Anggota Kelompok:
Febrianggi Caesar Immanuel (140610240077)
Muhamad Fadhli Alfarizi (140610240061)
Gregorius Adiyatma Pradana (140610240065)
Bijan Ramadhan Aditri (140610240093)

Persiapan Library

install.packages(c(
  "tidyverse", "rmarkdown", "WDI", "jsonlite", "httr2",
  "rvest", "xml2", "readxl", "writexl", "skimr", "janitor",
  "knitr", "kableExtra", "here", "scales", "plotly", "maps", "DT",
  "htmltools"
))
library(tidyverse)
library(rmarkdown)
library(WDI)
library(jsonlite)
library(httr2)
library(rvest)
library(xml2)
library(readxl)
library(writexl)
library(skimr)
library(janitor)
library(knitr)
library(kableExtra)
library(here)
library(scales)
library(plotly)
library(maps)
library(DT)
library(htmltools)
theme_praktikum <- function(base_size = 13) {
  theme_minimal(base_size = base_size) +
    theme(
      plot.title    = element_text(face = "bold", color = "#1B5E20", size = rel(1.15)),
      plot.subtitle = element_text(color = "#C0392B"),
      panel.grid.minor = element_blank(),
      panel.grid.major = element_line(color = "#FDEDEC"),
      legend.position  = "right"
    )
}

Membaca Dataset

happiness_demo <- read_csv(
  here("world_happiness_report_2019.csv"),
  show_col_types = FALSE
)

head(happiness_demo)
dim(happiness_demo)
## [1] 156   9
glimpse(happiness_demo)
## Rows: 156
## Columns: 9
## $ `Overall rank`                 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, …
## $ `Country or region`            <chr> "Finland", "Denmark", "Norway", "Icelan…
## $ Score                          <dbl> 7.769, 7.600, 7.554, 7.494, 7.488, 7.48…
## $ `GDP per capita`               <dbl> 1.340, 1.383, 1.488, 1.380, 1.396, 1.45…
## $ `Social support`               <dbl> 1.587, 1.573, 1.582, 1.624, 1.522, 1.52…
## $ `Healthy life expectancy`      <dbl> 0.986, 0.996, 1.028, 1.026, 0.999, 1.05…
## $ `Freedom to make life choices` <dbl> 0.596, 0.592, 0.603, 0.591, 0.557, 0.57…
## $ Generosity                     <dbl> 0.153, 0.252, 0.271, 0.354, 0.322, 0.26…
## $ `Perceptions of corruption`    <dbl> 0.393, 0.410, 0.341, 0.118, 0.298, 0.34…

Membersihkan Nama Variabel

happiness_demo <- happiness_demo %>%
  clean_names()

names(happiness_demo)
## [1] "overall_rank"                 "country_or_region"           
## [3] "score"                        "gdp_per_capita"              
## [5] "social_support"               "healthy_life_expectancy"     
## [7] "freedom_to_make_life_choices" "generosity"                  
## [9] "perceptions_of_corruption"

Memeriksa Missing Value

colSums(is.na(happiness_demo))
##                 overall_rank            country_or_region 
##                            0                            0 
##                        score               gdp_per_capita 
##                            0                            0 
##               social_support      healthy_life_expectancy 
##                            0                            0 
## freedom_to_make_life_choices                   generosity 
##                            0                            0 
##    perceptions_of_corruption 
##                            0
skim(happiness_demo)
Data summary
Name happiness_demo
Number of rows 156
Number of columns 9
_______________________
Column type frequency:
character 1
numeric 8
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
country_or_region 0 1 4 24 0 156 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
overall_rank 0 1 78.50 45.18 1.00 39.75 78.50 117.25 156.00 ▇▇▇▇▇
score 0 1 5.41 1.11 2.85 4.54 5.38 6.18 7.77 ▂▇▇▇▃
gdp_per_capita 0 1 0.91 0.40 0.00 0.60 0.96 1.23 1.68 ▃▅▇▇▃
social_support 0 1 1.21 0.30 0.00 1.06 1.27 1.45 1.62 ▁▁▂▆▇
healthy_life_expectancy 0 1 0.73 0.24 0.00 0.55 0.79 0.88 1.14 ▁▃▃▇▅
freedom_to_make_life_choices 0 1 0.39 0.14 0.00 0.31 0.42 0.51 0.63 ▁▃▆▇▆
generosity 0 1 0.18 0.10 0.00 0.11 0.18 0.25 0.57 ▆▇▆▁▁
perceptions_of_corruption 0 1 0.11 0.09 0.00 0.05 0.09 0.14 0.45 ▇▅▁▁▁

Memilih Variabel

happiness2_demo <- happiness_demo %>%
  select(
    country_or_region,
    score,
    gdp_per_capita,
    social_support,
    healthy_life_expectancy,
    freedom_to_make_life_choices
  )

head(happiness2_demo)

Menyimpan Dataset

if (!dir.exists(here("output"))) {
  dir.create(here("output"))
}

write_csv(
  happiness2_demo,
  here("output", "happiness_clean.csv")
)

Visualisasi Awal

happiness2_demo %>%
  slice_max(score, n = 10) %>%
  ggplot(aes(
    x = reorder(country_or_region, score),
    y = score,
    fill = score
  )) +
  geom_col() +
  coord_flip() +
  scale_fill_gradient(low = "#E74C3C", high = "#27AE60", guide = "none") +
  labs(
    title = "10 Negara dengan Happiness Score Tertinggi (2019)",
    x = NULL,
    y = "Happiness Score"
  ) +
  theme_praktikum()

Langkah Analisis, Hasil, dan Pembahasan

Nomor 1 Instalasi dan Pemanggilan Package

Package yang digunakan dalam tugas ini sudah diinstal dan dipanggil pada bagian Persiapan Library. Daftar package yang aktif dan digunakan:

sort(.packages())
##  [1] "base"       "datasets"   "dplyr"      "DT"         "forcats"   
##  [6] "ggplot2"    "graphics"   "grDevices"  "here"       "htmltools" 
## [11] "httr2"      "janitor"    "jsonlite"   "kableExtra" "knitr"     
## [16] "lubridate"  "maps"       "methods"    "plotly"     "purrr"     
## [21] "readr"      "readxl"     "rmarkdown"  "rvest"      "scales"    
## [26] "skimr"      "stats"      "stringr"    "tibble"     "tidyr"     
## [31] "tidyverse"  "utils"      "WDI"        "writexl"    "xml2"

Nomor 2 Pengambilan Data Menggunakan Kaggle API

Persiapan Kaggle API

system2("python", "--version", stdout = TRUE)
## [1] "Python 3.13.14"
system2(
  "python",
  c("-m", "kaggle", "--version"),
  stdout = TRUE,
  stderr = TRUE
)
##  [1] "Authentication required to call the Kaggle API."                                    
##  [2] ""                                                                                   
##  [3] "First, you will need a Kaggle account. You can sign up at"                          
##  [4] "  https://www.kaggle.com/account/login"                                             
##  [5] ""                                                                                   
##  [6] "Recommended: log in with OAuth via a web-based authorization flow."                 
##  [7] "No token to manage; credentials are cached locally for you."                        
##  [8] "    kaggle auth login"                                                              
##  [9] ""                                                                                   
## [10] "If you'd rather not use OAuth, generate an API token at"                            
## [11] "  https://www.kaggle.com/settings/api  (click \"Generate New Token\" under \"API\")"
## [12] "and supply it to the CLI in one of these ways:"                                     
## [13] ""                                                                                   
## [14] "  Option A: Environment variable"                                                   
## [15] "    export KAGGLE_API_TOKEN=xxxxxxxxxxxxxx  # token copied from the settings UI"    
## [16] ""                                                                                   
## [17] "  Option B: API token file"                                                         
## [18] "    Save the token to ~/.kaggle/access_token"                                       
## [19] "Kaggle CLI 2.2.4"

