AED
Haidar Ramdhani
2024-02-09
Read Data
library(readxl)## Warning: package 'readxl' was built under R version 4.3.2library(sf)## Warning: package 'sf' was built under R version 4.3.2## Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUElibrary(plyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr)
library(ggplot2)
library(reshape2)
data <- read_xlsx("D:\\Campss\\Season 4\\AED\\3\\Data Tugas 1 AED.xlsx")## New names:
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`View(data)
data1 <- data[-(1:2),]
data1 <- data1[-35,]
data1 <- as.data.frame(lapply(data1, function(x) gsub("\\,", ".", x)))
data1$Provinsi <- lapply(data1$Provinsi, function(x) str_to_title(x))
data1[11,1] <- "DKI Jakarta"
data1[14,1] <- "DI Yogyakarta"
colnames(data1) <- c("Provinsi", "Februari 2022", "Agustus 2022", "Februari 2023", "Agustus 2023")
str(data1)## 'data.frame': 34 obs. of 5 variables:
## $ Provinsi :List of 34
## ..$ : chr "Aceh"
## ..$ : chr "Sumatera Utara"
## ..$ : chr "Sumatera Barat"
## ..$ : chr "Riau"
## ..$ : chr "Jambi"
## ..$ : chr "Sumatera Selatan"
## ..$ : chr "Bengkulu"
## ..$ : chr "Lampung"
## ..$ : chr "Kep. Bangka Belitung"
## ..$ : chr "Kep. Riau"
## ..$ : chr "DKI Jakarta"
## ..$ : chr "Jawa Barat"
## ..$ : chr "Jawa Tengah"
## ..$ : chr "DI Yogyakarta"
## ..$ : chr "Jawa Timur"
## ..$ : chr "Banten"
## ..$ : chr "Bali"
## ..$ : chr "Nusa Tenggara Barat"
## ..$ : chr "Nusa Tenggara Timur"
## ..$ : chr "Kalimantan Barat"
## ..$ : chr "Kalimantan Tengah"
## ..$ : chr "Kalimantan Selatan"
## ..$ : chr "Kalimantan Timur"
## ..$ : chr "Kalimantan Utara"
## ..$ : chr "Sulawesi Utara"
## ..$ : chr "Sulawesi Tengah"
## ..$ : chr "Sulawesi Selatan"
## ..$ : chr "Sulawesi Tenggara"
## ..$ : chr "Gorontalo"
## ..$ : chr "Sulawesi Barat"
## ..$ : chr "Maluku"
## ..$ : chr "Maluku Utara"
## ..$ : chr "Papua Barat"
## ..$ : chr "Papua"
## $ Februari 2022: chr "5.97" "5.47" "6.17" "4.40" ...
## $ Agustus 2022 : chr "6.17" "6.16" "6.28" "4.37" ...
## $ Februari 2023: chr "5.75" "5.24" "5.9" "4.25" ...
## $ Agustus 2023 : chr "6.03" "5.89" "5.94" "4.23" ...data1$`Februari 2022`<- as.numeric(data1$`Februari 2022`)
data1$`Februari 2023`<- as.numeric(data1$`Februari 2023`)
data1$`Agustus 2022`<- as.numeric(data1$`Agustus 2022`)
data1$`Agustus 2023`<- as.numeric(data1$`Agustus 2023`)
{
feb22 <- as.numeric(data1$`Februari 2022`)
feb23 <- as.numeric(data1$`Februari 2023`)
agt22 <- as.numeric(data1$`Agustus 2022`)
agt23 <- as.numeric(data1$`Agustus 2023`)
}
yr2022 <- data.frame(feb22, agt22)
yr2023 <- data.frame(feb23, agt23)
provinsi <- as.factor(unlist(data1$Provinsi))
data2 <- data.frame(provinsi, feb22, agt22, feb23, agt23)
mean22 <- mean(c(mean(feb22),mean(agt22)))
mean23 <- mean(c(mean(feb23),mean(agt23)))
data1$`Tahun 2022` <- rowMeans(data2[, c("feb22", "agt22")])
data1$`Tahun 2023` <- rowMeans(data2[, c("feb23", "agt23")])
data3 <- data.frame(provinsi, feb22, agt22, feb23, agt23, data1$`Tahun 2022`, data1$`Tahun 2023`)
colnames(data3) <- c("Provinsi", "Februari 2022", "Agustus 2022", "Februari 2023", "Agustus 2023", "Tahun 2022", "Tahun 2023")
