Assignment1-Konuganti-Old-Age-Dependency-Data-Wrangling-Visualization.Rmd
Narender Reddy Konuganti
3/8/2022
Old-age dependency ratio 65+/(15-64) , 1950-2100
This data will be used to analyze the ratio of people aged 65+ with population 15-64. It gives us a measure of the World Population Prospects and how old-age population have increased or decreased, over the years.
This data was sourced from the website https://population.un.org/wpp/Download/Standard/Population/. I have used one dataset which which shows “Old-age dependency ratio (ratio of population aged 65+ per 100 population 15-64)” from 1950 to 2020 by “Region, subregion, country or area”
Here are the variable descriptions for dataset:
Region, subregion, country or area= Data are presented by region, subregion, country or area using the four classifications to group countries”Country code = Specifies the country code
Type = Type of classification used to group countries
1950, 1955, .., 2020 = Years specifying “ratio of population aged 65+ per 100 population 15-64”
Coding:
Loading the required packages:
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(kableExtra)##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowsNote: Using Kable, I’m only displaying first 30 rows as the number of rows in dataset is more than 200
Data Cleaning:
Reading the data from csv file and updating “null and …” records to “NA”
agedep <- read_csv(file="Assignment1-Konuganti-Old-Age-Dependency-Data-Wrangling-Visualization.csv",na = c("", "...", "NA", "N/A"))## Rows: 255 Columns: 22
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Variant, Region, subregion, country or area *, Notes, Type
## dbl (18): Index, Country code, Parent code, 1950, 1955, 1960, 1965, 1970, 19...
##
## 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.kbl(agedep[1:30,]) %>%
kable_paper(bootstrap_options = "striped", full_width = F)| Index | Variant | Region, subregion, country or area * | Notes | Country code | Type | Parent code | 1950 | 1955 | 1960 | 1965 | 1970 | 1975 | 1980 | 1985 | 1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2020 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Estimates | WORLD | NA | 900 | World | 0 | 8.4 | 8.5 | 8.6 | 8.9 | 9.3 | 9.7 | 10.0 | 9.9 | 10.1 | 10.6 | 10.9 | 11.2 | 11.6 | 12.6 | 14.3 |
| 2 | Estimates | UN development groups | a | 1803 | Label/Separator | 900 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 3 | Estimates | More developed regions | b | 901 | Development Group | 1803 | 11.9 | 12.6 | 13.4 | 14.3 | 15.5 | 16.7 | 17.8 | 17.5 | 18.7 | 20.4 | 21.3 | 22.6 | 23.7 | 26.6 | 30.0 |
| 4 | Estimates | Less developed regions | c | 902 | Development Group | 1803 | 6.5 | 6.4 | 6.2 | 6.3 | 6.6 | 6.8 | 7.1 | 7.3 | 7.4 | 7.8 | 8.2 | 8.5 | 8.8 | 9.6 | 11.3 |
| 5 | Estimates | Least developed countries | d | 941 | Development Group | 902 | 5.9 | 5.4 | 5.3 | 5.4 | 5.5 | 5.8 | 5.9 | 5.9 | 5.9 | 6.0 | 6.1 | 6.1 | 6.1 | 6.2 | 6.2 |
| 6 | Estimates | Less developed regions, excluding least developed countries | e | 934 | Development Group | 902 | 6.6 | 6.5 | 6.4 | 6.4 | 6.7 | 7.0 | 7.3 | 7.4 | 7.6 | 8.0 | 8.5 | 8.8 | 9.2 | 10.2 | 12.1 |
| 7 | Estimates | Less developed regions, excluding China | NA | 948 | Development Group | 1803 | 6.2 | 6.1 | 6.1 | 6.4 | 6.5 | 6.6 | 6.7 | 6.7 | 6.9 | 7.2 | 7.5 | 7.7 | 8.0 | 8.5 | 9.4 |
| 8 | Estimates | Land-locked Developing Countries (LLDC) | f | 1636 | Special other | 1803 | 6.8 | 6.7 | 6.6 | 6.7 | 6.8 | 6.9 | 7.0 | 6.7 | 6.8 | 7.0 | 6.9 | 6.8 | 6.5 | 6.3 | 6.6 |
| 9 | Estimates | Small Island Developing States (SIDS) | g | 1637 | Special other | 1803 | 6.8 | 6.8 | 6.7 | 7.2 | 7.9 | 8.5 | 9.0 | 9.3 | 9.5 | 9.7 | 10.0 | 10.5 | 11.0 | 11.9 | 13.7 |
