Type:	Package
Title:	Retrieve, Harmonise and Map Open Data Regarding the Italian School System
Version:	0.2.7
Author:	Leonardo Cefalo [aut, cre], Alessio Pollice [ctb, ths], Paolo Maranzano [ctb]
Maintainer:	Leonardo Cefalo <leonardo.cefalo@uniba.it>
Description:	Compiles and displays the available data sets regarding the Italian school system, with a focus on the infrastructural aspects. Input datasets are downloaded from the web, with the aim of updating everything to real time. The functions are divided in four main modules, namely 'Get', to scrape raw data from the web 'Util', various utilities needed to process raw data 'Group', to aggregate data at the municipality or province level 'Map', to visualize the output datasets.
License:	GPL (≥ 3)
Encoding:	UTF-8
URL:	https://github.com/lcef97/SchoolDataIT
LazyData:	true
RoxygenNote:	7.3.2
Imports:	curl, dplyr, ggplot2, grDevices, httr, leafpop, magrittr, mapview, readr, rlang, rvest, sf, stringr, tidyr, utils, xml2
Suggests:	knitr, readxl, rmarkdown, testthat (≥ 3.0.0), tidyverse
Config/testthat/edition:	3
Depends:	R (≥ 2.10)
NeedsCompilation:	no
Packaged:	2025-07-11 10:11:54 UTC; Leonardo
Repository:	CRAN
Date/Publication:	2025-07-11 10:40:07 UTC

SchoolDataIT: Retrieve, Harmonise and Map Open Data Regarding the Italian School System

Description

Compiles and displays the available data sets regarding the Italian school system, with a focus on the infrastructural aspects. Input datasets are downloaded from the web, with the aim of updating everything to real time. The functions are divided in four main modules, namely 'Get', to scrape raw data from the web 'Util', various utilities needed to process raw data 'Group', to aggregate data at the municipality or province level 'Map', to visualize the output datasets.

Author(s)

Maintainer: Leonardo Cefalo leonardo.cefalo@uniba.it (ORCID)

Other contributors:

• Alessio Pollice alessio.pollice@uniba.it (ORCID) [contributor, thesis advisor]

• Paolo Maranzano pmaranzano.ricercastatistica@gmail.com (ORCID) [contributor]

See Also

Useful links:

• https://github.com/lcef97/SchoolDataIT

Download the names and codes of Italian LAU and NUTS-3 administrative units

Description

This function downloads a file provided by the Italian National Institute of Statistics including all the codes of administrative units in Italy. As of today, it is the easiest way to map directly cadastral codes to municipality codes.

Usage

Get_AdmUnNames(Date = Sys.Date(), autoAbort = FALSE)

Arguments

Value

An object of class tbl_df, tbl and data.frame, including: NUTS-3 code, NUTS-3 abbreviation, LAU code, LAU name (description) and cadastral code. All variables are characters except for the NUTS-3 code.

Source

<https://situas.istat.it/web/#/territorio>

Examples

  Get_AdmUnNames("2025-01-01", autoAbort = TRUE)

Download the data regarding the broad band connection activation in Italian schools

Description

Retrieves the data regarding the activation date of the ultra-broadband connection in schools and indicates whether the connection was activated or not at a certain date.

Usage

Get_BroadBand(
  Date = as.Date(format(as.Date(format(Sys.Date(), "%Y-%m-01")) - 1, "%Y-%m-01")),
  include_municipality_code = TRUE,
  input_School2mun = NULL,
  input_Registry = NULL,
  input_AdmUnNames = NULL,
  verbose = TRUE,
  autoAbort = FALSE
)

Arguments

Details

Ultra - Broadband is defined as everlasting internet connection with a maximum speed of 1 gigabit per second, with a minimum guaranteed speed of 100 megabits/second both on the uploading and downloading operations, until the peering point is reached, as declared on the data provider's website: <https://bandaultralarga.italia.it/scuole-voucher/progetto-scuole/>. In the example the broadband availability at the beginning of school year 2022/23 (1st september 2022) is shown.

Value

An object of class tbl_df, tbl and data.frame. The variables BB_Activation_date and BB_Activation_staus indicate the activation date and activation status of the broadband connection at the selected date.

Source

Broadband dashboard: <https://bandaultralarga.italia.it/scuole-voucher/dashboard-scuole/>

Examples

  Broadband_220901 <- Get_BroadBand(Date = as.Date("2022-09-01"), autoAbort = TRUE)

  Broadband_220901

  Broadband_220901[, c(9,6,13,14)]

Download the database of Italian public schools buildings

Description

This function downloads the School Buildings Open Database provided by the Italian Ministry of Education, University and Research.

It is one of the main sources of information regarding the infrastructure system of public schools in Italy. For a given year, all available data are downloaded (except for the structural units section, which has a different level of detail) and gathered into a unique dataframe.

Usage

Get_DB_MIUR(
  Year = 2023,
  verbose = TRUE,
  input_Registry = NULL,
  input_AdmUnNames = NULL,
  show_col_types = FALSE,
  certifications = FALSE,
  autoAbort = FALSE
)

Arguments

Details

This function downloads the raw data; missing observations are not edited; all variables are characters. Since certifications are defined at the level of structural units of the single buildings, here the fields read as the percentage of structural units in a building having a given certificate. To edit the output of this function and convert the relevant variables to numeric or Boolean, please Util_DB_MIUR_num. Schools different from primary, middle or high schools are classified as "NR". In the example, the data for school year 2022/23 are retrieved.

Value

An object of class tbl_df, tbl and data.frame.

Source

Homepage

Examples

  input_DB23_MIUR <- Get_DB_MIUR(2023, autoAbort = TRUE)

  input_DB23_MIUR[-c(1,4,6,9)]

Download the classification of peripheral municipalities

Description

Retrieves the classification of Italian municipalities into six categories; classes D, E, and F are the so-called internal/inner areas; classes A, B and C are the central areas.

