Analisis Demografi - Machine Learning

Skenario

Anda dipekerjakan sebagai Data Scientist oleh Bank Dunia dan Anda sedang mengerjakan sebuah proyek untuk menganalisis tren demografis Dunia.
Anda diminta untuk membuat scatterplot yang menggambarkan Angka Kelahiran dan Stattistik Penggunaan Internet menurut Negara.
Scatterplot juga perlu dikategorikan berdasarkan Kelompok Pendapatan Negara.
Anda mendapat pekerjaan pembaruan mendesak dan secepatnya dilaksanakan dari manajer Anda.
Anda diminta untuk membuat scatterplot kedua yang juga menggambarkan Angka Kelahiran dan Statistik Penggunaan Internet menurut Negara.
Namun, kali ini scatterplot perlu dikategorikan berdasarkan Wilayah Negara.
Data tambahan telah disediakan dalam bentuk vektor R.

Analisa Data Demografi

#Pertama baca data dari file csv
dt_demografi <- read.csv("P2-Demographic-Data.csv")
head(dt_demografi, n=10)

##            Country.Name Country.Code Birth.rate Internet.users
## 1                 Aruba          ABW     10.244        78.9000
## 2           Afghanistan          AFG     35.253         5.9000
## 3                Angola          AGO     45.985        19.1000
## 4               Albania          ALB     12.877        57.2000
## 5  United Arab Emirates          ARE     11.044        88.0000
## 6             Argentina          ARG     17.716        59.9000
## 7               Armenia          ARM     13.308        41.9000
## 8   Antigua and Barbuda          ATG     16.447        63.4000
## 9             Australia          AUS     13.200        83.0000
## 10              Austria          AUT      9.400        80.6188
##           Income.Group
## 1          High income
## 2           Low income
## 3  Upper middle income
## 4  Upper middle income
## 5          High income
## 6          High income
## 7  Lower middle income
## 8          High income
## 9          High income
## 10         High income

#menampilkan ringkasan dari data demografi
summary(dt_demografi)

##  Country.Name       Country.Code         Birth.rate    Internet.users 
##  Length:195         Length:195         Min.   : 7.90   Min.   : 0.90  
##  Class :character   Class :character   1st Qu.:12.12   1st Qu.:14.52  
##  Mode  :character   Mode  :character   Median :19.68   Median :41.00  
##                                        Mean   :21.47   Mean   :42.08  
##                                        3rd Qu.:29.76   3rd Qu.:66.22  
##                                        Max.   :49.66   Max.   :96.55  
##  Income.Group      
##  Length:195        
##  Class :character  
##  Mode  :character  
##                    
##                    
##

#Menampilkan data dengan Birth Rate lebih dari 2
filter_br <- dt_demografi$Birth.rate > 2
head(dt_demografi[filter_br,], n=10)

##            Country.Name Country.Code Birth.rate Internet.users
## 1                 Aruba          ABW     10.244        78.9000
## 2           Afghanistan          AFG     35.253         5.9000
## 3                Angola          AGO     45.985        19.1000
## 4               Albania          ALB     12.877        57.2000
## 5  United Arab Emirates          ARE     11.044        88.0000
## 6             Argentina          ARG     17.716        59.9000
## 7               Armenia          ARM     13.308        41.9000
## 8   Antigua and Barbuda          ATG     16.447        63.4000
## 9             Australia          AUS     13.200        83.0000
## 10              Austria          AUT      9.400        80.6188
##           Income.Group
## 1          High income
## 2           Low income
## 3  Upper middle income
## 4  Upper middle income
## 5          High income
## 6          High income
## 7  Lower middle income
## 8          High income
## 9          High income
## 10         High income

#Ambil data yang Angka Kelahirannya > 40
head(dt_demografi[dt_demografi$Birth.rate > 40,], n=10)

##     Country.Name Country.Code Birth.rate Internet.users        Income.Group
## 3         Angola          AGO     45.985           19.1 Upper middle income
## 12       Burundi          BDI     44.151            1.3          Low income
## 15  Burkina Faso          BFA     40.551            9.1          Low income
## 66   Gambia, The          GMB     42.525           14.0          Low income
## 116         Mali          MLI     44.138            3.5          Low income
## 128        Niger          NER     49.661            1.7          Low income
## 129      Nigeria          NGA     40.045           38.0 Lower middle income
## 157      Somalia          SOM     43.891            1.5          Low income
## 168         Chad          TCD     45.745            2.3          Low income
## 179       Uganda          UGA     43.474           16.2          Low income

