R Data Science Homework
Masengesho Donatien 20251MBI014
2026-05-24
1 MAsters in Big Data Analitics
2 Introduction
This homework demonstrates important concepts in R programming and Data Science including:
- Importing data from different sources
- Merging datasets
- Using
%>%pipes andgroup_by() - Creating visualizations using
ggplot2geoms - Investigating
trace()andrecover()debugging functions - Using functional programming tools such as
sapply(),lapply(),map(),vapply(),split(), andtapply()
3 Setup and loading Libraries
library(tidyverse)
library(ggplot2)
library(dplyr)
library(readr)
library(corrplot)
library(reshape2)
library(skimr)
library(janitor)
library(plotly)
library(purrr)
library(DBI)
library(RPostgres)4 1. Importing Data from Two Different Sources(CSV and Postgres)
4.1 1.1 Loading Data from my local PostgreSQL Database
# Create PostgreSQL connection
connection <- dbConnect(
RPostgres::Postgres(),
dbname = "r_conn_test_db",
host = "localhost",
port = 5432,
user = "donatien",
password = "MYSQLdon2020."
)
# Display available tables
dbListTables(connection)## [1] "student"4.2 Reading from Student Table of r_conn_test_db database
# Read the student table
students <- dbReadTable(connection, "student")
# Alternative using SQL query
students <- dbGetQuery(connection, "SELECT * FROM student")
# Display first rows
head(students)## id reg_number name age gender height
## 1 1 REG0001 Student_1 18 Male 168.63
## 2 2 REG0002 Student_2 16 Female 194.89
## 3 3 REG0003 Student_3 16 Female 160.26
## 4 4 REG0004 Student_4 17 Female 165.74
## 5 5 REG0005 Student_5 23 Female 157.51
## 6 6 REG0006 Student_6 19 Female 162.854.4 1.2 Importing date from CSV Datasets (files on my computer : /home/donatient/Documents/r_assignment/data_sets/)
4.5 I am loading two files because will need them for merging purpose
4.6 Population and C02 emission files will be used till the end of this homwork
# Load population dataset
population <- read.csv(
"/home/donatient/Documents/r_assignment/data_sets/world_population.csv"
)
# Load CO2 emission dataset
co2Emission <- read.csv(
"/home/donatient/Documents/r_assignment/data_sets/CO2_emission.csv"
