4 2
#> Datsun 710 NA 4 108.0 NA 3.85 2.320 18.61 1 1 4 1 2
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 0
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 0
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 0
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 0
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 0
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 0
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 0
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 0
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 0
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 0
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 0
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 0
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 0
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 0
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 0
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 0
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 0
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 0
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 0
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 0
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 0
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 0
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 0
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 0
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 0
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 0
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 0
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 0
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 06 Way 3: rowSums
mtcars %>%
mutate(Na_n = rowSums(is.na(mtcars)))
#> mpg cyl disp hp drat wt qsec vs am gear carb Na_n
#> Mazda RX4 NA 6 160.0 NA 3.90 2.620 16.46 0 1 4 4 2
#> Mazda RX4 Wag NA 6 160.0 NA 3.90 2.875 17.02 0 1 4 4 2
#> Datsun 710 NA 4 108.0 NA 3.85 2.320 18.61 1 1 4 1 2
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 0
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 0
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 0
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 0
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 0
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 0
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 0
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 0
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 0
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 0
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 0
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 0
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 0
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 0
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 0
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 0
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 0
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 0
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 0
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 0
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 0
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 0
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 0
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 0
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 0
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 0
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 0
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 0
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 07 Way 4: cur_data()
cur_data() refers to the data in the current group, which is, in the case
of rowwise() the current row.
mtcars %>%
rowwise() %>%
mutate(NA_n = sum_isna(cur_data()))
#> # A tibble: 32 × 12
#> # Rowwise:
#> mpg cyl disp hp drat wt qsec vs am gear carb NA_n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 NA 6 160 NA 3.9 2.62 16.5 0 1 4 4 2
#> 2 NA 6 160 NA 3.9 2.88 17.0 0 1 4 4 2
#> 3 NA 4 108 NA 3.85 2.32 18.6 1 1 4 1 2
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 0
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 0
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 0
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 0
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 0
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 0
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 0
#> # … with 22 more rows8 Why not map()?
When using map() in relation with mutate(), we map a column of the data frame to some function.
However, when counting missing values per row, we would like to map a row to a function,
which is not possible using map().
9 Reproducibility
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