Course Info
What Will I Learn From This Course?
Use the ggplot2 package to explore distributions and relationships in a dataset.
Use the dplyr and tidyr packages to manipulate data into a format amenable for analysis.
Understand issues related to importing CSV and Excel file formats and how to overcome these issues.
Pre-requisite
A laptop with the RStudio IDE and R (version 3.2 and above) installed is ideal. R’s system requirements are minimal, however we recommend a system with 4GB RAM or more. https://support.rstudio.com/hc/en- us/articles/201853926-What-are-your-system- recommendations-for-the-RStudio-IDE-
We highly recommend that trainees install the tidyverse packages before coming to class with install.packages(“tidyverse”) Trainees may also use RStudio Cloud, an online- hosted instance of R designed for data science training, provided a stable internet connection at the venue can be guaranteed.
Pre-requisite
Course Outline for This Programme
Data frames
– Data types: integer, logical, double, string
– dplyr: filter, arrange, mutate
Data frames are a common representation for tables in R. Learn about the structure of a data frame, acceptable data types for data frame columns and how to filter and arrange rows and modify columns of a data frame using the dplyr package.
Grammar of graphics
– geoms and aesthetics
– facets
– labels
The grammar of graphics defines a consistent vocabulary for declaring visuals that is easily extensible to the creation of complex graphics. The ggplot2 package provides the capability to define visuals using this consistent vocabulary for declaring graphics. Learn to explore distributions and relationships between categorical and quantitative data types using the ggplot2 package.
Grammar of data manipulation
– select
– group_by
– vector functions (log, operators, ifelse(), etc.)
– summary functions (mean, median, etc.)
– variants (distinct, count, etc.)
Building on data frames, this section expands on the grammar for data manipulation defined by the dplyr package.
Tidy data
– pivot_wider, pivot_longer
– unite, separate
Data must be properly formatted for visualization and modelling tasks. Learn about the tidy data framework and how data can be transformed ‘wider’ and ‘longer’ with the tidyr package.
Importing data
– CSV, XLSX
– Use RStudio interface to generate import codes
– Aware of issues with CSV files, fixes
– Aware of spreadsheet organization best practices for working in R
CSV and Excel file formats are commonly encountered in analysis. This section explores how to import data in RStudio and common issues encountered when processing data in these file formats.
Mini project
Students will apply the skills learned in this course to a simple dataset and produce a report in R Markdown.
