# Introduction to mpindex

This guide presents an illustration of a simple application of `mpindex` package for computing Multidimensional Poverty Index (MPI) using the Alkire-Foster (AF) counting method developed by Sabina Alkire and James Foster.

## 1. Installation

To install the `mpindex` package from CRAN:

    install.packages('mpindex')

If you want to get the latest development version of `mpindex`, install it from GitHub. Note that you may need to install `devtools`.

    # install.packages("devtools")
    devtools::install_github('yng-me/mpindex')

Load the package once you have successfully completed the installation.

    library(mpindex)

## 2. MPI specifications

The initial step is to prepare an MPI specification file which will serve as references in the computation as well as generation of output in the later part of the process. This file should contain information about MPI dimensions, indicators and their corresponding weights.

This file should also be easy to create using the most common and accessible file types such as `.xlsx` (Excel), `.json`, `.csv`, or `.txt` (TSV).

### Built-in specification files

For convenience, `mpindex` has included built-in specification files (in different formats). Each file contains dimensions, indicators, weight, and other relevant information of the Global MPI.

To see the list of files available:

    system.file("extdata", package = "mpindex") |> list.files()
    #> [1] "global-mpi-specs.csv"  "global-mpi-specs.json" "global-mpi-specs.txt"
    #> [4] "global-mpi-specs.xlsx"

To use a built-in specification file, say the `.csv` file, use below script to first get the full path of the file.

    specs_file <- system.file("extdata", "global-mpi-specs.csv", package = "mpindex")

Global MPI – Dimensions, Indicators, Deprivation Cutoffs, and Weights
Dimension | Indicator | Variable | Weight | Description
--- | --- | --- | --- | ---
Health | Nutrition | nutrition | 0.167 | Any person under 70 years of age for whom there is nutritional information is undernourished.
Health | Child mortality | child_mortality | 0.167 | A child under 18 has died in the household in the five-year period preceding the survey.
Education | Years of schooling | year_schooling | 0.167 | No eligible household member has completed six years of schooling.
Education | School attendance | school_attendance | 0.167 | Any school-aged child is not attending school up to the age at which he/she would complete class 8.
Living Standards | Cooking fuel | cooking_fuel | 0.056 | A household cooks using solid fuel, such as dung, agricultural crop, shrubs, wood, charcoal, or coal.
Living Standards | Sanitation | sanitation | 0.056 | The household has unimproved or no sanitation facility or it is improved but shared with other households.
Living Standards | Drinking water | drinking_water | 0.056 | The household's source of drinking water is not safe or safe drinking water is a 30-minute or longer walk from home, roundtrip.
Living Standards | Electricity | electricity | 0.056 | The household has no electricity.
Living Standards | Housing | housing | 0.056 | The household has inadequate housing materials in any of the three components: floor, roof, or walls.
Living Standards | Assets | assets | 0.056 | The household does not own more than one of these assets: radio, TV, telephone, computer, animal cart, bicycle, motorbike, or refrigerator, and does not own a car or truck.
Source: Alkire, S., Kanagaratnam, U. and Suppa, N. (2020). ‘The global Multidimensional Poverty Index (MPI): 2020 revision’, OPHI MPI Methodological Note 49, Oxford Poverty and Human Development Initiative, University of Oxford.

### User-defined specification file

You can also define and create your own specification file if you prefer to or if you happen to use different sets of dimensions and indicators. At the minimum, however, this file should contain the following columns/variables:

- `Dimension`,
- `Indicator`,
- `Variable`,
- `Weight`,
- `Description` (optional).

Note that the order in which you put these columns does not matter and also the names are not case sensitive, but make sure to spell the column names correctly.

You may download the template here if you do not want to start from scratch: MPI specification file sample.

### Using `define_mpi_specs`

Once you have prepared the specification file, load it using the `define_mpi_specs` function (type `?define_mpi_specs` for a help text).

For demonstration purposes, we will use the built-in specification file, discussed above.

    specs_file <- system.file("extdata", "global-mpi-specs.csv", package = "mpindex")
    define_mpi_specs(specs_file)
    #> # A tibble: 10 × 7
    #>    dimension        indicator    variable weight description variable_name label
    #>    <chr>            <chr>        <chr>     <dbl> <chr>       <chr>         <chr>
    #>  1 Health           Nutrition    nutriti… 0.167  Any person… d01_i01_nutr… Heal…
    #>  2 Health           Child morta… child_m… 0.167  A child un… d01_i02_chil… Heal…
    #>  3 Education        Years of sc… year_sc… 0.167  No eligibl… d02_i01_year… Educ…
    #>  4 Education        School atte… school_… 0.167  Any school… d02_i02_scho… Educ…
    #>  5 Living Standards Cooking fuel cooking… 0.0556 A househol… d03_i01_cook… Livi…
    #>  6 Living Standards Sanitation   sanitat… 0.0556 The househ… d03_i02_sani… Livi…
    #>  7 Living Standards Drinking wa… drinkin… 0.0556 The househ… d03_i03_drin… Livi…
    #>  8 Living Standards Electricity  electri… 0.0556 The househ… d03_i04_elec… Livi…
    #>  9 Living Standards Housing      housing  0.0556 The househ… d03_i05_hous… Livi…
    #> 10 Living Standards Assets       assets   0.0556 The househ… d03_i06_asse… Livi…