Mencari Dataset

hasil_pencarian <- system2(
  "python",
  c("-m", "kaggle", "datasets", "list", "-s", "world-happiness"),
  stdout = TRUE,
  stderr = TRUE
)

cat(hasil_pencarian, sep = "\n")
## Authentication required to call the Kaggle API.
## 
## First, you will need a Kaggle account. You can sign up at
##   https://www.kaggle.com/account/login
## 
## Recommended: log in with OAuth via a web-based authorization flow.
## No token to manage; credentials are cached locally for you.
##     kaggle auth login
## 
## If you'd rather not use OAuth, generate an API token at
##   https://www.kaggle.com/settings/api  (click "Generate New Token" under "API")
## and supply it to the CLI in one of these ways:
## 
##   Option A: Environment variable
##     export KAGGLE_API_TOKEN=xxxxxxxxxxxxxx  # token copied from the settings UI
## 
##   Option B: API token file
##     Save the token to ~/.kaggle/access_token
## Authentication required to call the Kaggle API.
## 
## First, you will need a Kaggle account. You can sign up at
##   https://www.kaggle.com/account/login
## 
## Recommended: log in with OAuth via a web-based authorization flow.
## No token to manage; credentials are cached locally for you.
##     kaggle auth login
## 
## If you'd rather not use OAuth, generate an API token at
##   https://www.kaggle.com/settings/api  (click "Generate New Token" under "API")
## and supply it to the CLI in one of these ways:
## 
##   Option A: Environment variable
##     export KAGGLE_API_TOKEN=xxxxxxxxxxxxxx  # token copied from the settings UI
## 
##   Option B: API token file
##     Save the token to ~/.kaggle/access_token

Mengunduh dan Mengekstraksi Dataset

system2(
  "python",
  c(
    "-m", "kaggle", "datasets", "download",
    "-d", "unsdsn/world-happiness",
    "-p", "data",
    "--force"
  ),
  stdout = TRUE,
  stderr = TRUE
)
##  [1] "Authentication required to call the Kaggle API."                                                    
##  [2] ""                                                                                                   
##  [3] "First, you will need a Kaggle account. You can sign up at"                                          
##  [4] "  https://www.kaggle.com/account/login"                                                             
##  [5] ""                                                                                                   
##  [6] "Recommended: log in with OAuth via a web-based authorization flow."                                 
##  [7] "No token to manage; credentials are cached locally for you."                                        
##  [8] "    kaggle auth login"                                                                              
##  [9] ""                                                                                                   
## [10] "If you'd rather not use OAuth, generate an API token at"                                            
## [11] "  https://www.kaggle.com/settings/api  (click \"Generate New Token\" under \"API\")"                
## [12] "and supply it to the CLI in one of these ways:"                                                     
## [13] ""                                                                                                   
## [14] "  Option A: Environment variable"                                                                   
## [15] "    export KAGGLE_API_TOKEN=xxxxxxxxxxxxxx  # token copied from the settings UI"                    
## [16] ""                                                                                                   
## [17] "  Option B: API token file"                                                                         
## [18] "    Save the token to ~/.kaggle/access_token"                                                       
## [19] "Dataset URL: https://www.kaggle.com/datasets/unsdsn/world-happiness"                                
## [20] "License(s): CC0-1.0"                                                                                
## [21] "Downloading world-happiness.zip to data"                                                            
## [22] "\r  0%|          | 0.00/36.8k [00:00<?, ?B/s]\r100%|██████████| 36.8k/36.8k [00:00<00:00, 25.4MB/s]"
## [23] ""
list.files("data")
## [1] "2015.csv"            "2016.csv"            "2017.csv"           
## [4] "2018.csv"            "2019.csv"            "world-happiness.zip"
unzip(
  zipfile = "data/world-happiness.zip",
  exdir = "data"
)

Membaca dan Memeriksa Struktur Dataset (2019)

happiness_kaggle <- read_csv(
  "data/2019.csv",
  show_col_types = FALSE
) |>
  clean_names()

head(happiness_kaggle)
dim(happiness_kaggle)
## [1] 156   9
glimpse(happiness_kaggle)
## Rows: 156
## Columns: 9
## $ overall_rank                 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13…
## $ country_or_region            <chr> "Finland", "Denmark", "Norway", "Iceland"…
## $ score                        <dbl> 7.769, 7.600, 7.554, 7.494, 7.488, 7.480,…
## $ gdp_per_capita               <dbl> 1.340, 1.383, 1.488, 1.380, 1.396, 1.452,…
## $ social_support               <dbl> 1.587, 1.573, 1.582, 1.624, 1.522, 1.526,…
## $ healthy_life_expectancy      <dbl> 0.986, 0.996, 1.028, 1.026, 0.999, 1.052,…
## $ freedom_to_make_life_choices <dbl> 0.596, 0.592, 0.603, 0.591, 0.557, 0.572,…
## $ generosity                   <dbl> 0.153, 0.252, 0.271, 0.354, 0.322, 0.263,…
## $ perceptions_of_corruption    <dbl> 0.393, 0.410, 0.341, 0.118, 0.298, 0.343,…
summary(happiness_kaggle)
##   overall_rank    country_or_region      score       gdp_per_capita  
##  Min.   :  1.00   Length:156         Min.   :2.853   Min.   :0.0000  
##  1st Qu.: 39.75   Class :character   1st Qu.:4.545   1st Qu.:0.6028  
##  Median : 78.50   Mode  :character   Median :5.380   Median :0.9600  
##  Mean   : 78.50                      Mean   :5.407   Mean   :0.9051  
##  3rd Qu.:117.25                      3rd Qu.:6.184   3rd Qu.:1.2325  
##  Max.   :156.00                      Max.   :7.769   Max.   :1.6840  
##  social_support  healthy_life_expectancy freedom_to_make_life_choices
##  Min.   :0.000   Min.   :0.0000          Min.   :0.0000              
##  1st Qu.:1.056   1st Qu.:0.5477          1st Qu.:0.3080              
##  Median :1.272   Median :0.7890          Median :0.4170              
##  Mean   :1.209   Mean   :0.7252          Mean   :0.3926              
##  3rd Qu.:1.452   3rd Qu.:0.8818          3rd Qu.:0.5072              
##  Max.   :1.624   Max.   :1.1410          Max.   :0.6310              
##    generosity     perceptions_of_corruption
##  Min.   :0.0000   Min.   :0.0000           
##  1st Qu.:0.1087   1st Qu.:0.0470           
##  Median :0.1775   Median :0.0855           
##  Mean   :0.1848   Mean   :0.1106           
##  3rd Qu.:0.2482   3rd Qu.:0.1412           
##  Max.   :0.5660   Max.   :0.4530

