collapse (version 1.7.6)

recode-replace: Recode and Replace Values in Matrix-Like Objects

Description

A small suite of functions to efficiently perform common recoding and replacing tasks in matrix-like objects (vectors, matrices, arrays, data frames, lists of atomic objects):

• recode_num and recode_char can be used to efficiently recode multiple numeric or character values, respectively. The syntax is inspired by dplyr::recode, but the functionality is enhanced in the following respects: (1) they are faster than dplyr::recode, (2) when passed a data frame / list, all appropriately typed columns will be recoded. (3) They preserve the attributes of the data object and of columns in a data frame / list, and (4) recode_char also supports regular expression matching using grepl.

• replace_NA efficiently replaces NA/NaN with a value (default is 0L). data can be multi-typed, in which case appropriate columns can be selected through the cols argument. For numeric data a more versatile alternative is provided by data.table::nafill and data.table::setnafill.

• replace_Inf replaces Inf/-Inf (or optionally NaN/Inf/-Inf) with a value (default is NA). replace_Inf skips non-numeric columns in a data frame.

• replace_outliers replaces values falling outside a 1- or 2-sided numeric threshold or outside a certain number of standard deviations with a value (default is NA). replace_outliers skips non-numeric columns in a data frame.

Usage

recode_num(X, …, default = NULL, missing = NULL, set = FALSE)
recode_char(X, …, default = NULL, missing = NULL, regex = FALSE,
            ignore.case = FALSE, fixed = FALSE, set = FALSE)
replace_NA(X, value = 0L, cols = NULL, set = FALSE)
replace_Inf(X, value = NA, replace.nan = FALSE)
replace_outliers(X, limits, value = NA,
                 single.limit = c("SDs", "min", "max", "overall_SDs"))

Arguments

X

a vector, matrix, array, data frame or list of atomic objects.

…

comma-separated recode arguments of the form: value = replacement, `2` = 0, Secondary = "SEC" etc.. recode_char with regex = TRUE also supports regular expressions i.e. `^S|D$` = "STD" etc.

default

optional argument to specify a scalar value to replace non-matched elements with.

missing

optional argument to specify a scalar value to replace missing elements with. Note that to increase efficiency this is done before the rest of the recoding i.e. the recoding is performed on data where missing values are filled!

set

logical. TRUE does (some) replacements by reference (i.e. in-place modification of the data). For replace_NA this feature is mature, and the result will be returned invisibly. For recode_num and recode_char, replacement by reference is still partial, so you need to assign the result to an object to materialize all changes.

regex

logical. If TRUE, all recode-argument names are (sequentially) passed to grepl as a pattern to search X. All matches are replaced. Note that NA's are also matched as strings by grepl.

value

a single (scalar) value to replace matching elements with.

cols

select columns to replace missing values in using a function, column names, indices or logical vector.

replace.nan

logical. TRUE replaces NaN/Inf/-Inf. FALSE (default) replaces only Inf/-Inf.

limits

either a vector of two-numeric values c(minval, maxval) constituting a two-sided outlier threshold, or a single numeric value constituting either a factor of standard deviations (default), or the minimum or maximum of a one-sided outlier threshold. See also single.limit.

single.limit

a character or integer (argument only applies if length(limits) == 1):

• 1 - "SDs" specifies that limits will be interpreted as a (two-sided) threshold in column standard-deviations on standardized data. The underlying code is equivalent to X[abs(fscale(X)) > limits] <- value but faster. Since fscale is S3 generic with methods for grouped_df, pseries and pdata.frame, the standardizing will be grouped if such objects are passed (i.e. the outlier threshold is then measured in within-group standard deviations).

• 2 - "min" specifies that limits will be interpreted as a (one-sided) minimum threshold. The underlying code is equivalent to X[X < limits] <- value.

• 3 - "max" specifies that limits will be interpreted as a (one-sided) maximum threshold. The underlying code is equivalent to X[X > limits] <- value.

• 4 - "overall_SDs" is equivalent to "SDs" but ignores groups when a grouped_df, pseries or pdata.frame is passed (i.e. standardizing and determination of outliers is by the overall column standard deviation).

ignore.case, fixed

logical. Passed to grepl and only applicable if regex = TRUE.

See Also

pad, Efficient Programming, Collapse Overview

Examples

# NOT RUN {
recode_char(c("a","b","c"), a = "b", b = "c")
recode_char(month.name, ber = NA, regex = TRUE)
mtcr <- recode_num(mtcars, `0` = 2, `4` = Inf, `1` = NaN)
replace_Inf(mtcr)
replace_Inf(mtcr, replace.nan = TRUE)
replace_outliers(mtcars, c(2, 100))                 # Replace all values below 2 and above 100 w. NA
replace_outliers(mtcars, 2, single.limit = "min")   # Replace all value smaller than 2 with NA
replace_outliers(mtcars, 100, single.limit = "max") # Replace all value larger than 100 with NA
replace_outliers(mtcars, 2)                         # Replace all values above or below 2 column-
                                                    # standard-deviations from the column-mean w. NA
replace_outliers(fgroup_by(iris, Species), 2)       # Passing a grouped_df, pseries or pdata.frame
                                                    # allows to remove outliers according to
                                                    # in-group standard-deviation. see ?fscale
# }