| 10 | Estimates | World Bank income groups | NA | 1802 | Label/Separator | 900 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 11 | Estimates | High-income countries | h | 1503 | Income Group | 1802 | 12.1 | 13.0 | 13.7 | 14.5 | 15.5 | 16.4 | 17.3 | 17.1 | 18.2 | 19.3 | 20.3 | 21.3 | 22.5 | 25.3 | 28.2 |
| 12 | Estimates | Middle-income countries | h | 1517 | Income Group | 1802 | 6.9 | 6.8 | 6.7 | 6.8 | 7.2 | 7.6 | 7.9 | 7.9 | 8.1 | 8.5 | 8.9 | 9.2 | 9.5 | 10.4 | 12.3 |
| 13 | Estimates | Upper-middle-income countries | h | 1502 | Income Group | 1517 | 7.2 | 7.2 | 7.1 | 7.1 | 7.6 | 8.1 | 8.6 | 8.6 | 8.9 | 9.6 | 10.2 | 10.7 | 11.1 | 12.6 | 15.8 |
| 14 | Estimates | Lower-middle-income countries | h | 1501 | Income Group | 1517 | 6.5 | 6.3 | 6.2 | 6.5 | 6.7 | 6.9 | 7.0 | 7.0 | 7.0 | 7.3 | 7.5 | 7.7 | 7.8 | 8.2 | 9.1 |
| 15 | Estimates | Low-income countries | h | 1500 | Income Group | 1802 | 5.8 | 5.6 | 5.5 | 5.5 | 5.6 | 5.7 | 5.9 | 5.9 | 6.0 | 6.1 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 |
| 16 | Estimates | No income group available | NA | 1518 | Income Group | 1802 | 8.4 | 8.4 | 8.5 | 8.1 | 8.2 | 9.3 | 9.7 | 10.1 | 10.2 | 10.8 | 11.4 | 11.8 | 13.1 | 15.2 | 17.7 |
| 17 | Estimates | Geographic regions | i | 1840 | Label/Separator | 900 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 18 | Estimates | Africa | j | 903 | Region | 1840 | 5.9 | 5.7 | 5.8 | 5.9 | 6.0 | 6.1 | 6.1 | 6.2 | 6.2 | 6.2 | 6.2 | 6.0 | 6.0 | 6.1 | 6.3 |
| 19 | Estimates | Asia | k | 935 | Region | 1840 | 6.8 | 6.6 | 6.4 | 6.4 | 6.8 | 7.1 | 7.5 | 7.7 | 8.0 | 8.5 | 9.1 | 9.5 | 10.0 | 11.0 | 13.1 |
| 20 | Estimates | Europe | l | 908 | Region | 1840 | 12.1 | 12.7 | 13.6 | 14.8 | 16.3 | 17.8 | 18.9 | 17.8 | 19.0 | 20.9 | 21.8 | 23.3 | 24.0 | 26.4 | 29.5 |
| 21 | Estimates | Latin America and the Caribbean | m | 904 | Region | 1840 | 6.3 | 6.4 | 6.7 | 7.1 | 7.3 | 7.5 | 7.8 | 7.9 | 8.2 | 8.7 | 9.1 | 9.8 | 10.5 | 11.6 | 13.4 |
| 22 | Estimates | Northern America | n | 905 | Region | 1840 | 12.6 | 14.1 | 15.0 | 15.4 | 15.9 | 16.3 | 17.2 | 18.0 | 18.9 | 19.2 | 18.7 | 18.5 | 19.5 | 22.3 | 25.8 |
| 23 | Estimates | Oceania | o | 909 | Region | 1840 | 11.6 | 12.2 | 12.4 | 12.2 | 11.7 | 12.1 | 12.9 | 13.3 | 14.1 | 14.9 | 15.3 | 15.7 | 16.3 | 18.1 | 20.1 |
| 24 | Estimates | Sustainable Development Goal (SDG) regions | p | 1828 | Label/Separator | 900 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 25 | Estimates | SUB-SAHARAN AFRICA | NA | 947 | SDG region | 1828 | 5.9 | 5.6 | 5.6 | 5.6 | 5.7 | 5.7 | 5.8 | 5.9 | 5.9 | 5.8 | 5.7 | 5.5 | 5.4 | 5.4 | 5.5 |
| 26 | Estimates | Eastern Africa | NA | 910 | Subregion | 947 | 5.5 | 5.6 | 5.5 | 5.6 | 5.6 | 5.7 | 5.8 | 5.7 | 5.7 | 5.6 | 5.5 | 5.3 | 5.2 | 5.2 | 5.3 |
| 27 | Estimates | Burundi | NA | 108 | Country/Area | 910 | 5.8 | 5.6 | 5.5 | 5.7 | 5.8 | 6.4 | 6.0 | 5.8 | 5.6 | 5.4 | 5.3 | 4.5 | 4.1 | 4.0 | 4.5 |
| 28 | Estimates | Comoros | NA | 174 | Country/Area | 910 | 6.6 | 6.0 | 5.8 | 5.7 | 5.8 | 6.0 | 6.2 | 6.3 | 6.2 | 6.0 | 5.7 | 5.5 | 5.3 | 5.1 | 5.4 |
| 29 | Estimates | Djibouti | NA | 262 | Country/Area | 910 | 3.9 | 4.1 | 4.3 | 4.4 | 4.6 | 4.8 | 4.6 | 4.8 | 4.9 | 5.2 | 5.4 | 5.6 | 6.0 | 6.6 | 7.1 |
| 30 | Estimates | Eritrea | NA | 232 | Country/Area | 910 | 6.2 | 5.6 | 5.1 | 4.9 | 4.8 | 4.8 | 4.9 | 5.0 | 5.3 | 7.0 | 7.6 | 6.6 | 7.1 | 8.2 | 8.3 |
Removing the unnecessary columns (Column Names - Index, Notes)
agedep_rm <- agedep[ -c(1,4) ]
kbl(agedep_rm[1:30,]) %>%
