Classwork 11

Map Visualization I

Loading R packages

library(tidyverse)
library(rmarkdown)
library(skimr)
library(ggthemes)
library(socviz)
library(geofacet)

Part 1. Climate Opinion Map

The following data is for Part 1:

climate_opinion_long <- read_csv(
  'https://bcdanl.github.io/data/climate_opinion_2021.csv')

Variable Description

• belief:
  • human: Estimated percentage who think that global warming is caused mostly by human activities.
  • happening: Estimated percentage who think that global warming is happening.

Question 1

• Filter climate_opinion_long, so that climate_opinion_long has only estimated percentage of people who think that global warming is caused mostly by human activities.

Answer:

Question 2

• Join the two data.frames, socviz::county_map and the resulting data.frame in Question 1.

county_map <- 
  read_csv("https://bcdanl.github.io/data/socviz_county_map.csv")

Answer:

Question 3

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

p_caption <- "Sources: Yale Program on Climate Change Communication\n(https://climatecommunication.yale.edu/visualizations-data/ycom-us/)"

Answer:

Part 2. Pitbull in NYC

The following data set is for Part 2:

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')

Question 4

• Replicate the above 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 = quantile(DATAFRAME$Y, .60, na.rm = T), 
         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
```

Answer:

Question 5

• Write an R code to identify the zip_code with the highest proportion of Pit Bull (or Mix), as shown above.

Answer:

Part 3. Unemployment Rate Maps with geofacet::facet_geo()

The following data is for Part 3:

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

Question 6

Use geom_area(), geom_line(), and facet_geo(~state, labeller = adjust_labels) to replicate the above figure.

• Since the date column in the unemp_house_prices data.frame is of type Date, you may need to use ymd() to convert a character string like "2008-01-01" into a Date value:

unemp_house_prices |> 
  filter(
    date >= ymd("2008-01-01")
  )

scale_x_date(
    breaks = ymd(c("2009-01-01", "2011-01-01", 
                   "2013-01-01", "2015-01-01", "2017-01-01"))
  )

• Use the following as_labeller() for labeling lengthy state names:

adjust_labels <- as_labeller(
  function(x) {
    case_when(
      x == "New Hampshire" ~ "N. Hampshire",
      x == "District of Columbia" ~ "DC",
      x == "North Carolina" ~ "N. Carolina",
      x == "South Carolina" ~ "S. Carolina",
      TRUE ~ x
    )
  }
)

Answer:

Discussion

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