Last updated: 2019-11-26

Checks: 7 0

Knit directory: PSYMETAB/

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Rmd 2db9bcf Jenny 2019-11-26 wflow_publish(all = T)
html 2db9bcf Jenny 2019-11-26 wflow_publish(all = T)
html 5fb939e Jenny Sjaarda 2019-11-26 Build site.
Rmd 7bae873 Jenny Sjaarda 2019-11-26 Start workflowr project.

Describe your project.

see data/README.md for information on data inputs see scripst/README.md for information on scripts

Last updated: 2019-11-26

Code version: 2db9bcfcc7febefb29ff93ea2a30e41c0f0156f2

We used this site to collaborate and share our results. Please feel free to explore. The results that made it into the final paper are in the section Finalizing below. Here are some useful links:

Setting up the project

Finalizing

Process sequence data

Analysis

Download data

One-time investigations

Other

LCL data from a full flowcell


Divvy bikes
Photo by Steven Vance / CC BY 2.0

Project overview

The purpose of this project is to gain some insight into city-wide biking trends by analyzing the Divvy trip data. Also, I examine trip data from one bike station at the University of Chicago to compare the biking patterns at the university against city-wide trends.

All the results and plots presented in the pages below should be reproduceable on your computer. Follow the Setup Instructions if you are interested in reproducing the results for yourself.

These are the results of my analyses. They were generated by rendering the R Markdown documents into webpages.

  1. A first glance at the Divvy data.

  2. A map of the Divvy stations in Chicago.

  3. Exploring daily bike commuting trends from the Divvy data.

  4. Exploring seasonal biking trends from the Divvy data.

Credits

This workflowr project was developed by Peter Carbonetto at the University of Chicago.

Thanks to John Blischak and Matthew Stephens for their assistance and support. Also, thanks to Larry Layne and Austin Wehrwein for sharing their analyses of the Divvy trip data that inspired some of the investigations here.


sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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     

loaded via a namespace (and not attached):
 [1] workflowr_1.5.0 Rcpp_1.0.2      rprojroot_1.3-2 digest_0.6.20  
 [5] later_0.8.0     R6_2.4.0        backports_1.1.4 git2r_0.26.1   
 [9] magrittr_1.5    evaluate_0.14   stringi_1.4.3   fs_1.3.1       
[13] promises_1.0.1  whisker_0.4     rmarkdown_1.15  tools_3.6.1    
[17] stringr_1.4.0   glue_1.3.1      httpuv_1.5.2    xfun_0.9       
[21] yaml_2.2.0      compiler_3.6.1  htmltools_0.3.6 knitr_1.25