1 Objectives

In order to use graphs and figures to effectively communicate with our audience, we need to consider a few things:

Recognize the needs of your audience (who are they, and where are they coming from?)

level of data literacy, subject matter expertise, etc.

Communicate the quality of the data with stakeholders (can we answer their question(s) with the available data?)

let them know the good and the bad news

Identify the correct data visualization (based on the data and the problem/question)

single variable, bivariate, and multivariate graphs

2 Materials

The slides for this lesson are here
RStudio.Cloud Project

3 Previous lessons

All of the exercises and lessons are available here. Read more about ggplot2 on the tidyverse website, and in the Data Visualisation chapter of R for Data Science.

4 Load the packages

The main packages we’re going to use are dplyr, tidyr, and ggplot2. These are all part of the tidyverse, so we’ll import this package below:

install.packages("tidyverse")
library(tidyverse)

5 Example: COVID and Mobility

Assume we received the following questions from a stakeholder:

How has COVID changed our modes of transportation?

Are people using fewer or different forms of transportation since the COVID pandemic?

Questions we should be considering:

What kind of measurements would this be?

how people travel (walk, drive, etc.)

What would these data look like?

what would the columns and rows look like?

5.1 Data Import

We’re going to use the following data to answer the stakeholder’s questions:

Apple mobility data: https://covid19.apple.com/mobility

Import the data below

AppleMobRaw <- readr::read_csv("https://bit.ly/36tTVpe")

5.1.1 Tidy `AppleMobRaw`

These data need to be restructured into a tidy format.

AppleMobRaw %>% 
  tidyr::pivot_longer(cols = -c(geo_type:country), 
                      names_to = "date", 
                      values_to = "dir_request")

5.1.2 Wrangle `AppleMobRaw`

We will remove the missing values from country and sub-region

AppleMobRaw %>% 
  tidyr::pivot_longer(cols = -c(geo_type:country), 
                      names_to = "date", values_to = "dir_request") %>% 
    # remove missing country and missing sub-region data
  dplyr::filter(!is.na(country) & !is.na(`sub-region`))

Use mutate() to create a properly formatted date variable, and rename() the transportation_type variable to trans_type. Apply janitor::clean_names() to the entire dataset and assign the final output to TidyApple.

AppleMobRaw %>% 
  tidyr::pivot_longer(cols = -c(geo_type:country), 
                      names_to = "date", values_to = "dir_request") %>% 
    # remove missing country and missing sub-region data
  dplyr::filter(!is.na(country) & !is.na(`sub-region`)) %>% 
  # format date
  mutate(date = lubridate::ymd(date)) %>% 
  # change name of transportation types
  rename(trans_type = transportation_type) %>% 
  # clean names 
  janitor::clean_names() -> TidyApple

5.2 Counting

One of the most important jobs of analytic work is counting things. There are many ways to accomplish this in R, but we’ll stick with the dplyr package because it’s part of the tidyverse.

The dplyr function for counting responses of a categorical or factor variable is count(), and it works like this:

Data %>% 
  count(variable)

5.2.1 Count

So, if we wanted to count the number of different transportation types in the TidyApple data frame, it would look like this,

TidyApple %>% 
  dplyr::count(trans_type)

5.2.2 Count & sort

We can also sort the responses using the sort = TRUE argument.

TidyApple %>% 
  dplyr::count(trans_type, sort = TRUE)

5.2.3 Iterate with count

We can also combine dplyr::select_if() and purrr::map() to pass the count() function to all the character variables in TidyApple.

TidyApple %>% 
  select_if(is.character) %>% 
  map(~count(data.frame(x = .x), x, sort = TRUE)) -> tidy_apple_counts

We can example the counts of each value by using the $ to subset the tidy_apple_counts list.

tidy_apple_counts$sub_region

tidy_apple_counts$region

6 Visualizing Distributions

Before we start looking at relationships between variables, we should examine each variable’s underlying distribution. In the next section, we’re going to cover a few graphs that display variable distributions: histograms, density, violin, and ridgeline plots,

6.1 Histograms

A histogram is a special kind of bar graph–it only takes a single continuous variable (in this case, dir_request), and it displays a relative breakdown of the values.

The x axis for the histogram will have the direction requests, and the y variable will display a count of the values.

lab_hist <- labs(x = "Apple directions requests",
                 y = "Count",
     title = "Distribution of Direction Requests",
     subtitle = "source: https://covid19.apple.com/mobility")

6.1.1 exercise

Create a histogram of direction requests using dir_request

TidyApple %>% ggplot() + 
  geom_histogram(aes(x = ____________)) + 
  lab_hist

6.1.2 solution

See blow:

TidyApple %>% ggplot() + 
  geom_histogram(aes(x = dir_request)) + 
  lab_hist

6.2 Adjusting Y Axes

We can see the y axis of the histogram is in scientific notation. This might be hard for some audiences to interpret, so we will change this to use the whole number with commas with the scales package.

6.2.1 exercise

Add the scales::comma value to the scale_y_continuous() function.

library(scales)
TidyApple %>% ggplot() + 
  geom_histogram(aes(x = dir_request)) + 
  scale_y_continuous(labels = __________) +
  lab_hist

6.2.2 solution

See below:

library(scales)
TidyApple %>% ggplot() + 
  geom_histogram(aes(x = dir_request)) + 
  scale_y_continuous(labels = scales::comma) +
  lab_hist

6.3 Histogram Shape

We can control the shape of the histogram with the bins argument. The default is 30.

6.3.1 exercie

Set bins to 15.

TidyApple %>% ggplot() + 
  geom_histogram(aes(x = dir_request), bins = __) + 
  scale_y_continuous(labels = scales::comma) +
  lab_hist

6.3.2 solution

See below:

TidyApple %>% ggplot() + 
  geom_histogram(aes(x = dir_request), bins = 15) + 
  scale_y_continuous(labels = scales::comma) +
  lab_hist

6.3.3 exercise

Set bins to 45 and assign it to gg_hist45.

TidyApple %>% ggplot() + 
  geom_histogram(aes(x = dir_request), bins = __) + 
  scale_y_continuous(labels = scales::comma) +
  lab_hist -> _____________

6.3.4 solution

See below:

TidyApple %>% ggplot() + 
  geom_histogram(aes(x = dir_request), bins = 45) + 
  scale_y_continuous(labels = scales::comma) +
  lab_hist -> gg_hist45

6.4 Density Plots

What if we want to see how a continuous variable is distributed across a categorical variable? We did this in the previous lesson with a boxplot.

Density plots come in handy here (so do geom_boxplot()s!). Read more about the density geom here.

