Tugas Prakt Visdat 11
Rose Dwi Aulia Amaradhani
2024-05-14
#Library
library(ggplot2)
library(tidyverse) ## Warning: package 'tidyverse' was built under R version 4.3.2## Warning: package 'lubridate' was built under R version 4.3.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggspatial)## Warning: package 'ggspatial' was built under R version 4.3.3library(dplyr)
library(reshape2)##
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data <- read_excel("D:/tugas ngampus hampir gila/smt 4/visdut/data anrex.xlsx")
data[] <- lapply(data, as.numeric)## Warning in lapply(data, as.numeric): NAs introduced by coerciondata <- subset(data, select = -c(Wilayah))
data## # A tibble: 35 × 13
## No Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 60.8 1047226 16.4 84.7 11.8 6.86 10226 69.1 380046 139. 14.9
## 2 2 52.6 1828573 24.8 90.5 13.3 7.87 12492 73.9 479027 216. 12.5
## 3 3 49.9 828883 21.3 86.3 12.2 7.07 10470 70.2 378858 70.0 8.92
## 4 4 64.3 901621 21.3 91.2 12.5 7.08 10541 70.6 425135 99.6 11.5
## 5 5 45.6 1090129 24.2 92.4 12.7 8.09 13716 75.4 420339 97.5 9.81
## 6 6 62.5 2043077 18.0 87.3 12.4 6.4 10993 68.0 513339 286. 15.8
## 7 7 45.5 2007829 50.7 88.6 12.7 7.39 11432 71.8 419429 191 11.0
## 8 8 50.8 1240510 16.8 90.7 13.3 8.27 11166 74.1 511145 143. 12.0
## 9 9 40.5 1492891 15.0 83.4 12.5 7.28 11083 71.5 464614 163. 11.7
## 10 10 58.8 1221086 20.0 88.9 12.8 8.26 11306 73.8 479131 86.8 6.61
## # ℹ 25 more rows
## # ℹ 1 more variable: X11 <dbl>#Correlation
##Correlation Scatterplot
plotkesatu <- ggplot(data, aes(x = X1, y = Y)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Jumlah Penduduk", y = "Skor IPLM")
plotkedua <- ggplot(data, aes(x = X2, y = Y)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "PRDB Kapita", y = "Skor IPLM")
plotketiga <- ggplot(data, aes(x = X7, y = Y)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "IPM", y = "Skor IPLM")
plotkeempat <- ggplot(data, aes(x = X11, y = Y)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Jumlah Perpustakaan", y = "Skor IPLM")
grid.arrange(plotkesatu, plotkedua, plotketiga, plotkeempat, nrow = 2)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
Dari 4 scatter plot di atas, kita mendapatkan informasi bahwa jumlah
penduduk miskin, PRDB kapita, dan jumlah perpustakaan menunjukkan
hubungan linear positif yang menandakan bahwa semakin tinggi angka
jumlah penduduk miskin, PRDB kapita, dan jumlah perpustakaan, maka nilai
IPLM cenderung meningkat. Sebaliknya, jumlah penduduk menunjukkan
hubungan linear negatif yang menandakan bahwa semakin sedikit jumlah
penduduk , maka nilai IPLM menurun.
##Correlation HeatMap
datacorr <- select_if(data, is.numeric)
str(datacorr)## tibble [35 × 13] (S3: tbl_df/tbl/data.frame)
