utils_na_zero function

Utilities for handling with NA and zero values

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(): Fills NA in selected columns using the next or previous entry.

• has_na(), has_zero(): Check for NAs and 0s in the data and return a logical value.

• prop_na() returns the proportion of NAs in each column of a data frame.

• random_na(): Generate random NA values in a two-way table based on a desired proportion.

• remove_cols_na(), remove_rows_na(): Remove columns and rows that contains at least one NA value.

• remove_cols_all_na(), remove_rows_all_na(): Remove columns and rows where all values are NAs.

• remove_cols_zero(), remove_rows_zero(): Remove columns and rows that contains at least one 0 value, respectively.

• select_cols_na(), select_cols_zero(): Select columns with NAs

  and 0s, respectively.

• select_rows_na(), select_rows_zero(): Select rows with NAs

  and 0s, respectively.

• replace_na(), replace_zero(): Replace NAs and 0s, respectively, with a replacement value.

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 NAs in fill_na(), replace NAs in replace_na() or zeros in replace_zero(). If ... is null then all variables in .data will 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 NA values to generate in .data.

Returns

A data frame with rows or columns with NA values deleted.

Examples

library(metan)
data_naz <- iris %>%
              group_by(Species) %>%
              doo(~head(., n = 3)) %>%
              as_character(Species)
data_naz
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)
has_zero(data_naz)

# Fill NA values of column GEN
fill_na(data_naz, Species)

# Remove columns
remove_cols_na(data_naz)
remove_cols_zero(data_naz)
remove_rows_na(data_naz)
remove_rows_zero(data_naz)

# Select columns
select_cols_na(data_naz)
select_cols_zero(data_naz)
select_rows_na(data_naz)
select_rows_zero(data_naz)

# Replace values
replace_na(data_naz)
replace_zero(data_naz)

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com