在R传单中包含风数据的交互式动态地图？

我有一些这样的数据：

     longitude  latitude          speed        direction
1    6.10722222 46.23639           4           360
2    6.95416667 43.54694           4           360
3    7.21472222 43.66556          13           330
4    4.01666667 48.32167           7           290
5    2.30833333 43.21611          14           290
6    2.48305556 44.40806          13           320
7    5.21500000 43.43694          19           330
8    4.92361111 43.52278          32           320
9    5.10805556 43.60306          26           330
10  -0.44972222 49.17389           6           290
11   0.16000000 49.36389           3             0
12   2.41666667 44.89750           5           290
13  -0.31361111 45.65583           8           310
14   0.21888889 45.72972           7            70
15  -1.19527778 46.17917          10           330
16   2.64250000 47.05722           4           270
17   1.48555556 45.03972           8           320
18   8.80250000 41.92417           5            60
19   9.09638889 41.50306           2             0
20   9.40527778 41.92667          15           350
21   9.48472222 42.55083          13            10
22   8.79277778 42.52056           9            50
23   5.09083333 47.26639           9           330
24  -3.47444444 48.75556           6           330
25  -2.85666667 48.53778           6           330
26   0.52055556 44.82444           4           280
27   4.96833333 44.91611          22           360
28   1.20666667 49.01833           6           280
29   1.37944444 48.05806           5           330
30  -3.81666667 48.60111           9           330
31  -4.42194444 48.44778          10           330
32  -4.16805556 47.97556          12           340
33  -4.15166667 48.53028          10           340
34  -4.44583333 48.28194          12           330
35   4.41611111 43.75778          14           360
36   1.36777778 43.63500          14           310
37  -0.71527778 44.82861           4           290
38  -1.13138889 44.53528           2           360
39   3.96111111 43.58333           5            30
40   3.35333333 43.32333          20           320
41  -2.08000000 48.58833          12           340
42  -1.73222222 48.07194           4           310
43   1.72250000 46.85889           3           310
44   0.72333333 47.43306           3           300
45   5.33222222 45.36333          13           360
46   5.42444444 47.04222           8           340
47  -1.06888889 43.68917           4           310
48  -0.50916667 43.91167           6           300
49   1.68916667 47.31750           0             0
50   4.29722222 45.53389           8           340
51  -1.60805556 47.15778           4           330
52  -2.15666667 47.31056           8           300
53   1.75944444 47.98944           4           320
54   0.59055556 44.17500           8           290
55  -0.31194444 47.56028           3            60
56  -1.47527778 49.65083           8           340
57   4.15666667 49.20861           3             0
58   4.20611111 48.77333           4           320
59   4.90805556 48.63389           4            30
60  -0.74277778 48.03222           3           330
61   6.22583333 48.69278           5           340
62   5.95500000 48.58389           4           310
63  -3.43944444 47.76083           4           250
64  -2.72805556 47.72194           4           310
65   6.24638889 48.97861           6           320
66   3.11083333 47.00417           3           280
67   3.08638889 50.56417           6           280
68   2.11277778 49.45500           4           310
69   2.51916667 49.25333           0             0
70   1.62750000 50.51472           5           330
71   3.16222222 45.78639           5           350
72  -1.53138889 43.46889          16           330
73  -0.41833333 43.38083           7           350
74   0.00000000 43.18694           7           340
75   2.86972222 42.74083          15           330
76   7.63416667 48.54222           7           320
77   7.52916667 47.59028           1             0
78   7.35916667 48.11028           3             0
79   5.08111111 45.72556          10           350
80   4.93861111 45.73028          10           350
81   6.35972222 47.78806           3             0
82   4.02111111 46.40639           8           310
83   0.20166667 47.94917           1             0
84   5.88166667 45.63806           2             0
85   6.09888889 45.92944           8           360
86   1.18388889 49.39222           5           290
87   0.08805556 49.53417           7            10
88   2.67027778 48.60667           3           300
89   2.11111111 48.74972           4           310
90   2.19166667 48.77417           4           360
91   2.69222222 49.97111           4           360
92   2.28916667 43.55694          13           330
93   6.14583333 43.09722           7           290
94   4.90194444 43.90694          14           290
95   4.85972222 44.14833          13           320
96  -1.38166667 46.70250          19           330
97   0.30666667 46.58750          32           320
98   1.18027778 45.86111          26           330
99   6.06666667 48.32528           6           290
100  2.43222222 48.94972           3             0
101  2.35944444 48.72556           5           290
102  2.54861111 49.01000           8           310
103  2.04083333 49.09667           7            70

