has_na()
: Check for NA
values in the data and return a logical value.
random_na()
: Generate random NA
values in a two-way table
based on a desired proportion.
remove_cols_na()
: Remove columns with NA
values.
remove_rows_na()
: Remove rows with NA
values.
select_cols_na()
: Select columns with NA
values.
select_rows_na()
: Select rows with NA
values.
replace_na()
Replace missing values
remove_rows_na(.data, verbose = TRUE) remove_cols_na(.data, verbose = TRUE) select_cols_na(.data, verbose = TRUE) select_rows_na(.data, verbose = TRUE) has_na(.data) replace_na(.data, ..., replace = 0) random_na(.data, prop)
.data | A data frame or tibble |
---|---|
verbose | Logical argument. If |
... | Variables to replace |
replace | The value used for replacement. Defaults to |
prop | The proportion (percentage) of |
A data frame with rows or columns with NA
values deleted.
# \donttest{ library(metan) data_with_na <- data_g data_with_na[c(1, 5, 10), c(3:5, 10:15)] <- NA data_with_na#> # A tibble: 39 x 17 #> GEN REP PH EH EP EL ED CL CD CW KW NR #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 H1 1 NA NA NA 15.7 49.9 30.5 16.6 NA NA NA #> 2 H1 2 2.20 1.09 0.492 13.7 49.2 30.5 14.7 22.3 130. 16.4 #> 3 H1 3 2.29 1.15 0.502 15.1 52.6 31.7 16.2 29.6 176. 15.6 #> 4 H10 1 1.79 0.888 0.514 13.9 44.1 26.2 15.0 12.9 116. 14.8 #> 5 H10 2 NA NA NA 13.6 43.9 23.5 14.4 NA NA NA #> 6 H10 3 2.27 1.11 0.491 14.5 43.7 24.6 16.1 12.5 128. 15.2 #> 7 H11 1 1.71 0.808 0.489 15.5 45.2 25.0 16.7 15.2 140. 15.6 #> 8 H11 2 2.09 1.06 0.509 12.2 46.9 26.5 14.3 13.5 114. 16.8 #> 9 H11 3 2.5 1.44 0.577 15.0 49.0 27.5 15.2 19.4 168. 16.4 #> 10 H12 1 NA NA NA 14.4 49.2 28.4 15 NA NA NA #> # ... with 29 more rows, and 5 more variables: NKR <dbl>, CDED <dbl>, #> # PERK <dbl>, TKW <dbl>, NKE <dbl>has_na(data_with_na)#> [1] TRUEremove_cols_na(data_with_na)#> Warning: Column(s) PH, EH, EP, CW, KW, NR, NKR, CDED, PERK with NA values deleted.#> # A tibble: 39 x 8 #> GEN REP EL ED CL CD TKW NKE #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 H1 1 15.7 49.9 30.5 16.6 347. 458. #> 2 H1 2 13.7 49.2 30.5 14.7 337. 386. #> 3 H1 3 15.1 52.6 31.7 16.2 422. 431. #> 4 H10 1 13.9 44.1 26.2 15.0 258. 446. #> 5 H10 2 13.6 43.9 23.5 14.4 233. 496. #> 6 H10 3 14.5 43.7 24.6 16.1 251. 524. #> 7 H11 1 15.5 45.2 25.0 16.7 264. 535. #> 8 H11 2 12.2 46.9 26.5 14.3 288. 397 #> 9 H11 3 15.0 49.0 27.5 15.2 315. 532. #> 10 H12 1 14.4 49.2 28.4 15 291. 525. #> # ... with 29 more rowsremove_rows_na(data_with_na)#> Warning: Row(s) 1, 5, 10 with NA values deleted.#> # A tibble: 36 x 17 #> GEN REP PH EH EP EL ED CL CD CW KW NR NKR #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 H1 2 2.20 1.09 0.492 13.7 49.2 30.5 14.7 22.3 130. 