mapineqr

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The goal of {mapineqr} is to access the data from the Mapineq.org API and dashboard (product of the Mapineq project).

For Python package/module, see https://github.com/e-kotov/mapineqpy.

Installation

Install latest release from R-multiverse:

r install.packages('mapineqr', repos = c('https://e-kotov.r-universe.dev', 'https://cloud.r-project.org') )

You can also install the development version of mapineqr from GitHub:

r if (!require("pak")) install.packages("pak") pak::pak("e-kotov/mapineqr")

``` r

load packages used in the examples on this page

library(mapineqr) library(dplyr) library(ggplot2) library(eurostat) library(sf) library(biscale) ```

Basic Example - univariate data and maps

1. Get the full list of available data at NUTS 3 level:

``` r library(mapineqr)

availabledata <- misources(level = "3") head(available_data) ```

# A tibble: 52 × 3
   source_name    short_description      description                                                         
   <chr>          <chr>                  <chr>                                                               
 1 DEMO_R_D3AREA  "Area by NUTS 3 regio" Area by NUTS 3 region (ESTAT)                                       
 2 PROJ_19RAASFR3 "Assumptions for fert" Assumptions for fertility rates by age, type of projection and NUTS…
 3 PROJ_19RAASMR3 "Assumptions for prob" Assumptions for probability of dying by age, sex, type of projectio…
 4 BD_HGNACE2_R3  "Business demography " Business demography and high growth enterprise by NACE Rev. 2 and N…
 5 BD_SIZE_R3     "Business demography " Business demography by size class and NUTS 3 regions (ESTAT)        
 6 CENS_11DWOB_R3 "Conventional dwellin" Conventional dwellings by occupancy status, type of building and NU…
 7 CRIM_GEN_REG   "Crimes recorded by t" Crimes recorded by the police by NUTS 3 regions (ESTAT)             
 8 DEMO_R_MAGEC3  "Deaths by age group," Deaths by age group, sex and NUTS 3 region (ESTAT)                  
 9 DEMO_R_MWK3_T  "Deaths by week and N" Deaths by week and NUTS 3 region (ESTAT)                            
10 DEMO_R_MWK3_TS "Deaths by week, sex " Deaths by week, sex and NUTS 3 region (ESTAT)                       
# ℹ 42 more rows
# ℹ Use `print(n = ...)` to see more rows

1. Select data source by source_name column and check it’s year and NUTS level coverage:

r mi_source_coverage("CRIM_GEN_REG")

# A tibble: 10 × 5
   nuts_level year  source_name  short_description    description                                            
   <chr>      <chr> <chr>        <chr>                <chr>                                                  
 1 0          2008  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)
 2 0          2009  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)
 3 0          2010  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)
 4 1          2008  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)
 5 1          2009  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)
 6 1          2010  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)
 7 2          2008  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)
 8 2          2009  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)
 9 2          2010  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)
10 3          2008  CRIM_GEN_REG Crimes recorded by t Crimes recorded by the police by NUTS 3 regions (ESTAT)

1. Check the available filters for the data source:

r mi_source_filters("CRIM_GEN_REG", year = 2010, level = "2")

# A tibble: 6 × 4
  field field_label                                                           label                                    value     
  <chr> <chr>                                                                 <chr>                                    <chr>     
1 unit  Unit of measure                                                       Number                                   NR        
2 freq  Time frequency                                                        Annual                                   A         
3 iccs  International classification of crime for statistical purposes (ICCS) Intentional homicide                     ICCS0101  
4 iccs  International classification of crime for statistical purposes (ICCS) Robbery                                  ICCS0401  
5 iccs  International classification of crime for statistical purposes (ICCS) Burglary of private residential premises ICCS05012 
6 iccs  International classification of crime for statistical purposes (ICCS) Theft of a motorized land vehicle        ICCS050211

1. Choose the indicator to filter (let it be burglaries) to and get the data:

r x <- mi_data(x_source = "CRIM_GEN_REG", year = 2010, level = "2", x_filters = list(iccs = "ICCS05012")) head(x)