## $ No : num [1:35] 1 2 3 4 5 6 7 8 9 10 ...
## $ Y : num [1:35] 60.8 52.6 49.9 64.3 45.6 ...
## $ X1 : num [1:35] 1047226 1828573 828883 901621 1090129 ...
## $ X2 : num [1:35] 16.4 24.8 21.3 21.3 24.2 ...
## $ X3 : num [1:35] 84.7 90.5 86.3 91.2 92.4 ...
## $ X4 : num [1:35] 11.8 13.3 12.2 12.5 12.7 ...
## $ X5 : num [1:35] 6.86 7.87 7.07 7.08 8.09 6.4 7.39 8.27 7.28 8.26 ...
## $ X6 : num [1:35] 10226 12492 10470 10541 13716 ...
## $ X7 : num [1:35] 69.1 73.9 70.2 70.6 75.4 ...
## $ X8 : num [1:35] 380046 479027 378858 425135 420339 ...
## $ X9 : num [1:35] 139 216.5 70 99.6 97.5 ...
## $ X10: num [1:35] 14.9 12.53 8.92 11.49 9.81 ...
## $ X11: num [1:35] 50 48 9 8 73 22 67 22 27 20 ...data_melt <- cor(datacorr[sapply(datacorr,is.numeric)])
data_melt <- melt(data_melt)
ggplot(data_melt, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
labs(title = "Correlation Heatmap",
x = " ",
y = " ")
##Corellogram
ggplot(data_melt, aes(Var1, Var2, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "cyan", mid = "green", high = "darkblue", midpoint = 0, limits = c(-1,1), name="Korelasi") +
labs(title = "Corellogram") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
datacorr1 <- round(cor(datacorr), 1)
datacorr1 <- cor(datacorr)
ggcorrplot(datacorr1, method = "circle")
Dari gambar-gambar di atas, dapat disimpulkan bahwa terdapat indikasi
multikolinieritas antara beberapa variabel X, seperti antara harapan
lama sekolah dan rata-rata lama sekolah. Korelasi yang cukup besar
antara kedua variabel ini, yang ditunjukkan dengan warna yang lebih
gelap atau lebih terang dalam plot, menandakan adanya hubungan yang
signifikan di antara keduanya. Oleh karena itu, dapat disimpulkan bahwa
harapan lama sekolah dan rata-rata lama sekolah saling mempengaruhi
karena adanya korelasi yang signifikan.
#Time Series
datalagi <- read_csv("D:/tugas ngampus hampir gila/smt 4/visdut/BBNII.csv")## Rows: 235 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (2): % Change, % Change vs Average
## num (5): Open, High, Low, Close, Volume
## dttm (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(datalagi)## spc_tbl_ [235 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Date : POSIXct[1:235], format: "2023-05-14 17:00:00" "2023-05-15 17:00:00" ...
## $ Open : num [1:235] 4500 4475 4362 4400 4412 ...
## $ High : num [1:235] 4512 4488 4412 4450 4438 ...
## $ Low : num [1:235] 4425 4350 4325 4375 4388 ...
## $ Close : num [1:235] 4450 4375 4375 4412 4425 ...
## $ % Change : num [1:235] -1.11 -1.69 0 0.86 0.28 1.13 -1.4 1.98 -0.56 1.12 ...
## $ % Change vs Average: num [1:235] -1.16 -1.74 -0.05 0.81 0.23 1.08 -1.45 1.93 -0.61 1.07 ...
## $ Volume : num [1:235] 7.82e+07 1.02e+08 1.02e+08 9.64e+07 8.85e+07 ...
## - attr(*, "spec")=
## .. cols(
## .. Date = col_datetime(format = ""),
## .. Open = col_number(),
## .. High = col_number(),
## .. Low = col_number(),
## .. Close = col_number(),
## .. `% Change` = col_double(),
## .. `% Change vs Average` = col_double(),
## .. Volume = col_number()
## .. )
## - attr(*, "problems")=<externalptr>datalagi## # A tibble: 235 × 8
## Date Open High Low Close `% Change` `% Change vs Average`
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2023-05-14 17:00:00 4500 4512. 4425 4450 -1.11 -1.16
## 2 2023-05-15 17:00:00 4475 4488. 4350 4375 -1.69 -1.74
## 3 2023-05-16 17:00:00 4362. 4412. 4325 4375 0 -0.05
## 4 2023-05-18 17:00:00 4400 4450 4375 4412. 0.86 0.81
## 5 2023-05-21 17:00:00 4412. 4438. 4388. 4425 0.28 0.23
## 6 2023-05-22 17:00:00 4412. 4500 4412. 4475 1.13 1.08
## 7 2023-05-23 17:00:00 4450 4475 4400 4412. -1.4 -1.45
## 8 2023-05-24 17:00:00 4412. 4512. 4400 4500 1.98 1.93
## 9 2023-05-25 17:00:00 4500 4538. 4475 4475 -0.56 -0.61
## 10 2023-05-28 17:00:00 4500 4525 4475 4525 1.12 1.07
## # ℹ 225 more rows
## # ℹ 1 more variable: Volume <dbl>##Point
datalagi <- head(datalagi, -1)
ggplot(datalagi, aes(x =Date, y = Open)) +