)5 Display first rows from each data set Population and Co2 emmision
## Rank CCA3 Country.Territory Capital Continent X2022.Population
## 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
## X2020.Population X2015.Population X2010.Population X2000.Population
## 1 38972230 33753499 28189672 19542982
## 2 2866849 2882481 2913399 3182021
## 3 43451666 39543154 35856344 30774621
## 4 46189 51368 54849 58230
## 5 77700 71746 71519 66097
## 6 33428485 28127721 23364185 16394062
## X1990.Population X1980.Population X1970.Population Area..km..
## 1 10694796 12486631 10752971 652230
## 2 3295066 2941651 2324731 28748
## 3 25518074 18739378 13795915 2381741
## 4 47818 32886 27075 199
## 5 53569 35611 19860 468
## 6 11828638 8330047 6029700 1246700
## Density..per.km.. Growth.Rate World.Population.Percentage
## 1 63.0587 1.0257 0.52
## 2 98.8702 0.9957 0.04
## 3 18.8531 1.0164 0.56
## 4 222.4774 0.9831 0.00
## 5 170.5641 1.0100 0.00
## 6 28.5466 1.0315 0.45## Country.Name country_code Region
## 1 Aruba ABW Latin America & Caribbean
## 2 Afghanistan AFG South Asia
## 3 Angola AGO Sub-Saharan Africa
## 4 Albania ALB Europe & Central Asia
## 5 Andorra AND Europe & Central Asia
## 6 United Arab Emirates ARE Middle East & North Africa
## Indicator.Name X1990 X1991 X1992
## 1 CO2 emissions (metric tons per capita) NA NA NA
## 2 CO2 emissions (metric tons per capita) 0.1917451 0.1676816 0.09595774
## 3 CO2 emissions (metric tons per capita) 0.5536620 0.5445386 0.54355722
## 4 CO2 emissions (metric tons per capita) 1.8195416 1.2428102 0.68369983
## 5 CO2 emissions (metric tons per capita) 7.5218317 7.2353792 6.96307870
## 6 CO2 emissions (metric tons per capita) 30.1951886 31.7784962 29.08092584
## X1993 X1994 X1995 X1996 X1997 X1998
## 1 NA NA NA NA NA NA
## 2 0.08472111 0.07554583 0.06846796 0.06258803 0.05682662 0.05269086
## 3 0.70898423 0.83680440 0.91214149 1.07216847 1.08663697 1.09182531
## 4 0.63830704 0.64535519 0.60543625 0.61236736 0.46692147 0.57215370
## 5 6.72417752 6.54157891 6.73347949 6.99159455 7.30744115 7.63953851
## 6 29.27567777 30.84933296 31.12501806 30.92802588 30.48633262 29.66358052
## X1999 X2000 X2001 X2002 X2003 X2004
## 1 NA NA NA NA NA NA
## 2 0.04015697 0.0365737 0.03378536 0.04557366 0.05151838 0.04165539
## 3 1.10985966 0.9880774 0.94182891 0.89557767 0.92486944 0.93026295
## 4 0.95535931 1.0262131 1.05549588 1.23237878 1.33898498 1.40405869
## 5 7.92319165 7.9522863 7.72154906 7.56623988 7.24241557 7.34426233
## 6 28.88710798 27.0351591 29.43026994 28.50146173 27.96926982 27.03893822
## X2005 X2006 X2007 X2008 X2009 X2010
## 1 NA NA NA NA NA NA
## 2 0.06041878 0.06658329 0.06531235 0.1284166 0.1718624 0.2436140
## 3 0.81353929 0.82184008 0.81175351 0.8886580 0.9394040 0.9761842
## 4 1.33820940 1.33999574 1.39393137 1.3843112 1.4414936 1.5276237
## 5 7.35378001 6.79054277 6.53104692 6.4393039 6.1566875 6.1571978
## 6 25.38238104 22.93510429 21.37028576 22.0114692 19.8323489 19.0397698
## X2011 X2012 X2013 X2014 X2015 X2016 X2017
## 1 NA NA NA NA NA NA NA
## 2 0.2965062 0.2592953 0.1856237 0.1462356 0.1728967 0.1497893 0.1316946
## 3 0.9855223 0.9506959 1.0362939 1.0997791 1.1350441 1.0318113 0.8133007
## 4 1.6694232 1.5032405 1.5336300 1.6683374 1.6037751 1.5576644 1.7887861
## 5 5.8508861 5.9446542 5.9428004 5.8071277 6.0261818 6.0806003 6.1041339
## 6 18.5094574 19.2078011 20.0556476 20.0516980 21.0776420 21.4806686 20.7690223
## X2018 X2019 X2019.1
## 1 NA NA NA
## 2 0.1632953 0.1598244 0.1598244
## 3 0.7776749 0.7921371 0.7921371
## 4 1.7827389 1.6922483 1.6922483
## 5 6.3629754 6.4812174 6.4812174
## 6 18.3906781 19.3295633 19.32956336 2. Data Cleaning before merging 2 Datasets(population and C02 Emission)
6.1 Cleaning Population Dataset from misisng values prior to merging
## Rank CCA3
## 0 0
## Country.Territory Capital
## 0 0
## Continent X2022.Population
## 0 0
## X2020.Population X2015.Population
## 0 0
## X2010.Population X2000.Population
## 0 0
## X1990.Population X1980.Population
## 0 0
## X1970.Population Area..km..
## 0 0
## Density..per.km.. Growth.Rate
## 0 0
## World.Population.Percentage
## 0# Remove missing values
population <- na.omit(population)