kable_paper(bootstrap_options = "striped", full_width = F)| Variant | Region, subregion, country or area * | Country code | Type | Parent code | 1950 | 1955 | 1960 | 1965 | 1970 | 1975 | 1980 | 1985 | 1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2020 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimates | WORLD | 900 | World | 0 | 8.4 | 8.5 | 8.6 | 8.9 | 9.3 | 9.7 | 10.0 | 9.9 | 10.1 | 10.6 | 10.9 | 11.2 | 11.6 | 12.6 | 14.3 |
| Estimates | UN development groups | 1803 | Label/Separator | 900 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Estimates | More developed regions | 901 | Development Group | 1803 | 11.9 | 12.6 | 13.4 | 14.3 | 15.5 | 16.7 | 17.8 | 17.5 | 18.7 | 20.4 | 21.3 | 22.6 | 23.7 | 26.6 | 30.0 |
| Estimates | Less developed regions | 902 | Development Group | 1803 | 6.5 | 6.4 | 6.2 | 6.3 | 6.6 | 6.8 | 7.1 | 7.3 | 7.4 | 7.8 | 8.2 | 8.5 | 8.8 | 9.6 | 11.3 |
| Estimates | Least developed countries | 941 | Development Group | 902 | 5.9 | 5.4 | 5.3 | 5.4 | 5.5 | 5.8 | 5.9 | 5.9 | 5.9 | 6.0 | 6.1 | 6.1 | 6.1 | 6.2 | 6.2 |
| Estimates | Less developed regions, excluding least developed countries | 934 | Development Group | 902 | 6.6 | 6.5 | 6.4 | 6.4 | 6.7 | 7.0 | 7.3 | 7.4 | 7.6 | 8.0 | 8.5 | 8.8 | 9.2 | 10.2 | 12.1 |
| Estimates | Less developed regions, excluding China | 948 | Development Group | 1803 | 6.2 | 6.1 | 6.1 | 6.4 | 6.5 | 6.6 | 6.7 | 6.7 | 6.9 | 7.2 | 7.5 | 7.7 | 8.0 | 8.5 | 9.4 |
| Estimates | Land-locked Developing Countries (LLDC) | 1636 | Special other | 1803 | 6.8 | 6.7 | 6.6 | 6.7 | 6.8 | 6.9 | 7.0 | 6.7 | 6.8 | 7.0 | 6.9 | 6.8 | 6.5 | 6.3 | 6.6 |
| Estimates | Small Island Developing States (SIDS) | 1637 | Special other | 1803 | 6.8 | 6.8 | 6.7 | 7.2 | 7.9 | 8.5 | 9.0 | 9.3 | 9.5 | 9.7 | 10.0 | 10.5 | 11.0 | 11.9 | 13.7 |
| Estimates | World Bank income groups | 1802 | Label/Separator | 900 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Estimates | High-income countries | 1503 | Income Group | 1802 | 12.1 | 13.0 | 13.7 | 14.5 | 15.5 | 16.4 | 17.3 | 17.1 | 18.2 | 19.3 | 20.3 | 21.3 | 22.5 | 25.3 | 28.2 |
| Estimates | Middle-income countries | 1517 | Income Group | 1802 | 6.9 | 6.8 | 6.7 | 6.8 | 7.2 | 7.6 | 7.9 | 7.9 | 8.1 | 8.5 | 8.9 | 9.2 | 9.5 | 10.4 | 12.3 |
| Estimates | Upper-middle-income countries | 1502 | Income Group | 1517 | 7.2 | 7.2 | 7.1 | 7.1 | 7.6 | 8.1 | 8.6 | 8.6 | 8.9 | 9.6 | 10.2 | 10.7 | 11.1 | 12.6 | 15.8 |
| Estimates | Lower-middle-income countries | 1501 | Income Group | 1517 | 6.5 | 6.3 | 6.2 | 6.5 | 6.7 | 6.9 | 7.0 | 7.0 | 7.0 | 7.3 | 7.5 | 7.7 | 7.8 | 8.2 | 9.1 |
| Estimates | Low-income countries | 1500 | Income Group | 1802 | 5.8 | 5.6 | 5.5 | 5.5 | 5.6 | 5.7 | 5.9 | 5.9 | 6.0 | 6.1 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 |
| Estimates | No income group available | 1518 | Income Group | 1802 | 8.4 | 8.4 | 8.5 | 8.1 | 8.2 | 9.3 | 9.7 | 10.1 | 10.2 | 10.8 | 11.4 | 11.8 | 13.1 | 15.2 | 17.7 |
| Estimates | Geographic regions | 1840 | Label/Separator | 900 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Estimates | Africa | 903 | Region | 1840 | 5.9 | 5.7 | 5.8 | 5.9 | 6.0 | 6.1 | 6.1 | 6.2 | 6.2 | 6.2 | 6.2 | 6.0 | 6.0 | 6.1 | 6.3 |
| Estimates | Asia | 935 | Region | 1840 | 6.8 | 6.6 | 6.4 | 6.4 | 6.8 | 7.1 | 7.5 | 7.7 | 8.0 | 8.5 | 9.1 | 9.5 | 10.0 | 11.0 | 13.1 |
| Estimates | Europe | 908 | Region | 1840 | 12.1 | 12.7 | 13.6 | 14.8 | 16.3 | 17.8 | 18.9 | 17.8 | 19.0 | 20.9 | 21.8 | 23.3 | 24.0 | 26.4 | 29.5 |
| Estimates | Latin America and the Caribbean | 904 | Region | 1840 | 6.3 | 6.4 | 6.7 | 7.1 | 7.3 | 7.5 | 7.8 | 7.9 | 8.2 | 8.7 | 9.1 | 9.8 | 10.5 | 11.6 | 13.4 |
| Estimates | Northern America | 905 | Region | 1840 | 12.6 | 14.1 | 15.0 | 15.4 | 15.9 | 16.3 | 17.2 | 18.0 | 18.9 | 19.2 | 18.7 | 18.5 | 19.5 | 22.3 | 25.8 |
