The maps featured in the shiny application provide the US population estimates by state as of July 1, 2017. The datasets for the estimates are publicly available here (for the population csv) and here (the population change table). The estimate methodology details are here.

The dataset (the population csv) is reformatted for further processing in R:

• Extract the columns E through H and rename to:
  • State, Population, Popu18andUp (population of age 18 and older), and Popu18Pct (percentage of population >= age 18)

• Change Popu18Pct to reflect the real values by dividing by 100.

• Add the Code column for US State 2-character codes.

• Lastly, copy 2016 population numbers (columns B of the population change table) and paste into the csv.

• Calculate the population changes and the % of the changes.

• Write the reformatted data frame to USpopstates.csv.

library(dplyr); library(xlsx)
df = read.csv("https://www2.census.gov/programs-surveys/popest/datasets/2010-2017/state/asrh/scprc-est2017-18+pop-res.csv")
df = df[2:52,5:8] # remove the first and the last rows
statecodes = append(state.abb,"DC",8)
df = df %>% mutate(Code=statecodes) 
names(df) = c("State","Population","Popu18andUp","Popu18Pct","Code")
df = df %>%  mutate(Popu18Pct = Popu18Pct/100)
download.file(
   "https://www2.census.gov/programs-surveys/popest/tables/2010-2017/state/totals/nst-est2017-03.xlsx",
   "popuchangetable.xlsx",mode="wb")
table = read.xlsx("popuchangetable.xlsx", header=FALSE, startRow=11, endRow=61, 
                  sheetName="NST03")
popu2016.tbl = table$X2  # 2nd column hosts the 2016 population numbers

df = df %>% mutate(Popu2016 = popu2016.tbl) %>% mutate(PopuChg = Population - Popu2016) %>%
            mutate(PctChg = PopuChg / Popu2016)
write.csv(df,"USpopstates.csv")

• The application shows 3 maps in 3 tabs on the main pane.
  • US Population by State
  • The % of US Population Changes since July 2016
  • The % of US Population of Age 18 and older

• The stats of each state show on hover.

• On the left pane, Enter a valid state code (i.e. NY for New York) to see the stats related to the state.

• The links to the data source and the estimate methodology employed by the US Census Bureau are located on the left pane as well.

• The ui.R and server.R files are located at GitHub.