# Clean column names
population <- clean_names(population)
co2Emission <- clean_names(co2Emission)
# Display cleaned column names
colnames(population)## [1] "rank" "cca3"
## [3] "country_territory" "capital"
## [5] "continent" "x2022_population"
## [7] "x2020_population" "x2015_population"
## [9] "x2010_population" "x2000_population"
## [11] "x1990_population" "x1980_population"
## [13] "x1970_population" "area_km"
## [15] "density_per_km" "growth_rate"
## [17] "world_population_percentage"6.2 Merging Population and CO2 emission Datasets
merged <- merge(
population,
co2Emission,
by.x = "country_territory",
by.y = "country_name"
)
# Display merged dataset information
head(merged)## country_territory rank cca3 capital continent x2022_population
## 1 Afghanistan 36 AFG Kabul Asia 41128771
## 2 Albania 138 ALB Tirana Europe 2842321
## 3 Algeria 34 DZA Algiers Africa 44903225
## 4 American Samoa 213 ASM Pago Pago Oceania 44273
## 5 Andorra 203 AND Andorra la Vella Europe 79824
## 6 Angola 42 AGO Luanda Africa 35588987
## x2020_population x2015_population x2010_population x2000_population
## 1 38972230 33753499 28189672 19542982
## 2 2866849 2882481 2913399 3182021
## 3 43451666 39543154 35856344 30774621
## 4 46189 51368 54849 58230
## 5 77700 71746 71519 66097
## 6 33428485 28127721 23364185 16394062
## x1990_population x1980_population x1970_population area_km density_per_km
## 1 10694796 12486631 10752971 652230 63.0587
## 2 3295066 2941651 2324731 28748 98.8702
## 3 25518074 18739378 13795915 2381741 18.8531
## 4 47818 32886 27075 199 222.4774
## 5 53569 35611 19860 468 170.5641
## 6 11828638 8330047 6029700 1246700 28.5466
## growth_rate world_population_percentage country_code
## 1 1.0257 0.52 AFG
## 2 0.9957 0.04 ALB
## 3 1.0164 0.56 DZA
## 4 0.9831 0.00 ASM
## 5 1.0100 0.00 AND
## 6 1.0315 0.45 AGO
## region indicator_name x1990
## 1 South Asia CO2 emissions (metric tons per capita) 0.1917451
## 2 Europe & Central Asia CO2 emissions (metric tons per capita) 1.8195416
## 3 Middle East & North Africa CO2 emissions (metric tons per capita) 2.4434300
## 4 East Asia & Pacific CO2 emissions (metric tons per capita) NA
## 5 Europe & Central Asia CO2 emissions (metric tons per capita) 7.5218317
## 6 Sub-Saharan Africa CO2 emissions (metric tons per capita) 0.5536620
## x1991 x1992 x1993 x1994 x1995 x1996 x1997
## 1 0.1676816 0.09595774 0.08472111 0.07554583 0.06846796 0.06258803 0.05682662
## 2 1.2428102 0.68369983 0.63830704 0.64535519 0.60543625 0.61236736 0.46692147
## 3 2.5162433 2.47296078 2.61330374 2.60900907 2.65806257 2.60093353 2.50243923
## 4 NA NA NA NA NA NA NA
## 5 7.2353792 6.96307870 6.72417752 6.54157891 6.73347949 6.99159455 7.30744115
## 6 0.5445386 0.54355722 0.70898423 0.83680440 0.91214149 1.07216847 1.08663697
## x1998 x1999 x2000 x2001 x2002 x2003 x2004
## 1 0.05269086 0.04015697 0.0365737 0.03378536 0.04557366 0.05151838 0.04165539
## 2 0.57215370 0.95535931 1.0262131 1.05549588 1.23237878 1.33898498 1.40405869
## 3 2.47244786 2.53107052 2.5787445 2.50067461 2.58671220 2.73337366 2.73735406
## 4 NA NA NA NA NA NA NA
## 5 7.63953851 7.92319165 7.9522863 7.72154906 7.56623988 7.24241557 7.34426233
## 6 1.09182531 1.10985966 0.9880774 0.94182891 0.89557767 0.92486944 0.93026295
## x2005 x2006 x2007 x2008 x2009 x2010 x2011
## 1 0.06041878 0.06658329 0.06531235 0.1284166 0.1718624 0.2436140 0.2965062
## 2 1.33820940 1.33999574 1.39393137 1.3843112 1.4414936 1.5276237 1.6694232
## 3 2.84135137 2.96691468 3.00728985 3.1024511 3.1745733 3.1736545 3.2947426
## 4 NA NA NA NA NA NA NA
## 5 7.35378001 6.79054277 6.53104692 6.4393039 6.1566875 6.1571978 5.8508861
## 6 0.81353929 0.82184008 0.81175351 0.8886580 0.9394040 0.9761842 0.9855223
## x2012 x2013 x2014 x2015 x2016 x2017 x2018
## 1 0.2592953 0.1856237 0.1462356 0.1728967 0.1497893 0.1316946 0.1632953
