Undeployed shiny DV Capstone

[dimasrepo]

Data Visualisasi Capstone yang tidak bisa di deploy

Dear Team Algoritma ,
Berikut syntax untuk Data Visualisasi Capstone yang tidak bisa di deploy

#---------------------- Q&A/Problem-------------
Tidak bisa melakukan deploy tetapi bisa di running app dengan baik
Apakah ini berkaitan mengenai penggunakan file sf untuk map ? 
berikut Markdown untuk setiap file


``````
#--------------------------------File Problem--------------------------------
#--------------------------------ui.R--------------------------------
library(shiny)

library(shinydashboard)

dashboardPage(
  skin = "black",
  #------------- Title-----------
  dashboardHeader(
    title = "Energy Analysis"
  ),
  
  
  
  #-------------- Menu -------------
  dashboardSidebar(
    sidebarMenu(
    menuItem("Dashboard", tabName = "page1", icon = icon("earth")),
    menuItem("Graph", tabName = "page2", icon = icon("chart-simple")),
    menuItem("Map", tabName = "page3", icon = icon("map")),
    menuItem("Data", tabName = "page4", icon = icon("th"))
  )
),
  
  
  #------------- Isi ------------ 
  dashboardBody(
    tabItems(
#-----------Dashboard ---------------
      tabItem(
        tabName = "page1",
        fluidRow(
          infoBox("Record",
                  value = nrow(data2),
                  icon = icon("earth"),
                  width = 4,
                  color = "blue"),
          infoBox("Country",
                  value = length(unique(data$Country)),
                  icon = icon("city"),
                  width = 4,
                  color = "blue"),
          infoBox("Time Series",
                  value = length(unique(data$Year)),
                  icon = icon("clock"),
                  width = 4,
                  color = "blue")
        ),
        fluidRow(
          box(
            width = 12,
            plotlyOutput(outputId = "plot1")
          )
        )
      ),
# Add additional tabItems as needed
      
#-----------menu 1 ---------------
tabItem(
  tabName = "page2",
  fluidRow(
    box(
      width = 12,
      selectInput(
        inputId = "Category",
        label = "Input Category",
        choices = unique(data2$Category),
        selected = "Total energy consumption"
      )
    )
  ),
  fluidRow(
    box(
      width = 12,
      plotlyOutput(outputId = "plot2")
    ),
    box(
      width = 12,
      plotlyOutput(outputId = "plot3")
    )
  )
),
#-------------menu2-----------
tabItem(
  tabName = "page3",
  fluidRow(
    box(
      width = 9,
      radioButtons(
        inputId = "Category",
        label = "Category",
        choices = unique(data$Category),  # Assuming 'Country' is a column in your data
        selected = "Total energy production",          # Default selected country
        inline = TRUE                    # Display buttons inline (optional)
      )
    )
  ),
  fluidRow(
    box(
      width = 12,
      plotlyOutput(outputId = "plot4")
    )
  )
),

#-------------menu3-----------
tabItem(
  tabName = "page4",
  fluidRow(
    box(
      width = 12,
      title = "Dataset Energy Statistical",
      dataTableOutput(outputId = "data")
    )
  )
))))

#------------------------ Global.R ----------------------------------------

# Mempersiapkan libraries

library(shiny)
library(shinydashboard)
library(DT)
library(dplyr)
library(lubridate)
library(ggplot2)
library(plotly)
library(glue)
library(scales)
library(ggpubr)
library(tidyr)
library(treemapify)
library(sf)
library(hrbrthemes)
library(viridis)
# install.packages(c("Matrix", "MatrixModels"))

# library(lme4)
# library(MatrixModels)
# library(car)
# library(pbkrtest)

# remove.packages("Matrix")
# install.packages("Matrix")
# 
# remove.packages("MatrixModels")
# install.packages("MatrixModels")
# me-non-aktifkan scientific notation
options(scipen = 9999) 

# # Membaca data
 data <- read.csv("data_input/Energydata.csv")
# 
# # Mengubah tipe data
# vids_clean <- vids %>% 
#   mutate_at(.vars = c("title", "category_id", "channel_title", 
#                       "publish_when",  "publish_wday"), 
#             .funs = as.factor) %>% 
#   mutate(trending_date = ymd(trending_date),
#          publish_time = ymd_hms(publish_time))

#-----------------Pivot---------
 data <- data %>% 
   #Filter
   #Merubah bentuk ke long format
   pivot_longer(cols = c(X1990 : X2022),
                names_to = "Year",
                values_to = "values")
#-----------------------Year Edit
 data$Year <- substr(data$Year, 2,5)

#---------------------Select Data
 data <- select(data, Country, Category, Year,values)

#-------------------------Mutate type
 
 data <- data %>% 
   mutate(
     Category = as.character(Category),
     values = as.numeric(values),
     Year = ymd(paste0(Year, "-12-31"))
   )

#-----------------Na Handling
 
 # Menghitung rata-rata values berdasarkan Country, Category, dan Year
 average_values <- data %>%
   ungroup() %>%  # Menghilangkan pengelompokan dari hasil summarize sebelumnya
   group_by(Country, Category, Year) %>%
   summarize(avg_value = mean(values, na.rm = TRUE))
 
 # Mengganti NA dengan rata-rata yang sesuai
 data1 <- data %>%
   left_join(average_values, by = c("Country", "Category", "Year")) %>%
   mutate(values = ifelse(is.na(values), avg_value, values)) %>%
   select(-avg_value)  # Menghapus kolom avg_value yang tidak diperlukan lagi
 
#------------------------Filtering Country 
 
 exclude_countries <- c("World", "OECD", "Asia", "G7", "BRICS", "Europe", "European Union", "CIS", "America", "North America")
 
 # Filter the dataset
 data2 <- data1 %>% 
   filter(!Country %in% exclude_countries)
 
 
 
 #---------------------Penyesuaian dengan data MAP SF
 data2 <- data2 %>%
   mutate(
     Country = case_when(
       Country == "Czechia"        ~ "Czech Republic",
       Country == "Turkiye"        ~ "Turkey",
       Country == "North America"  ~ "United States Minor Outlying Islands",
       Country == "Latin America"  ~ "Brazil",
       Country == "South Korea"    ~ "Korea, Republic of",
       Country == "Pacific"        ~ "Australia",