# Di sini demografi_data$Birth.rate > 40 adalah vektor yang berisi nilai BENAR seperti di bawah ini

#Ambil data yang Tingkat Kelahirannya> 40 dan pengguna Internet <2
head(dt_demografi[dt_demografi$Birth.rate > 40 & dt_demografi$Internet.users <2,])

##     Country.Name Country.Code Birth.rate Internet.users Income.Group
## 12       Burundi          BDI     44.151            1.3   Low income
## 128        Niger          NER     49.661            1.7   Low income
## 157      Somalia          SOM     43.891            1.5   Low income

#Ambil data Income group yang High Income, disini IncomeGroup berisi Data Kategori
head(dt_demografi[dt_demografi$Income.Group == "High income",], n=10)

##            Country.Name Country.Code Birth.rate Internet.users Income.Group
## 1                 Aruba          ABW     10.244       78.90000  High income
## 5  United Arab Emirates          ARE     11.044       88.00000  High income
## 6             Argentina          ARG     17.716       59.90000  High income
## 8   Antigua and Barbuda          ATG     16.447       63.40000  High income
## 9             Australia          AUS     13.200       83.00000  High income
## 10              Austria          AUT      9.400       80.61880  High income
## 13              Belgium          BEL     11.200       82.17020  High income
## 18              Bahrain          BHR     15.040       90.00004  High income
## 19         Bahamas, The          BHS     15.339       72.00000  High income
## 23              Bermuda          BMU     10.400       95.30000  High income

#Ambil detail Negara Argentina
head(dt_demografi[dt_demografi$Country.Name == "Argentina",])

##   Country.Name Country.Code Birth.rate Internet.users Income.Group
## 6    Argentina          ARG     17.716           59.9  High income

Plotting Data Demografi

#load ggplot2
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.5

#menampilkan scatterplot yang menggambarkan Angka Kelahiran (birth.rate) dan Statistik Penggunaan Internet (Internet.users)
qplot(data = dt_demografi, x=Internet.users, y = Birth.rate, size=I(4))

#memberikan warna pada node plot
qplot(data = dt_demografi, x=Internet.users, y = Birth.rate, size=I(4), colour=I("blue"))

#Scatterplot juga perlu dikategorikan berdasarkan Kelompok Pendapatan Negara (Income.Group).
qplot(data = dt_demografi, x=Internet.users, y = Birth.rate, size=I(3), colour=Income.Group)

# Namun, kali ini scatterplot perlu dikategorikan berdasarkan Wilayah Negara.
# Data tambahan telah disediakan dalam bentuk vektor R.

