Wk 6 Data Transformation: World Population
Dirk Hartog
2023-10-08
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
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## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── 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 errorsWe will looking at a data set containing global population counts for 234 countries or territories.
Read in the untidy .csv file from github
url <- "https://raw.githubusercontent.com/D-hartog/DATA607/main/PROJECT2/worldpop_untidy.csv"
world_pop <- read_csv(url)## Rows: 234 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): CCA3, Country/Territory, Capital, Continent
## dbl (13): Rank, 2022 Population, 2020 Population, 2015 Population, 2010 Popu...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(world_pop)## # A tibble: 6 × 17
## Rank CCA3 `Country/Territory` Capital Continent `2022 Population`
## <dbl> <chr> <chr> <chr> <chr> <dbl>
## 1 36 AFG Afghanistan Kabul Asia 41128771
## 2 138 ALB Albania Tirana Europe 2842321
## 3 34 DZA Algeria Algiers Africa 44903225
## 4 213 ASM American Samoa Pago Pago Oceania 44273
## 5 203 AND Andorra Andorra la Vella Europe 79824
## 6 42 AGO Angola Luanda Africa 35588987
## # ℹ 11 more variables: `2020 Population` <dbl>, `2015 Population` <dbl>,
## # `2010 Population` <dbl>, `2000 Population` <dbl>, `1990 Population` <dbl>,
## # `1980 Population` <dbl>, `1970 Population` <dbl>, `Area (km²)` <dbl>,
## # `Density (per km²)` <dbl>, `Growth Rate` <dbl>,
## # `World Population Percentage` <dbl>glimpse(world_pop)## Rows: 234
## Columns: 17
## $ Rank <dbl> 36, 138, 34, 213, 203, 42, 224, 201, 33,…
## $ CCA3 <chr> "AFG", "ALB", "DZA", "ASM", "AND", "AGO"…
## $ `Country/Territory` <chr> "Afghanistan", "Albania", "Algeria", "Am…
## $ Capital <chr> "Kabul", "Tirana", "Algiers", "Pago Pago…
## $ Continent <chr> "Asia", "Europe", "Africa", "Oceania", "…
## $ `2022 Population` <dbl> 41128771, 2842321, 44903225, 44273, 7982…
## $ `2020 Population` <dbl> 38972230, 2866849, 43451666, 46189, 7770…
## $ `2015 Population` <dbl> 33753499, 2882481, 39543154, 51368, 7174…
## $ `2010 Population` <dbl> 28189672, 2913399, 35856344, 54849, 7151…
## $ `2000 Population` <dbl> 19542982, 3182021, 30774621, 58230, 6609…
## $ `1990 Population` <dbl> 10694796, 3295066, 25518074, 47818, 5356…
## $ `1980 Population` <dbl> 12486631, 2941651, 18739378, 32886, 3561…
## $ `1970 Population` <dbl> 10752971, 2324731, 13795915, 27075, 1986…
## $ `Area (km²)` <dbl> 652230, 28748, 2381741, 199, 468, 124670…
## $ `Density (per km²)` <dbl> 63.0587, 98.8702, 18.8531, 222.4774, 170…
## $ `Growth Rate` <dbl> 1.0257, 0.9957, 1.0164, 0.9831, 1.0100, …
## $ `World Population Percentage` <dbl> 0.52, 0.04, 0.56, 0.00, 0.00, 0.45, 0.00…CLEANING THE DATA
- First I want to change the column names for later transformtion of the year columns .
colnames(world_pop)[c(1:17)] <- c("RANK","CCAS", "COUNTRY_TERR", "CAPITAL","CONTINENT", "2022", "2020", "2015", "2010", "2000", "1990", "1980", "1970","AREA", "DENSITY", "GROWTH_RATE", "WORLD_POP_PCT")TIDY/TRANSFORMING THE DATA: For the analysis I want to compare growth rates by year as well as among continents. In order to make do this analysis we will need to created an observation for each country and year.
- I don’t think that there is much transformation that needs to be done to the data execpt taking the year columns pivoting those columns to rows.
