2022 UC Carpentries Fall Workshop, R Day 3, Notes and Cheatsheet

plotting and report building with `ggplot2` and `knitr`

Lesson Data

We're using a teaching version of the gapminder dataset. There are several ways to obtain this. Just choose one.

Direct csv download
1. Right-click to save-as onto your computer, or
2. Right-click to copy link to read into R directly from the github-hosted file
  - gapminder <- read.csv("copied-link"), or

Load gapminder package in R
- install.packages("gapminder") (as with all packages, you only need to do this once)
- library("gapminder")

Gapminder Documentation

Original source, gapminder.org
R Package documentation on cran.r-project.org
Many references within r-project.org
R Documentation, powered by DataCamp

Intro - A Quick look at rstudio.cloud

Just an introduction, how to check for installed libraries.

Plotting with `ggplot2`

The first session of Day 3 is Episode 8 from the Software Carpentry R for Reproducible Scientific Analysis Lesson.

`ggplot` Function Basics

Syntax of a Basic Call

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + 
  geom_point()

Putting geom_point() on its own line aids in readability, which is useful for a plot with many layers.

`ggplot` Elements

ggplot() creates a new ggplot.
aes() is how aesthetic mappings are constructed and associated with the data.
- there are arguments for colors, how things are grouped, line size & shapes, positions, etc.
- Aesthetic arguments which are called outside of the aes() function will map to all data points.
geom_() are geometric objects. These are added to the plot in layers, which is why a ggplot call includes + something (geom/stat)
- how are your data displayed? lines, bar charts, heatmaps, contours, polygons, segments, etc
- individual geom_...() calls can include their own aesthetic mappings, both using the aes() function, and directly (remember, aesthetic arguments assigned outside the aes() function will apply to all variables/data points)

Challenge #1

Part A

In the gapminder example we've been using,

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point()

use the column "year" to show how life expectancy has changed over time.

Part B

We’ve been using the aes function to tell the scatterplot geom about the x and y locations of each point. Another aesthetic property we can modify is the point color. Modify the code from the Part A to color the points by the “continent” column. Is it easier to detect trends?

Challenge #1 Solutions

Part A

ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp)) + geom_point()

Part B

ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp, color=continent)) +
   geom_point()

About Layers

ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, color=continent)) +
  geom_line()

add by=:

ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line()

add points:

ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line() + geom_point()

move color mapping:

ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country)) +
  geom_line(mapping = aes(color=continent)) + geom_point()

Challenge #2 (2-minute challenge)

Using the previous example:

ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country)) +
 geom_line(mapping = aes(color=continent)) + geom_point()

Switch the order of the point and line layers from the previous example. What happened?

Answer: the layers are drawn in a different order, so the lines now cover the points

Transformations and statistics

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

change scale:

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + scale_x_log10()

add smoothing function, lm stands for linear model:

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + scale_x_log10() + geom_smooth(method="lm")

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + scale_x_log10() + geom_smooth(method="lm", size=1.5)

About the Tilde `~`

The tilde symbol ~ is often used as an operator to describe a statistical model formula.

The left side is optional, and denotes the target or dependent variable. The right side is the predictor or independent variable(s).

R documentation on ~

Challenge #3

Part A

Given

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
  geom_point(alpha = 0.5) + scale_x_log10() + geom_smooth(method="lm", size=1.5)

Modify the color and size of the points on the point layer, but don't use the aes() function in that layer.

Part B

Modify your solution to Part A so that the points are now a different shape and are colored by continent with new trendlines. Hint: The color argument can be used inside the aes() function.

This cheatsheet helps with syntax on assigning arguments. A PDF version is at RSudio Cheatsheets.

Challenge #3 Solutions

Part A

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
 geom_point(size=3, color="orange") + scale_x_log10() +
 geom_smooth(method="lm", size=1.5)

Part B

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) +
 geom_point(size=3, shape=17) + scale_x_log10() +
 geom_smooth(method="lm", size=1.5)

Multipaneled Figures

americas <- gapminder[gapminder$continent == "Americas",]
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
  geom_line() +
  facet_wrap( ~ country) +
  theme(axis.text.x = element_text(angle = 45))

ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  ) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

Exporting Plots

lifeExp_plot <- ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  labs(
    x = "Year",              # x axis title
    y = "Life expectancy",   # y axis title
    title = "Figure 1",      # main title of figure
    color = "Continent"      # title of legend
  ) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

ggsave(filename = "results/lifeExp.png", plot = lifeExp_plot, width = 12, height = 10, dpi = 300, units = "cm")

Cheatsheets, et al

ggplot function reference on tidyverse.
RSudio cheatsheets

ggplot_cheatsheet_page1

ggplot_cheatsheet_page2

Create Reports with `knitr`

The second session of Day 3 is Episode 15 from the Software Carpentry R for Reproducible Scientific Analysis Lesson.

