Geomapping
Packages
rnaturalearth- provides access for the Natural Earth. The Natural Earth is a public domain map dataset including vector country and other administrative boundariessffor setting the map object class and plot itmapview,tmap, andggplot2for plotting the map objectscoronavirus,covid19italyto pull Covid19 data at different levels (e.g., region, country, etc.)
pacman::p_load(sf,
rnaturalearth,
mapview,
tmap,
# rnaturalearthhires,
rgeos,
ggplot2,
dplyr,
tidyr,
rvest,
coronavirus)devtools::install_github("ropensci/rnaturalearthhires")## Skipping install of 'rnaturalearthhires' from a github remote, the SHA1 (2ed7a937) has not changed since last install.
## Use `force = TRUE` to force installationIntroduction
Polygons
plot(x = 1,
y = 1,
ylab = "",
xlab = "",
main = "Point")
plot(x = c(1, 2, 3), y = c(1, 4, 2), type = "l")
plot(x = c(1, 2, 4, 3, 1), y = c(1, 4, 3, 1, 1), type = "l")
labels + polygons = map boarders
labels + polygons + data = choropleth map
World
world <- ne_countries(type = 'countries', scale = 'small', returnclass = "sf")
str(world)## Classes 'sf' and 'data.frame': 177 obs. of 64 variables:
## $ scalerank : int 1 1 1 1 1 1 1 3 1 1 ...
## $ featurecla: chr "Admin-0 country" "Admin-0 country" "Admin-0 country" "Admin-0 country" ...
## $ labelrank : num 3 3 6 4 2 6 4 6 2 4 ...
## $ sovereignt: chr "Afghanistan" "Angola" "Albania" "United Arab Emirates" ...
## $ sov_a3 : chr "AFG" "AGO" "ALB" "ARE" ...
## $ adm0_dif : num 0 0 0 0 0 0 0 1 1 0 ...
## $ level : num 2 2 2 2 2 2 2 2 2 2 ...
## $ type : chr "Sovereign country" "Sovereign country" "Sovereign country" "Sovereign country" ...
## $ admin : chr "Afghanistan" "Angola" "Albania" "United Arab Emirates" ...
## $ adm0_a3 : chr "AFG" "AGO" "ALB" "ARE" ...
## $ geou_dif : num 0 0 0 0 0 0 0 0 0 0 ...
## $ geounit : chr "Afghanistan" "Angola" "Albania" "United Arab Emirates" ...
## $ gu_a3 : chr "AFG" "AGO" "ALB" "ARE" ...
## $ su_dif : num 0 0 0 0 0 0 0 0 0 0 ...
## $ subunit : chr "Afghanistan" "Angola" "Albania" "United Arab Emirates" ...
## $ su_a3 : chr "AFG" "AGO" "ALB" "ARE" ...
## $ brk_diff : num 0 0 0 0 0 0 0 0 0 0 ...
## $ name : chr "Afghanistan" "Angola" "Albania" "United Arab Emirates" ...
## $ name_long : chr "Afghanistan" "Angola" "Albania" "United Arab Emirates" ...
## $ brk_a3 : chr "AFG" "AGO" "ALB" "ARE" ...
## $ brk_name : chr "Afghanistan" "Angola" "Albania" "United Arab Emirates" ...
## $ brk_group : chr NA NA NA NA ...
## $ abbrev : chr "Afg." "Ang." "Alb." "U.A.E." ...
## $ postal : chr "AF" "AO" "AL" "AE" ...
## $ formal_en : chr "Islamic State of Afghanistan" "People's Republic of Angola" "Republic of Albania" "United Arab Emirates" ...
## $ formal_fr : chr NA NA NA NA ...
## $ note_adm0 : chr NA NA NA NA ...
## $ note_brk : chr NA NA NA NA ...
## $ name_sort : chr "Afghanistan" "Angola" "Albania" "United Arab Emirates" ...
## $ name_alt : chr NA NA NA NA ...
## $ mapcolor7 : num 5 3 1 2 3 3 4 7 1 3 ...
## $ mapcolor8 : num 6 2 4 1 1 1 5 5 2 1 ...
## $ mapcolor9 : num 8 6 1 3 3 2 1 9 2 3 ...
## $ mapcolor13: num 7 1 6 3 13 10 NA 11 7 4 ...
## $ pop_est : num 28400000 12799293 3639453 4798491 40913584 ...
## $ gdp_md_est: num 22270 110300 21810 184300 573900 ...
## $ pop_year : num NA NA NA NA NA NA NA NA NA NA ...
## $ lastcensus: num 1979 1970 2001 2010 2010 ...
## $ gdp_year : num NA NA NA NA NA NA NA NA NA NA ...
## $ economy : chr "7. Least developed region" "7. Least developed region" "6. Developing region" "6. Developing region" ...
