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#' Get Row Means
#'
#' Does what \code{rowMeans()} does but without having to cbind the variables. Makes it easier to use
#' with the tidyverse
#'
#' @param ... the variables (unquoted) to be included in the row means
#' @param na.rm should the missing values be ignored? default is FALSE
#'
#' @return the row means
#'
#' @examples
#'
#' \dontrun{
#'
#' library(furniture)
#' library(tidyverse)
#'
#' data <- data.frame(
#' x = sample(c(1,2,3,4), 100, replace=TRUE),
#' y = rnorm(100),
#' z = rnorm(100)
#' )
#'
#' data2 <- data %>%
#' mutate(y_z_mean = rowmeans(y, z))
#' data2 <- data %>%
#' mutate(y_z_mean = rowmeans(y, z, na.rm=TRUE))
#'
#' }
#'
#' @export
rowmeans = function(..., na.rm=FALSE){
rowMeans(cbind(...), na.rm = na.rm)
}
#' Get Row Means With N Missing Values Per Row
#'
#' Does what \code{furniture::rowmeans()} does while allowing a certain number (\code{n}) to have missing values.
#'
#' @param ... the variables (unquoted) to be included in the row means
#' @param n the number of values without missingness required to get the row mean
#'
#' @return the row means
#'
#' @examples
#'
#' \dontrun{
#'
#' library(furniture)
#' library(dplyr)
#'
#' data <- data.frame(
#' x = sample(c(1,2,3,4), 100, replace=TRUE),
#' y = rnorm(100),
#' z = rnorm(100)
#' )
#'
#' data2 <- mutate(data, x_y_z_mean = rowmeans.n(x, y, z, n = 2))
#'
#' }
#'
#' @export
rowmeans.n <- function(..., n){
ifelse(rowmeans(is.na(cbind(...)) <= n),
rowmeans(..., na.rm = TRUE),
NA)
}
#' Get Row Sums
#'
#' Does what \code{rowSums()} does but without having to cbind the variables. Makes it easier to use
#' with the tidyverse
#'
#' @param ... the variables to be included in the row sums
#' @param na.rm should the missing values be ignored? default is FALSE
#'
#' @return the row sums
#'
#'
#' @examples
#'
#' \dontrun{
#'
#' library(furniture)
#' library(tidyverse)
#'
#' data <- data.frame(
#' x = sample(c(1,2,3,4), 100, replace=TRUE),
#' y = rnorm(100),
#' z = rnorm(100)
#' )
#'
#' data2 <- data %>%
#' mutate(y_z_sum = rowsums(y, z))
#' data2 <- data %>%
#' mutate(y_z_sum = rowsums(y, z, na.rm=TRUE))
#'
#' }
#'
#'
#' @export
rowsums = function(..., na.rm=FALSE){
rowSums(cbind(...), na.rm = na.rm)
}
#' Get Row Sums With N Missing Values Per Row
#'
#' Does what \code{furniture::rowsums()} does while allowing a certain number (\code{n}) to have missing values.
#'
#' @param ... the variables (unquoted) to be included in the row means
#' @param n the number of values without missingness required to get the row mean
#'
#' @return the row sums
#'
#' @examples
#'
#' \dontrun{
#'
#' library(furniture)
#' library(dplyr)
#'
#' data <- data.frame(
#' x = sample(c(1,2,3,4), 100, replace=TRUE),
#' y = rnorm(100),
#' z = rnorm(100)
#' )
#'
#' data2 <- mutate(data, x_y_z_mean = rowsums.n(x, y, z, n = 2))
#'
#' }
#'
#' @export
rowsums.n <- function(..., n){
ifelse(rowsums(is.na(cbind(...)) <= n),
rowsums(..., na.rm = TRUE),
NA)
}
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