My First Notebook
Miriam
31 10 2018
R Markdown
This is my first G4D Assignment
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: 1.3-1Loading required package: sp
library(sf)## Linking to GEOS 3.5.1, GDAL 2.1.3, PROJ 4.9.2library(sp)library(cartography)library(rgdal)##Getting Vector data
download.file("http://data.biogeo.ucdavis.edu/data/diva/adm/TZA_adm.zip",
destfile = "./data/TZA_adm.zip" , mode='wb')unzip("./data/TZA_adm.zip", exdir = "./data")##Creating an object sp color
spcol <- readOGR(dsn = "./data/TZA_adm1.shp", verbose = FALSE)
class(spcol)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"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 = "Tanzania's Departments")
### And for Library sf
library(sf)
sfcol <- st_read(dsn = "./data/TZA_adm1.shp", quiet = TRUE)
class(sfcol)## [1] "sf" "data.frame"sfcol$COLOUR <- spcol@data$COLOUR
plot(st_geometry(sfcol), col=sfcol$COLOUR, main = "Tanzania's Departments")
library(cartography)data(nuts2006)##For color plots
plot(nuts0.spdf, border = NA, col = NA, bg = "#A6CAE0")
# Plot non european space
plot(world.spdf, col = "#E3DEBF", border=NA, add=TRUE)
# Plot a layer of countries borders
plot(nuts0.spdf, border = "grey20", lwd = 3, add = TRUE)
# Plot a layer of NUTS1
plot(nuts1.spdf, border = "grey30", lwd = 2, add = TRUE)
# Plot a layer of NUTS2
plot(nuts2.spdf, border = "grey40", lwd = 0.5, add = TRUE)
# Plot a layer of NUTS3
plot(nuts3.spdf, border = "grey20", lwd = 0.1, add = TRUE)
####Plot 10 Most polulated countries in Europe
# Layout plot
layoutLayer(title = "Most Populated Countries in Europe", # title of the map
author = "Author:Miriam Kasebele", #
sources = "Sources: Eurostat", #
scale = NULL, # no scale
col = NA, # no color for the title box
coltitle = "black", # color of the title
frame = TRUE,
bg = "#A6CAE0", # background of the map
extent = nuts0.spdf) # set the extent of the map
# Non European space
plot(world.spdf, col = "#AAB7B8", border = NA, add = TRUE)
# European (EU28) countries
plot(nuts0.spdf, col = "#F12BD0",border = "white", lwd = 1, add = TRUE)
# Selection of the 10 most populated countries of Europe
dflab <- nuts0.df[order(nuts0.df$pop2008, decreasing = TRUE),][1:10,]
# Label creation
dflab$lab <- paste(dflab$id, "\n", round(dflab$pop2008/1000000,0), "M", sep ="")
# Label plot of the 10 most populated countries
labelLayer(spdf = nuts0.spdf, # SpatialPolygonsDataFrame used to plot he labels
df = dflab, # data frame containing the lables
txt = "lab", # label field in df
col = "#690409", # color of the labels
cex = 0.6, # size of the labels
font = 2) # label font
# Add an explanation text
text(x = 5477360, y = 4177311, labels = "The 10 most populated countries of Europe
Total population 2008 [millions]", cex = 0.7, adj = 0)
##Computation of growth rate
# Compute the compound annual growth rate
nuts2.df$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####Loading required bitops
urlfile <- "https://raw.github.com/bhaskarvk/user2017.geodataviz/master/inst/extdata/africa-internet_usage-2015.csv"
internet_usage <- read.csv(urlfile)
head(internet_usage)## Country.Name Country.Code
## 1 Algeria DZA
## 2 Angola AGO
## 3 Bahrain BHR
## 4 Benin BEN
## 5 Botswana BWA
## 6 Burkina Faso BFA
## Series.Name Series.Code
## 1 Individuals using the Internet (% of population) IT.NET.USER.ZS
## 2 Individuals using the Internet (% of population) IT.NET.USER.ZS
## 3 Individuals using the Internet (% of population) IT.NET.USER.ZS
## 4 Individuals using the Internet (% of population) IT.NET.USER.ZS
## 5 Individuals using the Internet (% of population) IT.NET.USER.ZS
## 6 Individuals using the Internet (% of population) IT.NET.USER.ZS
## X2014..YR2014. X2015..YR2015. X2016..YR2016.
## 1 25.00000 38.200000 ..
## 2 10.20000 12.400000 ..
## 3 90.50313 93.478301 ..
## 4 6.00000 6.787703 ..
## 5 18.50000 27.500000 ..
## 6 9.40000 11.387646 ..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] 175print(world)## Simple feature collection with 177 features and 63 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## First 10 features:
## scalerank featurecla labelrank sovereignt sov_a3 adm0_dif
## 0 1 Admin-0 country 3 Afghanistan AFG 0
## 1 1 Admin-0 country 3 Angola AGO 0
## 2 1 Admin-0 country 6 Albania ALB 0
## 3 1 Admin-0 country 4 United Arab Emirates ARE 0
## 4 1 Admin-0 country 2 Argentina ARG 0
## 5 1 Admin-0 country 6 Armenia ARM 0
## 6 1 Admin-0 country 4 Antarctica ATA 0
## 7 3 Admin-0 country 6 France FR1 1
## 8 1 Admin-0 country 2 Australia AU1 1
## 9 1 Admin-0 country 4 Austria AUT 0
## level type admin adm0_a3
## 0 2 Sovereign country Afghanistan AFG
## 1 2 Sovereign country Angola AGO
## 2 2 Sovereign country Albania ALB
## 3 2 Sovereign country United Arab Emirates ARE
## 4 2 Sovereign country Argentina ARG
## 5 2 Sovereign country Armenia ARM
## 6 2 Indeterminate Antarctica ATA
## 7 2 Dependency French Southern and Antarctic Lands ATF
## 8 2 Country Australia AUS
## 9 2 Sovereign country Austria AUT
## geou_dif geounit gu_a3 su_dif
## 0 0 Afghanistan AFG 0
## 1 0 Angola AGO 0
## 2 0 Albania ALB 0
## 3 0 United Arab Emirates ARE 0
## 4 0 Argentina ARG 0
## 5 0 Armenia ARM 0
## 6 0 Antarctica ATA 0
## 7 0 French Southern and Antarctic Lands ATF 0
## 8 0 Australia AUS 0
## 9 0 Austria AUT 0
## subunit su_a3 brk_diff
## 0 Afghanistan AFG 0
## 1 Angola AGO 0
## 2 Albania ALB 0
## 3 United Arab Emirates ARE 0
## 4 Argentina ARG 0
## 5 Armenia ARM 0
## 6 Antarctica ATA 0
## 7 French Southern and Antarctic Lands ATF 0
## 8 Australia AUS 0
## 9 Austria AUT 0
## name name_long brk_a3
## 0 Afghanistan Afghanistan AFG
## 1 Angola Angola AGO
## 2 Albania Albania ALB
## 3 United Arab Emirates United Arab Emirates ARE
## 4 Argentina Argentina ARG
## 5 Armenia Armenia ARM
## 6 Antarctica Antarctica ATA
## 7 Fr. S. Antarctic Lands French Southern and Antarctic Lands ATF
## 8 Australia Australia AUS
## 9 Austria Austria AUT
## brk_name brk_group abbrev postal
## 0 Afghanistan <NA> Afg. AF
## 1 Angola <NA> Ang. AO
## 2 Albania <NA> Alb. AL
## 3 United Arab Emirates <NA> U.A.E. AE
## 4 Argentina <NA> Arg. AR
## 5 Armenia <NA> Arm. ARM
## 6 Antarctica <NA> Ant. AQ
## 7 Fr. S. and Antarctic Lands <NA> Fr. S.A.L. TF
## 8 Australia <NA> Auz. AU
## 9 Austria <NA> Aust. A
## formal_en formal_fr note_adm0
## 0 Islamic State of Afghanistan <NA> <NA>
## 1 People's Republic of Angola <NA> <NA>
## 2 Republic of Albania <NA> <NA>
## 3 United Arab Emirates <NA> <NA>
## 4 Argentine Republic <NA> <NA>
## 5 Republic of Armenia <NA> <NA>
## 6 <NA> <NA> <NA>
## 7 Territory of the French Southern and Antarctic Lands <NA> Fr.
## 8 Commonwealth of Australia <NA> <NA>
## 9 Republic of Austria <NA> <NA>
## note_brk name_sort
## 0 <NA> Afghanistan
## 1 <NA> Angola
## 2 <NA> Albania
## 3 <NA> United Arab Emirates
## 4 <NA> Argentina
## 5 <NA> Armenia
## 6 Multiple claims held in abeyance Antarctica
## 7 <NA> French Southern and Antarctic Lands
## 8 <NA> Australia
## 9 <NA> Austria
## name_alt mapcolor7 mapcolor8 mapcolor9 mapcolor13 pop_est gdp_md_est
## 0 <NA> 5 6 8 7 28400000 22270.0
## 1 <NA> 3 2 6 1 12799293 110300.0
## 2 <NA> 1 4 1 6 3639453 21810.0
## 3 <NA> 2 1 3 3 4798491 184300.0
## 4 <NA> 3 1 3 13 40913584 573900.0
## 5 <NA> 3 1 2 10 2967004 18770.0
## 6 <NA> 4 5 1 NA 3802 760.4
## 7 <NA> 7 5 9 11 140 16.0
## 8 <NA> 1 2 2 7 21262641 800200.0
## 9 <NA> 3 1 3 4 8210281 329500.0
## pop_year lastcensus gdp_year economy
## 0 NA 1979 NA 7. Least developed region
## 1 NA 1970 NA 7. Least developed region
## 2 NA 2001 NA 6. Developing region
## 3 NA 2010 NA 6. Developing region
## 4 NA 2010 NA 5. Emerging region: G20
## 5 NA 2001 NA 6. Developing region
## 6 NA NA NA 6. Developing region
## 7 NA NA NA 6. Developing region
## 8 NA 2006 NA 2. Developed region: nonG7
## 9 NA 2011 NA 2. Developed region: nonG7
## income_grp wikipedia fips_10 iso_a2 iso_a3 iso_n3 un_a3
## 0 5. Low income NA <NA> AF AFG 004 004
## 1 3. Upper middle income NA <NA> AO AGO 024 024
## 2 4. Lower middle income NA <NA> AL ALB 008 008
## 3 2. High income: nonOECD NA <NA> AE ARE 784 784
## 4 3. Upper middle income NA <NA> AR ARG 032 032
## 5 4. Lower middle income NA <NA> AM ARM 051 051
## 6 2. High income: nonOECD NA <NA> AQ ATA 010 <NA>
## 7 2. High income: nonOECD NA <NA> TF ATF 260 <NA>
## 8 1. High income: OECD NA <NA> AU AUS 036 036
## 9 1. High income: OECD NA <NA> AT AUT 040 040
## wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us adm0_a3_un adm0_a3_wb
## 0 AF AFG NA AFG AFG NA NA
## 1 AO AGO NA AGO AGO NA NA
## 2 AL ALB NA ALB ALB NA NA
## 3 AE ARE NA ARE ARE NA NA
## 4 AR ARG NA ARG ARG NA NA
## 5 AM ARM NA ARM ARM NA NA
## 6 <NA> <NA> NA ATA ATA NA NA
## 7 <NA> <NA> NA ATF ATF NA NA
## 8 AU AUS NA AUS AUS NA NA
## 9 AT AUT NA AUT AUT NA NA
## continent region_un
## 0 Asia Asia
## 1 Africa Africa
## 2 Europe Europe
## 3 Asia Asia
## 4 South America Americas
## 5 Asia Asia
## 6 Antarctica Antarctica
## 7 Seven seas (open ocean) Seven seas (open ocean)
## 8 Oceania Oceania
## 9 Europe Europe
## subregion region_wb name_len long_len
## 0 Southern Asia South Asia 11 11
## 1 Middle Africa Sub-Saharan Africa 6 6
## 2 Southern Europe Europe & Central Asia 7 7
## 3 Western Asia Middle East & North Africa 20 20
## 4 South America Latin America & Caribbean 9 9
## 5 Western Asia Europe & Central Asia 7 7
## 6 Antarctica Antarctica 10 10
## 7 Seven seas (open ocean) Sub-Saharan Africa 22 35
## 8 Australia and New Zealand East Asia & Pacific 9 9
## 9 Western Europe Europe & Central Asia 7 7
## abbrev_len tiny homepart geometry
## 0 4 NA 1 MULTIPOLYGON (((61.21082 35...
## 1 4 NA 1 MULTIPOLYGON (((16.32653 -5...
## 2 4 NA 1 MULTIPOLYGON (((20.59025 41...
## 3 6 NA 1 MULTIPOLYGON (((51.57952 24...
## 4 4 NA 1 MULTIPOLYGON (((-65.5 -55.2...
## 5 4 NA 1 MULTIPOLYGON (((43.58275 41...
## 6 4 NA 1 MULTIPOLYGON (((-59.57209 -...
## 7 10 2 NA MULTIPOLYGON (((68.935 -48....
## 8 4 NA 1 MULTIPOLYGON (((145.398 -40...
## 9 5 NA 1 MULTIPOLYGON (((16.97967 48...africa <- dplyr::filter(world, region_un=='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') %>%
st_transform(crs="+proj=laea +lon_0=18.984375")## Warning: Column `iso_a3` joining character vector and factor, coercing into
## character vectorafrica$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 16.360000africa.centers <- st_centroid(africa)
africa.spdf <- methods::as(africa, 'Spatial')
africa.spdf@data$id <- row.names(africa.spdf@data)
africa.tidy <- broom::tidy(africa.spdf)## Regions defined for each Polygonslibrary(colormap)
library(widgetframe)## Loading required package: htmlwidgetsNow we plot Internet usage
africa.tidy <- dplyr::left_join(africa.tidy, africa.spdf@data, by='id')
g <- ggplot(africa.tidy) +
geom_polygon_interactive(
color='black',
aes(long, lat, group=group, fill=internet.usage.2015,
tooltip=sprintf("%s<br/>%s",iso_a3,internet.usage.2015))) +
hrbrthemes::theme_ipsum() +
colormap::scale_fill_colormap(
colormap=colormap::colormaps$copper, reverse = T) +
labs(title='Internet Usage in Africa in 2015', subtitle='As Percent of Population',
caption='Source: World Bank Open Data.')