geom_point() +
labs(title = "Time series of BBNI in almost a year",
x = "Date",
y = "Open")
##Line
ggplot(datalagi, aes(x =Date, y = Open)) +
geom_line() +
labs(title = "Time series of BBNI in almost a year",
x = "Date",
y = "Value (open)")
calculate_moving_average <- function(datalagi, window_size) {
ma_values <- zoo::rollmean(datalagi$Open, k = window_size, align = "right", fill = NA)
ma_values_padded <- c(rep(NA, window_size - 1), ma_values)
datalagi$ma <- ma_values_padded[1:nrow(datalagi)]
return(datalagi)
}
window_size <- 3
datalagi <- calculate_moving_average(datalagi, window_size)
ggplot(datalagi, aes(x = Date)) +
geom_line(aes(y = Open), color = "white", size = 1) +
geom_line(aes(y = ma), color = "red", linetype = "dashed", size = 1) +
geom_ribbon(aes(ymin = -Inf, ymax = ma), fill = "red", alpha = 0.2) +
labs(title = paste("BBNI data with Moving Average (Window Size:", window_size, ")"),
x = "Date",
y = "Value (open)") +
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Removed 4 rows containing missing values (`geom_line()`).
##Perbandingan Time Series
datatsp1 <- read_csv("D:/tugas ngampus hampir gila/smt 4/visdut/BBSAHAM.csv")## Rows: 61 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (6): Open, High, Low, Close, % Change, % Change vs Average
## num (1): Volume
## dttm (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.datatsp2 <- read_csv("D:/tugas ngampus hampir gila/smt 4/visdut/BABASAHAM.csv")## Rows: 61 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (6): Open, High, Low, Close, % Change, % Change vs Average
## num (1): Volume
## dttm (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.datatsp1## # A tibble: 61 × 8
## Date Open High Low Close `% Change` `% Change vs Average`
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2023-03-19 17:00:00 80.7 3.9 3.59 3.84 3.23 3.15
## 2 2023-03-26 17:00:00 85.4 4.7 3.8 4.56 18.8 18.7
## 3 2023-04-02 17:00:00 102. 4.69 4.45 4.55 -0.22 -0.29
## 4 2023-04-09 17:00:00 98.3 4.64 4.38 4.51 -0.88 -0.95
## 5 2023-04-16 17:00:00 98.8 4.54 4.18 4.23 -6.21 -6.28
## 6 2023-04-23 17:00:00 92.2 4.22 3.83 3.91 -7.57 -7.64
## 7 2023-04-30 17:00:00 85.6 4.75 3.85 4.69 20.0 19.9
## 8 2023-05-07 17:00:00 104. 5.37 4.73 5.01 6.82 6.75
## 9 2023-05-14 17:00:00 110 5.51 4.97 5.38 7.39 7.31
## 10 2023-05-21 17:00:00 118. 5.49 5.14 5.23 -2.79 -2.86
## # ℹ 51 more rows
## # ℹ 1 more variable: Volume <dbl>datatsp2## # A tibble: 61 × 8
## Date Open High Low Close `% Change` `% Change vs Average`
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2023-03-19 17:00:00 80.2 88.4 79.5 86.9 6.4 6.23
## 2 2023-03-26 17:00:00 87.1 105. 85.5 102. 17.6 17.4
## 3 2023-04-02 17:00:00 101. 103. 97.3 103. 0.55 0.37
## 4 2023-04-09 17:00:00 101. 103. 93.6 94.6 -7.97 -8.15
## 5 2023-04-16 17:00:00 96.7 99.2 88.3 89.1 -5.73 -5.91
## 6 2023-04-23 17:00:00 88.4 88.5 82.2 84.7 -4.98 -5.16
## 7 2023-04-30 17:00:00 84.2 85.3 80.5 83.2 -1.74 -1.91
## 8 2023-05-07 17:00:00 83.6 88.5 80.2 85.3 2.55 2.37
## 9 2023-05-14 17:00:00 87.1 91.4 83.5 84.0 -1.59 -1.77
## 10 2023-05-21 17:00:00 85.7 87.5 78.1 81.0 -3.58 -3.76
## # ℹ 51 more rows
## # ℹ 1 more variable: Volume <dbl>datatsp1 <- head(datatsp1)
datatsp2 <- head(datatsp2)
# Memplot kedua seri waktu
ggplot() +
geom_line(data = datatsp1, aes(x = Date, y = Open, color = "BB")) +
geom_line(data = datatsp2, aes(x = Date, y = Open, color = "BABA")) +
labs(title = "BB vs. BABA",
x = "Date",
y = "Value",
color = "Series") +
scale_color_manual(values = c("BB" = "darkblue", "BABA" = "red")) +
theme_minimal()
Grafik-grafik tersebut menggambarkan pola kenaikan dan penurunan harga
saham BB seiring waktu. Terlihat bahwa saham BB mengalami tren kenaikan
dan mulai menurun di bulan Maret, sedangkan saham BABA mengalami tren
kenaikan dan mulai menurun di bulan April. Di sisi lain, terlihat bahwa
saham BB dan BABA mengalami peningkatan yang cukup konsisten pada awal
tahun.