       Country == "Africa"         ~ "South Africa",
       Country == "Middle-East"    ~ "Iran",
       Country == "Iran"           ~ "Iran (Islamic Republic of)",
       TRUE                        ~ as.character(Country)
     )
   )

#----------------------------------Server.R-----------------------------
function(input, output, session) {
  #--------------------Agg plotdash---------- 
  output$plot1 <- renderPlotly({
    
    datakom <- data2 %>%
      filter(!Category %in% c("Average emission factor", "Share of electricity in total final energy consumption", "Average emission factor", "Total energy production")) %>% 
      group_by(Category, Year) %>%
      summarize(mean = round(mean(values, na.rm = TRUE), digits = 2)) %>%
      arrange(Category)
    #----------------- Mutate plotdash ----------------------
    # datakom <- datakom %>%
    #   mutate(labeling = glue ("Category : {Category}
    #                       Count : {total_values}"))
    #--------------------ggplot --------------------
    plot1 <- ggplot(datakom, aes(x = Year, y = mean, fill = Category)) +
      geom_area() + 
      scale_x_date(date_breaks = "5 years", date_labels = "%Y") +
      theme_classic() +
      labs(title = "Global Production Comodity vs Consumtion Overtime",
           subtitle = "Energy Production Data",
           caption = "Source: Energdata.info",
           x = "Year", y = NULL) +
      theme(legend.position = "top", 
            plot.title = element_text(size = 15, hjust = 0.5),
            plot.subtitle = element_text(size = 8, hjust = 0.5)) +
      scale_fill_brewer(palette = "Blues")
    #--------------------ggplotly plotdash ---------------------
    # ggplotly(plot1, tooltip = "text")
    
    
  })

  
#--------------------Agg plot2----------
    output$plot2 <- renderPlotly({

      datacon_top10 <- data2 %>%
        filter(Category == input$Category) %>%
        group_by(Country, Category) %>% 
        summarise(Mean = round(mean(values), digits = 2)) %>% 
        ungroup() %>% arrange(-Mean) %>%  head(10)
#----------------- Mutate plot1 ----------------------
      datacon_top10 <- datacon_top10 %>% 
        mutate(labeling = glue ("Country : {Country}
                          Count : {Mean}"))
#--------------------ggplot --------------------
      plot2 <- ggplot(datacon_top10, aes(x = Mean, y = reorder(Country, Mean),text = labeling)) +
        geom_col(aes(fill = Mean)) + geom_hline(yintercept = 0) +
        scale_fill_gradient2(low = "#02293b", mid = "#94c3e0", high = "#b3d2e6", midpoint = median(datacon_top10$Mean)) + 
        labs(title = glue ("Top 10 Country {input$Category}"),
             subtitle = "Top 10 Energy Consumtion",
             caption = "Source: Energdata.info",
             x = NULL, y = NULL) +
        theme(legend.position = "bottom", 
              plot.title = element_text(size = 15, hjust = 0.5),
              plot.subtitle = element_text(size = 8, hjust = 0.5))
#--------------------ggplotly plot2 ---------------------
      ggplotly(plot2, tooltip = "text")


    })
    

    
#--------------------Agg plot3----------
    output$plot3 <- renderPlotly({
      
      dataconpro <- data2 %>%
        filter(!Category %in% c("Average emission factor","Coal and lignite production", "Natural gas production", "Refined oil products production", "Share of electricity in total final energy consumption", "Share of renewables in electricity production", "Share of wind and solar in electricity production", "Average emission factor")) %>% 
        group_by(Category, Year) %>%
        summarize(total_values = round(mean(values), digits = 2)) %>%
        arrange(Year,Category)
      #----------------- Mutate plot1 ----------------------
      dataconpro <- dataconpro %>% 
        mutate(labeling1 = glue ("Category : {Category}
                          Count : {total_values}
                          Year : {Year}"))
      #--------------------ggplot --------------------
      custom_colors <- c("Total energy consumption" = "#3885bb",  # Blue
                         "Total energy production" = "#bdd7e7")  # Grey
      
      # Create the plot with manual colors and points
      plot3 <- ggplot(dataconpro, aes(x = Year, y = total_values, color = Category, group = Category)) +
        geom_line(size = 1, alpha = 0.9, linetype = 1) +
        geom_point(aes(text = labeling1), size = 2 ) + 
        scale_color_manual(values = custom_colors) +# Add points for each line
        theme_minimal() +
        labs(title = "Global Energy Overtime",
             subtitle = "Production | Consumtion",
             caption = "Source: Energdata.info",
             x = NULL, y = NULL) +
        theme(legend.position = "top", 
              plot.title = element_text(size = 15, hjust = 0.5),
              plot.subtitle = element_text(size = 8, hjust = 0.5)) +
        scale_fill_brewer(palette = "Blues") 
      #--------------------ggplotly plot2 ---------------------
      ggplotly(plot3, tooltip = "text")
      
      
    })
#--------------------Agg plot4----------
    output$plot4 <- renderPlotly({
      
      datacatall <- data2 %>%
        filter(Category == input$Category) %>% 
        group_by(Country) %>% 
        summarise(Mean = round(mean(values), digits = 2)) %>% 
        ungroup() %>% 
        arrange(desc(Mean))
      #------------------ read ------------------------#
      shapefile_path <- "C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/data_input/world_shape_file/TM_WORLD_BORDERS_SIMPL-0.3.shp"
      world <- st_read(shapefile_path)
      #------------------ join ------------------------#
      map_data <- left_join(world, datacatall, by = c("NAME" = "Country"))
      #----------------- Mutate plot1 ----------------------
      map_data <- map_data %>% 
        mutate(Country = glue ("{NAME}"))
      #--------------------ggplot --------------------
      breaks <- pretty_breaks(n = 6)(map_data$Mean)
      plot4 <-
        ggplot(map_data) +
        geom_sf(aes(label = Country, fill = Mean)) +
        scale_fill_gradient2(name = NULL,
