[R 데이터분석 with 샤이니] 커피 전문점 접근성 분석 (Shiny)

> ## 12-4 커피 전문점 접근성 분석하기
> 
> # Step 1 : 데이터 준비하기
> 
> setwd(dirname(rstudioapi::getSourceEditorContext()$path))
> load("./01_code/coffee/coffee_shop.rdata")
> head(coffee_shop)
Simple feature collection with 6 features and 7 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 126.9136 ymin: 37.44907 xmax: 127.0142 ymax: 37.58296
CRS:           +proj=longlat +datum=WGS84 +no_defs
         brand          name                             juso        x        y    station metro_idx
1   이디야커피      신길역점 서울특별시 영등포구 영등포로 353 126.9181 37.51512       신길      51.5
2   이디야커피      라이프점      서울특별시 영등포구 63로 40 126.9392 37.51954   여의나루      44.3
3     스타벅스  동숭로아트점     서울특별시 종로구 동숭길 110 127.0039 37.58296       혜화      69.3
4     스타벅스 남부터미널2점     서울특별시 서초구 효령로 274 127.0142 37.48439 남부터미널      64.0
5   이디야커피        시흥점     서울특별시 금천구 금하로 750 126.9136 37.44907       신림      45.0
6 투썸플레이스    서울타워점 서울특별시 용산구 남산공원길 105 126.9879 37.55111       명동      59.4
                   geometry
1 POINT (126.9181 37.51512)
2 POINT (126.9392 37.51954)
3 POINT (127.0039 37.58296)
4 POINT (127.0142 37.48439)
5 POINT (126.9136 37.44907)
6 POINT (126.9879 37.55111)
> 
> # Step 2 : 사용자 화면 구현하기
> 
> library(shiny)
> library(leaflet)
> library(leaflet.extras)
> library(dplyr)
> 
> ui <- bootstrapPage(
+   #---# 사용자 화면 페이지 스타일 설정
+   tags$style(type = "text/css", "html, body {width:100%;height:100%}"),
+   #---# 지도 생성
+   leafletOutput("map", width = "100%", height = "100%"),
+   #---# 메뉴 패널
+   absolutePanel(top = 10, right = 10,
+                 selectInput(
+                   inputId = "sel_brand",
+                   label = tags$span(style="color:black;", "프랜차이즈를 선택하시오"),
+                   choices = unique(coffee_shop$brand),
+                   selected = unique(coffee_shop$brand)[2]
+                 ),
+                 sliderInput(
+                   inputId = "range",
+                   label = tags$span(style="color:black;", "접근성 범위를 선택하시오"),
+                   min = 0, max = 100, value = c(60, 80), step = 10
+                 ),
+                 plotOutput("density", height = 230)
+   )
+ )
> 

> # Step 3 : 서버 구현하기
> 
> server <- function(input, output, session) {
+   #---# 반응식 1 : 브랜드 선택 + 접근성 범위
+   brand_sel <- reactive({
+     brand_sel <- subset(coffee_shop,
+                         brand == input$sel_brand &
+                           metro_idx >= input$range[1] & metro_idx <= input$range[2]
+     )
+   })
+   #---# 반응식 2 : 브랜드 선택
+   plot_sel <- reactive({
+     plot_sel <- subset(coffee_shop,
+                         brand == input$sel_brand
+     )
+   })
+   #---# 밀도 함수 출력
+   output$density <- renderPlot({
+     ggplot(data = with(density(plot_sel()$metro_idx), data.frame(x, y)),
+            mapping = aes(x = x, y = y)) +
+       geom_line() +
+       xlim(0, 100) +
+       xlab("접근성 지수") + ylab("빈도") +
+       geom_vline(xintercept = input$range[1], color = "red", size = 0.5) +
+       geom_vline(xintercept = input$range[2], color = "red", size = 0.5) +
+       theme(axis.text.x = element_blank(), axis.ticks.y = element_blank())
+   })
+   #---# 지도 출력
+   output$map <- renderLeaflet({
+     leaflet(brand_sel(), width = "100%", height = "100%") %>%
+       addTiles() %>%
+       setView(lng = 127.0381, lat = 37.59512, zoom = 11) %>%
+       addPulseMarkers(lng = ~x, lat = ~y,
+                       label = ~name,
+                       icon = makePulseIcon())
+   })
+ }
> 
> # Step 4 : 애플리케이션 실행하기
> 
> shinyApp(ui = ui, server = server)

Listening on http://127.0.0.1:5417

 

 

 

 

 

 

 

출처 : 김철민, ⌜공공데이터로 배우는 R 데이터분석 with 샤이니⌟, 이지스퍼블리싱, 2022