Menyimpan Dataset Hasil Unduhan

write_csv(
  happiness_kaggle,
  here("output", "happiness_2019.csv")
)

Membaca dan Menyamakan Kolom Tiap Tahun (2015–2019)

file_tahun <- c(
  "2015" = "data/2015.csv",
  "2016" = "data/2016.csv",
  "2017" = "data/2017.csv",
  "2018" = "data/2018.csv",
  "2019" = "data/2019.csv"
)

file_tahun <- file_tahun[file.exists(file_tahun)]
file_tahun
##            2015            2016            2017            2018            2019 
## "data/2015.csv" "data/2016.csv" "data/2017.csv" "data/2018.csv" "data/2019.csv"
kolom_per_tahun <- purrr::map(
  file_tahun,
  ~ names(read_csv(.x, n_max = 0, show_col_types = FALSE) |> clean_names())
)

kolom_per_tahun
## $`2015`
##  [1] "country"                     "region"                     
##  [3] "happiness_rank"              "happiness_score"            
##  [5] "standard_error"              "economy_gdp_per_capita"     
##  [7] "family"                      "health_life_expectancy"     
##  [9] "freedom"                     "trust_government_corruption"
## [11] "generosity"                  "dystopia_residual"          
## 
## $`2016`
##  [1] "country"                     "region"                     
##  [3] "happiness_rank"              "happiness_score"            
##  [5] "lower_confidence_interval"   "upper_confidence_interval"  
##  [7] "economy_gdp_per_capita"      "family"                     
##  [9] "health_life_expectancy"      "freedom"                    
## [11] "trust_government_corruption" "generosity"                 
## [13] "dystopia_residual"          
## 
## $`2017`
##  [1] "country"                     "happiness_rank"             
##  [3] "happiness_score"             "whisker_high"               
##  [5] "whisker_low"                 "economy_gdp_per_capita"     
##  [7] "family"                      "health_life_expectancy"     
##  [9] "freedom"                     "generosity"                 
## [11] "trust_government_corruption" "dystopia_residual"          
## 
## $`2018`
## [1] "overall_rank"                 "country_or_region"           
## [3] "score"                        "gdp_per_capita"              
## [5] "social_support"               "healthy_life_expectancy"     
## [7] "freedom_to_make_life_choices" "generosity"                  
## [9] "perceptions_of_corruption"   
## 
## $`2019`
## [1] "overall_rank"                 "country_or_region"           
## [3] "score"                        "gdp_per_capita"              
## [5] "social_support"               "healthy_life_expectancy"     
## [7] "freedom_to_make_life_choices" "generosity"                  
## [9] "perceptions_of_corruption"
ganti_nama <- function(data, dari, ke) {
  if (dari %in% names(data) && !(ke %in% names(data))) {
    names(data)[names(data) == dari] <- ke
  }
  data
}

baca_happiness_tahun <- function(path, tahun) {
  df <- read_csv(path, show_col_types = FALSE) |> clean_names()

  df <- df |>
    ganti_nama("country", "country_or_region") |>
    ganti_nama("happiness_score", "score") |>
    ganti_nama("economy_gdp_per_capita", "gdp_per_capita") |>
    ganti_nama("family", "social_support") |>
    ganti_nama("health_life_expectancy", "healthy_life_expectancy") |>
    ganti_nama("freedom", "freedom_to_make_life_choices")

  df |>
    mutate(year = tahun) |>
    select(
      year, country_or_region, score, gdp_per_capita,
      social_support, healthy_life_expectancy, freedom_to_make_life_choices
    )
}
happiness_multi_tahun <- purrr::map2_dfr(
  file_tahun,
  as.integer(names(file_tahun)),
  baca_happiness_tahun
)

glimpse(happiness_multi_tahun)
## Rows: 782
## Columns: 7
## $ year                         <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015,…
## $ country_or_region            <chr> "Switzerland", "Iceland", "Denmark", "Nor…
## $ score                        <dbl> 7.587, 7.561, 7.527, 7.522, 7.427, 7.406,…
## $ gdp_per_capita               <dbl> 1.39651, 1.30232, 1.32548, 1.45900, 1.326…
## $ social_support               <dbl> 1.34951, 1.40223, 1.36058, 1.33095, 1.322…
## $ healthy_life_expectancy      <dbl> 0.94143, 0.94784, 0.87464, 0.88521, 0.905…
## $ freedom_to_make_life_choices <dbl> 0.66557, 0.62877, 0.64938, 0.66973, 0.632…
table(happiness_multi_tahun$year)
## 
## 2015 2016 2017 2018 2019 
##  158  157  155  156  156

Nomor 3 Web Scraping HDI dari Wikipedia

Membaca Halaman HTML

url <- "https://en.wikipedia.org/wiki/List_of_countries_by_Human_Development_Index"
halaman <- read_html(url)

Mengambil dan Mengidentifikasi Tabel

tabel <- halaman |> html_table(fill = TRUE)