g
###Reproduction of Poverty gap
poverty_gap <- read.csv("./data/poverty-gap-index.csv")
#unique(poverty_gap$Code)
length(unique(poverty_gap$Code))## [1] 133#For selection of one year we employ dplyr library
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionnpov <- filter(poverty_gap, Year==1992)
names(npov) <- c("Entity", "Code", "Year", "Pov_1992")
head(npov)## Entity Code Year Pov_1992
## 1 Argentina ARG 1992 1.18
## 2 Bolivia BOL 1992 4.81
## 3 Brazil BRA 1992 9.09
## 4 Bulgaria BGR 1992 0.00
## 5 Burundi BDI 1992 36.79
## 6 Central African Republic CAF 1992 58.85###Joining our world geospatial object with the poverty_gap non-spatial object
nworld <- left_join(world, npov, by = c('iso_a3' = 'Code'))## Warning: Column `iso_a3`/`Code` joining character vector and factor,
## coercing into character vectornworld %>% st_transform(crs="+proj=laea +lon_0=18.984375")## Simple feature collection with 177 features and 66 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -10326570 ymin: -10821730 xmax: 11718260 ymax: 12476540
## 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
## 1 1 Admin-0 country 3 Afghanistan AFG
## 2 1 Admin-0 country 3 Angola AGO
## 3 1 Admin-0 country 6 Albania ALB
## 4 1 Admin-0 country 4 United Arab Emirates ARE
## 5 1 Admin-0 country 2 Argentina ARG
## 6 1 Admin-0 country 6 Armenia ARM
## 7 1 Admin-0 country 4 Antarctica ATA
## 8 3 Admin-0 country 6 France FR1
## 9 1 Admin-0 country 2 Australia AU1
## 10 1 Admin-0 country 4 Austria AUT
## adm0_dif level type admin
## 1 0 2 Sovereign country Afghanistan
## 2 0 2 Sovereign country Angola
## 3 0 2 Sovereign country Albania
## 4 0 2 Sovereign country United Arab Emirates
## 5 0 2 Sovereign country Argentina
## 6 0 2 Sovereign country Armenia
## 7 0 2 Indeterminate Antarctica
## 8 1 2 Dependency French Southern and Antarctic Lands
## 9 1 2 Country Australia
## 10 0 2 Sovereign country Austria
## adm0_a3 geou_dif geounit gu_a3 su_dif
## 1 AFG 0 Afghanistan AFG 0
## 2 AGO 0 Angola AGO 0
## 3 ALB 0 Albania ALB 0
## 4 ARE 0 United Arab Emirates ARE 0
## 5 ARG 0 Argentina ARG 0
## 6 ARM 0 Armenia ARM 0
## 7 ATA 0 Antarctica ATA 0
## 8 ATF 0 French Southern and Antarctic Lands ATF 0
## 9 AUS 0 Australia AUS 0
## 10 AUT 0 Austria AUT 0
## subunit su_a3 brk_diff
## 1 Afghanistan AFG 0
## 2 Angola AGO 0
## 3 Albania ALB 0
## 4 United Arab Emirates ARE 0
## 5 Argentina ARG 0
## 6 Armenia ARM 0
## 7 Antarctica ATA 0
## 8 French Southern and Antarctic Lands ATF 0
## 9 Australia AUS 0
## 10 Austria AUT 0
## name name_long brk_a3
## 1 Afghanistan Afghanistan AFG
## 2 Angola Angola AGO
## 3 Albania Albania ALB
## 4 United Arab Emirates United Arab Emirates ARE
## 5 Argentina Argentina ARG
## 6 Armenia Armenia ARM
## 7 Antarctica Antarctica ATA
## 8 Fr. S. Antarctic Lands French Southern and Antarctic Lands ATF
## 9 Australia Australia AUS
## 10 Austria Austria AUT
## brk_name brk_group abbrev postal
## 1 Afghanistan <NA> Afg. AF
## 2 Angola <NA> Ang. AO
## 3 Albania <NA> Alb. AL
## 4 United Arab Emirates <NA> U.A.E. AE
## 5 Argentina <NA> Arg. AR
## 6 Armenia <NA> Arm. ARM
## 7 Antarctica <NA> Ant. AQ
## 8 Fr. S. and Antarctic Lands <NA> Fr. S.A.L. TF
## 9 Australia <NA> Auz. AU
## 10 Austria <NA> Aust. A
## formal_en formal_fr
## 1 Islamic State of Afghanistan <NA>
## 2 People's Republic of Angola <NA>
## 3 Republic of Albania <NA>
## 4 United Arab Emirates <NA>
## 5 Argentine Republic <NA>
## 6 Republic of Armenia <NA>
## 7 <NA> <NA>
## 8 Territory of the French Southern and Antarctic Lands <NA>
## 9 Commonwealth of Australia <NA>
## 10 Republic of Austria <NA>
## note_adm0 note_brk
## 1 <NA> <NA>
## 2 <NA> <NA>
## 3 <NA> <NA>
## 4 <NA> <NA>
## 5 <NA> <NA>
## 6 <NA> <NA>
## 7 <NA> Multiple claims held in abeyance
## 8 Fr. <NA>
## 9 <NA> <NA>
## 10 <NA> <NA>
## name_sort name_alt mapcolor7 mapcolor8
## 1 Afghanistan <NA> 5 6
## 2 Angola <NA> 3 2
## 3 Albania <NA> 1 4
## 4 United Arab Emirates <NA> 2 1
## 5 Argentina <NA> 3 1
## 6 Armenia <NA> 3 1
## 7 Antarctica <NA> 4 5
## 8 French Southern and Antarctic Lands <NA> 7 5
## 9 Australia <NA> 1 2
## 10 Austria <NA> 3 1
## mapcolor9 mapcolor13 pop_est gdp_md_est pop_year lastcensus gdp_year
## 1 8 7 28400000 22270.0 NA 1979 NA
## 2 6 1 12799293 110300.0 NA 1970 NA
## 3 1 6 3639453 