This course will be delivered live but will also have all sessions recorded allowing you to take the course in your own time should you choose.
Course Overview:
In this course, we provide a comprehensive practical introduction to data wrangling and data visualization using R. In the coverage of data wrangling, we will cover tools provided by R’s tidyverse, including dplyr, tidyr, purrr, etc. We will cover how to read data of different types into R using readr and related packages, and then cover in detail all the dplyr tools such as select, filter, mutate, summarize, etc. 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 will also how to reshape data using pivots, and how to merge data sets using merge operations. For the topic of visualization, we provide a comprehensive introduction to data visualization in R using ggplot. We begin by covering the major types of plots for visualizing distributions of univariate data: histograms, density plots, barplots, and Tukey boxplots. In all of these cases, we will consider how to visualize multiple distributions simultaneously on the same plot using different colours and “facet” plots. We then turn to the visualization of bivariate data using scatterplots. Here, we will explore how to apply linear and nonlinear smoothing functions to the data, how to add marginal histograms to the scatterplot, add labels to points, and scale each point by the value of a third variable.
Email oliverhooker@prstatistics.com with any questions
ONLINE COURSE – Introduction to Data Wrangling and Data Visualization using R (DWDV01)
https://www.prstatistics.com/course/introduction-to-data-wrangling-and-data-visualization-using-r-dwdv01/
This course will be delivered live but will also have all sessions recorded allowing you to take the course in your own time should you choose.
Course Overview:
In this course, we provide a comprehensive practical introduction to data wrangling and data visualization using R. In the coverage of data wrangling, we will cover tools provided by R’s tidyverse, including dplyr, tidyr, purrr, etc. We will cover how to read data of different types into R using readr and related packages, and then cover in detail all the dplyr tools such as select, filter, mutate, summarize, etc. 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 will also how to reshape data using pivots, and how to merge data sets using merge operations. For the topic of visualization, we provide a comprehensive introduction to data visualization in R using ggplot. We begin by covering the major types of plots for visualizing distributions of univariate data: histograms, density plots, barplots, and Tukey boxplots. In all of these cases, we will consider how to visualize multiple distributions simultaneously on the same plot using different colours and “facet” plots. We then turn to the visualization of bivariate data using scatterplots. Here, we will explore how to apply linear and nonlinear smoothing functions to the data, how to add marginal histograms to the scatterplot, add labels to points, and scale each point by the value of a third variable.
Email oliverhooker@prstatistics.com with any questions