                             low = "#ffffcc", mid = "#bdd7e7", high = "#08519c",  
                             breaks = breaks) +
        labs(title = glue ("Global Energy | {input$Category}"),
             subtitle = NULL,
             caption = "Source: Energdata.info") +                          
        theme_minimal() +
        theme(
          # Legend
          legend.position = "top",                       
          legend.justification = 0.5,                   
          legend.key.size = unit(0.5, "cm"),            
          legend.key.width = unit(1, "cm"),           
          legend.text = element_text(size = 8),         
          legend.margin = margin(),                      
          # Judul dan Subjudul
          plot.title = element_text(size = 20, hjust = 0.5),  
          plot.subtitle = element_text(size = 8, hjust = 0.5)                
        )
      
      
    })
#--------------------Data Table ----------
output$data <- renderDataTable(
    data2,
    options = list(scrollx = T,
                  sccrolly = T))

}


#------------------ Console Log -----------
R version 4.3.1 (2023-06-16 ucrt) -- "Beagle Scouts"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

[Workspace loaded from C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/.RData]

> shiny::runApp()
Loading required package: shiny
Warning: package ‘shiny’ was built under R version 4.3.3
Warning: package ‘shinydashboard’ was built under R version 4.3.3

Attaching package: ‘shinydashboard’

The following object is masked from ‘package:graphics’:

    box

Warning: package ‘DT’ was built under R version 4.3.3

Attaching package: ‘DT’

The following objects are masked from ‘package:shiny’:

    dataTableOutput, renderDataTable

Warning: package ‘dplyr’ was built under R version 4.3.3

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

Warning: package ‘lubridate’ was built under R version 4.3.3

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union

Warning: package ‘ggplot2’ was built under R version 4.3.3
Warning: package ‘plotly’ was built under R version 4.3.3

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

Warning: package ‘glue’ was built under R version 4.3.3
Warning: package ‘scales’ was built under R version 4.3.3
Warning: package ‘ggpubr’ was built under R version 4.3.3
Warning: package ‘tidyr’ was built under R version 4.3.3
Warning: package ‘treemapify’ was built under R version 4.3.3
Warning: package ‘sf’ was built under R version 4.3.3
Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE
Warning: package ‘hrbrthemes’ was built under R version 4.3.3
Warning: package ‘viridis’ was built under R version 4.3.3
Loading required package: viridisLite
Warning: package ‘viridisLite’ was built under R version 4.3.3

Attaching package: ‘viridis’

The following object is masked from ‘package:scales’:

    viridis_pal

Warning: package ‘lme4’ was built under R version 4.3.3
Loading required package: Matrix

Attaching package: ‘Matrix’

The following objects are masked from ‘package:tidyr’:

    expand, pack, unpack

Warning in check_dep_version() :
  ABI version mismatch: 
lme4 was built with Matrix ABI version 1
Current Matrix ABI version is 0
Please re-install lme4 from source or restore original ‘Matrix’ package
Warning: package ‘MatrixModels’ was built under R version 4.3.3
Error: package or namespace load failed for ‘MatrixModels’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]):
 namespace ‘Matrix’ 1.5-4.1 is already loaded, but >= 1.6.0 is required
> runApp()
Warning: package ‘MatrixModels’ was built under R version 4.3.3
Error: package or namespace load failed for ‘MatrixModels’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]):
 namespace ‘Matrix’ 1.5-4.1 is already loaded, but >= 1.6.0 is required
> runApp()
Removing package from ‘C:/Dimas/Docs/Me/Algoritma/App/R-4.3.1/R-4.3.1/library’
(as ‘lib’ is unspecified)
Error in install.packages : Updating loaded packages
Removing package from ‘C:/Dimas/Docs/Me/Algoritma/App/R-4.3.1/R-4.3.1/library’
(as ‘lib’ is unspecified)

Restarting R session...

> install.packages("Matrix")
Warning in install.packages :
  package ‘Matrix’ is not available for this version of R

A version of this package for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
> shiny::runApp()
Loading required package: shiny
Warning: package ‘shiny’ was built under R version 4.3.3
Warning: package ‘shinydashboard’ was built under R version 4.3.3

Attaching package: ‘shinydashboard’

The following object is masked from ‘package:graphics’:

    box

Warning: package ‘DT’ was built under R version 4.3.3

Attaching package: ‘DT’

The following objects are masked from ‘package:shiny’:

    dataTableOutput, renderDataTable

Warning: package ‘dplyr’ was built under R version 4.3.3

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

Warning: package ‘lubridate’ was built under R version 4.3.3

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union

Warning: package ‘ggplot2’ was built under R version 4.3.3
Warning: package ‘plotly’ was built under R version 4.3.3

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

Warning: package ‘glue’ was built under R version 4.3.3
Warning: package ‘scales’ was built under R version 4.3.3
Warning: package ‘ggpubr’ was built under R version 4.3.3
Warning: package ‘tidyr’ was built under R version 4.3.3
Warning: package ‘treemapify’ was built under R version 4.3.3
Warning: package ‘sf’ was built under R version 4.3.3
Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE
Warning: package ‘hrbrthemes’ was built under R version 4.3.3
Warning: package ‘viridis’ was built under R version 4.3.3
Loading required package: viridisLite
Warning: package ‘viridisLite’ was built under R version 4.3.3

Attaching package: ‘viridis’

The following object is masked from ‘package:scales’:

    viridis_pal

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `values = as.numeric(values)`.
Caused by warning:
! NAs introduced by coercion
`summarise()` has grouped output by 'Country', 'Category'. You can override using the `.groups` argument.