`define_mpi_specs` requires a specification file as its first argument. The default poverty cutoff is set to `1/3` (based on Global MPI). You can also define a list of poverty cutoffs by specifying in the `.poverty_cutoffs` argument to achieve gradient MPIs.

    define_mpi_specs(
      .mpi_specs_file = specs_file,
      .poverty_cutoffs = c(1/3, 0.2, 0.8)
    )

If your dataset contains unique ID, like `uuid`, it is recommended to define it here using the `.uid` argument.

    define_mpi_specs(
      .mpi_specs_file = specs_file,
      .uid = 'uuid'
    )

You can also set the aggregation level. Make sure it corresponds to the column name present in your dataset (see `?df_househod` and more below).

    define_mpi_specs(
      .mpi_specs_file = specs_file,
      .poverty_cutoffs = c(1/3, 0.2, 0.8),
      .uid = 'uuid',
      .aggregation = 'class'
    )

`.unit_of_analysis`, `.source_of_data`, and `.names_separator` are merely used for auto labels when generating the output later.

Note: `define_mpi_specs` returns a data frame of MPI specs defined in the specs file. By default, it saves a global option named `mpi_specs` which can be accessed using `getOption('mpi_specs')`.

For our demonstration, we will use the `use_global_mpi_specs()` wrapper function, which yields the same result as calling `define_mpi_specs` and passing the built-in specs file.

    use_global_mpi_specs(
      .uid = 'uuid',
      .aggregation = 'class'
    )

## 3. Data preparation

The user of `mpindex` is assumed to have basic familiarity with the concept of tidy data as well as able to perform data wrangling and transformation using the `tidyverse` ecosystem. Under the hood, `mpindex` uses `dplyr` verbs to perform data manipulation.

We also assume that your dataset is already tidy and ready for analysis. See R for Data Science by Hadley Wickham and Garrett Grolemund if you need a refresher.

### Dataset

For this demonstration, we will use two (2) synthetic datasets available within the package:

- `df_household` household-level data (type `?df_household` for more info)
- `df_household_roster` individual-level data (type `?df_household_roster` for more info)

    library(dplyr)

    glimpse(df_household)
    #> Rows: 198
    #> Columns: 21
    #> $ uuid               <chr> "5dbec60a-ebda-47bd-ae18-3b017a221125", "8b70c208-8…
    #> $ class              <chr> "Rural", "Rural", "Rural", "Rural", "Rural", "Rural…
    #> $ drinking_water     <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
    #> $ toilet             <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 4, 1, 1, 1, …
    #> $ with_child_died    <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, …
    #> $ roof               <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
    #> $ walls              <int> 1, 1, 3, 3, 3, 5, 5, 1, 3, 1, 1, 4, 8, 2, 2, 10, 2,…
    #> $ floor              <int> 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 5, 2, 4, 1, 2, …
    #> $ electricity        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, …
    #> $ cooking_fuel       <int> 3, 5, 5, 5, 5, 5, 5, 3, 3, 1, 3, 5, 5, 5, 5, 5, 5, …
    #> $ asset_refrigerator <int> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, …
    #> $ asset_radio        <int> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, …
    #> $ asset_tv           <int> 1, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 0, 0, 0, 0, 0, 0, …
    #> $ asset_telephone    <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
    #> $ asset_mobile_phone <int> 4, 3, 2, 2, 5, 2, 1, 6, 2, 4, 1, 2, 3, 0, 0, 2, 0, …
    #> $ asset_animal_cart  <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
    #> $ asset_computer     <int> 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
    #> $ asset_motorcycle   <int> 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, …
    #> $ asset_bicycle      <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
    #> $ asset_car          <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
    #> $ asset_truck        <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …

    glimpse(df_household_roster)
    #> Rows: 905
    #> Columns: 8
    #> $ uuid                     <chr> "5dbec60a-ebda-47bd-ae18-3b017a221125", "5dbe…
    #> $ line_number              <int> 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 1, 2, 3, 4, 1, …
    #> $ class                    <chr> "Rural", "Rural", "Rural", "Rural", "Rural", …
    #> $ sex                      <chr> "Male", "Female", "Female", "Male", "Female",…
    #> $ age                      <int> 55, 48, 17, 10, 30, 26, 3, 19, 66, 62, 34, 34…
    #> $ attending_school         <int> NA, NA, 1, 1, NA, NA, NA, 1, NA, NA, NA, NA, …
    #> $ undernourished           <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
    #> $ completed_6yrs_schooling <int> 1, 1, 1, 2, 1, 1, NA, 2, 1, 1, 2, 1, 2, NA, 2…