## 2 1.5032405 1.5336300 1.6683374 1.6037751 1.5576644 1.7887861 1.7827389
## 3 3.6093077 3.6449793 3.7956323 3.9334959 3.8200903 3.8256380 3.9201091
## 4 NA NA NA NA NA NA NA
## 5 5.9446542 5.9428004 5.8071277 6.0261818 6.0806003 6.1041339 6.3629754
## 6 0.9506959 1.0362939 1.0997791 1.1350441 1.0318113 0.8133007 0.7776749
## x2019 x2019_1
## 1 0.1598244 0.1598244
## 2 1.6922483 1.6922483
## 3 3.9776505 3.9776505
## 4 NA NA
## 5 6.4812174 6.4812174
## 6 0.7921371 0.7921371## [1] "country_territory" "rank"
## [3] "cca3" "capital"
## [5] "continent" "x2022_population"
## [7] "x2020_population" "x2015_population"
## [9] "x2010_population" "x2000_population"
## [11] "x1990_population" "x1980_population"
## [13] "x1970_population" "area_km"
## [15] "density_per_km" "growth_rate"
## [17] "world_population_percentage" "country_code"
## [19] "region" "indicator_name"
## [21] "x1990" "x1991"
## [23] "x1992" "x1993"
## [25] "x1994" "x1995"
## [27] "x1996" "x1997"
## [29] "x1998" "x1999"
## [31] "x2000" "x2001"
## [33] "x2002" "x2003"
## [35] "x2004" "x2005"
## [37] "x2006" "x2007"
## [39] "x2008" "x2009"
## [41] "x2010" "x2011"
## [43] "x2012" "x2013"
## [45] "x2014" "x2015"
## [47] "x2016" "x2017"
## [49] "x2018" "x2019"
## [51] "x2019_1"7 3. Using Pipes (%>%) and Grouping
7.1 Boxplot Visualization
population %>%
select(density_per_km, growth_rate, area_km) %>%
boxplot(
main = "Population Visualization",
col = c("lightblue", "lightgreen", "lightpink")
)
7.2 Top 10 Countries by Population in 2022
top10 <- population %>%
arrange(desc(x2022_population)) %>%
head(10)
# Display top 10 countries
top10## rank cca3 country_territory capital continent x2022_population
## 1 1 CHN China Beijing Asia 1425887337
## 2 2 IND India New Delhi Asia 1417173173
## 3 3 USA United States Washington, D.C. North America 338289857
## 4 4 IDN Indonesia Jakarta Asia 275501339
## 5 5 PAK Pakistan Islamabad Asia 235824862
## 6 6 NGA Nigeria Abuja Africa 218541212
## 7 7 BRA Brazil Brasilia South America 215313498
## 8 8 BGD Bangladesh Dhaka Asia 171186372
## 9 9 RUS Russia Moscow Europe 144713314
## 10 10 MEX Mexico Mexico City North America 127504125
## x2020_population x2015_population x2010_population x2000_population
## 1 1424929781 1393715448 1348191368 1264099069
## 2 1396387127 1322866505 1240613620 1059633675
## 3 335942003 324607776 311182845 282398554
## 4 271857970 259091970 244016173 214072421
## 5 227196741 210969298 194454498 154369924
## 6 208327405 183995785 160952853 122851984
## 7 213196304 205188205 196353492 175873720
## 8 167420951 157830000 148391139 129193327
## 9 145617329 144668389 143242599 146844839
## 10 125998302 120149897 112532401 97873442
## x1990_population x1980_population x1970_population area_km density_per_km
## 1 1153704252 982372466 822534450 9706961 146.8933
## 2 870452165 696828385 557501301 3287590 431.0675
## 3 248083732 223140018 200328340 9372610 36.0935
## 4 182159874 148177096 115228394 1904569 144.6529
## 5 115414069 80624057 59290872 881912 267.4018
## 6 95214257 72951439 55569264 923768 236.5759
## 7 150706446 122288383 96369875 8515767 25.2841
## 8 107147651 83929765 67541860 147570 1160.0350
## 9 148005704 138257420 130093010 17098242 8.4636
## 10 81720428 67705186 50289306 1964375 64.9082
## growth_rate world_population_percentage
## 1 1.0000 17.88
## 2 1.0068 17.77
## 3 1.0038 4.24
## 4 1.0064 3.45
## 5 1.0191 2.96
## 6 1.0241 2.74
## 7 1.0046 2.70
## 8 1.0108 2.15
## 9 0.9973 1.81
## 10 1.0063 1.607.3 Grouping by Continent
population %>%
group_by(continent) %>%
summarise(
average_density = mean(density_per_km),
average_growth = mean(growth_rate),
total_population = sum(x2022_population)
)## # A tibble: 6 × 4
## continent average_density average_growth total_population
## <chr> <dbl> <dbl> <dbl>
## 1 Africa 125. 1.02 1426730932
## 2 Asia 1025. 1.01 4721383274
## 3 Europe 663. 1.00 743147538
## 4 North America 273. 1.00 600296136
## 5 Oceania 133. 1.01 45038554
## 6 South America 21.0 1.01 4368166088 4. Data Visualization Using Geoms
8.1 Preparing CO2 Dataset for Plotting
