| Title: | Geography Vizualisations |
|---|---|
| Description: | Provides function and objects to establish vizualisations for my Geography lessons. |
| Authors: | Pascal Burkhard [aut, cre] (ORCID: <https://orcid.org/0009-0001-5504-5084>) |
| Maintainer: | Pascal Burkhard <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.2.0.9000 |
| Built: | 2026-10-17 09:43:10 UTC |
| Source: | https://github.com/Nenuial/geographer |
These functions showcase different map projections.
df_cartography_project_equirectangular() df_cartography_project_gallpeters() df_cartography_project_hobodyer() df_cartography_project_mercator() df_cartography_project_winkeltripel() df_cartography_project_robinson() df_cartography_project_goodehomolosine() df_cartography_project_equalearth()df_cartography_project_equirectangular() df_cartography_project_gallpeters() df_cartography_project_hobodyer() df_cartography_project_mercator() df_cartography_project_winkeltripel() df_cartography_project_robinson() df_cartography_project_goodehomolosine() df_cartography_project_equalearth()
The following projections are available:
Equi-rectangular: df_cartography_project_equirectangular()
Gall-Peters: df_cartography_project_gallpeters()
Hobo-Dyer: df_cartography_project_hobodyer()
Mercator: df_cartography_project_mercator()
Wikel-Tripel: df_cartography_project_winkeltripel()
Robinson: df_cartography_project_robinson()
Goode Homolosine: df_cartography_project_goodehomolosine()
Equal Earth: df_cartography_project_equalearth()
A ggplot2 object
df_cartography_project_equirectangular() df_cartography_project_gallpeters() df_cartography_project_hobodyer() df_cartography_project_mercator() df_cartography_project_winkeltripel() df_cartography_project_robinson() df_cartography_project_goodehomolosine() df_cartography_project_equalearth()df_cartography_project_equirectangular() df_cartography_project_gallpeters() df_cartography_project_hobodyer() df_cartography_project_mercator() df_cartography_project_winkeltripel() df_cartography_project_robinson() df_cartography_project_goodehomolosine() df_cartography_project_equalearth()
Velocity against distance of the 24 nebulae of Hubble (1929), printed for a figure 17 × 11 cm. The student version is an empty grid to plot the points by hand (distance 0 to 2.2 million parsecs, velocity -400 to 1200 km/s, a fine grid every 0.1 and 100). The key version adds the points, the least-squares line through the origin (K = Σrv / Σr² ≈ 424 km/s per million parsecs) and Hubble's rounded K ≈ 500 (dashed).
df_cosmology_graph_hubble(key = FALSE, theme = ggplot2::theme_minimal())df_cosmology_graph_hubble(key = FALSE, theme = ggplot2::theme_minimal())
key |
|
theme |
A ggplot2 theme |
A ggplot2 graph
df_cosmology_graph_hubble() df_cosmology_graph_hubble(key = TRUE)df_cosmology_graph_hubble() df_cosmology_graph_hubble(key = TRUE)
World population graph
df_demography_graph_world_population_current(theme = ggplot2::theme_minimal())df_demography_graph_world_population_current(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
df_demography_graph_world_population_current()df_demography_graph_world_population_current()
World population growth rate graph
df_demography_graph_world_population_growth(theme = ggplot2::theme_minimal())df_demography_graph_world_population_growth(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
df_demography_graph_world_population_growth()df_demography_graph_world_population_growth()
World population graph from 10'000 BCE to 1200 CE
df_demography_graph_world_population_historical( theme = ggplot2::theme_minimal() )df_demography_graph_world_population_historical( theme = ggplot2::theme_minimal() )
theme |
A ggplot2 theme |
A ggplot2 graph
df_demography_graph_world_population_historical()df_demography_graph_world_population_historical()
Malthus's own example (1798): the population doubles every 25 years (1, 2, 4, 8... 256) while food grows by the same amount (1, 2, 3... 9).
df_demography_hc_malthus()df_demography_hc_malthus()
A highcharter graph
df_demography_hc_malthus()df_demography_hc_malthus()
Crude birth and death rates of a country of the Human Mortality Database
(geodata::gdt_hmd_demography), the natural increase shaded between them.
df_demography_hc_transition(code, events = NULL)df_demography_hc_transition(code, events = NULL)
code |
The HMD country code, e.g. |
events |
A named numeric vector of years to mark with a dashed line,
e.g. |
A highcharter graph
df_demography_hc_transition("SWE", c(famine = 1773, "Spanish flu" = 1918))df_demography_hc_transition("SWE", c(famine = 1773, "Spanish flu" = 1918))
Every country of the world with the Human Development Index from 1990 to 2021.
df_demography_hc_world_hdi()df_demography_hc_world_hdi()
A hicharter graph
df_demography_hc_world_hdi()df_demography_hc_world_hdi()
World population since year 0 (historical estimates, then the World Bank since 1960) with the dates of the billions (United Nations) and the Black Death.
df_demography_hc_world_population(from = 0)df_demography_hc_world_population(from = 0)
from |
The first year |
A highcharter graph
df_demography_hc_world_population()df_demography_hc_world_population()
Humand Development Index (HDI) map
df_demography_map_hc_world_hdi(year)df_demography_map_hc_world_hdi(year)
year |
The year |
A highcharter map
df_demography_map_hc_world_hdi(2020)df_demography_map_hc_world_hdi(2020)
Depth of the earthquakes against their distance from the trench, along a
line across a subduction zone (answer key of the plate detective exercise).
Earthquakes within half_width degrees of latitude of the line are kept;
volcanoes near the line are shown at the surface.
df_geology_graph_cross_section( region = c("A", "B"), half_width = 2, lat = NULL, from = NULL, to = NULL, title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_hc_cross_section( region = c("A", "B"), half_width = 2, lat = NULL, from = NULL, to = NULL, title = TRUE )df_geology_graph_cross_section( region = c("A", "B"), half_width = 2, lat = NULL, from = NULL, to = NULL, title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_hc_cross_section( region = c("A", "B"), half_width = 2, lat = NULL, from = NULL, to = NULL, title = TRUE )
region |
|
half_width |
Half width of the band around the line, in degrees of latitude |
lat, from, to
|
Latitude of the line and longitudes of its start (the
trench side) and end, in degrees (west and south negative). |
title |
|
theme |
A ggplot2 theme |
A ggplot2 graph (df_geology_graph_cross_section()) or a
highcharts chart (df_geology_hc_cross_section())
df_geology_graph_cross_section("A") df_geology_hc_cross_section("B")df_geology_graph_cross_section("A") df_geology_hc_cross_section("B")
Synthetic seismograms of the same earthquake at the three stations of the epicentre exercise, stacked on the same time axis (seconds after the earthquake, a tick every second): first the small P wave, then the larger S wave. The key version marks the arrivals of the P and S waves.
df_geology_graph_seismograms( key = FALSE, title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_hc_seismograms(key = FALSE, title = TRUE)df_geology_graph_seismograms( key = FALSE, title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_hc_seismograms(key = FALSE, title = TRUE)
key |
|
title |
|
theme |
A ggplot2 theme |
A ggplot2 graph (df_geology_graph_seismograms()) or a
highcharts chart (df_geology_hc_seismograms())
df_geology_graph_seismograms() df_geology_hc_seismograms(key = TRUE)df_geology_graph_seismograms() df_geology_hc_seismograms(key = TRUE)
Map of Switzerland with the three seismic stations of the epicentre exercise
(Sion, Bern and Lugano), for drawing circles with a compass. The map is in
the Swiss projection LV95, so distances are true, and its panel has a fixed
size: printed at 100 %, the map has the scale 1:scale. The key version adds
the circles (radius: distance to the epicentre) and the epicentre of the
Visp earthquake of 1855.
df_geology_map_epicentre( key = FALSE, scale = 2500000, title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_map_leaflet_epicentre(key = FALSE)df_geology_map_epicentre( key = FALSE, scale = 2500000, title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_map_leaflet_epicentre(key = FALSE)
key |
|
scale |
Denominator of the printed scale |
title |
|
theme |
A ggplot2 theme |
The panel of df_geology_map_epicentre() measures 360 km × 300 km at the
chosen scale (14.4 cm × 12 cm at 1:2 500 000, 18 cm × 15 cm at 1:2 000 000):
give the figure at least about 2 cm more in each direction and do not let
the document rescale it.