We are going to create the graph labels so we know what to expect when we build our graph.

We want to see the distribution of the directions request, filled by the levels of transportation type.

lab_density <- labs(x = "Apple directions requests",
                    fill = "Transit Type",
     title = "Distribution of Direction Requests vs. Transportation Type",
     subtitle = "source: https://covid19.apple.com/mobility")

Now we build the density plot, passing the variables so they match our labels above.

6.4.1 exercise

Create a density plot of direction requests colored by the type of transportation.

TidyApple %>% 
  ggplot() +
  geom_density(aes(x = __________, fill = __________)) + 
  lab_density

6.4.2 solution

See below:

TidyApple %>% 
  ggplot() +
  geom_density(aes(x = dir_request, fill = trans_type)) + 
  lab_density

One drawback to density plots is the y axis can be hard to interpret

6.4.3 exercise

Adjust the overlapping densities by setting alpha to 1/3. Assign this plot to gg_density.

TidyApple %>% 
  ggplot() +
  geom_density(aes(x = dir_request, fill = trans_type), 
               alpha = __________) + 
  
  lab_density -> __________

6.4.4 solution

See below:

TidyApple %>% 
  ggplot() +
  geom_density(aes(x = dir_request, fill = trans_type), 
               alpha = 1/3) + 
  lab_density -> gg_density

gg_density

6.5 Ridgeline Plots

Another option is a ridgeline plot (from the ggridges package). These display multiple densities.

lab_ridges <- labs(
     title = "Direction Requests by Transportation Type",
     subtitle = "source: https://covid19.apple.com/mobility",
     fill = "Transit type",
     x = "Apple directions requests",
     y = "Transportation Types")

library(ggridges)
TidyApple %>%  
  ggplot() + 
  geom_density_ridges(aes(x = dir_request, 
                          y = trans_type, 
                          fill = trans_type), 
                      alpha = 1/5) + 
  lab_ridges

6.6 Violin Plots

Another alternative to the density plot is the violin plot.

6.6.1 exercise

assign "Apple directions requests" to the x axis
assign "Transit Type" to the y axis

lab_violin <- labs(x = _________________________,
                    y = _________________________,
                   fill = "Transit Type",
     title = "Distribution of Direction Requests vs. Transportation Type",
     subtitle = "source: https://covid19.apple.com/mobility")

6.6.2 solution

lab_violin <- labs(x = "Apple directions requests",
                   y = "Transit Type",
                   fill = "Transit Type",
     title = "Distribution of Direction Requests vs. Transportation Type",
     subtitle = "source: https://covid19.apple.com/mobility")

6.6.3 exercise

Add a geom_violin() to the code below:

TidyApple %>% 
  ggplot() +
  ____________(aes(y = dir_request, x = trans_type, 
                  fill = trans_type)) + 
  lab_violin

6.6.4 solution

TidyApple %>% 
  ggplot() +
  geom_violin(aes(y = dir_request, x = trans_type, 
                  fill = trans_type)) + 
  lab_violin

6.6.5 exercise

The great thing about ggplot2s layered syntax, is that we can add geoms with similar aesthetics to the same graph! For example, we can see how geom_violins and geom_boxplots are related by adding a geom_boxplot() layer to the graph above.

TidyApple %>% 
  ggplot() +
  geom_violin(aes(y = dir_request, x = trans_type, 
                  fill = trans_type), alpha = 1/5) + 
  ___________(aes(y = dir_request, x = trans_type, 
                   color = trans_type)) + 
  lab_violin

6.6.6 solution

Note we set the alpha to 1/5 for the geom_violin(), and the color to trans_type for the geom_boxplot().

TidyApple %>% 
  ggplot() +
  geom_violin(aes(y = dir_request, x = trans_type, 
                  fill = trans_type), alpha = 1/5) + 
  geom_boxplot(aes(y = dir_request, x = trans_type, 
                   color = trans_type)) + 
  lab_violin

7 Visualizing Trends

We’ve seen the distribution of dir_request, and how it varies across trans_type.

Now we’ll look at how the relationship between these two variables varies over in a subset of US cities and across a specific date range.

7.1 Focus on US Cities

We’ll start by narrowing down the data by filtering to only US cities.

7.1.1 exercise

Filter the geo_type to "city" and the country to "United States", and pass the date variable to skimr::skim()

TidyApple %>% 
  filter(geo_type == ___________ &
              country == ___________) %>% 
                # use skimr to check date
                skimr::skim(___________)

7.1.2 solution

Here we reduce the dataset to only cities in the US, and we check the date range with skimr::skim(). If this looks OK, we assign to USCities

TidyApple %>% 
  filter(geo_type == "city" &
              country == "United States") %>% 
                # use skimr to check date
                skimr::skim(date)

Data summary
Name	Piped data
Number of rows	99538
Number of columns	7
_______________________
Column type frequency:
Date	1
________________________
Group variables	None

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-13	2020-11-24	2020-06-19	317

TidyApple %>% 
  filter(geo_type == "city" &
         country == "United States") -> USCities

7.2 Updating Labels

We can see this date range is from 2020-01-13 to 2020-11-24.

7.2.1 exercise

Use paste0() to combine the first and last date in the filtered USCities dataset.

paste0(min(______________$____), 
       " through ", 
       max(______________$____))

7.2.2 solution

paste0(min(USCities$date), 
       " through ", 
       max(USCities$date))

## [1] "2020-01-13 through 2020-11-24"

We want to specify this in our labels object (lab_line_update), so we will use the paste0() function to have the labels update every time the data changes.

labs(x = "Date",
     y = "Direction Requests",
     title = "Direction Requests Over Time (US Cities Only)",
     subtitle = paste0(min(USCities$date), 
                       " through ", 
                       max(USCities$date)),
     caption = "source: https://covid19.apple.com/mobility", 
     color = "Transit Type") -> lab_line_update

We’re going to create a line graph of direction requests over time, colored by color.

7.2.3 exercise

Pass the filtered data to the geom_line(), mapping the following variables to their relative aesthetics:

date to x
dir_request to y
trans_type to both group and color

Include lab_line_update to see how the new labels look!

USCities %>% 
  ggplot() +
  geom_line(aes(x = __________, y = __________, 
             group = __________, color = __________)) + 
  __________

7.2.4 solution

Let’s see what happens when we use lab_line_update.

USCities %>% 
  ggplot() +
  geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type)) + 
  lab_line_update

The dates updated to the min and max date in USCities.

7.3 Adjusting Line Size

These lines in this graph are overlapping each other, so we will adjust the size to 0.20.

