R 闪亮的过滤器地图按接近位置
R shiny filter map by proximity to location
我有一些邮政编码数据以及经度和纬度数据，我用这些数据来使用 Leaflet 包在英国绘制某些类型的犯罪。我正在尝试使用 Rshiny
来做到这一点
我设法绘制了这些犯罪地点。然而，我想要的是能够按半径过滤来自特定位置的所有犯罪。例如，如果我输入邮政编码，例如RG1 3YL，我将能够过滤用户能够 select 例如 5 英里、10 英里等半径范围内的所有犯罪
下面我包括了应用程序以及犯罪数据集的值
# Loads the Shiny and leaflet libraries.
library(shiny)
library(leaflet)
library(shinyjs)
# Saves the dataset
crime <- read.csv("crime.csv", header=TRUE, stringsAsFactors=FALSE)
ui <- fluidPage(shinyjs::useShinyjs(),
fluidPage(
# Give the page a title
titlePanel("Crime Map"),
mainPanel(leafletOutput("map")),
fluidRow(column(3,sliderInput("miles", "Miles from location",
min = 1, max = 100, value = 10,width='120px'))
)))
server<-function (input, output, session) {
output$map <- renderLeaflet({
leaflet(crime) %>%
addTiles()%>%
setView(lng = -1.525, lat = 55, zoom =5)%>%
addMarkers(lng=crime$Longitude, lat=crime$Latitude, popup="The birthplace of R")
})
}
shinyApp(ui=ui, server=server)
数据：
crime <-
structure(list(Postcode = c("WN1 3LU", "BL7 9YH", "BT36 7WE",
"B6 6BS", "BD20 6LJ", "OL3 7EX", "M18 8RX", "BD23 1LY", "FY7 8PU",
"M19 2FT", "FY4 2BL", "SK9 7QL", "HX4 8PQ", "LA12 7NP", "BL0 9UN",
"DL10 7DP", "OL11 5SS", "OL12 6EL", "AB43 9QW", "SL2 3UR", "B15 3JH",
"BB8 9HA", "B24 9LP", "B36 0JN", "BL7 0QG", "B43 5JG", "B23 5GJ",
"WS4 1LH", "B45 8DP", "B46 2LH", "B60 2AQ", "SK9 1BY", "B63 3RA",
"B67 7DJ", "B75 5HP", "B15 2ER", "BB8 0PH", "M33 4HZ", "B76 2TN",
"BD23 1HN", "HG3 4AN", "BB6 8HN", "BB1 1BQ", "BB1 1DR", "BB1 1DU",
"BB1 1DZ", "BB2 2NA", "BB1 1HN", "BB1 1LS", "BB1 1RU"), crime = c(2L,
7L, 1L, 2L, 7L, 1L, 1L, 10L, 3L, 1L, 3L, 1L, 1L, 1L, 5L, 3L,
2L, 4L, 1L, 1L, 1L, 6L, 2L, 3L, 8L, 4L, 3L, 3L, 2L, 1L, 4L, 3L,
2L, 1L, 1L, 2L, 7L, 3L, 2L, 6L, 1L, 16L, 1L, 2L, 2L, 1L, 5L,
3L, 6L, 3L), Latitude = c(53.546367, 53.613982, 54.666528, 52.504583,
53.874506, 53.53092, 53.464027, 53.966432, 53.905032, 53.443761,
53.791965, 53.312478, 53.687511, 54.198119, 53.626566, 54.407691,
53.626655, 53.630964, 57.682959, 51.555254, 52.464528, 53.861084,
52.519347, 52.500382, 53.647756, 52.543856, 52.542246, 52.602772,
52.385279, 52.521596, 52.328812, 53.324304, 52.45635, 52.491619,
52.592831, 52.462017, 53.857882, 53.40895, 52.558527, 53.962755,
54.038168, 53.820994, 53.745103, 53.74722, 53.747415, 53.745182,
53.740827, 53.745517, 53.74505, 53.740014), Longitude = c(-2.620909,
-2.406439, -5.981969, -1.89328, -1.91552, -2.009768, -2.161701,
-2.026868, -3.039515, -2.19589, -3.03992, -2.236681, -1.883202,
-3.113402, -2.323867, -1.717372, -2.212356, -2.157153, -2.011322,
-0.616795, -1.936503, -2.171532, -1.820899, -1.752919, -2.39153,
-1.939161, -1.848246, -1.952058, -2.003599, -1.639788, -2.055299,
