Analisis Trend Dunia - Machine Learning
Penulis : Ismail Akbar | Totok Chamidy,M.Kom | Magister Informatika | UIN Maulana Malik Ibrahim Malang
08 Desember 2021
Skenario
Saya sangat terkesan dengan hasil tugas anda sebelumnya dan sekarang saya mempunyai proyek baru untuk Anda.
Saya minta anda membuat scatterplot yang menggambarkan Harapan Hidup (sumbu y) dan Statistik Tingkat Kesuburan (sumbu x) menurut Negara.
Scatterplot juga perlu dikategorikan berdasarkan Wilayah Negara.
Anda diberikan data selama 2 tahun: 1960 dan 2013 dan Anda diminta untuk menghasilkan visualisasi untuk setiap tahun ini.
Beberapa data telah disediakan dalam file csv, beberapa - dalam vektor R.
File csv berisi: data gabungan untuk kedua tahun.
Semua manipulasi data harus dilakukan di R karena tugas proyek ini mungkin akan diaudit pada tahap selanjutnya.
Anda juga diminta untuk memberikan wawasan tentang bagaimana kedua periode tersebut dibandingkan.(Ini yang - terpenting di akhir presentasi)
Setelah Anda mengimpor file csv ke R, langkah pertama adalah membagi dataframe menjadi dua: data 1960 dan data 2013. Gunakan memfileteran dataframe.
Trend Dunia
#Pertama baca data dari file csv
data <- read.csv("DatasetTugasproyekMachineLearning3.csv")
head(data, n=10)## Country.Name Country.Code Region Year Fertility.Rate
## 1 Aruba ABW The Americas 1960 4.820
## 2 Afghanistan AFG Asia 1960 7.450
## 3 Angola AGO Africa 1960 7.379
## 4 Albania ALB Europe 1960 6.186
## 5 United Arab Emirates ARE Middle East 1960 6.928
## 6 Argentina ARG The Americas 1960 3.109
## 7 Armenia ARM Asia 1960 4.550
## 8 Antigua and Barbuda ATG The Americas 1960 4.425
## 9 Australia AUS Oceania 1960 3.453
## 10 Austria AUT Europe 1960 2.690str(data)## 'data.frame': 374 obs. of 5 variables:
## $ Country.Name : chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Country.Code : chr "ABW" "AFG" "AGO" "ALB" ...
## $ Region : chr "The Americas" "Asia" "Africa" "Europe" ...
## $ Year : int 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
## $ Fertility.Rate: num 4.82 7.45 7.38 6.19 6.93 ...summary(data)## Country.Name Country.Code Region Year
## Length:374 Length:374 Length:374 Min. :1960
## Class :character Class :character Class :character 1st Qu.:1960
## Mode :character Mode :character Mode :character Median :1986
## Mean :1986
## 3rd Qu.:2013
## Max. :2013
## Fertility.Rate
## Min. :1.124
## 1st Qu.:2.243
## Median :3.994
## Mean :4.191
## 3rd Qu.:6.252
## Max. :8.187Menyelidiki berapa tahun yang berbeda dalam kumpulan data
data$Year## [1] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [16] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [31] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [46] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [61] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [76] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [91] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [106] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [121] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [136] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [151] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [166] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960
## [181] 1960 1960 1960 1960 1960 1960 1960 2013 2013 2013 2013 2013 2013 2013 2013
## [196] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [211] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [226] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [241] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [256] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [271] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [286] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [301] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [316] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [331] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [346] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013
## [361] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013Filtering the dataframes
data1960 <- data[data$Year==1960,]
data2013 <- data[data$Year==2013,]Memeriksa jumlah baris untuk 1960
