Last updated: 2018-01-23
Code version: 96c320f
Here, we will take a brief look at the data provided by Divvy.
I begin by loading a few packages, as well as some additional functions I wrote for this project.
library(data.table)
source("../code/functions.R")I wrote a function, read.divvy.data, that reads in the trip and station data from the Divvy CSV files. This function uses fread from the data.table package to quickly read in the data (it is much faster than read.table). This function also prepares the data, including the departure dates and times, so that they are easier to work with.
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.We have data on 581 Divvy stations across the city.
nrow(divvy$stations)
# [1] 581
print(head(divvy$stations),row.names = FALSE)
# name latitude longitude dpcapacity online_date
# 2112 W Peterson Ave 41.99118 -87.68359 15 5/12/2015
# 63rd St Beach 41.78102 -87.57612 23 4/20/2015
# 900 W Harrison St 41.87468 -87.65002 19 8/6/2013
# Aberdeen St & Jackson Blvd 41.87773 -87.65479 15 6/21/2013
# Aberdeen St & Monroe St 41.88042 -87.65560 19 6/26/2013
# Ada St & Washington Blvd 41.88283 -87.66121 15 10/10/2013We also have information about the >3 million trips taken on Divvy bikes in 2016.
nrow(divvy$trips)
# [1] 3595383
print(head(divvy$trips),row.names = FALSE)
# trip_id starttime bikeid tripduration from_station_id
# 9080551 2016-03-31 23:53:00 155 841 344
# 9080550 2016-03-31 23:46:00 4831 649 128
# 9080549 2016-03-31 23:42:00 4232 210 350
# 9080548 2016-03-31 23:37:00 3464 1045 303
# 9080547 2016-03-31 23:33:00 1750 202 334
# 9080546 2016-03-31 23:31:00 4302 638 67
# from_station_name to_station_id to_station_name
# Ravenswood Ave & Lawrence Ave 458 Broadway & Thorndale Ave
# Damen Ave & Chicago Ave 213 Leavitt St & North Ave
# Ashland Ave & Chicago Ave 210 Ashland Ave & Division St
# Broadway & Cornelia Ave 458 Broadway & Thorndale Ave
# Lake Shore Dr & Belmont Ave 329 Lake Shore Dr & Diversey Pkwy
# Sheffield Ave & Fullerton Ave 304 Broadway & Waveland Ave
# usertype gender birthyear start.week start.day start.hour
# Subscriber Male 1986 13 Thursday 23
# Subscriber Male 1980 13 Thursday 23
# Subscriber Male 1979 13 Thursday 23
# Subscriber Male 1980 13 Thursday 23
# Subscriber Male 1969 13 Thursday 23
# Subscriber Male 1991 13 Thursday 23Out of all the Divvy stations in Chicago, the one on Navy Pier (near the corner of Streeter and Grand) had the most activity by far.
departures <- table(divvy$trips$from_station_name)
as.matrix(head(sort(departures,decreasing = TRUE)))
# [,1]
# Streeter Dr & Grand Ave 90042
# Lake Shore Dr & Monroe St 51090
# Theater on the Lake 47927
# Clinton St & Washington Blvd 47125
# Lake Shore Dr & North Blvd 45754
# Clinton St & Madison St 41744I would also like to take a close look at the trip data for the main Divvy station on the University of Chicago campus. The Divvy bikes were rented almost 8,000 times in 2016 at that location.
sum(divvy$trips$from_station_name == "University Ave & 57th St",na.rm = TRUE)
# [1] 7944This is the version of R and the packages that were used to generate these results.
sessionInfo()
# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.2
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
#
# 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] data.table_1.10.4-3
#
# loaded via a namespace (and not attached):
# [1] compiler_3.4.3 backports_1.1.2 magrittr_1.5 rprojroot_1.3-2
# [5] tools_3.4.3 htmltools_0.3.6 yaml_2.1.16 Rcpp_0.12.15
# [9] stringi_1.1.6 rmarkdown_1.8 knitr_1.18 git2r_0.21.0
# [13] stringr_1.2.0 digest_0.6.13 evaluate_0.10.1This R Markdown site was created with workflowr 0.10.1.