R Programming: World pupulation and most popular countries.
Skills
Author
Benjamini Mpinga
Published
April 15, 2023
WORLD POPULATION DATA.
WORLD POPULATION DATA: This post is the result of R programming analysis, which basically demonstrate the most current world population of each continent and most popular countries within a continental level. R is a powerful language when it comes to analysis and documentation, this post is the one of R’s multiple way of communication. i have also generated pdf and word documents through from R Studio editor.
Questions.
Find the world’s most popular continent.
The most popular country in every continent.
The trend of the world population based on data given.
world’s population data from 1970 to 2022.
Data Summary.
The data below requires, to find the most popular continent on Earth, the most popular country on Earth and the general population growth trend. So we are going to import our data in R studio then the next step is to look weather the data are tidy or not, if Yes then we straight move to answer the question and if Not then we need to tidy them so that we are able to use them.
Choosen packages.
Tidyverse::
readr::
Lubridate::
readxl::
magrittr::
janitor::
dplyr::
About the chosen package.
These packages are chosen specifically to handle all activities in this task. Each package functions accordingly. This packages provide several functions that are key tools to facilitate the coding. One can choose the way makes him or her relevant in doing activities in coding. Especially when it comes to package arrangements, firstly understanding of the result required, is usually every coder’s concerns. But how one apply the calling of packages differ from one another.
My self i prefer to call all the package in the very first Chunk, this is because i already know all the necessary function() I’ll be required to use when coding. So the mentioned package in this assignment are already set in a first chunk.
require(tidyverse)
Loading required package: tidyverse
-- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
v dplyr 1.1.1 v readr 2.1.4
v forcats 1.0.0 v stringr 1.5.0
v ggplot2 3.4.2 v tibble 3.2.1
v lubridate 1.9.2 v tidyr 1.3.0
v purrr 1.0.1
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Loading required package: janitor
Attaching package: 'janitor'
The following objects are masked from 'package:stats':
chisq.test, fisher.test
require(magrittr)
Loading required package: magrittr
Attaching package: 'magrittr'
The following object is masked from 'package:purrr':
set_names
The following object is masked from 'package:tidyr':
extract
require(readr)require(readxl)
Data Entry.
world =read_csv("e:/my_staffs/Blogs/teneson/posts/Current-world-pop/The world population data/world_population.csv")
Rows: 234 Columns: 17
-- Column specification --------------------------------------------------------
Delimiter: ","
chr (4): CCA3, Country, 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.
The table above has some input that can not be proper managed with R language, so we must clean them and left with what we are going to use to answer the question asked.
pop = world %>%clean_names() %>%select(-c(1:2, 4, 7:17)) %>%rename("population"=3)
con = pop %>%filter(continent =="Asia")con1 = pop %>%filter(continent =="Africa")con2 = pop %>%filter(continent =="North America")con3 = pop %>%filter(continent =="South America")con4 = pop %>%filter(continent =="Europe")con5 = pop %>%filter(continent =="Oceania")
Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
i Please use the `linewidth` argument instead.
Finally a graph above demonstrate the world population by continent, the most to miner populated continent on Earth. Asia is leading the chart followed by Africa.
ASIA
The graphs below show Asian continent population from high to low level, graph number one is not well arranged, sample were entered to the database the way they were collected, in fact without consideration of proper arrangement from the highest to the lowest number, it shows the original data, so we had to arranged it in an order that can be proper viewed In graph number two in ascending order. We use the function reorder() of the package tidyverse.
Therefor The Red graph shows countries with population more than Fifty millions, The Blue graph shows countries with population between Ten to Fifty millions, While the green graph shows the countries with less than Ten millions.
The first graph doesn’t allow eyes of the reader to relax when reading, So we have to generate another graph by flip our graph so that the names of the countries can be well viewed, we use the function Coord_flip() of the package tidyverse.
asia
# A tibble: 50 x 3
country continent population
<chr> <chr> <dbl>
1 Afghanistan Asia 41128771
2 Armenia Asia 2780469
3 Azerbaijan Asia 10358074
4 Bahrain Asia 1472233
5 Bangladesh Asia 171186372
6 Bhutan Asia 782455
7 Brunei Asia 449002
8 Cambodia Asia 16767842
9 China Asia 1425887337
10 Georgia Asia 3744385
# i 40 more rows
The graphs below show Africa continent population from high to low level, graph number one is not well arranged, sample were entered to the database the way they were collected, in fact without consideration of proper arrangement from the highest to the lowest number, it shows the original data, so we had to arranged it in an order that can be proper viewed In graph number two in ascending order. We use the function reorder() of the package tidyverse.
Therefor The Red graph shows countries with population more than Fifty millions, The Blue graph shows countries with population between Ten to Fifty millions, While the green graph shows the countries with less than Ten millions.