Pulau <- c(rep("Sumatera",10), rep("Jawa",6), rep("Nusa Tenggara",3), rep("Kalimantan",5), rep("Sulawesi",6), rep("Maluku",2), rep("Papua",2))
data.pulau <- data.frame(data1, Pulau)
{
sumatera <- subset(data.pulau, Pulau == "Sumatera")
jawa <- subset(data.pulau, Pulau == "Jawa")
kalimantan <- subset(data.pulau, Pulau == "Kalimantan")
sulawesi <- subset(data.pulau, Pulau == "Sulawesi")
papua <- subset(data.pulau, Pulau == "Papua")
}
datpulau5 <- rbind(sumatera, jawa, kalimantan, sulawesi, papua)
colnames(datpulau5) <- c("Provinsi", "Februari 2022", "Agustus 2022", "Februari 2023", "Agustus 2023", "Tahun 2022", "Tahun 2023", "Pulau")Statistik Deskriptif
statdesc <- function(a){
x <- as.numeric(a)
Max <- round(max(x), 2)
Min <- round(min(x),2)
Mean <- round(mean(x),2)
Qrt.1 <- round(quantile(x,0.25),2)
Med <- round(median(x),2)
Qrt.3 <- round(quantile(x,0.75),2)
Var <- round(var(x),2)
Stdev <- round(sqrt(Var),2)
Range <- round((Max-Min),2)
frek <- table(x)
Mod <- as.numeric(names(frek)[frek == max(frek)])
dt <- as.data.frame(rbind(Max, Min, Mod, Mean, Qrt.1, Med, Qrt.3, Var, Stdev, Range))
colnames(dt) <- "Statistik Deskriptif"
return(dt)
}
statdesc2 <- function(a,b){
x <- as.numeric(a)
Max <- round(max(x), 3)
Min <- round(min(x),3)
Mean <- round(mean(x),3)
Qrt.1 <- round(quantile(x,0.25),3)
Med <- round(median(x),3)
Qrt.3 <- round(quantile(x,0.75),3)
Var <- round(var(x),3)
Stdev <- round(sqrt(Var),3)
Range <- round((Max-Min),3)
dt <- as.data.frame(rbind(Max, NULL, Min, NULL, Mean, NULL, Qrt.1, NULL, Med, NULL, Qrt.3, NULL, Var,NULL, Stdev, NULL, Range))
colnames(dt) <- b
return(t(dt))
}
(statdesc2(agt23, "Agustus 2023"))## Max Min Mean Qrt.1 Med Qrt.3 Var Stdev Range
## Agustus 2023 7.52 2.27 4.614 3.487 4.32 5.762 2.014 1.419 5.25summary(agt23)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.270 3.487 4.320 4.614 5.763 7.520Redefine New Data
Data per Bulan
{
feb22name <- rep("Februari 2022", 34)
feb23name <- rep("Februari 2023", 34)
agt22name <- rep("Agustus 2022", 34)
agt23name <- rep("Agustus 2023", 34)
}
data1melt <- melt(data = data1, id.var = "Provinsi", variable.name = "Bulan",
value.name = "Nilai TPT")
Bulan <- as.factor(rbind(c(feb22name, agt22name, feb23name, agt23name)))
Angka <- as.numeric(rbind(c(feb22, agt22, feb23, agt23)))
newdat <- data.frame(Bulan,Angka)
reord <- reorder(newdat$Bulan, newdat$Angka, FUN = median)
ggplot()+
geom_boxplot(data=newdat, aes(x = Bulan, y = Angka))+
labs(x = NULL, y = "Tingkat Pengangguran Terbuka Indonesia")
Data per Tahun
{
tahun22 <- rep("Tahun 2022", 34)
tahun23 <- rep("Tahun 2023", 34)
}
Tahun <- as.factor(rbind(c(tahun22, tahun23)))
Tahun22 <- as.factor(tahun22)
Tahun23 <- as.factor(tahun23)
Nilai <- as.numeric(rbind(c(data1$`Tahun 2022`, data1$`Tahun 2023`)))
Nilai22 <- as.numeric(data1$`Tahun 2022`)
Nilai23 <- as.numeric(data1$`Tahun 2023`)
newdatyr <- data.frame(Tahun, Nilai)
newdatyr22 <- data.frame(Tahun22, Nilai22)
newdatyr23 <- data.frame(Tahun23, Nilai23)
