Maps: In-Class Exercise & Exercise
2020-Spring [Data Management] / Instructor: Prof. SHEU, Ching-Fan
CHIU, Ming-Tzu
2020-05-25
In-Class Exercise
Q1
Indicate countries you have visited so far on a world map in the style of the ebola outbreaks example.
library(rworldmap)## Loading required package: sp## ### Welcome to rworldmap ##### For a short introduction type : vignette('rworldmap')dta1 <- read.table("C:/Users/TheorEco Lab/Desktop/108-2/DataManagement/0518/visit_country.txt", h = T)
library(countrycode)
# a data.frame with the ISO3 country names plus a variable to
# merge to the map data
dta1$Country <- countrycode(dta1[,1], "country.name", "iso3c")
dta1$visit <- rep(1, length(dta1$Country))
mapDevice("x11")
# join the data.frame to the country map data
visitMap <- joinCountryData2Map(dta1, joinCode = "ISO3", nameJoinColumn = "Country")## 3 codes from your data successfully matched countries in the map
## 0 codes from your data failed to match with a country code in the map
## 240 codes from the map weren't represented in your data# plot it, the color palette's first color is red
mapCountryData(visitMap, nameColumnToPlot = "visit", catMethod = "categorical",
addLegend = FALSE, mapTitle =" ", missingCountryCol = gray(.9))
Q2
Plot places in administratice areas of Taiwan you have visited so far.
dta2 <- sf::st_read("C:/Users/TheorEco Lab/Desktop/108-2/DataManagement/0518/TWN_adm2.shp")## Reading layer `TWN_adm2' from data source `C:\Users\TheorEco Lab\Desktop\108-2\DataManagement\0518\TWN_adm2.shp' using driver `ESRI Shapefile'
## Simple feature collection with 22 features and 11 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 116.71 ymin: 20.6975 xmax: 122.1085 ymax: 25.63431
