jared
82b8db7f84
Gitea Organization/civilyticsR/pipeline/head There was a failure building this commit
379 lines
10 KiB
R
379 lines
10 KiB
R
#' Safe maximum
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#' Take the max but ignore missing values
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#'
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#' @param x a numeric vector which may contain NA values
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#'
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#' @return the maximum non-missing value
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#' @export
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#'
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#' @examples
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#' safe_max(1:10)
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#' safe_max(c(1:10, NA))# 10
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safe_max <- function(x) {
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if (all(is.na(x))) {
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return(NA)
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} else {
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max(x, na.rm = TRUE)
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}
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}
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#' Prettify proportion data
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#' Make data expresed as proportions print out as prettily formatted characters with percentages
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#'
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#' @param x a numeric vector of proportions to convert to percentages
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#' @param ndigit a vector of length 1 indicating the number of digits to display in the percent, by
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#' default it represents the digits after the decimal point in the percentage form
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#'
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#' @return a character vector with a % attached
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#' @export
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#'
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#' @examples
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#' pretty_per(0.2, ndigit = 1)
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#' pretty_per(c(0.2, 0.332423, 0.4, 0.342342), ndigit = 2)
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pretty_per <- function(x, ndigit = 1) {
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if (any(x >= 100) & !all(is.na(x))) {
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message("Values over 100 found, did you mean to use proportions?")
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}
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x <- format(round(x, digits = ndigit + 2) * 100, nsmall = ndigit)
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x <- paste0(x, "%")
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x[x == "NA%"] <- " - "
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x <- trimws(x)
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return(x)
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}
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#' Zero out missing values
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#'
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#' @param x a numeric vector with missing values
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#'
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#' @return a numeric vector with missing values replaced by 0
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#' @export
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#'
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#' @examples
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#' na_zero(1:10)
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#' na_zero(c(NA, NA, 2:10))
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na_zero <- function(x) {
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x[is.na(x)] <- 0
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return(x)
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}
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#' Prettify count data
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#' Make integers propertly formatted as strings which include commas suitable for printing
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#' @param x a numeric vector
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#'
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#' @return a character vector with commas introduced in place positions in the numbers
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#' @export
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#'
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#' @examples
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#' pretty_count(10)
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#' pretty_count(1000)
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#' pretty_count(1e8)
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pretty_count <- function(x) {
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x <- prettyNum(x, big.mark = ",")
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return(x)
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}
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#' Unsuppress data using sampling
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#'
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#' @param x a vector
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#' @param replace_char the character you want to replace in the vector
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#' @param zeros the number of zeroes to oversample when replacing replace_char
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#' @param max_value the numeric maximum value the replacement for the "*" can be
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#' @return a numeric vector with no characters representing suppressed values
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#' @export
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#'
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#' @examples
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#' suppr_data <- c("2", "8", "*", "*", "7", "9", "100")
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#' star_subs(suppr_data, zeros = 1, max_value = 10)
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#' star_subs(suppr_data, zeros = 1, max_value = 200)
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star_subs <- function(x, replace_char = "*",
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zeros = 15, max_value = 20) {
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x[x == "*"] <- sample(c(rep("0", zeros), as.character(0:max_value)),
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sum(x==replace_char), replace = TRUE)
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x <- as.numeric(x)
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return(x)
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}
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#' Recode grade level from character to numeric
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#'
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#' @param x character description of grade levels from NCES style data
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#'
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#' @return a numeric vector
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#' @export
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#'
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#' @examples
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#' grade_level_to_num(c("KG", "Pre-K", "12", "10", "09"))
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grade_level_to_num <- function(x) {
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# Cannot generate new levels if it is a factor so we coerce to character first
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x <- as.character(x)
