I have been using R for a while to analyse data sets at the University and write up reports from these. My typical workflow is to use Rmarkdown and Knitr to do this.

My basic template for knitr has the following chunk after the YAML header (excluding backticks) that loads up :

{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
libs <- c("tidyr", "dplyr", "readr", "tibble", "knitr", "ggplot2","lubridate","stringi","purrr","RPostgres")
lapply(libs, library, character.only = TRUE)```

Loading data

Typically, one loads up data from files exported from other systems. These are often in CSV format, but R can also handle Excel documents and a wide variety of files. I usually place by input data in a sub-folder data/.

units <- read_csv(paste0('data/','sru-enrolment-counts','.csv'))  #, col_types = "dcccccdcccc")

Rather than loading via CSV, R supports database connections and querying in a similar fashion to other programming languages.

And further, knitre chunks can actually contain SQL code that uses a previously established connection, in this case one via RPostgres, and stores the result in the variable specified.

{sql, connection=con, output.var="units_df"}
SELECT units.id, code,

Manipulating data

I often turn to the Tidyverse for the tools that I use for this step.

Quartiles and Distributions

R has a quantile funtion thatdefaults to quartiles, but can do deciles, percentils and more. See some examples that

  • https://www.statisticshowto.datasciencecentral.com/cumulative-distribution-function/


Another part of the Tidyverse that I frequently utilise is ggplot. Once you begin dealing with complex data you need to think through the visualisation aspects and from Data to Viz is an excellent resource.

I sometimes have multiple factors that require plotting concurrently which creates an issue for colouring. You can adjust the adjust the colour palette and the number of colours available

Bigger data

When dealing with larger data sets, long computation times, or ‘expensive’ operations (such as a database connection and query) it can be useful to cache the results in some fashion. In fact, there is [an online book on Efficient R programming(https://csgillespie.github.io/efficientR/)