Library
lapply(c("readxl","factoextra","ggcorrplot","ggplot2","hrbrthemes","profileR"),library,character.only=T)[[1]]## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.## Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and## if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow## Loading required package: RColorBrewer## Loading required package: reshape## Loading required package: lavaan## This is lavaan 0.6-12
## lavaan is FREE software! Please report any bugs.## [1] "readxl" "stats" "graphics" "grDevices" "utils" "datasets"
## [7] "methods" "base"Dataset
data<-data.frame(read_excel("C:/Users/falco/Downloads/Agg.xlsx"))## New names:
## • `` -> `...1`data <- as.data.frame(data)
data <- data[,-1]
head(data)## Group.1 isi proses kompetensi pendidik sarana pengelolaan
## 1 KAB. BANDUNG 94.64894 91.85106 90.57447 76.92553 77.57447 91.60638
## 2 KAB. BANDUNG BARAT 93.02703 90.27027 89.10811 72.91892 72.43243 89.81081
## 3 KAB.BEKASI 95.45763 92.55932 92.69492 79.74576 82.93220 92.06780
## 4 KAB. BOGOR 95.93103 93.79310 93.66207 79.66207 83.77241 93.26207
## 5 KAB. CIAMIS 95.08696 92.00000 92.17391 81.30435 80.91304 92.26087
## 6 KAB. CIANJUR 92.73333 88.90000 86.03333 73.66667 74.86667 90.43333
## biaya nilai
## 1 94.80851 93.30851
## 2 92.91892 92.54054
## 3 93.47458 93.52542
## 4 95.18621 94.14483
## 5 93.47826 93.78261
## 6 91.40000 89.73333str(data)## 'data.frame': 27 obs. of 9 variables:
## $ Group.1 : chr "KAB. BANDUNG" "KAB. BANDUNG BARAT" "KAB.BEKASI" "KAB. BOGOR" ...
## $ isi : num 94.6 93 95.5 95.9 95.1 ...
## $ proses : num 91.9 90.3 92.6 93.8 92 ...
## $ kompetensi : num 90.6 89.1 92.7 93.7 92.2 ...
## $ pendidik : num 76.9 72.9 79.7 79.7 81.3 ...
## $ sarana : num 77.6 72.4 82.9 83.8 80.9 ...
## $ pengelolaan: num 91.6 89.8 92.1 93.3 92.3 ...
## $ biaya : num 94.8 92.9 93.5 95.2 93.5 ...
## $ nilai : num 93.3 92.5 93.5 94.1 93.8 ...Data Formatting
rownames(data) <- data$Group.1
data <- data[,-1]Analisis Data
Eksplorasi Data
cor_tpg <- cor(data)
cor_tpg## isi proses kompetensi pendidik sarana pengelolaan
## isi 1.0000000 0.8071484 0.6959006 0.3766905 0.5505898 0.8812228
## proses 0.8071484 1.0000000 0.7418726 0.3937596 0.5608541 0.8418333
## kompetensi 0.6959006 0.7418726 1.0000000 0.2307085 0.5449026 0.7155040
## pendidik 0.3766905 0.3937596 0.2307085 1.0000000 0.8056812 0.4362039
## sarana 0.5505898 0.5608541 0.5449026 0.8056812 1.0000000 0.6628535
## pengelolaan 0.8812228 0.8418333 0.7155040 0.4362039 0.6628535 1.0000000
## biaya 0.6836386 0.6744211 0.6566229 0.3836942 0.4990374 0.7719458
## nilai 0.8364676 0.8325131 0.6258339 0.3823415 0.4984915 0.7825059
## biaya nilai
## isi 0.6836386 0.8364676
## proses 0.6744211 0.8325131
## kompetensi 0.6566229 0.6258339
## pendidik 0.3836942 0.3823415
## sarana 0.4990374 0.4984915
## pengelolaan 0.7719458 0.7825059
## biaya 1.0000000 0.5847264
## nilai 0.5847264 1.0000000ggcorrplot(cor_tpg,type = "lower",lab=TRUE)
profil<-cbind(data,Mean=rowMeans(data))
prof<-profil[order(profil$Mean,decreasing = F),]
profileplot(head(prof[,-9]),rownames(head(prof[,-9])),standardize = F)
PCA
PCA_TPG <- prcomp(data,scale.=FALSE,center=TRUE)
summary(PCA_TPG)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 6.3619 3.2894 1.8479 1.5181 1.19486 0.85257 0.77100
## Proportion of Variance 0.6744 0.1803 0.0569 0.0384 0.02379 0.01211 0.00991
## Cumulative Proportion 0.6744 0.8547 0.9116 0.9500 0.97378 0.98590 0.99580
## PC8
## Standard deviation 0.5020
## Proportion of Variance 0.0042
## Cumulative Proportion 1.0000fviz_screeplot(PCA_TPG,geom="line")
PCA_TPG$rotation## PC1 PC2 PC3 PC4 PC5
## isi 0.2067832 -0.2334372 0.2064738 -0.109115952 0.17470553
## proses 0.2618846 -0.2962338 0.2113085 -0.182425467 -0.10128329
## kompetensi 0.3333331 -0.4596500 -0.5871044 0.006042511 -0.54140958
## pendidik 0.3550398 0.4842286 0.4324293 0.198800083 -0.59711651
## sarana 0.6785004 0.4502229 -0.3594469 -0.160072620 0.37421308
## pengelolaan 0.2452959 -0.2079651 0.1308886 0.034129919 0.36530781
## biaya 0.2523350 -0.2708452 0.1416405 0.835958026 0.18741906
## nilai 0.2593976 -0.3044500 0.4632868 -0.435479916 -0.03059536
## PC6 PC7 PC8
## isi -0.33801135 -0.61608687 -0.5690987
## proses 0.83891661 0.02320560 -0.2261721
## kompetensi -0.15268289 -0.07657507 0.1028719
## pendidik -0.04163116 -0.20498582 0.1124825
## sarana 0.01256343 0.16985077 -0.1143185
## pengelolaan 0.08106943 -0.42940269 0.7442278
## biaya -0.05107448 0.29543844 -0.1380712
## nilai -0.38416577 0.52105251 0.1254212- PC1 buat ngeliat provinsi dengan 8 standar pendidikan tertinggi/terendah
- PC2 buat ngeliat provinsi yang standar pendidik dan standar sarananya tinggi tapi 6 standar sisanya rendah
pcx<-fviz_pca_biplot(PCA_TPG,col.ind = "x",labelsize=3,
ggtheme=theme_ft_rc(axis_title_just = "center", grid=F)+
theme(plot.title = element_text(hjust=0.5)),
col.var = "coral", gradient.cols = "blue",title="PCA Biplot Base On PC1");pcx
- Kota bandung dan depok paling tinggi 8 standar pendidikan
- Kabupaten cianjur dan paten bandung baratpaling rendah 8 standar pendidikan
pcy<-fviz_pca_biplot(PCA_TPG,col.ind = "y",labelsize=3,
ggtheme=theme_ft_rc(axis_title_just = "center", grid=F)+
theme(plot.title = element_text(hjust=0.5)),
col.var = "coral", gradient.cols = "green",title="PCA Biplot Base On PC2");pcy
- Kabupaten sumedang standar pendidik dan standar sarananya rendah tapi 6 standar sisanya tinggi
- Kota Sukabumi standar pendidik dan standar sarananya tinggi tapi 6 standar sisanya rendah
-
Interpretasi lain:
- semua standar pendidikan hubungannya berbanding lurus
- standar sarana paling tinggi kontribusinya dibandingkan 7 standar pendidikan lainnya
profileR::profileplot()