reord2 <- reorder(newdatyr$Tahun, newdatyr$Nilai, FUN = median)Visualisasi Data
Boxplot
ggplot(data=newdat, aes(x = reord, y = Angka, fill = Bulan), color = "black") +
geom_boxplot()
ggplot(data=newdat, aes(x = reord,
y = Angka, color = Bulan)) +
geom_boxplot(width = 0.5)+
geom_jitter(alpha = 0.5) +
theme(legend.position = "top",
plot.margin = unit(c(1, 1, 1, 3), "cm"))+
labs(x = NULL, y = "Tingkat Pengangguran Terbuka Indonesia")+
scale_x_discrete(limits = rev(levels(reord)))
reord <- reorder(newdat$Bulan, newdat$Angka, FUN = median)
orderp <- reorder(data.pulau$Pulau, data.pulau$Tahun.2023, FUN = median)
neworder <- reorder(datpulau5$Pulau, datpulau5$`Tahun 2023`, FUN = median)
ggplot(data=datpulau5, aes(x = neworder, y = `Tahun 2023`, color = Pulau)) +
geom_boxplot(width = 0.5)+
geom_jitter(alpha = 0.5) +
theme(legend.position = "top",
plot.margin = unit(c(1, 1, 1, 3), "cm"))+
labs(x = NULL, y = "TPT Indonesia Tahun 2023")+
scale_x_discrete()
ggplot(data=newdatyr, aes(x = reord2,
y = Nilai, color = Tahun)) +
geom_boxplot()+
geom_jitter(alpha = 0.5) +
scale_x_discrete(limits = rev(levels(reord2))) +
theme()+
labs(x = NULL, y = "Tingkat Pengangguran Terbuka Indonesia")
Histogram
ggplot(data = newdat, aes(x=Angka)) +
geom_histogram(aes(fill=Bulan)) +
scale_fill_brewer(palette="Set2") +
facet_wrap( ~ Bulan, ncol=1) +
xlab("TPT Indonesia") +
ylab("Frekuensi") +
theme_bw() +
ggtitle("Angka TPT per Bulan\n")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
library(plyr)
library(dplyr)
rataan<-ddply(newdat, "Bulan", summarise, rata2=mean(Angka))
rataan2 <- ddply(newdatyr, "Tahun", summarise, rata2=mean(Nilai))
ggplot(newdat, aes(x=Angka, color=Bulan)) +
geom_histogram(fill="white")+
geom_vline(data=rataan, aes(xintercept=rata2, color=Bulan),
linetype="dashed")+
theme(legend.position="top")+
labs(y = "Frekuensi", x = "Angka TPT Indonesia") +
geom_text(data = rataan, aes(x = rata2-0.1, y = 11.7, label = paste(round(rata2,1))), vjust = -1, size = 2.2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Violin Plot
ggplot(newdatyr,aes(reord2,Nilai))+
geom_violin(aes(col = Tahun), fill = "white", width = 0.7)+
geom_boxplot(width=.1, fill= "#4B5768", outlier.colour=NA)+
geom_jitter(alpha = 0.5, aes(color = Tahun))+
geom_hline(data=rataan2, aes(yintercept=rata2, color=Tahun),
linetype="dashed", alpha = 2)+
labs(x = NULL, y = "Tingkat Pengangguran Terbuka")+
theme(legend.position = "top")+
stat_summary(fun=median,geom="point",fill="blue",shape=21,size=2.5) +
scale_x_discrete(limits = rev(levels(reord2))) + coord_flip()
Density Plot
# Density
ggplot(data3)+
geom_density(aes(x=`Februari 2022`,fill= "Februari 2022" ),color="coral", alpha=0.4)+
geom_density(aes(x=`Februari 2023`,fill= "Februari 2023" ),color="#e9ecef", alpha=0.4)+
geom_density(aes(x=`Agustus 2022`,fill="Agustus 2022"), color="#e9ecef", alpha=0.4)+
geom_density(aes(x=`Agustus 2023`,fill= "Agustus 2023" ),color="cyan", alpha=0.4)+
labs(title="Density Plot Sebaran Data Tingkat Pengangguran Terbuka Indonesia")+
xlab("Tingkat Pengangguran Terbuka")+
xlim(1,10.2)+
scale_fill_manual(name = NULL,