length(tabel)
## [1] 14
for (i in seq_along(tabel)) {
  cat("\n====================\n")
  cat("Tabel", i, "\n")
  print(head(tabel[[i]], 3))
}
## 
## ====================
## Tabel 1 
## # A tibble: 3 × 3
##   Dimensions                  Indicators                       `Dimension index`
##   <chr>                       <chr>                            <chr>            
## 1 Long and healthy life       Life expectancy at birth         Life expectancy …
## 2 Knowledge                   Expected years of schoolingMean… Education index  
## 3 A decent standard of living GNI per capita (PPP $)           GNI index        
## 
## ====================
## Tabel 2 
## # A tibble: 3 × 5
##    Rank Changesince2015 `Country or territory` `HDI value`
##   <int> <chr>           <chr>                        <dbl>
## 1     1 "(2)"           Iceland                      0.972
## 2     2 "(1)"           Norway                       0.97 
## 3     2 ""              Switzerland                  0.97 
## # ℹ 1 more variable: `%annual growth(2010–2023)` <chr>
## 
## ====================
## Tabel 3 
## # A tibble: 3 × 10
##   `Region or group`      `1990` `2000` `2010` `2015` `2020` `2021` `2022` `2023`
##   <chr>                   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 OECD                    0.801  0.846  0.883  0.899  0.903  0.904  0.91   0.916
## 2 Very high human devel…  0.797  0.838  0.879  0.898  0.901  0.903  0.908  0.914
## 3 Europe and Central As…  0.674  0.686  0.753  0.789  0.802  0.803  0.815  0.818
## # ℹ 1 more variable: `Annualgrowth(1990-2023)` <chr>
## 
## ====================
## Tabel 4 
## # A tibble: 3 × 6
##   .mw-parser-output .navbar{dis…¹ .mw-parser-output .n…² ``    ``    ``    ``   
##   <chr>                           <chr>                  <chr> <chr> <chr> <chr>
## 1 Social                          "Topics:\nActing out\… Topi… "Act… Meas… Soci…
## 2 Topics:                         "Acting out\nChild ab… <NA>   <NA> <NA>  <NA> 
## 3 Measures:                       "Social Progress Inde… <NA>   <NA> <NA>  <NA> 
## # ℹ abbreviated names:
## #   ¹​`.mw-parser-output .navbar{display:inline;font-size:88%;font-weight:normal}.mw-parser-output .navbar-collapse{float:left;text-align:left}.mw-parser-output .navbar-boxtext{word-spacing:0}.mw-parser-output .navbar ul{display:inline-block;white-space:nowrap;line-height:inherit}.mw-parser-output .navbar-brackets::before{margin-right:-0.125em;content:"[ "}.mw-parser-output .navbar-brackets::after{margin-left:-0.125em;content:" ]"}.mw-parser-output .navbar li{word-spacing:-0.125em}.mw-parser-output .navbar a>span,.mw-parser-output .navbar a>abbr{text-decoration:inherit}.mw-parser-output .navbar-mini abbr{font-variant:small-caps;border-bottom:none;text-decoration:none;cursor:inherit}.mw-parser-output .navbar-ct-full{font-size:114%;margin:0 7em}.mw-parser-output .navbar-ct-mini{font-size:114%;margin:0 4em}html.skin-theme-clientpref-night .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}@media(prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}}@media print{.mw-parser-output .navbar{display:none!important}}vteDeprivation and poverty indicators`,
## #   ²​`.mw-parser-output .navbar{display:inline;font-size:88%;font-weight:normal}.mw-parser-output .navbar-collapse{float:left;text-align:left}.mw-parser-output .navbar-boxtext{word-spacing:0}.mw-parser-output .navbar ul{display:inline-block;white-space:nowrap;line-height:inherit}.mw-parser-output .navbar-brackets::before{margin-right:-0.125em;content:"[ "}.mw-parser-output .navbar-brackets::after{margin-left:-0.125em;content:" ]"}.mw-parser-output .navbar li{word-spacing:-0.125em}.mw-parser-output .navbar a>span,.mw-parser-output .navbar a>abbr{text-decoration:inherit}.mw-parser-output .navbar-mini abbr{font-variant:small-caps;border-bottom:none;text-decoration:none;cursor:inherit}.mw-parser-output .navbar-ct-full{font-size:114%;margin:0 7em}.mw-parser-output .navbar-ct-mini{font-size:114%;margin:0 4em}html.skin-theme-clientpref-night .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}@media(prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .navbar li a abbr{color:var(--color-base)!important}}@media print{.mw-parser-output .navbar{display:none!important}}vteDeprivation and poverty indicators`
## 
## ====================
## Tabel 5 
## # A tibble: 2 × 2
##   X1        X2                                                                  
##   <chr>     <chr>                                                               
## 1 Topics:   "Acting out\nChild abuse\nDisadvantaged\nDiseases of poverty\nEnvir…
## 2 Measures: "Social Progress Index"                                             
## 
## ====================
## Tabel 6 
## # A tibble: 1 × 2
##   X1      X2                                                                    
##   <chr>   <chr>                                                                 
## 1 Topics: "Cruel, inhuman or degrading treatment\nLearned helplessness\nPsychol…
## 
## ====================
## Tabel 7 
## # A tibble: 2 × 2
##   X1        X2                                                                  
##   <chr>     <chr>                                                               
## 1 Topics:   "Asset poverty\nCulture of poverty\nDebt bondage\nEnergy poverty\nE…
## 2 Measures: "Below Poverty Line (India)\nHomeless Vulnerability Index\nMisery i…
## 
## ====================
## Tabel 8 
## # A tibble: 2 × 2
##   X1        X2                                                                  
##   <chr>     <chr>                                                               
## 1 Topics:   "Disability and poverty\nFood insecurity\nPhysical abuse\nSex traff…
## 2 Measures: "India State Hunger Index\nGlobal Hunger Index\nDisability-adjusted…
## 
## ====================
## Tabel 9 
## # A tibble: 2 × 2
##   X1        X2                                                           
##   <chr>     <chr>                                                        
## 1 Topics:   "Feminization of poverty"                                    
## 2 Measures: "Gender-related Development Index (GDI)\nGender Parity Index"
## 
## ====================
## Tabel 10 
## # A tibble: 3 × 6
##   vteEconomic classification of…¹ vteEconomic classifi…² ``    ``    ``    ``   
##   <chr>                           <chr>                  <chr> <chr> <chr> <chr>
## 1 "Developed country\nAdvanced e… "Developed country\nA… <NA>   <NA> <NA>   <NA>
## 2 "Three/Four-World Model"        "First World\nSecond … <NA>   <NA> <NA>   <NA>
## 3 "Gross domestic product (GDP)"  "Nominal\nBy country\… Nomi… "By … Purc… "By …
## # ℹ abbreviated names: ¹​`vteEconomic classification of countries`,
## #   ²​`vteEconomic classification of countries`
## 
## ====================
## Tabel 11 
## # A tibble: 2 × 2
##   X1                             X2                                             
##   <chr>                          <chr>                                          
## 1 Nominal                        "By country\npast and projected\nper capita\np…
## 2 Purchasing  power parity (PPP) "By country\nfuture estimates\nper capita\nper…
## 
## ====================
## Tabel 12 
## # A tibble: 3 × 2
##   `vteLists of countries by population statistics` vteLists of countries by po…¹
##   <chr>                                            <chr>                        
## 1 Global                                           "Current population\nUnited …
## 2 Continents/subregions                            "Africa\nAntarctica\nAsia\nE…
## 3 Intercontinental                                 "Americas\nArab world\nCommo…
## # ℹ abbreviated name: ¹​`vteLists of countries by population statistics`
## 
## ====================
## Tabel 13 
## # A tibble: 3 × 2
##   `vteLists of countries by quality of life rankings` vteLists of countries by…¹
##   <chr>                                               <chr>                     
## 1 General                                             "Life expectancy\nAfrica\…
## 2 Economic                                            "Net take-home pay\nLong-…
## 3 Environment                                         "Greenhouse gas emissions…
## # ℹ abbreviated name: ¹​`vteLists of countries by quality of life rankings`
## 
## ====================
## Tabel 14 
## # A tibble: 3 × 2
##   vteLists of subnational entities by Human Development…¹ vteLists of subnatio…²
##   <chr>                                                   <chr>                 
## 1 Africa                                                  "Algeria\nAngola\nBen…
## 2 Asia                                                    "Afghanistan\nArmenia…
## 3 Europe                                                  "Albania\nAustria\nBa…
## # ℹ abbreviated names:
## #   ¹​`vteLists of subnational entities by Human Development Index rankings`,
## #   ²​`vteLists of subnational entities by Human Development Index rankings`

Memilih dan Membersihkan Tabel HDI

hdi <- tabel[[2]] |>
  clean_names() |>
  rename(
    country_name = country_or_territory,
    hdi = hdi_value
  ) |>
  select(country_name, hdi)

head(hdi)
glimpse(hdi)
## Rows: 193
## Columns: 2
## $ country_name <chr> "Iceland", "Norway", "Switzerland", "Denmark", "Germany",…
## $ hdi          <dbl> 0.972, 0.970, 0.970, 0.962, 0.959, 0.959, 0.958, 0.955, 0…

Menyimpan Dataset Hasil Scraping

write_csv(
  hdi,
  here("output", "hdi_wikipedia.csv")
)

Nomor 4 Integrasi Dataset (Left Join)

Membaca Ulang Dataset yang Tersimpan

Kedua dataset dibaca kembali dari folder output/ agar proses integrasi dapat dijalankan ulang secara independen.

