# R/results.R In AlfonsoRReyes/RepDataPeerAssessment1: Reproducible Research Assignment 1. Developed as a package

#### Documented in get.imp.dfinfo.imp

```#' Gets a summary statistics for a data frame
#'
#' It could be before or after imputation
#'
#' @param x data frame
#' @param uniVar variable to analyze
#' @param colName name of the column in the report table
#' @param decs number of decimals. Default is 3.
#'
#' @return data frame with one column
#'
#' @importFrom stats median sd
#'
#' @export
#'
info.imp <- function(x, uniVar, colName, decs = 3) {
if (anyNA(x[, uniVar])) {
indNA <- which(is.na(x[, uniVar]))             # which rows are NA
indNA.not <- which(!((1:nrow(x)) %in% indNA))  # which rows are not NA
count.NA <- length(indNA)

x <- x[indNA.not, ]    # get the observations only; not NAs

}

tmp <- data.frame(nrows = nrow(x),
median = median(x[, uniVar]),
mean = mean(x[, uniVar]),
sd = sd(x[, uniVar]),
ses = sd(x[, uniVar]) / sqrt(sum(!is.na(x[, uniVar])))
)
tmp <- data.frame(round(t(tmp), decs))
names(tmp) <- colName
return(tmp)

}

#' Get a complete data frame including the imputed values
#'
#' @param x data frame
#' @param mice.imp is an object of mice imputation
#' @param uniVar is the variable to look into
#'
get.imp.df <- function(x, mice.imp, uniVar) {
imp.df <- x
indNA <- which(is.na(x[, uniVar]))            # which rows are NA
indNA.not <- which(!((1:nrow(x)) %in% indNA)) # rows that are not NA

steps.imp <- mice.imp\$imp[uniVar][[1]]   # all imputed values ONLY; no observations

imp.df[indNA, uniVar] <- steps.imp
return(imp.df)

}
```
AlfonsoRReyes/RepDataPeerAssessment1 documentation built on May 5, 2019, 4:53 a.m.