title: “R Notebook” output: html_notebook —

As a first step we load the required librarys which should be installed in advance:

library (rgdal)

## Loading required package: sp

## rgdal: version: 1.3-6, (SVN revision 773)
##  Geospatial Data Abstraction Library extensions to R successfully loaded
##  Loaded GDAL runtime: GDAL 2.1.3, released 2017/20/01
##  Path to GDAL shared files: /usr/share/gdal/2.1
##  GDAL binary built with GEOS: TRUE 
##  Loaded PROJ.4 runtime: Rel. 4.9.2, 08 September 2015, [PJ_VERSION: 492]
##  Path to PROJ.4 shared files: (autodetected)
##  Linking to sp version: 1.3-1

library (sf)

## Linking to GEOS 3.5.1, GDAL 2.1.3, PROJ 4.9.2

library (sp)
library (cartography)
library (rgdal)

Downloading, extracting and reading the vector data for the selected country:

download.file    ("http://biogeo.ucdavis.edu/data/diva/adm/LKA_adm.zip", destfile = ".//LKA_adm.zip" , mode='wb')

unzip            (".//LKA_adm.zip", exdir = "C://Users//david//Desktop//geomatics")

spcol <- readOGR(dsn = "C://Users//david//Desktop//geomatics//LKA_adm1.shp", verbose = FALSE)
class(spcol)

## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"

Choosing html colors and adding a title for the plot:

spcol@data$COLOUR <- "#FFFFFF"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 0] <- "#006837"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 1] <- "#52BE80"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 2] <- "#28B463"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 3] <- "#D4EFDF"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 4] <- "#138D75"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 5] <- "#76D7C4"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 6] <- "#A3E4D7"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 7] <- "#D0ECE7"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 8] <- "#FAE5D3"
spcol@data$COLOUR[(as.numeric(as.character(spcol@data$ID_1)) %% 10) == 9] <- "#A9CCE3"

plot(spcol, col=spcol$COLOUR, main = "Sri Lanka´s Departments")

library(sf)

sfcol <- st_read(dsn = "C://Users//david//Desktop//geomatics//LKA_adm1.shp", quiet = TRUE)
class(sfcol)

## [1] "sf"         "data.frame"

Using the cartography package with geospatial data for europe:

data(nuts2006)
plot(nuts0.spdf, border = NA, col = NA, bg = "#A6CAE0")
# Plot non european space
plot(world.spdf, col  = "#E3DEBF", 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)

# Layout plot
layoutLayer(title = "3 Least Populated Countries in Europe", # title of the map
            author = "Author: Your name here",  # 
            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 = "#A0B27B",border = "white", lwd = 1, add = TRUE)

# Selection of the 3 least populated countries of Europe
dflab <- nuts0.df[order(nuts0.df$pop2008, decreasing = FALSE),][1:3,]

# Label creation
dflab$lab <- paste(dflab$id, "\n", round(dflab$pop2008/100000,1), "M", sep ="")

# Label plot of the 10 most populated countries
labelLayer(spdf = nuts0.spdf, # SpatialPolygonsDataFrame used to plot he labels
           df = dflab, # data frame containing the lables
           txt = "lab", # label field in df
           col = "#690409", # color of the labels
           cex = 0.9, # size of the labels
           font = 2) # label font

# Add an explanation text
text(x = 5477360, y = 4177311, labels = "The 3 least populated countries of Europe
Total population 2008 [millions]", cex = 0.7, adj = 0)

# 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

library(ggplot2)
library(ggiraph)
library(rnaturalearth)
library(readr)
library(RCurl)

## Loading required package: bitops

library(bitops)

urlfile <- "https://raw.github.com/bhaskarvk/user2017.geodataviz/master/inst/extdata/africa-internet_usage-2015.csv"

internet_usage <- read.csv(urlfile)

names(internet_usage) <- c("Country Name",  "Country Code",   "Series Name",  "Series Code", 
"2014 [YR2014]", "2015 [YR2015]", "2015 [YR20156]")


names(internet_usage)

## [1] "Country Name"   "Country Code"   "Series Name"    "Series Code"   
## [5] "2014 [YR2014]"  "2015 [YR2015]"  "2015 [YR20156]"

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

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

## [1] 175

africa <- dplyr::filter(world, region_un=='Africa')  

africa2= africa %>% dplyr::left_join(internet_usage %>% dplyr::select(
    `Country Code`, `2015 [YR2015]`
  ) %>% dplyr::rename(iso_a3=`Country Code`, internet.usage.2015=`2015 [YR2015]`),
  by = 'iso_a3')