#Geospasial
world <- ne_countries(scale = "medium", returnclass = "sf")
ggplot(data = world) +
geom_sf()
ggplot(data = world) +
geom_sf(aes(fill = pop_est)) +
scale_fill_viridis_c(option = "volcano")## Warning in viridisLite::viridis(n, alpha, begin, end, direction, option):
## Option 'volcano' does not exist. Defaulting to 'viridis'.
spasial <- read.csv("C:/Users/Asus/Downloads/2019.csv")
spasial## Overall.rank Country.or.region Score GDP.per.capita Social.support
## 1 1 Finland 7.769 1.340 1.587
## 2 2 Denmark 7.600 1.383 1.573
## 3 3 Norway 7.554 1.488 1.582
## 4 4 Iceland 7.494 1.380 1.624
## 5 5 Netherlands 7.488 1.396 1.522
## 6 6 Switzerland 7.480 1.452 1.526
## 7 7 Sweden 7.343 1.387 1.487
## 8 8 New Zealand 7.307 1.303 1.557
## 9 9 Canada 7.278 1.365 1.505
## 10 10 Austria 7.246 1.376 1.475
## 11 11 Australia 7.228 1.372 1.548
## 12 12 Costa Rica 7.167 1.034 1.441
## 13 13 Israel 7.139 1.276 1.455
## 14 14 Luxembourg 7.090 1.609 1.479
## 15 15 United Kingdom 7.054 1.333 1.538
## 16 16 Ireland 7.021 1.499 1.553
## 17 17 Germany 6.985 1.373 1.454
## 18 18 Belgium 6.923 1.356 1.504
## 19 19 United States 6.892 1.433 1.457
## 20 20 Czech Republic 6.852 1.269 1.487
## 21 21 United Arab Emirates 6.825 1.503 1.310
## 22 22 Malta 6.726 1.300 1.520
## 23 23 Mexico 6.595 1.070 1.323
## 24 24 France 6.592 1.324 1.472
## 25 25 Taiwan 6.446 1.368 1.430
## 26 26 Chile 6.444 1.159 1.369
## 27 27 Guatemala 6.436 0.800 1.269
## 28 28 Saudi Arabia 6.375 1.403 1.357
## 29 29 Qatar 6.374 1.684 1.313
## 30 30 Spain 6.354 1.286 1.484
## 31 31 Panama 6.321 1.149 1.442
## 32 32 Brazil 6.300 1.004 1.439
## 33 33 Uruguay 6.293 1.124 1.465
## 34 34 Singapore 6.262 1.572 1.463
## 35 35 El Salvador 6.253 0.794 1.242
## 36 36 Italy 6.223 1.294 1.488
## 37 37 Bahrain 6.199 1.362 1.368
## 38 38 Slovakia 6.198 1.246 1.504
## 39 39 Trinidad & Tobago 6.192 1.231 1.477
## 40 40 Poland 6.182 1.206 1.438
## 41 41 Uzbekistan 6.174 0.745 1.529
## 42 42 Lithuania 6.149 1.238 1.515
## 43 43 Colombia 6.125 0.985 1.410
## 44 44 Slovenia 6.118 1.258 1.523
## 45 45 Nicaragua 6.105 0.694 1.325
## 46 46 Kosovo 6.100 0.882 1.232
## 47 47 Argentina 6.086 1.092 1.432
## 48 48 Romania 6.070 1.162 1.232
## 49 49 Cyprus 6.046 1.263 1.223
## 50 50 Ecuador 6.028 0.912 1.312
## 51 51 Kuwait 6.021 1.500 1.319
## 52 52 Thailand 6.008 1.050 1.409
## 53 53 Latvia 5.940 1.187 1.465
## 54 54 South Korea 5.895 1.301 1.219
## 55 55 Estonia 5.893 1.237 1.528
## 56 56 Jamaica 5.890 0.831 1.478
## 57 57 Mauritius 5.888 1.120 1.402
## 58 58 Japan 5.886 1.327 1.419
## 59 59 