1.1 The cartography way:

# Load data
data(nuts2006)
# Plot a layer with the extent of the EU28 countries with only a background color
plot(nuts0.spdf, border = NA, col = NA, bg = "#A6CAE0")
# Plot non european space
plot(world.spdf, col  = "lightgreen", border=NA, add=TRUE)
# Plot a layer of countries borders
plot(nuts0.spdf, border = "grey20", lwd = 3, add = TRUE)
# Plot a layer of NUTS1
plot(nuts1.spdf, border = "grey30", lwd = 2, add = TRUE)
# Plot a layer of NUTS2
plot(nuts2.spdf, border = "grey40", lwd = 0.5, add = TRUE)
# Plot a layer of NUTS3
plot(nuts3.spdf, border = "grey20", lwd = 0.1, add = TRUE)

1.2 Creating a map with the five european countries with the most deaths in 2008:

# Layout plot
layoutLayer(title = "Countries with the highest deaths in 2008", # title of the map
            author = "Author: Schnepel (2018)",  # 
            sources = "Sources: Please give credit", # 
            scale = NULL, # no scale
            col = NA, # no color for the title box 
            coltitle = "black", # color of the title
            frame = FALSE,  # no frame around the map
            bg = "#A6CAE0", # background of the map
            extent = nuts0.spdf) # set the extent of the map

# Non European space
plot(world.spdf, col = "#AAB7B8", border = NA, add = TRUE)
# European (EU28) countries
plot(nuts0.spdf, col = "lightgreen",border = "black", lwd = 1, add = TRUE)

# Selection of the 10 most populated countries of Europe
dflab <- nuts0.df[order(nuts0.df$death_2008, decreasing = TRUE),][1:5,]
# Label creation
dflab$lab <- paste(dflab$id, "\n", round(dflab$death_2008/1000,0), "k", sep ="")

labelLayer(spdf = nuts0.spdf, # SpatialPolygonsDataFrame used to plot he labels
           df = dflab, # data frame containing the lables
           txt = "lab", # label field in df
           col = "red", # color of the labels
           cex = 0.6, # size of the labels
           font = 3) # label font

# Add an explanation text
text(x = 5477360, y = 4177311, labels = "The 5 European countries 
     with the most deaths
     in 2008 [thousands]", cex = 0.7, adj = 0)

1.3 Choropleth Map:

# Compute the compound annual growth rate
nuts2.df$cagr <- (((nuts2.df$pop2008 / nuts2.df$pop1999)^(1/9)) - 1) * 100
# Set a custom color palette
cols <- carto.pal(pal1 = "green.pal", # first color gradient
                  n1 = 2, # number of colors in the first gradiant
                  pal2 = "red.pal", # second color gradient
                  n2 = 4) # number of colors in the second gradiant
# Plot a layer with the extent of the EU28 countries with only a background color
plot(nuts0.spdf, border = NA, col = NA, bg = "#A6CAE0")
# Plot non european space
plot(world.spdf, col  = "#E3DEBF", border=NA, add=TRUE)
# Plot the compound annual growth rate
choroLayer(spdf = nuts2.spdf, # SpatialPolygonsDataFrame of the regions
           df = nuts2.df, # data frame with compound annual growth rate
           var = "cagr", # compound annual growth rate field in df
           breaks = c(-2.43,-1,0,0.5,1,2,3.1), # list of breaks
           col = cols, # colors 
           border = "grey40", # color of the polygons borders
           lwd = 0.5, # width of the borders
           legend.pos = "right", # position of the legend
           legend.title.txt = "Compound Annual\nGrowth Rate", # title of the legend
           legend.values.rnd = 2, # number of decimal in the legend values
           add = TRUE) # add the layer to the current plot
# Plot a layer of countries borders
plot(nuts0.spdf,border = "grey20", lwd=0.75, add=TRUE)
# Layout plot
layoutLayer(title = "Demographic Trends", author = "cartography", 
            sources = "Eurostat, 2008", frame = TRUE, col = NA, 
            scale = NULL,coltitle = "black",
            south = TRUE) # add a south arrow

2.1 Creating a filter for european countries:

seleurope = worldborder$REGION == 150 & worldborder$ISO3 != "RUS"
summary(seleurope)

##    Mode   FALSE    TRUE 
## logical     196      50

europefilter <- dplyr::filter(worldborder, seleurope)

2.2 Creating a map for Europe that shows the share of the population with alcohol use disorders in 2016:

alc <- read.csv("./Data/share-with-alcohol-use-disorders.csv")
head(alc)

nalc <- filter(alc, Year==2016)#2016
names(nalc) <-   c("Entity", "Code",   "Year",   "Alc_2016")  
head(nalc)

testworldalc <- left_join(europefilter, nalc, by = c('ISO3' = 'Code'))

## Warning: Column `ISO3`/`Code` joining factors with different levels,
## coercing to character vector

a=testworldalc %>%  st_transform(crs="+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs")
head(a)

testworld.centersalc <- st_centroid(a)
testworld.spdfalc <- methods::as(a, 'Spatial')
testworld.spdfalc@data$id <- row.names(testworld.spdfalc@data)
testworld.tidyalc <- broom::tidy(testworld.spdfalc)

testworld.tidyalc <- dplyr::left_join(testworld.tidyalc, testworld.spdfalc@data, by='id')
summary(testworld.tidyalc$Alc_2016)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.482   1.419   1.687   1.867   1.905   4.200    3649

testgalc <- ggplot(testworld.tidyalc) +
  geom_polygon_interactive(
    color='gray18',
    aes(long, lat, group=group, fill=(Alc_2016),
        tooltip=sprintf("%s<br/>%s",ISO3,Alc_2016))) +
 hrbrthemes::theme_ipsum() +
  colormap::scale_fill_colormap(
    colormap=colormap::colormaps$velocity_blue, reverse = T) +
  labs(title='Share of the population with alcohol 
      use disorders in 2016', subtitle='As Percent of total Population',
       caption='Source: IHME, Global Burden of Disease (GBD)',
       fill="Percent [%]")
testgalc