A ggplot2 map (df_geology_map_epicentre()) or a leaflet map
(df_geology_map_leaflet_epicentre())
df_geology_map_epicentre() df_geology_map_epicentre(key = TRUE) df_geology_map_leaflet_epicentre(key = TRUE)df_geology_map_epicentre() df_geology_map_epicentre(key = TRUE) df_geology_map_leaflet_epicentre(key = TRUE)
Earthquakes (magnitude 5 or more, 1975–2025) and Holocene volcanoes of one of the five regions of the plate detective exercise, without plate boundaries: finding them is the exercise. Epicentres are coloured (and shaped, for greyscale printing) by depth class: 0–70 km, 70–300 km and 300–700 km. The size of the points shows the magnitude.
df_geology_map_plate_detective( region = c("A", "B", "C", "D", "E"), title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_map_hc_plate_detective( region = c("A", "B", "C", "D", "E"), title = TRUE )df_geology_map_plate_detective( region = c("A", "B", "C", "D", "E"), title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_map_hc_plate_detective( region = c("A", "B", "C", "D", "E"), title = TRUE )
region |
Region code: |
title |
|
theme |
A ggplot2 theme |
A ggplot2 map (df_geology_map_plate_detective()) or a highcharts
chart (df_geology_map_hc_plate_detective())
df_geology_map_plate_detective("A") df_geology_map_hc_plate_detective("B")df_geology_map_plate_detective("A") df_geology_map_hc_plate_detective("B")
Map of the municipalities around Vesuvius with their names and population, the crater, the ancient sites of Pompeii and Herculaneum and rings showing the approximate reach of the pyroclastic flows of past eruptions. The key version colours the red zone of the national emergency plan (municipalities entirely or partly in it).
df_geology_map_vesuvius( key = FALSE, rings = c(`1631` = 10, `79` = 15), half_width = 25, title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_map_leaflet_vesuvius(key = FALSE, rings = c(`1631` = 10, `79` = 15))df_geology_map_vesuvius( key = FALSE, rings = c(`1631` = 10, `79` = 15), half_width = 25, title = TRUE, theme = ggplot2::theme_minimal() ) df_geology_map_leaflet_vesuvius(key = FALSE, rings = c(`1631` = 10, `79` = 15))
key |
|
rings |
Named vector of ring radii in km around the crater; the names label the rings (approximate reach of pyroclastic flows) |
half_width |
Half width of the map around the crater in km (ggplot version) |
title |
|
theme |
A ggplot2 theme |
A ggplot2 map (df_geology_map_vesuvius()) or a leaflet map
(df_geology_map_leaflet_vesuvius())
df_geology_map_vesuvius() df_geology_map_vesuvius(key = TRUE) df_geology_map_leaflet_vesuvius(key = TRUE)df_geology_map_vesuvius() df_geology_map_vesuvius(key = TRUE) df_geology_map_leaflet_vesuvius(key = TRUE)
Daily number of ships passing a maritime chokepoint (IMF PortWatch, AIS
estimates) over a period: daily total, 7-day centred mean of all ships and
7-day centred mean of tankers. The student version shows only label as
title; the key version (key = TRUE) shows the name of the chokepoint and
the period, and marks the events. Texts are in English or French,
following geotools::gtl_opt_set_i18n() (label and events are passed
as they are).
df_geopolitics_graph_chokepoint( chokepoint, from = NULL, to = NULL, key = FALSE, label = NULL, events = NULL, theme = ggplot2::theme_minimal() ) df_geopolitics_hc_chokepoint( chokepoint, from = NULL, to = NULL, key = FALSE, label = NULL, events = NULL, daily_color = "#cfd8e3", total_color = "#1f3a5f", tanker_color = "#d95f02" )df_geopolitics_graph_chokepoint( chokepoint, from = NULL, to = NULL, key = FALSE, label = NULL, events = NULL, theme = ggplot2::theme_minimal() ) df_geopolitics_hc_chokepoint( chokepoint, from = NULL, to = NULL, key = FALSE, label = NULL, events = NULL, daily_color = "#cfd8e3", total_color = "#1f3a5f", tanker_color = "#d95f02" )
chokepoint |
PortWatch identifier, e.g. |
from, to
|
First and last day (dates or |
key |
|
label |
Title of the student version (e.g. |
events |
A data frame with columns |
theme |
A ggplot2 theme |
daily_color, total_color, tanker_color
|
Colours of the daily bars and of the 7-day means of all ships and of tankers (highcharts version, e.g. for a dark theme) |
A ggplot2 graph (df_geopolitics_graph_chokepoint()) or a
highcharts chart (df_geopolitics_hc_chokepoint(), to be themed with
e.g. ggeo::hc_web_theme())
df_geopolitics_graph_chokepoint( "chokepoint1", "2021-02-15", "2021-05-15", key = TRUE, events = tibble::tibble( date = c("2021-03-23", "2021-03-29"), label = c("Ever Given runs aground", "freed") ) ) df_geopolitics_hc_chokepoint("chokepoint2", "2023-01-01", "2024-12-31", label = "Chokepoint A")df_geopolitics_graph_chokepoint( "chokepoint1", "2021-02-15", "2021-05-15", key = TRUE, events = tibble::tibble( date = c("2021-03-23", "2021-03-29"), label = c("Ever Given runs aground", "freed") ) ) df_geopolitics_hc_chokepoint("chokepoint2", "2023-01-01", "2024-12-31", label = "Chokepoint A")
Static maps for the exercise "Read a conflict".
df_geopolitics_map_preah_vihear(theme = ggplot2::theme_minimal()) df_geopolitics_map_iran_war(theme = ggplot2::theme_minimal())df_geopolitics_map_preah_vihear(theme = ggplot2::theme_minimal()) df_geopolitics_map_iran_war(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
df_geopolitics_map_preah_vihear(): the Thailand–Cambodia border along the
Dângrêk mountains, with the relief (the 1904 treaty put the border on the
watershed, along the escarpment), the provinces and the disputed temples of
Preah Vihear, Ta Muen Thom and Ta Krabey.
df_geopolitics_map_iran_war(): the region of the war around Iran, from
Israel to Pakistan, with the capitals, the nuclear sites of Fordow, Natanz
and Isfahan, the Al Udeid air base and the Strait of Hormuz.
Coordinates of the sites from Wikidata, borders from Natural Earth, relief
from the Copernicus DEM. Both maps are drawn for a figure about 17 cm wide.
Names, legends and sources are in English or French, following
geotools::gtl_opt_set_i18n().
