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Bar Chart Jumlah Tenaga Medis menurut Provinsi di Indonesia 2025

Data = read.table(file.choose(), header = TRUE)
Data

##                     Provinsi Tenaga_Medis
## 1                       Aceh           21
## 2             Sumatera_Utara           24
## 3             Sumatera_Barat           12
## 4                       Riau           13
## 5                      Jambi            8
## 6           Sumatera_Selatan           17
## 7                   Bengkulu            5
## 8                    Lampung           13
## 9  Kepulauan_Bangka_Belitung            4
## 10            Kepulauan_Riau            5
## 11               DKI_Jakarta           39
## 12                Jawa_Barat           74
## 13               Jawa_Tengah           66
## 14             DI_Yogyakarta           12
## 15                Jawa_Timur           74
## 16                    Banten           19
## 17                      Bali           13
## 18       Nusa_Tenggara_Barat           13
## 19       Nusa_Tenggara_Timur           16
## 20          Kalimantan_Barat           11
## 21         Kalimantan_Tengah            8
## 22        Kalimantan_Selatan           10
## 23          Kalimantan_Timur           11
## 24          Kalimantan_Utara            3
## 25            Sulawesi_Utara           10
## 26           Sulawesi_Tengah            9
## 27          Sulawesi_Selatan           27
## 28         Sulawesi_Tenggara           10
## 29                 Gorontalo            4
## 30            Sulawesi_Barat            4
## 31                    Maluku            7
## 32              Maluku_Utara            4
## 33               Papua_Barat            2
## 34          Papua_Barat_Daya            2
## 35                     Papua            4
## 36             Papua_Selatan            2
## 37              Papua_Tengah            3
## 38          Papua_Pegunungan            1

library(ggplot2)
ggplot(Data, aes(
  x = Tenaga_Medis,
  y = reorder(Provinsi, Tenaga_Medis)
)) +
  geom_col(
    fill = "red",
    color = "black",
    width = 0.6
  ) +
  labs(
    title = "Jumlah Tenaga Medis Menurut Provinsi di Indonesia",
    x = "Jumlah Tenaga Medis",
    y = "Provinsi"
  ) +
  theme_classic() +
  theme(
    axis.text.y = element_text(size = 7),
    axis.text.x = element_text(size = 9),
    axis.title = element_text(face = "bold"),
    plot.title = element_text(
      hjust = 0.5,
      face = "bold",
      size = 14
    )
  )

Histogram Jumlah Tenaga Medis menurut Provinsi di Indonesia 2025

library(ggplot2)

ggplot(Data, aes(x = Tenaga_Medis)) +
  geom_histogram(
    binwidth = 5,
    boundary = 0,
    fill = "red",
    color = "black"
  ) +
  scale_x_continuous(
    breaks = seq(0, 75, by = 5),
    limits = c(0, 75)
  ) +
  labs(
    title = "Distribusi Jumlah Tenaga Medis di Indonesia",
    x = "Jumlah Tenaga Medis",
    y = "Freq"
  ) +
  theme_classic() +
  theme(
    axis.text.x = element_text(
      angle = 0,
      hjust = 0.5
    ),
    plot.title = element_text(
      hjust = 0.5,
      face = "bold"
    )
  )

Bar Chart Jumlah Tenaga Medis menurut Provinsi di Pulau Kalimantan 2025

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ lubridate 1.9.5     ✔ tibble    3.3.1
## ✔ purrr     1.2.2     ✔ tidyr     1.3.2
## ✔ readr     2.2.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggplot2)

Data_Kalimantan <- Data %>%
  filter(str_starts(Provinsi, "Kalimantan"))

Data_Kalimantan

##             Provinsi Tenaga_Medis
## 1   Kalimantan_Barat           11
## 2  Kalimantan_Tengah            8
## 3 Kalimantan_Selatan           10
## 4   Kalimantan_Timur           11
## 5   Kalimantan_Utara            3

ggplot(Data_Kalimantan, aes(x = Provinsi, y = Tenaga_Medis, fill = Provinsi)) +
  geom_col() +
  labs(
    title = "Jumlah Tenaga Medis di Pulau Kalimantan",
    x = "Provinsi",
    y = "Jumlah Tenaga Medis"
  ) +
  scale_fill_manual(
    values = c(
      "Kalimantan_Barat" = "pink",
      "Kalimantan_Tengah" = "lightblue",
      "Kalimantan_Selatan" = "lightgreen",
      "Kalimantan_Timur" = "plum",
      "Kalimantan_Utara" = "khaki"
    )
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(hjust = 0.5, face = "bold"),
    legend.position = "none"
  )