| Estimates | Oceania | 909 | Region | 1840 | 11.6 | 12.2 | 12.4 | 12.2 | 11.7 | 12.1 | 12.9 | 13.3 | 14.1 | 14.9 | 15.3 | 15.7 | 16.3 | 18.1 | 20.1 |
| Estimates | Sustainable Development Goal (SDG) regions | 1828 | Label/Separator | 900 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Estimates | SUB-SAHARAN AFRICA | 947 | SDG region | 1828 | 5.9 | 5.6 | 5.6 | 5.6 | 5.7 | 5.7 | 5.8 | 5.9 | 5.9 | 5.8 | 5.7 | 5.5 | 5.4 | 5.4 | 5.5 |
| Estimates | Eastern Africa | 910 | Subregion | 947 | 5.5 | 5.6 | 5.5 | 5.6 | 5.6 | 5.7 | 5.8 | 5.7 | 5.7 | 5.6 | 5.5 | 5.3 | 5.2 | 5.2 | 5.3 |
| Estimates | Burundi | 108 | Country/Area | 910 | 5.8 | 5.6 | 5.5 | 5.7 | 5.8 | 6.4 | 6.0 | 5.8 | 5.6 | 5.4 | 5.3 | 4.5 | 4.1 | 4.0 | 4.5 |
| Estimates | Comoros | 174 | Country/Area | 910 | 6.6 | 6.0 | 5.8 | 5.7 | 5.8 | 6.0 | 6.2 | 6.3 | 6.2 | 6.0 | 5.7 | 5.5 | 5.3 | 5.1 | 5.4 |
| Estimates | Djibouti | 262 | Country/Area | 910 | 3.9 | 4.1 | 4.3 | 4.4 | 4.6 | 4.8 | 4.6 | 4.8 | 4.9 | 5.2 | 5.4 | 5.6 | 6.0 | 6.6 | 7.1 |
| Estimates | Eritrea | 232 | Country/Area | 910 | 6.2 | 5.6 | 5.1 | 4.9 | 4.8 | 4.8 | 4.9 | 5.0 | 5.3 | 7.0 | 7.6 | 6.6 | 7.1 | 8.2 | 8.3 |
Dropping rows/records with NA values
agedep_complete <- na.omit(agedep_rm) # Remove NA
kbl(agedep_complete[1:30,]) %>%
kable_paper(bootstrap_options = "striped", full_width = F)| Variant | Region, subregion, country or area * | Country code | Type | Parent code | 1950 | 1955 | 1960 | 1965 | 1970 | 1975 | 1980 | 1985 | 1990 | 1995 | 2000 | 2005 | 2010 | 2015 | 2020 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimates | WORLD | 900 | World | 0 | 8.4 | 8.5 | 8.6 | 8.9 | 9.3 | 9.7 | 10.0 | 9.9 | 10.1 | 10.6 | 10.9 | 11.2 | 11.6 | 12.6 | 14.3 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 11.9 | 12.6 | 13.4 | 14.3 | 15.5 | 16.7 | 17.8 | 17.5 | 18.7 | 20.4 | 21.3 | 22.6 | 23.7 | 26.6 | 30.0 |
| Estimates | Less developed regions | 902 | Development Group | 1803 | 6.5 | 6.4 | 6.2 | 6.3 | 6.6 | 6.8 | 7.1 | 7.3 | 7.4 | 7.8 | 8.2 | 8.5 | 8.8 | 9.6 | 11.3 |
| Estimates | Least developed countries | 941 | Development Group | 902 | 5.9 | 5.4 | 5.3 | 5.4 | 5.5 | 5.8 | 5.9 | 5.9 | 5.9 | 6.0 | 6.1 | 6.1 | 6.1 | 6.2 | 6.2 |
| Estimates | Less developed regions, excluding least developed countries | 934 | Development Group | 902 | 6.6 | 6.5 | 6.4 | 6.4 | 6.7 | 7.0 | 7.3 | 7.4 | 7.6 | 8.0 | 8.5 | 8.8 | 9.2 | 10.2 | 12.1 |
| Estimates | Less developed regions, excluding China | 948 | Development Group | 1803 | 6.2 | 6.1 | 6.1 | 6.4 | 6.5 | 6.6 | 6.7 | 6.7 | 6.9 | 7.2 | 7.5 | 7.7 | 8.0 | 8.5 | 9.4 |
| Estimates | Land-locked Developing Countries (LLDC) | 1636 | Special other | 1803 | 6.8 | 6.7 | 6.6 | 6.7 | 6.8 | 6.9 | 7.0 | 6.7 | 6.8 | 7.0 | 6.9 | 6.8 | 6.5 | 6.3 | 6.6 |
| Estimates | Small Island Developing States (SIDS) | 1637 | Special other | 1803 | 6.8 | 6.8 | 6.7 | 7.2 | 7.9 | 8.5 | 9.0 | 9.3 | 9.5 | 9.7 | 10.0 | 10.5 | 11.0 | 11.9 | 13.7 |
| Estimates | High-income countries | 1503 | Income Group | 1802 | 12.1 | 13.0 | 13.7 | 14.5 | 15.5 | 16.4 | 17.3 | 17.1 | 18.2 | 19.3 | 20.3 | 21.3 | 22.5 | 25.3 | 28.2 |
| Estimates | Middle-income countries | 1517 | Income Group | 1802 | 6.9 | 6.8 | 6.7 | 6.8 | 7.2 | 7.6 | 7.9 | 7.9 | 8.1 | 8.5 | 8.9 | 9.2 | 9.5 | 10.4 | 12.3 |
| Estimates | Upper-middle-income countries | 1502 | Income Group | 1517 | 7.2 | 7.2 | 7.1 | 7.1 | 7.6 | 8.1 | 8.6 | 8.6 | 8.9 | 9.6 | 10.2 | 10.7 | 11.1 | 12.6 | 15.8 |
| Estimates | Lower-middle-income countries | 1501 | Income Group | 1517 | 6.5 | 6.3 | 6.2 | 6.5 | 6.7 | 6.9 | 7.0 | 7.0 | 7.0 | 7.3 | 7.5 | 7.7 | 7.8 | 8.2 | 9.1 |
| Estimates | Low-income countries | 1500 | Income Group | 1802 | 5.8 | 5.6 | 5.5 | 5.5 | 5.6 | 5.7 | 5.9 | 5.9 | 6.0 | 6.1 | 6.0 | 6.0 | 6.0 | 6.0 | 6.0 |
