In kmcconeghy/mdsr: A Suite of Functions for Working with Nursing Home Minimum Data Set Assessments.

knitr::opts_chunk$set(collapse = T, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)
library(mdsR)
library(tidyverse)

Introduction

A primary purpose of this package is to check raw MDS data, format and prepare it for a 'tidy' analytical dataset.

The following sections demonstrate examples:

Test that mds data.frame is canonical

For the package to work it must:

1) Be a 'data.frame', 'tbl' class object.

2) Have named variables consistent with mdsR internal data dictionary

Calling mdsR internal data

Most functions will call an internal dictionary, when checking whether variables are properly formatted etc. The guides mdsR to what kind of a variable a column is, what to label it, whether it is a common mds ResDAC item or something Brown generates for its own server purposes etc. It only works if the data frame is set-up properly.

Test non-canon file

not_mds_dta <- cars
mds_as_canon(not_mds_dta)

Test canon file

mds_dta <- mdsR::mds_dta  
mds_dta <- mds_as_canon(mds_dta)

quick check

mds_is_canon(mds_dta)

Check missingness

mds_flagmiss(mds_dta, .table=F)

Report table of missingness

mds_flagmiss(mds_dta, .cutoff=0, .table=T)

Check variables for formatting

Brown specialty variables

kmcconeghy/mdsr documentation built on May 3, 2019, 1:47 p.m.