Honduras 5.860 0.642 1.236
## 60 60 Kazakhstan 5.809 1.173 1.508
## 61 61 Bolivia 5.779 0.776 1.209
## 62 62 Hungary 5.758 1.201 1.410
## 63 63 Paraguay 5.743 0.855 1.475
## 64 64 Northern Cyprus 5.718 1.263 1.252
## 65 65 Peru 5.697 0.960 1.274
## 66 66 Portugal 5.693 1.221 1.431
## 67 67 Pakistan 5.653 0.677 0.886
## 68 68 Russia 5.648 1.183 1.452
## 69 69 Philippines 5.631 0.807 1.293
## 70 70 Serbia 5.603 1.004 1.383
## 71 71 Moldova 5.529 0.685 1.328
## 72 72 Libya 5.525 1.044 1.303
## 73 73 Montenegro 5.523 1.051 1.361
## 74 74 Tajikistan 5.467 0.493 1.098
## 75 75 Croatia 5.432 1.155 1.266
## 76 76 Hong Kong 5.430 1.438 1.277
## 77 77 Dominican Republic 5.425 1.015 1.401
## 78 78 Bosnia and Herzegovina 5.386 0.945 1.212
## 79 79 Turkey 5.373 1.183 1.360
## 80 80 Malaysia 5.339 1.221 1.171
## 81 81 Belarus 5.323 1.067 1.465
## 82 82 Greece 5.287 1.181 1.156
## 83 83 Mongolia 5.285 0.948 1.531
## 84 84 North Macedonia 5.274 0.983 1.294
## 85 85 Nigeria 5.265 0.696 1.111
## 86 86 Kyrgyzstan 5.261 0.551 1.438
## 87 87 Turkmenistan 5.247 1.052 1.538
## 88 88 Algeria 5.211 1.002 1.160
## 89 89 Morocco 5.208 0.801 0.782
## 90 90 Azerbaijan 5.208 1.043 1.147
## 91 91 Lebanon 5.197 0.987 1.224
## 92 92 Indonesia 5.192 0.931 1.203
## 93 93 China 5.191 1.029 1.125
## 94 94 Vietnam 5.175 0.741 1.346
## 95 95 Bhutan 5.082 0.813 1.321
## 96 96 Cameroon 5.044 0.549 0.910
## 97 97 Bulgaria 5.011 1.092 1.513
## 98 98 Ghana 4.996 0.611 0.868
## 99 99 Ivory Coast 4.944 0.569 0.808
## 100 100 Nepal 4.913 0.446 1.226
## 101 101 Jordan 4.906 0.837 1.225
## 102 102 Benin 4.883 0.393 0.437
## 103 103 Congo (Brazzaville) 4.812 0.673 0.799
## 104 104 Gabon 4.799 1.057 1.183
## 105 105 Laos 4.796 0.764 1.030
## 106 106 South Africa 4.722 0.960 1.351
## 107 107 Albania 4.719 0.947 0.848
## 108 108 Venezuela 4.707 0.960 1.427
## 109 109 Cambodia 4.700 0.574 1.122
## 110 110 Palestinian Territories 4.696 0.657 1.247
## 111 111 Senegal 4.681 0.450 1.134
## 112 112 Somalia 4.668 0.000 0.698
## 113 113 Namibia 4.639 0.879 1.313
## 114 114 Niger 4.628 0.138 0.774
## 115 115 Burkina Faso 4.587 0.331 1.056
## 116 116 Armenia 4.559 0.850 1.055
## 117 117 Iran 4.548 1.100 0.842
## 118 118 Guinea 4.534 0.380 0.829
## 119 119 Georgia 4.519 0.886 0.666
## 120 120 Gambia 4.516 0.308 0.939
## 121 121 Kenya 4.509 0.512 0.983
## 122 122 Mauritania 4.490 0.570 1.167
## 123 123 Mozambique 4.466 0.204 0.986
## 124 124 Tunisia 4.461 0.921 1.000
## 125 125 Bangladesh 4.456 0.562 0.928
## 126 126 Iraq 4.437 1.043 0.980
## 127 127 Congo (Kinshasa) 4.418 0.094 1.125
