I am trying to plot flow map (for singapore) . I have Entry(Lat,Long) and Exit (Lat,long). I am trying to map the flow from entry to exit in singapore map.
structure(list(token_id = c(1.12374e+19, 1.12374e+19, 1.81313e+19,
1.85075e+19, 1.30752e+19, 1.30752e+19, 1.32828e+19, 1.70088e+19,
1.70088e+19, 1.70088e+19, 1.05536e+19, 1.44818e+19, 1.44736e+19,
1.44736e+19, 1.44736e+19, 1.44736e+19, 1.89909e+19, 1.15795e+19,
1.15795e+19, 1.15795e+19, 1.70234e+19, 1.70234e+19, 1.44062e+19,
1.21512e+19, 1.21512e+19, 1.95909e+19, 1.95909e+19, 1.50179e+19,
1.50179e+19, 1.24174e+19, 1.36445e+19, 1.98549e+19, 1.92068e+19,
1.18468e+19, 1.18468e+19, 1.92409e+19, 1.92409e+19, 1.21387e+19,
1.9162e+19, 1.9162e+19, 1.40385e+19, 1.40385e+19, 1.32996e+19,
1.32996e+19, 1.69103e+19, 1.69103e+19, 1.57387e+19, 1.40552e+19,
1.40552e+19, 1.00302e+19), Entry_Station_Lat = c(1.31509, 1.33261,
1.28425, 1.31812, 1.33858, 1.29287, 1.39692, 1.37773, 1.33858,
1.33322, 1.28179, 1.30036, 1.43697, 1.39752, 1.27637, 1.39752,
1.41747, 1.35733, 1.28405, 1.37773, 1.35898, 1.42948, 1.32774,
1.42948, 1.349, 1.36017, 1.34971, 1.38451, 1.31509, 1.31509,
1.37002, 1.34971, 1.31231, 1.39169, 1.31812, 1.44909, 1.29341,
1.41747, 1.33759, 1.44062, 1.31509, 1.38451, 1.29461, 1.32388,
1.41747, 1.27614, 1.39752, 1.39449, 1.33261, 1.31231), Entry_Station_Long = c(103.76525,
103.84718, 103.84329, 103.89308, 103.70611, 103.8526, 103.90902,
103.76339, 103.70611, 103.74217, 103.859, 103.85563, 103.7865,
103.74745, 103.84596, 103.74745, 103.83298, 103.9884, 103.85152,
103.76339, 103.75191, 103.83505, 103.67828, 103.83505, 103.74956,
103.88504, 103.87326, 103.74437, 103.76525, 103.76525, 103.84955,
103.87326, 103.83793, 103.89548, 103.89308, 103.82004, 103.78479,
103.83298, 103.69742, 103.80098, 103.76525, 103.74437, 103.80605,
103.93002, 103.83298, 103.79156, 103.74745, 103.90051, 103.84718,
103.83793), Exit_Station_Lat = structure(c(48L, 34L, 118L, 60L,
14L, 54L, 10L, 49L, 49L, 74L, 71L, 65L, 102L, 5L, 102L, 119L,
116L, 10L, 13L, 88L, 117L, 66L, 40L, 62L, 117L, 37L, 67L, 34L,
85L, 44L, 102L, 44L, 115L, 29L, 92L, 17L, 121L, 70L, 120L, 52L,
85L, 34L, 42L, 11L, 4L, 115L, 62L, 48L, 92L, 14L), .Label = c("1.27082",
"1.27091", "1.27236", "1.27614", "1.27637", "1.27646", "1.27935",
"1.28221", "1.28247", "1.28405", "1.28621", "1.28819", "1.28932",
"1.29287", "1.29309", "1.29338", "1.29341", "1.29461", "1.29694",
"1.29959", "1.29974", "1.30034", "1.30252", "1.30287", "1.30392",
"1.30394", "1.30619", "1.30736", "1.30842", "1.31139", "1.3115",
"1.31167", "1.31188", "1.31509", "1.31654", "1.31756", "1.31913",
"1.31977", "1.32008", "1.3205", "1.32104", "1.32388", "1.32573",
"1.32725", "1.32774", "1.33119", "1.33155", "1.33261", "1.33322",
"1.33474", "1.33554", "1.33759", "1.33764", "1.33858", "1.33921",
"1.34037", "1.34225", "1.34293", "1.3432", "1.34426", "1.34857",
"1.349", "1.34905", "1.35158", "1.35733", "1.35898", "1.36017",
"1.3625", "1.36849", "1.37002", "1.37121", "1.37304", "1.37666",
"1.37775", "1.3786", "1.37862", "1.38001", "1.38029", "1.3803",
"1.38178", "1.38269", "1.38295", "1.38399", "1.38423", "1.38451",
"1.38671", "1.38672", "1.38777", "1.38814", "1.3894", "1.39147",
"1.39169", "1.39189", "1.39208", "1.39389", "1.39449", "1.39452",
"1.39628", "1.39692", "1.39717", "1.39732", "1.39752", "1.39821",