7.3.1 exercise

Change the size of the geom_line() (outside of aes()).

USCities %>% 
  ggplot() +
  geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
            size = ____________) + 
  lab_line01

7.3.2 solution

See below:

USCities %>% 
  ggplot() +
  geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
            size = 0.20) + 
  lab_line_update

Now the trends are easier to see.

7.4 Setting Date Range

We are going to only look at the trends between February and August of 2020, but we’re going to use an alternative method to filter the data and create the labels.

We will create two new objects (start_date and end_date), which we can use to narrow the dates using the filter() function (and anywhere else we need to use this date range).

This method is better than passing the dates as a character (i.e. in quotes), because we would only have to change it in one place. However, the option above makes better use of R functional programming syntax.

7.4.1 exercise

Pass the start_date and end_date to the as_date() functions, and take a look at the date variable with skimr::skim() and if it looks correct, assign it to USCitiesFebJul

# create date objects
start_date <- "2020-02-01"
end_date <- "2020-08-01"
# check with skimr
TidyApple %>% 
  filter(geo_type == "city" &
              country == "United States", 
                date >= as_date(_____________) & 
                  date <= as_date(_____________)) %>% 
                  skimr::skim(_____________)

7.4.2 solution

See below:

# create date objects
start_date <- "2020-02-01"
end_date <- "2020-08-01"
# check with skimr
TidyApple %>% 
  filter(geo_type == "city" &
              country == "United States", 
                date >= as_date(start_date) & 
                  date <= as_date(end_date)) -> USCitiesFebJul

USCitiesFebJul %>% 
  skimr::skim(date)

Data summary
Name	Piped data
Number of rows	57462
Number of columns	7
_______________________
Column type frequency:
Date	1
________________________
Group variables	None

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-02-01	2020-08-01	2020-05-02	183

7.4.3 exercise

Create the new labels (lab_line_paste) with the paste0() function by passing both start_date and end_date.

lab_line_paste <- labs(x = "Date",
                 y = "Direction Requests",
                 title = "Direction Requests Over Time (US Cities Only)",
                 subtitle = paste0(___________, " through ", ___________),
                 caption = "source: https://covid19.apple.com/mobility", 
                 color = "Transit Type")

7.4.4 solution

lab_line_paste <- labs(x = "Date",
                 y = "Direction Requests",
                 title = "Direction Requests Over Time (US Cities Only)",
                 subtitle = paste0(start_date, " through ", end_date),
                 caption = "source: https://covid19.apple.com/mobility", 
                 color = "Transit Type")

USCitiesFebJul %>% 
  ggplot() +
  geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
            # make these slightly larger...
            size = 0.30) + 
  lab_line_paste

8 Adding Text to Graphs

We can see there is a gap in the direct request data (this is documented in the data source).

“Data for May 11-12 is not available and will appear as blank columns in the data set.”

We should communicate this gap with our audience, and we might include a text annotation on the graph so our audience isn’t distracted by the gap.

In the previous lesson, we introduced the ggrepel package to show the points on this graph of top performing pharmaceutical companies.

We’re going to use labels to annotate and highlight US cities between March and June of 2020.

8.1 Annotations

Being able to manually add text and annotations as layers to your graph makes it easier to communicate the nuances of your data to your audience. We are going to start by accounting for the missing data in TidyApple.

8.1.1 exercise

Build a dataset from TidyApple that only has US cities, and ranges from March 1, 2020 to June 30, 2020.

USCitiesMarJun <-  TidyApple %>% 
  filter(geo_type == ___________ &
              country == ___________, 
                date >= as_date(___________) & 
                  date <= as_date(___________)) 

USCitiesMarJun %>% 
                  skimr::skim()

8.1.2 solution

See below:

USCitiesMarJun <-  TidyApple %>% 
  filter(geo_type == "city" &
              country == "United States", 
                date >= as_date("2020-03-01") & 
                  date <= as_date("2020-07-01")) 

USCitiesMarJun %>% skimr::skim()

Data summary
Name	Piped data
Number of rows	38622
Number of columns	7
_______________________
Column type frequency:
character	5
Date	1
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
geo_type	1	4	4	1
region	1	5	39	111
trans_type	1	7	7	3
sub_region	1	4	14	39
country	1	13	13	1

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-03-01	2020-07-01	2020-05-01	123

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
dir_request	628	0.98	82.06	41.11	4.37	50.87	74.38	109.11	525.44	▇▃▁▁▁

8.1.3 exercise

We’re going to build labels using the paste0() function. Fill in the appropriate dataset for the min() and max() date.

lab_annotate <- labs(x = "Date",
                 y = "Direction Requests",
                 title = "Direction Requests Over Time (US Cities Only)",
                 subtitle = paste0(min(______________$date), 
                                   " through ", 
                                   max(______________$date)),
                 caption = "source: https://covid19.apple.com/mobility", 
                 color = "Transit Type")

8.1.4 solution

See below:

lab_annotate <- labs(x = "Date",
                 y = "Direction Requests",
                 title = "Direction Requests Over Time (US Cities Only)",
                 subtitle = paste0(min(USCitiesMarJun$date), 
                                   " through ", 
                                   max(USCitiesMarJun$date)),
                 caption = "source: https://covid19.apple.com/mobility", 
                 color = "Transit Type")

8.1.5 exercise

The previous code for the graph has been added. We’re going to add the following layers:

Inside coord_cartesian():

map min(USCitiesMarJun$date) and max(USCitiesMarJun$date) inside c() to xlim
map min(USCitiesMarJun$dir_request, na.rm = TRUE) and max(USCitiesMarJun$dir_request, na.rm = TRUE) inside c() to ylim

Inside the # horizontal annotate():

set geom to "segment"
map 0.5 to size
map "firebrick3" to color
map lubridate::as_date("2020-05-10") to x
map lubridate::as_date("2020-05-13") to xend
map 100 to y and yend

Inside the # big vertical annotate():

set geom to "segment"
map 1 to size
map "firebrick3" to color
map lubridate::as_date("2020-05-11") to x
map lubridate::as_date("2020-05-11") to xend
map 270 to y and 100 to yend

Inside the # text annotate():

set geom to "text"
map 8 to size
map "red" to color
map 0.5 to hjust
map lubridate::as_date("2020-05-07") to x
map 280 to y
map "Data not available" to label