nrow(data1960)## [1] 187Checking row counts for 2013
nrow(data2013)## [1] 187Visualisasi Jumlah Data tiap Tahun
barplot (table(data$Year),beside=T, main="Perbandingan jumlah dataset Trend Dunia", xlab="Tahun", col=c(4, 2))
### vektor yang kita perlukan
Country_Code <- c("ABW","AFG","AGO","ALB","ARE","ARG","ARM","ATG","AUS","AUT","AZE","BDI","BEL","BEN","BFA","BGD","BGR","BHR","BHS","BIH","BLR","BLZ","BOL","BRA","BRB","BRN","BTN","BWA","CAF","CAN","CHE","CHL","CHN","CIV","CMR","COG","COL","COM","CPV","CRI","CUB","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","GTM","GUM","GUY","HKG","HND","HRV","HTI","HUN","IDN","IND","IRL","IRN","IRQ","ISL","ITA","JAM","JOR","JPN","KAZ","KEN","KGZ","KHM","KIR","KOR","KWT","LAO","LBN","LBR","LBY","LCA","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","SSD","STP","SUR","SVK","SVN","SWE","SWZ","SYR","TCD","TGO","THA","TJK","TKM","TLS","TON","TTO","TUN","TUR","TZA","UGA","UKR","URY","USA","UZB","VCT","VEN","VIR","VNM","VUT","WSM","YEM","ZAF","COD","ZMB","ZWE")
Life_Expectancy_At_Birth_1960 <- c(65.5693658536586,32.328512195122,32.9848292682927,62.2543658536585,52.2432195121951,65.2155365853659,65.8634634146342,61.7827317073171,70.8170731707317,68.5856097560976,60.836243902439,41.2360487804878,69.7019512195122,37.2782682926829,34.4779024390244,45.8293170731707,69.2475609756098,52.0893658536585,62.7290487804878,60.2762195121951,67.7080975609756,59.9613658536585,42.1183170731707,54.2054634146342,60.7380487804878,62.5003658536585,32.3593658536585,50.5477317073171,36.4826341463415,71.1331707317073,71.3134146341463,57.4582926829268,43.4658048780488,36.8724146341463,41.523756097561,48.5816341463415,56.716756097561,41.4424390243903,48.8564146341463,60.5761951219512,63.9046585365854,69.5939268292683,70.3487804878049,69.3129512195122,44.0212682926829,72.1765853658537,51.8452682926829,46.1351219512195,53.215,48.0137073170732,37.3629024390244,69.1092682926829,67.9059756097561,38.4057073170732,68.819756097561,55.9584878048781,69.8682926829268,57.5865853658537,39.5701219512195,71.1268292682927,63.4318536585366,45.8314634146342,34.8863902439024,32.0422195121951,37.8404390243902,36.7330487804878,68.1639024390244,59.8159268292683,45.5316341463415,61.2263414634146,60.2787317073171,66.9997073170732,46.2883170731707,64.6086585365854,42.1000975609756,68.0031707317073,48.6403170731707,41.1719512195122,69.691756097561,44.945512195122,48.0306829268293,73.4286585365854,69.1239024390244,64.1918292682927,52.6852682926829,67.6660975609756,58.3675853658537,46.3624146341463,56.1280731707317,41.2320243902439,49.2159756097561,53.0013170731707,60.3479512195122,43.2044634146342,63.2801219512195,34.7831707317073,42.6411951219512,57.303756097561,59.7471463414634,46.5107073170732,69.8473170731707,68.4463902439024,69.7868292682927,64.6609268292683,48.4466341463415,61.8127804878049,39.9746829268293,37.2686341463415,57.0656341463415,60.6228048780488,28.2116097560976,67.6017804878049,42.7363902439024,63.7056097560976,48.3688048780488,35.0037073170732,43.4830975609756,58.7452195121951,37.7736341463415,59.4753414634146,46.8803902439024,58.6390243902439,35.5150487804878,37.1829512195122,46.9988292682927,73.3926829268293,73.549756097561,35.1708292682927,71.2365853658537,42.6670731707317,45.2904634146342,60.8817073170732,47.6915853658537,57.8119268292683,38.462243902439,67.6804878048781,68.7196097560976,62.8089268292683,63.7937073170732,56.3570487804878,61.2060731707317,65.6424390243903,66.0552926829268,42.2492926829268,45.6662682926829,48.1876341463415,38.206,65.6598292682927,49.3817073170732,30.3315365853659,49.9479268292683,36.9658780487805,31.6767073170732,50.4513658536585,59.6801219512195,69.9759268292683,68.9780487804878,73.0056097560976,44.2337804878049,52.768243902439,38.0161219512195,40.2728292682927,54.6993170731707,56.1535365853659,54.4586829268293,33.7271219512195,61.3645365853659,62.6575853658537,42.009756097561,45.3844146341463,43.6538780487805,43.9835609756098,68.2995365853659,67.8963902439025,69.7707317073171,58.8855365853659,57.7238780487805,59.2851219512195,63.7302195121951,59.0670243902439,46.4874878048781,49.969512195122,34.3638048780488,49.0362926829268,41.0180487804878,45.1098048780488,51.5424634146342)
