ggmap

Pecu PPT / Code :

https://docs.google.com/…/1pO9H2MwVIUi6YJtrSDSPTFvR2P…/edit…

http://datascienceandr.org/…/03-RVisualization-04-Javascrip…

# ggmap 上課教材

https://blog.gtwang.org/r/r-ggmap-package-spatial-data-visualization/

# 如果遇到版本問題：Update R 到 3.3.3

Mac :https://cran.r-project.org/src/base/R-3/

Windows : https://cran.rstudio.com/bin/windows/base/old/3.3.3/

# Windows 出現 f error 在 console 將ggplot2 版本改成2.2.0

library(devtools)

install_github("hadley/[email protected]")

# 輸入地址轉換經緯度

http://gps.uhooamber.com/address-to-lat-lng.html

# Code -- 繪製基本地圖

library(ggplot2)

library(ggmap)

library(mapproj)

map <- get_map(location = 'Taiwan',zoom = 7,language = "zh-TW")

map <- get_map(location =c(lan = 120.233937,lat = 22.993013),zoom = 10, language = "zh-TW")

ggmap(map)

# Code -- 將資料畫在地圖上

首先從政府資料開放平臺上下載紫外線即時監測資料的 csv 檔，接著將資料讀進 R 中

uv <-read.csv("Whatyouwant.csv",encoding="big-5")

# 繪製經緯度

lon.deg <- sapply((strsplit(as.character(uv$WGS84Lon), ",")), as.numeric)

uv$lon <- lon.deg[1, ] + lon.deg[2, ]/60 + lon.deg[3, ]/3600

lat.deg <- sapply((strsplit(as.character(uv$WGS84Lat), ",")), as.numeric)

uv$lat <- lat.deg[1, ] + lat.deg[2, ]/60 + lat.deg[3, ]/3600

# 把資料加到地圖中

library(ggmap)

map <- get_map(location = 'Taiwan', zoom = 7)

ggmap(map) + geom_point(aes(x = lon, y = lat, size = UVI), data = uv)

#　Google 地圖的標記（marker）與路徑（path）

d <- function(x=-95.36, y=29.76, n,r,a){

round(data.frame(

lon = jitter(rep(x,n), amount = a),

lat = jitter(rep(y,n), amount = a)

), digits = r)

}

df <- d(n = 50,r = 3,a = .3) # random 製造經緯度

map <- get_googlemap(markers = df, path = df,, scale = 2)

ggmap(map)

# 小提醒 -- lon 跟 lat 的表示型式需修改成：121.123455 (原為121,123,455的話)

# 老師提供完整code

#install.packages("devtools")

#library(devtools)

#install.packages("ggplot2")

library(ggplot2)

library(ggmap)

library(mapproj)

map <- get_map(location ='taiwan', zoom =9, language ="zh-TW")

map <- get_map(location = c(lon =-95.36, lat =29.76),

zoom =10, language ="zh-TW")

ggmap(map)

uv <- read.csv("UV_20151116152215.csv", encoding="big-5")

uv$lon <- uv$WGS84Lon

uv$lat <- uv$WGS84Lat

map <- get_map(location ='Taiwan', zoom =7)

ggmap(map) +

geom_point(aes(x = lon, y = lat, size = uv), data = uv) +

scale_size(range = c(0,3))

#d <- function(x=-95.36, y=29.76, n,r,a){

# round(data.frame(

# lon = jitter(rep(x,n), amount = a),

# lat = jitter(rep(y,n), amount = a)

# ), digits = r)

#}

#df1 <- d(n = 50,r = 3,a = .3)

#map <- get_googlemap(markers = df1, path = df1, scale = 2)

#ggmap(map)

df2 <- uv[,5:6]

names(df2) <- c('lon','lat')

map2 <- get_googlemap(center ='Taiwan', zoom =7,

markers = df2, path = df2, scale =2)

ggmap(map2)

mu <- c(-95.3632715,29.7632836)

nDataSets <- sample(4:10,1)

chkpts <- NULL

for(k in1:nDataSets){

a <- rnorm(2); b <- rnorm(2);

si <-1/3000* (outer(a,a) + outer(b,b))

chkpts <- rbind(chkpts,

cbind(MASS::mvrnorm(rpois(1,50), jitter(mu,.01), si), k))

}

chkpts <- data.frame(chkpts)

names(chkpts) <- c("lon","lat","class")

chkpts$class <- factor(chkpts$class)

qplot(lon, lat, data = chkpts, colour = class)

df2$uv <- as.factor(as.integer(uv$uv))

qplot(lon, lat, data = df2, colour = uv)

df2$air <- sample(1:10, length(df2$uv), replace=T)

ggmap(get_map(location ='taiwan', zoom =7), extent ="panel") +

geom_point(aes(x = lon, y = lat, colour = factor(uv), size = air), data = df2) +

scale_size(range = c(0,6))

# 進階設定顏色 http://ggplot2.tidyverse.org/reference/scale_gradient.html