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<h1 class="title toc-ignore">Divvy usage by time-of-day</h1>
<h4 class="author"><em>Peter Carbonetto</em></h4>

</div>


<!-- Define defaults shared by all workflowr files. -->
<!-- Update knitr chunk options -->
<!-- Insert the date the file was last updated -->
<p><strong>Last updated:</strong> 2018-03-09</p>
<!-- Insert the code version (Git commit SHA1) if Git repository
 exists and R package git2r is installed -->
<p><strong>Code version:</strong> 599efc1</p>
<p>Here we use the Divvy trip data to examine biking trends over the course of a typical day in Chicago.</p>
<p>I begin by loading a few packages, as well as some additional functions I wrote for this project.</p>
<pre class="r"><code>library(data.table)
library(ggplot2)
source(&quot;../code/functions.R&quot;)</code></pre>
<div id="read-the-data" class="section level2">
<h2>Read the data</h2>
<p>Following <a href="first-glance.html">my earlier steps</a>, I use function <code>read.divvy.data</code> to read the trip and station data from the CSV files.</p>
<pre class="r"><code>divvy &lt;- 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.</code></pre>
<p>To make it easier to compile statistics by time of day, I convert the “start hour” column to a factor.</p>
<pre class="r"><code>divvy$trips &lt;- transform(divvy$trips,start.hour = factor(start.hour,0:23))</code></pre>
</div>
<div id="count-departures-by-time-of-day" class="section level2">
<h2>Count departures by time-of-day</h2>
<p>Now that <code>start.hour</code> is a factor, it is easy to create a bar chart showing the total number of departures at each hour. Unsurprisingly, we see little biking activity at night. Further, the two peaks (“modes”) in the bar chart nicely recapitulate the morning and afternoon rush hours.</p>
<pre class="r"><code>ggplot(divvy$trips,aes(start.hour)) +
  geom_bar(fill = &quot;dodgerblue&quot;,width = 0.6) +
  theme_minimal() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())</code></pre>
<p><img src="figure/time-of-day-trends.Rmd/timeofday-barchart-1.png" width="480" style="display: block; margin: auto;" /></p>
<p>However, this bar chart is a bit muddled because it is counting trips during the week and on the weekends. Consider that the bin count <span class="math inline">\(x[h]\)</span> for hour <span class="math inline">\(h\)</span> in the histogram above is a sum of the counts for each day of the week:</p>
<p><span class="math display">\[
\begin{align}
x[h] 
&amp; = \sum_{i\;\in\;\mathsf{DaysOfTheWeek}} x_i[h] \\
&amp; = x_{\mathsf{Mon}}[h] + x_{\mathsf{Tue}}[h] + x_{\mathsf{Wed}}[h] +
x_{\mathsf{Thu}}[h] + x_{\mathsf{Fri}}[h] + x_{\mathsf{Sat}}[h] +
x_{\mathsf{Sun}}[h]
\end{align}
\]</span></p>
<p><em>Note:</em> The math above is embedded in the webpage using <a href="https://github.com/mathjax/MathJax">MathJax</a>. See <a href="https://math.meta.stackexchange.com/questions/5020/mathjax-basic-tutorial-and-quick-reference">here</a> for an excellent reference on MathJax.</p>
<p>Once we plot the counts separately for each the day of the week, the rush-hour trends become more obvious. (Also notice that the rush-hour weeks disappear on Saturday and Sunday.)</p>
<pre class="r"><code>ggplot(divvy$trips,aes(start.hour)) +
  geom_bar(fill = &quot;dodgerblue&quot;,width = 0.75) +
  facet_wrap(~start.day,ncol = 2) +
  scale_x_discrete(breaks = seq(0,24,2)) +
  theme_minimal() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())</code></pre>
<p><img src="figure/time-of-day-trends.Rmd/timeofday-barchart-2-1.png" width="576" style="display: block; margin: auto;" /></p>
</div>
<div id="time-of-day-trends-at-university-of-chicago" class="section level2">
<h2>Time-of-day trends at University of Chicago</h2>
<p>The commuting trends are different at the University of Chicago Divvy station—there isn’t much of a morning rush hour. This may be because students and staff don’t regularly use the Divvy bikes for commuting.</p>
<pre class="r"><code>ggplot(subset(divvy$trips,from_station_name == &quot;University Ave &amp; 57th St&quot;),
       aes(start.hour)) +
  geom_bar(fill = &quot;dodgerblue&quot;,width = 0.75) +
  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())</code></pre>
<p><img src="figure/time-of-day-trends.Rmd/timeofday-uchicago-barchart-1.png" width="576" style="display: block; margin: auto;" /></p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<p>This is the version of R and the packages that were used to generate these results.</p>
<pre class="r"><code>sessionInfo()
# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.3
# 
# 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] ggplot2_2.2.1       data.table_1.10.4-3
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_0.12.15     knitr_1.20       magrittr_1.5     munsell_0.4.3   
#  [5] colorspace_1.3-2 rlang_0.2.0.9000 stringr_1.3.0    plyr_1.8.4      
#  [9] tools_3.4.3      grid_3.4.3       gtable_0.2.0     git2r_0.21.0    
# [13] htmltools_0.3.6  yaml_2.1.17      lazyeval_0.2.1   rprojroot_1.3-2 
# [17] digest_0.6.15    tibble_1.4.2     evaluate_0.10.1  rmarkdown_1.9   
# [21] labeling_0.3     stringi_1.1.6    compiler_3.4.3   pillar_1.2.1    
# [25] scales_0.5.0     backports_1.1.2</code></pre>
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