Week Two Highlights

Here is some of the tidycensus work that I performed

library(tidycensus)
library(ggplot2)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ lubridate 1.9.2     ✔ tibble    3.2.1
✔ purrr     1.0.1     ✔ tidyr     1.3.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(scales)


Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

library(plotly)


Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

library(ggiraph)

median_income <- get_acs(
  geography = "county",
  variables = "B19013_001",
  state = "IA", 
  year = 2021
)

Getting data from the 2017-2021 5-year ACS

Warning: • You have not set a Census API key. Users without a key are limited to 500
queries per day and may experience performance limitations.
ℹ For best results, get a Census API key at
http://api.census.gov/data/key_signup.html and then supply the key to the
`census_api_key()` function to use it throughout your tidycensus session.
This warning is displayed once per session.

medinc_data <- median_income %>%
  #reducing the number of countys the graph can display
  slice(floor(99 * 0.25):floor(99 * 0.75)+1) %>%
  #ordering from estimate highest to estimate lowest
  arrange(desc(estimate))

median_barplot <- ggplot(medinc_data, aes(x = estimate, 
                                    y = reorder(NAME, estimate),
                                    tooltip = estimate,
                                    data_id = GEOID)) +
  geom_errorbar(aes(xmin = estimate - moe, 
                    xmax = estimate + moe),
                    width = 0.75, 
                    size = 1) + 
  geom_point_interactive(color = "red", size = 1.5) +
  scale_x_continuous(labels = label_dollar()) + 
  labs(title = "Plot describing median income by counties in the State of Iowa",
       x = "Estimated median Income",
       y = "Counties in Iowa") + 
  theme_minimal(base_size = 8)

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

mapping_median <- get_acs(
  geography = "tract",
  variables = "B19013_001",
  state = "IA",
  year = 2021,
  geometry = TRUE
)

Getting data from the 2017-2021 5-year ACS

Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.


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median_map <- ggplot(mapping_median, aes(fill = estimate)) + 
  geom_sf() + 
  theme_void() + 
  scale_fill_viridis_c(option = "H", n.breaks = 10) + 
  labs(title = "Mapping median income by census in the State of Iowa",
       subtitle = "",
       fill = "Census count")
median_barplot

median_map

Also worked on completing DataCamp courses and added on more than 10000 XP.