Data Visualization with ggplot2 (part 2)

1 Outline

1. Import Apple Mobility Data
2. Wrangle Apple Mobility Data
3. Trends
   
4. Adding text
   
5. Labeling values
   
6. Reference lines

2 Materials

View the slides for this section here.

3 Import TidyApple data

We’re going to import the data from the previous exercises.

TidyApple <- vroom::vroom(file = "https://bit.ly/3IiwcJs", delim = ",")
head(TidyApple)

4 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.

4.1 Focus on US Cities

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

4.1.1 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(___________)

4.1.2 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

4.2 Updating Labels

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

4.2.1 exercise

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

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

4.2.2 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.

4.2.3 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 = __________)) +
  __________

4.2.4 solution

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

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

4.3 Adjusting Line Size

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

4.3.1 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

4.3.2 solution

Now the trends are easier to see.

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

4.4 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.

4.4.1 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(_____________)

4.4.2 solution

# 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

4.4.3 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")

4.4.4 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

5 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.

5.1 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.

5.1.1 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()

5.1.2 solution

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	n_missing	complete_rate	min	max	empty	n_unique	whitespace
geo_type	0	1	4	4	0	1	0
region	0	1	5	39	0	111	0
trans_type	0	1	7	7	0	3	0
sub_region	0	1	4	14	0	39	0
country	0	1	13	13	0	1	0

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	▇▃▁▁▁

5.1.3 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")

5.1.4 solution

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")

5.1.5 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

5.1.6 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

5.1.7 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

5.1.8 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

5.2 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

5.3 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

5.3.1 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

5.3.2 solution

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

5.3.3 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")

5.3.4 solution

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")

5.3.5 exercise

Create max_driving_labels using paste0() with region and date.

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

5.3.6 solution

See below:

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

5.3.7 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

5.3.8 solution

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

5.4 Labeling Values 2

Min Walking Requests

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

5.4.1 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

5.4.2 solution

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

5.4.3 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")

5.4.4 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")

5.4.5 exercise

Create min_walking_labels using paste0() with region and date

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

5.4.6 solution

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

5.4.7 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

5.4.8 solution

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

6 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).

6.1 Top Cities

Top Cities

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

6.1.1 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)

6.1.2 solution

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", "Chic…
## $ trans_type  <chr> "driving", "driving", "driving", "driv…
## $ sub_region  <chr> "Illinois", "Illinois", "Illinois", "I…
## $ country     <chr> "United States", "United States", "Uni…
## $ date        <date> 2020-01-13, 2020-01-14, 2020-01-15, 2…
## $ dir_request <dbl> 100.00, 103.68, 104.45, 108.72, 132.80…

6.1.3 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 = _________)

6.1.4 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")

6.2 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!

6.2.1 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

6.2.2 solution

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

6.3 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().

6.3.1 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

6.3.2 solution

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.