values = c("Februari 2022" = "#e41a1c", "Februari 2023" = "#4daf4a",
"Agustus 2022" = "#377eb8", "Agustus 2023" = "#984ea3"))
ggplot(data3)+
geom_density(aes(x=`Tahun 2022`,fill= "Tahun 2022" ),color="#e9ecef", alpha=0.4)+
geom_density(aes(x=`Tahun 2023`,fill= "Tahun 2023" ),color="#e9ecef", alpha=0.4)+
labs(title="Density Plot Sebaran Data Tingkat Pengangguran Terbuka Indonesia")+
xlab("TPT")+
ylab("Jumlah")+
xlim(1,10.2)
Histogram (gif)
library(gganimate)## Warning: package 'gganimate' was built under R version 4.3.2library(gifski)## Warning: package 'gifski' was built under R version 4.3.2library(av)## Warning: package 'av' was built under R version 4.3.2animasi<- ggplot(newdat,aes(Angka)) +
geom_histogram(col = "white",fill = "blue") +
transition_states(Bulan,5,0.5, wrap = F)+
view_follow(fixed_x = T)
animasi## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Bar Chart
ggplot(data = data3,
aes(x = Provinsi, y = `Tahun 2023`)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Tingkat Pengangguran Terbuka", subtitle="Tingkat Pengangguran Terbuka di Indonesia Tahun 2023",
caption="Sumber: Badan Pusat Statistik", y = "TPT", x = "Provinsi")
ggplot(data = data3, aes(x = Provinsi)) +
geom_bar(aes(y = `Tahun 2022`, fill = "Tahun 2022"), stat = "identity", position = position_dodge(width = 0.8)) +
geom_bar(aes(y = `Tahun 2023`, fill = "Tahun 2023"), stat = "identity", position = position_dodge(width = 0.8)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
title = "Tingkat Pengangguran Terbuka",
subtitle = "Tingkat Pengangguran Terbuka di Indonesia Tahun 2023",
caption = "Sumber: Badan Pusat Statistik",
y = "TPT",
x = "Provinsi"
) +
scale_fill_manual(values = c("Tahun 2022" = "#f44369", "Tahun 2023" = "#3e3b92"), name = "Tahun")
ggplot(data = data3, aes(x = Provinsi)) +
geom_bar(aes(y = `Agustus 2022`, fill = "Agustus 2022"), alpha = 0.9, stat = "identity", position = position_dodge(width = 0.8)) +
geom_bar(aes(y = `Agustus 2023`, fill = "Agustus 2023"), alpha = 0.8, stat = "identity", position = position_dodge(width = 0.8)) +
theme(axis.text.x = element_text(angle = 0, hjust = 1),
axis.text.y = element_text(size = 6.5, angle = 0, hjust = 1)) +
labs(
title = "Tingkat Pengangguran Terbuka Indonesia",
subtitle = "Per Bulan Agustus",
caption = "Sumber: Badan Pusat Statistik",
y = "TPT",
x = "Provinsi"
) +
scale_fill_manual(values = c("Agustus 2022" = "#1ECE70", "Agustus 2023" = "#3e3b92"), name = "Periode Waktu") +
coord_flip()
data3_melted <- melt(data3[,c(1,6:7)], id.vars = "Provinsi", variable.name = "Tahun", value.name = "TPT")
data4 <- melt(data3[,c(1:5)], id.vars = "Provinsi", variable.name = "Bulan", value.name = "TPT")
# Plotting
ggplot(data = data3_melted, aes(x = Provinsi, y = TPT, fill = Tahun)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
theme(axis.text.x = element_text(size = 8,angle = 45, hjust = 1)) +
labs(
title = "Tingkat Pengangguran Terbuka",
subtitle = "Tingkat Pengangguran Terbuka di Indonesia Tahun 2023",
caption = "Sumber: Badan Pusat Statistik",
y = "TPT",