USCitiesMarJun %>% 
    ggplot() +
    geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
             # make these slightly larger...
             size = 0.30) + 
   # coordinate system
   coord_cartesian(xlim = c(_______________, _______________), 
                   
                   ylim = c(_______________, na.rm = __________), 
                            _______________, na.rm = __________))) +
   # horizontal
   annotate(geom = ___________, size = ___________, color = ___________,
                  x = ___________, 
                  xend = ___________,
                  y = ___________, 
                  yend = ___________) + 
    # big vertical 
    annotate(geom = ___________, 
            size = ___________, 
            color = ___________,
            x = ___________, 
            xend = ___________, 
            y = ___________, yend = ___________) + 
    # text
    annotate(geom = "text",
               size = 8,
               color = "red", 
               hjust = 0.5, 
               x = lubridate::as_date("2020-05-07"), 
               y = 280, 
               label = "Data not available") +

    lab_annotate

8.1.6 solution

# plot
USCitiesMarJun %>% 
    ggplot() +
    geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
             # make these slightly larger...
             size = 0.30) + 
   # coordinate system
   coord_cartesian(xlim = c(min(USCitiesMarJun$date), 
                            max(USCitiesMarJun$date)), 
                   
                   ylim = c(min(USCitiesMarJun$dir_request, na.rm = TRUE), 
                            max(USCitiesMarJun$dir_request, na.rm = TRUE))) +
   # horizontal
   annotate(geom = "segment", 
            size = 0.5, 
            color = "firebrick3",
                  x = lubridate::as_date("2020-05-10"), 
                  xend = lubridate::as_date("2020-05-13"),
                             y = 100, 
                             yend = 100) + 
    # big vertical 
    annotate(geom = "segment", 
            size = 1, 
            color = "firebrick3",
            x = lubridate::as_date("2020-05-11"), 
            xend = lubridate::as_date("2020-05-11"), 
            y = 270, yend = 100) + 
  
    # text
    annotate(geom = "text",
               color = "red", 
               hjust = 0.5, 
               size = 8,
               x = lubridate::as_date("2020-05-07"), 
               y = 280, 
               label = "Data not available") +

    lab_annotate

8.1.7 exercise

Add a second and third vertical segment to create a fence or bracket for the dates with missing data.

# plot
USCitiesMarJun %>% 
    ggplot() +
    geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
             # make these slightly larger...
             size = 0.30) + 
   # coordinate system
   coord_cartesian(xlim = c(min(USCitiesMarJun$date), 
                            max(USCitiesMarJun$date)), 
                   
                   ylim = c(min(USCitiesMarJun$dir_request, na.rm = TRUE), 
                            max(USCitiesMarJun$dir_request, na.rm = TRUE))) +
   # horizontal
   annotate(geom = "segment", 
            size = 0.5, 
            color = "firebrick3",
                  x = lubridate::as_date("2020-05-10"), 
                  xend = lubridate::as_date("2020-05-13"),
                             y = 100, 
                             yend = 100) +
    # big vertical 
    annotate(geom = "segment", 
            size = 1, 
            color = "firebrick3",
            x = lubridate::as_date("2020-05-11"), 
            xend = lubridate::as_date("2020-05-11"), 
            y = 270, yend = 100) + 
    # text
    annotate(geom = "text",
               color = "red", 
               hjust = 0.5, 
               size = 8,
               x = lubridate::as_date("2020-05-07"), 
               y = 280, 
               label = "Data not available") +
   # second vertical 
   annotate(geom = "segment", 
            size = 0.5, 
            color = "firebrick3",
            x = _____________________________, 
            xend = _____________________________, 
            y = _____________________________, 
            yend = _____________________________) + 
    # third vertical 
     annotate(geom = "segment", 
            size = 0.5, 
            color = "firebrick3",
            x = _____________________________, 
            xend = _____________________________, 
            y = _____________________________, 
            yend = _____________________________) + 
  
   lab_annotate

8.1.8 solution

See below:

# plot
USCitiesMarJun %>% 
    ggplot() +
    geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
             # make these slightly larger...
             size = 0.30) + 
   # coordinate system
   coord_cartesian(xlim = c(min(USCitiesMarJun$date), 
                            max(USCitiesMarJun$date)), 
                   
                   ylim = c(min(USCitiesMarJun$dir_request, na.rm = TRUE), 
                            max(USCitiesMarJun$dir_request, na.rm = TRUE))) +
   # horizontal
   annotate(geom = "segment", 
            size = 0.5, 
            color = "firebrick3",
                  x = lubridate::as_date("2020-05-10"), 
                  xend = lubridate::as_date("2020-05-13"),
                             y = 100, 
                             yend = 100) +
    # big vertical 
    annotate(geom = "segment", 
            size = 1, 
            color = "firebrick3",
            x = lubridate::as_date("2020-05-11"), 
            xend = lubridate::as_date("2020-05-11"), 
            y = 270, yend = 100) + 
    # text
    annotate(geom = "text",
               color = "red", 
               hjust = 0.5, 
               size = 8,
               x = lubridate::as_date("2020-05-07"), 
               y = 280, 
               label = "Data not available") +
   # second vertical 
   annotate(geom = "segment", 
            size = 0.5, 
            color = "firebrick3",
            x = lubridate::as_date("2020-05-10"), 
            xend = lubridate::as_date("2020-05-10"), 
            y = 100, yend = 90) + 
    # third vertical 
     annotate(geom = "segment", 
            size = 0.5, 
            color = "firebrick3",
            x = lubridate::as_date("2020-05-13"), 
            xend = lubridate::as_date("2020-05-13"), 
            y = 100, yend = 90) + 
  
   lab_annotate

8.2 Plotting area

Another option is to use geom_rect() to black out the missing data.

# plot
USCitiesMarJun %>% 
    ggplot() +
    geom_line(aes(x = date, y = dir_request, 
             group = trans_type, color = trans_type), 
             # make these slightly larger...
             size = 0.30) + 
   # coordinate system
   coord_cartesian(xlim = c(min(USCitiesMarJun$date), 
                            max(USCitiesMarJun$date)), 
                   
                   ylim = c(min(USCitiesMarJun$dir_request, na.rm = TRUE), 
                            max(USCitiesMarJun$dir_request, na.rm = TRUE))) +
  geom_rect(xmin = lubridate::as_date("2020-05-10"), 
            xmax = lubridate::as_date("2020-05-12"), 
            ymin = -Inf, 
            ymax = Inf, 
            color = NA) + 
  geom_text(x = as.Date("2020-05-11"), 
            y = 100, label = "Data Not Available", 
            angle = 90, color = "white") +
  lab_annotate

8.3 Labeling Values

Max Driving Requests

The code below creates a subset of the data (TopUSCities). We will use this to add the labels.