co2_long <- co2Emission %>%
pivot_longer(
cols = starts_with("x"),
names_to = "year",
values_to = "co2_emission"
)
head(co2_long)## # A tibble: 6 × 6
## country_name country_code region indicator_name year co2_emission
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 Aruba ABW Latin America & C… CO2 emissions… x1990 NA
## 2 Aruba ABW Latin America & C… CO2 emissions… x1991 NA
## 3 Aruba ABW Latin America & C… CO2 emissions… x1992 NA
## 4 Aruba ABW Latin America & C… CO2 emissions… x1993 NA
## 5 Aruba ABW Latin America & C… CO2 emissions… x1994 NA
## 6 Aruba ABW Latin America & C… CO2 emissions… x1995 NA
9 5. Investigating trace() and recover()
9.1 Creating a Buggy Function
9.2 Using trace() function for debugging
## [1] "process_population_df"## Tracing process_population_df(population) on entry
## [1] "TRACE ACTIVE"
## rank cca3 country_territory capital continent x2022_population
## 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
## x2020_population x2015_population x2010_population x2000_population
## 1 38972230 33753499 28189672 19542982
## 2 2866849 2882481 2913399 3182021
## 3 43451666 39543154 35856344 30774621
## 4 46189 51368 54849 58230
## 5 77700 71746 71519 66097
## 6 33428485 28127721 23364185 16394062
## x1990_population x1980_population x1970_population area_km density_per_km
## 1 10694796 12486631 10752971 652230 63.0587
## 2 3295066 2941651 2324731 28748 98.8702
## 3 25518074 18739378 13795915 2381741 18.8531
## 4 47818 32886 27075 199 222.4774
## 5 53569 35611 19860 468 170.5641
## 6 11828638 8330047 6029700 1246700 28.5466
## growth_rate world_population_percentage
## 1 1.0257 0.52
## 2 0.9957 0.04
## 3 1.0164 0.56
## 4 0.9831 0.00
## 5 1.0100 0.00
## 6 1.0315 0.45## [1] NA9.3 Using recover() function for debugging
## Tracing process_population_df(population) on entry
## [1] "TRACE ACTIVE"
## rank cca3 country_territory capital continent x2022_population
## 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
## x2020_population x2015_population x2010_population x2000_population
## 1 38972230 33753499 28189672 19542982
## 2 2866849 2882481 2913399 3182021
## 3 43451666 39543154 35856344 30774621
## 4 46189 51368 54849 58230
## 5 77700 71746 71519 66097
## 6 33428485 28127721 23364185 16394062
## x1990_population x1980_population x1970_population area_km density_per_km
## 1 10694796 12486631 10752971 652230 63.0587
## 2 3295066 2941651 2324731 28748 98.8702
## 3 25518074 18739378 13795915 2381741 18.8531
## 4 47818 32886 27075 199 222.4774
## 5 53569 35611 19860 468 170.5641
## 6 11828638 8330047 6029700 1246700 28.5466
## growth_rate world_population_percentage
## 1 1.0257 0.52
## 2 0.9957 0.04
## 3 1.0164 0.56
## 4 0.9831 0.00
## 5 1.0100 0.00
## 6 1.0315 0.45## [1] NA10 6. Applying different functions to data sets/dataframe in R
10.1 6.1 Using sapply()
## density_per_km growth_rate area_km
## 4.521270e+02 1.009577e+00 5.814494e+0510.2 6.2 Using lapply()
## $density_per_km
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.610e-02 3.842e+01 9.535e+01 4.521e+02 2.389e+02 2.317e+04
##
## $growth_rate
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.912 1.002 1.008 1.010 1.017 1.069
##
## $area_km
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 2650 81200 581449 430426 1709824210.3 6.3 Using vapply()
## density_per_km growth_rate area_km
## 4.521270e+02 1.009577e+00 5.814494e+0510.4 6.4 Using split()
## List of 6
## $ Africa :'data.frame': 57 obs. of 17 variables:
## ..$ rank : int [1:57] 34 42 77 144 58 78 53 171 117 69 ...
## ..$ cca3 : chr [1:57] "DZA" "AGO" "BEN" "BWA" ...
## ..$ country_territory : chr [1:57] "Algeria" "Angola" "Benin" "Botswana" ...
## ..$ capital : chr [1:57] "Algiers" "Luanda" "Porto-Novo" "Gaborone" ...
## ..$ continent : chr [1:57] "Africa" "Africa" "Africa" "Africa" ...
## ..$ x2022_population : int [1:57] 44903225 35588987 13352864 2630296 22673762 12889576 27914536 593149 5579144 17723315 ...