The first graph doesn’t allow eyes of the reader to relax when reading, So we have to generate another graph by flip our graph so that the names of the countries can be well viewed, we use the function Coord_flip() of the package tidyverse.
africa
# A tibble: 57 x 3
country continent population
<chr> <chr> <dbl>
1 Algeria Africa 44903225
2 Angola Africa 35588987
3 Benin Africa 13352864
4 Botswana Africa 2630296
5 Burkina Faso Africa 22673762
6 Burundi Africa 12889576
7 Cameroon Africa 27914536
8 Cape Verde Africa 593149
9 Central African Republic Africa 5579144
10 Chad Africa 17723315
# i 47 more rows
The graphs below show North America continent population from high to low level, graph number one is not well arranged, sample were entered to the database the way they were collected, in fact without consideration of proper arrangement from the highest to the lowest number, it shows the original data, so we had to arranged it in an order that can be proper viewed In graph number two in ascending order. We use the function reorder() of the package tidyverse.
Therefor The Red graph shows countries with population more than Fifty millions, The Blue graph shows countries with population between Ten to Fifty millions, While the green graph shows the countries with less than Ten millions.
The first graph doesn’t allow eyes of the reader to relax when reading, So we have to generate another graph by flip our graph so that the names of the countries can be well viewed, we use the function Coord_flip() of the package tidyverse.
notame
# A tibble: 40 x 3
country continent population
<chr> <chr> <dbl>
1 Anguilla North America 15857
2 Antigua and Barbuda North America 93763
3 Aruba North America 106445
4 Bahamas North America 409984
5 Barbados North America 281635
6 Belize North America 405272
7 Bermuda North America 64184
8 British Virgin Islands North America 31305
9 Canada North America 38454327
10 Cayman Islands North America 68706
# i 30 more rows
The graphs below show Europe continent population from high to low level, graph number one is not well arranged, sample were entered to the database the way they were collected, in fact without consideration of proper arrangement from the highest to the lowest number, it shows the original data, so we had to arranged it in an order that can be proper viewed In graph number two in ascending order. We use the function reorder() of the package tidyverse.
Therefor The Red graph shows countries with population more than Fifty millions, The Blue graph shows countries with population between Ten to Fifty millions, While the green graph shows the countries with less than Ten millions.
The first graph doesn’t allow eyes of the reader to relax when reading, So we have to generate another graph by flip our graph so that the names of the countries can be well viewed, we use the function Coord_flip() of the package tidyverse.
europe
# A tibble: 50 x 3
country continent population
<chr> <chr> <dbl>
1 Albania Europe 2842321
2 Andorra Europe 79824
3 Austria Europe 8939617
4 Belarus Europe 9534954
5 Belgium Europe 11655930
6 Bosnia and Herzegovina Europe 3233526
7 Bulgaria Europe 6781953
8 Croatia Europe 4030358
9 Cyprus Europe 1251488
10 Czech Republic Europe 10493986
# i 40 more rows
The graphs below show South America continent population from high to low level, graph number one is not well arranged, sample were entered to the database the way they were collected, in fact without consideration of proper arrangement from the highest to the lowest number, it shows the original data, so we had to arranged it in an order that can be proper viewed In graph number two in ascending order. We use the function reorder() of the package tidyverse.
Therefor The Red graph shows countries with population more than Fifty millions, The Blue graph shows countries with population between Ten to Fifty millions, While the green graph shows the countries with less than Ten millions.
The first graph doesn’t allow eyes of the reader to relax when reading, So we have to generate another graph by flip our graph so that the names of the countries can be well viewed, we use the function Coord_flip() of the package tidyverse.
sotame
# A tibble: 14 x 3
country continent population
<chr> <chr> <dbl>
1 Argentina South America 45510318
2 Bolivia South America 12224110
3 Brazil South America 215313498
4 Chile South America 19603733
5 Colombia South America 51874024
6 Ecuador South America 18001000
7 Falkland Islands South America 3780
8 French Guiana South America 304557
9 Guyana South America 808726
10 Paraguay South America 6780744
11 Peru South America 34049588
12 Suriname South America 618040
13 Uruguay South America 3422794
14 Venezuela South America 28301696
The graphs below show Oceania continent population from high to low level, graph number one is not well arranged, sample were entered to the database the way they were collected, in fact without consideration of proper arrangement from the highest to the lowest number, it shows the original data, so we had to arranged it in an order that can be proper viewed In graph number two in ascending order. We use the function reorder() of the package tidyverse.
Therefor The Red graph shows countries with population more than Fifty millions, The Blue graph shows countries with population between Ten to Fifty millions, While the green graph shows the countries with less than Ten millions.
The first graph doesn’t allow eyes of the reader to relax when reading, So we have to generate another graph by flip our graph so that the names of the countries can be well viewed, we use the function Coord_flip() of the package tidyverse.
ocean
# A tibble: 23 x 3
country continent population
<chr> <chr> <dbl>
1 American Samoa Oceania 44273
2 Australia Oceania 26177413
3 Cook Islands Oceania 17011
4 Fiji Oceania 929766
5 French Polynesia Oceania 306279
6 Guam Oceania 171774
7 Kiribati Oceania 131232
8 Marshall Islands Oceania 41569
9 Micronesia Oceania 114164
10 Nauru Oceania 12668
# i 13 more rows