Life_Expectancy_At_Birth_2013 <- c(75.3286585365854,60.0282682926829,51.8661707317073,77.537243902439,77.1956341463415,75.9860975609756,74.5613658536585,75.7786585365854,82.1975609756098,80.890243902439,70.6931463414634,56.2516097560976,80.3853658536585,59.3120243902439,58.2406341463415,71.245243902439,74.4658536585366,76.5459512195122,75.0735365853659,76.2769268292683,72.4707317073171,69.9820487804878,67.9134390243903,74.1224390243903,75.3339512195122,78.5466585365854,69.1029268292683,64.3608048780488,49.8798780487805,81.4011219512195,82.7487804878049,81.1979268292683,75.3530243902439,51.2084634146342,55.0418048780488,61.6663902439024,73.8097317073171,62.9321707317073,72.9723658536585,79.2252195121951,79.2563902439025,79.9497804878049,78.2780487804878,81.0439024390244,61.6864634146342,80.3024390243903,73.3199024390244,74.5689512195122,75.648512195122,70.9257804878049,63.1778780487805,82.4268292682927,76.4243902439025,63.4421951219512,80.8317073170732,69.9179268292683,81.9682926829268,68.9733902439024,63.8435853658537,80.9560975609756,74.079512195122,61.1420731707317,58.216487804878,59.9992682926829,54.8384146341464,57.2908292682927,80.6341463414634,73.1935609756098,71.4863902439024,78.872512195122,66.3100243902439,83.8317073170732,72.9428536585366,77.1268292682927,62.4011463414634,75.2682926829268,68.7046097560976,67.6604146341463,81.0439024390244,75.1259756097561,69.4716829268293,83.1170731707317,82.290243902439,73.4689268292683,73.9014146341463,83.3319512195122,70.45,60.9537804878049,70.2024390243902,67.7720487804878,65.7665853658537,81.459756097561,74.462756097561,65.687243902439,80.1288780487805,60.5203902439024,71.6576829268293,74.9127073170732,74.2402926829268,49.3314634146342,74.1634146341464,81.7975609756098,73.9804878048781,80.3391463414634,73.7090487804878,68.811512195122,64.6739024390244,76.6026097560976,76.5326585365854,75.1870487804878,57.5351951219512,80.7463414634146,65.6540975609756,74.7583658536585,69.0618048780488,54.641512195122,62.8027073170732,74.46,61.466,74.567512195122,64.3438780487805,77.1219512195122,60.8281463414634,52.4421463414634,74.514756097561,81.1048780487805,81.4512195121951,69.222,81.4073170731707,76.8410487804878,65.9636829268293,77.4192195121951,74.2838536585366,68.1315609756097,62.4491707317073,76.8487804878049,78.7111951219512,80.3731707317073,72.7991707317073,76.3340731707317,78.4184878048781,74.4634146341463,71.0731707317073,63.3948292682927,74.1776341463415,63.1670487804878,65.878756097561,82.3463414634146,67.7189268292683,50.3631219512195,72.4981463414634,55.0230243902439,55.2209024390244,66.259512195122,70.99,76.2609756097561,80.2780487804878,81.7048780487805,48.9379268292683,74.7157804878049,51.1914878048781,59.1323658536585,74.2469268292683,69.4001707317073,65.4565609756098,67.5223658536585,72.6403414634147,70.3052926829268,73.6463414634147,75.1759512195122,64.2918292682927,57.7676829268293,71.159512195122,76.8361951219512,78.8414634146341,68.2275853658537,72.8108780487805,74.0744146341464,79.6243902439024,75.756487804878,71.669243902439,73.2503902439024,63.583512195122,56.7365853658537,58.2719268292683,59.2373658536585,55.633)Membuat kerangka data tambahan
add1960 <- data.frame(Code=Country_Code, Life.Exp=Life_Expectancy_At_Birth_1960)
add2013 <- data.frame(Code=Country_Code, Life.Exp=Life_Expectancy_At_Birth_2013)summary(add1960)## Code Life.Exp
## Length:187 Min. :28.21
## Class :character 1st Qu.:43.47
## Mode :character Median :54.70
## Mean :53.73
## 3rd Qu.:64.05
## Max. :73.55summary(add2013)## Code Life.Exp
## Length:187 Min. :48.94
## Class :character 1st Qu.:64.52
## Mode :character Median :73.25
## Mean :70.76
## 3rd Qu.:76.84
## Max. :83.83Menggabungkan pasangan kerangka data
merged1960 <- merge(data1960, add1960, by.x="Country.Code", by.y="Code")
merged2013 <- merge(data2013, add2013, by.x="Country.Code", by.y="Code")Memeriksa struktur baru
str(merged1960)## 'data.frame': 187 obs. of 6 variables:
## $ Country.Code : chr "ABW" "AFG" "AGO" "ALB" ...
## $ Country.Name : chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Region : chr "The Americas" "Asia" "Africa" "Europe" ...
