On 28th November OxTalks will move to the new Halo platform and will become 'Oxford Events' (full details are available on the Staff Gateway).
There will be an OxTalks freeze beginning on Friday 14th November. This means you will need to publish any of your known events to OxTalks by then as there will be no facility to publish or edit events in that fortnight. During the freeze, all events will be migrated to the new Oxford Events site. It will still be possible to view events on OxTalks during this time.
If you have any questions, 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
