Project2-data3

Import libraries

library(dplyr)
library(tidyr)
library(ggplot2)
library(knitr)

Collect the data

This data was collected on Slack from the student: FOMBA KASSOH.

df3 = read.csv("https://raw.githubusercontent.com/Kossi-Akplaka/Data607-data_acquisition_and_management/main/Project%202/Data3-USA-Development-Indicators.csv")
kable(head(df3)) 

Country.Name	Country.Code	Series.Name	Series.Code	X1990..YR1990.	X2000..YR2000.	X2013..YR2013.	X2014..YR2014.	X2015..YR2015.	X2016..YR2016.	X2017..YR2017.	X2018..YR2018.	X2019..YR2019.	X2020..YR2020.	X2021..YR2021.	X2022..YR2022.
United States	USA	Population, total	SP.POP.TOTL	249623000	282162411	316059947	318386329	320738994	323071755	325122128	326838199	328329953	331511512	332031554	333287557
United States	USA	Population growth (annual %)	SP.POP.GROW	1	1	1	1	1	1	1	1	0	1	0	0
United States	USA	Surface area (sq. km)	AG.SRF.TOTL.K2	9629090	9632030	9831510	9831510	9831510	9831510	9831510	9831510	9831510	9831510	9831510	..
United States	USA	Population density (people per sq. km of land area)	EN.POP.DNST	27	31	35	35	35	35	36	36	36	36	36	..
United States	USA	Poverty headcount ratio at national poverty lines (% of population)	SI.POV.NAHC	..	..	..	..	..	..	..	..	..	..	..	..
United States	USA	Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)	SI.POV.DDAY	1	1	1	1	1	1	1	1	1	0	0	..

Let’s tidy the data

Remove columns 56 to 60

df3 <- df3[1:55,]

We can transform the data from wide to long

df3_long <- df3 %>%
  gather(Year, Count, X1990..YR1990.:X2022..YR2022.)
kable(head(df3_long))

Country.Name	Country.Code	Series.Name	Series.Code	Year	Count
United States	USA	Population, total	SP.POP.TOTL	X1990..YR1990.	249623000
United States	USA	Population growth (annual %)	SP.POP.GROW	X1990..YR1990.	1
United States	USA	Surface area (sq. km)	AG.SRF.TOTL.K2	X1990..YR1990.	9629090
United States	USA	Population density (people per sq. km of land area)	EN.POP.DNST	X1990..YR1990.	27
United States	USA	Poverty headcount ratio at national poverty lines (% of population)	SI.POV.NAHC	X1990..YR1990.	..
United States	USA	Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)	SI.POV.DDAY	X1990..YR1990.	1

Now we can tidy the column Year

df3_long$Year <- as.integer(sub("X(\\d+)\\.\\.YR\\d+\\.", "\\1", df3_long$Year))

head(df3_long$Year)

## [1] 1990 1990 1990 1990 1990 1990

Analysis

Let’s create another data frame with the total population of USA

USA_pop_total <- df3_long %>% 
  filter(Series.Name == "Population, total")
kable(USA_pop_total)

Country.Name	Country.Code	Series.Name	Series.Code	Year	Count
United States	USA	Population, total	SP.POP.TOTL	1990	249623000
United States	USA	Population, total	SP.POP.TOTL	2000	282162411
United States	USA	Population, total	SP.POP.TOTL	2013	316059947
United States	USA	Population, total	SP.POP.TOTL	2014	318386329
United States	USA	Population, total	SP.POP.TOTL	2015	320738994
United States	USA	Population, total	SP.POP.TOTL	2016	323071755
United States	USA	Population, total	SP.POP.TOTL	2017	325122128
United States	USA	Population, total	SP.POP.TOTL	2018	326838199
United States	USA	Population, total	SP.POP.TOTL	2019	328329953
United States	USA	Population, total	SP.POP.TOTL	2020	331511512
United States	USA	Population, total	SP.POP.TOTL	2021	332031554
United States	USA	Population, total	SP.POP.TOTL	2022	333287557

Now we can plot the total population of USA between 1990 to 2022

ggplot(data = USA_pop_total, aes(x = Year, y = Count, group = 1)) +
  geom_line()

The population of USA grew from 249623000 to 333287557.