TopUSCities <- TidyApple %>% 
  filter(country == "United States" & 
           region %in% c("New York City", "Los Angeles", 
                         "Chicago", "Houston", "Phoenix")) 
TopUSCities

8.3.1 exercise

Create MaxUSCitiesDriving by filtering trans_type, grouping on the region variable, and using dplyr::slice_max() to get the top value in dir_request.

TopUSCities %>% 
  filter(trans_type == __________) %>% 
  group_by(__________) %>% 
  slice_max(dir_request) %>% 
  ungroup() -> MaxUSCitiesDriving
MaxUSCitiesDriving

8.3.2 solution

See below:

TopUSCities %>% 
  filter(trans_type == "driving") %>% 
  group_by(region) %>% 
  slice_max(dir_request) %>% 
  ungroup() -> MaxUSCitiesDriving
MaxUSCitiesDriving

8.3.3 exercise

Create graph labels:

assign "Peak Driving Direction Requests in Largest US Cities" to title
assign "Max Driving Direction Requests & Date" to subtitle

lab_line_max_drivers <- labs(
                 x = "Date",
                 y = "Direction Requests",
                 title = "_________________________________",
                 subtitle = paste0(min(___________$date), 
                                   " through ", 
                                   max(___________$date)),
                 caption = "source: https://covid19.apple.com/mobility", 
                 color = "Transit Type")

8.3.4 solution

See below:

lab_line_max_drivers <- labs(
                 x = "Date",
                 y = "Direction Requests",
                 title = "Peak Driving Direction Requests in Largest US Cities",
                 subtitle = paste0(min(TopUSCities$date), 
                                   " through ", 
                                   max(TopUSCities$date)),
                 caption = "source: https://covid19.apple.com/mobility", 
                 color = "Transit Type")

8.3.5 exercise

Create max_driving_labels using paste0() with region and date.

MaxUSCitiesDriving %>% 
  mutate(
    max_driving_labels = paste0(______, ", ", ______)) -> MaxUSCitiesDriving
MaxUSCitiesDriving %>% 
  select(max_driving_labels)

8.3.6 solution

See below:

MaxUSCitiesDriving %>% 
  mutate(max_driving_labels = paste0(region, ", ", date)) -> MaxUSCitiesDriving
MaxUSCitiesDriving %>% 
  select(max_driving_labels)

8.3.7 exercise

Create a line plot, assigning the following values in geom_label_repel():

set the data argument to MaxUSCitiesDriving

Inside the aes():

map label to max_driving_labels

Outside the aes()

map color to "red"
map size to 3

TopUSCities %>% 
    ggplot() + 
    geom_line(aes(x = date, y = dir_request, 
                  group = trans_type, 
                  color = trans_type), 
                  # make these slightly smaller again...
                  size = 0.15) + 
    geom_label_repel(data = _____________,
              aes(x = date, y = dir_request, 
                  label = _____________), 
                  # set color and size...
                  color = _____, 
                  size = _) + 
  lab_line_max_drivers

8.3.8 solution

See below:

TopUSCities %>% 
    ggplot() + 
    geom_line(aes(x = date, y = dir_request, 
                  group = trans_type, 
                  color = trans_type), 
                  # make these slightly smaller again...
                  size = 0.15) + 
    geom_label_repel(data = MaxUSCitiesDriving,
              aes(x = date, y = dir_request, 
                  label = max_driving_labels), 
                  # set color and size...
                  color = "red", 
                  size = 3) + 
  lab_line_max_drivers

8.4 Labeling Values 2

Min Walking Requests

We are going to repeat the process above, but use the minimum value for walking direction requests.

8.4.1 exercise

filter() the trans_type to "walking"
group_by() the region
use slice_min() to get the minimum value for dir_request
Assign to MinUSCitiesWalking

TopUSCities %>% 
  filter(________ == ________) %>% 
  group_by(________) %>% 
  slice_min(dir_request) %>% 
  ungroup() -> MinUSCitiesWalking
MinUSCitiesWalking

8.4.2 solution

See below:

TopUSCities %>% 
  filter(trans_type == "walking") %>% 
  group_by(region) %>% 
  slice_min(dir_request) %>% 
  ungroup() -> MinUSCitiesWalking
MinUSCitiesWalking

8.4.3 exercise

assign "Lowest Walking Direction Requests in Largest US Cities" to title

lab_line_min_walking <- labs(
               x = "Date",
               y = "Direction Requests",
               title = "__________________________________________",
               subtitle = paste0(min(___________$date), 
                                   " through ", 
                                 max(___________$date)),
               caption = "source: https://covid19.apple.com/mobility", 
               color = "Transit Type")

8.4.4 solution

See below:

lab_line_min_walking <- labs(
               x = "Date",
               y = "Direction Requests",
               title = "Lowest Walking Direction Requests in Largest US Cities",
               subtitle = paste0(min(TopUSCities$date), 
                                   " through ", 
                                 max(TopUSCities$date)),
               caption = "source: https://covid19.apple.com/mobility", 
               color = "Transit Type")

8.4.5 exercise

Create min_walking_labels using paste0() with region and date

MinUSCitiesWalking %>% 
  mutate(min_walking_labels = paste0(_____, ", ", _____)) -> MinUSCitiesWalking
MinUSCitiesWalking

8.4.6 solution

See below:

MinUSCitiesWalking %>% 
  mutate(min_walking_labels = paste0(region, ", ", date)) -> MinUSCitiesWalking
MinUSCitiesWalking

8.4.7 exercise

Create a line plot, assigning the following values in geom_label_repel():

set data to MinUSCitiesWalking

Inside aes():

map min_walking_labels to label

Outside aes():

map "blue" to color
map 3 to size

TopUSCities %>% 
    ggplot() + 
    geom_line(aes(x = date, y = dir_request, 
                  group = trans_type, 
                  color = trans_type), 
                  # make these slightly smaller again...
                  size = 0.15) + 
    geom_label_repel(data = ____________,
              aes(x = date, y = dir_request, 
                  label = ____________), 
                  # set color and size...
                  color = _______, 
                  size = _) + 
  lab_line_min_walking

8.4.8 solution

See below:

TopUSCities %>% 
    ggplot() + 
    geom_line(aes(x = date, y = dir_request, 
                  group = trans_type, 
                  color = trans_type), 
                  # make these slightly smaller again...
                  size = 0.15) + 
    geom_label_repel(data = MinUSCitiesWalking,
              aes(x = date, y = dir_request, 
                  label = min_walking_labels), 
                  # set color and size...
                  color = "blue", 
                  size = 3) + 
  lab_line_min_walking

9 Reference lines

We’re going to focus on the top 8 cites according to their population (the date of this writing is 2020-11-26).

