Part 1

Load the libraries

library(readr)
library(ggplot2)
library(dplyr)
library(jhur)

Read Bike Lanes Dataset using read_bike() function from jhur package. Assign it to bike variable.

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_bike()

## 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.

Use ggplot2 package 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

ggplot(bike_agg, aes(x = dateInstalled, y = lane_avg_length)) + 
  geom_line() + 
  geom_point()

my_plot <- 
  ggplot(bike_agg, aes(x = dateInstalled, y = lane_avg_length)) + 
  geom_line() + 
  geom_point()

my_plot

“Update” your plot by adding a title and changing the x and y axis titles.

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

Use the scale_x_continuous() function and its arguments to modify x-axis look. Use scale_y_continuous() function and its arguments to modify the y-axis.

my_plot <- 
  my_plot + 
  scale_x_continuous(breaks = c(2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013)) + 
  scale_y_continuous(limits = c(0, NA))  # force y-axis to start from 0, do not force upper bound 
  
my_plot

Observe several different versions of the plot by displaying my_plot while adding a different “theme” to it.

my_plot + theme_bw()

my_plot + theme_classic()

my_plot + theme_dark()

my_plot + theme_gray()

my_plot + theme_void()

Part 2

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
## # … with 12 more rows

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 line with a different color (hint: use color = type in mapping).

ggplot(bike_agg_2, aes(x = dateInstalled, 
                       y = lane_count, 
                       color = type)) + 
  geom_line() + 
  geom_point()

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). Save the new plot as an object called facet_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_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

Bonus: Modify facet_plot to remove the legend and change the names of the axis titles to be “Number of bike lanes” for the x axis and “Date bike lane was installed” for the y axis.

facet_plot <- facet_plot +
  theme(legend.position = "none") +
  labs(x = "Number of bike lanes",
       y = "Date bike lane was installed")
facet_plot

## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

Data Visualization Lab Key

Part 1

Part 2