README.md
In mvuorre/systemaGlobalis: Gapminder's fact base in R
systemaGlobalis
systemaGlobalis is an R package that provides quick access to the
complete Gapminder data
repository.
Installation
You can install the development version of systemaGlobalis from
GitHub with:
devtools::install_github("mvuorre/systemaGlobalis")
It is not currently available on CRAN (nor is it likely to be in the
near future.)
Description
systemaGlobalis is a minimal R package interface to the complete
Systema
Globalis
data compilation gathered by Gapminder.
library(systemaGlobalis)
library(tidyverse)
theme_set(theme_linedraw() + theme(panel.grid = element_blank()))
It containts the following data frames (tibbles):
datapoints
- For each country and year, 532 variables
- The main data frame you’ll want to work with.
dim(datapoints)
#> [1] 79290 534
datapoints[1:5, 1:5] # First five rows and columns
#> # A tibble: 5 x 5
#> geo time adults_with_hiv_per… age_at_1st_marria… aged_15_24_employme…
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 abkh 2004 NA NA NA
#> 2 abkh 2005 NA NA NA
#> 3 abkh 2006 NA NA NA
#> 4 abkh 2007 NA NA NA
#> 5 abw 1700 NA NA NA
countries
- Additional information, such as naming schemes, regions, etc.,
about the countries included in
datapoints
countries
#> # A tibble: 273 x 33
#> country alt_5 alternative_1 alternative_2 alternative_3 alternative_4_c…
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 abkh <NA> <NA> <NA> <NA> <NA>
#> 2 abw <NA> <NA> <NA> <NA> Aruba
#> 3 afg <NA> Islamic Repu… <NA> <NA> Afghanistan
#> 4 ago <NA> <NA> <NA> <NA> Angola
#> 5 aia <NA> <NA> <NA> <NA> <NA>
#> 6 akr_a_… <NA> <NA> <NA> <NA> <NA>
#> 7 ala <NA> √Öland <NA> <NA> <NA>
#> 8 alb <NA> <NA> <NA> <NA> Albania
#> 9 and <NA> <NA> <NA> <NA> <NA>
#> 10 ant <NA> Neth. Antill… <NA> <NA> Netherland Anti…
#> # ... with 263 more rows, and 27 more variables: arb1 <chr>, arb2 <chr>,
#> # arb3 <chr>, arb4 <chr>, arb5 <chr>, arb6 <chr>,
#> # g77_and_oecd_countries <chr>, gapminder_list <chr>, god_id <chr>,
#> # gwid <chr>, income_groups <chr>, `is--country` <lgl>,
#> # iso3166_1_alpha2 <chr>, iso3166_1_alpha3 <chr>,
#> # iso3166_1_numeric <int>, iso3166_2 <chr>, landlocked <chr>,
#> # latitude <dbl>, longitude <dbl>, main_religion_2008 <chr>, name <chr>,
#> # pandg <chr>, un_state <lgl>, unicode_region_subtag <chr>,
#> # upper_case_name <chr>, world_4region <chr>, world_6region <chr>
concepts
- Descriptions of each of the variables in
datapoints, including
sources, tags, etc.
concepts
#> # A tibble: 534 x 8
#> concept description indicator_url name name_catalog name_short
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 adults… The estima… <NA> Adul… Adults with… Adults wi…
#> 2 age_at… The mean a… <NA> Age … Age at 1st … Age at 1s…
#> 3 aged_1… Percentage… <NA> Aged… Aged 15-24 Employmen…
#> 4 aged_1… Percentage… <NA> Aged… Aged 15-24 Unemploym…
#> 5 aged_1… For age gr… <NA> Aged… Aged 15-64 Labour fo…
#> 6 aged_1… Percentage… <NA> Aged… Aged 15+ Employmen…
#> 7 aged_1… For age gr… <NA> Aged… Aged 15+ Labour fo…
#> 8 aged_1… Percentage… <NA> Aged… Aged 15+ Unemploym…
#> 9 aged_2… For age gr… <NA> Aged… Aged 25-54 Labour fo…
#> 10 aged_2… Percentage… <NA> Aged… Aged 25-54 Unemploym…
#> # ... with 524 more rows, and 2 more variables: source_url <chr>,
#> # tags <chr>
That’s it. There are no functions, simply clean datasets that are easily
used in analyses.