9.1 Top Cities

Top Cities

We’re going to introduce another filtering method in this section to create the TopUSCities dataset.

9.1.1 exercise

Store top eight cities in the focus_on vector and use it to filter the TidyApple dataset.

focus_on <- c("New York City", "Los Angeles", 
                       "Chicago", "Houston", 
                       "Phoenix", "Philadelphia", 
                       "San Antonio", "San Diego")

TopUSCities <- TidyApple %>% 
  filter(region %in% _____________)

TopUSCities %>% glimpse(60)

9.1.2 solution

See below:

focus_on <- c("New York City", "Los Angeles", 
              "Chicago", "Houston", 
              "Phoenix", "Philadelphia", 
              "San Antonio", "San Diego")

TopUSCities <- TidyApple %>% 
  filter(region %in% focus_on)

TopUSCities %>% glimpse(60)

## Rows: 7,608
## Columns: 7
## $ geo_type    <chr> "city", "city", "city", "city", "city…
## $ region      <chr> "Chicago", "Chicago", "Chicago", "Chi…
## $ trans_type  <chr> "driving", "driving", "driving", "dri…
## $ sub_region  <chr> "Illinois", "Illinois", "Illinois", "…
## $ country     <chr> "United States", "United States", "Un…
## $ date        <date> 2020-01-13, 2020-01-14, 2020-01-15, …
## $ dir_request <dbl> 100.00, 103.68, 104.45, 108.72, 132.8…

9.1.3 exercise

Graph Labels

We’re going to place date on the x axis, and dir_request on the y. The tite will reflect a general description of what we’re expecting to see, and we’ll list the cities in the subtitle. color will be used to give a better description than trans_type.

Fill in names for the x, y, and color.

lab_top_cities <- labs(x = _____, y  = __________, 
     title = "Trends of Relative Activity in Selected US Cities",
     subtitle = "NY, LA, CH, HOU, PHA, PHL, SATX, SD", 
     color = _________)

9.1.4 solution

See below:

lab_top_cities <- labs(x = "Date", y  = "Direction Requests", 
     title = "Trends of Relative Activity in Selected US Cities",
     subtitle = "NY, LA, CH, HOU, PHA, PHL, SATX, SD", 
     color = "Type")

9.2 Set Global `aes()`

We’re going to set the global graph aesthetics inside ggplot(aes()) using our labels as a guide. This will serve as a base layer for us to add our reference line to!

9.2.1 exercise

Global aes()

Map trans_type to group and color
Also add a geom_line() layer with the size set to 0.1 (not inside the aes()!)

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             _____ = __________, 
             _____ = __________)) + 
  ____________(_____ = ___) + 
  lab_top_cities

9.2.2 solution

See below:

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  lab_top_cities

9.3 Reference Line Layer

The documentation for the data tells us each dir_request has a baseline value of 100. We’re going to add this as a reference line on the graph using ggplot2::geom_hline().

9.3.1 exercise

The geom_hline() function takes yintercept, size, and color arguments.

use our baseline value of 100 as the yintercept
set the size to 0.2
make the color of this line "gray20"

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) +
  geom_line(size = 0.1) + 
  # add reference line
  geom_hline(yintercept = ___, size = ___, color = _______) +
  lab_top_cities

9.3.2 solution

See below:

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  lab_top_cities

Reference lines are helpful when we want to examine trends in relation to a particular value.

10 Advanced Facets

In the previous lesson, we introduced the facet_wrap() function for viewing the relationship between two variables across the levels of a categorical variable. In the next section, we’re going to show how faceting can be used to explore ‘small multiples’ in a dataset with variation across multiple levels.

10.1 `facet_wrap()`

Now that we have a graph we can use to compare the 8 cities, we will use facet_wrap to create a subplot for each level of region.

10.1.1 exercise

Fill in the facet_wrap() (note the use of the ~) function with region and set the ncol to 2.

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  facet_wrap(~ _______,  ncol = _) + 
  lab_top_cities

10.1.2 solution

See below:

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  facet_wrap(~ region,  ncol = 2) + 
  lab_top_cities

10.1.3 exercise

Now map both region and trans_type to facet_wrap() and set the ncol to 6.

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = region, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  facet_wrap(_______ ~ _______, ncol = _) + 
  lab_top_cities

10.1.4 solution

See below:

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = region, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  facet_wrap(region ~ trans_type, ncol = 6) + 
  lab_top_cities

10.2 Complete Themes

ggplot2 comes with a few default theme settings. Read more about them here. These themes come with arguments to adjust the font_size and font_family on our plot.

theme_minimal(
  base_size = 11,
  base_family = "",
  base_line_size = base_size/22,
  base_rect_size = base_size/22
)

10.2.1 exercise

We’ll use the theme_minimal() function to reduce the number of elements on our graph (don’t add any arguments).

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = region, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  facet_wrap(region ~ trans_type, ncol = 6) + 
  ______________() +
  lab_top_cities

10.2.2 solution

See below:

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = region, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  facet_wrap(region ~ trans_type, ncol = 6) + 
  theme_minimal() +
  lab_top_cities

10.3 Theme elements

Themes (accessible with the theme function) give up the ability to customize various element_() settings, which affect the ‘look’ of our graph.

10.3.1 exercise

We will start by moving the legend.position to "top" so it’s not crowding the x axis on our graph.

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = region, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  facet_wrap(region ~ trans_type, ncol = 6) + 
  theme_minimal() + 
  theme(___________ = _____) +
  lab_top_cities

10.3.2 solution

See below:

TopUSCities %>% 
  ggplot(aes(x = date, y = dir_request, 
             group = region, 
             color = trans_type)) + 
  geom_line(size = 0.1) + 
  geom_hline(yintercept = 100, size = 0.2, color = "gray20") +
  facet_wrap(region ~ trans_type, ncol = 6) + 
  theme_minimal() + 
  theme(legend.position = "top") +
  lab_top_cities

Notice we pass the theme() layer after the theme_minimal() layer (if we ordered these the other way around, theme_minimal() would overwrite any custom settings we made in theme()).

10.4 Small Multiples

What if we wanted to facet more than 8 cities? Fortunately, we have a the ggforce and geofacet packages for doing just that!

library(sf)
library(geofacet)
library(ggforce)
library(jcolors)

Building the Graph Data

We will start by filtering the TidyApple to only the 50 US states (we’ve removed three US territories) and storing these data in TidyAppleUS.
Next we limit the date range to the beginning of the shelter in place (from February 1, 2020 to May 1, 2020). These data get stored in TidyAppleUST1.
We then create a dataset with only "transit" direction requests, and we count these by state (sub_region), arrange the data descending with sort = TRUE, and take the top 25 rows (Top25TransitStates).