我想在R leaflet包中显示风速数据（速度和方向），但是到目前为止还没有找到很多例子。

这次讨论非常有趣：简化R中不规则间隔的风数据的流线，但是如何在带有小叶的交互式地图上显示结果呢？

我想要这样的东西：http : //apps.socib.es/Leaflet.TimeDimension/examples/example3.html

我错过了数据集中的一些信息，例如CRS和时间戳；因此我创建了自己的数据集以提供可重复的示例。

这是创建交互式风向图的一种建议：一种是时间静态的，另一种是动态的：

1. 准备df包含地理参考数据的数据框（）：风速和风向。
2. df与辅助坐标互补，用于将风表示为箭头线。
3. 创建SpatialLinesDataFrame要在中使用的类的对象leaflet。
4. 生成风速和风向的交互式和静态地图。
5. 生成风速和风向的交互式动态地图（与R包集成shiny）。

请参见下面的注释代码和输出：

#------------------------------
#Step 1 - Prepare data frame (`df`) with georeferenced data: wind speed and wind direction.

#Sample data (n=12; data collected in 4 different days of December, 2016)

#Projected coordinates. CRS = EPSG3857 (http://spatialreference.org/ref/sr-org/7483/)
#Starting x and y coordinates (where wind data was observed).
start.x <- c(-5320000,-5316500,-5316020,-5316800,-5316050,-5320400,-5321800,-5320080,-5325000,-5320010,-5322165,-5320786) #longitude
start.y <- c(-2180000,-2185900,-2185300,-2184000,-2180700,-2180010,-2189000,-2187500,-2183030,-2184600,-2185025,-2182384) #latitute

#Wind variables (speed, direction and date)
w.speed <- c(10,75,93,40,23,8,65,45,29,54,35,28) #wind speed (km/h)
w.direction <- c(330,80,35,240,170,90,180,20,231,360,290,55) #wind azimuth angle (degrees)
w.date <- do.call("as.Date",
                 list(x = c("1-Dec-2016", "1-Dec-2016", "1-Dec-2016", "5-Dec-2016", "5-Dec-2016", "5-Dec-2016", "9-Dec-2016", "9-Dec-2016", "9-Dec-2016", "12-Dec-2016", "12-Dec-2016", "12-Dec-2016"),
                      format = "%d-%b-%Y")) #date of data collection (yyyy-mm-dd)
id <- c(1:length(start.x)) #id of sample data

#Dataframe with georeferenced wind data
df <- data.frame(id=id,start.x=start.x,start.y=start.y,w.speed=w.speed,w.direction=w.direction,w.date=w.date)
head(df,5)

#------------------------------
#Step 2 - Complement `df` with auxiliary coordinates for representing wind as arrowhead lines.

#Line parameters
line.length <- 1000 #length of polylines representing wind in the map (meters)
arrow.length <- 300 #lenght of arrowhead leg (meters)
arrow.angle <- 120 #angle of arrowhead leg (degrees azimuth)

#Generate data frame with auxiliary coordinates
end.xy.df <- data.frame(end.x=NA,end.y=NA,end.arrow.x=NA,end.arrow.y=NA)

for (i in c(1:nrow(df))){

#coordinates of end points for wind lines (the initial points are the ones where data was observed)
if (df$w.direction[i] <= 90) {
    end.x <- df$start.x[i] + (cos((90 - df$w.direction[i]) * 0.0174532925) * line.length)
} else if (df$w.direction[i] > 90 & df$w.direction[i] <= 180) {
    end.x <- df$start.x[i] + (cos((df$w.direction[i] - 90) * 0.0174532925) * line.length)
} else if (df$w.direction[i] > 180 & df$w.direction[i] <= 270) {
  end.x <- df$start.x[i] - (cos((270 - df$w.direction[i]) * 0.0174532925) * line.length)
} else {end.x <- df$start.x[i] - (cos((df$w.direction[i] - 270) * 0.0174532925) * line.length)}

if (df$w.direction[i] <= 90) {
    end.y <- df$start.y[i] + (sin((90 - df$w.direction[i]) * 0.0174532925) * line.length)
} else if (df$w.direction[i] > 90 & df$w.direction[i] <= 180) {
    end.y <- df$start.y[i] - (sin((df$w.direction[i] - 90) * 0.0174532925) * line.length)
} else if (df$w.direction[i] > 180 & df$w.direction[i] <= 270) {
    end.y <- df$start.y[i] - (sin((270 - df$w.direction[i]) * 0.0174532925) * line.length)
} else {end.y <- df$start.y[i] + (sin((df$w.direction[i] - 270) * 0.0174532925) * line.length)}

#coordinates of end points for arrowhead leg lines (the initial points are the previous end points)
end.arrow.x <- end.x + (cos((df$w.direction[i] + arrow.angle) * 0.0174532925) * arrow.length)
end.arrow.y <- end.y - (sin((df$w.direction[i] + arrow.angle) * 0.0174532925) * arrow.length)

end.xy.df <- rbind(end.xy.df,c(end.x,end.y,end.arrow.x,end.arrow.y)) 
}

end.xy <- end.xy.df[-1,]
df <- data.frame(df,end.xy) #df with observed and auxiliary variables
head(df,3)