"1.39928", "1.39962", "1.4023", "1.40455", "1.40511", "1.40524",
"1.40843", "1.40961", "1.41184", "1.41588", "1.41685", "1.41747",
"1.42526", "1.42948", "1.43256", "1.43697", "1.44062", "1.44909"
), class = "factor"), Exit_Station_Long = structure(c(59L, 19L,
27L, 4L, 65L, 3L, 63L, 6L, 6L, 21L, 93L, 121L, 9L, 56L, 9L, 32L,
16L, 63L, 44L, 23L, 50L, 12L, 54L, 11L, 50L, 71L, 87L, 19L, 7L,
118L, 9L, 118L, 49L, 90L, 96L, 31L, 45L, 61L, 38L, 2L, 7L, 19L,
117L, 47L, 34L, 49L, 11L, 59L, 96L, 65L), .Label = c("103.67828",
"103.69742", "103.70611", "103.72092", "103.73274", "103.74217",
"103.74437", "103.74529", "103.74745", "103.74905", "103.74956",
"103.75191", "103.7537", "103.75803", "103.76011", "103.76215",
"103.76237", "103.76449", "103.76525", "103.76648", "103.76667",
"103.76893", "103.7696", "103.77082", "103.77145", "103.77266",
"103.774", "103.77866", "103.78185", "103.78425", "103.78479",
"103.7865", "103.78744", "103.79156", "103.79631", "103.79654",
"103.79836", "103.80098", "103.803", "103.80605", "103.80745",
"103.80781", "103.80978", "103.81703", "103.82004", "103.82592",
"103.82695", "103.83216", "103.83298", "103.83505", "103.83918",
"103.83953", "103.83974", "103.84387", "103.84496", "103.84596",
"103.84673", "103.84674", "103.84718", "103.84823", "103.84955",
"103.85092", "103.85152", "103.85226", "103.8526", "103.85267",
"103.85436", "103.85446", "103.85452", "103.86088", "103.86149",
"103.86275", "103.86291", "103.86395", "103.86405", "103.86896",
"103.87087", "103.87135", "103.87534", "103.87563", "103.8763",
"103.87971", "103.88003", "103.88126", "103.88243", "103.88296",
"103.88504", "103.8858", "103.88816", "103.8886", "103.88934",
"103.89054", "103.89237", "103.89313", "103.8938", "103.89548",
"103.89719", "103.89723", "103.89854", "103.9003", "103.90051",
"103.90208", "103.90214", "103.9031", "103.90484", "103.90537",
"103.90597", "103.90599", "103.90663", "103.9086", "103.90902",
"103.9126", "103.9127", "103.91296", "103.91616", "103.9165",
"103.93002", "103.94638", "103.94929", "103.95337", "103.9884"
), class = "factor")), .Names = c("token_id", "Entry_Station_Lat",
"Entry_Station_Long", "Exit_Station_Lat", "Exit_Station_Long"
), row.names = c(10807L, 10808L, 10810L, 10815L, 10817L, 10818L,
10819L, 10820L, 10823L, 10824L, 10826L, 10827L, 10829L, 10831L,
10832L, 10833L, 10834L, 10835L, 10836L, 10838L, 10840L, 10841L,
10843L, 10847L, 10850L, 10852L, 10854L, 10855L, 10859L, 10861L,
10869L, 10872L, 10883L, 10886L, 10891L, 10895L, 10896L, 10897L,
10900L, 10902L, 10903L, 10906L, 10910L, 10911L, 10912L, 10913L,
10915L, 10920L, 10921L, 10924L), class = "data.frame")
I am trying to get something this : Map Flow
Just realized that the original solution usin geom_path was more complicated than necessary. geom_segmentworks without changing the data:
require(ggplot2)
require(ggmap)
basemap <- get_map("Singapore",
source = "stamen",
maptype = "toner",
zoom = 11)
g = ggplot(a)
map = ggmap(basemap, base_layer = g)
map = map + coord_cartesian() +
geom_curve(size = 1.3,
aes(x=as.numeric(Entry_Station_Long),
y=as.numeric(Entry_Station_Lat),
xend=as.numeric(as.character(Exit_Station_Long)),
yend=as.numeric(as.character(Exit_Station_Lat)),
color=as.factor(token_id)))
map
This solution leverages Draw curved lines in ggmap, geom_curve not working to implement curved lines on a map.