Listening on http://127.0.0.1:6097
`summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
> #------------------ read ------------------------#
> shapefile_path <- "C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/data_input/world_shape_file/TM_WORLD_BORDERS_SIMPL-0.3.shp"
> world <- st_read(shapefile_path)
Reading layer `TM_WORLD_BORDERS_SIMPL-0.3' from data source 
  `C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\world_shape_file\TM_WORLD_BORDERS_SIMPL-0.3.shp' using driver `ESRI Shapefile'
Simple feature collection with 246 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.57027
Geodetic CRS:  WGS 84
> #------------------ join ------------------------#
> map_data <- left_join(world, datacatall, by = c("NAME" = "Country"))
Error: object 'datacatall' not found
> datacatall1 <- data5 %>%
+   filter(Category == "Total energy consumption") %>% 
+   group_by(Country) %>% 
+   summarise(Mean = round(mean(values), digits = 2)) %>% 
+   ungroup() %>% 
+   arrange(desc(Mean))# Mengurutkan berdasarkan Mean dari yang terbesar ke terkecil
Error: object 'data5' not found
> datacatall1 <- data2 %>%
+   filter(Category == "Total energy consumption") %>% 
+   group_by(Country) %>% 
+   summarise(Mean = round(mean(values), digits = 2)) %>% 
+   ungroup() %>% 
+   arrange(desc(Mean))# Mengurutkan berdasarkan Mean dari yang terbesar ke terkecil
> 
> datacatall1
> #------------------ read ------------------------#
> shapefile_path <- "C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/data_input/world_shape_file/TM_WORLD_BORDERS_SIMPL-0.3.shp"
> world <- st_read(shapefile_path)
Reading layer `TM_WORLD_BORDERS_SIMPL-0.3' from data source 
  `C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\world_shape_file\TM_WORLD_BORDERS_SIMPL-0.3.shp' using driver `ESRI Shapefile'
Simple feature collection with 246 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.57027
Geodetic CRS:  WGS 84
> #------------------ join ------------------------#
> map_data <- left_join(world, datacatall, by = c("NAME" = "Country"))
Error: object 'datacatall' not found
> #------------------ read ------------------------#
> shapefile_path <- "C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/data_input/world_shape_file/TM_WORLD_BORDERS_SIMPL-0.3.shp"
> world <- st_read(shapefile_path)
Reading layer `TM_WORLD_BORDERS_SIMPL-0.3' from data source 
  `C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\world_shape_file\TM_WORLD_BORDERS_SIMPL-0.3.shp' using driver `ESRI Shapefile'
Simple feature collection with 246 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.57027
Geodetic CRS:  WGS 84
> #------------------ join ------------------------#
> map_data <- left_join(world, datacatall, by = c("NAME" = "Country"))
Error: object 'datacatall' not found
> datacatall <- data2 %>%
+   filter(Category == "Total energy consumption") %>% 
+   group_by(Country) %>% 
+   summarise(Mean = round(mean(values), digits = 2)) %>% 
+   ungroup() %>% 
+   arrange(desc(Mean))# Mengurutkan berdasarkan Mean dari yang terbesar ke terkecil
> 
> datacatall
> #------------------ read ------------------------#
> shapefile_path <- "C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/data_input/world_shape_file/TM_WORLD_BORDERS_SIMPL-0.3.shp"
> world <- st_read(shapefile_path)
Reading layer `TM_WORLD_BORDERS_SIMPL-0.3' from data source 
  `C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\world_shape_file\TM_WORLD_BORDERS_SIMPL-0.3.shp' using driver `ESRI Shapefile'
Simple feature collection with 246 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.57027
Geodetic CRS:  WGS 84
> #------------------ join ------------------------#
> map_data <- left_join(world, datacatall, by = c("NAME" = "Country"))
> map_data <- map_data %>% 
+   mutate(Country = glue ("{NAME}"))
> #------------------ plot ------------------------#
> breaks <- pretty_breaks(n = 6)(map_data$Mean)
> plot4 <-
+   ggplot(map_data) +
+   geom_sf(aes(label = Country, fill = Mean)) +
+   scale_fill_gradient2(name = NULL,
+                        low = "#ffffcc", mid = "#bdd7e7", high = "#08519c",  
+                        breaks = breaks) +
+   labs(title = "World Energy Consumtion",
+     subtitle = NULL,
+     caption = "Source: Energdata.info") +                          
+   theme_minimal() +
+   theme(
+     # Legend
+     legend.position = "top",                       
+     legend.justification = 0.5,                   
+     legend.key.size = unit(0.5, "cm"),            
+     legend.key.width = unit(1, "cm"),           
+     legend.text = element_text(size = 8),         
+     legend.margin = margin(),                      
+     # Judul dan Subjudul
+     plot.title = element_text(size = 20, hjust = 0.5),  
+     plot.subtitle = element_text(size = 8, hjust = 0.5)                
+   )
Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,  :
  Ignoring unknown aesthetics: label
> 
> ggplotly(plot4)
> #Data Preparation
> library(dplyr)
> library(lubridate)
> library(scales)
> library(tidyr)
> #visualisasi
> library(ggplot2)
> library(ggpubr)
> library(plotly)
> library(glue)
> library(treemapify)
> #Exporting Plot Statis
> library(ggpubr)
> library(sf)
> library(hrbrthemes)
> library(viridis)
> data <- read.csv("data_input/Energydata.csv") 
> data
> nrow(data1)
[1] 16929
> data <- data %>% 
+   #Filter
+   #Merubah bentuk ke long format
+   pivot_longer(cols = c(X1990 : X2022),
+                names_to = "Year",
+                values_to = "values")
> data
> data$Year <- substr(data$Year, 2,5)
> data
> data <- select(data, Country, Category, Year,values)
> data
> data <- data %>% 
+   mutate(
+     Category = as.character(Category),
+     values = as.numeric(values),
+     Year = ymd(paste0(Year, "-12-31"))
+   )
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `values = as.numeric(values)`.