## 128 128 Mali 4.390 0.385 1.105
## 129 129 Sierra Leone 4.374 0.268 0.841
## 130 130 Sri Lanka 4.366 0.949 1.265
## 131 131 Myanmar 4.360 0.710 1.181
## 132 132 Chad 4.350 0.350 0.766
## 133 133 Ukraine 4.332 0.820 1.390
## 134 134 Ethiopia 4.286 0.336 1.033
## 135 135 Swaziland 4.212 0.811 1.149
## 136 136 Uganda 4.189 0.332 1.069
## 137 137 Egypt 4.166 0.913 1.039
## 138 138 Zambia 4.107 0.578 1.058
## 139 139 Togo 4.085 0.275 0.572
## 140 140 India 4.015 0.755 0.765
## 141 141 Liberia 3.975 0.073 0.922
## 142 142 Comoros 3.973 0.274 0.757
## 143 143 Madagascar 3.933 0.274 0.916
## 144 144 Lesotho 3.802 0.489 1.169
## 145 145 Burundi 3.775 0.046 0.447
## 146 146 Zimbabwe 3.663 0.366 1.114
## 147 147 Haiti 3.597 0.323 0.688
## 148 148 Botswana 3.488 1.041 1.145
## 149 149 Syria 3.462 0.619 0.378
## 150 150 Malawi 3.410 0.191 0.560
## 151 151 Yemen 3.380 0.287 1.163
## 152 152 Rwanda 3.334 0.359 0.711
## 153 153 Tanzania 3.231 0.476 0.885
## 154 154 Afghanistan 3.203 0.350 0.517
## 155 155 Central African Republic 3.083 0.026 0.000
## 156 156 South Sudan 2.853 0.306 0.575
## Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1 0.986 0.596 0.153
## 2 0.996 0.592 0.252
## 3 1.028 0.603 0.271
## 4 1.026 0.591 0.354
## 5 0.999 0.557 0.322
## 6 1.052 0.572 0.263
## 7 1.009 0.574 0.267
## 8 1.026 0.585 0.330
## 9 1.039 0.584 0.285
## 10 1.016 0.532 0.244
## 11 1.036 0.557 0.332
## 12 0.963 0.558 0.144
## 13 1.029 0.371 0.261
## 14 1.012 0.526 0.194
## 15 0.996 0.450 0.348
## 16 0.999 0.516 0.298
## 17 0.987 0.495 0.261
## 18 0.986 0.473 0.160
## 19 0.874 0.454 0.280
## 20 0.920 0.457 0.046
## 21 0.825 0.598 0.262
## 22 0.999 0.564 0.375
## 23 0.861 0.433 0.074
## 24 1.045 0.436 0.111
## 25 0.914 0.351 0.242
## 26 0.920 0.357 0.187
## 27 0.746 0.535 0.175
## 28 0.795 0.439 0.080
## 29 0.871 0.555 0.220
## 30 1.062 0.362 0.153
## 31 0.910 0.516 0.109
## 32 0.802 0.390 0.099
## 33 0.891 0.523 0.127
## 34 1.141 0.556 0.271
## 35 0.789 0.430 0.093
## 36 1.039 0.231 0.158
## 37 0.871 0.536 0.255
## 38 0.881 0.334 0.121
## 39 0.713 0.489 0.185
## 40 0.884 0.483 0.117
## 41 0.756 0.631 0.322
## 42 0.818 0.291 0.043
## 43 0.841 0.470 0.099
## 44 0.953 0.564 0.144
## 45 0.835 0.435 0.200
## 46 0.758 0.489 0.262
## 47 0.881 0.471 0.066
## 48 0.825 0.462 0.083
## 49 1.042 0.406 0.190
## 50 0.868 0.498 0.126
## 51 0.808 0.493 0.142
## 52 0.828 0.557 0.359
## 53 0.812 0.264 0.075
## 54 1.036 0.159 0.175
## 55 0.874 0.495 0.103
## 56 0.831 0.490 0.107
## 57 0.798 0.498 0.215
## 58 1.088 0.445 0.069
## 59 0.828 0.507 0.246
## 60 0.729 