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data-workshop

This repository contains information and all required materials for the short workshop "Data Wrangling and Visualisation Using R", delivered at the TRiSS Social Science Methods Series

Data Wrangling and Visualisation Using R

This repository contains information and all required materials for the postgraduate workshop “Data Wrangling and Visualisation Using R”.

Date: 4 December 2017, 13-15pm

Place: TRiSS Seminar Room, 6th Floor of the Arts Building, Trinity College Dublin

Slides can be found here.

The workshop is part of the new TRiSS Social Science Methods Series organised by and for Trinity Research in Social Science (TRiSS) researchers. I will update and extend this repository continuously. If you have any questions about the event, please let me know.

Workshop Description

In most quantitative research, data analysis constitutes only the last part of a project. Prior to analysing data, often we need to reshape, merge, filter, recode, or rearrange our data. Moreover, reproducible data visualisation is an increasingly important and necessary tool for researchers in social science disciplines. The workshop introduces the R packages dplyr for data wrangling and the powerful ggplot2 package for data visualisation. After a general introduction to the “tidy data” approach, participants will apply the five most important functions for data transformation, and get to know the “grammar of graphics” of the ggplot2 package.

While the course is designed for those who have used R in some form previously, expertise in R is not required, and even those with no previous knowledge of R are welcome. You can view (preliminary) slides for the course here.

Prerequisites

I recommend to install the latest version of R and RStudio on your fully charged laptop. Both R and RStudio are free and open-source.

Introductions

Good introductory tutorials are the R Code School and (once you have R installed) the swirl package. In this short workshop, we will focus on the dplyr and ggplot2 packages, both are part of the “tidyverse”. For a comprehensive introduction to the tidyverse have a look at R for Data Science.

Registration

Please register for this workshop here.