## Warning: Column `iso_a3` joining character vector and factor, coercing into
## character vector

africa2$internet.usage.2015

##  [1] 12.400000  4.866224  6.787703 11.387646 27.500000  4.563264 21.000000
##  [8] 20.680148  3.800000  7.615975 11.922431 38.200000 37.819383  1.083733
## [15] 11.600000 23.500000 23.478128  4.700000 17.119821  3.540707 21.320000
## [22] 45.622801  5.903868 19.016080 16.071708 57.080000  4.173972 10.336925
## [29]  9.000000 15.199127  9.298148 22.307015  2.220165 47.442550 18.000000
## [36]        NA 26.614929 17.929787 21.690264  2.500000        NA  1.760000
## [43] 30.382701  2.700000  7.120000 48.519836  5.355144 19.221100 51.919116
## [50] 21.000000 16.360000

#the joint was successful

africa3=africa2 %>% st_transform(crs="+proj=laea +lon_0=18.984375") ## add the right crs from spatialreference.org(for example for asia)

africa.centers <- st_centroid(africa3)

africa.spdf <- methods::as(africa3, 'Spatial')
africa.spdf@data$id <- row.names(africa.spdf@data)
africa.tidy <- broom::tidy(africa.spdf)

library(htmlwidgets)
library(hrbrthemes)

hrbrthemes::import_roboto_condensed()

library(colormap)
library(widgetframe)

africa.tidy <- dplyr::left_join(africa.tidy, africa.spdf@data, by='id')

g <- ggplot(africa.tidy) +
  geom_polygon_interactive(
    color='black',
    aes(long, lat, group=group, fill=internet.usage.2015,
        tooltip=sprintf("%s<br/>%s",iso_a3,internet.usage.2015))) +
 hrbrthemes::theme_ipsum() +
  colormap::scale_fill_colormap(
    colormap=colormap::colormaps$copper, reverse = T) +
  labs(title='Internet Usage in Africa in 2015', subtitle='As Percent of Population',
       caption='Source: World Bank Open Data.')

g

Sys.setenv("plotly_username"="david.sena")
Sys.setenv("plotly_api_key"="7MjIFPXAp5F6ftToXRxt")

options(browser = 'false')
library(plotly)

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
h <- 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 = h
  )

# 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")

## Found a grid already named: 'nchoropleth-ag Grid'. Since fileopt='overwrite', I'll try to update it

## Found a plot already named: 'nchoropleth-ag'. Since fileopt='overwrite', I'll try to update it

p

chart_link

library(leaflet)
plt <- leaflet() %>%
  setView(lat =     50.935173, lng = 6.953101, zoom=13) %>%
  addTiles(group="OSM") %>%
  addProviderTiles(providers$CartoDB.DarkMatter, group="Dark") %>%
  addProviderTiles(providers$CartoDB.Positron, group="Light") %>%
  addLayersControl(baseGroups=c('OSM','Dark','Light'))
  
plt

urlfile2 <- "https://raw.github.com/amcrisan/EpiDesignPattern/master/data/ebola_metadata.RDS"
myfile <- download.file(urlfile2,"/cloud/project/C:/Users/david/Desktop/geomatics/ebola_metadata.RDS", method="auto")

metadata <- readRDS("/cloud/project/C:/Users/david/Desktop/geomatics/ebola_metadata.RDS")

library(ape)
#library(ggtree)
library(lubridate)

library(tidyr)

library(dplyr)
library(ggmap)

library(RColorBrewer)
library(dygraphs)
library(xts)

library(leaflet)
# First, we need to create some new data
# Counting number cases per country
aggDat<-metadata %>%
       filter(Country !="?") %>%
       group_by(Country,country_lon,country_lat) %>%
       dplyr::count()%>%
       mutate(popup=sprintf("%s = %d cases",Country,n)) #create a popup for the map
# Here's a very quick look at what this command generates for us:
aggDat

## # A tibble: 3 x 5
## # Groups:   Country, country_lon, country_lat [3]
##   Country country_lon country_lat     n popup          
##   <fct>         <dbl>       <dbl> <int> <chr>          
## 1 GIN           -9.70        9.95   367 GIN = 367 cases
## 2 LBR           -9.43        6.43    45 LBR = 45 cases 
## 3 SLE          -11.8         8.46   313 SLE = 313 cases

# Now, we'll define the color palette
pal <- colorFactor(
  palette = c('red', 'blue', 'green', 'purple', 'orange'),
  domain = aggDat$Country)