Finally, we filter TidyAppleUST1 using the 25 states in Top25TransitStates to create our graphing dataset, Top25TransitUSAllT1.

# create only US states (TidyAppleUS)
TidyApple %>% 
  filter(country == "United States" & 
         !sub_region %in% c("Guam", "Puerto Rico", 
                            "Virgin Islands")) -> TidyAppleUS
# create shelter in place time 1 (TidyAppleUST1)
TidyAppleUS %>% 
  filter(date >= as_date("2020-02-01") &
           date <= as_date("2020-05-01")) -> TidyAppleUST1

# create top 25 states (Top25TransitStates)
Top25TransitStates <- TidyAppleUST1 %>% 
  filter(trans_type == "transit") %>% 
  count(sub_region, trans_type, sort = TRUE) %>% 
  head(25)

# filter T1 to states with the most transit requests (Top25TransitUSAllT1)
TidyAppleUST1 %>% 
  filter(sub_region %in% 
           unique(Top25TransitStates$sub_region)) -> Top25TransitUSAllT1

Top25TransitUSAllT1 %>% skimr::skim()

Data summary
Name	Piped data
Number of rows	188643
Number of columns	7
_______________________
Column type frequency:
character	5
Date	1
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
geo_type	1	4	6	2
region	1	5	39	1058
trans_type	1	7	7	3
sub_region	1	4	14	25
country	1	13	13	1

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-02-01	2020-05-01	2020-03-17	91

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
dir_request	0	1	92.68	36.02	0.44	68.64	93.6	113.14	1379.02	▇▁▁▁▁

10.4.1 exercise

set title to "States With Highest Transit Direction Requests"
set subtitle to "Top 25 states based on number of total transit requests"

lab_facet_wrap_paginate <- labs(
       x = "Date", y = "Direction Requests", 
       title = _____________________________________,
       subtitle = _____________________________________)

10.4.2 solution

lab_facet_wrap_paginate <- labs(
       x = "Date", y = "Direction Requests", 
       title = "States With Highest Transit Direction Requests",
       subtitle = "Top 25 states based on number of total transit requests")

10.4.3 exercise

Inside ggforce::facet_wrap_paginate():

map sub_region as the variable to facet using the ~
map 5 to ncol
map "free_y" to scales

Inside theme()

map element_blank() to panel.border and panel.background
map element_text(size = 6) to axis.text.x and axis.text.y
map element_text(colour = 'black') to strip.text
map element_rect(fill = "gray93") to strip.background
map "top" to legend.position

Top25TransitUSAllT1 %>% 
  # global settings
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  # lines
  geom_hline(yintercept = 100, size = 0.3, color = "black") + 
  geom_line(size = 0.2) +
  # faceting
  ggforce::facet_wrap_paginate(~ __________, 
                               ncol = _, 
                               scales = _______) + 
  # theme settings
  theme(__________ = __________(), 
        __________ = __________(),
        
        __________ = __________(size = _), 
        __________ = __________(size = _),
        
        __________ = __________(colour = __________),
        
        __________ = __________(fill = __________), 
        
        __________ = __________) + 
  # labels
  lab_facet_wrap_paginate

10.4.4 solution

See below:

Top25TransitUSAllT1 %>% 
  # global settings
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  # lines
  geom_hline(yintercept = 100, size = 0.3, color = "black") + 
  geom_line(size = 0.2) +
  # faceting
  ggforce::facet_wrap_paginate(~ sub_region, 
                               ncol = 5, 
                               scales = "free_y") + 
  # theme settings
  theme(panel.border = element_blank(), 
        panel.background = element_blank(),
        axis.text.x = element_text(size = 6), 
        axis.text.y = element_text(size = 6),
        strip.text = element_text(colour = 'black'),
        strip.background = element_rect(fill = "gray93"), 
        legend.position = "top") + 
  # labels
  lab_facet_wrap_paginate

10.5 Adjusting Colors

Changing the colors on graphs gives us the ability to further customize their look. We can set these manually, or use one of the many complete color palettes from a user-written package. Below we’ll use the jcolors package to highlight the transit direction requests from the previous graph.

10.5.1 exercise

add scale_color_jcolors() and play with the palette argument to make the graph look like the solution.

Top25TransitUSAllT1 %>% 
  # global settings
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  # lines
  geom_hline(yintercept = 100, size = 0.3, color = "black") + 
  geom_line(size = 0.2) +
  # faceting
  ggforce::facet_wrap_paginate(~ sub_region, 
                               ncol = 5, 
                               scales = "free_y") + 
  # theme settings
  theme(panel.border = element_blank(), 
        panel.background = element_blank(),
        axis.text.x = element_text(size = 6), 
        axis.text.y = element_text(size = 6),
        strip.text = element_text(colour = 'black'),
        strip.background = element_rect(fill = "gray93"), 
        legend.position = "top") + 
  # adjust colors
  __________________________(palette = ____) + 
  
  lab_facet_wrap_paginate

10.5.2 solution

See below:

Top25TransitUSAllT1 %>% 
  # global settings
  ggplot(aes(x = date, y = dir_request, 
             group = trans_type, 
             color = trans_type)) + 
  # lines
  geom_hline(yintercept = 100, size = 0.3, color = "black") + 
  geom_line(size = 0.2) +
  # faceting
  ggforce::facet_wrap_paginate(~ sub_region, 
                               ncol = 5, 
                               scales = "free_y") + 
  # theme settings
  theme(panel.border = element_blank(), 
        panel.background = element_blank(),
        axis.text.x = element_text(size = 6), 
        axis.text.y = element_text(size = 6),
        strip.text = element_text(colour = 'black'),
        strip.background = element_rect(fill = "gray93"), 
        legend.position = "top") + 
  
  scale_color_jcolors(palette = "pal3") + 
  
  lab_facet_wrap_paginate

10.6 Extra: `geofacet`

We’re now going to look at all 50 states using the facet_geo() function from the geofacet package. To make this graph easier to interpret, we’re going to focus only on walking mobility data, and adjust the dir_request value to absolute change from baseline (set to 100 on 2020-01-13).

10.6.1 Adjusted Walking Data

Create the USWalkingAdj data by filtering the trans_type to "walking" and creating two new variables: above_below (a logical indicator for values being above or below the baseline value of 100), and dir_request_adj (the adjusted direction request value).

