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<h1 class="title toc-ignore">Divvy usage over the year</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> 2017-07-07</p>
<!-- Insert the code version (Git commit SHA1) if Git repository
exists and R package git2r is installed -->
<p><strong>Code version:</strong> 984143c</p>
<p>In this last notebook, I use the Divvy trip data to examine biking trends in Chicago over the course of one year.</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("../code/functions.R")</code></pre>
<p><br></p>
<div id="read-the-data" class="section level2">
<h2>Read the data</h2>
<p>First, I read in the Divvy trip and station data from the CSV files.</p>
<pre class="r"><code>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.</code></pre>
<p>I would like to analyze city-wide departures for each day of the year, so I create a new “day of year” column.</p>
<pre class="r"><code>divvy$trips <-
transform(divvy$trips,
start.dayofyear = factor(as.numeric(format(divvy$trips$starttime,"%j")),
1:366))</code></pre>
<p>I also convert the “start week” column to a factor to make it easier to compile trip statistics for each week in the year.</p>
<pre class="r"><code>divvy$trips <- transform(divvy$trips,start.week = factor(start.week,0:52))</code></pre>
<p><br></p>
</div>
<div id="plot-departures-per-day-and-per-week" class="section level2">
<h2>Plot departures per day and per week</h2>
<p>Here, I create a new vector containing the number of trips taken in each day of the year, and then I plot these numbers.</p>
<pre class="r"><code>counts.day <- as.vector(table(divvy$trips$start.dayofyear))
ggplot(data.frame(day = 1:366,departures = counts.day),
aes(x = day,y = departures)) +
geom_point(color = "darkblue",shape = 19,size = 1) +
geom_line(color = "darkblue") +
scale_x_continuous(breaks = seq(0,350,25)) +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())</code></pre>
<p><img src="figure/seasonal-trends.Rmd/plot-trips-by-day-1.png" width="576" style="display: block; margin: auto;" /></p>
<p>This plot shows a sizeable increase in bike trips during summer days, but since the number of trips varies widely from one day to the next, I think the plot will look nicer if instead we count the number of trips <em>per week</em> instead of per day.</p>
<pre class="r"><code>counts.week <- as.vector(table(divvy$trips$start.week))
ggplot(data.frame(week = 0:52,departures = counts.week),
aes(x = week,y = departures)) +
geom_point(color = "darkblue",shape = 19,size = 1.5) +
geom_line(color = "darkblue") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())</code></pre>
<p><img src="figure/seasonal-trends.Rmd/plot-trips-by-week-1.png" width="576" style="display: block; margin: auto;" /></p>
<p>Indeed, the seasonal trends are less noisy in this plot; the majority of Divvy bike trips in Chicago are taken when the weather is warmer (weeks 20–40), and very few people are using the Divvy bikes in the cold winter months.</p>
<p><br></p>
</div>
<div id="seasonal-trends-at-the-university-of-chicago" class="section level2">
<h2>Seasonal trends at the University of Chicago</h2>
<p>When we analyze trips taken at the University of Chicago bike station, the “bump” during warmer months flattens out. This is probably because a large fraction of University of Chicago students leave during the summer.</p>
<pre class="r"><code>dat <- subset(divvy$trips,from_station_name == "University Ave & 57th St")
counts.week.uchicago <- as.vector(table(dat$start.week))
ggplot(data.frame(week = 0:52,departures = counts.week.uchicago),
aes(x = week,y = departures)) +
geom_point(color = "darkblue",shape = 19,size = 1.5) +
geom_line(color = "darkblue") +
theme_minimal() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())</code></pre>
<p><img src="figure/seasonal-trends.Rmd/plot-trips-by-week-uchicago-1.png" width="576" style="display: block; margin: auto;" /></p>
<p><br></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 3.3.2 (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] ggplot2_2.2.1 data.table_1.10.4
#
# loaded via a namespace (and not attached):
# [1] Rcpp_0.12.11 assertthat_0.2.0 digest_0.6.12 rprojroot_1.2
# [5] plyr_1.8.4 grid_3.3.2 gtable_0.2.0 backports_1.0.5
# [9] git2r_0.18.0 magrittr_1.5 scales_0.4.1 evaluate_0.10.1
# [13] stringi_1.1.2 lazyeval_0.2.0 rmarkdown_1.6 labeling_0.3
# [17] tools_3.3.2 stringr_1.2.0 munsell_0.4.3 yaml_2.1.14
# [21] colorspace_1.3-2 htmltools_0.3.6 knitr_1.16 tibble_1.2</code></pre>
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