# Now, we'll create the Map
# This first command will creat an empty map
m<-leaflet(aggDat)
  m %>%
        addTiles()%>% 
        addCircleMarkers(
          lng=~country_lon,
          lat= ~country_lat,
          radius=~sqrt(n)*2,
          color = ~pal(Country),
          stroke = FALSE, fillOpacity = 0.7,
          label=~as.character(popup),
          labelOptions = labelOptions(noHide = T),
          options = leafletOptions(minZoom = 0, maxZoom = 10,scroolWheelZoom=FALSE))

aggDat<-metadata %>%
        filter(Country !="?") %>%
        group_by(Country,Region,region_lon,region_lat) %>%
        dplyr::count()%>% 
        mutate(popup=sprintf("%s (%s) = %d cases",Region,Country,n))
      
m<-leaflet(aggDat)
      
m %>%
  addTiles()%>% 
  addCircleMarkers(
    lng=~region_lon,
    lat= ~region_lat,
    radius=~sqrt(n)*2,
    color = ~pal(Country), #we actually colour the points by country here
    stroke = FALSE, fillOpacity = 0.7,
    label=~as.character(popup),
    labelOptions = labelOptions(noHide = F))

m<-leaflet(metadata) 
# by providing the region latitude and longtitude co-ordinates we allow clustering of regional samples
m %>%
  addTiles()%>%
  addCircleMarkers(
    lng=~region_lon,
    lat= ~region_lat,
    stroke = FALSE, fillOpacity = 0.5,
    clusterOptions= markerClusterOptions(titile="regional clusters") #cluster options
  )

Additional Content:

library(ggplot2)
library(ggiraph)
library(rnaturalearth)
library(readr)
library(RCurl)
library(bitops)

file <- "/cloud/project/C:/Users/david/Desktop/geomatics/economic-inequality-gini-index.csv"

economic_inequality <- read.csv(file)
unique(economic_inequality$Code)

##   [1] ALB      DZA      AGO      ARG      ARM      AUS      AUT     
##   [8] AZE      BGD      BLR      BEL      BLZ      BEN      BTN     
##  [15] BOL      BIH      BWA      BRA      BGR      BFA      BDI     
##  [22] CMR      CAN      CPV      CAF      TCD      CHL      CHN     
##  [29] COL      COM      COG      CRI      CIV      HRV      CYP     
##  [36] CZE      COD      DNK      DJI      DOM      ECU      EGY     
##  [43] SLV      EST      ETH      FJI      FIN      FRA      GAB     
##  [50] GMB      GEO      DEU      GHA      GRC      GTM      GIN     
##  [57] GNB      GUY      HTI      HND      HUN      ISL      IND     
##  [64] IDN      IRN      IRQ      IRL      ISR      ITA      JAM     
##  [71] JPN      JOR      KAZ      KEN      KIR      OWID_KOS KGZ     
##  [78] LAO      LVA      LBN      LSO      LBR      LTU      LUX     
##  [85] MKD      MDG      MWI      MYS      MDV      MLI      MRT     
##  [92] MUS      MEX      FSM      MDA      MNG      MNE      MAR     
##  [99] MOZ      MMR      NAM      NPL      NLD      NIC      NER     
## [106] NGA      NOR      PAK      PSE      PAN      PNG      PRY     
## [113] PER      PHL      POL      PRT      ROU      RUS      RWA     
## [120] LCA      WSM      STP      SEN      SRB      SYC      SLE     
## [127] SVK      SVN      SLB      ZAF      KOR      SSD      ESP     
## [134] LKA      SDN      SUR      SWZ      SWE      CHE      SYR     
## [141] TJK      TZA      THA      TLS      TGO      TON      TTO     
## [148] TUN      TUR      TKM      TUV      UGA      UKR      GBR     
## [155] USA      URY      UZB      VUT      VEN      VNM      YEM     
## [162] ZMB      ZWE     
## 163 Levels: AGO ALB ARG ARM AUS AUT AZE BDI BEL BEN BFA BGD BGR BIH ... ZWE

length(unique(economic_inequality$Code))

## [1] 163

library(dplyr)

fpov <- filter(economic_inequality, Year==2014, Entity=='Argentina' )

names(economic_inequality) <-   c("Entity", "Code",   "Year",   "GINI")  

head(economic_inequality)    