0.410 0.146
## 61 0.706 0.511 0.137
## 62 0.828 0.199 0.081
## 63 0.777 0.514 0.184
## 64 1.042 0.417 0.191
## 65 0.854 0.455 0.083
## 66 0.999 0.508 0.047
## 67 0.535 0.313 0.220
## 68 0.726 0.334 0.082
## 69 0.657 0.558 0.117
## 70 0.854 0.282 0.137
## 71 0.739 0.245 0.181
## 72 0.673 0.416 0.133
## 73 0.871 0.197 0.142
## 74 0.718 0.389 0.230
## 75 0.914 0.296 0.119
## 76 1.122 0.440 0.258
## 77 0.779 0.497 0.113
## 78 0.845 0.212 0.263
## 79 0.808 0.195 0.083
## 80 0.828 0.508 0.260
## 81 0.789 0.235 0.094
## 82 0.999 0.067 0.000
## 83 0.667 0.317 0.235
## 84 0.838 0.345 0.185
## 85 0.245 0.426 0.215
## 86 0.723 0.508 0.300
## 87 0.657 0.394 0.244
## 88 0.785 0.086 0.073
## 89 0.782 0.418 0.036
## 90 0.769 0.351 0.035
## 91 0.815 0.216 0.166
## 92 0.660 0.491 0.498
## 93 0.893 0.521 0.058
## 94 0.851 0.543 0.147
## 95 0.604 0.457 0.370
## 96 0.331 0.381 0.187
## 97 0.815 0.311 0.081
## 98 0.486 0.381 0.245
## 99 0.232 0.352 0.154
## 100 0.677 0.439 0.285
## 101 0.815 0.383 0.110
## 102 0.397 0.349 0.175
## 103 0.508 0.372 0.105
## 104 0.571 0.295 0.043
## 105 0.551 0.547 0.266
## 106 0.469 0.389 0.130
## 107 0.874 0.383 0.178
## 108 0.805 0.154 0.064
## 109 0.637 0.609 0.232
## 110 0.672 0.225 0.103
## 111 0.571 0.292 0.153
## 112 0.268 0.559 0.243
## 113 0.477 0.401 0.070
## 114 0.366 0.318 0.188
## 115 0.380 0.255 0.177
## 116 0.815 0.283 0.095
## 117 0.785 0.305 0.270
## 118 0.375 0.332 0.207
## 119 0.752 0.346 0.043
## 120 0.428 0.382 0.269
## 121 0.581 0.431 0.372
## 122 0.489 0.066 0.106
## 123 0.390 0.494 0.197
## 124 0.815 0.167 0.059
## 125 0.723 0.527 0.166
## 126 0.574 0.241 0.148
## 127 0.357 0.269 0.212
## 128 0.308 0.327 0.153
## 129 0.242 0.309 0.252
## 130 0.831 0.470 0.244
## 131 0.555 0.525 0.566
## 132 0.192 0.174 0.198
## 133 0.739 0.178 0.187
## 134 0.532 0.344 0.209
## 135 0.000 0.313 0.074
## 136 0.443 0.356 0.252
## 137 0.644 0.241 0.076
## 138 0.426 0.431 0.247
## 139 0.410 0.293 0.177
## 140 0.588 0.498 0.200
## 141 0.443 0.370 0.233
## 142 0.505 0.142 0.275
## 143 0.555 0.148 0.169
## 144 0.168 0.359 0.107
## 145 0.380 0.220 0.176
## 146 0.433 0.361 0.151
## 147 0.449 0.026 0.419
## 148 0.538 0.455 0.025
## 149 0.440 0.013 0.331
## 150 0.495 0.443 0.218
## 151 0.463 0.143 0.108
## 152 0.614 0.555 0.217
## 153 0.499 0.417 0.276
## 154 0.361 0.000 0.158
## 155 0.105 0.225 0.235
## 156 0.295 0.010 0.202
## Perceptions.of.corruption
## 1 0.393
## 2 0.410
## 3 0.341
## 4 0.118
## 5 0.298
## 6 0.343
## 7 0.373
## 8 0.380
## 9 0.308
## 10 0.226
## 11 0.290
## 12 0.093
## 13 0.082
## 14 0.316