Usage

Get_InnerAreas(verbose = TRUE, autoAbort = FALSE)

Arguments

Details

Classes are defined according to these criteria; see the methodological note (in Italian) for more detail:

• A - Standalone pole municipalities, the highest degree of centrality; they are characterised by a thorough and self-sufficient combined endowment of school, health and transport infrastructure, i.e. there are at least a lyceum and a technical high school; a railway station of medium dimensions and a hospital provided with an emergency ward.

• B - Intermunicipality poles; the endowment of such infrastructures is complete if a small set of contiguous municipalities is considered

The remaining classes are defined in terms of the national distribution of the road distances from a municipality to the closest pole:

• C - Belt municipalities, travel time below the median (< 27'42”) .

• D - Intermediate municipalities, travel time between the median and the third quartile (27'42” - 40'54”).

• E - Peripheral municipalities, travel time between the third quartile and 97.5th percentile (40'54” - 1h 6' 54”).

• F - Ultra-peripheral municipalities, travel time over the 97.5th percentile (>1h 6' 54”).

For more information regarding the dataset, it is possible to check the ISTAT methodological note (in Italian) available at <https://www.istat.it/it/files//2022/07/FOCUS-AREE-INTERNE-2021.pdf>

Value

An object of class tbl_df, tbl and data.frame.

Source

<https://www.istat.it/notizia/la-geografia-delle-aree-interne-nel-2020-vasti-territori-tra-potenzialita-e-debolezze/>

Examples

InnerAreas <- Get_InnerAreas(autoAbort = TRUE)
InnerAreas[, c(1,9,13)]

Download the Invalsi census survey data

Description

Downloads the full database of the Invalsi scores, detailed either at the municipality or province level.

Usage

Get_Invalsi_IS(
  level = "LAU",
  verbose = TRUE,
  show_col_types = FALSE,
  multiple_out = TRUE,
  autoAbort = FALSE,
  category = FALSE
)

Arguments

Details

Numeric variables provided are:

• Average_percentage_score Average direct score (percentage of sufficient tests)

• Std_dev_percentage_score Standard deviation of the direct score

• WLE_average_score Average WLE score. The WLE score is calculated through the Rasch's psychometric model and is suitable for middle and high schools in that it is cleaned from the effect of cheating (which would affect both the average score and the score variability). By construction it has a mean around 200 points.

• Std_dev_WLE_score Standard deviation of the WLE score. By construction it ranges around 40 points at the school level.

• Students_coverage Students coverage percentage

Additional numeric variables, not always available for all observational units, are:

• Mean and SD of ESCS indicator

• First-Fifth_Level: Distribution of the proficiency level of students

• Targets_percentage: Percentage of students reaching targets

Numeric codes 888 and 999 denote not applicable and not available fields respectively.

If multiple_out == TRUE, provides the following datasets:

• Municipality_data: LAU-level data

• Province_data: NUTS-3-level data

• Region_data: NUTS-2-level data

• LLS_data: data at the level of local labour systems (Sistemi Locali del Lavoro; see ISTAT webpage for details)

• Inner_Areas_2021_data aggregated data for inner areas according to the 2020 taxonomy

• Inner_Areas_2014_data aggregated data for inner areas according to the former 2014 taxonomy

• Macroarea_data data aggregated for North-West, North-East, Center, South and Islands

Value

Unless multiple_out == TRUE, an object of class tbl_df, tbl and data.frame. Otherwise, a list including objects of the aforementioned classes

Source

<https://serviziostatistico.invalsi.it/en/archivio-dati/?_sft_invalsi_ss_data_collective=open-data>

Examples

Get_Invalsi_IS(level = "NUTS-3", autoAbort = TRUE, verbose = FALSE)

Download the registry of Italian public schools from the school registry section

Description

This function returns two main pieces of information regarding Italian schools, namely:

• The denomination of the region, province and municipality to which the school belongs.

• The mechanographical code to the reference institute of each school.

It is possible to access schools in all the national territory, including the autonomous provinces of Aosta, Trento and Bozen.

Usage

Get_Registry(
  Year = 2023,
  filename = c("SCUANAGRAFESTAT", "SCUANAAUTSTAT"),
  show_col_types = FALSE,
  autoAbort = FALSE
)

Arguments

Details

Schools different from primary, middle or high schools are classified as "NR".

Value

An object of class tbl_df, tbl and data.frame.

Source

Homepage

Examples

  Get_Registry(2024, filename = "SCUANAGRAFESTAT", autoAbort = TRUE)

Associate a Municipality (LAU) code to each school

Description

This function associates the relevant municipality codes to all the schools listed in the two main registries provided by the Italian Ministry of Education, University and Research, namely:

• The registry of school buildings, here referred to as Registry_from_buildings (Get_DB_MIUR)

• The official schools registry, here referred to as Registry_from_registry (see Get_Registry)

Usage

Get_School2mun(
  Year = 2023,
  show_col_types = FALSE,
  verbose = TRUE,
  input_AdmUnNames = NULL,
  input_Registry = NULL,
  autoAbort = FALSE
)

Arguments

Value

An object of class list, including 4 elements:

• $Registry_from_buildings: Object of class tbl_df, tbl and data.frame: the schools listed in the buildings registry

• $Registry_from_registry: Object of class tbl_df, tbl and data.frame: the schools listed in the schools registry

• $Any: Object of class tbl_df, tbl and data.frame: schools listed anywhere

• $Both: Object of class tbl_df, tbl and data.frame: schools listed in both the sections

Source

Buildings registry (2021 onwards); Buindings registry(until 2019); Schools registry

Examples

Get_School2mun(Year = 2023, autoAbort = TRUE)

Download the shapefiles of Italian NUTS-3 and LAU administrative units

Description

Downloads either the boundaries or the centroids of the relevant administrative units, either provinces or municipalities, from the ISTAT website. Geometries are expressed in meters.

Usage

Get_Shapefile(
  Year,
  level = "LAU",
  lightShp = TRUE,
  autoAbort = FALSE,
  centroids = FALSE
)

Arguments

Value

A spatial data frame of class data.frame and sf.