happiness_final <- read_csv(
  here("output", "happiness_2019.csv"),
  show_col_types = FALSE
)

hdi_final <- read_csv(
  here("output", "hdi_wikipedia.csv"),
  show_col_types = FALSE
)

Menyamakan Format Nama Negara

hdi_final <- hdi_final |>
  mutate(
    country_name = str_trim(country_name),
    country_name = recode(
      country_name,
      "Korea (Republic of)"                = "South Korea",
      "Russian Federation"                 = "Russia",
      "Czechia"                            = "Czech Republic",
      "Türkiye"                            = "Turkey",
      "Viet Nam"                           = "Vietnam",
      "Iran (Islamic Republic of)"         = "Iran",
      "Bolivia (Plurinational State of)"   = "Bolivia",
      "Venezuela (Bolivarian Republic of)" = "Venezuela",
      "Tanzania (United Republic of)"      = "Tanzania",
      "Moldova (Republic of)"              = "Moldova",
      "Lao People's Democratic Republic"   = "Laos",
      "Syrian Arab Republic"               = "Syria",
      "Congo (Democratic Republic of the)" = "Congo (Kinshasa)",
      "Congo"                              = "Congo (Brazzaville)",
      "Hong Kong, China (SAR)"             = "Hong Kong",
      "Eswatini (Kingdom of)"              = "Swaziland",
      "Palestine, State of"                = "Palestinian Territories",
      "Cabo Verde"                         = "Cape Verde",
      "Gambia (Republic of the)"           = "Gambia",
      "North Macedonia"                    = "Macedonia"
    )
  )

Melakukan Left Join (2019 — dataset utama)

happiness_hdi <- happiness_final |>
  left_join(hdi_final, by = c("country_or_region" = "country_name"))

head(happiness_hdi)

Memeriksa dan Menangani Data yang Tidak Cocok

tidak_match <- happiness_hdi |>
  filter(is.na(hdi)) |>
  select(country_or_region)

n_tidak_match <- nrow(tidak_match)

n_tidak_match
## [1] 9
tidak_match

Terdapat 9 negara yang belum berhasil dipadankan dengan data HDI. Baris tersebut dikeluarkan agar variabel HDI tidak mengandung nilai kosong pada analisis selanjutnya.

happiness_hdi <- happiness_hdi |>
  filter(!is.na(hdi))

dim(happiness_hdi)
## [1] 147  10

Menyusun dan Menyimpan Dataset Akhir (2019)

happiness_hdi <- happiness_hdi |>
  rename(
    country_name    = country_or_region,
    happiness_score = score
  ) |>
  select(
    country_name,
    happiness_score,
    gdp_per_capita,
    social_support,
    healthy_life_expectancy,
    freedom_to_make_life_choices,
    hdi
  )

head(happiness_hdi)

Dataset hasil integrasi disimpan sebagai satu file CSV final, happiness_hdi_merged.csv, yang menjadi dataset utama untuk seluruh analisis pada bagian selanjutnya.

write_csv(
  happiness_hdi,
  here("output", "happiness_hdi_merged.csv")
)

Integrasi Per Tahun (2015–2019) — Semua Variabel

Dataset Kaggle multitahun digabungkan dengan HDI berdasarkan nama negara, sehingga setiap kombinasi negara × tahun memiliki seluruh variabel lengkap: happiness score, GDP per capita, social support, healthy life expectancy, freedom, dan HDI.

happiness_hdi_multi <- happiness_multi_tahun |>
  left_join(hdi_final, by = c("country_or_region" = "country_name")) |>
  filter(!is.na(hdi)) |>
  rename(
    country_name    = country_or_region,
    happiness_score = score
  ) |>
  select(
    country_name,
    year,
    happiness_score,
    gdp_per_capita,
    social_support,
    healthy_life_expectancy,
    freedom_to_make_life_choices,
    hdi
  ) |>
  arrange(year, country_name)

glimpse(happiness_hdi_multi)
## Rows: 743
## Columns: 8
## $ country_name                 <chr> "Afghanistan", "Albania", "Algeria", "Ang…
## $ year                         <int> 2015, 2015, 2015, 2015, 2015, 2015, 2015,…
## $ happiness_score              <dbl> 3.575, 4.959, 5.605, 4.033, 6.574, 4.350,…
## $ gdp_per_capita               <dbl> 0.31982, 0.87867, 0.93929, 0.75778, 1.053…
## $ social_support               <dbl> 0.30285, 0.80434, 1.07772, 0.86040, 1.248…
## $ healthy_life_expectancy      <dbl> 0.30335, 0.81325, 0.61766, 0.16683, 0.787…
## $ freedom_to_make_life_choices <dbl> 0.23414, 0.35733, 0.28579, 0.10384, 0.449…
## $ hdi                          <dbl> 0.496, 0.810, 0.763, 0.616, 0.865, 0.811,…
count(happiness_hdi_multi, year)
if (!dir.exists("output")) dir.create("output", recursive = TRUE)

write_csv(
  happiness_hdi_multi,
  here("output", "happiness_hdi_merged_2015_2019.csv")
)

list_per_tahun <- happiness_hdi_multi |>
  dplyr::group_split(year) |>
  purrr::set_names(
    happiness_hdi_multi |>
      dplyr::distinct(year) |>
      dplyr::arrange(year) |>
      dplyr::pull(year) |>
      as.character()
  )

writexl::write_xlsx(
  list_per_tahun,
  here("output", "happiness_hdi_per_tahun.xlsx")
)

Nomor 5 Pemeriksaan Dataset Hasil Integrasi

dim(happiness_hdi)
## [1] 147   7
glimpse(happiness_hdi)
## Rows: 147
## Columns: 7
## $ country_name                 <chr> "Finland", "Denmark", "Norway", "Iceland"…
## $ happiness_score              <dbl> 7.769, 7.600, 7.554, 7.494, 7.488, 7.480,…
## $ gdp_per_capita               <dbl> 1.340, 1.383, 1.488, 1.380, 1.396, 1.452,…
## $ social_support               <dbl> 1.587, 1.573, 1.582, 1.624, 1.522, 1.526,…
## $ healthy_life_expectancy      <dbl> 0.986, 0.996, 1.028, 1.026, 0.999, 1.052,…
## $ freedom_to_make_life_choices <dbl> 0.596, 0.592, 0.603, 0.591, 0.557, 0.572,…
## $ hdi                          <dbl> 0.948, 0.962, 0.970, 0.972, 0.955, 0.970,…
summary(happiness_hdi)
##  country_name       happiness_score gdp_per_capita   social_support 
##  Length:147         Min.   :2.853   Min.   :0.0000   Min.   :0.000  
##  Class :character   1st Qu.:4.541   1st Qu.:0.5760   1st Qu.:1.047  
##  Mode  :character   Median :5.386   Median :0.9600   Median :1.277  
##                     Mean   :5.413   Mean   :0.9064   Mean   :1.208  
##                     3rd Qu.:6.190   3rd Qu.:1.2290   3rd Qu.:1.454  
##                     Max.   :7.769   Max.   :1.6840   Max.   :1.624  
##  healthy_life_expectancy freedom_to_make_life_choices      hdi        
##  Min.   :0.1050          Min.   :0.0000               Min.   :0.3880  
##  1st Qu.:0.5530          1st Qu.:0.3070               1st Qu.:0.6130  
##  Median :0.7950          Median :0.4260               Median :0.7760  
##  Mean   :0.7302          Mean   :0.3944               Mean   :0.7477  
##  3rd Qu.:0.8825          3rd Qu.:0.5080               3rd Qu.:0.8895  
##  Max.   :1.1410          Max.   :0.6310               Max.   :0.9720
skim(happiness_hdi)
Data summary
Name happiness_hdi
Number of rows 147
Number of columns 7
_______________________
Column type frequency:
character 1
numeric 6
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
country_name 0 1 4 24 0 147 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
happiness_score 0 1 5.41 1.13 2.85 4.54 5.39 6.19 7.77 ▂▇▇▇▅
gdp_per_capita 0 1 0.91 0.40 0.00 0.58 0.96 1.23 1.68 ▃▃▆▇▃
social_support 0 1 1.21 0.30 0.00 1.05 1.28 1.45 1.62 ▁▁▂▅▇
healthy_life_expectancy 0 1 0.73 0.24 0.10 0.55 0.80 0.88 1.14 ▁▃▃▇▅
freedom_to_make_life_choices 0 1 0.39 0.15 0.00 0.31 0.43 0.51 0.63 ▁▃▅▇▆
hdi 0 1 0.75 0.16 0.39 0.61 0.78 0.89 0.97 ▂▃▃▇▇