## ..$ x2020_population : int [1:57] 43451666 33428485 12643123 2546402 21522626 12220227 26491087 582640 5343020 16644701 ...
## ..$ x2015_population : int [1:57] 39543154 28127721 10932783 2305171 18718019 10727148 23012646 552166 4819333 14140274 ...
## ..$ x2010_population : int [1:57] 35856344 23364185 9445710 2091664 16116845 9126605 19878036 521212 4660067 11894727 ...
## ..$ x2000_population : int [1:57] 30774621 16394062 6998023 1726985 11882888 6307659 15091594 458251 3759170 8259137 ...
## ..$ x1990_population : int [1:57] 25518074 11828638 5133419 1341474 9131361 5483793 11430520 364563 2809221 5827069 ...
## ..$ x1980_population : int [1:57] 18739378 8330047 3833939 938578 6932967 4312834 8519891 317234 2415276 4408230 ...
## ..$ x1970_population : int [1:57] 13795915 6029700 3023443 592244 5611666 3497834 6452787 287262 2067356 3667394 ...
## ..$ area_km : int [1:57] 2381741 1246700 112622 582000 272967 27834 475442 4033 622984 1284000 ...
## ..$ density_per_km : num [1:57] 18.85 28.55 118.56 4.52 83.06 ...
## ..$ growth_rate : num [1:57] 1.02 1.03 1.03 1.02 1.03 ...
## ..$ world_population_percentage: num [1:57] 0.56 0.45 0.17 0.03 0.28 0.16 0.35 0.01 0.07 0.22 ...
## $ Asia :'data.frame': 50 obs. of 17 variables:
## ..$ rank : int [1:50] 36 140 91 154 8 165 175 73 1 131 ...
## ..$ cca3 : chr [1:50] "AFG" "ARM" "AZE" "BHR" ...
## ..$ country_territory : chr [1:50] "Afghanistan" "Armenia" "Azerbaijan" "Bahrain" ...
## ..$ capital : chr [1:50] "Kabul" "Yerevan" "Baku" "Manama" ...
## ..$ continent : chr [1:50] "Asia" "Asia" "Asia" "Asia" ...
## ..$ x2022_population : int [1:50] 41128771 2780469 10358074 1472233 171186372 782455 449002 16767842 1425887337 3744385 ...
## ..$ x2020_population : int [1:50] 38972230 2805608 10284951 1477469 167420951 772506 441725 16396860 1424929781 3765912 ...
## ..$ x2015_population : int [1:50] 33753499 2878595 9863480 1362142 157830000 743274 421437 15417523 1393715448 3771132 ...
## ..$ x2010_population : int [1:50] 28189672 2946293 9237202 1213645 148391139 705516 396053 14363532 1348191368 3836831 ...
## ..$ x2000_population : int [1:50] 19542982 3168523 8190337 711442 129193327 587207 333926 12118841 1264099069 4265172 ...
## ..$ x1990_population : int [1:50] 10694796 3556539 7427836 517418 107147651 558442 261928 8910808 1153704252 5391636 ...
## ..$ x1980_population : int [1:50] 12486631 3135123 6383060 362595 83929765 415257 187921 6198959 982372466 5145843 ...
## ..$ x1970_population : int [1:50] 10752971 2534377 5425317 222555 67541860 298894 133343 6708525 822534450 4800426 ...