2.3 Creating a map for the global natural population growth in 2015:

popg <- read.csv("./Data/natural-population-growth.csv")#population growth
#unique(poverty_gap$Code)
head(popg)

npop <- filter(popg, Year==2015)#2015

names(npop) <-   c("Entity", "Code",   "Year",   "Pop_Growth")  

head(npop)

world <- sf::st_as_sf(rnaturalearth::countries110)

## str(world)
length(unique(world$iso_a3))

## [1] 175

nworldpop <- left_join(world, npop, by = c('iso_a3' = 'Code'))
                    
nworldpop %>%  st_transform(crs="+proj=laea +lon_0=18.984375")

head(nworldpop)

world.centerspop <- st_centroid(nworldpop)

world.spdfpop <- methods::as(nworldpop, 'Spatial')
world.spdfpop@data$id <- row.names(world.spdfpop@data)

world.tidypop <- broom::tidy(world.spdfpop)

world.tidypop <- dplyr::left_join(world.tidypop, world.spdfpop@data, by='id')
summary(world.tidypop$Pop_Growth)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## -0.5702  0.3642  1.0433  1.1995  1.8440  4.0183     882

gpop <- ggplot(world.tidypop) +
  geom_polygon_interactive(
    color='gray18',
    aes(long, lat, group=group, fill=(Pop_Growth),
        tooltip=sprintf("%s<br/>%s",iso_a3,Pop_Growth))) +
 hrbrthemes::theme_ipsum() +
  colormap::scale_fill_colormap(
    colormap=colormap::colormaps$autumn, reverse = T) +
  labs(title='Global Natural Population Growth in 2015', subtitle='',
       caption='Source: UN Population Division (2015)',
       fill="Percent [%]")

gpop

2.4 Plotly

The plotly package provides access to an online visualitation platform. Before running code in this section, you need to sign up at plotly[https://plot.ly/r/getting-started/]

df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')

# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)

# specify map projection/options
g <- list(
  showframe = FALSE,
  showcoastlines = FALSE,
  projection = list(type = 'Mercator')
)

p <- plot_geo(df) %>%
  add_trace(
    z = ~GDP..BILLIONS., color = ~GDP..BILLIONS., colors = 'Blues',
    text = ~COUNTRY, locations = ~CODE, marker = list(line = l)
  ) %>%
  colorbar(title = 'GDP Billions US$', tickprefix = '$') %>%
  layout(
    title = '2014 Global GDP<br>Source:<a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">CIA World Factbook</a>',
    geo = g
  )

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = api_create(p, filename="nchoropleth-ag")

chart_link

#https://plot.ly/~gnarsk/1/

Ials’s interactive graph and data of “2014 Global GDPSource:CIA World Factbook” is a choropleth. The x-axis shows values from 0 to 0. The y-axis shows values from 0 to 0.

2.5 Leaflet

The following code is a just a plain visualization:

library(leaflet)
plt <- leaflet() %>%
  setView(lat = 50.85045, lng = 4.34878, zoom=13) %>%
  addTiles(group="OSM") %>%
  addProviderTiles(providers$CartoDB.DarkMatter, group="Dark") %>%
  addProviderTiles(providers$CartoDB.Positron, group="Light") %>%
  addLayersControl(baseGroups=c('OSM','Dark','Light'))
  
plt

2.5.1 Creating a map of life expectancy for europe using leaflet

life <- read.csv("./Data/life-expectancy.csv")
head(life)

life15 <- filter(life, Year==2015)#

names(life15) <-   c("Entity", "Code",   "Year",   "Life")  

europelife <- left_join(europefilter, life15, by = c('ISO3' = 'Code'))

## Warning: Column `ISO3`/`Code` joining factors with different levels,
## coercing to character vector

europelife %>%  st_transform(crs="+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs")

qpal <- colorNumeric("Blues", europelife$Life)

mlife<-leaflet(europelife)
mlife %>% addPolygons(
  fillColor = ~qpal(Life),
  weight = 2,
  opacity = 1,
  color = "black",
  dashArray = "3",
  fillOpacity = 0.7)%>%
  leaflet::addLegend("bottomright", pal=qpal, values=europelife$Life,
                     title="Life expectancy [years]")%>%
  leaflet::addScaleBar("bottomleft")

Life expectancy in Europe in 2015 (70 (darkred) to 85 (white) Years) ### 2.5.2 Creating an interactive map of europe showing the the share of population with alcohol disorders in 2016:

# Now, we'll create the Map
# This first command will creat an empty map
apal <- colorNumeric("PuOr", domain = a$Alc_2016)
malc<-leaflet(a)
  malc %>%
        addTiles()%>% 
          addCircleMarkers(
          lng=~LON,
          lat= ~LAT,
          radius=~Alc_2016*4,
          color = ~apal(Alc_2016),
          stroke = FALSE, fillOpacity = 0.7,
          #label=~as.character(Alc_2016),
          labelOptions = labelOptions(noHide = T),
          options = leafletOptions(minZoom = 0, maxZoom = 10,scroolWheelZoom=FALSE))%>%
    leaflet::addLegend("bottomright",pal=apal,values=a$Alc_2016,title="Percent")