Caused by warning:
! NAs introduced by coercion
> data
> # Menghitung rata-rata values berdasarkan Country, Category, dan Year
> average_values <- data %>%
+   ungroup() %>%  # Menghilangkan pengelompokan dari hasil summarize sebelumnya
+   group_by(Country, Category, Year) %>%
+   summarize(avg_value = mean(values, na.rm = TRUE))
`summarise()` has grouped output by 'Country', 'Category'. You can override using the `.groups` argument.
> 
> # Mengganti NA dengan rata-rata yang sesuai
> data1 <- data %>%
+   left_join(average_values, by = c("Country", "Category", "Year")) %>%
+   mutate(values = ifelse(is.na(values), avg_value, values)) %>%
+   select(-avg_value)  # Menghapus kolom avg_value yang tidak diperlukan lagi
> 
> # Menampilkan hasil
> data1
> exclude_countries <- c("World", "OECD", "Asia", "G7", "BRICS", "Europe", "European Union", "CIS", "America", "North America")
> 
> # Filter the dataset
> data2 <- data1 %>% 
+   filter(!Country %in% exclude_countries)
> data2
> glimpse(data4)
Error: object 'data4' not found
> data2 <- data2 %>%
+   mutate(
+     Country = case_when(
+       Country == "Czechia"        ~ "Czech Republic",
+       Country == "Turkiye"        ~ "Turkey",
+       Country == "North America"  ~ "United States Minor Outlying Islands",
+       Country == "Latin America"  ~ "Brazil",
+       Country == "South Korea"    ~ "Korea, Republic of",
+       Country == "Pacific"        ~ "Australia",
+       Country == "Africa"         ~ "South Africa",
+       Country == "Middle-East"    ~ "Iran",
+       Country == "Iran"           ~ "Iran (Islamic Republic of)",
+       TRUE                        ~ as.character(Country)
+     )
+   )
> #Data Energy Consumption Top 10 Countries
> datacon_top10 <- data2 %>%
+   filter(Category == "Total energy consumption") %>% 
+   group_by(Country, Category) %>% 
+   summarise(Mean = round(mean(values), digits = 2)) %>% 
+   ungroup() %>% arrange(-Mean) %>%  head(10)
`summarise()` has grouped output by 'Country'. You can override using the `.groups` argument.
> datacon_top10
> #Data Energy Production Top 10 Countries
> dataemis_top10 <- data2 %>%
+   filter(Category == "Total energy production") %>% 
+   group_by(Country) %>% 
+   summarise(mean_values = mean(values)) %>% 
+   ungroup() %>% 
+   arrange(-mean_values) %>% head(10)
> dataemis_top10
> #Total Data Categories Per Year Per Country
> datacatall <- data5 %>%
+   filter(Category == "Total energy production") %>% 
+   group_by(Country) %>% 
+   summarise(Mean = round(mean(values), digits = 2)) %>% 
+   ungroup() %>% 
+   arrange(-Mean) 
Error: object 'data5' not found
> #Total Data Categories Per Year Per Country
> datacatall <- data2 %>%
+   filter(Category == "Total energy production") %>% 
+   group_by(Country) %>% 
+   summarise(Mean = round(mean(values), digits = 2)) %>% 
+   ungroup() %>% 
+   arrange(-Mean) 
> datacatall
> #Data Mean Consumtion vs Production Per Year
> dataconpro <- data5 %>%
+   filter(!Category %in% c("Average emission factor","Coal and lignite production", "Natural gas production", "Refined oil products production", "Share of electricity in total final energy consumption", "Share of renewables in electricity production", "Share of wind and solar in electricity production", "Average emission factor")) %>% 
+   group_by(Year,Category) %>%
+   summarize(total_values = round(mean(values), digits = 2)) %>%
+   arrange(Year,Category)  # Opsional: mengurutkan hasil berdasarkan Year dan Category
Error: object 'data5' not found
> #Data Preparation
> library(dplyr)
> library(lubridate)
> library(scales)
> library(tidyr)
> #visualisasi
> library(ggplot2)
> library(ggpubr)
> library(plotly)
> library(glue)
> library(treemapify)
> #Exporting Plot Statis
> library(ggpubr)
> library(sf)
> library(hrbrthemes)
> library(viridis)
> data <- read.csv("data_input/Energydata.csv") 
> data
> nrow(data1)
[1] 16929
> data <- data %>% 
+   #Filter
+   #Merubah bentuk ke long format
+   pivot_longer(cols = c(X1990 : X2022),
+                names_to = "Year",
+                values_to = "values")
> data
> data$Year <- substr(data$Year, 2,5)
> data
> data <- select(data, Country, Category, Year,values)
> data
> data <- data %>% 
+   mutate(
+     Category = as.character(Category),
+     values = as.numeric(values),
+     Year = ymd(paste0(Year, "-12-31"))
+   )
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `values = as.numeric(values)`.
Caused by warning:
! NAs introduced by coercion
> data
> # Menghitung rata-rata values berdasarkan Country, Category, dan Year
> average_values <- data %>%
+   ungroup() %>%  # Menghilangkan pengelompokan dari hasil summarize sebelumnya
+   group_by(Country, Category, Year) %>%
+   summarize(avg_value = mean(values, na.rm = TRUE))
`summarise()` has grouped output by 'Country', 'Category'. You can override using the `.groups` argument.