-2.225203, -2.068454, -1.973917, -1.833944, -1.910421, -2.156804,
-2.348656, -1.789616, -2.008168, -1.763933, -2.457171, -2.47016,
-2.471184, -2.471854, -2.47057, -2.489655, -2.472272, -2.467627,
-2.470512)), row.names = c(NA, 50L), class = "data.frame")
以下是一些使用犯罪数据邮政编码的修改。
library(shiny)
library(leaflet)
library(shinyjs)
library(rgeos)
#crime <- read.csv("crime.csv", header=TRUE, stringsAsFactors=FALSE)
crime_df <-
structure(
list(
Postcode = c(
"WN1 3LU",
"BL7 9YH",
"BT36 7WE",
"B6 6BS",
"BD20 6LJ",
"OL3 7EX",
"M18 8RX",
"BD23 1LY",
"FY7 8PU",
"M19 2FT",
"FY4 2BL",
"SK9 7QL",
"HX4 8PQ",
"LA12 7NP",
"BL0 9UN",
"DL10 7DP",
"OL11 5SS",
"OL12 6EL",
"AB43 9QW",
"SL2 3UR",
"B15 3JH",
"BB8 9HA",
"B24 9LP",
"B36 0JN",
"BL7 0QG",
"B43 5JG",
"B23 5GJ",
"WS4 1LH",
"B45 8DP",
"B46 2LH",
"B60 2AQ",
"SK9 1BY",
"B63 3RA",
"B67 7DJ",
"B75 5HP",
"B15 2ER",
"BB8 0PH",
"M33 4HZ",
"B76 2TN",
"BD23 1HN",
"HG3 4AN",
"BB6 8HN",
"BB1 1BQ",
"BB1 1DR",
"BB1 1DU",
"BB1 1DZ",
"BB2 2NA",
"BB1 1HN",
"BB1 1LS",
"BB1 1RU"
),
crime = c(
2L,
7L,
1L,
2L,
7L,
1L,
1L,
10L,
3L,
1L,
3L,
1L,
1L,
1L,
5L,
3L,
2L,
4L,
1L,
1L,
1L,
6L,
2L,
3L,
8L,
4L,
3L,
3L,
2L,
1L,
4L,
3L,
2L,
1L,
1L,
2L,
7L,
3L,
2L,
6L,
1L,
16L,
1L,
2L,
2L,
1L,
5L,
3L,
6L,
3L
),
Latitude = c(
53.546367,
53.613982,
54.666528,
52.504583,
53.874506,
53.53092,
53.464027,
53.966432,
53.905032,
53.443761,
53.791965,
53.312478,
53.687511,
54.198119,
53.626566,
54.407691,
53.626655,
53.630964,
57.682959,
51.555254,
52.464528,
53.861084,
52.519347,
52.500382,
53.647756,
52.543856,
52.542246,
52.602772,
52.385279,
52.521596,
52.328812,
53.324304,
52.45635,
52.491619,
52.592831,
52.462017,
53.857882,
53.40895,
52.558527,
53.962755,
54.038168,
53.820994,
53.745103,
53.74722,
53.747415,
53.745182,
53.740827,
53.745517,
53.74505,
53.740014
),
Longitude = c(
-2.620909,-2.406439,
-5.981969,
-1.89328,
-1.91552,
-2.009768,
-2.161701,-2.026868,
-3.039515,
-2.19589,
-3.03992,
-2.236681,
-1.883202,-3.113402,
-2.323867,
-1.717372,
-2.212356,
-2.157153,
-2.011322,-0.616795,
-1.936503,
-2.171532,
-1.820899,
-1.752919,
-2.39153,-1.939161,
-1.848246,
-1.952058,
-2.003599,
-1.639788,
-2.055299,-2.225203,
-2.068454,
-1.973917,
-1.833944,
-1.910421,
-2.156804,-2.348656,
-1.789616,
-2.008168,
-1.763933,
-2.457171,
-2.47016,-2.471184,
-2.471854,
-2.47057,
-2.489655,
-2.472272,
-2.467627,-2.470512
)
),
row.names = c(NA, 50L),
class = "data.frame"
)
postcodes = sort(unique(crime_df$Postcode))
crime = crime_df
coordinates(crime) = ~Longitude+Latitude
proj4string(crime) = CRS("+init=epsg:4326")
crime <- spTransform(x = crime, CRS = CRS("+init=epsg:27700"))
ui <- fluidPage(shinyjs::useShinyjs(),
fluidPage(