Examples
Let’s first look at a selection of variables containing the word
“income”:
datapoints %>%
select(contains("income")) %>%
names(.)
#> [1] "data_quality_income_per_person"
#> [2] "debt_servicing_costs_percent_of_exports_and_net_income_from_abroad"
#> [3] "income_mountains"
#> [4] "income_per_person_gdppercapita_ppp_inflation_adjusted"
#> [5] "income_per_person_long_series"
#> [6] "income_per_person_with_projections"
#> [7] "income_share_of_2nd_poorest_20percent"
#> [8] "income_share_of_2nd_richest_20percent"
#> [9] "income_share_of_middle_20percent"
#> [10] "income_share_of_poorest_10percent"
#> [11] "income_share_of_poorest_20percent"
#> [12] "income_share_of_richest_10percent"
#> [13] "income_share_of_richest_20percent"
#> [14] "subsistence_incomes_per_person"
datapoints %>%
ggplot(aes(time, income_per_person_long_series)) +
geom_line(aes(group = geo)) +
scale_x_continuous(limits = c(1900, 2018))
To find out more about this particular variable, look at the concepts
data frame
concepts %>%
filter(str_detect(concept, "income")) %>%
select(1:2) %>%
slice(1:4) %>%
kable()
| concept | description |
| :--------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| data_quality_income_per_person | Rates the quality for each observation of “Income per person - revised”. The ratings are 0 (data quality not known yet) and 5 (very bad). You can use this “indicator” as color together with income per person to highlight the roughest observations. |
| debt_servicing_costs_percent_of_exports_and_net_income_from_abroad | Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF. |
| income_per_person_gdppercapita_ppp_inflation_adjusted | Gross domestic product per person adjusted for differences in purchasing power (in international dollars, fixed 2011 prices, PPP based on 2011 ICP). |
| income_per_person_long_series | Income per person, version 15, including historical data & projections |
We can use the countries data frame to get more information about the
countries
datapoints %>%
left_join(rename(countries, geo = country)) %>%
filter(!is.na(world_4region)) %>%
ggplot(aes(time, income_per_person_long_series)) +
geom_line(aes(group = geo)) +
scale_x_continuous(limits = c(1800, 2018)) +
facet_wrap("world_4region")
#> Joining, by = "geo"
And, of course, animations! Here’s an animated version of the figure on
page 26 of Factfulness:
library(gganimate)
p <- datapoints %>%
select(geo,
time,
child = children_per_woman_total_fertility,
mortality = child_mortality_0_5_year_olds_dying_per_1000_born,
pop = population_total) %>%
filter(between(time, 1945, 2018)) %>%
ggplot(aes(child, (1000-mortality)/1000, size = pop)) +
annotate(geom="rect", fill = NA, col = "gray40",
xmin = 8.5, xmax = 5, ymin = .55, ymax = .95) +
annotate(geom="rect", fill = NA, col = "gray40",
xmin = 3.5, xmax = 1.1, ymin = .90, ymax = 1) +
geom_point(shape = 21, fill = "gray40", alpha = .8) +
scale_y_continuous("Children surviving to age 5",
breaks = 5:10/10,
limits = c(.5, 1),
labels = scales::percent) +
scale_x_reverse("Babies per woman",
breaks = 1:8) +
annotate(geom="text", col = "gray40",
x = 8.5, y = .55, vjust = 1, hjust = 0,
label = "Big families and many children die") +
annotate(geom="text", col = "gray40",
x = 3.5, y = .9, vjust = 1, hjust = 0,
label = "Small families and\nfew children die") +
theme(legend.position = "none") +
labs(title = "Year: {frame_time}") +
# Animation
transition_time(time)
animate(p, nframes = 100)
Acknowledgements
systemaGlobalis is simply a minimal R package wrapper to the dataset
created by Gapminder. As such, please refer to
https://www.gapminder.org/ for more information about the data,
including sources, and please acknowledge them for any reuse of the
data.
Gapminder created this dataset and provides it under Creative Common
Attribution 4.0 International.
The R package is therefore distributed under the same license.
There is another gapminder R package that contains a selection of the
gapminder data, gapminder, which
inspired this package.
mvuorre/systemaGlobalis documentation built on May 31, 2019, 5:22 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
systemaGlobalis
systemaGlobalis is an R package that provides quick access to the complete Gapminder data repository.