##    Entity Code Year GINI
## 1 Albania  ALB 1996 27.0
## 2 Albania  ALB 2002 31.7
## 3 Albania  ALB 2005 30.6
## 4 Albania  ALB 2008 30.0
## 5 Albania  ALB 2012 29.0
## 6 Algeria  DZA 1988 40.2

world2 <- filter(world, admin=='Argentina')

fworld <- left_join(world2, economic_inequality, by = c('iso_a3' = 'Code'))

                    
fworld %>%  st_transform(crs="+proj=laea +lon_0=18.984375")

## Simple feature collection with 27 features and 66 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -8055781 ymin: -7262960 xmax: -5003690 ymax: -3161631
## epsg (SRID):    NA
## proj4string:    +proj=laea +lat_0=0 +lon_0=18.984375 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs
## First 10 features:
##    scalerank      featurecla labelrank sovereignt sov_a3 adm0_dif level
## 1          1 Admin-0 country         2  Argentina    ARG        0     2
## 2          1 Admin-0 country         2  Argentina    ARG        0     2
## 3          1 Admin-0 country         2  Argentina    ARG        0     2
## 4          1 Admin-0 country         2  Argentina    ARG        0     2
## 5          1 Admin-0 country         2  Argentina    ARG        0     2
## 6          1 Admin-0 country         2  Argentina    ARG        0     2
## 7          1 Admin-0 country         2  Argentina    ARG        0     2
## 8          1 Admin-0 country         2  Argentina    ARG        0     2
## 9          1 Admin-0 country         2  Argentina    ARG        0     2
## 10         1 Admin-0 country         2  Argentina    ARG        0     2
##                 type     admin adm0_a3 geou_dif   geounit gu_a3 su_dif
## 1  Sovereign country Argentina     ARG        0 Argentina   ARG      0
## 2  Sovereign country Argentina     ARG        0 Argentina   ARG      0
## 3  Sovereign country Argentina     ARG        0 Argentina   ARG      0
## 4  Sovereign country Argentina     ARG        0 Argentina   ARG      0
## 5  Sovereign country Argentina     ARG        0 Argentina   ARG      0
## 6  Sovereign country Argentina     ARG        0 Argentina   ARG      0
## 7  Sovereign country Argentina     ARG        0 Argentina   ARG      0
## 8  Sovereign country Argentina     ARG        0 Argentina   ARG      0
## 9  Sovereign country Argentina     ARG        0 Argentina   ARG      0
## 10 Sovereign country Argentina     ARG        0 Argentina   ARG      0
##      subunit su_a3 brk_diff      name name_long brk_a3  brk_name brk_group
## 1  Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
## 2  Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
## 3  Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
## 4  Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
## 5  Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
## 6  Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
## 7  Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
## 8  Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
## 9  Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
## 10 Argentina   ARG        0 Argentina Argentina    ARG Argentina      <NA>
##    abbrev postal          formal_en formal_fr note_adm0 note_brk name_sort
## 1    Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
## 2    Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
## 3    Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
## 4    Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
## 5    Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
## 6    Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
## 7    Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
## 8    Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
## 9    Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
## 10   Arg.     AR Argentine Republic      <NA>      <NA>     <NA> Argentina
##    name_alt mapcolor7 mapcolor8 mapcolor9 mapcolor13  pop_est gdp_md_est
## 1      <NA>         3         1         3         13 40913584     573900
## 2      <NA>         3         1         3         13 40913584     573900
## 3      <NA>         3         1         3         13 40913584     573900
## 4      <NA>         3         1         3         13 40913584     573900
## 5      <NA>         3         1         3         13 40913584     573900
## 6      <NA>         3         1         3         13 40913584     573900
## 7      <NA>         3         1         3         13 40913584     573900
## 8      <NA>         3         1         3         13 40913584     573900
## 9      <NA>         3         1         3         13 40913584     573900
## 10     <NA>         3         1         3         13 40913584     573900
##    pop_year lastcensus gdp_year                 economy
## 1        NA       2010       NA 5. Emerging region: G20
## 2        NA       2010       NA 5. Emerging region: G20
## 3        NA       2010       NA 5. Emerging