R-Ladies RTP ggplot2 Workshop

October 15, 2016

Welcome to R-Ladies RTP!

Outline

Why ggplot2?
Conceptual ggplot2
Basic ggplot2 syntax
Common pitfalls
The power of layers
The power of groups
The power of facets
Scales and legends
Themes and refinements
Extensions

Materials

All source code at https://github.com/rladies/rtp-data-viz-ggplot2-10-15-16

Datasets

Datasets for today are here: https://www.dropbox.com/s/zhmn02ti0ggxdj7/rladies_ggplot2_datasets.rda?dl=1
You can download them from R:

download.file( 'https://www.dropbox.com/s/zhmn02ti0ggxdj7/ rladies_ggplot2_datasets.rda?dl=1', 'rladies_ggplot2_datasets.rda')

attach('rladies_ggplot2_datasets.rda')

Dataset Descriptions

Transit ride data
rides_df: one row per ride
daily_df: daily summary of rides
hourly_df: hourly summary of rides
Durham registered voter data
durham_voters_df: one row per voter

Why ggplot2?

Exploratory Power

explore data very quickly, and don't screw up
graphics "pit of success"
refine plots to be publication quality

Conceptually Cohesive

based on the grammar of graphics
principles and patterns
very complex plots from simple building blocks

Dominance

the most prominent static graphics package in R
help is easy to find
developers are contibuting extensions

Conceptual ggplot2

The basics

map variables to aethestics
add "geoms" for visual representation layers
scales can be independently managed
legends are automatically created
statistics are sometimes calculated by geoms…

Learn more

ggplot2 book

Basic ggplot2 syntax

The required elements

DATA
MAPPING
GEOM

Our first hands-on example …

download.file(
'https://www.dropbox.com/s/zhmn02ti0ggxdj7/rladies_ggplot2_datasets.rda?dl=1',
        'rladies_ggplot2_datasets.rda')
        
attach('rladies_ggplot2_datasets.rda')

library(tidyverse)

Basic plot

ggplot(data = daily_df) +
geom_point(mapping = aes(x = ride_date, y = n_rides))

Mappings can be general

ggplot(data = daily_df, mapping = aes(x = ride_date, y = n_rides)) +
geom_point()

Parameters can be unnamed

ggplot(daily_df, aes(x = ride_date, y = n_rides)) +
geom_point()

Data can be passed in

daily_df %>%
ggplot(aes(x = ride_date, y = n_rides)) +
geom_point()

Data can be passed in

daily_df %>%
ggplot(aes(x = ride_date, y = n_rides)) +
geom_point()

Mapping is more than x and y

daily_df %>%
ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
geom_point()

Mapping is more than x and y

daily_df %>%
ggplot(aes(x = ride_date, y = n_rides, size = n_riders)) +
geom_point()

Variable creation on the fly…

daily_df %>%
ggplot(aes(x = ride_date, y = n_rides, color = day_of_week %in% c('Sat', 'Sun'))) +
geom_point()

… or passed in

daily_df %>%
  mutate(day_type = if_else(day_of_week %in% c('Sat', 'Sun'),
                            'Weekend',
                            'Weekday')) %>%
ggplot(aes(x = ride_date, y = n_rides, color = day_type)) +
geom_point()

Common early pitfalls

Mappings that aren't

daily_df %>%
ggplot() +
geom_point(aes(x = ride_date, y = n_rides, color = 'blue'))

Mappings that aren't

daily_df %>%
ggplot() +
geom_point(aes(x = ride_date, y = n_rides), color = 'blue')

+ and %>%

daily_df %>% mutate(day_type = if_else(day_of_week %in% c('Sat', 'Sun'), 'Weekend', 'Weekday')) %>% ggplot(aes(x = ride_date, y = n_rides, color = day_type)) %>% geom_point()

Basic plot exercise

Plot the number of unique routes per day over time, colored by day of week. (n_unique_routes)

Basic plot exercise

daily_df %>%
  ggplot(aes(x = ride_date, y = n_unique_routes, color = day_of_week)) +
  geom_point()

The power of layers

Basic plot

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides)) +
  geom_point()

Two layers!