A ggplot2 map
df_geopolitics_map_preah_vihear() df_geopolitics_map_iran_war()df_geopolitics_map_preah_vihear() df_geopolitics_map_iran_war()
An imaginary coast with two states, Aldoria and Borania, for drawing the maritime zones of the United Nations Convention on the Law of the Sea (UNCLOS) with a compass: internal waters, territorial sea (12 nm), contiguous zone (24 nm), exclusive economic zone (200 nm), median line between the two states and the high seas. The key versions draw the zones, with the island of Kalos counting either as an island (it has its own zones) or as a rock (UNCLOS art. 121.3: territorial sea only, an enclave).
df_geopolitics_map_sea_zones( key = c("none", "island", "rock"), theme = ggplot2::theme_void() )df_geopolitics_map_sea_zones( key = c("none", "island", "rock"), theme = ggplot2::theme_void() )
key |
|
theme |
A ggplot2 theme |
Units are nautical miles. The map is 340 nm × 380 nm with no side margins: printed 17 cm wide (figure 17 cm × 19.9 cm), the scale is 1 cm = 20 nm. Pass a theme without axis text, titles or side margins, otherwise the panel shrinks and the scale is lost. A key version takes a few seconds (distances computed on a 0.5 nm grid for the median line).
A ggplot2 map
df_geopolitics_map_sea_zones() df_geopolitics_map_sea_zones("rock")df_geopolitics_map_sea_zones() df_geopolitics_map_sea_zones("rock")
This function renders a base map with the country highlighted.
gph_base_map(country) gph_base_tmap(country)gph_base_map(country) gph_base_tmap(country)
country |
Country to highlight |
A leaflet map with the country borders
# Using Leaflet gph_base_map("Switzerland") gph_base_map("United Kingdom") # Using tmap (v4) gph_base_tmap("Switzerland") gph_base_tmap("United Kingdom")# Using Leaflet gph_base_map("Switzerland") gph_base_map("United Kingdom") # Using tmap (v4) gph_base_tmap("Switzerland") gph_base_tmap("United Kingdom")
NaturalEarth Bound box for planet Earth
gph_boundbox()gph_boundbox()
A simple feature
gph_boundbox()gph_boundbox()
Crete a treemap plot of the world countries by area
gph_country_area_tree()gph_country_area_tree()
A highcharter plot
gph_country_area_tree()gph_country_area_tree()
Crete a treemap plot of the world countries by population
gph_country_population_tree()gph_country_population_tree()
A highcharter plot
gph_country_population_tree()gph_country_population_tree()
Provides basic ggplot2 graph with the following data on it :
the population curve (blue)
the birth rate curve (grey)
the death_rate curve (black)
gph_demogram( country, theme = ggplot2::theme_minimal(), population_color = "blue" )gph_demogram( country, theme = ggplot2::theme_minimal(), population_color = "blue" )
country |
A string with the country name |
theme |
A ggplot2 theme |
population_color |
A color value for the population curve |
A ggplot2 graph
gph_demogram("Switzerland")gph_demogram("Switzerland")
Provides basic ggplot2 graph with the following data on it :
the population curve (blue)
the birth rate curve (grey)
the death_rate curve (black)
gph_hc_demogram( country, population_color = "blue", birth_color = "grey", death_color = "black" )gph_hc_demogram( country, population_color = "blue", birth_color = "grey", death_color = "black" )
country |
A string with the country name |
population_color |
Color of the population curve |
birth_color |
Color of the birth rate curve |
death_color |
Color of the death rate curve |
A highcharter graph
gph_hc_demogram("Switzerland") # for a dark background gph_hc_demogram("Switzerland", "#6ea8fe", "#8f99a8", "#dee2e6")gph_hc_demogram("Switzerland") # for a dark background gph_hc_demogram("Switzerland", "#6ea8fe", "#8f99a8", "#dee2e6")
Provides basic ggplot2 graph with the following data on it :
the total life expectancy (black dotted)
the male life expectancy (blue)
the male life expectancy (red)
gph_hc_lexgram(country, men = "blue", women = "red", all = "black")gph_hc_lexgram(country, men = "blue", women = "red", all = "black")
country |
A string with the country name |
men |
Color for men line |
women |
Color for women line |
all |
Color for average line |
A highcharts graph
gph_hc_lexgram("Switzerland")gph_hc_lexgram("Switzerland")
Plot latest swiss votation using highcharts
gph_hc_map_swiss_votes( geolevel = c("canton", "district", "municipality"), votedates, id, language = "FR" )gph_hc_map_swiss_votes( geolevel = c("canton", "district", "municipality"), votedates, id, language = "FR" )
geolevel |
One of "canton", "district" or "municipality" |
votedates |
The date of the vote |
id |
The vote id |
language |
One of "DE", "FR", "IT" or "RM" |
A highcharter map
gph_hc_map_swiss_votes("canton", votedates = "2024-03-03", id = 6650)gph_hc_map_swiss_votes("canton", votedates = "2024-03-03", id = 6650)
Create highcharter population pyramid
gph_hc_pyramid(country, year)gph_hc_pyramid(country, year)
country |
A string with the country name |
year |
An integer for the year |
A highcharter graph
# Not run: need a valid IDB API key gph_hc_pyramid("Switzerland", 2020)# Not run: need a valid IDB API key gph_hc_pyramid("Switzerland", 2020)
Create highcharter population pyramid with relative values
gph_hc_pyramid_relative(country, year)gph_hc_pyramid_relative(country, year)
country |
A string with the country name |
year |
An integer for the year |
A highcharter graph
# Not run: need a valid IDB API key gph_hc_pyramid_relative("Switzerland", 2020)# Not run: need a valid IDB API key gph_hc_pyramid_relative("Switzerland", 2020)
The total fertility rate (children per woman) of one or more countries since 1960, with the replacement level (2.1). One country is drawn as an area, several as lines.
gph_hc_tfr( countries, events = NULL, periods = NULL, highlight = NULL, colors = c("#c03728", "#8a93a3", "#d9a441", "#919c4c", "#5b7fa6", "#6b452b", "#2b323f") )gph_hc_tfr( countries, events = NULL, periods = NULL, highlight = NULL, colors = c("#c03728", "#8a93a3", "#d9a441", "#919c4c", "#5b7fa6", "#6b452b", "#2b323f") )
countries |
A character vector of country names or iso codes |
events |
A named numeric vector of years to mark with a dashed line,
e.g. |
periods |
A named list of year ranges to shade, e.g. |
highlight |
Countries (as in |
colors |
Colours of the countries, in the order of |
A highcharter graph
gph_hc_tfr("China", events = c("one child" = 1980), periods = list(famine = c(1959, 1961))) gph_hc_tfr(c("France", "Germany", "Italy"), highlight = "France")gph_hc_tfr("China", events = c("one child" = 1980), periods = list(famine = c(1959, 1961))) gph_hc_tfr(c("France", "Germany", "Italy"), highlight = "France")
The population aged 15 to 64 of a country since 1990, in millions, with its peak.