## 15 0.278
## 16 0.310
## 17 0.265
## 18 0.210
## 19 0.128
## 20 0.036
## 21 0.182
## 22 0.151
## 23 0.073
## 24 0.183
## 25 0.097
## 26 0.056
## 27 0.078
## 28 0.132
## 29 0.167
## 30 0.079
## 31 0.054
## 32 0.086
## 33 0.150
## 34 0.453
## 35 0.074
## 36 0.030
## 37 0.110
## 38 0.014
## 39 0.016
## 40 0.050
## 41 0.240
## 42 0.042
## 43 0.034
## 44 0.057
## 45 0.127
## 46 0.006
## 47 0.050
## 48 0.005
## 49 0.041
## 50 0.087
## 51 0.097
## 52 0.028
## 53 0.064
## 54 0.056
## 55 0.161
## 56 0.028
## 57 0.060
## 58 0.140
## 59 0.078
## 60 0.096
## 61 0.064
## 62 0.020
## 63 0.080
## 64 0.162
## 65 0.027
## 66 0.025
## 67 0.098
## 68 0.031
## 69 0.107
## 70 0.039
## 71 0.000
## 72 0.152
## 73 0.080
## 74 0.144
## 75 0.022
## 76 0.287
## 77 0.101
## 78 0.006
## 79 0.106
## 80 0.024
## 81 0.142
## 82 0.034
## 83 0.038
## 84 0.034
## 85 0.041
## 86 0.023
## 87 0.028
## 88 0.114
## 89 0.076
## 90 0.182
## 91 0.027
## 92 0.028
## 93 0.100
## 94 0.073
## 95 0.167
## 96 0.037
## 97 0.004
## 98 0.040
## 99 0.090
## 100 0.089
## 101 0.130
## 102 0.082
## 103 0.093
## 104 0.055
## 105 0.164
## 106 0.055
## 107 0.027
## 108 0.047
## 109 0.062
## 110 0.066
## 111 0.072
## 112 0.270
## 113 0.056
## 114 0.102
## 115 0.113
## 116 0.064
## 117 0.125
## 118 0.086
## 119 0.164
## 120 0.167
## 121 0.053
## 122 0.088
## 123 0.138
## 124 0.055
## 125 0.143
## 126 0.089
## 127 0.053
## 128 0.052
## 129 0.045
## 130 0.047
## 131 0.172
## 132 0.078
## 133 0.010
## 134 0.100
## 135 0.135
## 136 0.060
## 137 0.067
## 138 0.087
## 139 0.085
## 140 0.085
## 141 0.033
## 142 0.078
## 143 0.041
## 144 0.093
## 145 0.180
## 146 0.089
## 147 0.110
## 148 0.100
## 149 0.141
## 150 0.089
## 151 0.077
## 152 0.411
## 153 0.147
## 154 0.025
## 155 0.035
## 156 0.091world_map <- map_data("world")
colnames(spasial)[colnames(spasial) == "Country.Territory"] <- "region"
merged_data <- left_join(world_map, spasial, by = c("region" = "Country.or.region"))
ggplot(merged_data, aes(x = long, y = lat, group = group, fill = Healthy.life.expectancy)) +
geom_polygon(color = "darkgreen") +
scale_fill_gradient(name = "Population (2019)", low = "blue", high = "black", guide = "legend") +
theme_void() +
labs(title = "Social.support 2019")
Kumpulan map chart yang disajikan di atas, terlihat distribusi populasi
global pada tahun 2022. Warna biru kehitaman menunjukkan negara-negara
dengan populasi yang tinggi, sementara warna biru muda menandakan
populasi yang lebih rendah. Dari peta tersebut, terlihat dengan jelas
bahwa China dan India memiliki populasi yang sangat besar.