> 
> # Mengganti NA dengan rata-rata yang sesuai
> data1 <- data %>%
+   left_join(average_values, by = c("Country", "Category", "Year")) %>%
+   mutate(values = ifelse(is.na(values), avg_value, values)) %>%
+   select(-avg_value)  # Menghapus kolom avg_value yang tidak diperlukan lagi
> 
> # Menampilkan hasil
> data1
> exclude_countries <- c("World", "OECD", "Asia", "G7", "BRICS", "Europe", "European Union", "CIS", "America", "North America")
> 
> # Filter the dataset
> data2 <- data1 %>% 
+   filter(!Country %in% exclude_countries)
> data2
> data2 <- data2 %>%
+   mutate(
+     Country = case_when(
+       Country == "Czechia"        ~ "Czech Republic",
+       Country == "Turkiye"        ~ "Turkey",
+       Country == "North America"  ~ "United States Minor Outlying Islands",
+       Country == "Latin America"  ~ "Brazil",
+       Country == "South Korea"    ~ "Korea, Republic of",
+       Country == "Pacific"        ~ "Australia",
+       Country == "Africa"         ~ "South Africa",
+       Country == "Middle-East"    ~ "Iran",
+       Country == "Iran"           ~ "Iran (Islamic Republic of)",
+       TRUE                        ~ as.character(Country)
+     )
+   )
> #Data Energy Consumption Top 10 Countries
> datacon_top10 <- data2 %>%
+   filter(Category == "Total energy consumption") %>% 
+   group_by(Country, Category) %>% 
+   summarise(Mean = round(mean(values), digits = 2)) %>% 
+   ungroup() %>% arrange(-Mean) %>%  head(10)
`summarise()` has grouped output by 'Country'. You can override using the `.groups` argument.
> datacon_top10
> #Data Energy Production Top 10 Countries
> dataemis_top10 <- data2 %>%
+   filter(Category == "Total energy production") %>% 
+   group_by(Country) %>% 
+   summarise(mean_values = mean(values)) %>% 
+   ungroup() %>% 
+   arrange(-mean_values) %>% head(10)
> dataemis_top10
> #Total Data Categories Per Year Per Country
> datacatall <- data2 %>%
+   filter(Category == "Total energy production") %>% 
+   group_by(Country) %>% 
+   summarise(Mean = round(mean(values), digits = 2)) %>% 
+   ungroup() %>% 
+   arrange(-Mean) 
> datacatall
> #Data Mean Consumtion vs Production Per Year
> dataconpro <- data2 %>%
+   filter(!Category %in% c("Average emission factor","Coal and lignite production", "Natural gas production", "Refined oil products production", "Share of electricity in total final energy consumption", "Share of renewables in electricity production", "Share of wind and solar in electricity production", "Average emission factor")) %>% 
+   group_by(Year,Category) %>%
+   summarize(total_values = round(mean(values), digits = 2)) %>%
+   arrange(Year,Category)  # Opsional: mengurutkan hasil berdasarkan Year dan Category
`summarise()` has grouped output by 'Year'. You can override using the `.groups` argument.
> 
> # Menampilkan hasil
> dataconpro
> #Data Mean Consumtion vs Production Per Year Per Country
> dataconpro <- data2 %>%
+   filter(!Category %in% c("Average emission factor","Coal and lignite production", "Natural gas production", "Refined oil products production", "Share of electricity in total final energy consumption", "Share of renewables in electricity production", "Share of wind and solar in electricity production", "Average emission factor")) %>% 
+   group_by(Category, Year) %>%
+   summarize(total_values = round(mean(values), digits = 2)) %>%
+   arrange(Year,Category)  # Opsional: mengurutkan hasil berdasarkan Year dan Category
`summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
> 
> # Menampilkan hasil
> dataconpro
> unique(datakom$Category)
Error: object 'datakom' not found
> datacon_top10 <- datacon_top10 %>% 
+   mutate(labeling = glue ("Category : {Category}
+                           Count : {Mean}"))
> datacon_top10
> datacon_top10 <- datacon_top10 %>% 
+   mutate(labeling = glue ("Category : {Category}
+                           Count : {Mean}"))
> datacon_top10
> 
> plot1 <- ggplot(datacon_top10, aes(x = Mean, y = reorder(Country, Mean),text = labeling)) +
+   geom_col(aes(fill = Mean)) + geom_hline(yintercept = 0) +
+   scale_fill_gradient2(low = "#02293b", mid = "#94c3e0", high = "#b3d2e6", midpoint = median(datacon_top10$Mean)) + 
+   labs(title = "Global Energy Overtime",
+        subtitle = "Top 10 Energy Consumtion",
+        caption = "Source: Energdata.info",
+        x = NULL, y = NULL) +
+   theme(legend.position = "bottom", 
+         plot.title = element_text(size = 15, hjust = 0.5),
+         plot.subtitle = element_text(size = 8, hjust = 0.5))
> plot1
> ggplotly(plot1, tooltip = "text")
> dataconpro <- dataconpro %>% 
+   mutate(labeling1 = glue ("Category : {Category}
+                           Count : {total_values}
+                           Year : {Year}"))
> # Define custom colors
> custom_colors <- c("Total energy consumption" = "#3885bb",  # Blue
+                    "Total energy production" = "#bdd7e7")  # Grey
> 
> # Create the plot with manual colors and points
> plot2 <- ggplot(dataconpro, aes(x = Year, y = total_values, color = Category, group = Category)) +
+   geom_line(size = 1, alpha = 0.9, linetype = 1) +
+   geom_point(aes(text = labeling1), size = 2 ) + 
+   scale_color_manual(values = custom_colors) +# Add points for each line
+   theme_minimal() +
+ labs(title = "Global Energy Overtime",
+        subtitle = "Production | Consumtion",
+        caption = "Source: Energdata.info",
+        x = NULL, y = NULL) +
+   theme(legend.position = "top", 
+         plot.title = element_text(size = 15, hjust = 0.5),
+         plot.subtitle = element_text(size = 8, hjust = 0.5)) +
+   scale_fill_brewer(palette = "Blues") 
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 in geom_point(aes(text = labeling1), size = 2) :
  Ignoring unknown aesthetics: text
> ggplotly(plot2, tooltip = "text")
> ggplotly(plot2, tooltip = "text")
> 
> # Library
> # library(streamgraph)
>  
> # # Create data:
> # data <- data.frame(
> #   year=rep(seq(1990,2016) , each=10),
> #   name=rep(letters[1:10] , 27),
> #   value=sample( seq(0,1,0.0001) , 270)
> # )
> #  
> # # Stream graph with a legend
> # pp <- streamgraph(data, key="name", value="value", date="year", height="300px", width="1000px") %>%
> #   sg_legend(show=TRUE, label="names: ")