| Estimates | No income group available | 1518 | Income Group | 1802 | 8.4 | 8.4 | 8.5 | 8.1 | 8.2 | 9.3 | 9.7 | 10.1 | 10.2 | 10.8 | 11.4 | 11.8 | 13.1 | 15.2 | 17.7 |
| Estimates | Africa | 903 | Region | 1840 | 5.9 | 5.7 | 5.8 | 5.9 | 6.0 | 6.1 | 6.1 | 6.2 | 6.2 | 6.2 | 6.2 | 6.0 | 6.0 | 6.1 | 6.3 |
| Estimates | Asia | 935 | Region | 1840 | 6.8 | 6.6 | 6.4 | 6.4 | 6.8 | 7.1 | 7.5 | 7.7 | 8.0 | 8.5 | 9.1 | 9.5 | 10.0 | 11.0 | 13.1 |
| Estimates | Europe | 908 | Region | 1840 | 12.1 | 12.7 | 13.6 | 14.8 | 16.3 | 17.8 | 18.9 | 17.8 | 19.0 | 20.9 | 21.8 | 23.3 | 24.0 | 26.4 | 29.5 |
| Estimates | Latin America and the Caribbean | 904 | Region | 1840 | 6.3 | 6.4 | 6.7 | 7.1 | 7.3 | 7.5 | 7.8 | 7.9 | 8.2 | 8.7 | 9.1 | 9.8 | 10.5 | 11.6 | 13.4 |
| Estimates | Northern America | 905 | Region | 1840 | 12.6 | 14.1 | 15.0 | 15.4 | 15.9 | 16.3 | 17.2 | 18.0 | 18.9 | 19.2 | 18.7 | 18.5 | 19.5 | 22.3 | 25.8 |
| Estimates | Oceania | 909 | Region | 1840 | 11.6 | 12.2 | 12.4 | 12.2 | 11.7 | 12.1 | 12.9 | 13.3 | 14.1 | 14.9 | 15.3 | 15.7 | 16.3 | 18.1 | 20.1 |
| Estimates | SUB-SAHARAN AFRICA | 947 | SDG region | 1828 | 5.9 | 5.6 | 5.6 | 5.6 | 5.7 | 5.7 | 5.8 | 5.9 | 5.9 | 5.8 | 5.7 | 5.5 | 5.4 | 5.4 | 5.5 |
| Estimates | Eastern Africa | 910 | Subregion | 947 | 5.5 | 5.6 | 5.5 | 5.6 | 5.6 | 5.7 | 5.8 | 5.7 | 5.7 | 5.6 | 5.5 | 5.3 | 5.2 | 5.2 | 5.3 |
| Estimates | Burundi | 108 | Country/Area | 910 | 5.8 | 5.6 | 5.5 | 5.7 | 5.8 | 6.4 | 6.0 | 5.8 | 5.6 | 5.4 | 5.3 | 4.5 | 4.1 | 4.0 | 4.5 |
| Estimates | Comoros | 174 | Country/Area | 910 | 6.6 | 6.0 | 5.8 | 5.7 | 5.8 | 6.0 | 6.2 | 6.3 | 6.2 | 6.0 | 5.7 | 5.5 | 5.3 | 5.1 | 5.4 |
| Estimates | Djibouti | 262 | Country/Area | 910 | 3.9 | 4.1 | 4.3 | 4.4 | 4.6 | 4.8 | 4.6 | 4.8 | 4.9 | 5.2 | 5.4 | 5.6 | 6.0 | 6.6 | 7.1 |
| Estimates | Eritrea | 232 | Country/Area | 910 | 6.2 | 5.6 | 5.1 | 4.9 | 4.8 | 4.8 | 4.9 | 5.0 | 5.3 | 7.0 | 7.6 | 6.6 | 7.1 | 8.2 | 8.3 |
| Estimates | Ethiopia | 231 | Country/Area | 910 | 5.7 | 5.2 | 4.9 | 4.8 | 5.1 | 5.3 | 6.2 | 5.9 | 6.3 | 6.1 | 6.1 | 6.2 | 6.4 | 6.4 | 6.3 |
| Estimates | Kenya | 404 | Country/Area | 910 | 7.0 | 7.3 | 7.5 | 7.5 | 7.1 | 6.7 | 6.1 | 5.6 | 5.1 | 4.7 | 4.3 | 3.9 | 3.5 | 3.7 | 4.3 |
| Estimates | Madagascar | 450 | Country/Area | 910 | 5.4 | 5.7 | 6.0 | 6.4 | 6.8 | 7.2 | 6.7 | 6.1 | 5.9 | 5.8 | 5.7 | 5.6 | 5.3 | 5.1 | 5.5 |
| Estimates | Malawi | 454 | Country/Area | 910 | 6.0 | 6.1 | 6.0 | 5.8 | 5.7 | 5.4 | 5.5 | 5.9 | 6.0 | 6.6 | 6.1 | 5.6 | 5.2 | 5.0 | 4.9 |
Data Wrangling:
It is untidy data as it does not satisfy tidy data principles:
- Not every row is an observation.
- Every variable does not form a column.
The process below tidies the data.
Using pivot_longer:
- In this dataset, we have a vector of column names(1950,1955,..,2020) that contain values, not variables. Creating 2 new columns named “Year” and “Age_Dependency_Ratio” with pivot longer to tidy the dataset.
agedep_longer=agedep_complete %>%
pivot_longer(c('1950', '1955','1960', '1965','1970', '1975','1980', '1985','1990', '1995',
'2000', '2005','2010', '2015','2020'), names_to = "Year", values_to = "Age_Dependency_Ratio")
kbl(agedep_longer[1:30,]) %>%
kable_paper(bootstrap_options = "striped", full_width = F)| Variant | Region, subregion, country or area * | Country code | Type | Parent code | Year | Age_Dependency_Ratio |
|---|---|---|---|---|---|---|
| Estimates | WORLD | 900 | World | 0 | 1950 | 8.4 |
| Estimates | WORLD | 900 | World | 0 | 1955 | 8.5 |
| Estimates | WORLD | 900 | World | 0 | 1960 | 8.6 |
| Estimates | WORLD | 900 | World | 0 | 1965 | 8.9 |