Nomor 6 Eksplorasi Data: Statistik Deskriptif

happiness_hdi |>
  select(where(is.numeric)) |>
  pivot_longer(everything(), names_to = "variabel", values_to = "nilai") |>
  group_by(variabel) |>
  summarise(
    n      = sum(!is.na(nilai)),
    mean   = mean(nilai, na.rm = TRUE),
    median = median(nilai, na.rm = TRUE),
    sd     = sd(nilai, na.rm = TRUE),
    min    = min(nilai, na.rm = TRUE),
    max    = max(nilai, na.rm = TRUE)
  ) |>
  kable(digits = 3, caption = "Statistik Deskriptif Variabel Numerik (2019)") |>
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))
Statistik Deskriptif Variabel Numerik (2019)
variabel n mean median sd min max
freedom_to_make_life_choices 147 0.394 0.426 0.146 0.000 0.631
gdp_per_capita 147 0.906 0.960 0.400 0.000 1.684
happiness_score 147 5.413 5.386 1.130 2.853 7.769
hdi 147 0.748 0.776 0.161 0.388 0.972
healthy_life_expectancy 147 0.730 0.795 0.237 0.105 1.141
social_support 147 1.208 1.277 0.305 0.000 1.624

Nomor 7 Visualisasi Data

7a. Distribusi Nilai HDI

ggplot(happiness_hdi, aes(x = hdi)) +
  geom_histogram(aes(y = after_stat(density), fill = after_stat(x)),
                 bins = 20, color = "white") +
  geom_density(color = "#1B5E20", linewidth = 1) +
  scale_fill_gradient(low = "#E74C3C", high = "#27AE60", guide = "none") +
  labs(
    title = "Distribusi Nilai Human Development Index (HDI)",
    x = "HDI", y = "Densitas"
  ) +
  theme_praktikum()

7b. Distribusi Happiness Score per Tahun

Pilih salah satu tab tahun di bawah ini untuk melihat distribusi Happiness Score pada tahun tersebut. Tab 2019 ditampilkan sebagai tampilan awal (default).

Tahun 2015
Ringkasan Happiness Score - 2015
Statistik Nilai
Jumlah Negara 150.000
Rata-rata 5.391
Median 5.261
SD 1.162
Minimum 2.839
Maksimum 7.587
Tahun 2016
Ringkasan Happiness Score - 2016
Statistik Nilai
Jumlah Negara 149.000
Rata-rata 5.383
Median 5.314
SD 1.152
Minimum 2.905
Maksimum 7.526
Tahun 2017
Ringkasan Happiness Score - 2017
Statistik Nilai
Jumlah Negara 148.000
Rata-rata 5.362
Median 5.283
SD 1.146
Minimum 2.693
Maksimum 7.537
Tahun 2018
Ringkasan Happiness Score - 2018
Statistik Nilai
Jumlah Negara 149.000
Rata-rata 5.376
Median 5.358
SD 1.133
Minimum 2.905
Maksimum 7.632
Tahun 2019
Ringkasan Happiness Score - 2019
Statistik Nilai
Jumlah Negara 147.000
Rata-rata 5.413
Median 5.386
SD 1.130
Minimum 2.853
Maksimum 7.769

7c. Eksplorasi HDI–Happiness per Tahun

Pilih salah satu tab tahun di bawah ini untuk melihat lima negara paling bahagia, scatter plot HDI vs Happiness Score, dan nilai korelasinya pada tahun tersebut. Tab 2019 ditampilkan sebagai tampilan awal (default).

Tahun 2015

Dataset tahun 2015 setelah digabungkan dengan HDI memuat 150 negara.

5 Negara Paling Bahagia - 2015
country_name happiness_score hdi
Switzerland 7.587 0.970
Iceland 7.561 0.972
Denmark 7.527 0.962
Norway 7.522 0.970
Canada 7.427 0.939

Korelasi HDI dengan Happiness Score pada tahun 2015 adalah r = 0.79.

Tahun 2016

Dataset tahun 2016 setelah digabungkan dengan HDI memuat 149 negara.

5 Negara Paling Bahagia - 2016
country_name happiness_score hdi
Denmark 7.526 0.962
Switzerland 7.509 0.970
Iceland 7.501 0.972
Norway 7.498 0.970
Finland 7.413 0.948

Korelasi HDI dengan Happiness Score pada tahun 2016 adalah r = 0.8.

Tahun 2017

Dataset tahun 2017 setelah digabungkan dengan HDI memuat 148 negara.

5 Negara Paling Bahagia - 2017
country_name happiness_score hdi
Norway 7.537 0.970
Denmark 7.522 0.962
Iceland 7.504 0.972
Switzerland 7.494 0.970
Finland 7.469 0.948

Korelasi HDI dengan Happiness Score pada tahun 2017 adalah r = 0.82.

Tahun 2018

Dataset tahun 2018 setelah digabungkan dengan HDI memuat 149 negara.

5 Negara Paling Bahagia - 2018
country_name happiness_score hdi
Finland 7.632 0.948
Norway 7.594 0.970
Denmark 7.555 0.962
Iceland 7.495 0.972
Switzerland 7.487 0.970

Korelasi HDI dengan Happiness Score pada tahun 2018 adalah r = 0.82.

Tahun 2019

Dataset tahun 2019 setelah digabungkan dengan HDI memuat 147 negara.

5 Negara Paling Bahagia - 2019
country_name happiness_score hdi
Finland 7.769 0.948
Denmark 7.600 0.962
Norway 7.554 0.970
Iceland 7.494 0.972
Netherlands 7.488 0.955

Korelasi HDI dengan Happiness Score pada tahun 2019 adalah r = 0.81.