> #Data Mean Comodity Vs Consumtion Per Year
> datakom <- data2 %>%
+   filter(!Category %in% c("Average emission factor", "Share of electricity in total final energy consumption", "Average emission factor", "Total energy production")) %>% 
+   group_by(Category, Year) %>%
+   summarize(total_values = mean(values, na.rm = TRUE)) %>%
+   arrange(Category)  # Opsional: mengurutkan hasil berdasarkan Year dan Category
`summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
> 
> # Menampilkan hasil
> datakom
> datakom <- datakom %>% 
+   mutate(labeling = glue ("Category : {Category}
+                           Count : {total_values}"))
> # print(head(datakom_filtered))
> 
> # Buat plot menggunakan ggplot2
> plot3 <- ggplot(datakom, aes(x = Year, y = total_values, fill = Category)) +
+   geom_area() + 
+   scale_x_date(date_breaks = "5 years", date_labels = "%Y") +
+   theme_classic() +
+   labs(title = "Global Energy Overtime",
+        subtitle = "Energy Production Data",
+        caption = "Source: Energdata.info",
+        x = "Year", y = NULL) +
+   theme(legend.position = "top", 
+         plot.title = element_text(size = 15, hjust = 0.5),
+         plot.subtitle = element_text(size = 8, hjust = 0.5)) +
+   scale_fill_brewer(palette = "Blues")
> # Menampilkan plot
> ggplotly(plot3)
> datacatall <- data2 %>%
+   filter(Category == "Total energy consumption") %>% 
+   group_by(Country) %>% 
+   summarise(Mean = round(mean(values), digits = 2)) %>% 
+   ungroup() %>% 
+   arrange(desc(Mean))# Mengurutkan berdasarkan Mean dari yang terbesar ke terkecil
> 
> datacatall
> #------------------ read ------------------------#
> shapefile_path <- "C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/data_input/world_shape_file/TM_WORLD_BORDERS_SIMPL-0.3.shp"
> world <- st_read(shapefile_path)
Reading layer `TM_WORLD_BORDERS_SIMPL-0.3' from data source 
  `C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\world_shape_file\TM_WORLD_BORDERS_SIMPL-0.3.shp' using driver `ESRI Shapefile'
Simple feature collection with 246 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.57027
Geodetic CRS:  WGS 84
> #------------------ join ------------------------#
> map_data <- left_join(world, datacatall, by = c("NAME" = "Country"))
> map_data <- map_data %>% 
+   mutate(Country = glue ("{NAME}"))
> #------------------ plot ------------------------#
> breaks <- pretty_breaks(n = 6)(map_data$Mean)
> plot4 <-
+   ggplot(map_data) +
+   geom_sf(aes(label = Country, fill = Mean)) +
+   scale_fill_gradient2(name = NULL,
+                        low = "#ffffcc", mid = "#bdd7e7", high = "#08519c",  
+                        breaks = breaks) +
+   labs(title = "World Energy Consumtion",
+     subtitle = NULL,
+     caption = "Source: Energdata.info") +                          
+   theme_minimal() +
+   theme(
+     # Legend
+     legend.position = "top",                       
+     legend.justification = 0.5,                   
+     legend.key.size = unit(0.5, "cm"),            
+     legend.key.width = unit(1, "cm"),           
+     legend.text = element_text(size = 8),         
+     legend.margin = margin(),                      
+     # Judul dan Subjudul
+     plot.title = element_text(size = 20, hjust = 0.5),  
+     plot.subtitle = element_text(size = 8, hjust = 0.5)                
+   )
Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,  :
  Ignoring unknown aesthetics: label
> 
> ggplotly(plot4)
> plot5 <- ggplot(map_data) +
+   geom_sf(aes(fill = Mean, label = NAME), lwd = 0.5, color = "white") +
+   scale_fill_gradient2(name = NULL,
+                        low = "#ffffcc", mid = "#bdd7e7", high = "#08519c",  # Warna rendah dan tinggi
+                        breaks = breaks) +            # Titik-titik breaks
+   labs(title = "Global Energy Overtime",
+     subtitle = "World Energy Consumtion 1990 - 2022",
+     caption = "Source: Energdata.info") +                          
+   theme_minimal() +                                # Tema minimal
+   theme(
+     # Legend
+     legend.position = "top",                       # Posisi legenda di atas
+     legend.justification = 0.5,                    # Posisi legenda di tengah
+     legend.key.size = unit(0.3, "cm"),            # Ukuran kotak kunci legenda
+     legend.key.width = unit(1, "cm"),           # Lebar kotak kunci legenda
+     legend.text = element_text(size = 5),         # Ukuran teks legenda
+     legend.margin = margin(),                      # Margin legenda
+     # Judul dan Subjudul
+     plot.title = element_text(size = 20, hjust = 0.5),  # Ukuran dan posisi horizontal judul
+     plot.subtitle = element_text(size = 8, hjust = 0.5)                # Menghapus subjudul
+   )
Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,  :
  Ignoring unknown aesthetics: label
> plot5
> runApp()
Warning in install.packages :
  package ‘Matrix’ is not available for this version of R

A version of this package for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
Warning in install.packages :
  dependency ‘Matrix’ is not available
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.3/MatrixModels_0.5-3.zip'
Content type 'application/zip' length 414579 bytes (404 KB)
downloaded 404 KB

package ‘MatrixModels’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
	C:\Users\sendd\AppData\Local\Temp\RtmpItCcci\downloaded_packages
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `values = as.numeric(values)`.
Caused by warning:
! NAs introduced by coercion
`summarise()` has grouped output by 'Country', 'Category'. You can override using the `.groups` argument.

Listening on http://127.0.0.1:6097
`summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
> #------------------ read ------------------------#
> shapefile_path <- "C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/data_input/TM_WORLD_BORDERS_SIMPL-0.3.shp"
> world <- st_read(shapefile_path)
Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet,  :
  GDAL Error 4: Unable to open C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\TM_WORLD_BORDERS_SIMPL-0.3.shx or C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\TM_WORLD_BORDERS_SIMPL-0.3.SHX. Set SHAPE_RESTORE_SHX config option to YES to restore or create it.