Source

<https://www.istat.it/it/archivio/222527>

Examples

  library(magrittr)


  Prov23_shp <- Get_Shapefile(2023, lightShp = TRUE, level = "NUTS-3", autoAbort = TRUE)
  ggplot2::ggplot() + ggplot2::geom_sf(data = Prov23_shp) +
    ggplot2::ggtitle("Italian provinces in 2023/01/01")
 

Download students' number data

Description

This functions downloads the data regarding the number of students, from the open website of the Italian Ministry of Education, University and Research

Usage

Get_nstud(
  Year = 2023,
  filename = c("ALUCORSOETASTA", "ALUCORSOINDCLASTA"),
  verbose = TRUE,
  show_col_types = FALSE,
  autoAbort = FALSE
)

Arguments

Value

By default, a list of two tbl_df, tbl and data.frame objects:

• $ALUCORSOETASTA: The number of students by school, school grade and age. It provides a higher number of school than the other element

• $ALUCORSOINDCLASTA: The number of students and classes by school and school grade. This is a long-format dataframe.

Source

Homepage

Examples

  Get_nstud(2023, filename = "ALUCORSOINDCLASTA", autoAbort = TRUE)

Download the number of teachers in Italian schools by province

Description

This functions downloads the number of teachers by province from the open website of the Italian Ministry of Education, University and Research.

Usage

Get_nteachers_prov(
  Year = 2023,
  verbose = TRUE,
  show_col_types = FALSE,
  filename = c("DOCTIT", "DOCSUP"),
  autoAbort = FALSE
)

Arguments

Details

Please notice that by default, the function returns the count of the number of tenured and temporary teachers. If either the count of non-teaching personnel or the count of a single category of teaching personnel is needed, please adapt the filename argument accordingly.

Value

An object of class tbl_df, tbl and data.frame.

Source

Homepage

Examples

nteachers23 <- Get_nteachers_prov(2023, filename = "DOCTIT", autoAbort = TRUE)
nteachers23[, c(3,4,5)]

Aggregate the database of Italian public schools buildings at the municipality and province level

Description

This function transforms the output of the Util_DB_MIUR_num function (which is detailed at the level of single school buildings) at the municipality/LAU and province/NUTS-3 level. It also allows the user to classify the grade of centrality of municipalities through the variable Inner_area.

Usage

Group_DB_MIUR(
  data = NULL,
  Year = 2023,
  count_units = TRUE,
  countname = "nbuildings",
  count_missing = TRUE,
  verbose = TRUE,
  track_deleted = TRUE,
  InnerAreas = TRUE,
  ord_InnerAreas = FALSE,
  input_InnerAreas = NULL,
  autoAbort = FALSE,
  ...
)

Arguments

Details

Numerical variables are summarised by the mean; Boolean variables are summarised by the mean as well, thus they become frequency indicators. Qualitative values, if included, are summarised by the mode. Summary measures do not include NAs. The output dataframes are also detailed at the school order level (i.e. Primary, Midde, High school, or different orders). This means that rows are unique combinations of territorial unities and school order.

Value

An object of class list including:

• $Municipality_data: object of class tbl_df, tbl and data.frame, the output dataframe detailed at the municipality level; all variables besides the first 5 (which identify the record) are numeric

• $Province_data: object of class 'tbl_df', 'tbl' and 'data.frame', the output dataframe detailad at the province level; all variables besides the first 3 (which identify the record) are numeric

• $Municipality_missing (Only if count_missing == TRUE); object of class tbl_df, tbl and data.frame, the percentage of NAs in each variable at the municipality level.

• $Province_missing: (Only if count_missing == TRUE); object of class 'tbl_df', 'tbl' and 'data.frame', the percentage of NAs in each variable at the province level.

• $deleted: character vector. The schools removed from the original dataframe for data quality reasons. This object is returned only if track_deleted == TRUE

Examples

library(magrittr)
DB23_MIUR <- example_input_DB23_MIUR %>% Util_DB_MIUR_num(verbose = FALSE) %>%
    Group_DB_MIUR(InnerAreas = FALSE)



DB23_MIUR$Municipality_data[, -c(1,2,4)]
summary(DB23_MIUR$Municipality_data)

DB23_MIUR$Province_data[, -c(1,3)]
summary(DB23_MIUR$Province_data)

Aggregate the students number data by class at the municipality and province level

Description

This function creates two dataframes with the number of students, classes and students by class, aggregated at the province and municipality level

Usage

Group_nstud(
  data = NULL,
  Year = 2023,
  check = TRUE,
  verbose = TRUE,
  check_registry = "Any",
  InnerAreas = TRUE,
  ord_InnerAreas = FALSE,
  check_ggplot = FALSE,
  missing_to_1 = FALSE,
  input_Registry = NULL,
  input_InnerAreas = NULL,
  input_Prov_shp = NULL,
  input_School2mun = NULL,
  input_AdmUnNames = NULL,
  autoAbort = FALSE,
  ...
)

Arguments

Details

Numerical variables are summarised by the mean; Boolean variables are summarised by the mean as well, thus they become frequency indicators. Qualitative values, if included, are summarised by the mode. Summary measures do not include NAs.

Value

An object of class list including:

• $Municipality_data: object of class tbl_df, tbl and data.frame, the output dataframe detailed at the municipality level

• $Province_data: object of class 'tbl_df', 'tbl' and 'data.frame', the output dataframe detailad at the province level

Examples

Year <- 2023

nstud23_aggr <- Group_nstud(data = example_input_nstud23, Year = Year,
                           input_Registry = example_input_Registry23,
                           InnerAreas = FALSE,
                           input_School2mun = example_School2mun23)

summary(nstud23_aggr$Municipality_data[,c(46,47,48)])

summary(nstud23_aggr$Province_data[,c(44,45,46)])

Arrange the number of teachers per students in public Italian schools at the province level

Description

This function provides the average number of teachers per students in Italian public schools at the province level.

