![[Stable]](figures/lifecycle-stable.svg)
NAs and zeros can increase the noise in multi-environment trial analysis. This collection of functions will make it easier to deal with them.
fill_na(): FillsNAin selected columns using the next or previous entry.has_na(), has_zero(): Check forNAsand0sin the data and return a logical value.prop_na()returns the proportion ofNAsin each column of a data frame.random_na(): Generate randomNAvalues in a two-way table based on a desired proportion.remove_cols_na(),remove_rows_na(): Remove columns and rows that contains at least oneNAvalue.remove_cols_all_na(),remove_rows_all_na(): Remove columns and rows where all values areNAs.remove_cols_zero(),remove_rows_zero(): Remove columns and rows that contains at least one0value, respectively.select_cols_na(), select_cols_zero(): Select columns withNAsand0s, respectively.select_rows_na(), select_rows_zero(): Select rows withNAsand0s, respectively.replace_na(), replace_zero(): ReplaceNAsand0s, respectively, with areplacementvalue.
Usage
fill_na(.data, ..., direction = "down")
has_na(.data)
prop_na(.data, ...)
remove_rows_na(.data, verbose = TRUE)
remove_rows_all_na(.data, verbose = TRUE)
remove_cols_na(.data, verbose = TRUE)
remove_cols_all_na(.data, verbose = TRUE)
select_cols_na(.data, verbose = TRUE)
select_rows_na(.data, verbose = TRUE)
replace_na(.data, ..., replacement = 0)
random_na(.data, prop)
has_zero(.data)
remove_rows_zero(.data, verbose = TRUE)
remove_cols_zero(.data, verbose = TRUE)
select_cols_zero(.data, verbose = TRUE)
select_rows_zero(.data, verbose = TRUE)
replace_zero(.data, ..., replacement = NA)Arguments
- .data
A data frame.
- ...
Variables to fill
NAsinfill_na(), replaceNAsinreplace_na()or zeros inreplace_zero(). If...is null then all variables in.datawill be evaluated. It must be a single variable name or a comma-separated list of unquoted variables names. Select helpers are also allowed.- direction
Direction in which to fill missing values. Currently either "down" (the default), "up", "downup" (i.e. first down and then up) or "updown" (first up and then down).
- verbose
Logical argument. If
TRUE(default) shows in console the rows or columns deleted.- replacement
The value used for replacement. Defaults to
0. Other possible values are Use"colmean","colmin", and"colmax"to replace missing values with column mean, minimum and maximum values, respectively.- prop
The proportion (percentage) of
NAvalues to generate in.data.
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
data_naz <- iris %>%
group_by(Species) %>%
doo(~head(., n = 3)) %>%
as_character(Species)
data_naz
#> # A tibble: 9 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.1 3.5 1.4 0.2
#> 2 setosa 4.9 3 1.4 0.2
#> 3 setosa 4.7 3.2 1.3 0.2
#> 4 versicolor 7 3.2 4.7 1.4
#> 5 versicolor 6.4 3.2 4.5 1.5
#> 6 versicolor 6.9 3.1 4.9 1.5
#> 7 virginica 6.3 3.3 6 2.5
#> 8 virginica 5.8 2.7 5.1 1.9
#> 9 virginica 7.1 3 5.9 2.1
data_naz[c(2:3, 6, 8), c(1:2, 4, 5)] <- NA
data_naz[c(2, 7, 9), c(2, 3, 4)] <- 0
has_na(data_naz)
#> [1] TRUE
has_zero(data_naz)
#> [1] TRUE
# Fill NA values of column GEN
fill_na(data_naz, Species)
#> # A tibble: 9 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.1 3.5 1.4 0.2
#> 2 setosa 0 0 0 NA
#> 3 setosa NA 3.2 NA NA
#> 4 versicolor 7 3.2 4.7 1.4
#> 5 versicolor 6.4 3.2 4.5 1.5
#> 6 versicolor NA 3.1 NA NA
#> 7 virginica 0 0 0 2.5
#> 8 virginica NA 2.7 NA NA
#> 9 virginica 0 0 0 2.1
# Remove columns
remove_cols_na(data_naz)
#> Warning: Column(s) Species, Sepal.Length, Petal.Length, Petal.Width with NA values deleted.