# Give the page a title
titlePanel("Crime Map"),
mainPanel(leafletOutput("map")),
fluidRow(column(
3,
sliderInput(
"miles",
"Miles from location",
min = 1,
max = 100,
value = 10,
width = '120px'
),
selectInput("postcode", 'postcode', choices = postcodes, selected = postcodes[2])
))
))
server <- function (input, output, session) {
output$map <- renderLeaflet({
inside_df <- inside_df()
leaflet(crime_df) %>%
addTiles() %>%
setView(lng = -1.525,
lat = 55,
zoom = 5) %>%
addMarkers(
lng = inside_df$Longitude,
lat = inside_df$Latitude,
popup = inside_df$Postcode
)
})
circle <- reactive({
postcode <- input$postcode
location <- crime_df %>% dplyr::filter(Postcode == postcode) %>% dplyr::select(Latitude, Longitude)
coordinates(location) <- ~Longitude+Latitude
proj4string(location) = CRS("+init=epsg:4326")
location <- spTransform(location, CRS = CRS("+init=epsg:27700"))
circle <- gBuffer(location, width = input$miles * 1609.34)
circle
})
inside_df <- reactive({
circle = circle()
inside = crime[circle,] # find points inside the circle
inside = spTransform(inside, CRS("+init=epsg:4326"))
inside_df = as.data.frame(inside)
inside_df
})
}
shinyApp(ui = ui, server = server)
它将 crime 数据框转换为空间对象，并使用选定的邮政编码创建另一个空间对象。然后将此邮政编码对象转换为所需半径的圆，以创建可用于在其中查找点的空间多边形（直线为 inside = crime[circle,]）。我转换为适当的空间投影以允许准确使用英里（可以忽略警告）。 Leaflet 需要纬度和经度，所以我在制作数据框之前转换回 4326 投影。
我有一些邮政编码数据以及经度和纬度数据，我用这些数据来使用 Leaflet 包在英国绘制某些类型的犯罪。我正在尝试使用 Rshiny
来做到这一点我设法绘制了这些犯罪地点。然而，我想要的是能够按半径过滤来自特定位置的所有犯罪。例如，如果我输入邮政编码，例如RG1 3YL，我将能够过滤用户能够 select 例如 5 英里、10 英里等半径范围内的所有犯罪
下面我包括了应用程序以及犯罪数据集的值
# Loads the Shiny and leaflet libraries.
library(shiny)
library(leaflet)
library(shinyjs)
# Saves the dataset
crime <- read.csv("crime.csv", header=TRUE, stringsAsFactors=FALSE)
ui <- fluidPage(shinyjs::useShinyjs(),
fluidPage(
# Give the page a title
titlePanel("Crime Map"),
mainPanel(leafletOutput("map")),
fluidRow(column(3,sliderInput("miles", "Miles from location",
min = 1, max = 100, value = 10,width='120px'))
)))
server<-function (input, output, session) {
output$map <- renderLeaflet({
leaflet(crime) %>%
addTiles()%>%
setView(lng = -1.525, lat = 55, zoom =5)%>%
addMarkers(lng=crime$Longitude, lat=crime$Latitude, popup="The birthplace of R")
})
}
shinyApp(ui=ui, server=server)
数据：
crime <-
structure(list(Postcode = c("WN1 3LU", "BL7 9YH", "BT36 7WE",
"B6 6BS", "BD20 6LJ", "OL3 7EX", "M18 8RX", "BD23 1LY", "FY7 8PU",
"M19 2FT", "FY4 2BL", "SK9 7QL", "HX4 8PQ", "LA12 7NP", "BL0 9UN",
"DL10 7DP", "OL11 5SS", "OL12 6EL", "AB43 9QW", "SL2 3UR", "B15 3JH",
"BB8 9HA", "B24 9LP", "B36 0JN", "BL7 0QG", "B43 5JG", "B23 5GJ",
"WS4 1LH", "B45 8DP", "B46 2LH", "B60 2AQ", "SK9 1BY", "B63 3RA",