21810.0 NA 2001 NA
## 4 3 3 4798491 184300.0 NA 2010 NA
## 5 3 13 40913584 573900.0 NA 2010 NA
## 6 2 10 2967004 18770.0 NA 2001 NA
## 7 1 NA 3802 760.4 NA NA NA
## 8 9 11 140 16.0 NA NA NA
## 9 2 7 21262641 800200.0 NA 2006 NA
## 10 3 4 8210281 329500.0 NA 2011 NA
## economy income_grp wikipedia fips_10
## 1 7. Least developed region 5. Low income NA <NA>
## 2 7. Least developed region 3. Upper middle income NA <NA>
## 3 6. Developing region 4. Lower middle income NA <NA>
## 4 6. Developing region 2. High income: nonOECD NA <NA>
## 5 5. Emerging region: G20 3. Upper middle income NA <NA>
## 6 6. Developing region 4. Lower middle income NA <NA>
## 7 6. Developing region 2. High income: nonOECD NA <NA>
## 8 6. Developing region 2. High income: nonOECD NA <NA>
## 9 2. Developed region: nonG7 1. High income: OECD NA <NA>
## 10 2. Developed region: nonG7 1. High income: OECD NA <NA>
## iso_a2 iso_a3 iso_n3 un_a3 wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us
## 1 AF AFG 004 004 AF AFG NA AFG AFG
## 2 AO AGO 024 024 AO AGO NA AGO AGO
## 3 AL ALB 008 008 AL ALB NA ALB ALB
## 4 AE ARE 784 784 AE ARE NA ARE ARE
## 5 AR ARG 032 032 AR ARG NA ARG ARG
## 6 AM ARM 051 051 AM ARM NA ARM ARM
## 7 AQ ATA 010 <NA> <NA> <NA> NA ATA ATA
## 8 TF ATF 260 <NA> <NA> <NA> NA ATF ATF
## 9 AU AUS 036 036 AU AUS NA AUS AUS
## 10 AT AUT 040 040 AT AUT NA AUT AUT
## adm0_a3_un adm0_a3_wb continent region_un
## 1 NA NA Asia Asia
## 2 NA NA Africa Africa
## 3 NA NA Europe Europe
## 4 NA NA Asia Asia
## 5 NA NA South America Americas
## 6 NA NA Asia Asia
## 7 NA NA Antarctica Antarctica
## 8 NA NA Seven seas (open ocean) Seven seas (open ocean)
## 9 NA NA Oceania Oceania
## 10 NA NA Europe Europe
## subregion region_wb name_len long_len
## 1 Southern Asia South Asia 11 11
## 2 Middle Africa Sub-Saharan Africa 6 6
## 3 Southern Europe Europe & Central Asia 7 7
## 4 Western Asia Middle East & North Africa 20 20
## 5 South America Latin America & Caribbean 9 9
## 6 Western Asia Europe & Central Asia 7 7
## 7 Antarctica Antarctica 10 10
## 8 Seven seas (open ocean) Sub-Saharan Africa 22 35
## 9 Australia and New Zealand East Asia & Pacific 9 9
## 10 Western Europe Europe & Central Asia 7 7
## abbrev_len tiny homepart Entity Year Pov_1992
## 1 4 NA 1 <NA> NA NA
## 2 4 NA 1 <NA> NA NA
## 3 4 NA 1 <NA> NA NA
## 4 6 NA 1 <NA> NA NA
## 5 4 NA 1 Argentina 1992 1.18
## 6 4 NA 1 <NA> NA NA
## 7 4 NA 1 <NA> NA NA
## 8 10 2 NA <NA> NA NA
## 9 4 NA 1 <NA> NA NA
## 10 5 NA 1 <NA> NA NA
## geometry
## 1 MULTIPOLYGON (((3897019 413...
## 2 MULTIPOLYGON (((-294686.1 -...
## 3 MULTIPOLYGON (((142770.1 45...
## 4 MULTIPOLYGON (((3333836 276...
## 5 MULTIPOLYGON (((-5003690 -7...
## 6 MULTIPOLYGON (((2183104 454...
## 7 MULTIPOLYGON (((-1509927 -8...
## 8 MULTIPOLYGON (((3831026 -56...
## 9 MULTIPOLYGON (((7426184 -79...
## 10 MULTIPOLYGON (((-163453.8 5...head(nworld)## Simple feature collection with 6 features and 66 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -73.41544 ymin: -55.25 xmax: 75.15803 ymax: 42.68825
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## scalerank featurecla labelrank sovereignt sov_a3 adm0_dif
## 1 1 Admin-0 country 3 Afghanistan AFG 0
## 2 1 Admin-0 country 3 Angola AGO 0
## 3 1 Admin-0 country 6 Albania ALB 0
## 4 1 Admin-0 country 4 United Arab Emirates ARE 0
## 5 1 Admin-0 country 2 Argentina ARG 0
## 6 1 Admin-0 country 6 Armenia ARM 0
## level type admin adm0_a3 geou_dif
## 1 2 Sovereign country Afghanistan AFG 0
## 2 2 Sovereign country Angola AGO 0
## 3 2 Sovereign country Albania ALB 0
## 4 2 Sovereign country United Arab Emirates ARE 0
## 5 2 Sovereign country Argentina ARG 0
## 6 2 Sovereign country Armenia ARM 0
## geounit gu_a3 su_dif subunit su_a3 brk_diff
## 1 Afghanistan AFG 0 Afghanistan AFG 0
## 2 Angola AGO 0 Angola AGO 0
## 3 Albania ALB 0 Albania ALB 0
## 4 United Arab Emirates ARE 0 United Arab Emirates ARE 0
## 5 Argentina ARG 0 Argentina ARG 0
## 6 Armenia ARM 0 Armenia ARM 0
## name name_long brk_a3 brk_name
## 1 Afghanistan Afghanistan AFG Afghanistan
## 2 Angola Angola AGO Angola
## 3 Albania Albania ALB Albania
## 4 United Arab Emirates United Arab Emirates ARE United Arab Emirates
## 5 Argentina Argentina ARG Argentina
## 6 Armenia Armenia ARM Armenia