7d. Dua Puluh Negara dengan HDI Tertinggi

hdi_top20 <- hdi_final |>
  slice_max(order_by = hdi, n = 20) |>
  arrange(hdi) |>
  mutate(country_name = factor(country_name, levels = unique(country_name)))
p_dot <- hdi_top20 |>
  ggplot(aes(x = hdi, y = country_name)) +
  geom_segment(
    aes(x = 0, xend = hdi, y = country_name, yend = country_name),
    color = "grey80", linewidth = 0.8
  ) +
  geom_point(
    aes(color = hdi, text = paste0("<b>", country_name, "</b>", "<br>HDI : ", round(hdi, 3))),
    size = 4
  ) +
  scale_color_gradient(low = "#E74C3C", high = "#27AE60", name = "HDI") +
  scale_x_continuous(limits = c(0, 1.05), breaks = seq(0, 1, 0.1), labels = number_format(accuracy = 0.01)) +
  labs(
    title = "20 Negara dengan Human Development Index (HDI) Tertinggi",
    subtitle = "Data hasil Web Scraping dari Wikipedia",
    x = "Nilai Human Development Index (HDI)", y = NULL
  ) +
  theme_praktikum() +
  theme(panel.grid.major.y = element_blank())

ggplotly(p_dot, tooltip = "text") |>
  layout(title = list(x = 0, xanchor = "left", font = list(size = 15)))

7e. Peta Dunia Persebaran HDI

world_map <- map_data("world")

hdi_map <- hdi_final |>
  mutate(
    country_name = recode(
      country_name,
      "United States"  = "USA",
      "United Kingdom" = "UK"
    )
  )

peta_hdi <- left_join(world_map, hdi_map, by = c("region" = "country_name"))
p_map <- ggplot(peta_hdi, aes(x = long, y = lat, group = group)) +
  geom_polygon(
    aes(
      fill = hdi,
      text = paste0(
        "<b>", region, "</b>",
        "<br>HDI : ", ifelse(is.na(hdi), "Data tidak tersedia", round(hdi, 3))
      )
    ),
    color = "white", linewidth = 0.15
  ) +
  coord_fixed(1.3) +
  scale_fill_gradient(low = "#E74C3C", high = "#27AE60",
                      na.value = "grey90", name = "HDI") +
  labs(
    title = "Persebaran Human Development Index (HDI) Dunia",
    subtitle = "Data hasil Web Scraping dari Wikipedia",
    x = NULL, y = NULL
  ) +
  theme_void(base_size = 12) +
  theme(legend.position = "bottom",
        plot.title = element_text(face = "bold", color = "#1B5E20"))

ggplotly(p_map, tooltip = "text") |>
  layout(
    annotations = list(
      x = 1, y = -0.10,
      text = "Sumber : Wikipedia (Human Development Index)",
      showarrow = FALSE, xref = "paper", yref = "paper", xanchor = "right",
      font = list(size = 10, color = "gray40")
    )
  )

Nomor 8 Analisis Korelasi

8a. Matriks Korelasi (2019)

var_korelasi <- happiness_hdi |>
  select(hdi, happiness_score, gdp_per_capita, social_support)

cor_matrix <- cor(var_korelasi, use = "complete.obs")

cor_matrix |>
  round(3) |>
  kable(caption = "Matriks Korelasi antar Variabel (2019)") |>
  kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover"))
Matriks Korelasi antar Variabel (2019)
hdi happiness_score gdp_per_capita social_support
hdi 1.000 0.806 0.949 0.775
happiness_score 0.806 1.000 0.796 0.781
gdp_per_capita 0.949 0.796 1.000 0.763
social_support 0.775 0.781 0.763 1.000

8b. Heatmap Korelasi (2019)

cor_df <- as.data.frame(as.table(cor_matrix))
names(cor_df) <- c("var1", "var2", "korelasi")

ggplot(cor_df, aes(x = var1, y = var2, fill = korelasi)) +
  geom_tile(color = "white") +
  geom_text(aes(label = round(korelasi, 2)), color = "white", size = 4, fontface = "bold") +
  scale_fill_gradient(
    low = "#E74C3C", high = "#27AE60", name = "Korelasi"
  ) +
  labs(
    title = "Heatmap Korelasi: HDI, Happiness Score, GDP per Capita, Social Support (2019)",
    x = NULL, y = NULL
  ) +
  theme_praktikum() +
  theme(
    panel.grid = element_blank(),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Nomor 9 Interpretasi Hasil

Pilih tahun pada tab di bawah ini untuk melihat interpretasi hasil analisis pada tahun tersebut. Tab 2019 ditampilkan sebagai tampilan awal (default).

Interpretasi Tahun 2015

a. Negara dengan HDI tertinggi berasal dari kawasan mana?
Lima negara dengan HDI tertinggi pada tahun 2015 adalah Iceland, Norway, Switzerland, Denmark, Germany, Sweden. Berdasarkan Cleveland dot plot (7d) dan peta dunia (7e), negara dengan HDI tertinggi secara umum berasal dari kawasan Eropa Barat/Utara serta sebagian negara maju di Asia Timur dan Oseania.

b. Apakah negara dengan HDI tinggi selalu memiliki Happiness Score tinggi?
Korelasi antara HDI dan Happiness Score pada tahun 2015 adalah r = 0.79, menunjukkan hubungan yang cukup kuat. Negara dengan HDI tinggi cenderung memiliki Happiness Score yang tinggi pula, meskipun tidak selalu berlaku mutlak — terlihat pada scatter plot (7c) masih terdapat variasi di sekitar garis regresi.

c. Bagaimana hubungan GDP per Capita terhadap Happiness Score?
Korelasi GDP per Capita dan Happiness Score pada tahun 2015 adalah r = 0.79, menunjukkan hubungan yang cukup kuat dan positif.

d. Bagaimana hubungan Social Support terhadap Happiness Score?
Korelasi Social Support dan Happiness Score pada tahun 2015 adalah r = 0.74, mengindikasikan hubungan cukup kuat. Dukungan sosial turut berkontribusi terhadap tingkat kebahagiaan suatu negara.

e. Variabel apa yang paling berhubungan dengan tingkat kebahagiaan?
Berdasarkan matriks korelasi (Nomor 8), variabel dengan korelasi absolut tertinggi terhadap Happiness Score pada tahun 2015 adalah gdp_per_capita dengan nilai korelasi sebesar 0.79.

Interpretasi Tahun 2016

a. Negara dengan HDI tertinggi berasal dari kawasan mana?
Lima negara dengan HDI tertinggi pada tahun 2016 adalah Iceland, Norway, Switzerland, Denmark, Germany, Sweden. Berdasarkan Cleveland dot plot (7d) dan peta dunia (7e), negara dengan HDI tertinggi secara umum berasal dari kawasan Eropa Barat/Utara serta sebagian negara maju di Asia Timur dan Oseania.

b. Apakah negara dengan HDI tinggi selalu memiliki Happiness Score tinggi?
Korelasi antara HDI dan Happiness Score pada tahun 2016 adalah r = 0.80, menunjukkan hubungan yang cukup kuat. Negara dengan HDI tinggi cenderung memiliki Happiness Score yang tinggi pula, meskipun tidak selalu berlaku mutlak — terlihat pada scatter plot (7c) masih terdapat variasi di sekitar garis regresi.

c. Bagaimana hubungan GDP per Capita terhadap Happiness Score?
Korelasi GDP per Capita dan Happiness Score pada tahun 2016 adalah r = 0.79, menunjukkan hubungan yang cukup kuat dan positif.

d. Bagaimana hubungan Social Support terhadap Happiness Score?
Korelasi Social Support dan Happiness Score pada tahun 2016 adalah r = 0.74, mengindikasikan hubungan cukup kuat. Dukungan sosial turut berkontribusi terhadap tingkat kebahagiaan suatu negara.

e. Variabel apa yang paling berhubungan dengan tingkat kebahagiaan?
Berdasarkan matriks korelasi (Nomor 8), variabel dengan korelasi absolut tertinggi terhadap Happiness Score pada tahun 2016 adalah hdi dengan nilai korelasi sebesar 0.80.