gph_hc_working_age( country, start = 1990, color = "#5b7fa6", peak_color = "#c03728" )gph_hc_working_age( country, start = 1990, color = "#5b7fa6", peak_color = "#c03728" )
country |
A string with the country name or iso code |
start |
The first year |
color |
Colour of the curve |
peak_color |
Colour of the peak and its label |
A highcharter graph
gph_hc_working_age("China")gph_hc_working_age("China")
Return Historical World Map
gph_historical_world_map(date)gph_historical_world_map(date)
date |
A date object |
An sf object
lubridate::ymd("1945-06-01") |> gph_historical_world_map() |> ggplot2::ggplot() + ggplot2::geom_sf()lubridate::ymd("1945-06-01") |> gph_historical_world_map() |> ggplot2::ggplot() + ggplot2::geom_sf()
Provides basic ggplot2 graph with the following data on it :
the total life expectancy (black dotted)
the male life expectancy (blue)
the male life expectancy (red)
gph_lexgram( country, theme = ggplot2::theme_minimal(), men = "blue", women = "red", all = "black" )gph_lexgram( country, theme = ggplot2::theme_minimal(), men = "blue", women = "red", all = "black" )
country |
A string with the country name |
theme |
A ggplot2 theme |
men |
Color for men line |
women |
Color for women line |
all |
Color for average line |
A ggplot2 graph
gph_lexgram("Switzerland")gph_lexgram("Switzerland")
Swiss lakes for maps
gph_map_swiss_lakes( fill_color = "skyblue", source = themakart::thema_topo("seen") )gph_map_swiss_lakes( fill_color = "skyblue", source = themakart::thema_topo("seen") )
fill_color |
A color for the lakes (default: skyblue) |
source |
The geometry for lakes (default: ThemaKart lakes) |
A ggplot2 layer for Swiss lakes
ggplot2::ggplot() + gph_map_swiss_relief() + gph_map_swiss_lakes()ggplot2::ggplot() + gph_map_swiss_relief() + gph_map_swiss_lakes()
Swiss relief background for maps
gph_map_swiss_relief()gph_map_swiss_relief()
A list of ggplot 2 layers for Swiss relief backgroup
ggplot2::ggplot() + gph_map_swiss_relief() + gph_map_swiss_lakes()ggplot2::ggplot() + gph_map_swiss_relief() + gph_map_swiss_lakes()
Plot latest swiss votation
gph_map_swiss_votes( geolevel = c("canton", "district", "municipality"), votedates, id, language = "FR" )gph_map_swiss_votes( geolevel = c("canton", "district", "municipality"), votedates, id, language = "FR" )
geolevel |
One of "canton", "district" or "municipality" |
votedates |
The date of the vote |
id |
The vote id |
language |
One of "DE", "FR", "IT" or "RM" |
A ggplot2 map
gph_map_swiss_votes("canton", votedates = "2024-03-03", id = 6650)gph_map_swiss_votes("canton", votedates = "2024-03-03", id = 6650)
Create a globe with the countries in the list highlighted
gph_mini_globe(countries)gph_mini_globe(countries)
countries |
A vector of country names |
A ggplot map
gph_mini_globe("Switzerland")gph_mini_globe("Switzerland")
Project simple world map
gph_project_world(crs)gph_project_world(crs)
crs |
A valid CRS string or object |
A ggplot map
gph_project_world("+proj=eqearth")gph_project_world("+proj=eqearth")
Projet world map with Tissot matrices
gph_project_world_tissot(crs)gph_project_world_tissot(crs)
crs |
A valid CRS string or object |
A ggplot object
gph_project_world_tissot("+proj=eqearth")gph_project_world_tissot("+proj=eqearth")
Create population pyramid
gph_pyramid(country, year, theme = ggplot2::theme_minimal())gph_pyramid(country, year, theme = ggplot2::theme_minimal())
country |
A string with the country name |
year |
An integer for the year |
theme |
A ggplot2 theme |
A ggplot graph
# Not run: needs a valid IDB API key gph_pyramid("Switzerland", 2020)# Not run: needs a valid IDB API key gph_pyramid("Switzerland", 2020)
Create relative population pyramid (5 year cohorts)
gph_pyramid_relative(country, year, theme = ggplot2::theme_minimal())gph_pyramid_relative(country, year, theme = ggplot2::theme_minimal())
country |
A string with the country name |
year |
An integer for the year |
theme |
A ggplot2 theme |
A ggplot graph
# Not run: needs a valid IDB API key gph_pyramid_relative("Switzerland", 2020)# Not run: needs a valid IDB API key gph_pyramid_relative("Switzerland", 2020)
Create world maps for an indicator
gph_wb_world_map(indicator) gph_wb_world_hc(indicator)gph_wb_world_map(indicator) gph_wb_world_hc(indicator)
indicator |
A list of lists. Each list should contain:
|
# Not run: saves the plots on file indicators <- list( list( code = c("population" = "EN.POP.DNST"), operation = rlang::expr(population), title = geotools::gtl_translate_enfr( "Population density", "Densité de population" ), file = "density", palette = list( palette = "viridis::viridis", type = "cont", dir = -1 ), breaks = c(10, 25, 50, 75, 100, 150, 300, 1000), center = -1, unit = "pp/km²", years = c(2019) ) ) gph_wb_world_map(indicators)# Not run: saves the plots on file indicators <- list( list( code = c("population" = "EN.POP.DNST"), operation = rlang::expr(population), title = geotools::gtl_translate_enfr( "Population density", "Densité de population" ), file = "density", palette = list( palette = "viridis::viridis", type = "cont", dir = -1 ), breaks = c(10, 25, 50, 75, 100, 150, 300, 1000), center = -1, unit = "pp/km²", years = c(2019) ) ) gph_wb_world_map(indicators)
Une carte du rapport du nombre d'hommes par rapport aux femmes par régions pour 2018.
oc_chine_carte_2018_sex_ratio_par_region(theme = ggplot2::theme_minimal())oc_chine_carte_2018_sex_ratio_par_region(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 map
oc_chine_carte_2018_sex_ratio_par_region()oc_chine_carte_2018_sex_ratio_par_region()
Sex-ratio à la naissance
oc_chine_hc_sex_ratio()oc_chine_hc_sex_ratio()
A highcharter plot
oc_chine_hc_sex_ratio()oc_chine_hc_sex_ratio()
Consommation d'énergie par source
oc_ecocitoyennete_graph_energy_per_source(theme = ggplot2::theme_minimal())oc_ecocitoyennete_graph_energy_per_source(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
oc_ecocitoyennete_graph_energy_per_source()oc_ecocitoyennete_graph_energy_per_source()
Carte mondiale intéractive de la prévalence de la contraception.
oc_geo_au_feminin_carte_hc_contraception()oc_geo_au_feminin_carte_hc_contraception()
A highcharts map
oc_geo_au_feminin_carte_hc_contraception()oc_geo_au_feminin_carte_hc_contraception()
Carte interactive du nombre de femmes au parlement.
oc_geo_au_feminin_carte_hc_femmes_parlement()oc_geo_au_feminin_carte_hc_femmes_parlement()
A highcharts map
oc_geo_au_feminin_carte_hc_femmes_parlement()oc_geo_au_feminin_carte_hc_femmes_parlement()
Carte interactive de l'accès au suffrage féminin par pays
oc_geo_au_feminin_carte_hc_mondiale_suffrage_feminin()oc_geo_au_feminin_carte_hc_mondiale_suffrage_feminin()
A highcharts map
oc_geo_au_feminin_carte_hc_mondiale_suffrage_feminin()oc_geo_au_feminin_carte_hc_mondiale_suffrage_feminin()
Carte interactive de l'accès au suffrage féminin par canton
oc_geo_au_feminin_carte_hc_suisse_suffrage_feminin()oc_geo_au_feminin_carte_hc_suisse_suffrage_feminin()
A highcharts map
oc_geo_au_feminin_carte_hc_suisse_suffrage_feminin()oc_geo_au_feminin_carte_hc_suisse_suffrage_feminin()
Cartes qui permettent de retracer l'évolution géopolitique de l'Afghanistan depuis 1912.