ggmaps used for simplicity - for more ambitious projects I would recommend leaflet.
Below the solution using a long data format with some prior data wrangling. It also uses straight lines instead of the curves above.
a %>%
mutate(path = row_number()) -> a
origin = select(a,token_id,Entry_Station_Lat,Entry_Station_Long,path)
origin$type = "origin"
dest = select(a,token_id,Exit_Station_Lat,Exit_Station_Long,path)
dest$type = "dest"
colnames(origin) = c("id","lat","long","path","type")
colnames(dest) = c("id","lat","long","path","type")
complete = rbind(origin,dest)
complete %>% arrange(path,type) -> complete
require(ggmap)
basemap <- get_map("Singapore",
source = "stamen",
maptype = "toner",
zoom = 11)
g = ggplot(complete, aes(x=as.numeric(long),
y=as.numeric(lat)))
map = ggmap(basemap, base_layer = g)
map + geom_path(aes(color = as.factor(id)),
size = 1.1)
If you want to plot it on an actual Google Map, and recreate the style of your linked map, you can use my googleway package that uses Google's Maps API. You need an API key to use their maps
library(googleway)
df$Exit_Station_Lat <- as.numeric(as.character(df$Exit_Station_Lat))
df$Exit_Station_Long <- as.numeric(as.character(df$Exit_Station_Long))
df$polyline <- apply(df, 1, function(x) {
lat <- c(x['Entry_Station_Lat'], x['Exit_Station_Lat'])
lon <- c(x['Entry_Station_Long'], x['Exit_Station_Long'])
encode_pl(lat = lat, lon = lon)
})
mapKey <- 'your_api_key'
style <- '[ { "stylers": [{ "visibility": "simplified"}]},{"stylers": [{"color": "#131314"}]},{"featureType": "water","stylers": [{"color": "#131313"},{"lightness": 7}]},{"elementType": "labels.text.fill","stylers": [{"visibility": "on"},{"lightness": 25}]}]'
google_map(key = mapKey, style = style) %>%
add_polylines(data = df,
polyline = "polyline",
mouse_over_group = "Entry_Station_Lat",
stroke_weight = 0.7,
stroke_opacity = 0.5,
stroke_colour = "#ccffff")
Note, to recreate the map using flight data, see the example given in ?add_polylines
You can also show other types of routes, for example, driving between the locations by using Google's Directions API to encode the driving routes.
