This course will be largely practical, hands-on, and workshop based. THIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES 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. 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. We then turn to combining and merging data. On Day 2, we cover how to perform descriptive or summary statistics on our data using dplyr’s summarize and group_by functions. 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. On Day 1 of this course, having covered how to read data of different types into R, we cover in detail all the dplyr tools such as select, filter, mutate, etc. 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 consequence for ease and speed with which we analyse data. Done poorly, it can be a time consuming, labourious, and error-prone.
Data wrangling is the art of taking raw and messy data and formating and cleaning it so that data analysis and visualization etc may be performed on it. In particular, we focus on tools provided by R’s tidyverse, including dplyr, tidyr, purrr, etc. A shared link will be deleted if it has been passive for almost 3 months.In this two day course, we provide a comprehensive practical introduction to data wrangling using R. Just click Share Button and it will create a short link, which can be shared through Email, WhatsApp or even through Social Media.
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