#------------------------------
#Step 3 - Create an object of class `SpatialLinesDataFrame` to use within `leaflet`.

lines <- data.frame(cbind(lng=c(df$start.x,df$end.x,df$end.arrow.x),
                          lat=c(df$start.y,df$end.y,df$end.arrow.y),
                          id=c(rep(df$id,3))))

lines.list <- list()

library(sp)

for (i in c(1:max(lines$id))){
line <- subset(lines,lines$id==i)
line <- as.matrix(line[,c(1:2)])
line <- Line(line) #object of class 'Line'
lines.list[[i]] <- Lines(list(line), ID = i) #list of 'objects'Lines' 
}

sp.lines <- SpatialLines(lines.list) #object of class 'SpatialLines'
proj4string(sp.lines) <- CRS("+init=epsg:3857") #define CRS

#Convert CRS to geographic coordinates (http://spatialreference.org/ref/epsg/4326/)
#for overlaying on OpenStreetMaps tiles in Leaflet
sp.lines <- spTransform(sp.lines, CRS("+init=epsg:4326"))

rownames(df) = df$id
#Join wind variables (id, speed, direction and date) to object of class 'SpatialLines'
sp.lines.df <- SpatialLinesDataFrame(sp.lines, df[,c(1,4:6)]) #object of class 'SpatialLinesDataFrame'
str(sp.lines.df) #inspect object structure

#------------------------------
# Code for next steps mostly adapted from https://rstudio.github.io/leaflet/
#------------------------------

#------------------------------
#Step 4 - Generate interactive and **static** map of wind speed and direction.

library(leaflet)

#popup settings
labels <- paste0("ID: ",sp.lines.df@data$id,
                 "<br>Wind speed: ",sp.lines.df@data$w.speed," Km/h<br>",
                 "Wind direction: ",sp.lines.df@data$w.direction," degrees azimuth<br>",
                 "Date: ", sp.lines.df@data$w.date)

#pallete settings
pal <- colorNumeric(palette = colorRampPalette(c("red", "blue"))(5),
                    domain = 0:max(sp.lines.df@data$w.speed))

#Create object fo class 'leaflet' 'htmlwidget'
m <- leaflet(sp.lines.df) %>%
  addTiles() %>%  # add default OpenStreetMap map tiles
  addPolylines(color = ~pal(w.speed), opacity=1, weigh = 3, popup = labels) %>%
  addLegend("bottomright", pal = pal, values = ~w.speed,
          title = "Wind speed <br> (km/h)",
          opacity = 1) %>%
  fitBounds(sp.lines.df@bbox[1,1], sp.lines.df@bbox[2,1], sp.lines.df@bbox[1,2], sp.lines.df@bbox[2,2])

#Plot map
m

#------------------------------
#Step 5 - Generate interactive and **dynamic** map of wind speed and direction.

library(shiny)

#User interface (UI) settings
ui <- fluidPage(leafletOutput("m.dynamic"),
                absolutePanel(top = 10,
                              right = 10,
                              draggable = TRUE,
                              sliderInput("range",
                                          "Time of data collection:",
                                          min = min(sp.lines.df@data$w.date),
                                          max = max(sp.lines.df@data$w.date),
                                          value = min(sp.lines.df@data$w.date),
                                          step = 4,
                                          animate=TRUE)))

#Name @coords slot of SpatialLinesDataFrame: 'lng' and 'lat'
#task necessary for 'observer' within 'server' function
for (i in c(1:max(sp.lines.df@data$id))) {
  colnames(sp.lines.df@lines[[i]]@Lines[[1]]@coords) <- c("lng","lat")
}

#Server logic
server <- function(input, output){
  filteredData <- reactive({
    sp.lines.df[sp.lines.df@data$w.date == input$range[1],]
  })
  output$m.dynamic <- renderLeaflet({
    leaflet(sp.lines.df) %>%
      addTiles() %>%  # Add default OpenStreetMap map tiles
      addLegend("bottomright",pal = pal, values = ~w.speed, title = "Wind speed <br> (km/h)", opacity = 0.9) %>%
      fitBounds(sp.lines.df@bbox[1,1], sp.lines.df@bbox[2,1], sp.lines.df@bbox[1,2], sp.lines.df@bbox[2,2])
  })
  observe({
    leafletProxy("m.dynamic", data = filteredData()) %>%
      clearShapes() %>%
      addPolylines(color = ~pal(w.speed), opacity=1, weigh = 3, popup = labels)
  })
}

# Complete app with UI and server components
shinyApp(ui, server)