## geographic CRS: WGS 84str(dta2)## Classes 'sf' and 'data.frame': 22 obs. of 12 variables:
## $ ID_0 : num 225 225 225 225 225 225 225 225 225 225 ...
## $ ISO : Factor w/ 1 level "TWN": 1 1 1 1 1 1 1 1 1 1 ...
## $ NAME_0 : Factor w/ 1 level "Taiwan": 1 1 1 1 1 1 1 1 1 1 ...
## $ ID_1 : num 1 2 3 4 4 4 4 4 4 4 ...
## $ NAME_1 : Factor w/ 4 levels "Kaohsiung","Pratas Islands",..: 1 2 3 4 4 4 4 4 4 4 ...
## $ ID_2 : num 1 2 3 4 5 6 7 8 9 10 ...
## $ NAME_2 : Factor w/ 21 levels "Changhwa","Chiayi",..: 7 NA 18 1 2 3 4 5 6 8 ...
## $ TYPE_2 : Factor w/ 3 levels "Chuan-shih","District|Hsien",..: 1 NA 1 2 2 2 2 2 2 3 ...
## $ ENGTYPE_2: Factor w/ 3 levels "County","Municipality",..: 3 NA 3 1 1 1 1 1 1 2 ...
## $ NL_NAME_2: Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
## $ VARNAME_2: Factor w/ 16 levels "Gaoxiong","Gaoxiong Shi",..: 2 NA 8 16 4 13 3 14 1 5 ...
## $ geometry :sfc_MULTIPOLYGON of length 22; first list element: List of 2
## ..$ :List of 1
## .. ..$ : num [1:4, 1:2] 120.2 120.2 120.2 120.2 22.8 ...
## ..$ :List of 1
## .. ..$ : num [1:827, 1:2] 120 120 120 120 120 ...
## ..- attr(*, "class")= chr "XY" "MULTIPOLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "names")= chr "ID_0" "ISO" "NAME_0" "ID_1" ...dta2$NAME_2## [1] Kaohsiung City <NA> Taipei City Changhwa Chiayi
## [6] Hsinchu Hualien Ilan Kaohsiung Keelung City
## [11] Miaoli Nantou Penghu Pingtung Taichung City
## [16] Taichung Tainan City Tainan Taipei Taitung
## [21] Taoyuan Yunlin
## 21 Levels: Changhwa Chiayi Hsinchu Hualien Ilan Kaohsiung ... Yunlindta2$visited <- c("red", "lightgray", "red", "lightgray", "lightgray", "red",
"lightgray", "red", "red", "lightgray", "lightgray", "lightgray",
"red", "red", "red", "red", "red", "red",
"red", "red", "lightgray", "lightgray")plot(dta2, col = dta2$visited, max.plot = 1, main = "")
legend("topleft", inset=.02, c("have visited", "haven't visited"), fill=c("lightgray","red"))
Q3
Map an area of Tainan city to include three of your favorite places to eat as landmarks.
dta3 <- list(lat = c(22.998402, 23.004596, 23.005070, 23.005013,22.983063, 22.990121),
lng = c(120.233564, 120.221126, 120.222829, 120.218745, 120.205673, 120.233056),
loc = c("生命樹咖啡", "小邁時光", "濰克早午餐(長榮店)", "芯恬也咖啡早午餐","城南舊肆", "誠品生活南紡店"))library(leaflet)m <- leaflet() %>%
addTiles() %>%
addMarkers(lng = dta3$lng,
lat = dta3$lat,
popup = dta3$loc)
mExercise
Q1
Build a thematic plot of the results of Taiwan 2020 presidential election between the DDP and the KMT. The geographical data (maps) for Taiwan can be obtained from DIVA-GIS: Geographic Information System for Biodiversity Research.
Source: Taiwan presidential election 2020 . Wikipedia.
dta3 <- sf::st_read("C:/Users/TheorEco Lab/Desktop/108-2/DataManagement/0518/TWN_adm2.shp")## Reading layer `TWN_adm2' from data source `C:\Users\TheorEco Lab\Desktop\108-2\DataManagement\0518\TWN_adm2.shp' using driver `ESRI Shapefile'
## Simple feature collection with 22 features and 11 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 116.71 ymin: 20.6975 xmax: 122.1085 ymax: 25.63431
## geographic CRS: WGS 84dta3$NAME_2## [1] Kaohsiung City <NA> Taipei City Changhwa Chiayi
## [6] Hsinchu Hualien Ilan Kaohsiung Keelung City
## [11] Miaoli Nantou Penghu Pingtung Taichung City
## [16] Taichung Tainan City Tainan Taipei Taitung
## [21] Taoyuan Yunlin
## 21 Levels: Changhwa Chiayi Hsinchu Hualien Ilan Kaohsiung ... Yunlindta3$votes <- c(62.2, 0, 53.7, 57.2, 64.2, 47.5,
60.4, 63.3, 62.2, 50.8, 50.3, 50.8,
53.9, 62.2, 57.0, 57.0, 67.4, 67.4,
56.5, 58.3, 54.8, 61.6)
dta3$color <- c("green", "gray", "green", "green", "green", "blue",
"blue", "green", "green", "green", "blue", "green",
"green", "green", "green", "green", "green", "green",
"green", "blue", "green", "green")plot(dta3, col = dta3$color, max.plot = 1, main = "")
legend("topleft", inset=.02, c("Tsai Ing-wen,
William Lai", "Han Kuo-yu,
Chang San-cheng"), fill=c("green","blue"))
Q2
Traffic accidents on roads in Taiwan in 2011 is available on-line from the Department of Transportation. Plot the number of deaths per 10,000 vehicles over the administrative units.