x = "Provinsi"
)
datpul_melt <- melt(datpulau5[,c(2:5, 8)],id.vars = "Pulau", variable.name = "Bulan", value.name = "TPT")
ggplot(data = datpul_melt, aes(x = Pulau, y = TPT, fill = Bulan)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
theme(axis.text.x = element_text(size = 10,angle = 0, hjust = 0.5)) +
labs(
title = "Tingkat Pengangguran Terbuka",
subtitle = "Tingkat Pengangguran Terbuka di Indonesia Tahun 2022 - 2023",
caption = "Sumber: Badan Pusat Statistik",
y = "TPT",
x = "Pulau"
)
# Plot chart vertikal
ggplot(data = data3_melted, aes(x = TPT, y = Provinsi, fill = Tahun)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 6),
axis.text.x = element_text(size = 8, angle = 0, hjust = 1)) +
labs(
title = "Tingkat Pengangguran Terbuka",
subtitle = "Perbandingan Tingkat Pengangguran Terbuka di Indonesia Tahun 2022 dan 2023",
caption = "Sumber: Badan Pusat Statistik",
x = "TPT",
y = "Provinsi"
)
View(data3_melted)
ggplot(data = data4, aes(x = TPT, y = Provinsi, fill = Bulan)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
theme(axis.text.y = element_text(angle = 0, hjust = 1, size = 6),
axis.text.x = element_text(size = 8, angle = 0, hjust = 1)) +
labs(
title = "Tingkat Pengangguran Terbuka",
subtitle = "Perbandingan Tingkat Pengangguran Terbuka di Indonesia Tahun 2022 dan 2023",
caption = "Sumber: Badan Pusat Statistik",
x = "TPT",
y = "Provinsi"
)
Peta Konsentrasi
#Base Map
library(indonesia)
prov <- id_map("indonesia", "provinsi")
colnames(prov) <- c("Provinsi", "geometry")
View(prov)
prov_merge <- merge(prov, data1, by = "Provinsi") ## old-style crs object detected; please recreate object with a recent sf::st_crs()ggplot(prov) +
geom_sf()## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
#Continuous
library(viridis)## Warning: package 'viridis' was built under R version 4.3.2## Loading required package: viridisLiteggplot(prov_merge) +
geom_sf(aes(fill = `Februari 2022`)) +
scale_fill_viridis_c(option = "F")
#Discrete
prov_mod <- prov_merge
{
prov_mod$`Februari 2022` <- as.numeric(as.character(prov_mod$`Februari 2022`))
prov_mod$`Februari 2023` <- as.numeric(as.character(prov_mod$`Februari 2023`))
prov_mod$`Agustus 2022` <- as.numeric(as.character(prov_mod$`Agustus 2022`))
prov_mod$`Agustus 2023` <- as.numeric(as.character(prov_mod$`Februari 2023`))
}
ggplot(prov_mod) +
geom_sf(aes(fill = `Tahun 2022`)) +
scale_fill_gradient(high = c("#DC443F","darkblue"), low = "#FFEDD2")+
labs(title = "Tahun 2022")
ggplot(prov_mod) +
geom_sf(aes(fill = `Tahun 2023`)) +
scale_fill_gradient(high = c("#DC443F","darkblue"), low = "#FFEDD2")+
labs(title = "Tahun 2023")
#hehe
ggplot(prov_mod) +
geom_sf(aes(fill = `Agustus 2023`)) +
scale_fill_gradient(high = c("#fff1bf", "#ec458d"), low = "#474ed7")
ggplot(prov_mod) +
geom_sf(aes(fill = `Tahun 2023`)) +
scale_fill_gradient(high = c("blue", "#ec458d"), low = "#fff1bf")+
labs(title = "Tahun 2023")
ggplot(prov_mod) +
geom_sf(aes(fill = `Tahun 2022`)) +
scale_fill_gradient(high = c("#474ed7", "#ec458d"), low = "#fff1bf")+
labs(title = "Tahun 2022")