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x[x %in% c("KG", "Kindergarten")] <- "0"
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x[x %in% c("Pre-K", "Pre-k", "Preschool", "Pre-Kindergarten")] <- "-1"
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x[x %in% c("Adult", "Adult Education")] <- "13"
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y <- as.numeric(x)
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return(y)
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}
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#' Recode NCES race categories to shorter names
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#'
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#' @param x a character vector with NCES race codes, often from Urban Institute
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#'
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#' @return recoded race categories following NCES race codes
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#' @export
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#'
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#' @examples
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#' race_short_names(c("Black", "Hispanic Or Latino", "Two Or More Races"))
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race_short_names <- function(x) {
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x <- as.character(x)
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x[x %in% c("Black", "Black Or African American", "Black or African American",
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"African American")] <- "black"
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x[x %in% c("Hispanic", "Hispanic Or Latino", "Hispanic or Latino")] <-
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"hisp_lat"
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x[x %in% c("White", "white", "White and Not Hispanic")] <- "white"
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x[x %in% c("Asian", "Asian American")] <- "asian"
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x[x %in% c("Two Or More Races", "Two or More Races")] <- "two_or_more"
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x[x %in% c("Native Hawaiian Or Other Pacific Islander",
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"Native Hawaiian or Other Pacific Islander",
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"Native Hawaiian Pacific Islander")] <- "native_haw"
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x[x %in% c("American Indian", "American Indian Or Alaska Native",
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"American Indian or Alaska Native", "American Indian or Native Alaskan")] <- "amind"
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x[x %in% c("Not Reported")] <- "other"
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return(x)
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}
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#' Sum a numeric that contains missing values and ignore missing values
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#'
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#' @param x a numeric vector
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#'
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#' @return the sum, ignoring any missing values
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#' @export
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#'
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#' @examples
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#' x <- c(2, NA, 4, 9)
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#' na_sum(x) # 15
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na_sum <- function(x) {
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stopifnot(is.numeric(x))
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message("Taking a sum with missing values equal to 0, be careful! \u1F601")
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x <- na_zero(x)
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return(sum(x))
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}
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# This function looks up the appropriate postal code for states from the
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# state name.
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# It also substitutes in PR and DC for Puerto Rico and District of Columbia
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# which are not included in the lookup table of states and state abbreviations
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# that comes with R.
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#' Title
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#'
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#' @param x a vector of state names
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## #' @importFrom datasets state.abb state.name
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#' @return state abbreviations matching state naems provided in X
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#' @export
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#'
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#' @examples
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#' postcode_lookup("Montana")
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postcode_lookup <- function(x) {
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modify_name <- c(state.name, "District of Columbia", "Puerto Rico")
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modify_abb <- c(state.abb, "DC", "PR")
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abb <- modify_abb[match(x, modify_name)]
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return(abb)
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}
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#' Truncated matching function
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#'
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#' @param x, the character value to match
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#' @param y, a vector of multiple character values to look for a match in
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#' @param n, an integer, how many matches to return
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#' @importFrom stringdist stringsim
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#'
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#' @return an integer giving the position of the table with the most characters
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trunc_match <- function(x, y, n) {
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out <- y[order(stringdist::stringsim(x, y, method = "lv"),
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decreasing = TRUE)]
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if (length(out) < n) {
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n <- length(out)
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}
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out <- out[1:n]
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return(out)
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}
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#' Compute the outersection of two fectors
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#'
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#' @param x first vector, of any type
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#' @param y second vector, same type as x
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#' @param ... additional vectors to be checked
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#'
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#' @return unique values across all of the vectors
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#' @export
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#'
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#' @examples
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#' # desired result is c(1, 2, 3, 6, 9, 10)
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#' outersect(1:5, 4:8, 7:10)
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outersect <- function(x, y, ...) {
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big.vec <- c(x, y, ...)