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides)) +
  geom_point() +
  geom_line()

Iterate on layers

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides)) +
  geom_point() + 
  geom_smooth(span = .1) # try changing span

The power of groups

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() + 
  geom_line()

Now we've got it

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_smooth(span = .2, se = FALSE)

Control data by layer

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point(data = filter(daily_df, !(day_of_week %in% c('Sat', 'Sun')) & n_rides < 200),
             size = 5, color = 'gray') +
  geom_point()

Control data by layer

low_weekdays_df <- daily_df %>%
  filter(!(day_of_week %in% c('Sat', 'Sun')) & n_rides < 100)

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, 
             color = day_of_week, label = ride_date)) +
  geom_point(data = low_weekdays_df, size = 5, color = 'gray') +
  geom_text(data = low_weekdays_df, aes(y = n_rides + 15), 
            size = 2, color = 'black') +
  geom_point()

The power of facets

facet_wrap

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides)) +
  geom_point() + 
  geom_line() +
  facet_wrap( ~ day_of_week)

facet_grid

durham_voters_df %>%
  group_by(race_code, gender_code, age) %>%
  summarize(n_voters = n(),
            n_rep = sum(party == 'REP')) %>%
  filter(gender_code %in% c('F','M') & 
           race_code %in% c('W', 'B', 'A') &
           age != 'Age < 18 Or Invalid Birth Date') %>%
  ggplot(aes(x = age, y = n_voters)) +
  geom_bar(stat = 'identity') +
  facet_grid(race_code ~ gender_code)

Free scales

durham_voters_df %>%
  group_by(race_code, gender_code, age) %>%
  summarize(n_voters = n(),
            n_rep = sum(party == 'REP')) %>%
  filter(gender_code %in% c('F','M') & 
           race_code %in% c('W', 'B', 'A') &
           age != 'Age < 18 Or Invalid Birth Date') %>%
  ggplot(aes(x = age, y = n_voters)) +
  geom_bar(stat = 'identity') +
  facet_grid(race_code ~ gender_code, scales = 'free_y')

Facets + layers

Note: better to use gather

durham_voters_df %>%
  group_by(race_code, gender_code, age) %>%
  summarize(n_voters = n(),
            n_rep = sum(party == 'REP')) %>%
  filter(gender_code %in% c('F','M') & 
           race_code %in% c('W', 'B', 'A') &
           age != 'Age < 18 Or Invalid Birth Date') %>%
  mutate(age_cat = as.numeric(as.factor(age))) %>%
  ggplot(aes(x = age, y = n_voters)) +
  geom_point() +
  geom_line(aes(x = age_cat)) +
  geom_line(aes(x = age_cat, y = n_rep), color = 'red') +
  geom_point(aes(y = n_rep), color = 'red') +
  facet_grid(race_code ~ gender_code, scales = 'free_y') +
  expand_limits(y = 0)

Everyday conveniences

Labels and titles

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_smooth(span = .2, se = FALSE) +
  xlab('') +
  ylab('# of Transit Rides') +
  ggtitle('Transit Rides over time by Day of Week') +
  scale_color_discrete('Day of Week')

Scales and legends

Scale transformation

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  scale_y_reverse()

Scale transformation

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  scale_y_sqrt()

Scale details

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  scale_y_continuous(breaks = c(0, 200, 500))

Themes and refinements

Overall Themes

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  theme_bw()

Overall Themes

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  theme_dark()

Changing Details

daily_df %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  theme(axis.text.x = element_text(angle = 90))

Themes Vignette

To really master themes:

http://docs.ggplot2.org/dev/vignettes/themes.html

Documentation

Online Docs

Better than the regular documentation:

http://docs.ggplot2.org/current/

Extensions

Contributed extensions

Check it out!

http://www.ggplot2-exts.org/

Questions?

Next Meetup

Monday, November 14th, 6pm

Location:

TransLoc, near RTP
4505 Emperor Blvd, Durham
Parking is easy!

Topic:

Getting data into R

Topic/format suggestions?