10.6.2 exercise

Fill in the correct variables in the wrangling steps below:

USWalkingAdj <- TidyAppleUS %>% 
  filter(trans_type == _________) %>%
  mutate(above_below = _________ < 100, 
         dir_request_adj = _________ - 100)

USWalkingAdj %>% 
  skimr::skim()

10.6.3 solution

See below:

USWalkingAdj <- TidyAppleUS %>% 
  filter(trans_type == "walking") %>%
  mutate(above_below = dir_request < 100, 
         dir_request_adj = dir_request - 100)

USWalkingAdj %>% 
  skimr::skim()

Data summary
Name	Piped data
Number of rows	160085
Number of columns	9
_______________________
Column type frequency:
character	5
Date	1
logical	1
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
geo_type	1	4	6	2
region	1	5	39	452
trans_type	1	7	7	1
sub_region	1	4	14	48
country	1	13	13	1

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-13	2020-11-24	2020-06-19	317

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
above_below	220	1	0.32	FAL: 108071, TRU: 51794

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
dir_request	220	1	129.36	63.68	0.44	89.44	122.7	163.82	1379.02	▇▁▁▁▁
dir_request_adj	220	1	29.36	63.68	-99.56	-10.56	22.7	63.82	1279.02	▇▁▁▁▁

10.6.4 exercise

Assign the following to the labels:

set "US Trends in Mobility Data for Walking (Adjusted)" to title
set "https://covid19.apple.com/mobility" to caption

lab_facet_geo <- labs(x = "Date", 
                      y = "Direction Requests (Walking)", 
       title = ____________________________________________, 
       subtitle = paste0("Original data uses 100 as baseline for usage at ", 
                         min(USWalkingAdj$date)), 
       caption = ____________________________________________, 
       fill = "Below Baseline")

10.6.5 solution

See below:

lab_facet_geo <- labs(x = "Date", 
                      y = "Direction Requests (Walking)", 
       title = "US Trends in Mobility Data for Walking (Adjusted)", 
       subtitle = paste0("Original data uses 100 as baseline for usage at ", 
                         min(USWalkingAdj$date)), 
       caption = "https://covid19.apple.com/mobility",
       fill = "Below Baseline")

10.6.6 exercise

set the colors in color_bl_or as c("#8470FF", "#7FFFD4")
set yintercept to 0 in geom_hline()
set the values in scale_fill_manual() to color_bl_or
map sub_region to facet_geo using ~

Inside theme()

set the panel.border and panel.background to element_blank()
set the axis.text.x and axis.text.y to element_text(size = 6)
set the strip.text.x to element_text(size = 7)
set strip.text to element_text(colour = 'white')
set strip.background to element_rect(fill = "black")
set legend.position to "bottom"

# set colors
color_bl_or <- c(____________, ____________)

USWalkingAdj %>% 
  ggplot(aes(x = date, y = dir_request_adj, 
             group = sub_region, fill = above_below)) + 
  
  geom_col() + 
  
  geom_hline(yintercept = _, color = "gray7") + 
  
  scale_fill_manual(values = ____________) +
  
  facet_geo(~ sub_region) +
  
  theme_bw() + 
  
  theme(______________ = ______________(), 
        ______________ = ______________(),
        
        ______________ = ______________(size = _), 
        ______________ = ______________(size = _),
        ______________ = ______________(size = _),
        
        ______________ = ______________(colour = ______________),
        ______________ = ______________(fill = ______________), 
        
        ______________ = ______________) + 
  
  lab_facet_geo

10.6.7 solution

See below:

# set colors
color_bl_or <- c("#8470FF", "#7FFFD4")

USWalkingAdj %>% 
  ggplot(aes(x = date, y = dir_request_adj, 
             group = sub_region, fill = above_below)) + 
  
  geom_col() + 
  
  geom_hline(yintercept = 0, 
             color = "gray7") + 
  
  scale_fill_manual(values = color_bl_or) +
  
  facet_geo(~ sub_region) +
  
  theme_bw() + 
  
  theme(panel.border = element_blank(), 
        panel.background = element_blank(),
        axis.text.x = element_text(size = 6), 
        axis.text.y = element_text(size = 6),
        strip.text.x = element_text(size = 7),
        strip.text = element_text(colour = 'white'),
        strip.background = element_rect(fill = "black"), 
        legend.position = "bottom") + 
  
  lab_facet_geo

11 Wrap Up

Original Question: How has COVID changed our modes of transportation?

Which graphs do you feel are best at answering this question? Why?
What other information (tables, annotations, etc.) would you include with the graphs?

Data Visualization as Communication

1 Objectives

2 Materials

3 Previous lessons

4 Load the packages

5 Example: COVID and Mobility

5.1 Data Import

5.1.1 Tidy AppleMobRaw

5.1.2 Wrangle AppleMobRaw

5.2 Counting

5.2.1 Count

5.2.2 Count & sort

5.2.3 Iterate with count

6 Visualizing Distributions

6.1 Histograms

6.1.1 exercise

6.1.2 solution

6.2 Adjusting Y Axes

6.2.1 exercise

6.2.2 solution

6.3 Histogram Shape

6.3.1 exercie

6.3.2 solution

6.3.3 exercise

6.3.4 solution

6.4 Density Plots

6.4.1 exercise

6.4.2 solution

6.4.3 exercise

6.4.4 solution

6.5 Ridgeline Plots

6.6 Violin Plots

6.6.1 exercise

6.6.2 solution

6.6.3 exercise

6.6.4 solution

6.6.5 exercise

6.6.6 solution

7 Visualizing Trends

7.1 Focus on US Cities

7.1.1 exercise

7.1.2 solution

7.2 Updating Labels

7.2.1 exercise

7.2.2 solution

7.2.3 exercise

7.2.4 solution

7.3 Adjusting Line Size

7.3.1 exercise

7.3.2 solution

7.4 Setting Date Range

7.4.1 exercise

7.4.2 solution

7.4.3 exercise

7.4.4 solution

8 Adding Text to Graphs

8.1 Annotations

8.1.1 exercise

8.1.2 solution

8.1.3 exercise

8.1.4 solution

8.1.5 exercise

8.1.6 solution

8.1.7 exercise

8.1.8 solution

8.2 Plotting area

8.3 Labeling Values

8.3.1 exercise

8.3.2 solution

8.3.3 exercise

8.3.4 solution

8.3.5 exercise

8.3.6 solution

8.3.7 exercise

8.3.8 solution

8.4 Labeling Values 2

8.4.1 exercise

8.4.2 solution

8.4.3 exercise

8.4.4 solution

5.1.1 Tidy `AppleMobRaw`

5.1.2 Wrangle `AppleMobRaw`

9.2 Set Global `aes()`

10.1 `facet_wrap()`

10.6 Extra: `geofacet`