oc_geo_au_feminin_carte_afghanistan_independance( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_carte_afghanistan(theme = ggplot2::theme_minimal())oc_geo_au_feminin_carte_afghanistan_independance( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_carte_afghanistan(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
Carte disponibles:
1912: oc_geo_au_feminin_carte_afghanistan_independance()
1950: oc_geo_au_feminin_carte_afghanistan()
A ggplot2 map
oc_geo_au_feminin_carte_afghanistan_independance() oc_geo_au_feminin_carte_afghanistan()oc_geo_au_feminin_carte_afghanistan_independance() oc_geo_au_feminin_carte_afghanistan()
Cartes des droits en matière d'avortement ainsi que des taux d'avortement.
oc_geo_au_feminin_carte_hc_abortion_rights() oc_geo_au_feminin_carte_abortion_rights(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_carte_hc_taux_avortement() oc_geo_au_feminin_carte_taux_avortement(theme = ggplot2::theme_minimal())oc_geo_au_feminin_carte_hc_abortion_rights() oc_geo_au_feminin_carte_abortion_rights(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_carte_hc_taux_avortement() oc_geo_au_feminin_carte_taux_avortement(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A map
oc_geo_au_feminin_carte_hc_abortion_rights() oc_geo_au_feminin_carte_abortion_rights() oc_geo_au_feminin_carte_hc_taux_avortement() oc_geo_au_feminin_carte_taux_avortement()oc_geo_au_feminin_carte_hc_abortion_rights() oc_geo_au_feminin_carte_abortion_rights() oc_geo_au_feminin_carte_hc_taux_avortement() oc_geo_au_feminin_carte_taux_avortement()
Cartes pour mettre en évidence les différences d'espérence de vie entre hommes et femmes.
oc_geo_au_feminin_carte_esperance_de_vie_wpp2012_2005_a_2010( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_carte_esperance_de_vie_wpp2019_2005_a_2010( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_carte_esperance_de_vie_wpp2019_2015_a_2020( theme = ggplot2::theme_minimal() )oc_geo_au_feminin_carte_esperance_de_vie_wpp2012_2005_a_2010( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_carte_esperance_de_vie_wpp2019_2005_a_2010( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_carte_esperance_de_vie_wpp2019_2015_a_2020( theme = ggplot2::theme_minimal() )
theme |
A ggplot2 theme |
A map
oc_geo_au_feminin_carte_esperance_de_vie_wpp2012_2005_a_2010() oc_geo_au_feminin_carte_esperance_de_vie_wpp2019_2005_a_2010() oc_geo_au_feminin_carte_esperance_de_vie_wpp2019_2015_a_2020()oc_geo_au_feminin_carte_esperance_de_vie_wpp2012_2005_a_2010() oc_geo_au_feminin_carte_esperance_de_vie_wpp2019_2005_a_2010() oc_geo_au_feminin_carte_esperance_de_vie_wpp2019_2015_a_2020()
Ces différentes cartes présente plusieurs indicateurs de l'inégalité entre hommes et femmes.
oc_geo_au_feminin_carte_sigi_2023(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_carte_hc_sigi_2023() oc_geo_au_feminin_carte_hc_gii_2021() oc_geo_au_feminin_carte_hc_wbl()oc_geo_au_feminin_carte_sigi_2023(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_carte_hc_sigi_2023() oc_geo_au_feminin_carte_hc_gii_2021() oc_geo_au_feminin_carte_hc_wbl()
theme |
A ggplot2 theme |
Indicateurs à disposition:
SIGI 2023: oc_geo_au_feminin_carte_sigi_2023() et oc_geo_au_feminin_carte_hc_sigi_2023()
GII 2021: oc_geo_au_feminin_carte_hc_gii_2021()
WB WBL 2022: oc_geo_au_feminin_carte_hc_wbl()
A map
# Sigi 2023 oc_geo_au_feminin_carte_sigi_2023() oc_geo_au_feminin_carte_hc_sigi_2023() # GII 2021 oc_geo_au_feminin_carte_hc_gii_2021() # WB WBL 2022 oc_geo_au_feminin_carte_hc_wbl()# Sigi 2023 oc_geo_au_feminin_carte_sigi_2023() oc_geo_au_feminin_carte_hc_sigi_2023() # GII 2021 oc_geo_au_feminin_carte_hc_gii_2021() # WB WBL 2022 oc_geo_au_feminin_carte_hc_wbl()
Cartes du sex ratio à la naissance.
oc_geo_au_feminin_carte_sex_ratio(year, theme = ggplot2::theme_minimal()) oc_geo_au_feminin_carte_hc_sex_ratio()oc_geo_au_feminin_carte_sex_ratio(year, theme = ggplot2::theme_minimal()) oc_geo_au_feminin_carte_hc_sex_ratio()
year |
The year |
theme |
A ggplot2 theme |
A ggplot2 map
oc_geo_au_feminin_carte_sex_ratio(2015) oc_geo_au_feminin_carte_hc_sex_ratio()oc_geo_au_feminin_carte_sex_ratio(2015) oc_geo_au_feminin_carte_hc_sex_ratio()
Cartes du taux de viols déclarés en Europe.
oc_geo_au_feminin_carte_viol_europe(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_carte_hc_viol_europe()oc_geo_au_feminin_carte_viol_europe(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_carte_hc_viol_europe()
theme |
A ggplot2 theme |
A map
oc_geo_au_feminin_carte_viol_europe() oc_geo_au_feminin_carte_hc_viol_europe()oc_geo_au_feminin_carte_viol_europe() oc_geo_au_feminin_carte_hc_viol_europe()
Pourcentage de femmes dans les parlements nationaux par régions
oc_geo_au_feminin_graph_proportion_parlements(theme = ggplot2::theme_minimal())oc_geo_au_feminin_graph_proportion_parlements(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
oc_geo_au_feminin_graph_proportion_parlements()oc_geo_au_feminin_graph_proportion_parlements()
Graphiques visualisant les résultats de l'enquête sur les violences sexuelles en Suisse réalisée par GFS.Bern sur demande d'Amnesty international en avril 2019.
oc_geo_au_feminin_graph_2019_violences_sexuelles_experiences( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_2019_violences_sexuelles_actes( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_2019_violences_sexuelles_police( theme = ggplot2::theme_minimal() )oc_geo_au_feminin_graph_2019_violences_sexuelles_experiences( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_2019_violences_sexuelles_actes( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_2019_violences_sexuelles_police( theme = ggplot2::theme_minimal() )
theme |
A ggplot2 theme |
A graph
oc_geo_au_feminin_graph_2019_violences_sexuelles_experiences() oc_geo_au_feminin_graph_2019_violences_sexuelles_actes() oc_geo_au_feminin_graph_2019_violences_sexuelles_police()oc_geo_au_feminin_graph_2019_violences_sexuelles_experiences() oc_geo_au_feminin_graph_2019_violences_sexuelles_actes() oc_geo_au_feminin_graph_2019_violences_sexuelles_police()
Graphiques présentant les différences salariale en Suisse par formation et par position.