## brk_group abbrev postal formal_en formal_fr note_adm0
## 1 <NA> Afg. AF Islamic State of Afghanistan <NA> <NA>
## 2 <NA> Ang. AO People's Republic of Angola <NA> <NA>
## 3 <NA> Alb. AL Republic of Albania <NA> <NA>
## 4 <NA> U.A.E. AE United Arab Emirates <NA> <NA>
## 5 <NA> Arg. AR Argentine Republic <NA> <NA>
## 6 <NA> Arm. ARM Republic of Armenia <NA> <NA>
## note_brk name_sort name_alt mapcolor7 mapcolor8 mapcolor9
## 1 <NA> Afghanistan <NA> 5 6 8
## 2 <NA> Angola <NA> 3 2 6
## 3 <NA> Albania <NA> 1 4 1
## 4 <NA> United Arab Emirates <NA> 2 1 3
## 5 <NA> Argentina <NA> 3 1 3
## 6 <NA> Armenia <NA> 3 1 2
## mapcolor13 pop_est gdp_md_est pop_year lastcensus gdp_year
## 1 7 28400000 22270 NA 1979 NA
## 2 1 12799293 110300 NA 1970 NA
## 3 6 3639453 21810 NA 2001 NA
## 4 3 4798491 184300 NA 2010 NA
## 5 13 40913584 573900 NA 2010 NA
## 6 10 2967004 18770 NA 2001 NA
## economy income_grp wikipedia fips_10
## 1 7. Least developed region 5. Low income NA <NA>
## 2 7. Least developed region 3. Upper middle income NA <NA>
## 3 6. Developing region 4. Lower middle income NA <NA>
## 4 6. Developing region 2. High income: nonOECD NA <NA>
## 5 5. Emerging region: G20 3. Upper middle income NA <NA>
## 6 6. Developing region 4. Lower middle income NA <NA>
## iso_a2 iso_a3 iso_n3 un_a3 wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us
## 1 AF AFG 004 004 AF AFG NA AFG AFG
## 2 AO AGO 024 024 AO AGO NA AGO AGO
## 3 AL ALB 008 008 AL ALB NA ALB ALB
## 4 AE ARE 784 784 AE ARE NA ARE ARE
## 5 AR ARG 032 032 AR ARG NA ARG ARG
## 6 AM ARM 051 051 AM ARM NA ARM ARM
## adm0_a3_un adm0_a3_wb continent region_un subregion
## 1 NA NA Asia Asia Southern Asia
## 2 NA NA Africa Africa Middle Africa
## 3 NA NA Europe Europe Southern Europe
## 4 NA NA Asia Asia Western Asia
## 5 NA NA South America Americas South America
## 6 NA NA Asia Asia Western Asia
## region_wb name_len long_len abbrev_len tiny homepart
## 1 South Asia 11 11 4 NA 1
## 2 Sub-Saharan Africa 6 6 4 NA 1
## 3 Europe & Central Asia 7 7 4 NA 1
## 4 Middle East & North Africa 20 20 6 NA 1
## 5 Latin America & Caribbean 9 9 4 NA 1
## 6 Europe & Central Asia 7 7 4 NA 1
## Entity Year Pov_1992 geometry
## 1 <NA> NA NA MULTIPOLYGON (((61.21082 35...
## 2 <NA> NA NA MULTIPOLYGON (((16.32653 -5...
## 3 <NA> NA NA MULTIPOLYGON (((20.59025 41...
## 4 <NA> NA NA MULTIPOLYGON (((51.57952 24...
## 5 Argentina 1992 1.18 MULTIPOLYGON (((-65.5 -55.2...
## 6 <NA> NA NA MULTIPOLYGON (((43.58275 41...Checking(and solve) if there are missing values (denoted as NA in R language Checking
nworld$Pov_1992## [1] NA NA NA NA 1.18 NA NA NA NA NA NA
## [12] 36.79 NA NA NA NA 0.00 NA NA NA NA 4.81
## [23] 9.09 NA NA NA 58.85 NA NA 1.48 NA 6.64 NA
## [34] NA NA 4.48 4.83 NA NA NA NA NA NA NA
## [45] 1.50 NA NA NA NA NA NA NA NA NA NA
## [56] NA NA NA NA NA NA NA NA NA NA NA
## [67] NA 12.48 10.80 NA NA NA NA NA NA NA NA
## [78] NA NA NA NA NA NA NA 7.93 NA NA NA
## [89] NA NA 4.83 NA NA NA NA NA NA NA NA
## [100] NA 3.28 NA 2.88 NA NA NA NA NA NA NA
## [111] NA 0.10 NA NA 34.12 27.37 NA NA NA NA NA
## [122] NA NA NA NA NA NA NA NA NA NA NA
## [133] NA NA 0.30 NA NA NA NA NA NA NA NA
## [144] NA NA NA NA NA NA 0.00 NA NA NA NA
## [155] NA NA 1.13 NA NA NA 0.86 NA NA NA NA
## [166] 28.61 0.68 0.15 NA NA 2.79 14.95 NA NA NA NA
## [177] NAnworld$Pov_1992[is.na(nworld$Pov_1992)] <- 0###More processing on the spatial objects. Type ?sf::st_centroid in the console if you need help.
world.centers <- st_centroid(nworld)
world.spdf <- methods::as(nworld, 'Spatial')
world.spdf@data$id <- row.names(world.spdf@data)
world.tidy <- broom::tidy(world.spdf)## Regions defined for each Polygons####Joining tables, again:
world.tidy <- dplyr::left_join(world.tidy, world.spdf@data, by='id')
summary(world.tidy$Pov_1992)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 1.429 0.000 58.850Now we Map
g <- ggplot(world.tidy) +
geom_polygon_interactive(
color='black',
aes(long, lat, group=group, fill=(Pov_1992),
tooltip=sprintf("%s<br/>%s",iso_a3,Pov_1992))) +
hrbrthemes::theme_ipsum() +
colormap::scale_fill_colormap(
colormap=colormap::colormaps$freesurface_red, reverse = T) +
labs(title='Poverty Gap in the World in 1992', subtitle='As Percent of Population',
caption='Source: World Bank Open Data.')