Countries_2012_Dataset <- c("Aruba","Afghanistan","Angola","Albania","United Arab Emirates","Argentina","Armenia","Antigua and Barbuda","Australia","Austria","Azerbaijan","Burundi","Belgium","Benin","Burkina Faso","Bangladesh","Bulgaria","Bahrain","Bahamas, The","Bosnia and Herzegovina","Belarus","Belize","Bermuda","Bolivia","Brazil","Barbados","Brunei Darussalam","Bhutan","Botswana","Central African Republic","Canada","Switzerland","Chile","China","Cote d'Ivoire","Cameroon","Congo, Rep.","Colombia","Comoros","Cabo Verde","Costa Rica","Cuba","Cayman Islands","Cyprus","Czech Republic","Germany","Djibouti","Denmark","Dominican Republic","Algeria","Ecuador","Egypt, Arab Rep.","Eritrea","Spain","Estonia","Ethiopia","Finland","Fiji","France","Micronesia, Fed. Sts.","Gabon","United Kingdom","Georgia","Ghana","Guinea","Gambia, The","Guinea-Bissau","Equatorial Guinea","Greece","Grenada","Greenland","Guatemala","Guam","Guyana","Hong Kong SAR, China","Honduras","Croatia","Haiti","Hungary","Indonesia","India","Ireland","Iran, Islamic Rep.","Iraq","Iceland","Israel","Italy","Jamaica","Jordan","Japan","Kazakhstan","Kenya","Kyrgyz Republic","Cambodia","Kiribati","Korea, Rep.","Kuwait","Lao PDR","Lebanon","Liberia","Libya","St. Lucia","Liechtenstein","Sri Lanka","Lesotho","Lithuania","Luxembourg","Latvia","Macao SAR, China","Morocco","Moldova","Madagascar","Maldives","Mexico","Macedonia, FYR","Mali","Malta","Myanmar","Montenegro","Mongolia","Mozambique","Mauritania","Mauritius","Malawi","Malaysia","Namibia","New Caledonia","Niger","Nigeria","Nicaragua","Netherlands","Norway","Nepal","New Zealand","Oman","Pakistan","Panama","Peru","Philippines","Papua New Guinea","Poland","Puerto Rico","Portugal","Paraguay","French Polynesia","Qatar","Romania","Russian Federation","Rwanda","Saudi Arabia","Sudan","Senegal","Singapore","Solomon Islands","Sierra Leone","El Salvador","Somalia","Serbia","South Sudan","Sao Tome and Principe","Suriname","Slovak Republic","Slovenia","Sweden","Swaziland","Seychelles","Syrian Arab Republic","Chad","Togo","Thailand","Tajikistan","Turkmenistan","Timor-Leste","Tonga","Trinidad and Tobago","Tunisia","Turkey","Tanzania","Uganda","Ukraine","Uruguay","United States","Uzbekistan","St. Vincent and the Grenadines","Venezuela, RB","Virgin Islands (U.S.)","Vietnam","Vanuatu","West Bank and Gaza","Samoa","Yemen, Rep.","South Africa","Congo, Dem. Rep.","Zambia","Zimbabwe")
Codes_2012_Dataset <- c("ABW","AFG","AGO","ALB","ARE","ARG","ARM","ATG","AUS","AUT","AZE","BDI","BEL","BEN","BFA","BGD","BGR","BHR","BHS","BIH","BLR","BLZ","BMU","BOL","BRA","BRB","BRN","BTN","BWA","CAF","CAN","CHE","CHL","CHN","CIV","CMR","COG","COL","COM","CPV","CRI","CUB","CYM","CYP","CZE","DEU","DJI","DNK","DOM","DZA","ECU","EGY","ERI","ESP","EST","ETH","FIN","FJI","FRA","FSM","GAB","GBR","GEO","GHA","GIN","GMB","GNB","GNQ","GRC","GRD","GRL","GTM","GUM","GUY","HKG","HND","HRV","HTI","HUN","IDN","IND","IRL","IRN","IRQ","ISL","ISR","ITA","JAM","JOR","JPN","KAZ","KEN","KGZ","KHM","KIR","KOR","KWT","LAO","LBN","LBR","LBY","LCA","LIE","LKA","LSO","LTU","LUX","LVA","MAC","MAR","MDA","MDG","MDV","MEX","MKD","MLI","MLT","MMR","MNE","MNG","MOZ","MRT","MUS","MWI","MYS","NAM","NCL","NER","NGA","NIC","NLD","NOR","NPL","NZL","OMN","PAK","PAN","PER","PHL","PNG","POL","PRI","PRT","PRY","PYF","QAT","ROU","RUS","RWA","SAU","SDN","SEN","SGP","SLB","SLE","SLV","SOM","SRB","SSD","STP","SUR","SVK","SVN","SWE","SWZ","SYC","SYR","TCD","TGO","THA","TJK","TKM","TLS","TON","TTO","TUN","TUR","TZA","UGA","UKR","URY","USA","UZB","VCT","VEN","VIR","VNM","VUT","PSE","WSM","YEM","ZAF","COD","ZMB","ZWE")
Regions_2012_Dataset <- c("The Americas","Asia","Africa","Europe","Middle East","The Americas","Asia","The Americas","Oceania","Europe","Asia","Africa","Europe","Africa","Africa","Asia","Europe","Middle East","The Americas","Europe","Europe","The Americas","The Americas","The Americas","The Americas","The Americas","Asia","Asia","Africa","Africa","The Americas","Europe","The Americas","Asia","Africa","Africa","Africa","The Americas","Africa","Africa","The Americas","The Americas","The Americas","Europe","Europe","Europe","Africa","Europe","The Americas","Africa","The Americas","Africa","Africa","Europe","Europe","Africa","Europe","Oceania","Europe","Oceania","Africa","Europe","Asia","Africa","Africa","Africa","Africa","Africa","Europe","The Americas","The Americas","The Americas","Oceania","The Americas","Asia","The Americas","Europe","The Americas","Europe","Asia","Asia","Europe","Middle East","Middle East","Europe","Middle East","Europe","The Americas","Middle East","Asia","Asia","Africa","Asia","Asia","Oceania","Asia","Middle East","Asia","Middle East","Africa","Africa","The Americas","Europe","Asia","Africa","Europe","Europe","Europe","Asia","Africa","Europe","Africa","Asia","The Americas","Europe","Africa","Europe","Asia","Europe","Asia","Africa","Africa","Africa","Africa","Asia","Africa","Oceania","Africa","Africa","The Americas","Europe","Europe","Asia","Oceania","Middle East","Asia","The Americas","The Americas","Asia","Oceania","Europe","The Americas","Europe","The Americas","Oceania","Middle East","Europe","Europe","Africa","Middle East","Africa","Africa","Asia","Oceania","Africa","The Americas","Africa","Europe","Africa","Africa","The Americas","Europe","Europe","Europe","Africa","Africa","Middle East","Africa","Africa","Asia","Asia","Asia","Asia","Oceania","The Americas","Africa","Europe","Africa","Africa","Europe","The Americas","The Americas","Asia","The Americas","The Americas","The Americas","Asia","Oceania","Middle East","Oceania","Middle East","Africa","Africa","Africa","Africa")