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duplicates <- big.vec[duplicated(big.vec)]
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setdiff(big.vec, unique(duplicates))
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}
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# desired result is c(1, 2, 3, 6, 9, 10)
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#outersect(1:5, 4:8, 7:10)
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#[1] 1 2 3 6 9 10
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#' Get the FIPS code for a given state abbreviation
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#'
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#' @param stabbr a two letter abbreviation for a US state
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#'
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#' @return FIPS codes that match the abbreviation
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#' @import tidycensus
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#' @export
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#'
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#' @examples
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#' get_fips("MT")
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#' get_fips("PR")
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#' get_fips("CC")
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get_fips <- function(stabbr) {
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fips <- tidycensus::fips_codes[, 1:2]
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fips <- fips[!duplicated(fips),]
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out <- fips[fips$state == stabbr, 2]
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return(out)
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}
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#' Get the state abbreviation from a given FIPS Code
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#'
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#' @param fips a character value that captures the FIPS code with leading 0
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#'
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#' @return a character value, length 2, with the state abbreviation
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#' @import tidycensus
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#' @export
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#'
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#' @examples
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#' get_stabbr("06")
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get_stabbr <- function(fips) {
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fips_codes <- tidycensus::fips_codes[, 1:2]
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fips_codes <- fips_codes[!duplicated(fips_codes),]
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if (length(fips) != 1) {
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out <- rep(NA, length(fips))
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for (i in length(fips)) {
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out[i] <- fips_codes[fips_codes$state_code == fips, 1]
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}
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return(out)
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} else {
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out <- fips_codes[fips_codes$state_code == fips, 1]
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return(out)
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}
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}
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## Let's calculate the z-score for the gap as well
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# Test statistic needs 4 values
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# Proporation A, Numerator A
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# Proportion B, Numerator B
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#' Calculate a Z-Score for a comparison between two proportions
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#'
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#' @param a_prop the proportion for group a
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#' @param a_count the count of the population in group a
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#' @param b_prop the proportion for group b
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#' @param b_count the count of the population in group b
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#'
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#' @return a numeric z score
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#' @export
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#'
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#' @examples
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#' z_gap_test(0.0002, 1e4, 0.0003, 1e4)
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z_gap_test <- function(a_prop, a_count, b_prop, b_count) {
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num <- (a_prop - b_prop) - 0
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denom_a <- (a_prop * (1-a_prop)) / a_count
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denom_b <- (b_prop * (1-b_prop)) / b_count
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denom <- sqrt(denom_a + denom_b)
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z = num / denom
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#if (is.nan)
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return(z)
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}
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# TODO: Consider vectorizing
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#z_univariate_v <- Vectorize(z_univariate, SIMPLIFY = TRUE)
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#' Add a random jitter to a count variable to mask its true value
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#'
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#' @param x the vector of numerics
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#' @param fac the range of values to add or subtract to perturb the count
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#'
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#' @details The count in the name means that this function enforces a floor of
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#' 0 on values, so values perturbed to have less than 0 will be capped at 0.
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#'
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#' @return a numeric vector
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#' @export
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#'
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#' @examples
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#' perturb_count(20:30, fac = 3)
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perturb_count <- function(x, fac = 3) {
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x <- sapply(x, function(x) x + sample(-fac:fac, 1))
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x[x < 0] <- 0
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return(x)
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}
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#' Add random noise to a variable before rounding
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#'
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#' @param x a numeric we want to round
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#'
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#' @return rounded values
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#' @export
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#' @details Credit to Jens von Bergmann for this algo https://github.com/mountainMath/dotdensity/blob/master/R/dot-density.R
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#' @importFrom stats runif
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#'
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#' @examples
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#' random_round(1.93)
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random_round <- function(x) {
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v = as.integer(x)
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r = x-v
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test = runif(length(r), 0.0, 1.0)
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add = rep(as.integer(0),length(r))
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add[r>test] <- as.integer(1)
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value = v + add
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ifelse(is.na(value) | value<0, 0, value)
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return(value)
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}
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#' Safely take a ratio and do not fail if 0 is in the denominator
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#'
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#' @param num numerator, a numeric
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#' @param denom denominator, a numeric
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#'
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#' @return The proportion, safely calculated with 0.1 substituting for 0
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#' @export
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#'
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#' @examples
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#' safe_ratio(100, 1)
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#' safe_ratio(100, 0)
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safe_ratio <- function(num, denom) {
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denom <- ifelse(denom == 0, 0.1, denom)
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y <- num / denom
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return(y)
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}
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