region: G20
## 4        NA       2010       NA 5. Emerging region: G20
## 5        NA       2010       NA 5. Emerging region: G20
## 6        NA       2010       NA 5. Emerging region: G20
## 7        NA       2010       NA 5. Emerging region: G20
## 8        NA       2010       NA 5. Emerging region: G20
## 9        NA       2010       NA 5. Emerging region: G20
## 10       NA       2010       NA 5. Emerging region: G20
##                income_grp wikipedia fips_10 iso_a2 iso_a3 iso_n3 un_a3
## 1  3. Upper middle income        NA    <NA>     AR    ARG    032   032
## 2  3. Upper middle income        NA    <NA>     AR    ARG    032   032
## 3  3. Upper middle income        NA    <NA>     AR    ARG    032   032
## 4  3. Upper middle income        NA    <NA>     AR    ARG    032   032
## 5  3. Upper middle income        NA    <NA>     AR    ARG    032   032
## 6  3. Upper middle income        NA    <NA>     AR    ARG    032   032
## 7  3. Upper middle income        NA    <NA>     AR    ARG    032   032
## 8  3. Upper middle income        NA    <NA>     AR    ARG    032   032
## 9  3. Upper middle income        NA    <NA>     AR    ARG    032   032
## 10 3. Upper middle income        NA    <NA>     AR    ARG    032   032
##    wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us adm0_a3_un adm0_a3_wb
## 1     AR   ARG     NA        ARG        ARG         NA         NA
## 2     AR   ARG     NA        ARG        ARG         NA         NA
## 3     AR   ARG     NA        ARG        ARG         NA         NA
## 4     AR   ARG     NA        ARG        ARG         NA         NA
## 5     AR   ARG     NA        ARG        ARG         NA         NA
## 6     AR   ARG     NA        ARG        ARG         NA         NA
## 7     AR   ARG     NA        ARG        ARG         NA         NA
## 8     AR   ARG     NA        ARG        ARG         NA         NA
## 9     AR   ARG     NA        ARG        ARG         NA         NA
## 10    AR   ARG     NA        ARG        ARG         NA         NA
##        continent region_un     subregion                 region_wb
## 1  South America  Americas South America Latin America & Caribbean
## 2  South America  Americas South America Latin America & Caribbean
## 3  South America  Americas South America Latin America & Caribbean
## 4  South America  Americas South America Latin America & Caribbean
## 5  South America  Americas South America Latin America & Caribbean
## 6  South America  Americas South America Latin America & Caribbean
## 7  South America  Americas South America Latin America & Caribbean
## 8  South America  Americas South America Latin America & Caribbean
## 9  South America  Americas South America Latin America & Caribbean
## 10 South America  Americas South America Latin America & Caribbean
##    name_len long_len abbrev_len tiny homepart    Entity Year GINI
## 1         9        9          4   NA        1 Argentina 1980 40.8
## 2         9        9          4   NA        1 Argentina 1986 42.8
## 3         9        9          4   NA        1 Argentina 1987 45.3
## 4         9        9          4   NA        1 Argentina 1991 46.8
## 5         9        9          4   NA        1 Argentina 1992 45.5
## 6         9        9          4   NA        1 Argentina 1993 44.9
## 7         9        9          4   NA        1 Argentina 1994 45.9
## 8         9        9          4   NA        1 Argentina 1995 48.9
## 9         9        9          4   NA        1 Argentina 1996 49.5
## 10        9        9          4   NA        1 Argentina 1997 49.1
##                          geometry
## 1  MULTIPOLYGON (((-5003690 -7...
## 2  MULTIPOLYGON (((-5003690 -7...
## 3  MULTIPOLYGON (((-5003690 -7...
## 4  MULTIPOLYGON (((-5003690 -7...
## 5  MULTIPOLYGON (((-5003690 -7...
## 6  MULTIPOLYGON (((-5003690 -7...
## 7  MULTIPOLYGON (((-5003690 -7...
## 8  MULTIPOLYGON (((-5003690 -7...
## 9  MULTIPOLYGON (((-5003690 -7...
## 10 MULTIPOLYGON (((-5003690 -7...

fworld$economic_inequality

## NULL

fworld$economic_inequality

## NULL

fworld$GINI[is.na(fworld$GINI)] <- 0

world.centers <- st_centroid(fworld)

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

world.tidy <- broom::tidy(world.spdf)

world.tidy <- dplyr::left_join(world.tidy, world.spdf@data, by='id')
summary(world.tidy$GINI)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   40.80   44.50   46.80   47.19   49.80   53.80

f <- ggplot(world.tidy) +
  geom_polygon_interactive(
    color='black',
    aes(long, lat, group=group, fill=(GINI),
        tooltip=sprintf("%s<br/>%s",iso_a3,GINI))) +
 hrbrthemes::theme_ipsum() +
  colormap::scale_fill_colormap(
    colormap=colormap::colormaps$freesurface_red, reverse = T) +
  labs(title='Economic inequality in Argentina 2014', subtitle='GINI index',
       caption='Source: World Bank Open Data.')

f