Last updated: 2017-06-29

Code version: 5c4fd93

I begin by loading packages and some useful function definitions into the R environment.

library(data.table)
source("../code/functions.R")


Reading the data

I wrote a function, read.divvy.data to read the trip and station data from the CSV files that were downloaded to the data directory. This function uses fread from the data.table packages to read the CSV files, which is much faster than the read.table function. This also take a few additional steps to prepare the data so that they are in a format that is 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.


A first glance at the Divvy data

dim(divvy$stations)
# [1] 581   5
names(divvy$stations)
# [1] "name"        "latitude"    "longitude"   "dpcapacity"  "online_date"
dim(divvy$trips)
# [1] 3595383      12
names(divvy$trips)
#  [1] "trip_id"           "starttime"         "stoptime"         
#  [4] "bikeid"            "tripduration"      "from_station_id"  
#  [7] "from_station_name" "to_station_id"     "to_station_name"  
# [10] "usertype"          "gender"            "birthyear"
  • Number of stations
  • Number of trips in 2016

Which station(s) had the most activity?

Session information

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] data.table_1.10.4
# 
# loaded via a namespace (and not attached):
#  [1] backports_1.0.5 magrittr_1.5    rprojroot_1.2   tools_3.3.2    
#  [5] htmltools_0.3.6 yaml_2.1.14     Rcpp_0.12.11    stringi_1.1.2  
#  [9] rmarkdown_1.6   knitr_1.16      git2r_0.18.0    stringr_1.2.0  
# [13] digest_0.6.12   evaluate_0.10.1

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