RStudio Workflow

Must-know Quarto Shortcuts

RStudio Workflow

Shortcuts for RStudio and RScript

Workflow for the Course

• Save your Quarto document for each class.
  • Go to File and select Save as…/ ( e.g., danl-200-mynote-lec-08-2024-0215.qmd)
• Store all your class Quarto documents in a dedicated directory, separate from your website project directory.

Workflow for Your Personal Website

File management

• Your website project directory should include files specifically dedicated to your website.

• In your website project directory, avoid having

  • Any file that exceeds 30 MB in size;
  • Quarto documents that you do not use for your website.

Workflow for Your Personal Website

Quarto Documents and _quarto.yml

• Ensure all Quarto documents are rendered well without any errors.

• After making edits to _quarto.yml, save the changes by clicking the floppy disk icon (💾).

• Run quarto render in Terminal, whenever you have some changes in _quarto.yml.

• Once quarto render completes, view the index.html in your website working directory to see the updates from a web browser.

• After confirming your local website files are well updated, use the 3-step git commands (add-commit-push) to update your online website.

RStudio Workflow

RStudio Project

• We use a RStudio project with Version Control and GitHub for the website project and its management.
  • e.g., bcdanl.github.io

RStudio Workflow

RStudio Project

• We use a RStudio project with Version Control and GitHub for the website project and its management.
  • e.g., bcdanl.github.io

RStudio Workflow

RStudio Project

# getting the pathname of the current working directory
getwd() 

• Check that the “home” of your project is the current working directory.

RStudio Workflow

Absolute Pathnames

• An absolute path is a complete path from the root directory to the target file or directory.

• Example (Mac): /Users/bchoe/Documents/bcdanl.github.io/data/car_data.csv

• Example (Windows): C:/Users/bchoe/Documents/bcdanl.github.io/data/car_data.csv

RStudio Workflow

Relative Pathnames

• A relative path is a path relative to the current working directory.

• Example (Mac): If the current directory is /Users/bchoe/Documents/bcdanl.github.io/, the relative path to car_data.csv is data/car_data.csv

• Example (Windows): If the current directory is C:/Users/bchoe/Documents/bcdanl.github.io, the relative path to car_data.csv is data/car_data.csv

• For any RStudio project, it is recommended to use a relative path.

RStudio Workflow

Example: Absolute vs. Relative Pathnames

path_abs <- "/Users/bchoe/Documents/websites/bcdanl.github.io/data/car_data.csv"
df_car_a <- read_csv(path_abs)

getwd()    # "/Users/bchoe/Documents/websites/bcdanl.github.io"
path_rel <- "data/car_data.csv"
df_car_r <- read_csv(path_rel)

R Basics

Working with Data from Files

uciCar <- read_csv('HERE WE PROVIDE A PATHNAME FOR car_data.csv')
View(uciCar)

R Basics

Examining data.frames

class(uciCar)

dim(uciCar)
nrow(uciCar)
ncol(uciCar)

library(skimr)
skim(uciCar)

• dim() shows how many rows and columns are in the data for data.frame.
• nrow() and ncol() shows the number of rows and columns for data.frame respectively.
• skimr::skim() provides a more detailed summary.
  • skimr is the R package that provides the function skim().

R Basics

Reading data.frames from an URL

tvshows <- read_csv(
        'https://bcdanl.github.io/data/tvshows.csv')

• We can import the CSV file from the web.

R Basics

Tidy data.frame: Variables, Observations, and Values

• There are three rules which make a data.frame tidy:

  1. Each variable must have its own column.
  2. Each observation must have its own row.
  3. Each value must have its own cell.

R Basics

R Variable and Data Types

• Questions 3-5 in Classwork 5

Exploratory Data Analysis

• In data visualization, you’ll turn data into plots.

• In data transformation, you’ll learn the key verbs that allow you to select important variables, filter out key observations, create new variables, and compute summaries.

• In exploratory data analysis, you’ll combine summary statistics (skim()), visualization, and transformation with your curiosity and skepticism to ask and answer interesting questions about data.