Usage

Group_teachers4stud(
  Year = 2023,
  input_nteachers = NULL,
  nteachers_filename = c("DOCTIT", "DOCSUP"),
  verbose = TRUE,
  input_nstud_raw = NULL,
  input_nstud_aggr = NULL,
  autoAbort = FALSE,
  ...
)

Arguments

Value

An object of class tbl_df, tbl and data.frame

Examples

  input_nstud23 <- Get_nstud(2023, filename ="ALUCORSOINDCLASTA", autoAbort = TRUE)
  Registry23 <- Get_Registry(2023, autoAbort = TRUE)
  School2mun23 <- Get_School2mun(2023, input_Registry = Registry23, autoAbort = TRUE)


  nstud23.aggr <- Group_nstud(Year = 2023, data = input_nstud23,
    input_Registry = Registry23, input_School2mun = School2mun23,
    autoAbort = TRUE)

  input_nteachers23 <- Get_nteachers_prov(2023, autoAbort = TRUE)

  teachers4stud <- Group_teachers4stud(Year = 2023,
                   input_nteachers = input_nteachers23,
                   input_nstud_aggr = nstud23.aggr, autoAbort = TRUE)

  teachers4stud[, -c(1, 2, 10, 11)]

  summary(teachers4stud)

Map school data

Description

This function displays a map of the data arranged trough the function Set_DB. It supports two kinds of map:

• Interactive map (default option), which allows the user to visualize all the data in scope through the interactive popup, and

• Static map (ggplot), which can be easily exported in .pdf objects.

The user must select a variable to display. It is possible to insert either a readily-downloaded database obtained through the function Set_DB or the basic inputs to plug in that function, other than an input shapefile. Relevant arguments not provided by the user will be download automatically, but not saved into the global environment. However we suggest to plug in at least some inputs, as otherwise the running time may be long. This function generalises the functionalities of the more data-specific functions Map_School_Buildings and Map_Invalsi.

Usage

Map_DB(
  data = NULL,
  Year = 2023,
  field,
  level = "LAU",
  plot = "mapview",
  popup_height = 200,
  col_rev = FALSE,
  pal = "viridis",
  input_shp = NULL,
  region_code = c(1:20),
  main_pos = "top",
  main = "",
  order = NULL,
  autoAbort = FALSE,
  only_observed = FALSE,
  ...
)

Arguments

Value

If plot == "mapview", an object of class mapview. Otherwise, if plot == "ggplot", an object of class gg and ggplot.

Examples

DB23 <- Set_DB(Year = 2023, level = "NUTS-3",
       Invalsi_grade = c(10,13), NA_autoRM = TRUE,
       input_Invalsi_IS = example_Invalsi23_prov, input_nstud = example_input_nstud23,
       input_InnerAreas = example_InnerAreas,
       input_School2mun = example_School2mun23,
       input_AdmUnNames = example_AdmUnNames20220630,
       nteachers = FALSE, BroadBand = FALSE, SchoolBuildings = FALSE)




Map_DB(DB23, field = "Students_per_class_13", input_shp = example_Prov22_shp, level = "NUTS-3",
 col_rev = TRUE, plot = "ggplot")

Map_DB(DB23, field = "Inner_area", input_shp = example_Prov22_shp, order = "High",
 level = "NUTS-3",col_rev = TRUE, plot = "ggplot")

Map_DB(DB23, field = "M_Mathematics_10", input_shp = example_Prov22_shp, level = "NUTS-3",
 plot = "ggplot")

Display a map of Invalsi scores

Description

This function displays either a static or interactive map of the Invalsi scores, either at the municipality or province level. It supports two kinds of map:

• Interactive map (default option), which allows the user to visualize all the data in scope through the interactive popup, and

• Static map (ggplot), which can be easily exported in .pdf objects.

Usage

Map_Invalsi(
  data = NULL,
  Year = 2023,
  subj_toplot = "ITA",
  grade = 8,
  level = "LAU",
  main = "",
  main_pos = "top",
  region_code = c(1:20),
  plot = "mapview",
  pal = "viridis",
  WLE = FALSE,
  col_rev = FALSE,
  popup_height = 200,
  only_observed = FALSE,
  verbose = TRUE,
  input_shp = NULL,
  autoAbort = FALSE
)

Arguments

Value

If plot == "mapview", an object of class mapview. Otherwise, if plot == "ggplot", an object of class gg and ggplot.

Examples

 Map_Invalsi(subj = "Italian", grade = 13, level = "NUTS-3", Year = 2023, WLE = FALSE,
  data = example_Invalsi23_prov, input_shp = example_Prov22_shp, plot = "ggplot")

 Map_Invalsi(subj = "Italian", grade = 5, level = "NUTS-3", Year = 2023, WLE = TRUE,
  data = example_Invalsi23_prov, input_shp = example_Prov22_shp, plot = "ggplot")

Display data fom the school buildings database

Description

This function displays a map of the data downloaded trough the Get_DB_MIUR function. It supports two kinds of map:

• Interactive map (default option), which allows the user to visualize all the data in scope through the interactive popup, and

• Static map (ggplot), which can be easily exported in .pdf objects.

Usage

Map_School_Buildings(
  data = NULL,
  field,
  order = NULL,
  level = "LAU",
  region_code = c(1:20),
  plot = "mapview",
  pal = "viridis",
  col_rev = FALSE,
  popup_height = 200,
  main_pos = "top",
  main = "",
  only_observed = FALSE,
  verbose = TRUE,
  input_shp = NULL,
  autoAbort = FALSE,
  ...
)

Arguments

Value

If plot == "mapview", an object of class mapview. Otherwise, if plot == "ggplot", an object of class gg and ggplot.

Examples

  library(magrittr)

  DB23_MIUR <- example_input_DB23_MIUR %>%
    Util_DB_MIUR_num(track.deleted = FALSE) %>%
    Group_DB_MIUR(InnerAreas = FALSE, count_missing = FALSE)

  DB23_MIUR %>% Map_School_Buildings(field = "School_bus",
     order = "Primary",level = "NUTS-3",  plot = "ggplot",
     input_shp = example_Prov22_shp)

  DB23_MIUR %>% Map_School_Buildings(field = "Railway_transport",
     order = "High",level = "NUTS-3", plot = "ggplot",
     input_shp = example_Prov22_shp)

  DB23_MIUR %>% Map_School_Buildings(field = "Context_without_disturbances",
     order = "Middle",level = "NUTS-3", plot = "ggplot",
     input_shp = example_Prov22_shp, col_rev = TRUE)

Build up a comprehensive database regarding the school system

Description

This function generates a unique dataframe of the school system data including a customary choice of available datasets. This function allows the user to aggregate the desired datasets, when available, among these:

• Invalsi census survey

• School buildings

• Number of students and school classes

• Number of teachers

• Broadband connection availability

To save as much time as possible it is possible to plug in ready-made input data; otherwise they will be downloaded automatically but not saved in the global environment When a new dataset is joined to the existing ones, it is possible that some observations in this datasets are missing. In this case, by default, the choice of keeping as much observational units as possible, or to remove units with missing variables is left to the user.

