Countries that I have visited

df_maps <- data.frame(Country = countrycode(levels(dta$Country)[1:10],
                                            'country.name', 'iso3c'),
                      Counts = as.numeric(table(dta$Country)[1:10]))
visitedMap <- joinCountryData2Map(df_maps, joinCode = "ISO3", nameJoinColumn = "Country")

10 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
233 codes from the map weren't represented in your data

mapCountryData(visitedMap, nameColumnToPlot = "Counts", catMethod = "categorical",
               addLegend = TRUE, missingCountryCol = gray(.9),
               colourPalette = c('deepskyblue', 'dodgerblue', 'dodgerblue4'),
               mapTitle = 'Countries that Jay Liao have visited (with visit counts label)')

Countries that I have stay (including my own country)

df_maps <- data.frame(Country = countrycode(levels(dta$Country),
                                            'country.name', 'iso3c'),
                      Counts = as.numeric(table(dta$Country)))
visitedMap <- joinCountryData2Map(df_maps, joinCode = "ISO3", nameJoinColumn = "Country")

11 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
232 codes from the map weren't represented in your data

mapCountryData(visitedMap, nameColumnToPlot = "Counts", catMethod = "categorical",
               addLegend = TRUE, missingCountryCol = gray(.9),
               colourPalette = c('deepskyblue', 'dodgerblue', 'dodgerblue4', 'black'),
               mapTitle = 'Countries that Jay Liao have stay (with stay months label)')

Great cafe’ for studying

d <- dta$cafe
m <- leaflet() %>% addTiles() %>%
  addMarkers(lat = d$lat, lng = d$lng, popup = d$loc)
m

Great brunch restaurants

d <- dta$Brunch
m <- leaflet() %>% addTiles() %>%
  addMarkers(lat = d$lat, lng = d$lng, popup = d$loc)
m

Great dinner restaurants

d <- dta$Dinner
m <- leaflet() %>% addTiles() %>%
  addMarkers(lat = d$lat, lng = d$lng, popup = d$loc)
m

Great bars

d <- dta$Bar
m <- leaflet() %>% addTiles() %>%
  addMarkers(lat = d$lat, lng = d$lng, popup = d$loc)
m

Lollipop plot

dta_sex %>% dplyr::filter(Country != 'Global') %>%
  mutate(Country = factor(Country, levels = Country[nrow(dta_sex):1]),
         Group = cut(Age, breaks = quantile(Age, probs = c(0, .25, .50, .75, 1)),
                     include.lowest = TRUE)) %>%
  qplot(data =., x = Age, y = Country, color = Group) +
  geom_segment(aes(xend = 14, yend = Country, color = Group)) +
  geom_vline(xintercept = dta_sex$Age[dta_sex$Country == 'Global'], lty = 2) +
  scale_x_continuous(breaks = seq(14, 22, by = 2)) +
  labs(x = 'Average age of the first sex', y = 'Country') +
  theme_bw() +
  theme(legend.position = 'none',
        axis.text.y = element_text(size = 10))

Map

df_maps <- data.frame(Country = countrycode(dta_sex$Country,
                                            'country.name', 'iso3c'),
                      Age = dta_sex$Age)

Warning in countrycode(dta_sex$Country, "country.name", "iso3c"): Some values were not matched unambiguously: Global, Serbia & Montenegro

visitedMap <- joinCountryData2Map(df_maps, joinCode = "ISO3", nameJoinColumn = "Country")

40 codes from your data successfully matched countries in the map
2 codes from your data failed to match with a country code in the map
203 codes from the map weren't represented in your data

par(oma = c(0, 0, 0, 0))
mapCountryData(visitedMap, nameColumnToPlot = "Age", catMethod = 'pretty',
               addLegend = FALSE, missingCountryCol = gray(.9), mapTitle = '',
               colourPalette = c('skyblue1', 'skyblue2', 'dodgerblue', 'dodgerblue4', 'black'))

Warning in rwmGetColours(colourPalette, numColours): 5 colours specified
and 9 required, using interpolation to calculate colours

This map is driven by the latest version of the dataset. Thus, it is lightly different from the target output.