## ..$ area_km : int [1:50] 652230 29743 86600 765 147570 38394 5765 181035 9706961 69700 ...
## ..$ density_per_km : num [1:50] 63.1 93.5 119.6 1924.5 1160 ...
## ..$ growth_rate : num [1:50] 1.026 0.996 1.004 1.006 1.011 ...
## ..$ world_population_percentage: num [1:50] 0.52 0.03 0.13 0.02 2.15 ...
## $ Europe :'data.frame': 50 obs. of 17 variables:
## ..$ rank : int [1:50] 138 203 99 96 81 137 108 130 158 88 ...
## ..$ cca3 : chr [1:50] "ALB" "AND" "AUT" "BLR" ...
## ..$ country_territory : chr [1:50] "Albania" "Andorra" "Austria" "Belarus" ...
## ..$ capital : chr [1:50] "Tirana" "Andorra la Vella" "Vienna" "Minsk" ...
## ..$ continent : chr [1:50] "Europe" "Europe" "Europe" "Europe" ...
## ..$ x2022_population : int [1:50] 2842321 79824 8939617 9534954 11655930 3233526 6781953 4030358 1251488 10493986 ...
## ..$ x2020_population : int [1:50] 2866849 77700 8907777 9633740 11561717 3318407 6979175 4096868 1237537 10530953 ...
## ..$ x2015_population : int [1:50] 2882481 71746 8642421 9700609 11248303 3524324 7309253 4254815 1187280 10523798 ...
## ..$ x2010_population : int [1:50] 2913399 71519 8362829 9731427 10877947 3811088 7592273 4368682 1129686 10464749 ...
## ..$ x2000_population : int [1:50] 3182021 66097 8010428 10256483 10264343 4179350 8097691 4548434 948237 10234710 ...
## ..$ x1990_population : int [1:50] 3295066 53569 7678729 10428525 9959560 4494310 8767778 4873707 788500 10301192 ...
## ..$ x1980_population : int [1:50] 2941651 35611 7547561 9817257 9828986 4199820 8980606 4680144 679327 10270060 ...
## ..$ x1970_population : int [1:50] 2324731 19860 7465301 9170786 9629376 3815561 8582950 4492638 640804 9795744 ...
## ..$ area_km : int [1:50] 28748 468 83871 207600 30528 51209 110879 56594 9251 78865 ...
## ..$ density_per_km : num [1:50] 98.9 170.6 106.6 45.9 381.8 ...
## ..$ growth_rate : num [1:50] 0.996 1.01 1.002 0.996 1.004 ...
## ..$ world_population_percentage: num [1:50] 0.04 0 0.11 0.12 0.15 0.04 0.09 0.05 0.02 0.13 ...
## $ North America:'data.frame': 40 obs. of 17 variables:
## ..$ rank : int [1:40] 224 201 198 176 186 177 206 221 39 205 ...
## ..$ cca3 : chr [1:40] "AIA" "ATG" "ABW" "BHS" ...
## ..$ country_territory : chr [1:40] "Anguilla" "Antigua and Barbuda" "Aruba" "Bahamas" ...
## ..$ capital : chr [1:40] "The Valley" "Saint John’s" "Oranjestad" "Nassau" ...
## ..$ continent : chr [1:40] "North America" "North America" "North America" "North America" ...
## ..$ x2022_population : int [1:40] 15857 93763 106445 409984 281635 405272 64184 31305 38454327 68706 ...
## ..$ x2020_population : int [1:40] 15585 92664 106585 406471 280693 394921 64031 30910 37888705 67311 ...
## ..$ x2015_population : int [1:40] 14525 89941 104257 392697 278083 359871 63144 29366 35732126 60911 ...
## ..$ x2010_population : int [1:40] 13172 85695 100341 373272 274711 322106 63447 27556 33963412 54074 ...
## ..$ x2000_population : int [1:40] 11047 75055 89101 325014 264657 240406 61371 20104 30683313 39658 ...
## ..$ x1990_population : int [1:40] 8316 63328 65712 270679 258868 182589 57470 15617 27657204 26027 ...
## ..$ x1980_population : int [1:40] 6560 64888 62267 223752 253575 145133 53565 11109 24511510 17100 ...
## ..$ x1970_population : int [1:40] 6283 64516 59106 179129 241397 120905 52019 9581 21434577 10533 ...
## ..$ area_km : int [1:40] 91 442 180 13943 430 22966 54 151 9984670 264 ...
## ..$ density_per_km : num [1:40] 174.3 212.1 591.4 29.4 655 ...
## ..$ growth_rate : num [1:40] 1.007 1.006 0.999 1.005 1.002 ...
## ..$ world_population_percentage: num [1:40] 0 0 0 0.01 0 0.01 0 0 0.48 0 ...
## $ Oceania :'data.frame': 23 obs. of 17 variables:
## ..$ rank : int [1:23] 213 55 223 162 183 191 192 215 194 225 ...
## ..