Last updated: 2017-07-07

Code version: 9077b6c

To reproduce the results on your own computer, please follow these setup instructions.

  1. Download or clone the git repository on your computer.

  2. Download the Divvy data files and copy the files to the “data” directory. I have provided a script code/retrieve_divvy_data.sh that can be used to automatically retrieve and prepare the data files. Alternatively, you can open this script and follow the steps by hand. After completing this step, all these files should be in the data directory:

Divvy_Stations_2016_Q1Q2.csv Divvy_Stations_2016_Q3.csv Divvy_Stations_2016_Q4.csv Divvy_Trips_2016_04.csv Divvy_Trips_2016_05.csv Divvy_Trips_2016_06.csv Divvy_Trips_2016_Q1.csv Divvy_Trips_2016_Q3.csv Divvy_Trips_2016_Q4.csv

  1. Install R and/or Rstudio.

  2. Install the R packages used for the analyses:

R install.packages(c("data.table","ggplot2"))

Once you have completed these steps, you may run the R code. When running the code, make sure your working directory is set to the “analysis” directory:

getwd()
# [1] "/Users/pcarbo/git/wflow-divvy/analysis"


Session information

This is the version of R and the packages that were used to generate the results from the R Markdown notebooks.

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     
# 
# 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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