oc_geo_au_feminin_graph_difference_salariale_brut_par_formation( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_difference_salariale_pourcent_par_formation( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_difference_salariale_brut_par_position( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_difference_salariale_pourcent_par_position( theme = ggplot2::theme_minimal() )oc_geo_au_feminin_graph_difference_salariale_brut_par_formation( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_difference_salariale_pourcent_par_formation( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_difference_salariale_brut_par_position( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_graph_difference_salariale_pourcent_par_position( theme = ggplot2::theme_minimal() )
theme |
A ggplot2 theme |
A graph
oc_geo_au_feminin_graph_difference_salariale_brut_par_formation() oc_geo_au_feminin_graph_difference_salariale_brut_par_position() oc_geo_au_feminin_graph_difference_salariale_pourcent_par_formation() oc_geo_au_feminin_graph_difference_salariale_pourcent_par_position()oc_geo_au_feminin_graph_difference_salariale_brut_par_formation() oc_geo_au_feminin_graph_difference_salariale_brut_par_position() oc_geo_au_feminin_graph_difference_salariale_pourcent_par_formation() oc_geo_au_feminin_graph_difference_salariale_pourcent_par_position()
Graphiques sur l'évolution de l'espérence de vie.
oc_geo_au_feminin_graph_esperances_de_vie(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_graph_difference_esperance_de_vie( theme = ggplot2::theme_minimal() )oc_geo_au_feminin_graph_esperances_de_vie(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_graph_difference_esperance_de_vie( theme = ggplot2::theme_minimal() )
theme |
A ggplot2 theme |
A graph
oc_geo_au_feminin_graph_esperances_de_vie() # Not run: need credentials for HMD database oc_geo_au_feminin_graph_difference_esperance_de_vie()oc_geo_au_feminin_graph_esperances_de_vie() # Not run: need credentials for HMD database oc_geo_au_feminin_graph_difference_esperance_de_vie()
Taux de jeunes apprenant à coder
oc_geo_au_feminin_graph_programmation_16_24_ans( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_hc_programmation_16_24_ans()oc_geo_au_feminin_graph_programmation_16_24_ans( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_hc_programmation_16_24_ans()
theme |
A ggplot2 theme |
A ggplot2 graph
oc_geo_au_feminin_graph_programmation_16_24_ans() oc_geo_au_feminin_hc_programmation_16_24_ans()oc_geo_au_feminin_graph_programmation_16_24_ans() oc_geo_au_feminin_hc_programmation_16_24_ans()
Scolarisation des filles en Afghanistan
oc_geo_au_feminin_graph_scolarisation_filles_afghanistan( theme = ggplot2::theme_minimal(), line_color = "black" ) oc_geo_au_feminin_hc_scolarisation_filles_afghanistan()oc_geo_au_feminin_graph_scolarisation_filles_afghanistan( theme = ggplot2::theme_minimal(), line_color = "black" ) oc_geo_au_feminin_hc_scolarisation_filles_afghanistan()
theme |
A ggplot2 theme |
line_color |
Graph line color |
A graph
oc_geo_au_feminin_graph_scolarisation_filles_afghanistan() oc_geo_au_feminin_hc_scolarisation_filles_afghanistan()oc_geo_au_feminin_graph_scolarisation_filles_afghanistan() oc_geo_au_feminin_hc_scolarisation_filles_afghanistan()
Graphiques avec l'évolution du PIB par habitant en Afghanistan
oc_geo_au_feminin_graph_situation_eco_afghanistan( theme = ggplot2::theme_minimal(), line_color = "black" ) oc_geo_au_feminin_hc_situation_eco_afghanistan()oc_geo_au_feminin_graph_situation_eco_afghanistan( theme = ggplot2::theme_minimal(), line_color = "black" ) oc_geo_au_feminin_hc_situation_eco_afghanistan()
theme |
A ggplot2 theme |
line_color |
Graph line color |
A graph
oc_geo_au_feminin_graph_situation_eco_afghanistan() oc_geo_au_feminin_hc_situation_eco_afghanistan()oc_geo_au_feminin_graph_situation_eco_afghanistan() oc_geo_au_feminin_hc_situation_eco_afghanistan()
Graphiques sur l'évolution du taux d'avortement en Suisse et l'évolution politique à ce sujet.
oc_geo_au_feminin_graph_suisse_avortements(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_hc_suisse_avortements_par_ages() oc_geo_au_feminin_graph_suisse_avortements_par_ages( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_hc_suisse_avortements_par_regions() oc_geo_au_feminin_graph_suisse_avortements_par_regions( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_hc_suisse_votations_avortement() oc_geo_au_feminin_graph_suisse_votations_avortement( theme = ggplot2::theme_minimal() )oc_geo_au_feminin_graph_suisse_avortements(theme = ggplot2::theme_minimal()) oc_geo_au_feminin_hc_suisse_avortements_par_ages() oc_geo_au_feminin_graph_suisse_avortements_par_ages( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_hc_suisse_avortements_par_regions() oc_geo_au_feminin_graph_suisse_avortements_par_regions( theme = ggplot2::theme_minimal() ) oc_geo_au_feminin_hc_suisse_votations_avortement() oc_geo_au_feminin_graph_suisse_votations_avortement( theme = ggplot2::theme_minimal() )
theme |
A ggplot2 theme |
A graph
oc_geo_au_feminin_graph_suisse_avortements() oc_geo_au_feminin_graph_suisse_avortements_par_ages() oc_geo_au_feminin_hc_suisse_avortements_par_ages() oc_geo_au_feminin_graph_suisse_avortements_par_regions() oc_geo_au_feminin_hc_suisse_avortements_par_regions() oc_geo_au_feminin_graph_suisse_votations_avortement() oc_geo_au_feminin_hc_suisse_votations_avortement()oc_geo_au_feminin_graph_suisse_avortements() oc_geo_au_feminin_graph_suisse_avortements_par_ages() oc_geo_au_feminin_hc_suisse_avortements_par_ages() oc_geo_au_feminin_graph_suisse_avortements_par_regions() oc_geo_au_feminin_hc_suisse_avortements_par_regions() oc_geo_au_feminin_graph_suisse_votations_avortement() oc_geo_au_feminin_hc_suisse_votations_avortement()
Victimes
oc_israel_palestine_graph_gaza_victimes(theme = ggplot2::theme_minimal()) oc_israel_palestine_hc_gaza_victimes()oc_israel_palestine_graph_gaza_victimes(theme = ggplot2::theme_minimal()) oc_israel_palestine_hc_gaza_victimes()
theme |
A ggplot2 theme |
A graph
Graphiques sur le thème de l'immigration en Israël.
oc_israel_palestine_graph_migration_israel(theme = ggplot2::theme_minimal()) oc_israel_palestine_hc_migration_israel() oc_israel_palestine_graph_population_nae(theme = ggplot2::theme_minimal()) oc_israel_palestine_hc_immigration_sioniste()oc_israel_palestine_graph_migration_israel(theme = ggplot2::theme_minimal()) oc_israel_palestine_hc_migration_israel() oc_israel_palestine_graph_population_nae(theme = ggplot2::theme_minimal()) oc_israel_palestine_hc_immigration_sioniste()
theme |
A ggplot2 theme |
A graph
oc_israel_palestine_graph_migration_israel() oc_israel_palestine_hc_migration_israel() oc_israel_palestine_graph_population_nae() oc_israel_palestine_hc_immigration_sioniste()oc_israel_palestine_graph_migration_israel() oc_israel_palestine_hc_migration_israel() oc_israel_palestine_graph_population_nae() oc_israel_palestine_hc_immigration_sioniste()
Une carte de l'accroissement naturel (2017 à 2024) au niveau régional en Russie.