#membuat data frame
dt_frame <- data.frame(Countries_2012_Dataset, Codes_2012_Dataset, Regions_2012_Dataset)
head(dt_frame, n=10)

##    Countries_2012_Dataset Codes_2012_Dataset Regions_2012_Dataset
## 1                   Aruba                ABW         The Americas
## 2             Afghanistan                AFG                 Asia
## 3                  Angola                AGO               Africa
## 4                 Albania                ALB               Europe
## 5    United Arab Emirates                ARE          Middle East
## 6               Argentina                ARG         The Americas
## 7                 Armenia                ARM                 Asia
## 8     Antigua and Barbuda                ATG         The Americas
## 9               Australia                AUS              Oceania
## 10                Austria                AUT               Europe

#Mengganti nama nama kolom data frame
colnames(dt_frame) <- c("Country","Codes","Region")
head(dt_frame, n=10)

##                 Country Codes       Region
## 1                 Aruba   ABW The Americas
## 2           Afghanistan   AFG         Asia
## 3                Angola   AGO       Africa
## 4               Albania   ALB       Europe
## 5  United Arab Emirates   ARE  Middle East
## 6             Argentina   ARG The Americas
## 7               Armenia   ARM         Asia
## 8   Antigua and Barbuda   ATG The Americas
## 9             Australia   AUS      Oceania
## 10              Austria   AUT       Europe

summary(dt_frame)

##    Country             Codes              Region         
##  Length:195         Length:195         Length:195        
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character

#Gabungkan myDF dengan demografi_data berdasarkan Codes dan Country.Code
merge_data <- merge(dt_demografi, dt_frame, by.x="Country.Code", by.y ="Codes")

#Pada data frame terdapat kolom negara yang sama, maka akan dihapus
merge_data$Country.Name <- NULL
head(merge_data, n=10)

##    Country.Code Birth.rate Internet.users        Income.Group
## 1           ABW     10.244        78.9000         High income
## 2           AFG     35.253         5.9000          Low income
## 3           AGO     45.985        19.1000 Upper middle income
## 4           ALB     12.877        57.2000 Upper middle income
## 5           ARE     11.044        88.0000         High income
## 6           ARG     17.716        59.9000         High income
## 7           ARM     13.308        41.9000 Lower middle income
## 8           ATG     16.447        63.4000         High income
## 9           AUS     13.200        83.0000         High income
## 10          AUT      9.400        80.6188         High income
##                 Country       Region
## 1                 Aruba The Americas
## 2           Afghanistan         Asia
## 3                Angola       Africa
## 4               Albania       Europe
## 5  United Arab Emirates  Middle East
## 6             Argentina The Americas
## 7               Armenia         Asia
## 8   Antigua and Barbuda The Americas
## 9             Australia      Oceania
## 10              Austria       Europe

str(merge_data)

## 'data.frame':    195 obs. of  6 variables:
##  $ Country.Code  : chr  "ABW" "AFG" "AGO" "ALB" ...
##  $ Birth.rate    : num  10.2 35.3 46 12.9 11 ...
##  $ Internet.users: num  78.9 5.9 19.1 57.2 88 ...
##  $ Income.Group  : chr  "High income" "Low income" "Upper middle income" "Upper middle income" ...
##  $ Country       : chr  "Aruba" "Afghanistan" "Angola" "Albania" ...
##  $ Region        : chr  "The Americas" "Asia" "Africa" "Europe" ...

#Scatter Plot antara Internet.users & Birth.rate dan dikategorikan berdasarkan Wilayah Negara
qplot(data=merge_data, x= Internet.users, y= Birth.rate, size=I(3), colour=Region)

#Mengubah bentuk Scatter plot menjadi persegi
qplot(data=merge_data, x= Internet.users, y= Birth.rate, size=I(3), colour=Region, shape = I(15))

#Mengubah bentuk Scatter plot menjadi transparan
qplot(data=merge_data, x= Internet.users, y= Birth.rate, size=I(3), colour=Region, shape = I(19), alpha=10)

Analisis Demografi - Machine Learning

Penulis : Ismail Akbar | Totok Chamidy,M.Kom | Magister Informatika | UIN Maulana Malik Ibrahim Malang

30 November 2021

Skenario

Analisa Data Demografi

Plotting Data Demografi