16.4 24.8 #> 2 H1 3 2.29 1.15 0.502 15.1 52.6 31.7 16.2 29.6 176. 15.6 29.2 #> 3 H10 1 1.79 0.888 0.514 13.9 44.1 26.2 15.0 12.9 116. 14.8 33 #> 4 H10 3 2.27 1.11 0.491 14.5 43.7 24.6 16.1 12.5 128. 15.2 34.6 #> 5 H11 1 1.71 0.808 0.489 15.5 45.2 25.0 16.7 15.2 140. 15.6 36 #> 6 H11 2 2.09 1.06 0.509 12.2 46.9 26.5 14.3 13.5 114. 16.8 26.2 #> 7 H11 3 2.5 1.44 0.577 15.0 49.0 27.5 15.2 19.4 168. 16.4 35 #> 8 H12 2 2.77 1.58 0.572 13.8 46.5 23.8 14.6 16.3 153. 17.6 31.4 #> 9 H12 3 2.00 0.782 0.386 13.7 47.5 25.3 14.3 18.9 139. 14.8 25.4 #> 10 H13 1 2.52 1.09 0.434 16.1 51.7 28.2 16.6 23.9 199. 18 30.8 #> # ... with 26 more rows, and 4 more variables: CDED <dbl>, PERK <dbl>, #> # TKW <dbl>, NKE <dbl>select_cols_na(data_with_na)#> Warning: Column(s) with NAs: PH, EH, EP, CW, KW, NR, NKR, CDED, PERK#> # A tibble: 39 x 9 #> PH EH EP CW KW NR NKR CDED PERK #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 NA NA NA NA NA NA NA NA NA #> 2 2.20 1.09 0.492 22.3 130. 16.4 24.8 0.619 85.2 #> 3 2.29 1.15 0.502 29.6 176. 15.6 29.2 0.603 85.9 #> 4 1.79 0.888 0.514 12.9 116. 14.8 33 0.596 89.8 #> 5 NA NA NA NA NA NA NA NA NA #> 6 2.27 1.11 0.491 12.5 128. 15.2 34.6 0.566 90.7 #> 7 1.71 0.808 0.489 15.2 140. 15.6 36 0.552 90.3 #> 8 2.09 1.06 0.509 13.5 114. 16.8 26.2 0.566 89.3 #> 9 2.5 1.44 0.577 19.4 168. 16.4 35 0.562 89.6 #> 10 NA NA NA NA NA NA NA NA NA #> # ... with 29 more rowsselect_rows_na(data_with_na)#> Warning: Rows(s) with NAs: 1, 5, 10#> # A tibble: 3 x 17 #> GEN REP PH EH EP EL ED CL CD CW KW NR NKR #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 H1 1 NA NA NA 15.7 49.9 30.5 16.6 NA NA NA NA #> 2 H10 2 NA NA NA 13.6 43.9 23.5 14.4 NA NA NA NA #> 3 H12 1 NA NA NA 14.4 49.2 28.4 15 NA NA NA NA #> # ... with 4 more variables: CDED <dbl>, PERK <dbl>, TKW <dbl>, NKE <dbl>replace_na(data_with_na)#> # A tibble: 39 x 17 #> GEN REP PH EH EP EL ED CL CD CW KW NR NKR #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 H1 1 0 0 0 15.7 49.9 30.5 16.6 0 0 0 0 #> 2 H1 2 2.20 1.09 0.492 13.7 49.2 30.5 14.7 22.3 130. 16.4 24.8 #> 3 H1 3 2.29 1.15 0.502 15.1 52.6 31.7 16.2 29.6 176. 15.6 29.2 #> 4 H10 1 1.79 0.888 0.514 13.9 44.1 26.2 15.0 12.9 116. 14.8 33 #> 5 H10 2 0 0 0 13.6 43.9 23.5 14.4 0 0 0 0 #> 6 H10 3 2.27 1.11 0.491 14.5 43.7 24.6 16.1 12.5 128. 15.2 34.6 #> 7 H11 1 1.71 0.808 0.489 15.5 45.2 25.0 16.7 15.2 140. 15.6 36 #> 8 H11 2 2.09 1.06 0.509 12.2 46.9 26.5 14.3 13.5 114. 16.8 26.2 #> 9 H11 3 2.5 1.44 0.577 15.0 49.0 27.5 15.2 19.4 168. 16.4 35 #> 10 H12 1 0 0 0 14.4 49.2 28.4 15 0 0 0 0 #> # ... with 29 more rows, and 4 more variables: CDED <dbl>, PERK <dbl>, #> # TKW <dbl>, NKE <dbl># }