## $ income_grp: chr "5. Low income" "3. Upper middle income" "4. Lower middle income" "2. High income: nonOECD" ...
## $ wikipedia : num NA NA NA NA NA NA NA NA NA NA ...
## $ fips_10 : chr NA NA NA NA ...
## $ iso_a2 : chr "AF" "AO" "AL" "AE" ...
## $ iso_a3 : chr "AFG" "AGO" "ALB" "ARE" ...
## $ iso_n3 : chr "004" "024" "008" "784" ...
## $ un_a3 : chr "004" "024" "008" "784" ...
## $ wb_a2 : chr "AF" "AO" "AL" "AE" ...
## $ wb_a3 : chr "AFG" "AGO" "ALB" "ARE" ...
## $ woe_id : num NA NA NA NA NA NA NA NA NA NA ...
## $ adm0_a3_is: chr "AFG" "AGO" "ALB" "ARE" ...
## $ adm0_a3_us: chr "AFG" "AGO" "ALB" "ARE" ...
## $ adm0_a3_un: num NA NA NA NA NA NA NA NA NA NA ...
## $ adm0_a3_wb: num NA NA NA NA NA NA NA NA NA NA ...
## $ continent : chr "Asia" "Africa" "Europe" "Asia" ...
## $ region_un : chr "Asia" "Africa" "Europe" "Asia" ...
## $ subregion : chr "Southern Asia" "Middle Africa" "Southern Europe" "Western Asia" ...
## $ region_wb : chr "South Asia" "Sub-Saharan Africa" "Europe & Central Asia" "Middle East & North Africa" ...
## $ name_len : num 11 6 7 20 9 7 10 22 9 7 ...
## $ long_len : num 11 6 7 20 9 7 10 35 9 7 ...
## $ abbrev_len: num 4 4 4 6 4 4 4 10 4 5 ...
## $ tiny : num NA NA NA NA NA NA NA 2 NA NA ...
## $ homepart : num 1 1 1 1 1 1 1 NA 1 1 ...
## $ geometry :sfc_MULTIPOLYGON of length 177; first list element: List of 1
## ..$ :List of 1
## .. ..$ : num [1:69, 1:2] 61.2 62.2 63 63.2 64 ...
## ..- attr(*, "class")= chr [1:3] "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 [1:63] "scalerank" "featurecla" "labelrank" "sovereignt" ...head(world)plot(world$geometry)
africa <- world %>%
filter(continent == "Africa")
plot(africa$geometry)
w_africa <- world %>%
filter(subregion == "Western Africa")
plot(
w_africa$geometry,
col = sf.colors(12, categorical = TRUE),
border = 'grey',
axes = TRUE,
main = "Western Africa"
)
nigeria <- ne_states(country = "Nigeria", returnclass = "sf")
head(nigeria)plot(nigeria$geometry, col = sf.colors(37, categorical = TRUE), border = 'grey',
axes = TRUE, main = "Nigeria")
Scrapping data
url <- "https://en.wikipedia.org/wiki/List_of_Nigerian_states_by_population"
page <- read_html(url)
tables <- html_node(page, ".wikitable")
pop_table <- html_table(tables, fill = TRUE) %>%
select(state_temp = State, pop_2006_temp = `Population (2006)`, pop_2016_temp = `Population (2016)`)
pop_table$pop_2006 <- as.numeric(gsub(x = pop_table$pop_2006_temp,pattern = ",", replacement = ""))
pop_table$pop_2016 <- as.numeric(gsub(x = pop_table$pop_2016_temp,pattern = ",", replacement = ""))
pop_table$state <- gsub(x = pop_table$state_temp,pattern = " State", replacement = "")
pop_table <- pop_table %>% select(-state_temp, -pop_2006_temp, - pop_2016_temp) %>%
select(state, pop_2006, pop_2016) %>%
mutate(state_fix = state)
pop_table$state_fix[which(pop_table$state_fix == "Nasarawa")] <- "Nassarawa"
head(pop_table)Combining everything
nigeria_pop <- nigeria %>%
left_join(pop_table, by = c("name" = "state_fix"))
plot(nigeria_pop["pop_2016"], key.pos = 1, axes = TRUE, key.width = lcm(1.3), key.length = 1.0)
tm_shape(nigeria_pop) +
tm_polygons(col = "pop_2016",
style = "order",
title = "Population",
palette = "Blues") +
tm_style("cobalt") +
tm_text("state", size = 0.7) +
tm_credits("Source: Wikipedia - List of Nigerian states by population",
position = c("LEFT", "BOTTOM")) +
tm_layout(title= "Nigeria Population by States",
title.position = c('right', 'top') ,
inner.margins = c(0.02, .02, .1, .15))
COVID example
covid19_daily <- refresh_coronavirus_jhu()
head(covid19_daily)df <- covid19_daily %>%
filter(location_type == "country") %>%
group_by(location, data_type) %>%
summarise(cases = sum(value),
.groups = "drop") %>%
pivot_wider(names_from = data_type, values_from = cases) %>%
setNames(c("country", "total_cases", "total_death"))
head(df)df1 <- covid19_daily %>%
filter(location_type == "country") %>%
group_by(location, location_code, data_type) %>%
summarise(cases = sum(value),
.groups = "drop") %>%
pivot_wider(names_from = data_type, values_from = cases) %>%
setNames(c("country", "total_cases", "total_death"))
world_covid <- world %>%
select(country = sovereignt, geometry) %>%
left_join(df, by = "country")
plot(world_covid)
plot(world_covid[, c("total_cases")])
Data Visualization
world <- ne_countries(type = 'countries', scale = 'large')
sp::plot(world)## Warning in wkt(obj): CRS object has no comment
world <- ne_countries(type = 'countries', scale = 'small', returnclass = "sf")
world$random <- stats::rnorm(n = nrow(world))
sf::plot_sf(world)
sp::plot(world[, "random"])
sp::plot(world[which(world$region_un == "Africa"), "random"])
x1 <- rnaturalearth::ne_countries(country = 'Nigeria', type='countries')
sp::plot(x1)## Warning in wkt(obj): CRS object has no comment
# x2 <- rnaturalearth::ne_states(country = 'Nigeria')