Data not available
Q3
DownloDownload the data for age fisrt have sex across several countries to make the following plot:
dta4 <- read.csv("C:/Users/TheorEco Lab/Desktop/108-2/DataManagement/0518/Age_When_You_First_Had_Sex.csv", header = TRUE)
str(dta4)## 'data.frame': 41 obs. of 2 variables:
## $ Country: Factor w/ 41 levels "Australia","Austria",..: 17 41 18 23 36 15 30 7 21 37 ...
## $ Age : num 19.8 19.6 19.1 19 18.9 18.6 18.4 18.3 18.1 18 ...library(choroplethr)
country_choropleth(dta4,
num_colors = 9) +
scale_fill_brewer (palette="Blues") +
labs(title=" Age When You First Had Sex")Error: Package choroplethrMaps is needed for this function to work. Please install it.
不知道 function loading 哪裡有問題，無法使用老師上課示範的方法，以下參照廖同學的做法。
library(countrycode)
dta4_maps <- data.frame(Country = countrycode(dta4$Country,"country.name", "iso3c"),
Age = dta4$Age)## Warning in countrycode(dta4$Country, "country.name", "iso3c"): Some values were not matched unambiguously: Serbia & Montenegrolibrary(rworldmap)
dta4Map <- joinCountryData2Map(dta4_maps, joinCode = "ISO3", nameJoinColumn = "Country")## 40 codes from your data successfully matched countries in the map
## 1 codes from your data failed to match with a country code in the map
## 203 codes from the map weren't represented in your datamapCountryData(dta4Map, nameColumnToPlot = "Age", catMethod = 'pretty',
addLegend = FALSE, missingCountryCol = gray(.9), mapTitle = '',
colourPalette = c('skyblue1', 'skyblue2', 'dodgerblue', 'dodgerblue4', 'black'))## You asked for 7 categories, 9 were used due to pretty() classification## Warning in rwmGetColours(colourPalette, numColours): 5 colours specified and 9
## required, using interpolation to calculate colours
Q4
Download all the files from github (click the downward triangle in the clone or download button in green) for flood in schools in Taipei to replicate the analysis with the markdown file included.
台北市國民小學洪災分析
降雨頻率分析與淹水潛勢是洪災分析重要的資料來源，利用水文分析進行降雨觀測及淹水分析結果，進一步整合社會統計資料，可評估人類社會承受災害的潛在風險。
假設將24小時延時200年重現期降雨的淹水潛勢的深度達50cm以上的區域，定義為「潛在受災區」。
1.設定所需的函式庫(libraries)
install.packages("rgdal") #讓R可以認識shp file
install.packages("sp")library(rgdal)## rgdal: version: 1.4-8, (SVN revision 845)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.2.3, released 2017/11/20
## Path to GDAL shared files: C:/Users/TheorEco Lab/Documents/R/win-library/3.6/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
## Path to PROJ.4 shared files: C:/Users/TheorEco Lab/Documents/R/win-library/3.6/rgdal/proj
## Linking to sp version: 1.4-1library(sp)
setwd("C:/Users/TheorEco Lab/Desktop/108-2/DataManagement/0518/flood-in-school-master") #設定working area
Flood <- readOGR(dsn = ".", layer = "flood50", encoding="big5")## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\TheorEco Lab\Desktop\108-2\DataManagement\0518\flood-in-school-master", layer: "flood50"