# A tibble: 6 × 4
  best_year geo   geo_name             x
  <chr>     <chr> <chr>            <int>
1 2008      AT11  Burgenland (A)     223
2 2008      AT12  Niederösterreich  2557
3 2008      AT13  Wien              9319
4 2008      AT21  Kärnten            507
5 2008      AT22  Steiermark        1163
6 2008      AT31  Oberösterreich     988

1. Map the indicator using NUTS2 polygons:

``` r library(eurostat) library(ggplot2)

load NUTS2 level polygons

nuts2 <- eurostat::geteurostatgeospatial(nuts_level = 2, year = "2010", crs = "4326")

join data to NUTS2 polygons

nuts2crime <- nuts2 |> leftjoin(x, by = "geo")

plot a map of burglaries

mapburglaries <- ggplot(nuts2crime) + geomsf(aes(fill = x)) + scalefillviridisc() + labs(title = "Number of burglaries of private residential premises in 2010") + theme_minimal()

ggsave("man/figures/mapburglaries.png", mapburglaries, width = 8, height = 6, dpi = 200, create.dir = TRUE) ```

Advanced Example - bivariate data and maps

1. Select two indicators.

Let those be (1) unemployment rate:

r mi_source_coverage("TGS00010") |> dplyr::arrange(desc(year))

# A tibble: 12 × 5
   nuts_level year  source_name short_description    description                                
   <chr>      <chr> <chr>       <chr>                <chr>                                      
 1 2          2022  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
 2 2          2021  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
 3 2          2020  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
 4 2          2019  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
 5 2          2018  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
 6 2          2017  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
 7 2          2016  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
 8 2          2015  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
 9 2          2014  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
10 2          2013  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
11 2          2012  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)
12 2          2011  TGS00010    Unemployment rate by Unemployment rate by NUTS 2 regions (ESTAT)

And (2) life expectancy:

r mi_source_coverage("DEMO_R_MLIFEXP") |> dplyr::arrange(desc(year))

# A tibble: 96 × 5
   nuts_level year  source_name    short_description    description                                          
   <chr>      <chr> <chr>          <chr>                <chr>                                                
 1 0          2021  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
 2 1          2021  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
 3 2          2021  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
 4 0          2020  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
 5 1          2020  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
 6 2          2020  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
 7 0          2019  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
 8 1          2019  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
 9 2          2019  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
10 0          2018  DEMO_R_MLIFEXP Life expectancy by a Life expectancy by age, sex and NUTS 2 region (ESTAT)
# ℹ 86 more rows
# ℹ Use `print(n = ...)` to see more rows

1. Check for available filters:

r mi_source_filters("TGS00010", year = 2018, level = "2")

# A tibble: 12 × 4
   field   field_label                                                     label                                                                      value 
   <chr>   <chr>                                                           <chr>                                                                      <chr> 
 1 unit    Unit of measure                                                 Percentage                                                                 PC    
 2 isced11 International Standard Classification of Education (ISCED 2011) All ISCED 2011 levels                                                      TOTAL 
 3 isced11 International Standard Classification of Education (ISCED 2011) Less than primary, primary and lower secondary education (levels 0-2)      ED0-2 
 4 isced11 International Standard Classification of Education (ISCED 2011) Upper secondary and post-secondary non-tertiary education (levels 3 and 4) ED3_4 
 5 isced11 International Standard Classification of Education (ISCED 2011) Tertiary education (levels 5-8)                                            ED5-8 
 6 isced11 International Standard Classification of Education (ISCED 2011) Unknown                                                                    UNK   
 7 isced11 International Standard Classification of Education (ISCED 2011) No response                                                                NRP   
 8 sex     Sex                                                             Total                                                                      T     
 9 sex     Sex                                                             Males                                                                      M     
10 sex     Sex                                                             Females                                                                    F     
11 freq    Time frequency                                                  Annual                                                                     A     
12 age     Age class                                                       15 years or over                                                           Y_GE15

r mi_source_filters("DEMO_R_MLIFEXP", year = 2018, level = "2") |> print(n=90)

# A tibble: 91 × 4
   field field_label     label            value
   <chr> <chr>           <chr>            <chr>
 1 unit  Unit of measure Year             YR   
 2 sex   Sex             Total            T    
 3 sex   Sex             Males            M    
 4 sex   Sex             Females          F    
 5 freq  Time frequency  Annual           A    
 6 age   Age class       Less than 1 year Y_LT1
 7 age   Age class       1 year           Y1   
 8 age   Age class       2 years          Y2   
 9 age   Age class       3 years          Y3   
10 age   Age class       4 years          Y4   
11 age   Age class       5 years          Y5   
12 age   Age class       6 years          Y6   
13 age   Age class       7 years          Y7   
14 age   Age class       8 years          Y8   
15 age   Age class       9 years          Y9   
16 age   Age class       10 years         Y10  
17 age   Age class       11 years         Y11  
...

1. Get the data for the two indicators:

r xy_data <- mi_data( year = 2018, level = "2", x_source = "TGS00010", x_filters = list(isced11 = "TOTAL", unit = "PC", age = "Y_GE15", sex = "T", freq = "A"), y_source = "DEMO_R_MLIFEXP", y_filters = list(unit = "YR", age = "Y_LT1", sex = "T", freq = "A") )

1. Plot the scratterplot:

