BDI R Code Clinic – 10 June 11am – Microsoft Teams (a link will be distributed prior to the session)

To register, please visit: oxford.onlinesurveys.ac.uk/bdi-r-code-clinic-10-june

Handling data frames the data.table way
Large datasets in R can take up a lot of memory, making some analyses very slow. The data.table package has some advantages in handling such data, but the syntax could be daunting for beginners or those used to dplyr. In this code clinic, we will run through some typical data analysis steps using the data.table package and occasionally compare with the dplyr / tidyverse syntax to figure out equivalent ways of doing the same thing.

Level of experience required: beginner to intermediate

Summary of the content: – The data.table philosophy – How data.table differs from base R & how it differs from dplyr – Run through with example dataset – loading data into R – subsetting and filtering – grouping and summarising – sorting – joining – reshaping – chaining several operations

Required packages – all available on CRAN, so installable with install.packages(): – data.table – dplyr – babynames – ggplot2