Creating a Markdown file

Within RStudio, click File → New File → R Markdown.

You might need to install packages

install libraries message

Fill out as much info as you want, and it will prepopulate the document header.

Markdown Basics

bold with double-asterisks

italics with underscores (or single asterisks)

code-type font with backticks

be consistent with your methods
otherwise it will confuse collaborators
or, maybe even your future self!

Title

Main section

Sub-section

Sub-sub section

with even smaller type

how small can it go?

When you knit the document, notice how RStudio jumps between the console and the render tab. Error messages, warnings, and other output involving creating the document will appear in the Render tab

Challenge 1

Create a new R Markdown document. Delete all of the R code chunks and write a bit of Markdown (some sections, some italicized text, and an itemized list).

Convert the document to a webpage.

More Markdown

hyperlinks: [Carpentries Home Page](https://carpentries.org/)
images: ![The Carpentries Logo](https://carpentries.org/assets/img/TheCarpentries.svg)
superscripts F² F^2^
subscripts F₂ F~2~
LaTeX: \[y = \mu + \sum_{i=1}^p \beta_i x_i + \epsilon\] $$y = \mu + \sum_{i=1}^p \beta_i x_i + \epsilon$$

R Code Chunks

```{r load_data}
gapminder <- read.csv("gapminder.csv")

How things are compiled

knitr uses Pandoc, which is a really cool tool for document conversion!

Chunk Options

```{r load_libraries, echo=FALSE, message=FALSE}
library("dplyr")
library("ggplot2")

```{r global_options, echo=FALSE}
knitr::opts_chunk$set(fig.path="Figs/", message=FALSE, warning=FALSE,
                      echo=FALSE, results="hide", fig.width=11)

Inline R

`r round(some_value, 2)`

Syntax	Example	Reference
# Header	Header	基本排版
- Unordered List	Unordered List
1. Ordered List	Ordered List
- [ ] Todo List	Todo List
> Blockquote	Blockquote
Bold font	Bold font
Italics font	Italics font
~~Strikethrough~~	~~Strikethrough~~
19^th^	19^th
H~2~O	H₂O
++Inserted text++	Inserted text
==Marked text==	Marked text
[link text](https:// "title")	Link
![image alt](https:// "title")	Image
`Code`	`Code`	在筆記中貼入程式碼
```javascript var i = 0; ```	`var i = 0;`	在筆記中貼入程式碼
:smile:		Emoji list
{%youtube youtube_id %}	Externals
$L^aT_eX$	L^aT_eX
:::info This is a alert area. :::	This is a alert area.

2022 UC Carpentries Fall Workshop, R Day 3, Notes and Cheatsheet

plotting and report building with ggplot2 and knitr

Lesson Data

Gapminder Documentation

Intro - A Quick look at rstudio.cloud

Plotting with ggplot2

ggplot Function Basics

Syntax of a Basic Call

ggplot Elements

Challenge #1

Part A

Part B

Challenge #1 Solutions

Part A

Part B

About Layers

Challenge #2 (2-minute challenge)

Transformations and statistics

About the Tilde ~

Challenge #3

Part A

Part B

Challenge #3 Solutions

Part A

Part B

Multipaneled Figures

Exporting Plots

Cheatsheets, et al

Create Reports with knitr

Creating a Markdown file

Markdown Basics

Title

Main section

Sub-section

Sub-sub section

with even smaller type

how small can it go?

Challenge 1

More Markdown

R Code Chunks

How things are compiled

Chunk Options

Inline R

plotting and report building with `ggplot2` and `knitr`

Plotting with `ggplot2`

`ggplot` Function Basics

`ggplot` Elements

About the Tilde `~`

Create Reports with `knitr`