Divvy usage by time-of-day

Read the data

Following my earlier steps, I use function read.divvy.data to read the trip and station data from the CSV files.

divvy <- read.divvy.data()
# Reading station data from ../data/Divvy_Stations_2016_Q4.csv.
# Reading trip data from ../data/Divvy_Trips_2016_Q1.csv.
# Reading trip data from ../data/Divvy_Trips_2016_04.csv.
# Reading trip data from ../data/Divvy_Trips_2016_05.csv.
# Reading trip data from ../data/Divvy_Trips_2016_06.csv.
# Reading trip data from ../data/Divvy_Trips_2016_Q3.csv.
# Reading trip data from ../data/Divvy_Trips_2016_Q4.csv.
# Preparing Divvy data for analysis in R.
# Converting dates and times.

To make it easier to compile statistics by time of day, I convert the “start hour” column to a factor (i.e., categorical variable).

divvy$trips <- transform(divvy$trips,start.hour = factor(start.hour,0:23))

Count departures

Now that start.hour is a factor, it is easy to create a bar chart showing the total number of departures at each hour.

Unsurprisingly, there is very little biking activity at night. The two peaks (“modes”) in the bar chart nicely recapitulate the morning and afternoon rush hours.

ggplot(divvy$trips,aes(start.hour)) +
  geom_bar(fill = "black",width = 0.6) +
  theme_minimal() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

This summary is a bit muddled because it is counting trips on the both weekdays and weekends; once we separate the counts by the day of the week, the rush-hour trends become more striking (and disappear completely on Saturday and Sunday).

ggplot(divvy$trips,aes(start.hour)) +
  geom_bar(fill = "black",width = 0.6) +
  facet_wrap(~start.day,ncol = 2) +
  scale_x_discrete(breaks = seq(0,24,2)) +
  theme_minimal() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

University of Chicago trends

ggplot(subset(divvy$trips,from_station_name == "University Ave & 57th St"),
       aes(start.hour)) +
  geom_bar(fill = "black",width = 0.6) +
  facet_wrap(~start.day,ncol = 2) +
  scale_x_discrete(breaks = seq(0,24,2)) +
  theme_minimal() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())

Session information

This is the version of R and the packages that were used to generate these results.

sessionInfo()
# R version 3.3.2 (2016-10-31)
# Platform: x86_64-apple-darwin13.4.0 (64-bit)
# Running under: macOS Sierra 10.12.5
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] ggplot2_2.2.1     data.table_1.10.4
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_0.12.11     assertthat_0.2.0 digest_0.6.12    rprojroot_1.2   
#  [5] plyr_1.8.4       grid_3.3.2       gtable_0.2.0     backports_1.0.5 
#  [9] git2r_0.18.0     magrittr_1.5     scales_0.4.1     evaluate_0.10.1 
# [13] stringi_1.1.2    lazyeval_0.2.0   rmarkdown_1.6    labeling_0.3    
# [17] tools_3.3.2      stringr_1.2.0    munsell_0.4.3    yaml_2.1.14     
# [21] colorspace_1.3-2 htmltools_0.3.6  knitr_1.16       tibble_1.2