df$drivingRoute <- lst_directions <- apply(df, 1, function(x){
orig <- as.numeric(c(x['Entry_Station_Lat'], x['Entry_Station_Long']))
dest <- as.numeric(c(x['Exit_Station_Lat'], x['Exit_Station_Long']))
dir <- google_directions(origin = orig, destination = dest, key = apiKey)
dir$routes$overview_polyline$points
})
google_map(key = mapKey, style = style) %>%
add_polylines(data = df,
polyline = "drivingRoute",
mouse_over_group = "Entry_Station_Lat",
stroke_weight = 0.7,
stroke_opacity = 0.5,
stroke_colour = "#ccffff")
Alternative answer using leaflet and geosphere
#get Packages
require(leaflet)
require(geosphere)
#format data
a$Entry_Station_Long = as.numeric(as.character(a$Entry_Station_Long))
a$Entry_Station_Lat = as.numeric(as.character(a$Entry_Station_Lat))
a$Exit_Station_Long = as.numeric(as.character(a$Exit_Station_Long))
a$Exit_Station_Lat = as.numeric(as.character(a$Exit_Station_Lat))
a$id = as.factor(as.numeric(as.factor(a$token_id)))
#create some colors
factpal <- colorFactor(heat.colors(30), pathList$id)
#create a list of interpolated paths
pathList = NULL
for(i in 1:nrow(a))
{
tmp = gcIntermediate(c(a$Entry_Station_Long[i],
a$Entry_Station_Lat[i]),
c(a$Exit_Station_Long[i],
a$Exit_Station_Lat[i]),n = 25,
addStartEnd=TRUE)
tmp = data.frame(tmp)
tmp$id = a[i,]$id
tmp$color = factpal(a[i,]$id)
pathList = c(pathList,list(tmp))
}
#create empty base leaflet object
leaflet() %>% addTiles() -> lf
#add each entry of pathlist to the leaflet object
for (path in pathList)
{
lf %>% addPolylines(data = path,
lng = ~lon,
lat = ~lat,
color = ~color) -> lf
}
#show output
lf
Note that as I mentioned before there is no way of geosphering the paths in such a small locality - the great circles are effectively straight lines. If you want the rounded edges for sake of aesthetics you may have to use the geom_curve way described in my other answer.
I've also written the mapdeck library to make visualisations like this more appealing*
library(mapdeck)
set_token("MAPBOX_TOKEN") ## set your mapbox token here
df$Exit_Station_Lat <- as.numeric(as.character(df$Exit_Station_Lat))
df$Exit_Station_Long <- as.numeric(as.character(df$Exit_Station_Long))
mapdeck(
style = mapdeck_style('dark')
, location = c(104, 1)
, zoom = 8
, pitch = 45
) %>%
add_arc(
data = df
, origin = c("Entry_Station_Long", "Entry_Station_Lat")
, destination = c("Exit_Station_Long", "Exit_Station_Lat")
, layer_id = 'arcs'
, stroke_from_opacity = 100
, stroke_to_opacity = 100
, stroke_width = 3
, stroke_from = "#ccffff"
, stroke_to = "#ccffff"
)
*subjectively speaking
I would like to leave an alternative approach for you. What you can do is to restructure your data. Right now you have two columns for entry stations and the other two for exit stations. You can create one column for long, and another for lat by combing these columns. The trick is to use rbind() and c().
Let's have a look of this simple example.
x <- c(1, 3, 5)
y <- c(2, 4, 6)
c(rbind(x, y))
#[1] 1 2 3 4 5 6
Imagine x is long for entry stations and y for exit stations. 1 is longitude for a starting point. 2 is longitude where the first journey ended. As far as I can see from your sample data, it seems that 3 is identical 2. You could remove duplicated data points for each token_id. If you have a large set of data, perhaps this is something you want to consider. Back to the main point, you can create a column with longitude in the sequence you want with the combination of the two functions. Since you said you have date information, make sure you order the data by date. Then, the sequence of each journey appears in the right way in tmp. You want to do this with latitude as well.
Now we look into your sample data. It seems that Exit_Station_Lat and Exit_Station_Long are in factor. The first operation is to convert them to numeric. Then, you apply the method above and create a data frame. I called your data mydf.
library(dplyr)
library(ggplot2)
library(ggalt)
library(ggthemes)
library(raster)
mydf %>%
mutate_at(vars(Exit_Station_Lat: Exit_Station_Long),
funs(as.numeric(as.character(.)))) -> mydf
group_by(mydf, token_id) %>%
do(data.frame(long = c(rbind(.$Entry_Station_Long,.$Exit_Station_Long)),
lat = c(rbind(.$Entry_Station_Lat, .$Exit_Station_Lat))
)
) -> tmp
Now let's get a map data from GADM. You can download data using the raster package.
getData(name = "GADM", country = "singapore", level = 0) %>%
fortify -> singapore
Finally, you draw a map. The key thing is to use group in aes in geom_path(). I hope this will let you move forward.
ggplot() +
geom_cartogram(data = singapore,
aes(x = long, y = lat, map_id = id),
map = singapore) +
geom_path(data = tmp,
aes(x = long, y = lat, group = token_id,
color = as.character(token_id)),
show.legend = FALSE) +
theme_map()
I am trying to convert point values to coordinates using the sp package to perform operations similar to this question. I have a list of data frames (hundreds in the full data set, 2 short ones here).