oc_russie_carte_accroissement( theme = ggplot2::theme_minimal(), barwidth = 40, greyscale = FALSE, year = 2024 ) oc_russie_carte_hc_accroissement(year = 2024)oc_russie_carte_accroissement( theme = ggplot2::theme_minimal(), barwidth = 40, greyscale = FALSE, year = 2024 ) oc_russie_carte_hc_accroissement(year = 2024)
theme |
A ggplot2 theme |
barwidth |
Width of legend bar |
greyscale |
Boolean: whether to create a grey scale map |
year |
Year of the data (2017 to 2024) |
A map
oc_russie_carte_accroissement() oc_russie_carte_hc_accroissement()oc_russie_carte_accroissement() oc_russie_carte_hc_accroissement()
Carte de l'adhésion à l'OTAN par années
oc_russie_carte_adhesion_otan(theme = ggplot2::theme_minimal()) oc_russie_carte_hc_adhesion_otan(layout = "proximate", zoom = 3.5)oc_russie_carte_adhesion_otan(theme = ggplot2::theme_minimal()) oc_russie_carte_hc_adhesion_otan(layout = "proximate", zoom = 3.5)
theme |
A ggplot2 theme |
layout |
Layout of the legend, either 'horizontal', 'vertical' or 'proximate' |
zoom |
Map zoom (defaults to 3.5) |
A ggplot2 map
oc_russie_carte_adhesion_otan() oc_russie_carte_hc_adhesion_otan()oc_russie_carte_adhesion_otan() oc_russie_carte_hc_adhesion_otan()
Une carte des divorces pour 1000 habitants par régions en 2019.
oc_russie_carte_divorces(theme = ggplot2::theme_minimal())oc_russie_carte_divorces(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 map
oc_russie_carte_divorces()oc_russie_carte_divorces()
Une carte de l'évolution démographique Russe entre 1990 et 2020.
oc_russie_carte_evolution_population( theme = ggplot2::theme_minimal(), barwidth = 40 )oc_russie_carte_evolution_population( theme = ggplot2::theme_minimal(), barwidth = 40 )
theme |
A ggplot2 theme |
barwidth |
Width of legend bar |
A ggplot2 map
oc_russie_carte_evolution_population()oc_russie_carte_evolution_population()
Une carte de l'indice de fécondité (2012 à 2024) au niveau régional en Russie.
oc_russie_carte_fecondite( theme = ggplot2::theme_minimal(), barwidth = 40, greyscale = FALSE, year = 2024 ) oc_russie_carte_hc_fecondite(year = 2024)oc_russie_carte_fecondite( theme = ggplot2::theme_minimal(), barwidth = 40, greyscale = FALSE, year = 2024 ) oc_russie_carte_hc_fecondite(year = 2024)
theme |
A ggplot2 theme |
barwidth |
Width of legend bar |
greyscale |
Boolean: whether to create a grey scale map |
year |
Year of the data (2012 to 2024) |
A map
oc_russie_carte_fecondite() oc_russie_carte_hc_fecondite()oc_russie_carte_fecondite() oc_russie_carte_hc_fecondite()
Cartes des élections présidentielles ukrainiennes en 2010, 2014 et 2019.
oc_russie_carte_hc_elections_ukraine_2010() oc_russie_carte_hc_elections_ukraine_2014() oc_russie_carte_hc_elections_ukraine_2019()oc_russie_carte_hc_elections_ukraine_2010() oc_russie_carte_hc_elections_ukraine_2014() oc_russie_carte_hc_elections_ukraine_2019()
A higcharts map
oc_russie_carte_hc_elections_ukraine_2010() oc_russie_carte_hc_elections_ukraine_2014() oc_russie_carte_hc_elections_ukraine_2019()oc_russie_carte_hc_elections_ukraine_2010() oc_russie_carte_hc_elections_ukraine_2014() oc_russie_carte_hc_elections_ukraine_2019()
Religion avec le plus de pourcentage en 2012 par région.
oc_russie_carte_hc_religion()oc_russie_carte_hc_religion()
A higcharts map
oc_russie_carte_hc_religion()oc_russie_carte_hc_religion()
Une carte des mariages pour 1000 habitants par régions en 2019.
oc_russie_carte_mariages(theme = ggplot2::theme_minimal())oc_russie_carte_mariages(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 map
oc_russie_carte_mariages()oc_russie_carte_mariages()
Une carte du taux de mortalité en 2019 au niveau régional en Russie.
oc_russie_carte_mortalite(theme = ggplot2::theme_minimal(), barwidth = 40)oc_russie_carte_mortalite(theme = ggplot2::theme_minimal(), barwidth = 40)
theme |
A ggplot2 theme |
barwidth |
Width of legend bar |
A ggplot2 map
oc_russie_carte_mortalite()oc_russie_carte_mortalite()
Une carte du taux de natalité en 2019 au niveau régional en Russie.
oc_russie_carte_natalite(theme = ggplot2::theme_minimal(), barwidth = 40)oc_russie_carte_natalite(theme = ggplot2::theme_minimal(), barwidth = 40)
theme |
A ggplot2 theme |
barwidth |
Width of legend bar |
A ggplot2 map
oc_russie_carte_natalite()oc_russie_carte_natalite()
Graphique de l'évolution démographique de la Russie entre 1980 et 2020. Graphique destiné à l'examen de maturité 2022.
oc_russie_graph_demo_exa(theme = ggplot2::theme_minimal())oc_russie_graph_demo_exa(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
oc_russie_graph_demo_exa()oc_russie_graph_demo_exa()
Graphique de l'évolution démographique de la Russie entre 1980 et 2023. Graphique destiné à l'examen de maturité 2025.
oc_russie_graph_demo_exa_2025(theme = ggplot2::theme_minimal())oc_russie_graph_demo_exa_2025(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
oc_russie_graph_demo_exa_2025()oc_russie_graph_demo_exa_2025()
"Race plot" avec l'évolution des dépenses militaires entre 2000 et 2020.
oc_russie_graph_depense_militaire(theme = ggplot2::theme_minimal())oc_russie_graph_depense_militaire(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
An animated ggplot2
# Not run: needs ffmpeg oc_russie_graph_depense_militaire()# Not run: needs ffmpeg oc_russie_graph_depense_militaire()
Graphique de l'immigration en Russie avec les 5 pays
les plus représentés et une catégorie autres.
oc_russie_graph_immigration(theme = ggplot2::theme_minimal())oc_russie_graph_immigration(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
oc_russie_graph_immigration()oc_russie_graph_immigration()
Graphiques des mariages et divorces en Russie
oc_russie_graph_mariages_et_divorces(theme = ggplot2::theme_minimal())oc_russie_graph_mariages_et_divorces(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
oc_russie_graph_mariages_et_divorces()oc_russie_graph_mariages_et_divorces()
Graphique des naissances et des avortements en Russie de 1999 à 2024.
oc_russie_graph_naissances(theme = ggplot2::theme_minimal()) oc_russie_graph_avortements(theme = ggplot2::theme_minimal()) oc_russie_graph_naissance_et_avortements(theme = ggplot2::theme_minimal()) oc_russie_hc_naissances_et_avortements()oc_russie_graph_naissances(theme = ggplot2::theme_minimal()) oc_russie_graph_avortements(theme = ggplot2::theme_minimal()) oc_russie_graph_naissance_et_avortements(theme = ggplot2::theme_minimal()) oc_russie_hc_naissances_et_avortements()
theme |
A ggplot2 theme |
A ggplot2 graph
A highcharts graph
oc_russie_graph_naissances() oc_russie_graph_avortements() oc_russie_hc_naissances_et_avortements() # Not run: needs patchwork to assemble the plot and only # works if the package is loaded oc_russie_graph_naissance_et_avortements()oc_russie_graph_naissances() oc_russie_graph_avortements() oc_russie_hc_naissances_et_avortements() # Not run: needs patchwork to assemble the plot and only # works if the package is loaded oc_russie_graph_naissance_et_avortements()
Graphique du solde migratoire en Russie par décénies.