Usage

Set_DB(
  Year = 2023,
  level = "LAU",
  conservative = TRUE,
  Invalsi = TRUE,
  SchoolBuildings = TRUE,
  nstud = TRUE,
  nteachers = TRUE,
  BroadBand = TRUE,
  verbose = TRUE,
  show_col_types = FALSE,
  Invalsi_subj = c("ELI", "ERE", "ITA", "MAT"),
  Invalsi_grade = c(2, 5, 8, 10, 13),
  Invalsi_WLE = FALSE,
  SchoolBuildings_certifications = FALSE,
  SchoolBuildings_include_numerics = TRUE,
  SchoolBuildings_include_qualitatives = FALSE,
  SchoolBuildings_row_cutout = FALSE,
  SchoolBuildings_col_cut_thresh = 20000,
  SchoolBuildings_flag_outliers = TRUE,
  SchoolBuildings_count_missing = FALSE,
  nstud_imputation_thresh = 19,
  nstud_missing_to_1 = FALSE,
  UB_nstud_byclass = 99,
  LB_nstud_byclass = 1,
  UB_nstud_byclass_grade = NULL,
  LB_nstud_byclass_grade = NULL,
  nstud_filter_by_grade = FALSE,
  InnerAreas = TRUE,
  ord_InnerAreas = FALSE,
  nstud_check = TRUE,
  nstud_check_registry = "Any",
  BroadBand_impute_missing = TRUE,
  Date = as.Date(paste0(substr(year.patternA(Year), 1, 4), "-09-01")),
  NA_autoRM = NULL,
  input_Invalsi_IS = NULL,
  input_Registry = NULL,
  input_SchoolBuildings = NULL,
  input_nstud = NULL,
  input_School2mun = NULL,
  input_AdmUnNames = NULL,
  input_InnerAreas = NULL,
  input_teachers4student = NULL,
  input_nteachers = NULL,
  input_BroadBand = NULL,
  autoAbort = FALSE
)

Arguments

Value

An object of class tbl_df, tbl and data.frame

See Also

Util_DB_MIUR_num, Group_DB_MIUR, Group_nstud, Util_Check_nstud_availability, Get_School2mun for similar arguments.

Examples

DB23_prov <- Set_DB(Year = 2023, level = "NUTS-3",Invalsi_grade = c(5, 8, 13),
      Invalsi_subj = "Italian",nteachers = FALSE, BroadBand = FALSE,
      SchoolBuildings_count_missing = FALSE,NA_autoRM= TRUE,
      input_SchoolBuildings = example_input_DB23_MIUR[, -c(11:18, 10:27)],
      input_Invalsi_IS = example_Invalsi23_prov,
      input_nstud = example_input_nstud23,
      input_InnerAreas = example_InnerAreas,
      input_School2mun = example_School2mun23,
      input_AdmUnNames = example_AdmUnNames20220630)


DB23_prov

summary(DB23_prov[, -c(22:62)])

Map schools included in the ultra-broadband plan to their LAU codes.

Description

Helper function to provide the ultra-broadband dataset obtained with Get_BroadBand with the statistical codes of the relevant municipalities, obtained with Get_School2mun, in case the ultra-broadband dataset has been downloaded with argument include_municipality_code = FALSE.

Usage

Util_BroadBand2mun(
  data,
  input_School2mun = NULL,
  input_Registry = NULL,
  input_AdmUnNames = NULL,
  verbose = FALSE,
  autoAbort = FALSE
)

Arguments

Details

see Get_BroadBand

Value

An object of class tbl_df, tbl and data.frame, identical to the output of Get_BroadBand with an additional column for LAU codes

Source

Broadband dashboard: <https://bandaultralarga.italia.it/scuole-voucher/dashboard-scuole/> . ISTAT LAU codes: <https://situas.istat.it/web/#/territorio>

Check how many schools in the school registries are included in the students count dataframe

Description

This function checks for which schools listed in the two registries (the buildings registry and the properly said schools registry) the count of students is available. The first registry is referred to as as Registry_from_buildings and the second one as Registry_from_registry.

Usage

Util_Check_nstud_availability(
  data,
  Year,
  cutout = c("IC", "IS", "NR"),
  verbose = TRUE,
  ggplot = TRUE,
  toplot_registry = "Any",
  InnerAreas = TRUE,
  ord_InnerAreas = FALSE,
  input_Registry = NULL,
  input_InnerAreas = NULL,
  input_Prov_shp = NULL,
  input_AdmUnNames = NULL,
  input_School2mun = NULL,
  autoAbort = FALSE
)

Arguments

Value

An object of class list including two elements:

• $Municipality_data

• $Province_data

Both the elements are objects of class list including four elements:

• $Registry_from_buildings: object of class of class tbl_df, tbl and data.frame: the availability of the number of students in the schools listed in the buildings section.

• $Registry_from_registry: object of class of class tbl_df, tbl and data.frame: the availability of the number of students in the schools listed in the registry section.

• $Any: object of class of class tbl_df, tbl and data.frame: the availability of the number of students in the schools listed anywhere.

• $Both: object of class of class tbl_df, tbl and data.frame: the availability of the number of students in the schools listed in both sections.

