Project 1

Instructions

Here’s what you need to do:

1. Download New York City’s database of rat sightings since 2010:

   • Rat_sightings.csv

2. Summarize the data somehow. The raw data has more than 100,000 rows, which means you’ll need to aggregate the data (filter(), group_by(), and summarize() will be your friends). Consider looking at the number of sightings per borough, per year, per dwelling type, etc., or a combination of these, like the change in the number sightings across the 5 boroughs between 2010 and 2016.

3. Create an appropriate visualization based on the data you summarized.

4. Write a polished, professional memo presenting your analysis. We are specifically looking for a discussion of the following:

   • What story are you telling with your new graphic?
   • How have you applied reasonable standards in visual storytelling?
   • What policy implication is there (if any)?

5. Upload the following outputs to D2L:

   • A PDF file of your memo with your graphics embedded in it.¹ This means you’ll need to do all your coding in an R Markdown file and embed your code in chunks.
   • Note that Part 2 of this project should be included in this PDF in it’s own section (see below).
   • Nothing else. No .Rmd, no code, nothing but your clean, polished memo with Part 1 and Part 2.

Some important notes on your assignment

• Your assignment should be clean and polished as if you were a city employee and you were turning in a work product. It should should flow nicely and use subsections (using ### at the start of the line) as appropriate.
• Do not “annotate” your thought process (e.g. do not write “we tried X but there were too many NA’s, so we did Y instead”). This should be a polished memo suitable for turning in as a work product.
• Your code should not appear in your output - it should be only your plots and memo writing.
  • To turn off code echoing, add echo = FALSE in each of your code chunk options (e.g. {r setup, echo = FALSE}), or set it globally in the first code chunk inside the knitr::opts_chunk$set function.

Starter code

I’ve provided some starter code below. A couple comments about it:

• By default, read_csv() treats cells that are empty or “NA” as missing values. This rat dataset uses “N/A” to mark missing values, so we need to add that as a possible marker of missingness (hence na = c("", "NA", "N/A"))
• To make life easier, I’ve renamed some of the key variables you might work with. You can rename others if you want.
• I’ve also created a few date-related variables (sighting_year, sighting_month, sighting_day, and sighting_weekday). You don’t have to use them, but they’re there if you need them. The functions that create these, like year() and wday() are part of the lubridate library.
• The date/time variables are formatted like 04/03/2017 12:00:00 AM, which R is not able to automatically parse as a date when reading the CSV file. You can use the mdy_hms() function in the lubridate library to parse dates that are structured as “month-day-year-hour-minute”. There are also a bunch of other iterations of this function, like ymd(), dmy(), etc., for other date formats.
• There’s one row with an unspecified borough, so I filter that out.

library(tidyverse)
library(lubridate)
rats_raw <- read_csv("data/Rat_Sightings.csv", na = c("", "NA", "N/A"))
# If you get an error that says "All formats failed to parse. No formats
# found", it's because the mdy_hms function couldn't parse the date. The date
# variable *should* be in this format: "04/03/2017 12:00:00 AM", but in some
# rare instances, it might load without the seconds as "04/03/2017 12:00 AM".
# If there are no seconds, use mdy_hm() instead of mdy_hms().
rats_clean <- rats_raw %>%
  rename(created_date = `Created Date`,
         location_type = `Location Type`,
         borough = Borough) %>%
  mutate(created_date = mdy_hms(created_date)) %>%
  mutate(sighting_year = year(created_date),
         sighting_month = month(created_date),
         sighting_day = day(created_date),
         sighting_weekday = wday(created_date, label = TRUE, abbr = FALSE)) %>%
  filter(borough != "Unspecified")

You’ll summarize the data with functions from dplyr, including stuff like count(), arrange(), filter(), group_by(), summarize(), and mutate(). Here are some examples of ways to summarize the data:

# See the count of rat sightings by weekday
rats_clean %>%
  count(sighting_weekday)
# Assign a summarized data frame to an object to use it in a plot
rats_by_weekday <- rats_clean %>%
  count(sighting_weekday, sighting_year)
ggplot(rats_by_weekday, aes(x = fct_rev(sighting_weekday), y = n)) +
  geom_col() +
  coord_flip() +
  facet_wrap(~ sighting_year)
# See the count of rat sightings by weekday and borough
rats_clean %>%
  count(sighting_weekday, borough, sighting_year)
# An alternative to count() is to specify the groups with group_by() and then
# be explicit about how you're summarizing the groups, such as calculating the
# mean, standard deviation, or number of observations (we do that here with
# `n()`).
rats_clean %>%
  group_by(sighting_weekday, borough) %>%
  summarize(n = n())