> dput(df)
list(structure(list(group = c(22, 43, 43, 36, 9, 20, 35, 18,
32, 2), mean_x_m = c(-2578373.61904762, -2082265, -1853701.875,
-2615961.89189189, -1538829.07815509, -1753235.6200847, -1690679.5,
-1694763.64583333, -1700343.15217391, -1416060), mean_y_m = c(3242738.76190476,
2563892.5, 1945883.125, 3130074.86486486, 1373724.65001039, 1468737.97186933,
2123413.5, 1442167.01388889, 2144261.73913043, 1352573.33333333
)), .Names = c("group", "mean_x_m", "mean_y_m"), row.names = c(72L,
140L, 142L, 121L, 27L, 66L, 114L, 60L, 105L, 5L), class = "data.frame"),
structure(list(group = c(12, 12, 47, 30, 39, 34, 47, 22,
10, 1), mean_x_m = c(-1830635.68663753, -2891058.33333333,
-1637448.59886202, -1974773.67400716, -1571853.24324324,
-2723090.33333333, -2704594.92760618, -2240863.49122807,
-1940748.88253242, -2176724.69924812), mean_y_m = c(2324222.49926225,
3261997.5, 2057096.55049787, 2411733.29933653, 1447883.78378379,
3406879.26666667, 3291053.77606178, 2788255.49473684, 2176919.6882151,
2920168.77443609)), .Names = c("group", "mean_x_m", "mean_y_m"
), row.names = c(67L, 68L, 243L, 155L, 202L, 173L, 244L,
114L, 61L, 3L), class = "data.frame"))
I can pull one data frame out at a time and convert to a SpatialPointsDataFrame without issue.
df1 = df[[1]]
coordinates(df1) = ~mean_x_m+mean_y_m
My problem is I can't get this to iterate over the entire list using a function, or even get the function to work for a single dataframe.
c = function(f){coordinates(f) = ~mean_x_m+mean_y_m}
df2 = c(df1)
c(df1)
df3 = lapply(df,c)
Would a for loop work better? I'm still learning about working with lists of data frames and matrices so any help on apply or for in this context would be appreciated. Thank you.
This is how you can use lapply:
fc <- function(f){coordinates(f) = ~mean_x_m + mean_y_m; f}
lapply(df, fc)
The problem was that your function did not return anything.
To make a single object:
x <- lapply(1:length(df), function(i) cbind(id=i, df[[i]]))
x <- do.call(rbind, x)
coordinates(x) <- ~mean_x_m+mean_y_m
If your dataframes have a consistent structure, it would be better to put them all into one dataframe.
library(dplyr)
library(sp)
result =
df %>%
bind_rows(.id = "list_number") %>%
as.data.frame %>%
`coordinates<-`(~mean_x_m+mean_y_m)
If you are working with geographic data I find it is easiest to use Spatial Points and SpatialPointsDataFrame classes to store data. To convert all elements of a list containing dataframes with the same column headings you could adapt this code:
library(sp)
# toy dataset X
X<-list(
x1 = data.frame(group =c("a","b","c"), X = c(-110.1,-110.2,-110), Y = c(44,44.2,44.3)),
x2 = data.frame(group =c("a","b","c"), X = c(-110.1,-110.2,-110), Y = c(44,44.2,44.3)))
# write a function based on the structure of your dfs
spdf_fxn<-function(df){
SpatialPointsDataFrame(coords= cbind(df$X,df$Y), data= data.frame(group = df$group),
proj4string=CRS("+proj=longlat +datum=WGS84"))
}
#apply this function over the list
Out_List<-lapply(X,spdf_fxn)
Write a function to convert the generic dataframe structure to a SpatialPointsDataframe, with group as the data appended to each point, then apply that function to the list. Note you will have to adapt the column names and use the appropriate proj4string (in this example it is longitude and latitude in WGS 84).