Source

Buildings Registry; Schools Registry

Examples

nstud23 <- Util_nstud_wide(example_input_nstud23, verbose = FALSE)

Util_Check_nstud_availability(nstud23, Year = 2023,
  input_Registry = example_input_Registry23, InnerAreas = FALSE,
  input_School2mun = example_School2mun23, input_Prov_shp = example_Prov22_shp)

Clean and convert the raw school buildings data to Boolean variables

Description

This function cleans the output of the Get_DB_MIUR function from missing values in two steps:

• First, it deletes both the columns exceeding a threshold of missing values (1000 by default) and the columns that cannot be converted into Boolean variables

• Then, it deletes the rows in which missing values remain

Finally, the remaining data are converted into Boolean variables. It is possible to keep track of the deleted rows.

Usage

Util_DB_MIUR_bool(
  data = NULL,
  cutout = NULL,
  col_cut_thresh = 10^3,
  verbose = TRUE,
  track_deleted = TRUE,
  autoAbort = autoAbort,
  ...
)

Arguments

Value

If track_deleted == TRUE, An object of class list including two objects:

• $data: object of class tbl_df, tbl and data.frame, the output dataframe. All variables besides the first 8 ones (which identify the record) are numeric.

• $deleted: character. The school codes corresponding to deleted rows

If track_deleted == FALSE, the output is only the first element of the list.

Convert the raw school buildings data to numeric or Boolean variables

Description

This function transforms the output variables of the Get_DB_MIUR into Boolean or Numeric. Additionally, it removes the columns with an excessive number of missing observations (20.000 by default), and if required it may also delete the rows including missing fields. In this case, it is possible to keep track of the deleted rows.

Usage

Util_DB_MIUR_num(
  data = NULL,
  include_numerics = TRUE,
  include_qualitatives = FALSE,
  row_cutout = FALSE,
  track_deleted = TRUE,
  verbose = TRUE,
  col_cut_thresh = 20000,
  flag_outliers = TRUE,
  autoAbort = FALSE,
  ...
)

Arguments

Details

The outliers to be set to NA if flag_outliers is active are defined as follows: School area or free area surface of less than 50 squared meters, building volume of less than 150 cubic meters, 0 floors in the building.

Value

If track_deleted == TRUE, An object of class list including two objects:

• $data: object of class tbl_df, tbl and data.frame, the output dataframe.

• $deleted: object of class tbl_df, tbl and data.frame. The school IDs of the deleted units.

If track_deleted == FALSE, the output is only the first element of the list.

Examples

library(magrittr)

DB23_MIUR_num <- example_input_DB23_MIUR %>% Util_DB_MIUR_num(track_deleted = FALSE)


DB23_MIUR_num[, -c(1,4,6,8,9,10)]
summary(DB23_MIUR_num)

Filter the Invalsi data by subject, school grade and year.

Description

This function filters the database of Invalsi scores (see Get_Invalsi_IS) by school year, education grade and subject and returns a dataframe in wide format. Each row corresponds to one territorial unit (either municipality or province); the numerical variables are three (the mean score, the score's standard deviation and the students coverage percentage) for each selected subject.

Usage

Util_Invalsi_filter(
  data = NULL,
  subj = c("ELI", "ERE", "ITA", "MAT"),
  grade = 8,
  level = "LAU",
  WLE = FALSE,
  Year = 2023,
  verbose = TRUE,
  autoAbort = FALSE
)

Arguments

Value

An object of class tbl_df, tbl and data.frame. For all subjects and school grades, the variables indicate:

• M The mean score, either WLE or percentage of sufficient tests

• S The standard deviation of the score

• C The students coverage percentage (expressed in the scale 1 - 100)

Examples

Util_Invalsi_filter(subj = c("Italian", "Mathematics"), grade = 5, level = "NUTS-3", Year = 2023,
                   WLE = FALSE, data = example_Invalsi23_prov)

Util_Invalsi_filter(subj = c("Italian", "Mathematics"), grade = 5, level = "NUTS-3", Year = 2023,
                    WLE = TRUE, data = example_Invalsi23_prov)

Invalsi23_high <- Util_Invalsi_filter(subj = "Italian", grade = c(10,13), level = "NUTS-3",
                                      Year = 2023, data = example_Invalsi23_prov)


 summary(Invalsi23_high)

Clean the raw dataframe of the number of students and arrange it in a wide format

Description

This function rearranges the output of the Get_nstud function in such a way to represent the counts of students and, if required, either the number of students by class and number of classes, or the counts of students per school timetable (running time) in a unique observation per school. If the focus is on class size, this function firstly cleans the data from the outliers in terms of average number of students by class at the school level and imputates the number of classes to 1 when missing.

Usage

Util_nstud_wide(
  data = NULL,
  missing_to_1 = FALSE,
  nstud_imputation_thresh = 19,
  UB_nstud_byclass = 99,
  LB_nstud_byclass = 1,
  filter_by_grade = FALSE,
  UB_nstud_byclass_grade = NULL,
  LB_nstud_byclass_grade = NULL,
  verbose = TRUE,
  autoAbort = FALSE,
  ...
)

Arguments

Details

In the example, we compare the dataframe obtained with the default settings and the one imposed setting narrow inclusion criteria

Value

An object of class tbl_df, tbl and data.frame

Examples

nstud.default <- Util_nstud_wide(example_input_nstud23)


nstud.narrow <- Util_nstud_wide(example_input_nstud23,
  UB_nstud_byclass = 35, LB_nstud_byclass = 5 )

nrow(nstud.default)
nrow(nstud.narrow)

nstud.default

summary(nstud.default)

Subset of the administrative codes of municipalities

Description

This table includes the administrative codes of the municipalities from four regions: Molise, Campania, Apulia and Basilicata, as of June 30th 2022; some strings in field Municipality_description including accents have been forced to ASCII. The whole dataset can be retrieved with the command Get_AdmUnNames(Date = "2022-06-30")

Usage

example_AdmUnNames20220630

Format

## 'example_AdmUnNames20220630' A data frame with 1,074 rows and 5 columns:

• Province_code Numeric; the NUTS-3 administrative code

• Province_initials Character;abbreviated NUTS-3 denomination.

• Municipality_code Character; the ISTAT LAU (municipality) ID.

• Municipality_description Character; the municipality name.