## $ Year : int 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
## $ Fertility.Rate: num 4.82 7.45 7.38 6.19 6.93 ...
## $ Life.Exp : num 65.6 32.3 33 62.3 52.2 ...str(merged2013)## 'data.frame': 187 obs. of 6 variables:
## $ Country.Code : chr "ABW" "AFG" "AGO" "ALB" ...
## $ Country.Name : chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Region : chr "The Americas" "Asia" "Africa" "Europe" ...
## $ Year : int 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ Fertility.Rate: num 1.67 5.05 6.16 1.77 1.8 ...
## $ Life.Exp : num 75.3 60 51.9 77.5 77.2 ...str(merged1960)## 'data.frame': 187 obs. of 6 variables:
## $ Country.Code : chr "ABW" "AFG" "AGO" "ALB" ...
## $ Country.Name : chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Region : chr "The Americas" "Asia" "Africa" "Europe" ...
## $ Year : int 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
## $ Fertility.Rate: num 4.82 7.45 7.38 6.19 6.93 ...
## $ Life.Exp : num 65.6 32.3 33 62.3 52.2 ...str(merged2013)## 'data.frame': 187 obs. of 6 variables:
## $ Country.Code : chr "ABW" "AFG" "AGO" "ALB" ...
## $ Country.Name : chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Region : chr "The Americas" "Asia" "Africa" "Europe" ...
## $ Year : int 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ Fertility.Rate: num 1.67 5.05 6.16 1.77 1.8 ...
## $ Life.Exp : num 75.3 60 51.9 77.5 77.2 ...Membuat visualisasi
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.5Memvisualisasikan data 1960
qplot(data=merged1960, x=Fertility.Rate, y=Life.Exp,
colour=Region, #colour
size=I(5),
alpha=I(0.6), #transparency
main="Harapan Hidup vs Kesuburan (1960)" #title
)
### Memvisualisasikan data 2013
qplot(data=merged2013, x=Fertility.Rate, y=Life.Exp,
colour=Region, #colour
size=I(5),
alpha=I(0.6), #transparency
main="Harapan Hidup vs Kesuburan (2013)" #title
)
Melihat dari kedua grafik, kita dapat melihat bahwa Angka Kelahiran telah turun dari tahun 1960 dan 2103. Angka Harapan Hidup juga meningkat. Selain itu, Angka Harapan Hidup terendah telah meningkat 20 tahun.
Anda juga diminta untuk memberikan wawasan tentang bagaimana kedua periode tersebut dibandingkan.
library(magrittr)## Warning: package 'magrittr' was built under R version 4.0.5library(dplyr) ## Warning: package 'dplyr' was built under R version 4.0.5##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniongabung = rbind(merged1960, merged2013)
head(gabung, n=10)## Country.Code Country.Name Region Year Fertility.Rate Life.Exp
## 1 ABW Aruba The Americas 1960 4.820 65.56937
## 2 AFG Afghanistan Asia 1960 7.450 32.32851
## 3 AGO Angola Africa 1960 7.379 32.98483
## 4 ALB Albania Europe 1960 6.186 62.25437
## 5 ARE United Arab Emirates Middle East 1960 6.928 52.24322
## 6 ARG Argentina The Americas 1960 3.109 65.21554
## 7 ARM Armenia Asia 1960 4.550 65.86346
## 8 ATG Antigua and Barbuda The Americas 1960 4.425 61.78273
## 9 AUS Australia Oceania 1960 3.453 70.81707
## 10 AUT Austria Europe 1960 2.690 68.58561str(gabung)## 'data.frame': 374 obs. of 6 variables:
## $ Country.Code : chr "ABW" "AFG" "AGO" "ALB" ...
## $ Country.Name : chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ Region : chr "The Americas" "Asia" "Africa" "Europe" ...
## $ Year : int 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
## $ Fertility.Rate: num 4.82 7.45 7.38 6.19 6.93 ...
## $ Life.Exp : num 65.6 32.3 33 62.3 52.2 ...filter(gabung, Year%in%c(1960, 2013)) %>%
ggplot(aes(Fertility.Rate, Life.Exp, col = Region)) +
geom_point() +
facet_grid(Region ~ Year)
# Visualisasi hanya dari segi tahun
filter(gabung, Year %in% c(1960, 2013)) %>%
ggplot(aes(Fertility.Rate, Life.Exp, col = Region)) +
geom_point() +
facet_grid(. ~ Year)