Releases: cwickham/r_intro_bc_stats
Version delivered at BC Stats July 2018
Overview
This course will give you a feel for the complete data analysis process in R - from importing and manipulating data through visualization. You'll see how using code to capture the analysis pipeline leads to deliverables that are documented, easily reproduced and easily automated.
We'll focus on tools in the tidyverse
a core set of R packages that are designed to be easy to learn, easy to use, and solve the most frequent data analysis problems.
During the course, we'll alternate between me introducing a new concept with some examples, and you applying that concept on your own. You should expect to spend at least 50% of your time writing code in RStudio on your own laptop.
The first half day is specifically for those who are new to R. Take a look at the prerequisites to see if you might be able to skip it.
Schedule
Session | Date/Time | Topic |
---|---|---|
Day 1: afternoon | Tue Jul 24th 1pm-4:30pm | Getting Started with R and RStudio |
Day 2: morning | Wed Jul 25th 8:30am-12pm |
Data Visualization with ggplot2
|
Day 2: afternoon | Wed Jul 25th 1pm-4:30pm |
Data Manipulation with dplyr and tidyr
|
Day 1 - Getting Started with R and RStudio
On your first afternoon you'll focus on getting comfortable writing code and executing it in RStudio. We'll take things slow as you learn to navigate RStudio, learn some syntax rules, and how to get help when you get stuck. Along the way you'll meet R's most ubiquitous objects for holding data and learn to import data whether it is a CSV, SPSS or Excel data file.
By the end of the day you will be able to:
- Open a notebook in RStudio and execute the code chunks in it
- Install and load an R package
- Open the help page for a function or built-in dataset
- Identify the components of an R function: the function name and arguments
- Assign the results of a function to a new variable
- Get an overview of a dataset that is in a data frame or tibble
- Import CSV, SPSS and SAS data files
Day 2 - Visualization and Manipulation of Data
We'll start the day with visualization of data in R using the package ggplot2
. You'll see how ggplot2
provides a framework for thinking about plots, which means you only need to learn one template to make almost any plot you can imagine. To practice, you'll make some of the most common kinds of data visualizations: histograms, scatterplots and time series plots, and continue building your skills as we continue through data manipulation.
In the afternoon we'll focus of the most common types of data manipulation: extracting subsets from data, adding new variables and creating grouped summaries. You'll find that doing this is quite intuitive using the dplyr
package which boils down manipulation into a set of verbs like: filter()
, mutate()
and summarise()
. Occasionally, data won't come in quite the right shape for manipulation or visualization you want to do, so we'll also talk about the key parts of the tidyr
package that help to reshape not not-so-tidy data.
By the end of the day you will be able to:
-
Create plots in
ggplot2
to explore data - Select variables and filter observations to subset data
- Add new variables, and transform variables
- Create grouped summaries of data
- Reshape data for use with tidy tools
Prerequisites
The first half-day is specifically for people that are new to R. You can safely join us starting on day 2 if you already:
- know how to define variables in R
-
have called a few basic functions (e.g.
mean()
), and - know how to open .R script files, and run code in the console
Introduction to R given in December 2017 @ BC Stats
Version of course delivered December 2017.
Overview
This course will give you a feel for the complete data analysis process
in R - from importing and manipulating data through visualization and
modelling, and finally communicating results. You'll see how using code
to capture the analysis pipeline leads to deliverables that are
documented, easily reproduced and easily automated.
We'll focus on tools in the tidyverse
a
core set of R packages that are designed to be easy to learn, easy to
use, and solve the most frequent data analysis problems.
During the course, we'll alternate between me introducing a new concept
with some examples, and you applying that concept on your own. You
should expect to spend at least 50% of your time writing code in RStudio
on your own laptop.
The first half day is specifically for those who are new to R. Take a
look at the prerequisites to see if you might be able
to skip it.
Schedule
Session | Date/Time | Topic |
---|---|---|
Day 1: afternoon | Tue Dec 12th 1pm-4pm | Getting Started with R and RStudio |
Day 2: morning | Wed Dec 13th 9am-12pm | Data Visualization with ggplot2 |
Day 2: afternoon | Wed Dec 13th 1pm-4pm | Data Manipulation with dplyr and tidyr |
Day 3: morning | Thu Dec 14th 9am-12pm | Reporting with Rmarkdown |
Day 3: afternoon | Thu Dec 14th 1pm-4pm | Workflow: list columns and iteration |