$ cca3 : chr [1:23] "ASM" "AUS" "COK" "FJI" ...
## ..$ country_territory : chr [1:23] "American Samoa" "Australia" "Cook Islands" "Fiji" ...
## ..$ capital : chr [1:23] "Pago Pago" "Canberra" "Avarua" "Suva" ...
## ..$ continent : chr [1:23] "Oceania" "Oceania" "Oceania" "Oceania" ...
## ..$ x2022_population : int [1:23] 44273 26177413 17011 929766 306279 171774 131232 41569 114164 12668 ...
## ..$ x2020_population : int [1:23] 46189 25670051 17029 920422 301920 169231 126463 43413 112106 12315 ...
## ..$ x2015_population : int [1:23] 51368 23820236 17695 917200 291787 167978 116707 49410 109462 11185 ...
## ..$ x2010_population : int [1:23] 54849 22019168 17212 905169 283788 164905 107995 53416 107588 10241 ...
## ..$ x2000_population : int [1:23] 58230 19017963 15897 832509 250927 160188 88826 54224 111709 10377 ...
## ..$ x1990_population : int [1:23] 47818 17048003 17123 780430 211089 138263 75124 46047 98603 9598 ...
## ..$ x1980_population : int [1:23] 32886 14706322 17651 644582 163591 110286 60813 31988 76299 7635 ...
## ..$ x1970_population : int [1:23] 27075 12595034 20470 527634 117891 88300 57437 23969 58989 6663 ...
## ..$ area_km : int [1:23] 199 7692024 236 18272 4167 549 811 181 702 21 ...
## ..$ density_per_km : num [1:23] 222.5 3.4 72.1 50.9 73.5 ...
## ..$ growth_rate : num [1:23] 0.983 1.01 1 1.006 1.007 ...
## ..$ world_population_percentage: num [1:23] 0 0.33 0 0.01 0 0 0 0 0 0 ...
## $ South America:'data.frame': 14 obs. of 17 variables:
## ..$ rank : int [1:14] 33 80 7 65 28 67 231 184 164 109 ...
## ..$ cca3 : chr [1:14] "ARG" "BOL" "BRA" "CHL" ...
## ..$ country_territory : chr [1:14] "Argentina" "Bolivia" "Brazil" "Chile" ...
## ..$ capital : chr [1:14] "Buenos Aires" "Sucre" "Brasilia" "Santiago" ...
## ..$ continent : chr [1:14] "South America" "South America" "South America" "South America" ...
## ..$ x2022_population : int [1:14] 45510318 12224110 215313498 19603733 51874024 18001000 3780 304557 808726 6780744 ...
## ..$ x2020_population : int [1:14] 45036032 11936162 213196304 19300315 50930662 17588595 3747 290969 797202 6618695 ...
## ..$ x2015_population : int [1:14] 43257065 11090085 205188205 17870124 47119728 16195902 3408 257026 755031 6177950 ...
## ..$ x2010_population : int [1:14] 41100123 10223270 196353492 17004162 44816108 14989585 3187 228453 747932 5768613 ...
## ..$ x2000_population : int [1:14] 37070774 8592656 175873720 15351799 39215135 12626507 3080 164351 759051 5123819 ...
## ..$ x1990_population : int [1:14] 32637657 7096194 150706446 13342868 32601393 10449837 2332 113931 747116 4059195 ...
## ..$ x1980_population : int [1:14] 28024803 5736088 122288383 11469828 26176195 8135845 2240 66825 778176 3078912 ...
## ..$ x1970_population : int [1:14] 23842803 4585693 96369875 9820481 20905254 6172215 2274 46484 705261 2408787 ...
## ..$ area_km : int [1:14] 2780400 1098581 8515767 756102 1141748 276841 12173 83534 214969 406752 ...
## ..$ density_per_km : num [1:14] 16.4 11.1 25.3 25.9 45.4 ...
## ..$ growth_rate : num [1:14] 1.01 1.01 1 1.01 1.01 ...
## ..$ world_population_percentage: num [1:14] 0.57 0.15 2.7 0.25 0.65 0.23 0 0 0.01 0.09 ...10.5 6.5 Using tapply()
## Africa Asia Europe North America Oceania
## 125.04765 1025.02414 663.32474 272.76176 132.54307
## South America
## 20.9719811 Conclusion
In this homework i explored major concepts bellow :
- Database connectivity with PostgreSQL
- CSV data importation
- CSV Data cleaning and merging
- Using functions p in R:map,tapply,split,vapply,lapply,sapply,merge,….
- Visualization using geoms for
ggplot2 - Debugging using
trace()andrecover() Data manipulation with pipes and grouping
These tools are essential in modern Data Science workflows and statistical analysis.