Last updated: 2019-04-10

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Knit directory: wflow-divvy/analysis/

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Ignored files:
    Ignored:    .DS_Store
    Ignored:    analysis/.DS_Store
    Ignored:    data/Divvy_Stations_2016_Q1Q2.csv
    Ignored:    data/Divvy_Stations_2016_Q3.csv
    Ignored:    data/Divvy_Stations_2016_Q4.csv
    Ignored:    data/Divvy_Trips_2016_04.csv
    Ignored:    data/Divvy_Trips_2016_05.csv
    Ignored:    data/Divvy_Trips_2016_06.csv
    Ignored:    data/Divvy_Trips_2016_Q1.csv
    Ignored:    data/Divvy_Trips_2016_Q3.csv
    Ignored:    data/Divvy_Trips_2016_Q4.csv
    Ignored:    data/README.txt
    Ignored:    data/data.tar.gz
    Ignored:    docs/.DS_Store

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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
html 54fcf4e Peter Carbonetto 2018-04-14 Re-built station-map, time-of-day-trends and seasonal-trends webpages
Rmd f163fe4 Peter Carbonetto 2018-04-14 Updates for new workflowr version, v0.11.0.9000.
html f163fe4 Peter Carbonetto 2018-04-14 Updates for new workflowr version, v0.11.0.9000.
html 51163d7 Peter Carbonetto 2018-03-12 Ran wflow_publish(“*.Rmd“) with version v0.11.0 of workflowr.
html ab9176e Peter Carbonetto 2018-03-09 Added code_hiding to the analysis R Markdown files.
html b32e833 Peter Carbonetto 2018-01-18 Re-built all webpages using workflowr v0.1.0.
html 93a3a86 Peter Carbonetto 2017-11-16 Re-built seasonal-trends.html using workflowr v0.8.0.
Rmd 5b8e33f Peter Carbonetto 2017-11-16 wflow_publish(“seasonal-trends.Rmd”)
Rmd 6b9ddf1 Peter Carbonetto 2017-08-02 Added header with between-section spacing adjustment, and removed <br> tags from R Markdown files.
Rmd c6e8686 Peter Carbonetto 2017-07-31 wflow_publish(Sys.glob(“*.Rmd“))
html 727b8d9 Peter Carbonetto 2017-07-13 Re-built all the analysis files; wflow_publish(Sys.glob(“*.Rmd“)).
Rmd 6d02ffc Peter Carbonetto 2017-07-13 Made a dozen or so small adjustments to the .Rmd files.
html bf818d8 Peter Carbonetto 2017-07-07 Ran wflow_publish(c(“index.Rmd”, “setup.Rmd”, “station-map.Rmd”,
Rmd e4ba033 Peter Carbonetto 2017-07-07 Removed use of word ‘notebook’.
html cbee77b Peter Carbonetto 2017-07-07 Ran wflow_publish(seasonal-trends.Rmd).
Rmd 984143c Peter Carbonetto 2017-07-07 Made a few small revisions to seasonal-trends.Rmd.
html 34518f3 Peter Carbonetto 2017-07-06 Built first draft of seasonal trends notebook.
Rmd 6ef7a6c Peter Carbonetto 2017-07-06 wflow_publish(“seasonal-trends.Rmd”)
Rmd c8f7e10 Peter Carbonetto 2017-07-06 Implemented first draft of seasonal trends notebook.

In this last analysis, I use the Divvy trip data to examine biking trends in Chicago over the course of one year.

I begin by loading a few packages, as well as some additional functions I wrote for this project.

library(data.table)
# Warning: package 'data.table' was built under R version 3.4.4
library(ggplot2)
# Warning: package 'ggplot2' was built under R version 3.4.4
source("../code/functions.R")

Read the data

First, I read in the Divvy trip and station data from the CSV files.

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.

I would like to analyze city-wide departures for each day of the year, so I create a new “day of year” column.

divvy$trips <-
  transform(divvy$trips,
    start.dayofyear = factor(as.numeric(format(divvy$trips$starttime,"%j")),
                             1:366))

I also convert the “start week” column to a factor to make it easier to compile trip statistics for each week in the year.

divvy$trips <- transform(divvy$trips,start.week = factor(start.week,0:52))

Plot departures per day and per week

Here, I create a new vector containing the number of trips taken in each day of the year, and then I plot these numbers.

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())

Version Author Date
f163fe4 Peter Carbonetto 2018-04-14
b32e833 Peter Carbonetto 2018-01-18
34518f3 Peter Carbonetto 2017-07-06

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 per week.

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())

Version Author Date
b32e833 Peter Carbonetto 2018-01-18
34518f3 Peter Carbonetto 2017-07-06

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.