g
USING PLOTLY
Getting started
Sys.setenv("plotly_username"="Mirii")
Sys.setenv("plotly_api_key"="hm0QKixOC2HPJDeKbzgQ")options(browser = 'false')
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplotting
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")## 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######creating chart link
chart_link###Using Leaflet
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#####Using a dataset from the recent 2014-2016 West African Ebola outbreak
urlfile2 <- "https://raw.github.com/amcrisan/EpiDesignPattern/master/data/ebola_metadata.RDS"
myfile <- download.file(urlfile2,"./datos/ebola_metadata.RDS", method="auto")metadata <- readRDS("./datos/ebola_metadata.RDS")library(ape)
#library(ggtree)
library(lubridate)##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## dateinstalling tidyr ilbrary
library(tidyr)##
## Attaching package: 'tidyr'## The following object is masked from 'package:RCurl':
##
## completelibrary(dplyr)
library(ggmap)##
## Attaching package: 'ggmap'## The following object is masked from 'package:plotly':
##
## wind###More packages
library(RColorBrewer)
library(dygraphs)
library(xts)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following object is masked from 'package:leaflet':
##
## addLegend## The following objects are masked from 'package:dplyr':
##
## first, last#######Aggregation of data
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###Definition of color Pallete
pal <- colorFactor(
palette = c('red', 'blue', 'green', 'purple', 'orange'),
domain = aggDat$Country)Now we create Map
m<-leaflet(aggDat)
m %>%
addTiles()%>%
addCircleMarkers(
lng=~country_lon,
lat= ~country_lat,
radius=~sqrt(n)*2,
color = ~pal(Country),
stroke = FALSE, fillOpacity = 0.7,
label=~as.character(popup),
labelOptions = labelOptions(noHide = T),
options = leafletOptions(minZoom = 0, maxZoom = 10,scroolWheelZoom=FALSE))Lets modify codes to get a dissagregated visualization
aggDat<-metadata %>%
filter(Country !="?") %>%
group_by(Country,Region,region_lon,region_lat) %>%
dplyr::count()%>%
mutate(popup=sprintf("%s (%s) = %d cases",Region,Country,n))
m<-leaflet(aggDat)
m %>%
addTiles()%>%
addCircleMarkers(
lng=~region_lon,
lat= ~region_lat,
radius=~sqrt(n)*2,
color = ~pal(Country), #we actually colour the points by country here
stroke = FALSE, fillOpacity = 0.7,
label=~as.character(popup),
labelOptions = labelOptions(noHide = F)
)## Warning in validateCoords(lng, lat, funcName): Data contains 3 rows with
## either missing or invalid lat/lon values and will be ignored######Lets automatically generate clusters that will vary by zooming in and out on the map
m<-leaflet(metadata)
# by providing the region latitude and longtitude co-ordinates we allow clustering of regional samples
m %>% addTiles()%>% addCircleMarkers(lng=~region_lon, lat=~region_lat,
stroke = FALSE, fillOpacity = 0.5,
clusterOptions= markerClusterOptions(titile="regional clusters")) #cluster options## Warning in validateCoords(lng, lat, funcName): Data contains 34 rows with
## either missing or invalid lat/lon values and will be ignoredNew content
The SDG looked at is water and sanitation
world_improved_drinking_water <- read.csv("./data/world-access-to-improved-drinking-water.csv") length(unique(world_improved_drinking_water$Code))## [1] 205Year ranges to be mapped
library(dplyr)nimprovedwater <- filter(world_improved_drinking_water, Year==2000)
names(nimprovedwater) <- c("Entity", "Code", "Year", "improvedwater_2000")
head(nimprovedwater)## Entity Code Year improvedwater_2000
## 1 Afghanistan AFG 2000 30.3
## 2 Albania ALB 2000 96.1
## 3 Algeria DZA 2000 89.5
## 4 American Samoa ASM 2000 97.8
## 5 Andorra AND 2000 100.0
## 6 Angola AGO 2000 45.7####join World geospatial object with the poverty_gap non-spatial object:
nworld <- left_join(world, nimprovedwater, by = c('iso_a3' = 'Code'))## Warning: Column `iso_a3`/`Code` joining character vector and factor,
## coercing into character vectornworld %>% st_transform(crs="+proj=laea +lon_0=18.984375")## Simple feature collection with 177 features and 66 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -10326570 ymin: -10821730 xmax: 11718260 ymax: 12476540
