The fourth course in our 8 part R-series is Data wrangling using R and Rstudio.
Online course - Data wrangling using R and Rstudio (DWRS03)
21st - 23rd November 2023
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Limited early bird tickets available priced @ £150.00 book before 8th November
Courses are recorded to accommodate different time zones. All attendees will have access to recordings for a further 3 months after the course to revisit any of the classes.
COURSE OVERVIEW - During this course we provide a comprehensive practical introduction to data wrangling using R. In particular, we focus on tools provided by R’s tidyverse, including dplyr, tidyr, purrr, etc. Data wrangling is the art of taking raw and messy data and formatting and cleaning it so that data analysis and visualization etc may be performed on it. Done poorly, it can be time consuming, laborious, and error-prone. Fortunately, the tools provided by R’s tidyverse allow us to do data wrangling in a fast, efficient, and high-level manner, which can have dramatic consequences for ease and speed with which we analyse data. We start with how to read data of different types into R, we then cover in detail all the dplyr tools such as select, filter, mutate, etc. Here, we will also cover the pipe operator (%>%) to create data wrangling pipelines that take raw messy data on the one end and return cleaned tidy data on the other. We then cover how to perform descriptive or summary statistics on our data using dplyr’s summarize and group_by functions. We then turn to combining and merging data. Here, we will consider how to concatenate data frames, including concatenating all data files in a folder, as well as cover the powerful SQL like join operations that allow us to merge information in different data frames. The final topic we will consider is how to “pivot” data from a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider.
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