Installation
You can install the development version of systemaGlobalis from GitHub with:
devtools::install_github("mvuorre/systemaGlobalis")
It is not currently available on CRAN (nor is it likely to be in the near future.)
Description
systemaGlobalis is a minimal R package interface to the complete Systema Globalis data compilation gathered by Gapminder.
library(systemaGlobalis)
library(tidyverse)
theme_set(theme_linedraw() + theme(panel.grid = element_blank()))
It containts the following data frames (tibbles):
datapoints- For each country and year, 532 variables
- The main data frame you’ll want to work with.
dim(datapoints)
#> [1] 79290 534
datapoints[1:5, 1:5] # First five rows and columns
#> # A tibble: 5 x 5
#> geo time adults_with_hiv_per… age_at_1st_marria… aged_15_24_employme…
#> <chr> <int> <dbl> <dbl> <dbl>
#> 1 abkh 2004 NA NA NA
#> 2 abkh 2005 NA NA NA
#> 3 abkh 2006 NA NA NA
#> 4 abkh 2007 NA NA NA
#> 5 abw 1700 NA NA NA
countries- Additional information, such as naming schemes, regions, etc.,
about the countries included in
datapoints
- Additional information, such as naming schemes, regions, etc.,
about the countries included in
countries
#> # A tibble: 273 x 33
#> country alt_5 alternative_1 alternative_2 alternative_3 alternative_4_c…
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 abkh <NA> <NA> <NA> <NA> <NA>
#> 2 abw <NA> <NA> <NA> <NA> Aruba
#> 3 afg <NA> Islamic Repu… <NA> <NA> Afghanistan
#> 4 ago <NA> <NA> <NA> <NA> Angola
#> 5 aia <NA> <NA> <NA> <NA> <NA>
#> 6 akr_a_… <NA> <NA> <NA> <NA> <NA>
#> 7 ala <NA> √Öland <NA> <NA> <NA>
#> 8 alb <NA> <NA> <NA> <NA> Albania
#> 9 and <NA> <NA> <NA> <NA> <NA>
#> 10 ant <NA> Neth. Antill… <NA> <NA> Netherland Anti…
#> # ... with 263 more rows, and 27 more variables: arb1 <chr>, arb2 <chr>,
#> # arb3 <chr>, arb4 <chr>, arb5 <chr>, arb6 <chr>,
#> # g77_and_oecd_countries <chr>, gapminder_list <chr>, god_id <chr>,
#> # gwid <chr>, income_groups <chr>, `is--country` <lgl>,
#> # iso3166_1_alpha2 <chr>, iso3166_1_alpha3 <chr>,
#> # iso3166_1_numeric <int>, iso3166_2 <chr>, landlocked <chr>,
#> # latitude <dbl>, longitude <dbl>, main_religion_2008 <chr>, name <chr>,
#> # pandg <chr>, un_state <lgl>, unicode_region_subtag <chr>,
#> # upper_case_name <chr>, world_4region <chr>, world_6region <chr>
concepts- Descriptions of each of the variables in
datapoints, including sources, tags, etc.
- Descriptions of each of the variables in
concepts
#> # A tibble: 534 x 8
#> concept description indicator_url name name_catalog name_short
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 adults… The estima… <NA> Adul… Adults with… Adults wi…
#> 2 age_at… The mean a… <NA> Age … Age at 1st … Age at 1s…
#> 3 aged_1… Percentage… <NA> Aged… Aged 15-24 Employmen…
#> 4 aged_1… Percentage… <NA> Aged… Aged 15-24 Unemploym…
#> 5 aged_1… For age gr… <NA> Aged… Aged 15-64 Labour fo…
#> 6 aged_1… Percentage… <NA> Aged… Aged 15+ Employmen…
#> 7 aged_1… For age gr… <NA> Aged… Aged 15+ Labour fo…
#> 8 aged_1… Percentage… <NA> Aged… Aged 15+ Unemploym…
#> 9 aged_2… For age gr… <NA> Aged… Aged 25-54 Labour fo…
#> 10 aged_2… Percentage… <NA> Aged… Aged 25-54 Unemploym…
#> # ... with 524 more rows, and 2 more variables: source_url <chr>,
#> # tags <chr>
That’s it. There are no functions, simply clean datasets that are easily used in analyses.
