Rのコンタープロット(スタックオーバーフローに関する多くのヒントを含む)のページを何回か試してみましたが、成功しませんでした。ルワンダの地図の追加を含む、輪郭を描くための私のデータは次のとおりです(データはx、y、zとして経度、緯度、雨の14の値で構成されています)。
Lon Lat Rain
28.92 -2.47 83.4
29.02 -2.68 144
29.25 -1.67 134.7
29.42 -2.07 174.9
29.55 -1.58 151.5
29.57 -2.48 224.1
29.6 -1.5 254.3
29.72 -2.18 173.9
30.03 -1.95 154.8
30.05 -1.6 152.2
30.13 -1.97 126.2
30.33 -1.3 98.5
30.45 -1.81 145.5
30.5 -2.15 151.3
これが私がstackoverflowから試したコードです:
datr <- read.table("Apr0130precip.txt",header=TRUE,sep=",")
x <- datr$x
y <- datr$y
z <- datr$z
require(akima)
fld <- interp(x,y,z)
par(mar=c(5,5,1,1))
filled.contour(fld)
補間は失敗します。ヘルプは高く評価されます。
base
Rグラフィックスとggplot
を使用したいくつかの異なる可能性があります。単純な等高線プロットとマップの上にあるプロットの両方が生成されます。
library(akima)
fld <- with(df, interp(x = Lon, y = Lat, z = Rain))
base
filled.contour
を使用したRプロットfilled.contour(x = fld$x,
y = fld$y,
z = fld$z,
color.palette =
colorRampPalette(c("white", "blue")),
xlab = "Longitude",
ylab = "Latitude",
main = "Rwandan rainfall",
key.title = title(main = "Rain (mm)", cex.main = 1))
geom_tile
およびstat_contour
を使用した基本的なggplot
代替library(ggplot2)
library(reshape2)
# prepare data in long format
df <- melt(fld$z, na.rm = TRUE)
names(df) <- c("x", "y", "Rain")
df$Lon <- fld$x[df$x]
df$Lat <- fld$y[df$y]
ggplot(data = df, aes(x = Lon, y = Lat, z = Rain)) +
geom_tile(aes(fill = Rain)) +
stat_contour() +
ggtitle("Rwandan rainfall") +
xlab("Longitude") +
ylab("Latitude") +
scale_fill_continuous(name = "Rain (mm)",
low = "white", high = "blue") +
theme(plot.title = element_text(size = 25, face = "bold"),
legend.title = element_text(size = 15),
axis.text = element_text(size = 15),
axis.title.x = element_text(size = 20, vjust = -0.5),
axis.title.y = element_text(size = 20, vjust = 0.2),
legend.text = element_text(size = 10))
ggplot
によって作成されたGoogleマップ上のggmap
# grab a map. get_map creates a raster object
library(ggmap)
rwanda1 <- get_map(location = c(lon = 29.75, lat = -2),
zoom = 9,
maptype = "toner",
source = "stamen")
# alternative map
# rwanda2 <- get_map(location = c(lon = 29.75, lat = -2),
# zoom = 9,
# maptype = "terrain")
# plot the raster map
g1 <- ggmap(rwanda1)
g1
# plot map and rain data
# use coord_map with default mercator projection
g1 +
geom_tile(data = df, aes(x = Lon, y = Lat, z = Rain, fill = Rain), alpha = 0.8) +
stat_contour(data = df, aes(x = Lon, y = Lat, z = Rain)) +
ggtitle("Rwandan rainfall") +
xlab("Longitude") +
ylab("Latitude") +
scale_fill_continuous(name = "Rain (mm)",
low = "white", high = "blue") +
theme(plot.title = element_text(size = 25, face = "bold"),
legend.title = element_text(size = 15),
axis.text = element_text(size = 15),
axis.title.x = element_text(size = 20, vjust = -0.5),
axis.title.y = element_text(size = 20, vjust = 0.2),
legend.text = element_text(size = 10)) +
coord_map()
ggplot
# Since I don't have your map object, I do like this instead:
# get map data from
# http://biogeo.ucdavis.edu/data/diva/adm/RWA_adm.Zip
# unzip files to folder named "rwanda"
# read shapefile with rgdal::readOGR
# just try the first out of three shapefiles, which seemed to work.
# 'dsn' (data source name) is the folder where the shapefile is located
# 'layer' is the name of the shapefile without the .shp extension.
library(rgdal)
rwa <- readOGR(dsn = "rwanda", layer = "RWA_adm0")
class(rwa)
# [1] "SpatialPolygonsDataFrame"
# convert SpatialPolygonsDataFrame object to data.frame
rwa2 <- fortify(rwa)
class(rwa2)
# [1] "data.frame"
# plot map and raindata
ggplot() +
geom_polygon(data = rwa2, aes(x = long, y = lat, group = group),
colour = "black", size = 0.5, fill = "white") +
geom_tile(data = df, aes(x = Lon, y = Lat, z = Rain, fill = Rain), alpha = 0.8) +
stat_contour(data = df, aes(x = Lon, y = Lat, z = Rain)) +
ggtitle("Rwandan rainfall") +
xlab("Longitude") +
ylab("Latitude") +
scale_fill_continuous(name = "Rain (mm)",
low = "white", high = "blue") +
theme_bw() +
theme(plot.title = element_text(size = 25, face = "bold"),
legend.title = element_text(size = 15),
axis.text = element_text(size = 15),
axis.title.x = element_text(size = 20, vjust = -0.5),
axis.title.y = element_text(size = 20, vjust = 0.2),
legend.text = element_text(size = 10)) +
coord_map()
もちろん、降水量データの補間とプロットは、 Rの空間データ用の素晴らしいツール を使用して、はるかに洗練された方法で行うことができます。私の答えはかなり早くて簡単なスタートです。