"B67 7DJ", "B75 5HP", "B15 2ER", "BB8 0PH", "M33 4HZ", "B76 2TN",
"BD23 1HN", "HG3 4AN", "BB6 8HN", "BB1 1BQ", "BB1 1DR", "BB1 1DU",
"BB1 1DZ", "BB2 2NA", "BB1 1HN", "BB1 1LS", "BB1 1RU"), crime = c(2L,
7L, 1L, 2L, 7L, 1L, 1L, 10L, 3L, 1L, 3L, 1L, 1L, 1L, 5L, 3L,
2L, 4L, 1L, 1L, 1L, 6L, 2L, 3L, 8L, 4L, 3L, 3L, 2L, 1L, 4L, 3L,
2L, 1L, 1L, 2L, 7L, 3L, 2L, 6L, 1L, 16L, 1L, 2L, 2L, 1L, 5L,
3L, 6L, 3L), Latitude = c(53.546367, 53.613982, 54.666528, 52.504583,
53.874506, 53.53092, 53.464027, 53.966432, 53.905032, 53.443761,
53.791965, 53.312478, 53.687511, 54.198119, 53.626566, 54.407691,
53.626655, 53.630964, 57.682959, 51.555254, 52.464528, 53.861084,
52.519347, 52.500382, 53.647756, 52.543856, 52.542246, 52.602772,
52.385279, 52.521596, 52.328812, 53.324304, 52.45635, 52.491619,
52.592831, 52.462017, 53.857882, 53.40895, 52.558527, 53.962755,
54.038168, 53.820994, 53.745103, 53.74722, 53.747415, 53.745182,
53.740827, 53.745517, 53.74505, 53.740014), Longitude = c(-2.620909,
-2.406439, -5.981969, -1.89328, -1.91552, -2.009768, -2.161701,
-2.026868, -3.039515, -2.19589, -3.03992, -2.236681, -1.883202,
-3.113402, -2.323867, -1.717372, -2.212356, -2.157153, -2.011322,
-0.616795, -1.936503, -2.171532, -1.820899, -1.752919, -2.39153,
-1.939161, -1.848246, -1.952058, -2.003599, -1.639788, -2.055299,
-2.225203, -2.068454, -1.973917, -1.833944, -1.910421, -2.156804,
-2.348656, -1.789616, -2.008168, -1.763933, -2.457171, -2.47016,
-2.471184, -2.471854, -2.47057, -2.489655, -2.472272, -2.467627,
-2.470512)), row.names = c(NA, 50L), class = "data.frame")
以下是一些使用犯罪数据邮政编码的修改。
library(shiny)
library(leaflet)
library(shinyjs)
library(rgeos)
#crime <- read.csv("crime.csv", header=TRUE, stringsAsFactors=FALSE)
crime_df <-
structure(
list(
Postcode = c(
"WN1 3LU",
"BL7 9YH",
"BT36 7WE",
"B6 6BS",
"BD20 6LJ",
"OL3 7EX",
"M18 8RX",
"BD23 1LY",
"FY7 8PU",
"M19 2FT",
"FY4 2BL",
"SK9 7QL",
"HX4 8PQ",
"LA12 7NP",
"BL0 9UN",
"DL10 7DP",
"OL11 5SS",
"OL12 6EL",
"AB43 9QW",
"SL2 3UR",
"B15 3JH",
"BB8 9HA",
"B24 9LP",
"B36 0JN",
"BL7 0QG",
"B43 5JG",
"B23 5GJ",
"WS4 1LH",
"B45 8DP",
"B46 2LH",
"B60 2AQ",
"SK9 1BY",
"B63 3RA",
"B67 7DJ",
"B75 5HP",
"B15 2ER",
"BB8 0PH",
"M33 4HZ",
"B76 2TN",
"BD23 1HN",
"HG3 4AN",
"BB6 8HN",
"BB1 1BQ",
"BB1 1DR",
"BB1 1DU",
"BB1 1DZ",
"BB2 2NA",
"BB1 1HN",
"BB1 1LS",
"BB1 1RU"
),
crime = c(
2L,
7L,
1L,
2L,
7L,
1L,
1L,
10L,
3L,
1L,
3L,
1L,
1L,
1L,
5L,
3L,
2L,
4L,
1L,
1L,
1L,
6L,
2L,
3L,
8L,
4L,
3L,
3L,
2L,
1L,
4L,
3L,
2L,
1L,
1L,
2L,
7L,
3L,
2L,
6L,
1L,
16L,
1L,
2L,
2L,
1L,
5L,
3L,
6L,
3L
),
Latitude = c(
53.546367,
53.613982,
54.666528,
52.504583,
53.874506,
53.53092,
53.464027,
53.966432,
53.905032,
