Logistics

Bulletin

  • Office hours up on website
  • label questions on gradescope pdf
  • Lab 1 can be turned in until Friday 11:59pm with no penalty.
  • Homework 1 to be released next week.
  • Due Thursday: prepare (watch videos before class)

Main Ideas

Lecture Notes and Exercises

Reminder

Before we start the exercise, we need to configure git so that RStudio can communicate with GitHub. This requires two pieces of information: your email address and your GitHub username.

Type the following lines of code in the console in RStudio, filling in your username and the email address associated with your GitHub account.

# library(usethis)
# use_git_config(user.name= "github username", user.email="your email")

Next load the tidyverse package. Recall, a package is just a bundle of shareable code.

library(tidyverse)

Exploratory data analysis (EDA) is an approach to analyzing datasets in order to summarize the main characteristics, often with visual representations of the data (today). We can also calculate summary statistics and perform data wrangling, manipulation, and transformation (next week).

We will use ggplot2 to construct visualizations. The gg in ggplot2 stands for “grammar of graphics”, a system or framework that allows us to describe the components of a graphic, building up an effective visualization layer by later.

Minneapolis Housing Data

We will introduce visualization using data on single-family homes sold in Minneapolis, Minnesota between 2005 and 2015.

Question: What happens when you click the green arrow in the code chunk below? What changes in the “Environment” pane?

mn_homes <- read_csv("data/mn_homes.csv")
glimpse(mn_homes)
## Rows: 495
## Columns: 13
## $ saleyear      <dbl> 2012, 2014, 2005, 2010, 2010, 2013, 2011, 2007, 2013, 20…
## $ salemonth     <dbl> 6, 7, 7, 6, 2, 9, 1, 9, 10, 6, 7, 8, 5, 2, 7, 6, 10, 6, …
## $ salesprice    <dbl> 690467.0, 235571.7, 272507.7, 277767.5, 148324.1, 242871…
## $ area          <dbl> 3937, 1440, 1835, 2016, 2004, 2822, 2882, 1979, 3140, 35…
## $ beds          <dbl> 5, 2, 2, 3, 3, 3, 4, 3, 4, 3, 3, 3, 2, 3, 3, 6, 2, 3, 2,…
## $ baths         <dbl> 4, 1, 1, 2, 1, 3, 3, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1,…
## $ stories       <dbl> 2.5, 1.7, 1.7, 2.5, 1.0, 2.0, 1.7, 1.5, 1.5, 2.5, 1.0, 2…
## $ yearbuilt     <dbl> 1907, 1919, 1913, 1910, 1956, 1934, 1951, 1929, 1940, 19…
## $ neighborhood  <chr> "Lowry Hill", "Cooper", "Hiawatha", "King Field", "Shing…
## $ community     <chr> "Calhoun-Isles", "Longfellow", "Longfellow", "Southwest"…
## $ lotsize       <dbl> 6192, 5160, 5040, 4875, 5060, 6307, 6500, 5600, 6350, 75…
## $ numfireplaces <dbl> 0, 0, 0, 0, 0, 2, 2, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,…
## $ fireplace     <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TR…

Question: What does each row represent? Each column?

[Write your answer here, you will do this for questions like this in your RMD file.]

First Visualization

ggplot creates the initial base coordinate system that we will add layers to. We first specify the dataset we will use with data = mn_homes. The mapping argument is paired with an aesthetic (aes), which tells us how the variables in our dataset should be mapped to the visual properties of the graph.

Question: What does the code chunk below do?

ggplot(data = mn_homes, 
       mapping = aes(x = area, y = salesprice))

ggplot(data = mn_homes, 
       mapping = aes(x = area, y = salesprice)) + 
   geom_point()

ggplot(data = mn_homes, 
       mapping = aes(x = area, y = salesprice)) + 
   geom_point() + 
   geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Run ?geom_smooth in the console. What does this function do?

This fits a loess regression line (moving regression) to the data.

ggplot(data = mn_homes, 
       mapping = aes(x = area, y = salesprice)) + 
   geom_point() + 
   geom_smooth() +
   labs(title = "Sales price vs. area of homes in Minneapolis, MN",
        x = "Area (square feet)", y = "Sales Price (dollars)")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

The procedure used to construct plots can be summarized using the code below.

ggplot(data = [dataset], 
       mapping = aes(x = [x-variable], y = [y-variable])) +
   geom_xxx() +
   geom_xxx() + 
  other options

Question: What do you think eval = FALSE is doing in the code chunk above?

Aesthetics

An aesthetic is a visual property of one of the objects in your plot.

  • shape
  • color
  • size
  • alpha (transparency)

We can map a variable in our dataset to a color, a size, a transparency, and so on. The aesthetics that can be used with each geom_ can be found in the documentation.

Question: What will the visualization look like below? Write your answer down before running the code.

Here we are going to use the viridis package, which has more color-blind accessible colors. scale_color_viridis specifies which colors you want to use. You can learn more about the options here.

Other sources that can be helpful in devising accessible color schemes include Color Brewer, the Wes Anderson package, and the cividis package.

