From 7f98dc7749ae7ab7932e0962139d097ac5aea716 Mon Sep 17 00:00:00 2001 From: James McMahon Date: Tue, 12 Dec 2023 15:13:18 +0000 Subject: [PATCH] Update README.Rmd (#64) Co-authored-by: Jennit07 <67372904+Jennit07@users.noreply.github.com> --- README.Rmd | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/README.Rmd b/README.Rmd index 82fc95c..d6dc4a8 100644 --- a/README.Rmd +++ b/README.Rmd @@ -23,13 +23,19 @@ knitr::opts_chunk$set( # slfhelper -The goal of slfhelper is to provide some easy-to-use functions that make working with the Source Linkage Files as painless and efficient as possible. +The goal of slfhelper is to provide some easy-to-use functions that make working with the Source Linkage Files as painless and efficient as possible. It is only intended for use by PHS employees and will only work on the PHS R infrastructure. ## Installation -The preferred method of installation is to use the [{`pak`} package](https://pak.r-lib.org/), which does an excellent job of handling the errors which sometimes occur. +The simplest way to install to the PHS Posit Workbench environment is to use the [PHS Package Manager](https://ppm.publichealthscotland.org/client/#/repos/3/packages/slfhelper), this will be the default setting and means you can install `slfhelper` as you would any other package. -```{r package_install} +``` {r package_install_ppm} +install.packages("slfhelper") +``` + +If this doesn't work you can install it directly from GitHub, there are a number of ways to do this, we recommend the [{`pak`} package](https://pak.r-lib.org/). + +```{r package_install_github} # Install pak (if needed) install.packages("pak") @@ -41,9 +47,9 @@ pak::pak("Public-Health-Scotland/slfhelper") ### Read a file -**Note:** Reading a full file is quite slow and will use a lot of memory, we would always recommend doing a column selection to only keep the variables that you need for your analysis. Just doing this will dramatically speed up the read-time. +**Note:** Reading a full file is quite slow and will use a lot of memory, we would always recommend doing a column selection to only keep the variables that you need for your analysis. Just doing this will dramatically speed up the read time. -We provide some data snippets to help with the column selection and filtering. +We provide some data snippets to help with column selection and filtering. ```{r helper_data} library(slfhelper) @@ -99,11 +105,11 @@ ep_1718 <- read_slf_episode(c("1718", "1819", "1920"), ) %>% get_chi() -# Change chi numbers from data above back to anon_chi +# Change chi numbers from the data above back to anon_chi ep_1718_anon <- ep_1718 %>% get_anon_chi(chi_var = "chi") -# Add anon_chi to cohort sample +# Add anon_chi to the cohort sample chi_cohort <- chi_cohort %>% get_anon_chi(chi_var = "upi_number") ```