| Estimates | WORLD | 900 | World | 0 | 1970 | 9.3 |
| Estimates | WORLD | 900 | World | 0 | 1975 | 9.7 |
| Estimates | WORLD | 900 | World | 0 | 1980 | 10.0 |
| Estimates | WORLD | 900 | World | 0 | 1985 | 9.9 |
| Estimates | WORLD | 900 | World | 0 | 1990 | 10.1 |
| Estimates | WORLD | 900 | World | 0 | 1995 | 10.6 |
| Estimates | WORLD | 900 | World | 0 | 2000 | 10.9 |
| Estimates | WORLD | 900 | World | 0 | 2005 | 11.2 |
| Estimates | WORLD | 900 | World | 0 | 2010 | 11.6 |
| Estimates | WORLD | 900 | World | 0 | 2015 | 12.6 |
| Estimates | WORLD | 900 | World | 0 | 2020 | 14.3 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1950 | 11.9 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1955 | 12.6 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1960 | 13.4 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1965 | 14.3 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1970 | 15.5 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1975 | 16.7 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1980 | 17.8 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1985 | 17.5 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1990 | 18.7 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 1995 | 20.4 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 2000 | 21.3 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 2005 | 22.6 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 2010 | 23.7 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 2015 | 26.6 |
| Estimates | More developed regions | 901 | Development Group | 1803 | 2020 | 30.0 |
Using pivot_wider:
- In this dataset, an observation(Column Name - Variant) is scattered across multiple rows. I have used pivot wider to create a new column “Estimates” and used the values from column “Age_Dependency_Ratio”
agedep_wider = agedep_longer %>% pivot_wider(names_from = Variant, values_from = Age_Dependency_Ratio)
kbl(agedep_wider[1:30,]) %>%
kable_paper(bootstrap_options = "striped", full_width = F)| Region, subregion, country or area * | Country code | Type | Parent code | Year | Estimates |
|---|---|---|---|---|---|
| WORLD | 900 | World | 0 | 1950 | 8.4 |
| WORLD | 900 | World | 0 | 1955 | 8.5 |
| WORLD | 900 | World | 0 | 1960 | 8.6 |
| WORLD | 900 | World | 0 | 1965 | 8.9 |
| WORLD | 900 | World | 0 | 1970 | 9.3 |
| WORLD | 900 | World | 0 | 1975 | 9.7 |
| WORLD | 900 | World | 0 | 1980 | 10.0 |
| WORLD | 900 | World | 0 | 1985 | 9.9 |
| WORLD | 900 | World | 0 | 1990 | 10.1 |
| WORLD | 900 | World | 0 | 1995 | 10.6 |
| WORLD | 900 | World | 0 | 2000 | 10.9 |
| WORLD | 900 | World | 0 | 2005 | 11.2 |
| WORLD | 900 | World | 0 | 2010 | 11.6 |
| WORLD | 900 | World | 0 | 2015 | 12.6 |
| WORLD | 900 | World | 0 | 2020 | 14.3 |
| More developed regions | 901 | Development Group | 1803 | 1950 | 11.9 |
| More developed regions | 901 | Development Group | 1803 | 1955 | 12.6 |
| More developed regions | 901 | Development Group | 1803 | 1960 | 13.4 |
| More developed regions | 901 | Development Group | 1803 | 1965 | 14.3 |
| More developed regions | 901 | Development Group | 1803 | 1970 | 15.5 |
| More developed regions | 901 | Development Group | 1803 | 1975 | 16.7 |
| More developed regions | 901 | Development Group | 1803 | 1980 | 17.8 |
| More developed regions | 901 | Development Group | 1803 | 1985 | 17.5 |
| More developed regions | 901 | Development Group | 1803 | 1990 | 18.7 |
| More developed regions | 901 | Development Group | 1803 | 1995 | 20.4 |
| More developed regions | 901 | Development Group | 1803 | 2000 | 21.3 |
| More developed regions | 901 | Development Group | 1803 | 2005 | 22.6 |
| More developed regions | 901 | Development Group | 1803 | 2010 | 23.7 |