## 4. Creating deprivation profile

### Using `define_deprivation`

First, we need to create an empty list, and name it `deprivation_profile` (but feel free to name it whatever you like).

    deprivation_profile <- list()

To create a deprivation profile for each indicator, we use the `define_deprivation` function (see `?define_deprivation`) and add to the `deprivation_profile` list we created above. Make sure that the deprivation profile for each indicator matches the variable name declared in the specification file.

### 1. Heath dimension

#### 1.1. Nutrition

For this indicator, we use the `df_household_roster` dataset. By default, `define_deprivation` sets the `.collapse = FALSE`. Since we need to collapse it to the household level, we need to set `.collapse = TRUE`.

    deprivation_profile$nutrition <- df_household_roster |>
      define_deprivation(
        .indicator = nutrition,
        .cutoff = undernourished == 1 & age < 70,
        .collapse = TRUE
      )

#### 1.2. Child mortality

For child mortality, we use the `df_household` dataset. But unlike in `nutrition`, we do not need to provide the `.collapse` argument since it is not applicable here.

    deprivation_profile$child_mortality <- df_household |>
      define_deprivation(
        .indicator = child_mortality,
        .cutoff = with_child_died == 1
      )

### 2. Education dimension

#### 2.1. Years of schooling

    deprivation_profile$year_schooling <- df_household_roster |>
      define_deprivation(
        .indicator = year_schooling,
        .cutoff = completed_6yrs_schooling == 2,
        .collapse = TRUE
      )

#### 2.2. School attendance

    deprivation_profile$school_attendance <- df_household_roster |>
      define_deprivation(
        .indicator = school_attendance,
        .cutoff = attending_school == 2 & age %in% c(5:24),
        .collapse = TRUE
      )

### 3. Living standards dimension

#### 3.1. Cooking fuel

    deprivation_profile$cooking_fuel <- df_household |>
      define_deprivation(
        .indicator = cooking_fuel,
        .cutoff = cooking_fuel %in% c(4:6, 9)
      )

#### 3.2. Sanitation

    deprivation_profile$sanitation <- df_household |>
      define_deprivation(
        .indicator = sanitation,
        .cutoff = toilet > 1
      )

#### 3.3. Drinking water

    deprivation_profile$drinking_water <- df_household |>
      define_deprivation(
        .indicator = drinking_water,
        .cutoff = drinking_water == 2
      )

#### 3.4. Electricity

    deprivation_profile$electricity <- df_household |>
      define_deprivation(
        .indicator = electricity,
        .cutoff = electricity == 2
      )

#### 3.5. Housing

    deprivation_profile$housing <- df_household |>
      define_deprivation(
        .indicator = housing,
        .cutoff = roof %in% c(5, 7, 9) | walls %in% c(5, 8, 9, 99) == 2 | floor %in% c(5, 6, 9)
      )

#### 3.6. Assets

For this indicator, we need additional transformation.

    deprivation_profile$assets <- df_household |>
      mutate_at(vars(starts_with('asset_')), ~ if_else(. > 0, 1L, 0L)) |>
      mutate(
        asset_phone = if_else(
          (asset_telephone + asset_mobile_phone) > 0,
          1L,
          0L
        )
      ) |>
      mutate(
        with_hh_conveniences = (
          asset_tv + asset_phone + asset_computer +
            asset_animal_cart + asset_bicycle +
            asset_motorcycle + asset_refrigerator) > 1,
        with_mobility_assets = (asset_car + asset_truck) > 0
      ) |>
      define_deprivation(
        .indicator = assets,
        .cutoff = !(with_hh_conveniences & with_mobility_assets)
      )

## 5. Computing the MPI

### Using `compute_mpi`

After completing the deprivation profile, use the `compute_mpi` function and pass the `deprivation_profile` list as the first argument.

    mpi_result <- df_household |>
      compute_mpi(deprivation_profile)

    names(mpi_result)
    #> [1] "index"              "contribution"       "headcount_ratio"
    #> [4] "deprivation_matrix"

### Outputs

#### 1. The MPI

    mpi_result$index

MPI Results using 33% Poverty Cutoff
Class | Number of cases | Headcount Ratio (H) | Intensity of Deprivation Among the Poor (A) | MPI (H x A)
--- | --- | --- | --- | ---
Rural | 98 | 0.480 | 0.513 | 0.246
Urban | 100 | 0.280 | 0.405 | 0.113