53.443761,
53.791965,
53.312478,
53.687511,
54.198119,
53.626566,
54.407691,
53.626655,
53.630964,
57.682959,
51.555254,
52.464528,
53.861084,
52.519347,
52.500382,
53.647756,
52.543856,
52.542246,
52.602772,
52.385279,
52.521596,
52.328812,
53.324304,
52.45635,
52.491619,
52.592831,
52.462017,
53.857882,
53.40895,
52.558527,
53.962755,
54.038168,
53.820994,
53.745103,
53.74722,
53.747415,
53.745182,
53.740827,
53.745517,
53.74505,
53.740014
),
Longitude = c(
-2.620909,-2.406439,
-5.981969,
-1.89328,
-1.91552,
-2.009768,
-2.161701,-2.026868,
-3.039515,
-2.19589,
-3.03992,
-2.236681,
-1.883202,-3.113402,
-2.323867,
-1.717372,
-2.212356,
-2.157153,
-2.011322,-0.616795,
-1.936503,
-2.171532,
-1.820899,
-1.752919,
-2.39153,-1.939161,
-1.848246,
-1.952058,
-2.003599,
-1.639788,
-2.055299,-2.225203,
-2.068454,
-1.973917,
-1.833944,
-1.910421,
-2.156804,-2.348656,
-1.789616,
-2.008168,
-1.763933,
-2.457171,
-2.47016,-2.471184,
-2.471854,
-2.47057,
-2.489655,
-2.472272,
-2.467627,-2.470512
)
),
row.names = c(NA, 50L),
class = "data.frame"
)
postcodes = sort(unique(crime_df$Postcode))
crime = crime_df
coordinates(crime) = ~Longitude+Latitude
proj4string(crime) = CRS("+init=epsg:4326")
crime <- spTransform(x = crime, CRS = CRS("+init=epsg:27700"))
ui <- fluidPage(shinyjs::useShinyjs(),
fluidPage(
# Give the page a title
titlePanel("Crime Map"),
mainPanel(leafletOutput("map")),
fluidRow(column(
3,
sliderInput(
"miles",
"Miles from location",
min = 1,
max = 100,
value = 10,
width = '120px'
),
selectInput("postcode", 'postcode', choices = postcodes, selected = postcodes[2])
))
))
server <- function (input, output, session) {
output$map <- renderLeaflet({
inside_df <- inside_df()
leaflet(crime_df) %>%
addTiles() %>%
setView(lng = -1.525,
lat = 55,
zoom = 5) %>%
addMarkers(
lng = inside_df$Longitude,
lat = inside_df$Latitude,
popup = inside_df$Postcode
)
})
circle <- reactive({
postcode <- input$postcode
location <- crime_df %>% dplyr::filter(Postcode == postcode) %>% dplyr::select(Latitude, Longitude)
coordinates(location) <- ~Longitude+Latitude
proj4string(location) = CRS("+init=epsg:4326")
location <- spTransform(location, CRS = CRS("+init=epsg:27700"))
circle <- gBuffer(location, width = input$miles * 1609.34)
circle
})
inside_df <- reactive({
circle = circle()
inside = crime[circle,] # find points inside the circle
inside = spTransform(inside, CRS("+init=epsg:4326"))
inside_df = as.data.frame(inside)
inside_df
})
}
shinyApp(ui = ui, server = server)
它将 crime 数据框转换为空间对象，并使用选定的邮政编码创建另一个空间对象。然后将此邮政编码对象转换为所需半径的圆，以创建可用于在其中查找点的空间多边形（直线为 inside = crime[circle,]）。我转换为适当的空间投影以允许准确使用英里（可以忽略警告）。 Leaflet 需要纬度和经度，所以我在制作数据框之前转换回 4326 投影。