Data visualization

“The simple graph has brought more information to the data analyst’s mind than any other device.” John Tukey

• Data visualization is the creation and study of the visual representation of data

• Many tools for visualizing data – R is one of them

• Many approaches/systems within R for making data visualizations – ggplot2 is one of them, and that’s what we’re going to use

Grammar of Graphics

Data Visualization - First Steps

library(tidyverse)
mpg
?mpg

• The mpg data frame, provided by ggplot2, contains observations collected by the US Environmental Protection Agency on 38 models of car.

• Q. Do cars with big engines use more fuel than cars with small engines?

  • displ: a car’s engine size, in liters.
  • hwy: a car’s fuel efficiency on the highway, in miles per gallon (mpg).

• What does the relationship between engine size and fuel efficiency look like?

Data Visualization - First Steps

Creating a ggplot

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy) )

• To plot mpg, run the above code to put displ on the x-axis and hwy on the y-axis.

Data Visualization - First Steps

Graphing Template

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

• To make a ggplot plot, replace the bracketed sections in the code below with a data.frame, a geom function, or a collection of mappings such as x = VAR_1 and y = VAR_2.

R Basics

R Variable and Data Types

• Question 1 in Classwork 6

Aesthetic Mappings

Aesthetic Mappings

• In the plot above, one group of points (highlighted in red) seems to fall outside of the linear trend.

  • How can you explain these cars? Are those hybrids?

Aesthetic Mappings

• An aesthetic is a visual property (e.g., size, shape, color) of the objects (e.g., class) in your plot.

• You can display a point in different ways by changing the values of its aesthetic properties.

Aesthetic Mappings

Adding a color to the plot

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy, 
                   color = class) )

Aesthetic Mappings

Adding a shape to the plot

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy, 
                   shape = class) )

Aesthetic Mappings

Adding a size to the plot

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy, 
                   size = class) )

Aesthetic Mappings

Adding an alpha (transparency) to the plot

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy, 
                   alpha = class) )

Aesthetic Mappings

Overplotting problem

• Many points overlap each other.

  • This problem is known as overplotting.

• When points overlap, it’s hard to know how many data points are at a particular location.

• Overplotting can obscure patterns and outliers, leading to potentially misleading conclusions.

• We can set a transparency level (alpha) between 0 (full transparency) and 1 (no transparency).

Aesthetic Mappings

Overplotting and alpha

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy),
             alpha = .2)

Aesthetic Mappings

Specifying a color to the plot

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy), 
             color = "blue")

Aesthetic Mappings

• To set an aesthetic manually, set the aesthetic by name as an argument of your geom_ function; i.e. it goes outside of aes().
  • You’ll need to pick a level that makes sense for that aesthetic:
    • The name of a color as a character string.
    • The size of a point in mm.
    • The shape of a point as a number, as shown below.

Aesthetic Mappings

Specifying a color to the plot?

ggplot(data = mpg) + 
  geom_point( mapping = 
                aes(x = displ, 
                    y = hwy, 
                    color = "blue") )

Common problems in ggplot()

ggplot(data = mpg) 
 + geom_point( mapping = 
                 aes(x = displ, 
                     y = hwy) )

• One common problem when creating ggplot2 graphics is to put the + in the wrong place.

R Basics

R Variable and Data Types

• Question 2 in Classwork 6

Facets

• One way to add a variable, particularly useful for categorical variables, is to use facets to split our plot into facets, subplots that each display one subset of the data.

Facets

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy), 
             alpha = .5) + 
  facet_wrap( . ~ class, nrow = 2)

• To facet our plot by a single variable, use facet_wrap().

Facets

• To facet our plot on the combination of two variables, add facet_grid( VAR_ROW ~ VAR_COL ) to our plot call.

Facets

• The first argument of facet_grid() is also a formula.
  • This time the formula should contain two variable names separated by a ~.

Facets

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy),
             alpha = .5) + 
  facet_grid(drv ~ cyl)

Facets

• Option scales in facet_*() is whether scales is
  • fixed ("fixed", the default),
  • free in one dimension ("free_x", "free_y"), or
  • free in two dimensions ("free").

Facets

ggplot(data = mpg) + 
  geom_point(mapping = 
               aes(x = displ, 
                   y = hwy),
             alpha = .5) + 
  facet_grid(drv ~ cyl, 
             scales = "free_x")

R Basics

R Variable and Data Types

• Question 3 in Classwork 6