Data Visualization Lab - Key
Part 1
Load the packages.
library(tidyverse)Read in the Bike Lanes Dataset using the read_csv function with the following link: http://jhudatascience.org/intro_to_r/data/Bike_Lanes.csv
Assign the data to an object called bike.
Then, use the provided code to compute a data frame bike_agg with aggregate summary of bike lanes: average length of lanes (lane_avg_length) for each year (dateInstalled).
bike <- read_csv(file = "http://jhudatascience.org/intro_to_r/data/Bike_Lanes.csv")## Rows: 1631 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): subType, name, block, type, project, route
## dbl (3): numLanes, length, dateInstalled
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.bike_agg <- bike %>%
# filter data to keep only these observations for which year is non-0
filter(dateInstalled != 0) %>%
group_by(dateInstalled) %>%
summarise(lane_avg_length = mean(length))
bike_agg## # A tibble: 8 × 2
## dateInstalled lane_avg_length
## <dbl> <dbl>
## 1 2006 1469.
## 2 2007 310.
## 3 2008 249.
## 4 2009 407.
## 5 2010 246.
## 6 2011 233.
## 7 2012 271.
## 8 2013 290.1.1
Use the object you just created (bike_agg) and the ggplot2 package to make plot of average length of lanes (lane_avg_length; y-axis) for each year (dateInstalled; x-axis). You can use lines layer (+ geom_line()) or points layer (+ geom_point()), or both!
Assign the plot to variable my_plot. Type my_plot in the console to have it displayed.
# General format
ggplot(???, aes(x = ???, y = ???)) +
??? +
???my_plot <-
ggplot(bike_agg, aes(x = dateInstalled, y = lane_avg_length)) +
geom_line() +
geom_point()
my_plot
1.2
“Update” your plot by adding a title and changing the x and y axis titles. (Hint: use the labs function.)
my_plot <- my_plot +
labs(
x = "Year of bike lane installation",
y = "Average bike lane length",
title = "Average bike lane length 2006-2013"
)
my_plot
1.3
Use the scale_x_continuous() function to plot the x axis with the following breaks c(2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013).
# General format
my_plot<- my_plot +
scale_x_continuous(breaks = ?????)my_plot <- my_plot +
scale_x_continuous(
breaks = c(2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013)
)
my_plot
my_plot <- my_plot +
scale_x_continuous(
breaks = seq(from = 2006, to = 2013, by = 1)
)## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.my_plot
1.4
Observe several different versions of the plot by displaying my_plot while adding a different “theme” to it.
# General format
my_plot + theme_bw()my_plot + theme_bw()
my_plot + theme_classic()
my_plot + theme_dark()
my_plot + theme_gray()
my_plot + theme_void()
Practice on Your Own!
P.1
Create a boxplot (with the geom_boxplot() function) using the Orange data, where Tree is plotted on the x axis and circumference is plotted on the y axis.
Orange %>%
ggplot(aes(x = Tree, y = circumference)) +
geom_boxplot()
Notice how the trees are ordered. We will learn more about this soon!
Part 2
2.1
Use the provided code to compute a data frame bike_agg_2 with aggregate summary of bike lanes: number of lanes (lane_count) – separately for each year (dateInstalled) and for each lane type.
bike_agg_2 <- bike %>%
filter(dateInstalled != 0) %>%
group_by(dateInstalled, type) %>%
summarise(lane_count = n())## `summarise()` has grouped output by 'dateInstalled'. You can override using the
## `.groups` argument.bike_agg_2## # A tibble: 22 × 3
## # Groups: dateInstalled [8]
## dateInstalled type lane_count
## <dbl> <chr> <int>
## 1 2006 BIKE LANE 2
## 2 2007 BIKE LANE 127
## 3 2007 SHARROW 95
## 4 2007 SIGNED ROUTE 146
## 5 2008 BIKE LANE 55
## 6 2008 SHARROW 148
## 7 2008 SIDEPATH 3
## 8 2009 BIKE LANE 46
## 9 2009 SHARED BUS BIKE 30
## 10 2009 SHARROW 10
## # ℹ 12 more rows2.2
Use ggplot2 package to make a plot showing trajectories of number of lanes (lane_count; y-axis) over year (dateInstalled; x-axis), where each bike line type has a different color (hint: use color = type in mapping).
# General format
ggplot(???, aes(
x = ???,
y = ???,
color = ???
)) +
geom_line() +
geom_point()ggplot(bike_agg_2, aes(
x = dateInstalled,
y = lane_count,
color = type
)) +
geom_line() +
geom_point()
2.3
Redo the above plot by adding a faceting (+ facet_wrap( ~ type, ncol = 3)) to have data for each bike line type in a separate plot panel.
(You may see geom_path: Each group consists of only one observation. Do you need to adjust the group aesthetic? warning as some bike lane types will have only 1 point plotted while trying to plot a line). Assign the new plot as an object called facet_plot.
Try adjusting the number of columns in the facet_wrap to see how this changes the plot.
facet_plot <- ggplot(bike_agg_2, aes(
x = dateInstalled,
y = lane_count,
color = type
)) +
geom_line() +
geom_point() +
facet_wrap(~type, ncol = 3)
facet_plot## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
2.4
Observe what happens when you remove either geom_line() OR geom_point() from one of your plots above.
# These elements are removed from the plot, like layersPractice on Your Own!
P.2
Modify facet_plot to remove the legend (hint use theme() and the legend.position argument) and change the names of the axis titles to be “Number of bike lanes” for the y axis and “Date bike lane was installed” for the x axis.
facet_plot <- facet_plot +
theme(legend.position = "none") +
labs(
y = "Number of bike lanes",
x = "Date bike lane was installed"
)
facet_plot## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
P.3
Modify facet_plot one more time with a fun theme! Look into the ThemePark package It has lots of fun themes! Try one out! Remember you will need to install it using remotes::install_github("MatthewBJane/ThemePark")and load in the library.
# remotes::install_github("MatthewBJane/ThemePark")
library(ThemePark)
facet_plot + theme_spiderman()## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?