Data WHO

Berikut ini adalah data demografi dari WHO yang diperoleh dari www.kaggle.com

Data Demografi menurut WHO
Country Year Status Life.expectancy Adult.Mortality infant.deaths Alcohol percentage.expenditure Hepatitis.B Measles BMI under.five.deaths Polio Total.expenditure Diphtheria HIV.AIDS GDP Population thinness..1.19.years thinness.5.9.years Income.composition.of.resources Schooling
Afghanistan 2015 Developing 65.0 263 62 0.01 71.279624 65 1154 19.1 83 6 8.16 65 0.1 584.25921 33736494 17.2 17.3 0.479 10.1
Afghanistan 2014 Developing 59.9 271 64 0.01 73.523582 62 492 18.6 86 58 8.18 62 0.1 612.69651 327582 17.5 17.5 0.476 10.0
Afghanistan 2013 Developing 59.9 268 66 0.01 73.219243 64 430 18.1 89 62 8.13 64 0.1 631.74498 31731688 17.7 17.7 0.470 9.9
Afghanistan 2012 Developing 59.5 272 69 0.01 78.184215 67 2787 17.6 93 67 8.52 67 0.1 669.95900 3696958 17.9 18.0 0.463 9.8
Afghanistan 2011 Developing 59.2 275 71 0.01 7.097109 68 3013 17.2 97 68 7.87 68 0.1 63.53723 2978599 18.2 18.2 0.454 9.5
Afghanistan 2010 Developing 58.8 279 74 0.01 79.679367 66 1989 16.7 102 66 9.20 66 0.1 553.32894 2883167 18.4 18.4 0.448 9.2

Rata-Rata Usia Harapan Hidup Setiap Negara

10 Negara dengan Harapan Hidup Tertinggi
Country RataRataHarapanHidup
Japan 82.53750
Sweden 82.51875
Iceland 82.44375
Switzerland 82.33125
France 82.21875
Italy 82.18750
Spain 82.06875
Australia 81.81250
Norway 81.79375
Canada 81.68750
10 Negara dengan Harapan Hidup Terendah
Country RataRataHarapanHidup
Nigeria 51.35625
Swaziland 51.32500
Zimbabwe 50.48750
Côte d’Ivoire 50.38750
Chad 50.38750
Malawi 49.89375
Angola 49.01875
Lesotho 48.78125
Central African Republic 48.51250
Sierra Leone 46.11250

Negara Japan memiliki rata-rata harapan hidup paling tinggi yaitu 82.5375 Tahun. Sementara negara Sierra Leone memiliki rata-rata harapan hidup paling rendah yaitu 46.1125 Tahun. Indonesia memiliki rata-rata hidup 67.55625 Tahun. Angka tersebut Lebih Rendah daripada rata-rata hidup global yaitu 69.2249317 Tahun

Usia Harapan Hidup Berdasarkan Status Negara [Berkembang/Maju]

Hasil pengujian shapiro menunjukan bahwa data rata-rata harapan hidup di negara maju 0.944 dan di negara berkembang 0.922 dengan p.value masing-masing adalah 0.406 dan 0.185

Grafik Normalitas Data Variabel Life.expectancy

ggplot(DataWHO, aes(x = Life.expectancy))+ geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 10 rows containing non-finite values (stat_bin).

Hasil pengujian kehomogenan menggunakan metode var.test dan bartlett.test menunjukan bahwa varian data rata-rata harapan hidup adalah 0.619 dengan nilai p.value keduanya adalah 0,364 lebih kecil daripada 0.05, masing-masing dengan nilai 0.619 dan 0.8221

## 
##  Two Sample t-test
## 
## data:  RataRataHarapanHidup by Status
## t = 21.671, df = 30, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group Developed and group Developing is not equal to 0
## 95 percent confidence interval:
##  10.94739 13.22538
## sample estimates:
##  mean in group Developed mean in group Developing 
##                 79.19785                 67.11147

Hasil Uji Beda Rata-Rata Usia Harapan Hidup antara negara berkembang dengan negara maju, secara statistik, diketahui nilai t = 21.671 dan beda rata-rata secara signifikan dengan p.value sebesar 2.2e-16, lebih kecil daripada 0.05

Perbandingan rata-rata harapan hidup di negara berkembang dan negara maju

DataKelompok <- DataWHO %>% 
  group_by(Status) %>%
  summarize(RataRataHarapanHidup = mean(Life.expectancy, na.rm=TRUE)) %>%
  filter(!is.nan(RataRataHarapanHidup))
ggplot(DataKelompok, aes(x=Status, y=RataRataHarapanHidup, fill=Status)) +
  geom_bar(stat = "identity")

### Perbandingan pada negara-negara ASEAN #### [1]

DataKelompok <- DataWHO %>% 
  filter(Country %in% c("Indonesia","Thailand","Malaysia","Singapore","Vietnam","Philippines")) %>%
  group_by(Country) %>%
  summarize(RataRataHarapanHidup = mean(Life.expectancy, na.rm=TRUE)) %>%
  filter(!is.nan(RataRataHarapanHidup))
ggplot(DataKelompok, aes(x=Country, y=RataRataHarapanHidup, fill=Country)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label=as.integer(RataRataHarapanHidup)), position=position_dodge(width=0.9), vjust=-0.25)

[2]

DataKelompok <- DataWHO %>% 
  filter(Country %in% c("Indonesia","Thailand","Malaysia","Singapore","Vietnam","Philippines")) %>%
  group_by(Year, Country) %>%
  summarize(RataRataHarapanHidup = mean(Life.expectancy, na.rm=TRUE)) %>%
  filter(!is.nan(RataRataHarapanHidup))
## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.
ggplot(DataKelompok,
   aes(x = Year, 
       y = RataRataHarapanHidup,
       color = Country))+
geom_point() +
geom_line()

Analisis Tambahan

Trend Rata-Rata Harapan Hidup Global

ggplot(TrenHarapanHidup) + 
  geom_point(aes(x = Year, y = RataRataHarapanHidup, colour = RataRataHarapanHidup), size = 3) +
  geom_line(data = TrenHarapanHidup, aes(x = Year, y = RataRataHarapanHidup))

Korelasi antara Harapan Hidup dengan Populasi

library(ggplot2)
Data <- DataWHO %>%
  mutate(GDPPerKapita = (GDP)/Population) %>%
  group_by(Country) %>%
  summarize(MeanGDP = mean(GDPPerKapita, na.rm=TRUE),
            MeanHarapanHidup = mean(Life.expectancy, na.rm=TRUE)) %>%
  filter(!is.na(MeanGDP) & !is.na(MeanHarapanHidup))
gg <- ggplot(Data, aes(x=MeanGDP, y=MeanHarapanHidup)) + 
  geom_point(aes(x=MeanGDP, y=MeanHarapanHidup)) + 
  geom_smooth(method="loess", se=F) + 
  labs(subtitle="Life Expectancy Vs Population", 
       y="Life Expectancy", 
       x="Population", 
       title="Scatterplot", 
       caption = "Source: WHO")

plot(gg)
## `geom_smooth()` using formula 'y ~ x'

Korelasi antara berbagai jenis penyakit

library(tidyr)
library(reshape2)
## 
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
## 
##     smiths
cordf <- DataWHO %>%
    drop_na() %>% 
    select(Hepatitis.B, Polio, Measles, HIV.AIDS, Diphtheria)

cormat <- cor(cordf)
melted <- melt(cormat, varnames = c("X","Y"))
ggplot(melted)+
    geom_tile(aes(X, Y, fill=value))

Selesai