• Cadastral_code Character; a LAU - level ID code, different from the official ISTAT municipality code. It is used in the school registry (see example_input_Registry23)

Source

<https://www.istat.it/it/archivio/6789>

See Also

Get_AdmUnNames

Subset of the school registry in school year 2022/23

Description

This dataframe includes the classification of municipalities , from four regions: Molise, Campania, Apulia and Basilicata. Only the first 10 columns are included; some strings in field Municipality_description including accents have been forced to ASCII. The whole dataset can be retrieved with the command Get_InnerAreas(). For the definition of ISTAT inner areas class, see Get_InnerAreas

Usage

example_InnerAreas

Format

## 'example_InnerAreas' A data frame with 1074 rows and 10 columns:

• Municipality_code Character; the ISTAT LAU (municipality) ID.

• Municipality_code_numeric Numeric; the ISTAT LAU (municipality) ID in numeric format.

• Cadastral_code Character; a LAU - level ID code, different from the official ISTAT municipality code.

• Region_code Numeric; the region (NUTS-2 administrative level) ID

• Region_description Character; the region (NUTS-2 administrative level) name.

• Province_code Numeric; the NUTS-3 administrative code.

• Province_initials Character; abbreviated NUTS-3 denomination.

• Province_description Character; the province (NUTS-3 administrative level) denomination.

• Municipality_description Character; the municipality name.

• Inner_area_code_2014_2020 Character; the ISTAT inner areas classification between 2014 and 2020.

• Inner_area_description_2014_2020 Character; the description of the classes identified in the previous column

• Inner_area_code_2021_2027 Character; the ISTAT inner areas classification between 2021 and 2027.

• Inner_area_description_2021_2027 Character; the description of the classes identified in the previous column

• Destination_municipality_code Character; For non-central municipalities (classes C, D, E, F), the ID of the closest pole municipality according to the 2021-2027 classification

• Destination_municipality_code Character; The denomination of the municipalities in the previous column

• Destination_pole_code Character; An internal ID convention for the destination poles; it includes a letter (the class of the destination pole, either A or B); a number of two digits (the region code of the destination pole) and the progressive number of poles within a region.

Source

<https://www.istat.it/it/archivio/273176>

See Also

Get_InnerAreas

Subset of the Invalsi scores in school year 2022/23

Description

This dataframe includes the Invalsi scores of the schools from four regions: Molise, Campania, Apulia and Basilicata, for the school year 2022/23. The whole dataset can be retrieved with the command Get_Invalsi_IS(level = "NUTS-3")

Usage

example_Invalsi23_prov

Format

## 'example_Invalsi23_prov' A data frame with 240 rows and 11 columns:

• Year Character; the school year.

• Grade Numeric; the school grade; only includes the school grades subjected to the Invalsi survey. Either 2, 5, 8, 10 or 13.

• Subject Character; the school subject in which the test is taken; either Italian, Mathematics, English reading or English listening.

• Province_code Numeric; the NUTS-3 administrative code.

• Province_initials Character; abbreviated NUTS-3 denomination.

• Province_description Character; the province (NUTS-3 administrative level) denomination.

• Average_percentage_score Numeric; the province-level percentage of sufficient tests, only for primary schools; ranges 0-100.

• Std_dev_percentage_score Numeric; the standard deviation of the percentage of sufficient tests, only for primary schools.

• WLE_average_score Numeric; the province-level average WLE (Weighted Likelihood Estimator) score.

• Std_dev_WLE_score Numeric; the standard deviation of WLE scores.

• Students_coverage Numeric; the percentage of students for which the Invalsi tests are reported.

Source

<https://serviziostatistico.invalsi.it/en/archivio-dati/?_sft_invalsi_ss_data_collective=open-data>

See Also

Get_Invalsi_IS

Subset of Italian provinces shapefile

Description

This is the shapefile for the provinces belonging to four regions: Molise, Campania, Apulia and Basilicata, as of January 1st 2022. These are the latest administrative units boundaries relevant at the beginning of the school year 2022/23. The whole shapefile can be retrieved with the command Get_Shapefile(Year = 2022, level = "NUTS-3")

Usage

example_Prov22_shp

Format

## 'example_Prov22_shp' A Spatial polygon data frame with 13 rows/polygons and 15 columns:

• COD_RIP Numeric; the code for the macroarea (1 for Northwest, 2 for Northeast, 3 for Center, 4 for South and 5 for Isles)

• COD_REG Numeric; the region (NUTS-2 administrative level) ID

• COD_PROV Numeric; the NUTS-3 administrative code

• COD_CM Numeric; the administrative code for Metropolitan Cities (which are always at the NUTS-3 level), obtained as 200 + NUTS-3 code, if the unit is a Metropolitan city; 0 otherwise.

• COD_UTS Numeric; the administrative code for Metropolitan cities if the unit is a Metropolitan City; the province code otherwise.

• DEN_PROV Character; the province (NUTS-3 administrative level) name, if the unit is not a Metropolitan City; blank otherwise.

• DEN_CM Character; the Metropolitan City (NUTS-3 administrative level) name, if the unit is a Metropolitan City; blank otherwise.

• DEN_UTS Character; the province or Metropolitan City (NUTS-3 administrative level) name.

• SIGLA Character; abbreviated NUTS-3 denomination.

• TIPO_UTS Character; the NUTS-3 type of the unit; either "Provincia" (Province) or "Citta metropolitana" (Metropolitan City)

• Shape_Leng Numeric; the polygon perimeter.

• Shape_Area Numeric; the polygon area.

• geometry the polygon geometry.

Source

<https://www.istat.it/it/archivio/222527>

See Also

Get_Shapefile

Association of the municipality code to a subset of public schools 2022/23

Description

This list maps the IDs of the schools from four regions (Molise, Campania, Apulia and Basilicata) to the corresponding LAU codes. The whole dataset can be retrieved with the command Get_School2mun(2023)

Usage

example_School2mun23

Format

## 'example_School2mun23' A list of four elements

• Registry_from_buildings A data frame of 5527 rows and 5 columns, including the schools listed in the buildings registry.

• Registry_from_registry A data frame of 5929 rows and 5 columns, including the schools listed in the schools registry.