#> # A tibble: 9 × 1
#> Sepal.Width
#> <dbl>
#> 1 3.5
#> 2 0
#> 3 3.2
#> 4 3.2
#> 5 3.2
#> 6 3.1
#> 7 0
#> 8 2.7
#> 9 0
remove_cols_zero(data_naz)
#> Warning: Column(s) Sepal.Length, Sepal.Width, Petal.Length with 0s deleted.
#> # A tibble: 9 × 2
#> Species Petal.Width
#> <chr> <dbl>
#> 1 setosa 0.2
#> 2 NA NA
#> 3 NA NA
#> 4 versicolor 1.4
#> 5 versicolor 1.5
#> 6 NA NA
#> 7 virginica 2.5
#> 8 NA NA
#> 9 virginica 2.1
remove_rows_na(data_naz)
#> Warning: Row(s) 2, 3, 6, 8 with NA values deleted.
#> # A tibble: 5 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.1 3.5 1.4 0.2
#> 2 versicolor 7 3.2 4.7 1.4
#> 3 versicolor 6.4 3.2 4.5 1.5
#> 4 virginica 0 0 0 2.5
#> 5 virginica 0 0 0 2.1
remove_rows_zero(data_naz)
#> Warning: Row(s) 2, 7, 9 with 0s deleted.
#> # A tibble: 6 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.1 3.5 1.4 0.2
#> 2 NA NA 3.2 NA NA
#> 3 versicolor 7 3.2 4.7 1.4
#> 4 versicolor 6.4 3.2 4.5 1.5
#> 5 NA NA 3.1 NA NA
#> 6 NA NA 2.7 NA NA
# Select columns
select_cols_na(data_naz)
#> Warning: Column(s) with NAs: Species, Sepal.Length, Petal.Length, Petal.Width
#> # A tibble: 9 × 4
#> Species Sepal.Length Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl>
#> 1 setosa 5.1 1.4 0.2
#> 2 NA 0 0 NA
#> 3 NA NA NA NA
#> 4 versicolor 7 4.7 1.4
#> 5 versicolor 6.4 4.5 1.5
#> 6 NA NA NA NA
#> 7 virginica 0 0 2.5
#> 8 NA NA NA NA
#> 9 virginica 0 0 2.1
select_cols_zero(data_naz)
#> Warning: Column(s) with 0s: Sepal.Length, Sepal.Width, Petal.Length
#> # A tibble: 9 × 3
#> Sepal.Length Sepal.Width Petal.Length
#> <dbl> <dbl> <dbl>
#> 1 5.1 3.5 1.4
#> 2 0 0 0
#> 3 NA 3.2 NA
#> 4 7 3.2 4.7
#> 5 6.4 3.2 4.5
#> 6 NA 3.1 NA
#> 7 0 0 0
#> 8 NA 2.7 NA
#> 9 0 0 0
select_rows_na(data_naz)
#> Warning: Rows(s) with NAs: 2, 3, 6, 8
#> # A tibble: 4 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 NA 0 0 0 NA
#> 2 NA NA 3.2 NA NA
#> 3 NA NA 3.1 NA NA
#> 4 NA NA 2.7 NA NA
select_rows_zero(data_naz)
#> Warning: Rows(s) with 0s: 1, 2, 3
#> # A tibble: 3 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 NA 0 0 0 NA
#> 2 virginica 0 0 0 2.5
#> 3 virginica 0 0 0 2.1
# Replace values
replace_na(data_naz)
#> # A tibble: 9 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.1 3.5 1.4 0.2
#> 2 0 0 0 0 0
#> 3 0 0 3.2 0 0
#> 4 versicolor 7 3.2 4.7 1.4
#> 5 versicolor 6.4 3.2 4.5 1.5
#> 6 0 0 3.1 0 0
#> 7 virginica 0 0 0 2.5
#> 8 0 0 2.7 0 0
#> 9 virginica 0 0 0 2.1
replace_zero(data_naz)
#> # A tibble: 9 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.1 3.5 1.4 0.2
#> 2 NA NA NA NA NA
#> 3 NA NA 3.2 NA NA
#> 4 versicolor 7 3.2 4.7 1.4
#> 5 versicolor 6.4 3.2 4.5 1.5
#> 6 NA NA 3.1 NA NA
#> 7 virginica NA NA NA 2.5
#> 8 NA NA 2.7 NA NA
#> 9 virginica NA NA NA 2.1
# }