## with 5103 features
## It has 5 fields# dsn = “.“ 是路徑，但因我們已將working area設定完成，所以打.就好
# dsn 是指路徑
# layer 是指shp的名稱
Schools <- readOGR(dsn = ".", layer = "tpecity_school", encoding="big5")## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\TheorEco Lab\Desktop\108-2\DataManagement\0518\flood-in-school-master", layer: "tpecity_school"
## with 198 features
## It has 3 fields
## Integer64 fields read as strings: STUDENTSSchools.Flood <- Schools[Flood, ] #把學校落在Flood中的排出來2.疊三個圖層
plot(Flood, col='cyan')
plot(Schools, col='gray', pch=20, add=TRUE)
plot(Schools.Flood, col='red', pch=20, add=TRUE)
nrow(Schools.Flood)## [1] 36#把36筆排出來
# 把 Schools.Flood 中計算筆數 (本例有36筆)
head(Schools.Flood, n=36)## coordinates TEXTSTRING ID STUDENTS
## 1 (301759.8, 2776380) 憯急\x9e\x9c葉 1 95
## 2 (301866.4, 2776287) 憯急\x9e\x9c\xb0\x8f 2 93
## 8 (301387.6, 2777951) \xe6\x98噸\xe5\x9c葉 33 113
## 9 (301094.7, 2777716) \xe6\x96\x9e\x9c\xb0\x8f 39 213
## 13 (297797.9, 2777667) 撖\xae\x9c\xb0\x8f 48 63
## 26 (302358.7, 2778549) 憯急\xe5\x9c\xb0\x8f 300 52
## 42 (309784.9, 2775764) 憭扳\xb9\x9c\xb0\x8f 681 92
## 52 (301512.1, 2775618) \xe7朣∪\x9c\xb0\x8f 730 163
## 61 (308530.7, 2773620) \xe6皝\x9c\xb0\x8f 837 24
## 67 (306388.8, 2772734) 瘞\x94\x9c葉 849 35
## 72 (304217, 2772849) 銝剖控\xe5\x9c葉 863 200
## 73 (303883.8, 2773194) 鈭虜\xe5\x9c葉 864 91
## 74 (303560.9, 2773155) 鈭虜\xe5\x9c\xb0\x8f 865 55
## 75 (302584.5, 2772046) \xe5\x90\x9e\x9c\xb0\x8f 882 91
## 78 (301662.6, 2774893) \xe5\x8a蔬\xe5\x9c\xb0\x8f 903 204
## 80 (301096.9, 2772347) \xe8\xe8\x90\x9c\xb0\x8f 913 119
## 81 (301075.9, 2772803) \xe9\x9b\xe5\x9c\xb0\x8f 915 24
## 82 (301247.5, 2772141) \xe6\xe6\xe5\x9c\xb0\x8f 916 201
## 83 (301079.5, 2773162) 瘞\xac\x9c葉 917 186
## 84 (301239.9, 2773257) 憭批\x90\x9c\xb0\x8f 918 25
## 87 (300894.4, 2772864) 憭芸像\xe5\x9c\xb0\x8f 924 91
## 89 (300805.4, 2773077) 憭扳\xa9\x9c\xb0\x8f 926 161
## 91 (298621, 2769869) \xe8瘙\x9c\xb0\x8f 979 99
## 98 (299149.1, 2769994) 樴控\xe5\x9c\xb0\x8f 1051 87
## 100 (304810, 2770048) 隞\x84\x9c\xb0\x8f 1059 56
## 101 (304749.5, 2770157) 隞\x84\x9c葉 1060 133
## 106 (303073.2, 2772220) \xe9\xe6\xe5\x9c\xb0\x8f 1085 48
## 119 (307068, 2771434) 瘞詨\x90\x9c葉 1199 101
## 123 (306471, 2764691) \xe6\xe6\xe5\x9c葉 1247 118
## 124 (306449.8, 2764558) \xe5\x9c\x85\x8a 1248 68
## 131 (304921.7, 2766373) \xe8\x88\x9a\x9c\xb0\x8f 1276 63
## 134 (304240.7, 2769283) 撱箏\xae\x9c\xb0\x8f 1287 43
## 138 (302582.5, 2770855) 敹\xad\x9c\xb0\x8f 1306 152
## 149 (299478.7, 2769410) \xe9\x9b\x9c\x9c\xb0\x8f 1362 110
## 174 (304714.8, 2766624) \xe8\x88\xa6\x9c葉 1575 85
## 175 (303576.3, 2765485) 皞芸\xe5\x9c\xb0\x8f 1576 168sum(as.integer(Schools.Flood$STUDENTS)) # sum 計算Schools.Flood中學生總數為多少## [1] 2645