Interpretasi Tahun 2017

a. Negara dengan HDI tertinggi berasal dari kawasan mana?
Lima negara dengan HDI tertinggi pada tahun 2017 adalah Iceland, Norway, Switzerland, Denmark, Germany, Sweden. Berdasarkan Cleveland dot plot (7d) dan peta dunia (7e), negara dengan HDI tertinggi secara umum berasal dari kawasan Eropa Barat/Utara serta sebagian negara maju di Asia Timur dan Oseania.

b. Apakah negara dengan HDI tinggi selalu memiliki Happiness Score tinggi?
Korelasi antara HDI dan Happiness Score pada tahun 2017 adalah r = 0.82, menunjukkan hubungan yang cukup kuat. Negara dengan HDI tinggi cenderung memiliki Happiness Score yang tinggi pula, meskipun tidak selalu berlaku mutlak — terlihat pada scatter plot (7c) masih terdapat variasi di sekitar garis regresi.

c. Bagaimana hubungan GDP per Capita terhadap Happiness Score?
Korelasi GDP per Capita dan Happiness Score pada tahun 2017 adalah r = 0.82, menunjukkan hubungan yang cukup kuat dan positif.

d. Bagaimana hubungan Social Support terhadap Happiness Score?
Korelasi Social Support dan Happiness Score pada tahun 2017 adalah r = 0.75, mengindikasikan hubungan cukup kuat. Dukungan sosial turut berkontribusi terhadap tingkat kebahagiaan suatu negara.

e. Variabel apa yang paling berhubungan dengan tingkat kebahagiaan?
Berdasarkan matriks korelasi (Nomor 8), variabel dengan korelasi absolut tertinggi terhadap Happiness Score pada tahun 2017 adalah hdi dengan nilai korelasi sebesar 0.82.

Interpretasi Tahun 2018

a. Negara dengan HDI tertinggi berasal dari kawasan mana?
Lima negara dengan HDI tertinggi pada tahun 2018 adalah Iceland, Norway, Switzerland, Denmark, Germany, Sweden. Berdasarkan Cleveland dot plot (7d) dan peta dunia (7e), negara dengan HDI tertinggi secara umum berasal dari kawasan Eropa Barat/Utara serta sebagian negara maju di Asia Timur dan Oseania.

b. Apakah negara dengan HDI tinggi selalu memiliki Happiness Score tinggi?
Korelasi antara HDI dan Happiness Score pada tahun 2018 adalah r = 0.82, menunjukkan hubungan yang cukup kuat. Negara dengan HDI tinggi cenderung memiliki Happiness Score yang tinggi pula, meskipun tidak selalu berlaku mutlak — terlihat pada scatter plot (7c) masih terdapat variasi di sekitar garis regresi.

c. Bagaimana hubungan GDP per Capita terhadap Happiness Score?
Korelasi GDP per Capita dan Happiness Score pada tahun 2018 adalah r = 0.80, menunjukkan hubungan yang cukup kuat dan positif.

d. Bagaimana hubungan Social Support terhadap Happiness Score?
Korelasi Social Support dan Happiness Score pada tahun 2018 adalah r = 0.75, mengindikasikan hubungan cukup kuat. Dukungan sosial turut berkontribusi terhadap tingkat kebahagiaan suatu negara.

e. Variabel apa yang paling berhubungan dengan tingkat kebahagiaan?
Berdasarkan matriks korelasi (Nomor 8), variabel dengan korelasi absolut tertinggi terhadap Happiness Score pada tahun 2018 adalah hdi dengan nilai korelasi sebesar 0.82.

Interpretasi Tahun 2019

a. Negara dengan HDI tertinggi berasal dari kawasan mana?
Lima negara dengan HDI tertinggi pada tahun 2019 adalah Iceland, Norway, Switzerland, Denmark, Germany, Sweden. Berdasarkan Cleveland dot plot (7d) dan peta dunia (7e), negara dengan HDI tertinggi secara umum berasal dari kawasan Eropa Barat/Utara serta sebagian negara maju di Asia Timur dan Oseania.

b. Apakah negara dengan HDI tinggi selalu memiliki Happiness Score tinggi?
Korelasi antara HDI dan Happiness Score pada tahun 2019 adalah r = 0.81, menunjukkan hubungan yang cukup kuat. Negara dengan HDI tinggi cenderung memiliki Happiness Score yang tinggi pula, meskipun tidak selalu berlaku mutlak — terlihat pada scatter plot (7c) masih terdapat variasi di sekitar garis regresi.

c. Bagaimana hubungan GDP per Capita terhadap Happiness Score?
Korelasi GDP per Capita dan Happiness Score pada tahun 2019 adalah r = 0.80, menunjukkan hubungan yang cukup kuat dan positif.

d. Bagaimana hubungan Social Support terhadap Happiness Score?
Korelasi Social Support dan Happiness Score pada tahun 2019 adalah r = 0.78, mengindikasikan hubungan cukup kuat. Dukungan sosial turut berkontribusi terhadap tingkat kebahagiaan suatu negara.

e. Variabel apa yang paling berhubungan dengan tingkat kebahagiaan?
Berdasarkan matriks korelasi (Nomor 8), variabel dengan korelasi absolut tertinggi terhadap Happiness Score pada tahun 2019 adalah hdi dengan nilai korelasi sebesar 0.81.

Nomor 10 Tabel Integrasi

Pilih tahun pada tab di bawah ini untuk melihat tabel integrasi World Happiness Report dan Human Development Index pada tahun tersebut.

list.files(here("output"))
## [1] "happiness_2015_2019.csv"            "happiness_2019.csv"                
## [3] "happiness_clean.csv"                "happiness_hdi_merged.csv"          
## [5] "happiness_hdi_merged_2015_2019.csv" "happiness_hdi_per_tahun.xlsx"      
## [7] "happiness_kaggle_2015_2019.csv"     "hdi_wikipedia.csv"                 
## [9] "hdi_wikipedia_2015_2019.csv"
150
NEGARA
0.752
RATA-RATA HDI
5.39
RATA-RATA HAPPINESS
149
NEGARA
0.752
RATA-RATA HDI
5.38
RATA-RATA HAPPINESS
148
NEGARA
0.748
RATA-RATA HDI
5.36
RATA-RATA HAPPINESS
149
NEGARA
0.748
RATA-RATA HDI
5.38
RATA-RATA HAPPINESS
147
NEGARA
0.748
RATA-RATA HDI
5.41
RATA-RATA HAPPINESS

Praktikum Data Mining · Data Acquisition & Exploratory Data Analysis