``` r eduvlifeexpplot <- ggplot(xydata, aes(x = x, y = y)) + geompoint() + labs(x = "Percentage of all adults aged 15 years or over with a degree", y = "Life expectancy at birth") + theme_minimal()

ggsave("man/figures/eduvlifeexpplot.png", eduvlifeexpplot, width = 8, height = 6, units = "in", dpi = 300)

```

1. Add the bivariate data to the NUTS2 polygons and create a plot:

r nuts2 <- eurostat::get_eurostat_geospatial(nuts_level = 2, year = "2016", crs = "4326") nuts2_edu_v_life_exp <- nuts2 |> left_join(xy_data, by = "geo")

``` r library(biscale) bidata <- biclass(nuts2eduvlife_exp, x = x, y = y, style = "quantile", dim = 3)

legend <- bi_legend(pal = "GrPink", dim = 3, xlab = " Higher % with a degree", ylab = " Higher life expectancy", size = 8) ```

``` r map <- ggplot() + geomsf(data = bidata, mapping = aes(fill = biclass), color = "white", size = 0.1, show.legend = FALSE) + biscalefill(pal = "GrPink", dim = 3) + labs( title = "Education vs Life Expectancy" ) + bi_theme()

png("man/figures/eduvlifeexpmap.png", width = 8, height = 6, units = "in", res = 300) print(map) print(legend, vp = grid::viewport(x = 0.4, y = .75, width = 0.2, height = 0.2, angle = -45)) dev.off() ```

Citation

To cite the R package and data in publications use:

Kotov E (2024). mapineqr. Access Mapineq inequality indicators via API. doi:10.32614/CRAN.package.mapineqr https://doi.org/10.32614/CRAN.package.mapineqr, https://github.com/e-kotov/mapineqr.

Mills M, Leasure D (2024). “Mapineq Link: Geospatial Dashboard and Database.” doi:10.5281/zenodo.13864000 https://doi.org/10.5281/zenodo.13864000.

BibTeX:

@Manual{mapineqr,
  title = {mapineqr. Access Mapineq inequality indicators via API},
  author = {Egor Kotov},
  year = {2024},
  url = {https://github.com/e-kotov/mapineqr},
  doi = {10.32614/CRAN.package.mapineqr},
}

@Misc{mapineq_link,
  title = {Mapineq Link: Geospatial Dashboard and Database},
  author = {Melinda C Mills and Douglas Leasure},
  year = {2024},
  month = {October},
  publisher = {Mapineq deliverables. Turku: INVEST Research Flagship Centre / University of Turku},
  doi = {10.5281/zenodo.13864000},
}