world_long <- world_pop %>%
pivot_longer(
cols = "2022":"1970",
names_to = "YEAR",
values_to = "POPULATION"
)
glimpse(world_long)## Rows: 1,872
## Columns: 11
## $ RANK <dbl> 36, 36, 36, 36, 36, 36, 36, 36, 138, 138, 138, 138, 138,…
## $ CCAS <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", …
## $ COUNTRY_TERR <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanista…
## $ CAPITAL <chr> "Kabul", "Kabul", "Kabul", "Kabul", "Kabul", "Kabul", "K…
## $ CONTINENT <chr> "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", …
## $ AREA <dbl> 652230, 652230, 652230, 652230, 652230, 652230, 652230, …
## $ DENSITY <dbl> 63.0587, 63.0587, 63.0587, 63.0587, 63.0587, 63.0587, 63…
## $ GROWTH_RATE <dbl> 1.0257, 1.0257, 1.0257, 1.0257, 1.0257, 1.0257, 1.0257, …
## $ WORLD_POP_PCT <dbl> 0.52, 0.52, 0.52, 0.52, 0.52, 0.52, 0.52, 0.52, 0.04, 0.…
## $ YEAR <chr> "2022", "2020", "2015", "2010", "2000", "1990", "1980", …
## $ POPULATION <dbl> 41128771, 38972230, 33753499, 28189672, 19542982, 106947…write.csv(world_long,file='/Users/dirkhartog/Desktop/CUNY_MSDS/DATA_607/PROJECT2/world_pop/worldpop_tidy.csv', row.names=FALSE)DATA ANALYSIS AND VISUALIZATIONS
- Finding the total, min and max populations for each year and each and continent
world_long %>%
group_by(CONTINENT, YEAR) %>%
summarize(TOTAL = sum(POPULATION, na.rm = TRUE),
MAX = max(POPULATION, na.rm = TRUE),
MIN = min(POPULATION, na.rm = TRUE))## `summarise()` has grouped output by 'CONTINENT'. You can override using the
## `.groups` argument.## # A tibble: 48 × 5
## # Groups: CONTINENT [6]
## CONTINENT YEAR TOTAL MAX MIN
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Africa 1970 365444348 55569264 35383
## 2 Africa 1980 481536377 72951439 52233
## 3 Africa 1990 638150629 95214257 71057
## 4 Africa 2000 818946032 122851984 80060
## 5 Africa 2010 1055228072 160952853 92409
## 6 Africa 2015 1201102442 183995785 99240
## 7 Africa 2020 1360671810 208327405 105530
## 8 Africa 2022 1426730932 218541212 107118
## 9 Asia 1970 2144906290 822534450 118007
## 10 Asia 1980 2635334228 982372466 164887
## # ℹ 38 more rowsworld_long %>% group_by(CONTINENT, YEAR) %>%
summarise(TOTAL = sum(POPULATION, na.rm = TRUE)) %>%
ggplot(aes(x = YEAR, y = TOTAL)) +
geom_point(aes(color = CONTINENT)) +
ggtitle("50 Year Trend in Continental Populations")## `summarise()` has grouped output by 'CONTINENT'. You can override using the
## `.groups` argument.
- We can now pull out information for a given year and compare attributes, in this case among continents. Looking at some statistics and trends in growth rates from Asian and African countries in 2022
world_long %>% filter(YEAR == "2022") %>%
group_by(CONTINENT) %>%
summarise(Average_gr = mean(GROWTH_RATE, na.rm = TRUE))## # A tibble: 6 × 2
## CONTINENT Average_gr
## <chr> <dbl>
## 1 Africa 1.02
## 2 Asia 1.01
## 3 Europe 1.00
## 4 North America 1.00
## 5 Oceania 1.01
## 6 South America 1.01world_long %>%
filter(YEAR == "2022" & CONTINENT %in% c("Asia", "Africa")) %>%
ggplot(aes(x = GROWTH_RATE)) +
geom_histogram(bins = 20, aes(fill = CONTINENT)) +
ggtitle("Histogram of Growth Rates Among Asian
and African Countries/Territories") +
ylab("Count") +
xlab("Growth Rate") +
theme(axis.title.x = element_text(color="darkgreen",size=15),
axis.title.y = element_text(color="red", size=15),
axis.text.x = element_text(size=10),
axis.text.y = element_text(size=10),
plot.title = element_text(color="darkblue",
size=18))
- Looking at any relationships in area size less than 5 million (km^2) and growth rate
world_long %>% filter(YEAR == "2022" & AREA < 5000000 & CONTINENT %in% c("Asia", "Africa")) %>%
ggplot(aes(x = AREA, y = GROWTH_RATE, color = CONTINENT)) +
geom_point() +
ggtitle("Area and Growth Rate of Asian
and African Countries/Territories in 2022") +
ylab("Growth Rate") +
xlab("Area (km^s)") +
theme(axis.title.x = element_text(color="darkgreen",size=15),
axis.title.y = element_text(color="red", size=15),
axis.text.x = element_text(size=10),
axis.text.y = element_text(size=10),
plot.title = element_text(color="darkblue",
size=18))
## CONCLUSIONS It is clear that Asian and African countries have seen a
larger trend in the growth of their populations over the past 50 years.
Despite this it does seem that currently average growth rates across the
globe are pretty similar between 1.002 - 1.02. This growth rate might
seem small but when talking about populations, a 1% grwoth rate is still
a lot of people!