Examples
Let’s first look at a selection of variables containing the word “income”:
datapoints %>%
select(contains("income")) %>%
names(.)
#> [1] "data_quality_income_per_person"
#> [2] "debt_servicing_costs_percent_of_exports_and_net_income_from_abroad"
#> [3] "income_mountains"
#> [4] "income_per_person_gdppercapita_ppp_inflation_adjusted"
#> [5] "income_per_person_long_series"
#> [6] "income_per_person_with_projections"
#> [7] "income_share_of_2nd_poorest_20percent"
#> [8] "income_share_of_2nd_richest_20percent"
#> [9] "income_share_of_middle_20percent"
#> [10] "income_share_of_poorest_10percent"
#> [11] "income_share_of_poorest_20percent"
#> [12] "income_share_of_richest_10percent"
#> [13] "income_share_of_richest_20percent"
#> [14] "subsistence_incomes_per_person"
datapoints %>%
ggplot(aes(time, income_per_person_long_series)) +
geom_line(aes(group = geo)) +
scale_x_continuous(limits = c(1900, 2018))
To find out more about this particular variable, look at the concepts
data frame
concepts %>%
filter(str_detect(concept, "income")) %>%
select(1:2) %>%
slice(1:4) %>%
kable()
| concept | description | | :--------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | data_quality_income_per_person | Rates the quality for each observation of “Income per person - revised”. The ratings are 0 (data quality not known yet) and 5 (very bad). You can use this “indicator” as color together with income per person to highlight the roughest observations. | | debt_servicing_costs_percent_of_exports_and_net_income_from_abroad | Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF. | | income_per_person_gdppercapita_ppp_inflation_adjusted | Gross domestic product per person adjusted for differences in purchasing power (in international dollars, fixed 2011 prices, PPP based on 2011 ICP). | | income_per_person_long_series | Income per person, version 15, including historical data & projections |
We can use the countries data frame to get more information about the
countries
datapoints %>%
left_join(rename(countries, geo = country)) %>%
filter(!is.na(world_4region)) %>%
ggplot(aes(time, income_per_person_long_series)) +
geom_line(aes(group = geo)) +
scale_x_continuous(limits = c(1800, 2018)) +
facet_wrap("world_4region")
#> Joining, by = "geo"
And, of course, animations! Here’s an animated version of the figure on page 26 of Factfulness:
library(gganimate)
p <- datapoints %>%
select(geo,
time,
child = children_per_woman_total_fertility,
mortality = child_mortality_0_5_year_olds_dying_per_1000_born,
pop = population_total) %>%
filter(between(time, 1945, 2018)) %>%
ggplot(aes(child, (1000-mortality)/1000, size = pop)) +
annotate(geom="rect", fill = NA, col = "gray40",
xmin = 8.5, xmax = 5, ymin = .55, ymax = .95) +
annotate(geom="rect", fill = NA, col = "gray40",
xmin = 3.5, xmax = 1.1, ymin = .90, ymax = 1) +
geom_point(shape = 21, fill = "gray40", alpha = .8) +
scale_y_continuous("Children surviving to age 5",
breaks = 5:10/10,
limits = c(.5, 1),
labels = scales::percent) +
scale_x_reverse("Babies per woman",
breaks = 1:8) +
annotate(geom="text", col = "gray40",
x = 8.5, y = .55, vjust = 1, hjust = 0,
label = "Big families and many children die") +
annotate(geom="text", col = "gray40",
x = 3.5, y = .9, vjust = 1, hjust = 0,
label = "Small families and\nfew children die") +
theme(legend.position = "none") +
labs(title = "Year: {frame_time}") +
# Animation
transition_time(time)
animate(p, nframes = 100)
Acknowledgements
systemaGlobalis is simply a minimal R package wrapper to the dataset created by Gapminder. As such, please refer to https://www.gapminder.org/ for more information about the data, including sources, and please acknowledge them for any reuse of the data.
Gapminder created this dataset and provides it under Creative Common Attribution 4.0 International.
The R package is therefore distributed under the same license.
There is another gapminder R package that contains a selection of the gapminder data, gapminder, which inspired this package.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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