## 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
## 1 1 Admin-0 country 3 Afghanistan AFG
## 2 1 Admin-0 country 3 Angola AGO
## 3 1 Admin-0 country 6 Albania ALB
## 4 1 Admin-0 country 4 United Arab Emirates ARE
## 5 1 Admin-0 country 2 Argentina ARG
## 6 1 Admin-0 country 6 Armenia ARM
## 7 1 Admin-0 country 4 Antarctica ATA
## 8 3 Admin-0 country 6 France FR1
## 9 1 Admin-0 country 2 Australia AU1
## 10 1 Admin-0 country 4 Austria AUT
## adm0_dif level type admin
## 1 0 2 Sovereign country Afghanistan
## 2 0 2 Sovereign country Angola
## 3 0 2 Sovereign country Albania
## 4 0 2 Sovereign country United Arab Emirates
## 5 0 2 Sovereign country Argentina
## 6 0 2 Sovereign country Armenia
## 7 0 2 Indeterminate Antarctica
## 8 1 2 Dependency French Southern and Antarctic Lands
## 9 1 2 Country Australia
## 10 0 2 Sovereign country Austria
## adm0_a3 geou_dif geounit gu_a3 su_dif
## 1 AFG 0 Afghanistan AFG 0
## 2 AGO 0 Angola AGO 0
## 3 ALB 0 Albania ALB 0
## 4 ARE 0 United Arab Emirates ARE 0
## 5 ARG 0 Argentina ARG 0
## 6 ARM 0 Armenia ARM 0
## 7 ATA 0 Antarctica ATA 0
## 8 ATF 0 French Southern and Antarctic Lands ATF 0
## 9 AUS 0 Australia AUS 0
## 10 AUT 0 Austria AUT 0
## subunit su_a3 brk_diff
## 1 Afghanistan AFG 0
## 2 Angola AGO 0
## 3 Albania ALB 0
## 4 United Arab Emirates ARE 0
## 5 Argentina ARG 0
## 6 Armenia ARM 0
## 7 Antarctica ATA 0
## 8 French Southern and Antarctic Lands ATF 0
## 9 Australia AUS 0
## 10 Austria AUT 0
## name name_long brk_a3
## 1 Afghanistan Afghanistan AFG
## 2 Angola Angola AGO
## 3 Albania Albania ALB
## 4 United Arab Emirates United Arab Emirates ARE
## 5 Argentina Argentina ARG
## 6 Armenia Armenia ARM
## 7 Antarctica Antarctica ATA
## 8 Fr. S. Antarctic Lands French Southern and Antarctic Lands ATF
## 9 Australia Australia AUS
## 10 Austria Austria AUT
## brk_name brk_group abbrev postal
## 1 Afghanistan <NA> Afg. AF
## 2 Angola <NA> Ang. AO
## 3 Albania <NA> Alb. AL
## 4 United Arab Emirates <NA> U.A.E. AE
## 5 Argentina <NA> Arg. AR
## 6 Armenia <NA> Arm. ARM
## 7 Antarctica <NA> Ant. AQ
## 8 Fr. S. and Antarctic Lands <NA> Fr. S.A.L. TF
## 9 Australia <NA> Auz. AU
## 10 Austria <NA> Aust. A
## formal_en formal_fr
## 1 Islamic State of Afghanistan <NA>
## 2 People's Republic of Angola <NA>
## 3 Republic of Albania <NA>
## 4 United Arab Emirates <NA>
## 5 Argentine Republic <NA>
## 6 Republic of Armenia <NA>
## 7 <NA> <NA>
## 8 Territory of the French Southern and Antarctic Lands <NA>
## 9 Commonwealth of Australia <NA>
## 10 Republic of Austria <NA>
## note_adm0 note_brk
## 1 <NA> <NA>
## 2 <NA> <NA>
## 3 <NA> <NA>
## 4 <NA> <NA>
## 5 <NA> <NA>
## 6 <NA> <NA>
## 7 <NA> Multiple claims held in abeyance
## 8 Fr. <NA>
## 9 <NA> <NA>
## 10 <NA> <NA>
## name_sort name_alt mapcolor7 mapcolor8
## 1 Afghanistan <NA> 5 6
## 2 Angola <NA> 3 2
## 3 Albania <NA> 1 4
## 4 United Arab Emirates <NA> 2 1
## 5 Argentina <NA> 3 1
## 6 Armenia <NA> 3 1
## 7 Antarctica <NA> 4 5
## 8 French Southern and Antarctic Lands <NA> 7 5
## 9 Australia <NA> 1 2
## 10 Austria <NA> 3 1
## mapcolor9 mapcolor13 pop_est gdp_md_est pop_year lastcensus gdp_year
## 1 8 7 28400000 22270.0 NA 1979 NA
## 2 6 1 12799293 110300.0 NA 1970 NA
## 3 1 6 3639453 21810.0 NA 2001 NA
## 4 3 3 4798491 184300.0 NA 2010 NA
## 5 3 13 40913584 573900.0 NA 2010 NA
## 6 2 10 2967004 18770.0 NA 2001 NA
## 7 1 NA 3802 760.4 NA NA NA
## 8 9 11 140 16.0 NA NA NA
## 9 2 7 21262641 800200.0 NA 2006 NA
## 10 3 4 8210281 329500.0 NA 2011 NA
## economy income_grp wikipedia fips_10
## 1 7. Least developed region 5. Low income NA <NA>
## 2 7. Least developed region 3. Upper middle income NA <NA>
## 3 6. Developing region 4. Lower middle income NA <NA>
## 4 6. Developing region 2. High income: nonOECD NA <NA>
## 5 5. Emerging region: G20 3. Upper middle income NA <NA>
## 6 6. Developing region 4. Lower middle income NA <NA>
## 7 6. Developing region 2. High income: nonOECD NA <NA>
## 8 6. Developing region 2. High income: nonOECD NA <NA>
## 9 2. Developed region: nonG7 1. High income: OECD NA <NA>
## 10 2. Developed region: nonG7 1. High income: OECD NA <NA>
## iso_a2 iso_a3 iso_n3 un_a3 wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us
## 1 AF AFG 004 004 AF AFG NA AFG AFG
## 2 AO AGO 024 024 AO AGO NA AGO AGO
## 3 AL ALB 008 008 AL ALB NA ALB ALB
## 4 AE ARE 784 784 AE ARE NA ARE ARE
## 5 AR ARG 032 032 AR ARG NA ARG ARG
## 6 AM ARM 051 051 AM ARM NA ARM ARM
## 7 AQ ATA 010 <NA> <NA> <NA> NA ATA ATA
## 8 TF ATF 260 <NA> <NA> <NA> NA ATF ATF
## 9 AU AUS 036 036 AU AUS NA AUS AUS
## 10 AT AUT 040 040 AT AUT NA AUT AUT
## adm0_a3_un adm0_a3_wb continent region_un
## 1 NA NA Asia Asia
## 2 NA NA Africa Africa
## 3 NA NA Europe Europe
## 4 NA NA Asia Asia
## 5 NA NA South America Americas
## 6 NA NA Asia Asia
## 7 NA NA Antarctica Antarctica
## 8 NA NA Seven seas (open ocean) Seven seas (open ocean)
## 9 NA NA Oceania Oceania
## 10 NA NA Europe Europe
## subregion region_wb name_len long_len
## 1 Southern Asia South Asia 11 11
## 2 Middle Africa Sub-Saharan Africa 6 6
## 3 Southern Europe Europe & Central Asia 7 7
## 4 Western Asia Middle East & North Africa 20 20
## 5 South America Latin America & Caribbean 9 9
## 6 Western Asia Europe & Central Asia 7 7
## 7 Antarctica Antarctica 10 10
## 8 Seven seas (open ocean) Sub-Saharan Africa 22 35
## 9 Australia and New Zealand East Asia & Pacific 9 9
## 10 Western Europe Europe & Central Asia 7 7
## abbrev_len tiny homepart Entity Year improvedwater_2000
## 1 4 NA 1 Afghanistan 2000 30.3
## 2 4 NA 1 Angola 2000 45.7
## 3 4 NA 1 Albania 2000 96.1
## 4 6 NA 1 United Arab Emirates 2000 99.7
## 5 4 NA 1 Argentina 2000 96.3
## 6 4 NA 1 Armenia 2000 92.6
## 7 4 NA 1 <NA> NA NA
## 8 10 2 NA <NA> NA NA
## 9 4 NA 1 Australia 2000 100.0
## 10 5 NA 1 Austria 2000 100.0
## geometry
## 1 MULTIPOLYGON (((3897019 413...
## 2 MULTIPOLYGON (((-294686.1 -...
## 3 MULTIPOLYGON (((142770.1 45...
## 4 MULTIPOLYGON (((3333836 276...
## 5 MULTIPOLYGON (((-5003690 -7...
## 6 MULTIPOLYGON (((2183104 454...
## 7 MULTIPOLYGON (((-1509927 -8...
## 8 MULTIPOLYGON (((3831026 -56...