oc_russie_graph_solde_migratoire(theme = ggplot2::theme_minimal())oc_russie_graph_solde_migratoire(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
oc_russie_graph_solde_migratoire()oc_russie_graph_solde_migratoire()
Graphiques avec l'évolution de l'attitude des russes envers l'étranger.
oc_russie_graph_attitude_us(theme = ggplot2::theme_minimal()) oc_russie_graph_attitude_eu(theme = ggplot2::theme_minimal())oc_russie_graph_attitude_us(theme = ggplot2::theme_minimal()) oc_russie_graph_attitude_eu(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
oc_russie_graph_attitude_us() oc_russie_graph_attitude_eu()oc_russie_graph_attitude_us() oc_russie_graph_attitude_eu()
Graphiques qui montrent l'évolution de l'espérance de vie de la Russie.
oc_russie_graph_esperance_europe(theme = ggplot2::theme_minimal()) oc_russie_hc_esperance_europe( others_color = "#d7d7d7", russia_color = "#41afeb" ) oc_russie_graph_esperance_65_femmes(theme = ggplot2::theme_minimal()) oc_russie_graph_esperance_65_hommes(theme = ggplot2::theme_minimal())oc_russie_graph_esperance_europe(theme = ggplot2::theme_minimal()) oc_russie_hc_esperance_europe( others_color = "#d7d7d7", russia_color = "#41afeb" ) oc_russie_graph_esperance_65_femmes(theme = ggplot2::theme_minimal()) oc_russie_graph_esperance_65_hommes(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
others_color |
Color of the other European countries (highcharts) |
russia_color |
Color of Russia (highcharts) |
A ggplot2 graph
oc_russie_graph_esperance_europe() oc_russie_hc_esperance_europe() # Not run: needs credentials for HMD database oc_russie_graph_esperance_65_femmes() oc_russie_graph_esperance_65_hommes()oc_russie_graph_esperance_europe() oc_russie_hc_esperance_europe() # Not run: needs credentials for HMD database oc_russie_graph_esperance_65_femmes() oc_russie_graph_esperance_65_hommes()
Graphique de l'évolution de l'indice de fécondité de la Russie comparé au reste de l'Europe.
oc_russie_graph_fertilite_europe(theme = ggplot2::theme_minimal()) oc_russie_hc_fertilite_europe()oc_russie_graph_fertilite_europe(theme = ggplot2::theme_minimal()) oc_russie_hc_fertilite_europe()
theme |
A ggplot2 theme |
A ggplot2 graph
A highcharts graph
oc_russie_graph_fertilite_europe() oc_russie_hc_fertilite_europe()oc_russie_graph_fertilite_europe() oc_russie_hc_fertilite_europe()
Graphique des 10 ethnies les plus fréquentes (hors Russes)
oc_russie_hc_ethnies(colors = palettetown::ichooseyou("salamence", 9))oc_russie_hc_ethnies(colors = palettetown::ichooseyou("salamence", 9))
colors |
Colours of the columns |
A highcharts graph
oc_russie_hc_ethnies() # for a dark background oc_russie_hc_ethnies(colors = c( "#f2c46d", "#7fd1a7", "#6ea8fe", "#ff8a7a", "#c9a0dc", "#9fd3ff", "#ffd27a", "#e8a65d", "#b8e08c", "#f4a6c6" ))oc_russie_hc_ethnies() # for a dark background oc_russie_hc_ethnies(colors = c( "#f2c46d", "#7fd1a7", "#6ea8fe", "#ff8a7a", "#c9a0dc", "#9fd3ff", "#ffd27a", "#e8a65d", "#b8e08c", "#f4a6c6" ))
PIB par habitant comparé entre la Russie et la Suisse
oc_russie_hc_pib_vs_suisse( russia_color = "#657062", switzerland_color = "#d14e3e" )oc_russie_hc_pib_vs_suisse( russia_color = "#657062", switzerland_color = "#d14e3e" )
russia_color |
Colour of the Russian curve |
switzerland_color |
Colour of the Swiss curve |
A highcharts graph
oc_russie_hc_pib_vs_suisse() # for a dark background oc_russie_hc_pib_vs_suisse(russia_color = "#9fd3ff", switzerland_color = "#ff8a7a")oc_russie_hc_pib_vs_suisse() # for a dark background oc_russie_hc_pib_vs_suisse(russia_color = "#9fd3ff", switzerland_color = "#ff8a7a")
Religion en Russie
oc_russie_hc_religion()oc_russie_hc_religion()
A highcharts graph
https://www.levada.ru/en/2023/06/02/religious-beliefs/
oc_russie_hc_religion()oc_russie_hc_religion()
Une carte du résultat de la votation sur le suffrage féminin au canton du Valais le 12 avril 1970.
oc_suisse_carte_1970_suffrage_feminin_valais(theme = ggplot2::theme_minimal())oc_suisse_carte_1970_suffrage_feminin_valais(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
ggplot2 map
oc_suisse_carte_1970_suffrage_feminin_valais()oc_suisse_carte_1970_suffrage_feminin_valais()
Religion par commune
oc_suisse_carte_hc_2020_religion()oc_suisse_carte_hc_2020_religion()
A highcharts map
oc_suisse_carte_hc_2020_religion()oc_suisse_carte_hc_2020_religion()
Une carte de l'introduction du suffrage féminin au niveau cantonal en Suisse.
oc_suisse_carte_suffrage_feminin(theme = ggplot2::theme_minimal())oc_suisse_carte_suffrage_feminin(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
ggplot2 map
oc_suisse_carte_suffrage_feminin()oc_suisse_carte_suffrage_feminin()
Un graphique avec l'évolution de l'immigration italienne en Suisse de 1981 jusqu'en 2021.
oc_suisse_graph_2021_immigration_italienne(theme = ggplot2::theme_minimal())oc_suisse_graph_2021_immigration_italienne(theme = ggplot2::theme_minimal())
theme |
A ggplot2 theme |
A ggplot2 graph
oc_suisse_graph_2021_immigration_italienne()oc_suisse_graph_2021_immigration_italienne()
Graphique des principales nationalité en Suisse en 2023 hors Suisses.
oc_suisse_hc_2023_nationalite(colors = palettetown::ichooseyou("salamence", 9))oc_suisse_hc_2023_nationalite(colors = palettetown::ichooseyou("salamence", 9))
colors |
Colours of the columns |
A highcharts graph
oc_suisse_hc_2023_nationalite() # for a dark background oc_suisse_hc_2023_nationalite(colors = c( "#f2c46d", "#7fd1a7", "#6ea8fe", "#ff8a7a", "#c9a0dc", "#9fd3ff", "#ffd27a", "#e8a65d", "#b8e08c", "#f4a6c6" ))oc_suisse_hc_2023_nationalite() # for a dark background oc_suisse_hc_2023_nationalite(colors = c( "#f2c46d", "#7fd1a7", "#6ea8fe", "#ff8a7a", "#c9a0dc", "#9fd3ff", "#ffd27a", "#e8a65d", "#b8e08c", "#f4a6c6" ))