index

Assignment 4 - Tables

Make a dynamic figure. Describe the following:

Source: https://apps.bea.gov/iTable/index_regional.cfm

This data was sourced from the Bureau of Economic Analysis and looks to understand the trends in personal income per capita across the 50 states and Washington DC.

The data has been tabled in two ways. The first leverages KableExtra and serves as a brief snapshot of the actual for 2020 and the percent change vs. 2019. This features a lightbar scroll option, and is meant to be taken in quickly by the viewer.

To further contextualize this, I created a second table using Reactable which allows for sorting by metric and additional information such as 2019 and 2016 actuals and percent change vs. 2016. In short the first table is to be used as a point in time for short-term trends, while the second allows us to manipulate the data and understand both the short and long-term trends quickly with our sorting function.

Step 1: Load Packages

library(kableExtra)
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter()     masks stats::filter()
## ✖ dplyr::group_rows() masks kableExtra::group_rows()
## ✖ dplyr::lag()        masks stats::lag()

library(dplyr)
library(sparkline)
library(reactable)
library(DT)

Load and manipulate data

#load and mutate data
econ <- read.csv("Regional - Level and Percent2.csv")

eco1 <- econ %>%
  filter(Description == "  Real per capita personal income 4/")%>%
  mutate(p5y = ((X2020 - X2016)/X2016)*100)

Create Table(s)

t_eco <- eco1 %>%
  select(GeoName, Levels, Per_Change_2020)%>%
  kbl(col.name = c("State/Region", "2020 Actuals", "Percent Change from 2019"),
      align = c("l","r","r"),
      digits = 1) %>% #check on this
  kable_styling(font_size = 18, fixed_thead = T) %>%
  add_header_above(c(" ","Real Per Capita Personal Income" = 2), font_size = 14) %>%
  kable_paper(lightable_options = c("hover","condensed"), full_width = T, html_font = "helvetica", font_size = 12)

t_eco 

	Real Per Capita Personal Income
State/Region	2020 Actuals	Percent Change from 2019
Alabama	46963	4.3
Alaska	55470	1.9
Arizona	45193	6.6
Arkansas	47765	3.5
California	57347	7.2
Colorado	55911	2.9
Connecticut	68533	2.1
Delaware	51689	4.3
District of Columbia	70040	3.7
Florida	49853	3.1
Georgia	49392	5.4
Hawaii	47234	4.0
Idaho	48216	7.5
Illinois	56482	4.6
Indiana	50624	5.9
Iowa	52969	5.7
Kansas	54773	5.2
Kentucky	47551	7.0
Louisiana	49483	4.6
Maine	50516	6.1
Maryland	56578	3.3
Massachusetts	65853	4.9
Michigan	51071	8.2
Minnesota	56696	5.1
Mississippi	43284	6.5
Missouri	50404	5.1
Montana	52054	7.3
Nebraska	55891	6.0
Nevada	49914	5.3
New Hampshire	58342	3.7
New Jersey	59594	4.1
New Mexico	45637	8.2
New York	60936	4.6
North Carolina	49396	4.8
North Dakota	60286	4.9
Ohio	52758	7.5
Oklahoma	49254	1.2
Oregon	49485	7.1
Pennsylvania	57030	6.4
Rhode Island	53859	6.0
South Carolina	47252	5.0
South Dakota	58414	8.1
Tennessee	49955	2.8
Texas	49945	2.0
Utah	49388	7.5
Vermont	53726	5.3
Virginia	55333	3.6
Washington	56385	5.2
West Virginia	46130	5.2
Wisconsin	53798	5.4
Wyoming	60463	1.5

t_reac <- eco1 %>%
  select(GeoName,Levels, X2019, X2016, Per_Change_2020, p5y)%>%
  reactable(columns = list(
    GeoName = colDef(name = "State"),
    Levels = colDef(name = "2020 Per Capita Income"),
    X2019 = colDef(name = "2019 Per Capita Income"),
    X2016 = colDef(name = "2016 Per Capita Income"),
    Per_Change_2020 = colDef(name = "Percent Change from 2019", format = colFormat(digits = 1)),
    p5y = colDef(name = "Percent Change from 2016", format = colFormat(digits = 1))),
  defaultSorted="GeoName",
  style = list(fontSize = "1rem"))
t_reac