• Any A data frame of 5954 rows and 5 columns, including schools listed in any of the registryes

• Both A data frame of 5510 rows and 5 columns, including schools listed in both registries

For each element, rows correspond to school IDs; the columns are:

• School_code Character; the school ID.

• Province_code Numeric; the NUTS-3 administrative code.

• Province_initials Character; abbreviated NUTS-3 denomination.

• Municipality_code Character; the ISTAT LAU (municipality) ID.

• Municipality_description Character; the municipality name.

Source

Buildings registry (2021 onwards); Buindings registry(until 2019); Schools registry

See Also

Get_School2mun

Subset of the school buildings database in school year 2022/23

Description

This dataframe includes the schools directly identifiable as primary, middle or high school, from four regions: Molise, Campania, Apulia and Basilicata. Only the first 35 columns are included. Some strings including accents in fields Other_disturbances_proximity, Other_specific_criticalities and Other have been forced to ASCII. The whole dataset can be retrieved with the command Get_DB_MIUR(2023)

Usage

example_input_DB23_MIUR

Format

## 'example_input_DB23_MIUR' A data frame with 7479 rows and 35 columns:

• Year Numeric; the school year.

• School_code Character; the school ID.

• Order Character; the school order, either primary, middle or high school.

• Reference_institute_code Character; the ID of the reference institute.

• Building_code Character; the building ID; the first 6 digits usually identify the municipality.

• Municipality_code Character; the ISTAT LAU (municipality) ID.

• Municipality_description Character; the municipality name.

• Province_initials Character; abbreviated NUTS-3 denomination.

• Postal_code Character; the ZIP code; slightly finer than municipality boundaries. for big municipalities.

• Context_without_disturbances Character; whether the school belongs to an environment devoid of disturbances; otherwise, the types of disturbances are listed in columns 11 - 18.

• Dumps_proximity Character; whether the school is close to dumps (disturbance element).

• Pollutant_industries_proximity Character; whether the school is close to pollutant industries (disturbance element).

• Pollutant_waters_proximity Character; whether the school is close to pollutant or stagnant streams or ponds (disturbance element).

• Air_pollution_sourcer_proximity Character; whether the school is close to sources of air pollution (disturbance element).

• Acoustic_pollution_sourcer_proximity Character; whether the school is close to sources of acoustic pollution (disturbance element).

• Electromagnetic_radiation_sources_proximity Character; whether the school is close to sources of electromagnetic radiation (disturbance element).

• Graveyards_proximity Character; whether the school is close to a graveyard (disturbance element).

• Other_disturbances_proximity Character; other disturbance elements to which the school is close, other than those already listed.

• School_area_specific_criticalities Character; whether any specific criticality element occurs inside the school area; specified in columns 20 - 27.

• Layby absence Character; whether the access to the area pertaining to the school building lacks a lay-by or pitch (school area criticality element).

• Unfenced area Character; whether the school building area lacks fences or enclosures (school area criticality element).

• Large_traffic Character; whether the school area is close to large traffic streams (school area criticality element).

• Railway_traffic Character; whether the school area is close to railway traffic streams (school area criticality element).

• Abandoned_industries Character; whether the school area is located in pre-existences of abandoned industries (school area criticality element).

• Decayed_urban_area Character; whether the school belongs or is close to a decayed area (school area criticality element).

• Risky_industries_proximity Character; whether the school is close to perilous industrial areas (school area criticality element).

• Other_specific_criticalities Character; specific criticality elements regarding the school area, other than those already listed.

• School_bus Character; whether the school is reached by school-bus service.

• Urban_public_transport Character; whether the school is served by a urban public transport station in the range of 250 meters.

• Interurban_public_transport Character; whether the school is served by a inter-urban public transport station in the range of 500 meters.

• Railway_transport Character; whether the school ranges 500 meters or less from a train station.

• Private_transport Character; whether the school can be reached by private transport.

• Disabled_people_transport Character; whether the school is provided with disabled people specific transport.

• Bicycle_lane Character; whether the building is in proximity of a bicycle/bike lane.

• Other Character; whether the building can be reached in any other specific way.

Source

Homepage; more in detail, the dataset blocks are downloaded respectively from: cols 10-18; cols 20-27; cols 28-35

See Also

Get_DB_MIUR

Subset of the school registry in school year 2022/23

Description

This dataframe includes the schools directly identifiable as primary, middle or high school, from four regions: Molise, Campania, Apulia and Basilicata. Only the first 10 columns are included. The whole dataset can be retrieved with the command Get_Registry(2023)

Usage

example_input_Registry23

Format

## 'example_input_Registry23' A data frame with 5929 rows and 10 columns:

• Year Numeric; the school year.

• Area Character; the macro-area of the municipality, i.e. North, Center or South.

• Region_description Character; the region (NUTS-2 administrative level) name.

• Province_description Character; the province (NUTS-3 administrative level) name.

• Reference_institute_code Character; the ID of the reference institute.

• School_code Character; the school ID.

• Cadastral_code Character; a LAU - level ID code, different from the official LAU municipality code. The Italian Ministry of Education does provide this code in the place of the LAU code for both the Schools registry and the early school buildings DBs.

• Municipality_description Character; the municipality name.

• School_address Character; the school physical address.

• Postal_code Character; the ZIP code, slightly finer than municipality boundaries for big municipalities.

Source

Source link

See Also

Get_Registry

Subset of the students and classes counts in school year 2022/23

Description

This dataframe includes students and classes counts for the schools from four regions: Molise, Campania, Apulia and Basilicata. The whole dataset can be retrieved with the command Get_nstud(2023, filename = "ALUCORSOINDCLASTA")

Usage

example_input_nstud23

Format

## 'example_input_nstud23' A data frame with 21208 rows and 7 columns:

• Year Numeric; the school year.

• School_code Character; the school ID.

• Order Character; the school order, either primary, middle or high school.

• Grade Numeric; the school grade.

• Classes Numeric; the count of classes of a given grade in each school

• Male_students Numeric; the count of male students in all classes of a given educational grade in each school

• Female_students Numeric; the count of female students in all classes of a given educational grade in each school

Source

Specific link

See Also

Get_nstud