## 9 MULTIPOLYGON (((7426184 -79...
## 10 MULTIPOLYGON (((-163453.8 5...head(nworld)## Simple feature collection with 6 features and 66 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -73.41544 ymin: -55.25 xmax: 75.15803 ymax: 42.68825
## epsg (SRID): 4326
## proj4string: +proj=longlat +datum=WGS84 +no_defs
## scalerank featurecla labelrank sovereignt sov_a3 adm0_dif
## 1 1 Admin-0 country 3 Afghanistan AFG 0
## 2 1 Admin-0 country 3 Angola AGO 0
## 3 1 Admin-0 country 6 Albania ALB 0
## 4 1 Admin-0 country 4 United Arab Emirates ARE 0
## 5 1 Admin-0 country 2 Argentina ARG 0
## 6 1 Admin-0 country 6 Armenia ARM 0
## level type admin adm0_a3 geou_dif
## 1 2 Sovereign country Afghanistan AFG 0
## 2 2 Sovereign country Angola AGO 0
## 3 2 Sovereign country Albania ALB 0
## 4 2 Sovereign country United Arab Emirates ARE 0
## 5 2 Sovereign country Argentina ARG 0
## 6 2 Sovereign country Armenia ARM 0
## geounit gu_a3 su_dif subunit su_a3 brk_diff
## 1 Afghanistan AFG 0 Afghanistan AFG 0
## 2 Angola AGO 0 Angola AGO 0
## 3 Albania ALB 0 Albania ALB 0
## 4 United Arab Emirates ARE 0 United Arab Emirates ARE 0
## 5 Argentina ARG 0 Argentina ARG 0
## 6 Armenia ARM 0 Armenia ARM 0
## name name_long brk_a3 brk_name
## 1 Afghanistan Afghanistan AFG Afghanistan
## 2 Angola Angola AGO Angola
## 3 Albania Albania ALB Albania
## 4 United Arab Emirates United Arab Emirates ARE United Arab Emirates
## 5 Argentina Argentina ARG Argentina
## 6 Armenia Armenia ARM Armenia
## brk_group abbrev postal formal_en formal_fr note_adm0
## 1 <NA> Afg. AF Islamic State of Afghanistan <NA> <NA>
## 2 <NA> Ang. AO People's Republic of Angola <NA> <NA>
## 3 <NA> Alb. AL Republic of Albania <NA> <NA>
## 4 <NA> U.A.E. AE United Arab Emirates <NA> <NA>
## 5 <NA> Arg. AR Argentine Republic <NA> <NA>
## 6 <NA> Arm. ARM Republic of Armenia <NA> <NA>
## note_brk name_sort name_alt mapcolor7 mapcolor8 mapcolor9
## 1 <NA> Afghanistan <NA> 5 6 8
## 2 <NA> Angola <NA> 3 2 6
## 3 <NA> Albania <NA> 1 4 1
## 4 <NA> United Arab Emirates <NA> 2 1 3
## 5 <NA> Argentina <NA> 3 1 3
## 6 <NA> Armenia <NA> 3 1 2
## mapcolor13 pop_est gdp_md_est pop_year lastcensus gdp_year
## 1 7 28400000 22270 NA 1979 NA
## 2 1 12799293 110300 NA 1970 NA
## 3 6 3639453 21810 NA 2001 NA
## 4 3 4798491 184300 NA 2010 NA
## 5 13 40913584 573900 NA 2010 NA
## 6 10 2967004 18770 NA 2001 NA
## economy income_grp wikipedia fips_10
## 1 7. Least developed region 5. Low income NA <NA>
## 2 7. Least developed region 3. Upper middle income NA <NA>
## 3 6. Developing region 4. Lower middle income NA <NA>
## 4 6. Developing region 2. High income: nonOECD NA <NA>
## 5 5. Emerging region: G20 3. Upper middle income NA <NA>
## 6 6. Developing region 4. Lower middle income NA <NA>
## iso_a2 iso_a3 iso_n3 un_a3 wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us
## 1 AF AFG 004 004 AF AFG NA AFG AFG
## 2 AO AGO 024 024 AO AGO NA AGO AGO
## 3 AL ALB 008 008 AL ALB NA ALB ALB
## 4 AE ARE 784 784 AE ARE NA ARE ARE
## 5 AR ARG 032 032 AR ARG NA ARG ARG
## 6 AM ARM 051 051 AM ARM NA ARM ARM
## adm0_a3_un adm0_a3_wb continent region_un subregion
## 1 NA NA Asia Asia Southern Asia
## 2 NA NA Africa Africa Middle Africa
## 3 NA NA Europe Europe Southern Europe
## 4 NA NA Asia Asia Western Asia
## 5 NA NA South America Americas South America
## 6 NA NA Asia Asia Western Asia
## region_wb name_len long_len abbrev_len tiny homepart
## 1 South Asia 11 11 4 NA 1
## 2 Sub-Saharan Africa 6 6 4 NA 1
## 3 Europe & Central Asia 7 7 4 NA 1
## 4 Middle East & North Africa 20 20 6 NA 1
## 5 Latin America & Caribbean 9 9 4 NA 1
## 6 Europe & Central Asia 7 7 4 NA 1
## Entity Year improvedwater_2000
## 1 Afghanistan 2000 30.3
## 2 Angola 2000 45.7
## 3 Albania 2000 96.1
## 4 United Arab Emirates 2000 99.7
## 5 Argentina 2000 96.3
## 6 Armenia 2000 92.6
## geometry
## 1 MULTIPOLYGON (((61.21082 35...
## 2 MULTIPOLYGON (((16.32653 -5...
## 3 MULTIPOLYGON (((20.59025 41...
## 4 MULTIPOLYGON (((51.57952 24...
## 5 MULTIPOLYGON (((-65.5 -55.2...
## 6 MULTIPOLYGON (((43.58275 41...#####checking missing values
nworld$improvedwater_2000## [1] 30.3 45.7 96.1 99.7 96.3 92.6 NA NA 100.0 100.0 74.1
## [12] 71.8 100.0 66.0 59.9 76.0 99.7 96.6 97.5 99.5 85.1 78.6
## [23] 93.5 NA 83.9 94.7 62.5 99.8 100.0 94.9 80.3 77.9 61.7
## [34] 47.1 69.2 89.9 95.1 90.6 NA 100.0 99.8 100.0 82.3 100.0
## [45] 86.8 89.5 79.7 95.9 51.0 99.9 99.0 28.9 100.0 90.7 NA
## [56] 100.0 83.8 100.0 89.3 70.5 62.7 82.7 52.1 47.3 98.8 100.0
## [67] 83.9 86.2 80.8 98.3 60.8 98.2 77.9 80.6 96.3 94.1 80.4
## [78] 100.0 100.0 100.0 93.5 96.8 100.0 93.8 51.8 78.4 41.6 93.4
## [89] NA 99.0 45.5 85.7 62.4 71.2 79.7 79.0 91.0 100.0 98.3
## [100] 78.3 85.1 38.0 88.6 99.3 46.6 66.6 97.1 56.3 41.1 42.0
## [111] 62.5 94.1 78.5 94.0 43.7 51.8 78.9 100.0 100.0 77.1 100.0
## [122] 84.0 88.5 89.5 79.8 87.1 35.1 95.9 93.6 99.9 98.0 73.4
## [133] 90.9 99.1 85.3 94.9 66.3 NA 95.0 62.0 NA 67.4 79.7
## [144] 47.2 81.6 NA 23.5 99.4 89.3 99.8 99.6 100.0 51.9 87.5
## [155] 44.7 53.6 91.9 59.6 59.6 54.3 93.3 89.9 92.6 NA 54.4
## [166] 56.4 97.6 96.8 98.8 88.7 91.1 77.4 75.8 59.9 86.5 53.0
## [177] 79.4nworld$improvedwater_2000 [is.na(nworld$improvedwater_2000)] <- 0world.centers <- st_centroid(nworld)
world.spdf <- methods::as(nworld, 'Spatial')
world.spdf@data$id <- row.names(world.spdf@data)
world.tidy <- broom::tidy(world.spdf)## Regions defined for each Polygonsworld.tidy <- dplyr::left_join(world.tidy, world.spdf@data, by='id')
summary(world.tidy$improvedwater_2000)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 62.70 89.50 77.05 98.80 100.00Now plotting Improved water status for the year 2000
g <- ggplot(world.tidy) +
geom_polygon_interactive(
color='blue',
aes(long, lat, group=group, fill=(improvedwater_2000),
tooltip=sprintf("%s<br/>%s",iso_a3,improvedwater_2000))) +
hrbrthemes::theme_ipsum() +
colormap::scale_fill_colormap(
colormap=colormap::colormaps$freesurface_red, reverse = T) +
labs(title='Worlds Improved water status for year 2000', subtitle='As Percent of Population',
caption='Source: Our World Data.')
g