Error: Cannot open "C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\TM_WORLD_BORDERS_SIMPL-0.3.shp"; The source could be corrupt or not supported. See `st_drivers()` for a list of supported formats.
> #------------------ read ------------------------#
> shapefile_path <- "C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/data_input/TM_WORLD_BORDERS_SIMPL-0.3.shp"
> world <- st_read(shapefile_path)
Warning in CPL_read_ogr(dsn, layer, query, as.character(options), quiet,  :
  GDAL Error 4: Unable to open C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\TM_WORLD_BORDERS_SIMPL-0.3.shx or C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\TM_WORLD_BORDERS_SIMPL-0.3.SHX. Set SHAPE_RESTORE_SHX config option to YES to restore or create it.
Error: Cannot open "C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\TM_WORLD_BORDERS_SIMPL-0.3.shp"; The source could be corrupt or not supported. See `st_drivers()` for a list of supported formats.
> #------------------ read ------------------------#
> shapefile_path <- "C:/Dimas/Docs/Me/Algoritma/Material/Capstone/DV/Global/data_input/world_shape_file/TM_WORLD_BORDERS_SIMPL-0.3.shp"
> world <- st_read(shapefile_path)
Reading layer `TM_WORLD_BORDERS_SIMPL-0.3' from data source 
  `C:\Dimas\Docs\Me\Algoritma\Material\Capstone\DV\Global\data_input\world_shape_file\TM_WORLD_BORDERS_SIMPL-0.3.shp' using driver `ESRI Shapefile'
Simple feature collection with 246 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.57027
Geodetic CRS:  WGS 84
> #------------------ join ------------------------#
> map_data <- left_join(world, datacatall, by = c("NAME" = "Country"))
> map_data <- map_data %>% 
+   mutate(Country = glue ("{NAME}"))
> #------------------ plot ------------------------#
> breaks <- pretty_breaks(n = 6)(map_data$Mean)
> plot4 <-
+   ggplot(map_data) +
+   geom_sf(aes(label = Country, fill = Mean)) +
+   scale_fill_gradient2(name = NULL,
+                        low = "#ffffcc", mid = "#bdd7e7", high = "#08519c",  
+                        breaks = breaks) +
+   labs(title = "World Energy Consumtion",
+     subtitle = NULL,
+     caption = "Source: Energdata.info") +                          
+   theme_minimal() +
+   theme(
+     # Legend
+     legend.position = "top",                       
+     legend.justification = 0.5,                   
+     legend.key.size = unit(0.5, "cm"),            
+     legend.key.width = unit(1, "cm"),           
+     legend.text = element_text(size = 8),         
+     legend.margin = margin(),                      
+     # Judul dan Subjudul
+     plot.title = element_text(size = 20, hjust = 0.5),  
+     plot.subtitle = element_text(size = 8, hjust = 0.5)                
+   )

#--------------------- Error Log Console ---------------------------
Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat, :
Ignoring unknown aesthetics: label

ggplotly(plot4)
runApp()
Warning in install.packages :
package ‘Matrix’ is not available for this version of R

A version of this package for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
Warning in install.packages :
dependency ‘Matrix’ is not available
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.3/MatrixModels_0.5-3.zip'
Content type 'application/zip' length 414579 bytes (404 KB)
downloaded 404 KB

package ‘MatrixModels’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
C:\Users\sendd\AppData\Local\Temp\RtmpItCcci\downloaded_packages
Warning: There was 1 warning in mutate().
ℹ In argument: values = as.numeric(values).
Caused by warning:
! NAs introduced by coercion
summarise() has grouped output by 'Country', 'Category'. You can override using the .groups argument.

Listening on http://127.0.0.1:6097
summarise() has grouped output by 'Category'. You can override using the .groups argument.

runApp()
Warning: There was 1 warning in mutate().
ℹ In argument: values = as.numeric(values).
Caused by warning:
! NAs introduced by coercion
summarise() has grouped output by 'Country', 'Category'. You can override using the .groups argument.

Listening on http://127.0.0.1:6097
summarise() has grouped output by 'Category'. You can override using the .groups argument.

runApp()
Warning: There was 1 warning in mutate().
ℹ In argument: values = as.numeric(values).
Caused by warning:
! NAs introduced by coercion
summarise() has grouped output by 'Country', 'Category'. You can override using the .groups argument.

Listening on http://127.0.0.1:6097
summarise() has grouped output by 'Category'. You can override using the .groups argument.``

#---------------------Dokumentasi-------------
![image](https://github.com/teamalgoritma/community/assets/141438958/a09bed14-81c9-4830-b435-c8b6aaac5ba6)
![image](https://github.com/teamalgoritma/community/assets/141438958/353c3550-da94-400f-b8a5-c53824991176)
![image](https://github.com/teamalgoritma/community/assets/141438958/a5436cc6-daef-4d3d-830e-734b5e42a185)
![image](https://github.com/teamalgoritma/community/assets/141438958/e54f497c-fb4e-4542-acec-b3fdc11f208f)

[ramdhinafinita]

Dear @senddimasadit
Terimakasih atas pertanyaannya. Berdasarkan error yang ada pada bagian bawah bisa dilihat bahwa packages matrix tidak sesuai dengan R yang digunakan.

Anda bisa coba untuk install ulang packages tersebut. Karena jika bisa di run app lokal, maka seharusnya bukan error dari shapefile, pada error log tidak menyebutkan error dari shp nya melainkan packages.
Untuk terkait geom_sf anda bisa merujuk link referensi berikut: https://r-charts.com/spatial/maps-ggplot2/
Jika masih terkendala error, silahkan zip project capstone anda dan kirimkan ke email [email protected]. Terimakasih

[dimasrepo]

Dear @ramdhinafinita
sudah di coba uninstall/install dan belum bisa di deploy juga.
sudah di email ke [email protected]
mohon bantuannya, terimakasih