ANALISIS DEMOGRAFI

  • 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.
datademographic <- read.csv("F:/uun/UIN/MACHINE LEARNING/TUGAS PROYEK 2/P2-Demographic-Data.csv", header=TRUE)

Angka Kelahiran dan Statistik Pengguna Internet Berdasarkan Kelompok Pendapatan Negara

library(ggplot2)
scatterplot <- ggplot(datademographic, aes(x = Birth.rate, y = Internet.users, shape = Income.Group, color=Income.Group))+
  geom_point()
scatterplot

Membuat Scatter Plot dengan Kategori Berdasarkan Wilayah Negara

Perlu penambahan data dalam bentuk vektor R terlebih dahulu

kode_negara <- 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")
wilayah_negara <- c("Amerika","Asia","Afrika","Eropa","Middle East","Amerika","Asia","Amerika","Oceania","Eropa","Asia","Afrika","Eropa","Afrika","Afrika","Asia","Eropa","Middle East","Amerika","Eropa","Eropa","Amerika","Amerika","Amerika","Amerika","Amerika","Asia","Asia","Afrika","Afrika","Amerika","Eropa","Amerika","Asia","Afrika","Afrika","Afrika","Amerika","Afrika","Afrika","Amerika","Amerika","Amerika","Eropa","Eropa","Eropa","Afrika","Eropa","Amerika","Afrika","Amerika","Afrika","Afrika","Eropa","Eropa","Afrika","Eropa","Oceania","Eropa","Oceania","Afrika","Eropa","Asia","Afrika","Afrika","Afrika","Afrika","Afrika","Eropa","Amerika","Amerika","Amerika","Oceania","Amerika","Asia","Amerika","Eropa","Amerika","Eropa","Asia","Asia","Eropa","Middle East","Middle East","Eropa","Middle East","Eropa","Amerika","Middle East","Asia","Asia","Afrika","Asia","Asia","Oceania","Asia","Middle East","Asia","Middle East","Afrika","Afrika","Amerika","Eropa","Asia","Afrika","Eropa","Eropa","Eropa","Asia","Afrika","Eropa","Afrika","Asia","Amerika","Eropa","Afrika","Eropa","Asia","Eropa","Asia","Afrika","Afrika","Afrika","Afrika","Asia","Afrika","Oceania","Afrika","Afrika","Amerika","Eropa","Eropa","Asia","Oceania","Middle East","Asia","Amerika","Amerika","Asia","Oceania","Eropa","Amerika","Eropa","Amerika","Oceania","Middle East","Eropa","Eropa","Afrika","Middle East","Afrika","Afrika","Asia","Oceania","Afrika","Amerika","Afrika","Eropa","Afrika","Afrika","Amerika","Eropa","Eropa","Eropa","Afrika","Afrika","Middle East","Afrika","Afrika","Asia","Asia","Asia","Asia","Oceania","Amerika","Afrika","Eropa","Afrika","Afrika","Eropa","Amerika","Amerika","Asia","Amerika","Amerika","Amerika","Asia","Oceania","Middle East","Oceania","Middle East","Afrika","Afrika","Afrika","Afrika")


# Membuat Data Frame
dataset_demographic <- data.frame(kode_negara, wilayah_negara)
head(dataset_demographic)

Menyatukan Dataset dari csv dengan Dataset dari vektor

merge_dataset <- merge(datademographic, dataset_demographic, by.x="Country.Code", by.y ="kode_negara")

head(merge_dataset)

Scatter Plot Angka Kelahiran dan Pengguna Internet Berdasarkan Wilayah Negara.

scatterplot2 <- ggplot(merge_dataset, aes(x = Birth.rate, y = Internet.users, shape = wilayah_negara, color=wilayah_negara))+
  geom_point()
scatterplot2