Load the packages and prepare the datasets

library(rgdal)
library(sp)
Flood <- readOGR(dsn = './flood-in-school', layer = "flood50", encoding = 'big5')

OGR data source with driver: ESRI Shapefile 
Source: "/Users/jayliao/Documents/NCKU_108_Course/dataM/exercise_0518/flood-in-school", layer: "flood50"
with 5103 features
It has 5 fields

Schools <- readOGR(dsn = './flood-in-school', layer = "tpecity_school", encoding = 'big5')

OGR data source with driver: ESRI Shapefile 
Source: "/Users/jayliao/Documents/NCKU_108_Course/dataM/exercise_0518/flood-in-school", layer: "tpecity_school"
with 198 features
It has 3 fields
Integer64 fields read as strings:  STUDENTS 

shp_tw <- readOGR('../data/mapdata201911261001/County_MOI_1081121.shp')

OGR data source with driver: ESRI Shapefile 
Source: "/Users/jayliao/Documents/NCKU_108_Course/dataM/data/mapdata201911261001/COUNTY_MOI_1081121.shp", layer: "COUNTY_MOI_1081121"
with 22 features
It has 4 fields

TP <- fortify(shp_tw) %>% dplyr::filter(id == 8)
fortify_Flood <- fortify(Flood)
Schools_Flood <- Schools[Flood, ]
df_Schools <- as.data.frame(Schools)

Plot the map with three layers using basic R graph tools

df_Schools_Flood <- as.data.frame(Schools_Flood)
par(oma = c(0, 0, 0, 0))    # Close the outter margins
plot(Flood, col = 'cyan')
plot(Schools, col = 'gray', pch = 20, add = TRUE)
plot(Schools_Flood, col = 'red', pch = 20, add = TRUE)
text(x = df_Schools_Flood$coords.x1, y = df_Schools_Flood$coords.x2,
     label = Schools_Flood$TEXTSTRING, family = '蘋方-繁 標準體', cex = .6)

I also try to label the name of the schools which are involved in the risky area. However, the texts of school names are too small to recognize. Some of them even overlap! I then try to use ggplot2 and ggrepel to fix it.

Plot the map with four layers using ggplot2

f_scale <- function(v1, v2, nIter) {
  n <- min(c(length(v1), length(v2)))
  s <- sapply(1:nIter, function(i) {
    v1 <- sort(sample(v1, n))
    v2 <- sort(sample(v2, n))
    coefficients(lm(v2 ~ v1))
  })
  return(rowMeans(s))
}
coeffi_long <- f_scale(TP$long, fortify_Flood$long, 5000)
coeffi_lat <- f_scale(TP$lat, fortify_Flood$lat, 5000)

library(ggrepel)
ggplot() +
  geom_polygon(aes(x = coeffi_long[1] + coeffi_long[2]*long, 
                   y = coeffi_lat[1] + coeffi_lat[2]*lat, group = group),
               fill = 'black', data = TP) +
  geom_polygon(aes(x = long, y = lat, group = group),
               fill = 'deepskyblue2', data = fortify_Flood) +
  geom_point(data = df_Schools, color = 'grey',
             mapping = aes(x = coords.x1, y = coords.x2)) +
  geom_point(data = df_Schools_Flood, color = 'hotpink',
             mapping = aes(x = coords.x1, y = coords.x2)) +
  geom_label_repel(aes(x = coords.x1, y = coords.x2, label = TEXTSTRING),
                   data = df_Schools_Flood,
                   family = '蘋方-繁 標準體', size = 1.5,
                   segment.color = 'grey10', segment.size = .25) +
  theme_void()

Plot the map with three layers using ggplot2

ggplot() +
  geom_polygon(aes(x = long, y = lat, group = group),
               fill = 'deepskyblue2', data = fortify_Flood) +
  geom_point(data = df_Schools, color = 'grey',
             mapping = aes(x = coords.x1, y = coords.x2)) +
  geom_point(data = df_Schools_Flood, color = 'hotpink',
             mapping = aes(x = coords.x1, y = coords.x2)) +
  geom_label_repel(aes(x = coords.x1, y = coords.x2, label = TEXTSTRING),
                   data = df_Schools_Flood,
                   family = '蘋方-繁 標準體', size = 2.5,
                   segment.color = 'grey10', segment.size = .25) +
  theme_void()

Compute the number of students under the risk

head(Schools_Flood@data, n = nrow(Schools_Flood@data)) 

Schools_Flood@data$STUDENTS %>% as.character() %>% as.numeric() %>% sum()

[1] 3722

台北市共有36個中小學在潛在受災區範圍內，同時可能影響的學生人數共為3722名