This visualization shows a scatterplot of area (x variable) and sales price (y variable). Using the viridis function, we make points for houses with a fireplace yellow and those without purple. We also add axis and an overall label.

library(viridis)
## Loading required package: viridisLite
ggplot(data = mn_homes, 
       mapping = aes(x = area, y = salesprice,
                     color = fireplace)) + 
   geom_point() + 
   labs(title = "Sales price vs. area of homes in Minneapolis, MN",
        x = "Area (square feet)", y = "Sales Price (dollars)") + 
        scale_color_viridis(discrete=TRUE, option = "D", name="Fireplace?")

Question: What about this one?

ggplot(data = mn_homes, 
       mapping = aes(x = area, y = salesprice,
                     shape = fireplace)) + 
   geom_point() + 
   labs(title = "Sales price vs. area of homes in Minneapolis, MN",
        x = "Area (square feet)", y = "Sales Price (dollars)",
        shape="Fireplace?")

Question: This one?

ggplot(data = mn_homes, 
       mapping = aes(x = area, y = salesprice,
                     color = fireplace,
                     size = lotsize)) + 
   geom_point() + 
   labs(title = "Sales price vs. area of homes in Minneapolis, MN",
        x = "Area (square feet)", y = "Sales Price (dollars)", 
        size = "Lot Size") +
  scale_color_viridis(discrete=TRUE, option = "D",name="Fireplace?")

Question: Are the above visualizations effective? Why or why not? How might you improve them?

Question: What is the difference between the two plots below?

ggplot(data = mn_homes) + 
  geom_point(mapping = aes(x = area, y = salesprice, color = "blue"))

ggplot(data = mn_homes) + 
  geom_point(mapping = aes(x = area, y = salesprice), color = "blue")

Use aes to map variables to plot features, use arguments in geom_xxx for customization not mapped to a variable.

Mapping in the ggplot function is global, meaning they apply to every layer we add. Mapping in a particular geom_xxx function treats the mappings as local.

Question: Create a scatterplot using variables of your choosing using the mn_homes data.

Question: Modify your scatterplot above by coloring the points for each community.

Faceting

We can use smaller plots to display different subsets of the data using faceting. This is helpful to examine conditional relationships.

Let’s try a few simple examples of faceting. Note that these plots should be improved by careful consideration of labels, aesthetics, etc.

# ggplot(data = mn_homes, 
#        mapping = aes(x = area, y = salesprice)) + 
#    geom_point() + 
#    labs(title = "Sales price vs. area of homes in Minneapolis, MN",
#         x = "Area (square feet)", y = "Sales Price (dollars)") + 
#    facet_grid(. ~ beds)
# ggplot(data = mn_homes, 
#        mapping = aes(x = area, y = salesprice)) + 
#    geom_point() + 
#    labs(title = "Sales price vs. area of homes in Minneapolis, MN",
#         x = "Area (square feet)", y = "Sales Price (dollars)") + 
#    facet_grid(beds ~ .)
# ggplot(data = mn_homes, 
#        mapping = aes(x = area, y = salesprice)) + 
#    geom_point() + 
#    labs(title = "Sales price vs. area of homes in Minneapolis, MN",
#         x = "Area (square feet)", y = "Sales Price (dollars)") + 
#    facet_grid(beds ~ baths)
# ggplot(data = mn_homes, 
#        mapping = aes(x = area, y = salesprice)) + 
#    geom_point() + 
#    labs(title = "Sales price vs. area of homes in Minneapolis, MN",
#         x = "Area (square feet)", y = "Sales Price (dollars)") + 
#    facet_wrap(~ community)

facet_grid()

  • 2d grid
  • rows ~ cols
  • use . for no plot

facet_wrap()

  • 1d ribbon wrapped into 2d

Practice

  1. Modify the code outline to make the changes described below.
  • Change the color of the points to green.
  • Add alpha to make the points more transparent.
  • Add labels for the x axis, y axis, and the color of the points.
  • Add an informative title.
  • Consider using the viridis palette. (Note, you can’t do all of these things at once in terms of color, these are just suggestions.)

When you are finished, remove eval = FALSE and knit the file to see the changes.

Here is some starter code:

ggplot(data = mn_homes, 
       mapping = aes(x = lotsize, y = salesprice)) + 
   geom_point(color = ____, alpha = ____) + 
   labs(____)
  1. Modify the code outline to make the changes described below.
  • Create a histogram of lotsize.
  • Modify the histogram by adding fill = "blue" inside the geom_histogram() function.
  • Modify the histogram by adding color = "red" inside the geom_histogram() function.

When you are finished, remove eval = FALSE and knit the file to see the changes.

ggplot(data = mn_homes, 
       mapping = aes(x = _____)) +
  geom_histogram(fill = ____, color = ____) +
  labs(title = "Histogram of Lot Size" , x = "Size of Lot", y = "Number of Homes")

Question: What is the difference between the color and fill arguments?

  1. Develop an effective visualization on your own using the code chunk provided below. Use three variables and at least one aesthetic mapping.