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IkFmcmlrYSIsIkFtZXJpa2EiLCJFcm9wYSIsIkFtZXJpa2EiLCJBc2lhIiwiQWZyaWthIiwiQWZyaWthIiwiQWZyaWthIiwiQW1lcmlrYSIsIkFmcmlrYSIsIkFmcmlrYSIsIkFtZXJpa2EiLCJBbWVyaWthIiwiQW1lcmlrYSIsIkVyb3BhIiwiRXJvcGEiLCJFcm9wYSIsIkFmcmlrYSIsIkVyb3BhIiwiQW1lcmlrYSIsIkFmcmlrYSIsIkFtZXJpa2EiLCJBZnJpa2EiLCJBZnJpa2EiLCJFcm9wYSIsIkVyb3BhIiwiQWZyaWthIiwiRXJvcGEiLCJPY2VhbmlhIiwiRXJvcGEiLCJPY2VhbmlhIiwiQWZyaWthIiwiRXJvcGEiLCJBc2lhIiwiQWZyaWthIiwiQWZyaWthIiwiQWZyaWthIiwiQWZyaWthIiwiQWZyaWthIiwiRXJvcGEiLCJBbWVyaWthIiwiQW1lcmlrYSIsIkFtZXJpa2EiLCJPY2VhbmlhIiwiQW1lcmlrYSIsIkFzaWEiLCJBbWVyaWthIiwiRXJvcGEiLCJBbWVyaWthIiwiRXJvcGEiLCJBc2lhIiwiQXNpYSIsIkVyb3BhIiwiTWlkZGxlIEVhc3QiLCJNaWRkbGUgRWFzdCIsIkVyb3BhIiwiTWlkZGxlIEVhc3QiLCJFcm9wYSIsIkFtZXJpa2EiLCJNaWRkbGUgRWFzdCIsIkFzaWEiLCJBc2lhIiwiQWZyaWthIiwiQXNpYSIsIkFzaWEiLCJPY2VhbmlhIiwiQXNpYSIsIk1pZGRsZSBFYXN0IiwiQXNpYSIsIk1pZGRsZSBFYXN0IiwiQWZyaWthIiwiQWZyaWthIiwiQW1lcmlrYSIsIkVyb3BhIiwiQXNpYSIsIkFmcmlrYSIsIkVyb3BhIiwiRXJvcGEiLCJFcm9wYSIsIkFzaWEiLCJBZnJpa2EiLCJFcm9wYSIsIkFmcmlrYSIsIkFzaWEiLCJBbWVyaWthIiwiRXJvcGEiLCJBZnJpa2EiLCJFcm9wYSIsIkFzaWEiLCJFcm9wYSIsIkFzaWEiLCJBZnJpa2EiLCJBZnJpa2EiLCJBZnJpa2EiLCJBZnJpa2EiLCJBc2lhIiwiQWZyaWthIiwiT2NlYW5pYSIsIkFmcmlrYSIsIkFmcmlrYSIsIkFtZXJpa2EiLCJFcm9wYSIsIkVyb3BhIiwiQXNpYSIsIk9jZWFuaWEiLCJNaWRkbGUgRWFzdCIsIkFzaWEiLCJBbWVyaWthIiwiQW1lcmlrYSIsIkFzaWEiLCJPY2VhbmlhIiwiRXJvcGEiLCJBbWVyaWthIiwiRXJvcGEiLCJBbWVyaWthIiwiT2NlYW5pYSIsIk1pZGRsZSBFYXN0IiwiRXJvcGEiLCJFcm9wYSIsIkFmcmlrYSIsIk1pZGRsZSBFYXN0IiwiQWZyaWthIiwiQWZyaWthIiwiQXNpYSIsIk9jZWFuaWEiLCJBZnJpa2EiLCJBbWVyaWthIiwiQWZyaWthIiwiRXJvcGEiLCJBZnJpa2EiLCJBZnJpa2EiLCJBbWVyaWthIiwiRXJvcGEiLCJFcm9wYSIsIkVyb3BhIiwiQWZyaWthIiwiQWZyaWthIiwiTWlkZGxlIEVhc3QiLCJBZnJpa2EiLCJBZnJpa2EiLCJBc2lhIiwiQXNpYSIsIkFzaWEiLCJBc2lhIiwiT2NlYW5pYSIsIkFtZXJpa2EiLCJBZnJpa2EiLCJFcm9wYSIsIkFmcmlrYSIsIkFmcmlrYSIsIkVyb3BhIiwiQW1lcmlrYSIsIkFtZXJpa2EiLCJBc2lhIiwiQW1lcmlrYSIsIkFtZXJpa2EiLCJBbWVyaWthIiwiQXNpYSIsIk9jZWFuaWEiLCJNaWRkbGUgRWFzdCIsIk9jZWFuaWEiLCJNaWRkbGUgRWFzdCIsIkFmcmlrYSIsIkFmcmlrYSIsIkFmcmlrYSIsIkFmcmlrYSIpDQoNCg0KIyBNZW1idWF0IERhdGEgRnJhbWUNCmRhdGFzZXRfZGVtb2dyYXBoaWMgPC0gZGF0YS5mcmFtZShrb2RlX25lZ2FyYSwgd2lsYXlhaF9uZWdhcmEpDQpoZWFkKGRhdGFzZXRfZGVtb2dyYXBoaWMpDQpgYGANCg0KIyBNZW55YXR1a2FuIERhdGFzZXQgZGFyaSBjc3YgZGVuZ2FuIERhdGFzZXQgZGFyaSB2ZWt0b3INCg0KYGBge3J9DQptZXJnZV9kYXRhc2V0IDwtIG1lcmdlKGRhdGFkZW1vZ3JhcGhpYywgZGF0YXNldF9kZW1vZ3JhcGhpYywgYnkueD0iQ291bnRyeS5Db2RlIiwgYnkueSA9ImtvZGVfbmVnYXJhIikNCmBgYA0KDQpgYGB7cn0NCg0KaGVhZChtZXJnZV9kYXRhc2V0KQ0KYGBgDQoNCg0KDQojIFNjYXR0ZXIgUGxvdCBBbmdrYSBLZWxhaGlyYW4gZGFuIFBlbmdndW5hIEludGVybmV0IEJlcmRhc2Fya2FuIFdpbGF5YWggTmVnYXJhLg0KDQpgYGB7cn0NCnNjYXR0ZXJwbG90MiA8LSBnZ3Bsb3QobWVyZ2VfZGF0YXNldCwgYWVzKHggPSBCaXJ0aC5yYXRlLCB5ID0gSW50ZXJuZXQudXNlcnMsIHNoYXBlID0gd2lsYXlhaF9uZWdhcmEsIGNvbG9yPXdpbGF5YWhfbmVnYXJhKSkrDQogIGdlb21fcG9pbnQoKQ0Kc2NhdHRlcnBsb3QyDQpgYGANCg0KDQoNCg==