Homework 3

ggplot Visualization; Maps; Quarto Blogging

Direction

• Please submit your Quarto Document for Part 1 in Homework 2 to Brightspace with the name below:

  • danl-310-hw3-LASTNAME-FIRSTNAME.qmd
    ( e.g., danl-310-hw3-choe-byeonghak.qmd )

• The due is April 5, 2025, 5:00 P.M.

• Please send Byeong-Hak an email (bchoe@geneseo.edu) if you have any questions.

Part 1. Map visualization

Question 1

The following data set is for Question 1:

nyc_dog_license <- read_csv(
  'https://bcdanl.github.io/data/nyc_dog_license.csv')

nyc_zips_coord <- read_csv(
  'https://bcdanl.github.io/data/nyc_zips_coord.csv')

nyc_zips_df <- read_csv(
  'https://bcdanl.github.io/data/nyc_zips_df.csv')

Q1a

• Replicate the following ggplot.

  • You should calculate the proportion of Pit Bull (or Mix) for each zip code.
  • You should join data.frames properly.
  • Choose the color palette from the viridis scales
    • https://ggplot2.tidyverse.org/reference/scale_viridis.html.
  • Use coord_map(projection = "albers", lat0 = 39, lat1 = 45).
  • To insert the image, use the following annotate():

# install.packages("ggtext")
library(ggtext)

annotate("richtext", 
         x =  , 
         y =  , 
         label = "<img src='https://bcdanl.github.io/lec_figs/pitbull.png' width='750'/>", 
         fill = NA,
         color = NA) 

• Note that the size of ggplot figure is 6.18 (width) x 6.84 (height)

```{.r}
#| fig-width: 6.18
#| fig-height: 6.84

# YOUR CODE IS HERE
```

Q1b

• Which zip_code has the highest proportion of Pit Bull (or Mix)?

Question 2

The following data is for Question 2:

election_panel <- read_csv(
  'https://bcdanl.github.io/data/election_panel.csv')

• Replicate the following map.
  • Do not use coord_map(projection = "albers", lat0 = 39, lat1 = 45).

Part 2. Quarto Blogging

• Use the following set of data.frames for Quarto Blogging:

nyc_dog_license <- read_csv(
  'https://bcdanl.github.io/data/nyc_dog_license.csv')

nyc_zips_coord <- read_csv(
  'https://bcdanl.github.io/data/nyc_zips_coord.csv')

nyc_zips_df <- read_csv(
  'https://bcdanl.github.io/data/nyc_zips_df.csv')