| More developed regions | 901 | Development Group | 1803 | 2015 | 26.6 |
| More developed regions | 901 | Development Group | 1803 | 2020 | 30.0 |
The data is now completely tidy and ready for data visualization:
- We removed unnecessary columns.
- We also checked for any NA & special values and removed the records
- Applied pivot_longer for addressing spread across multiple columns issue
- Applied pivot_wider for addressing observations scattered across multiple rows issue
DATA VISUALIZATION:
1. Box Plot of Year vs Age_Dependency_Ratio(%)
theme_set(theme_bw())
ggplot(data = agedep_wider, mapping = aes(x = Year, y = Estimates)) +
geom_boxplot()+ labs(x="Year", y="%Ratio of population aged 65+ per 100 population 15-64",title = "Box Plot of Old-age dependency ratio")
Regarding the above box plot, we can infer that, % of old-age people is increasing from 1950 to 2020 based on median values.
2. Scatter Plot of Year vs Age_Dependency_Ratio(%) grouped by type of classification
ggplot(data = agedep_wider) +
geom_point(mapping = aes(x = Year, y = Estimates)) +
facet_wrap(~ Type, nrow = 1) + coord_flip() + labs(x="Year", y="%Ratio of population aged 65+ per 100 population 15-64",title = "Scatter Plot of Old-age dependency ratio grouped by type of classification")
Regarding the above scatter plot, we can infer that, % of old-age people group by type of classification, there is % increase in last 10 years for all classifications.
As the number of regions/countries are more than 100, creating a vector of data(Continents) for better visualization
Region= c('Africa','Asia','Europe','Latin America and the Caribbean','Northern America','Oceania')
agedep_vis=agedep_wider[agedep_wider$`Region, subregion, country or area *` %in% Region,]
kbl(agedep_vis[1:30,]) %>%
kable_paper(bootstrap_options = "striped", full_width = F)| Region, subregion, country or area * | Country code | Type | Parent code | Year | Estimates |
|---|---|---|---|---|---|
| Africa | 903 | Region | 1840 | 1950 | 5.9 |
| Africa | 903 | Region | 1840 | 1955 | 5.7 |
| Africa | 903 | Region | 1840 | 1960 | 5.8 |
| Africa | 903 | Region | 1840 | 1965 | 5.9 |
| Africa | 903 | Region | 1840 | 1970 | 6.0 |
| Africa | 903 | Region | 1840 | 1975 | 6.1 |
| Africa | 903 | Region | 1840 | 1980 | 6.1 |
| Africa | 903 | Region | 1840 | 1985 | 6.2 |
| Africa | 903 | Region | 1840 | 1990 | 6.2 |
| Africa | 903 | Region | 1840 | 1995 | 6.2 |
| Africa | 903 | Region | 1840 | 2000 | 6.2 |
| Africa | 903 | Region | 1840 | 2005 | 6.0 |
| Africa | 903 | Region | 1840 | 2010 | 6.0 |
| Africa | 903 | Region | 1840 | 2015 | 6.1 |
| Africa | 903 | Region | 1840 | 2020 | 6.3 |
| Asia | 935 | Region | 1840 | 1950 | 6.8 |
| Asia | 935 | Region | 1840 | 1955 | 6.6 |
| Asia | 935 | Region | 1840 | 1960 | 6.4 |
| Asia | 935 | Region | 1840 | 1965 | 6.4 |
| Asia | 935 | Region | 1840 | 1970 | 6.8 |
| Asia | 935 | Region | 1840 | 1975 | 7.1 |
| Asia | 935 | Region | 1840 | 1980 | 7.5 |
| Asia | 935 | Region | 1840 | 1985 | 7.7 |
| Asia | 935 | Region | 1840 | 1990 | 8.0 |
| Asia | 935 | Region | 1840 | 1995 | 8.5 |
| Asia | 935 | Region | 1840 | 2000 | 9.1 |
| Asia | 935 | Region | 1840 | 2005 | 9.5 |
| Asia | 935 | Region | 1840 | 2010 | 10.0 |
| Asia | 935 | Region | 1840 | 2015 | 11.0 |
| Asia | 935 | Region | 1840 | 2020 | 13.1 |
3. Scatter Plot of Year vs Age_Dependency_Ratio(%) group by type of classification by Continent
ggplot(data = agedep_vis) +
geom_point(mapping = aes(x = Year, y = Estimates, color = `Region, subregion, country or area *`, size = Estimates))+ labs(x="Year", y="%Ratio of population aged 65+ per 100 population 15-64",title = "Scatter plot of Ratio of population aged 65+ per 100 by Continent")
