OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
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