Regarding the above scatter plot, we can infer that, % of old-age people by continent, Europe continent has highest proportion of old-aged people by 2020 which is 30% and Africa continent has the least proportion of old-aged people by 2020 which is close to 5%.
Creating a vector of data(Region/country) for better visualization
Region2= c('More developed regions','Less developed regions','High-income countries','Middle-income countries','Low-income countries','Small Island Developing States (SIDS)')
agedep_vis2=agedep_wider[agedep_wider$`Region, subregion, country or area *` %in% Region2,]
kbl(agedep_vis2[1:30,]) %>%
kable_paper(bootstrap_options = "striped", full_width = F)| Region, subregion, country or area * | Country code | Type | Parent code | Year | Estimates |
|---|---|---|---|---|---|
| More developed regions | 901 | Development Group | 1803 | 1950 | 11.9 |
| More developed regions | 901 | Development Group | 1803 | 1955 | 12.6 |
| More developed regions | 901 | Development Group | 1803 | 1960 | 13.4 |
| More developed regions | 901 | Development Group | 1803 | 1965 | 14.3 |
| More developed regions | 901 | Development Group | 1803 | 1970 | 15.5 |
| More developed regions | 901 | Development Group | 1803 | 1975 | 16.7 |
| More developed regions | 901 | Development Group | 1803 | 1980 | 17.8 |
| More developed regions | 901 | Development Group | 1803 | 1985 | 17.5 |
| More developed regions | 901 | Development Group | 1803 | 1990 | 18.7 |
| More developed regions | 901 | Development Group | 1803 | 1995 | 20.4 |
| More developed regions | 901 | Development Group | 1803 | 2000 | 21.3 |
| More developed regions | 901 | Development Group | 1803 | 2005 | 22.6 |
| More developed regions | 901 | Development Group | 1803 | 2010 | 23.7 |
| More developed regions | 901 | Development Group | 1803 | 2015 | 26.6 |
| More developed regions | 901 | Development Group | 1803 | 2020 | 30.0 |
| Less developed regions | 902 | Development Group | 1803 | 1950 | 6.5 |
| Less developed regions | 902 | Development Group | 1803 | 1955 | 6.4 |
| Less developed regions | 902 | Development Group | 1803 | 1960 | 6.2 |
| Less developed regions | 902 | Development Group | 1803 | 1965 | 6.3 |
| Less developed regions | 902 | Development Group | 1803 | 1970 | 6.6 |
| Less developed regions | 902 | Development Group | 1803 | 1975 | 6.8 |
| Less developed regions | 902 | Development Group | 1803 | 1980 | 7.1 |
| Less developed regions | 902 | Development Group | 1803 | 1985 | 7.3 |
| Less developed regions | 902 | Development Group | 1803 | 1990 | 7.4 |
| Less developed regions | 902 | Development Group | 1803 | 1995 | 7.8 |
| Less developed regions | 902 | Development Group | 1803 | 2000 | 8.2 |
| Less developed regions | 902 | Development Group | 1803 | 2005 | 8.5 |
| Less developed regions | 902 | Development Group | 1803 | 2010 | 8.8 |
| Less developed regions | 902 | Development Group | 1803 | 2015 | 9.6 |
| Less developed regions | 902 | Development Group | 1803 | 2020 | 11.3 |
4. Scatter Plot of Region/Country vs Type of classfication
ggplot(data = agedep_vis2) +
geom_point(mapping = aes(x = `Region, subregion, country or area *`, y = Type, size = Estimates))+ labs(x="Region/Country Grouping", y="Type of classification",title = "Scatter plot based on region/country by classification type") +
theme(plot.title = element_text(hjust = 0.5)) + coord_flip()
Regarding the above scatter plot, we can infer that, More developed and High-income countries have high % of old-aged people where as less developed and Low-income countries have less % of old-aged people.