#### 2. Contribution by dimension

    mpi_result$contribution

Contribution by Dimenstion and Indicator to MPI using 33% Poverty Cutoff
Class | Number of cases | Health | Education | Living Standards
--- | --- | --- | --- | ---
Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets
Rural | 98 | 20.7 | 1.4 | 32.5 | 18.0 | 9.0 | 1.4 | 0.7 | 2.1 | 3.7 | 10.6
Urban | 100 | 22.1 | 1.5 | 36.8 | 25.0 | 0.0 | 2.5 | 0.5 | 0.0 | 0.5 | 11.3

#### 3. Headcount ratio

    mpi_result$headcount_ratio$uncensored

Uncensored Headcount Ratio
Class | Number of cases | Health | Education | Living Standards
--- | --- | --- | --- | ---
Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets
Rural | 98 | 0.306 | 0.020 | 0.888 | 0.276 | 0.582 | 0.082 | 0.031 | 0.102 | 0.163 | 0.867
Urban | 100 | 0.170 | 0.010 | 0.740 | 0.240 | 0.010 | 0.100 | 0.010 | 0.000 | 0.010 | 0.660

    mpi_result$headcount_ratio$censored

Censored Headcount Ratio using 33% Poverty Cutoff
Class | Number of cases | Health | Education | Living Standards
--- | --- | --- | --- | ---
Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets
Rural | 98 | 0.306 | 0.020 | 0.480 | 0.265 | 0.398 | 0.061 | 0.031 | 0.092 | 0.163 | 0.469
Urban | 100 | 0.150 | 0.010 | 0.250 | 0.170 | 0.000 | 0.050 | 0.010 | 0.000 | 0.010 | 0.230

#### 4. Deprivation matrix (first 6 observations)

    mpi_result$deprivation_matrix$uncensored |> head()

Uncensored Deprivation Matrix
UUID | Class | Deprivation score | Health | Education | Living Standards
--- | --- | --- | --- | --- | ---
Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets
5dbec60a-ebda-47bd-ae18-3b017a221125 | Rural | 0.222 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1
8b70c208-8642-408c-8a51-30bcaa106069 | Rural | 0.278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1
aa7cb64d-ba16-4842-8994-8772060f432d | Rural | 0.111 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1
df3e5c9b-7218-451d-9917-cd552cf4f40f | Rural | 0.278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1
57babe6a-c163-4d8e-aa80-3a9bc9290d2d | Rural | 0.278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1
ba3f75cd-102d-482d-a979-b9098e9e0e18 | Rural | 0.278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1

    mpi_result$deprivation_matrix$censored |> head()

Censored Deprivation Matrix using 33% Poverty Cutoff
UUID | Class | Deprivation score | Health | Education | Living Standards
--- | --- | --- | --- | --- | ---
Nutrition | Child mortality | Years of schooling | School attendance | Cooking fuel | Sanitation | Drinking water | Electricity | Housing | Assets
5dbec60a-ebda-47bd-ae18-3b017a221125 | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
8b70c208-8642-408c-8a51-30bcaa106069 | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
aa7cb64d-ba16-4842-8994-8772060f432d | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
df3e5c9b-7218-451d-9917-cd552cf4f40f | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
57babe6a-c163-4d8e-aa80-3a9bc9290d2d | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
ba3f75cd-102d-482d-a979-b9098e9e0e18 | Rural | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0

Note: Deprivation matrices are included by default when you run `compute_mpi`. If you want to exclude these in your output, set `.include_deprivation_matrix` equal to `FALSE`.

## 6. Saving output

You may also save your output into an Excel file. You may choose to format the output or retain its tidy format by setting the `formatted_output` parameter appropriately.

    # Formatted output
    save_mpi(mpi_result, .filename = 'MPI Sample Output')

    # Not formatted
    save_mpi(mpi_result, .filename = 'MPI Sample Output (no format)', .formatted_output = FALSE)

## Full script

    # ----------------------------------
    # Load MPI specs from the built-in specs file
    use_global_mpi_specs(
      .uid = 'uuid',
      .aggregation = 'class'
    )

    # ----------------------------------
    # Create an empty list to store deprivation profile for each indicator
    deprivation_profile <- list()

    deprivation_profile$nutrition <- df_household_roster |>
      define_deprivation(
       .indicator = nutrition,
       .cutoff = undernourished == 1 & age < 70,
       .collapse = TRUE
      )

    deprivation_profile$child_mortality <- df_household |>
      define_deprivation(
       .indicator = child_mortality,
       .cutoff = with_child_died == 1
      )

    deprivation_profile$year_schooling <- df_household_roster |>
      define_deprivation(
       .indicator = year_schooling,
       .cutoff = completed_6yrs_schooling == 2,
       .collapse = TRUE
      )

    deprivation_profile$school_attendance <- df_household_roster |>
      define_deprivation(
       .indicator = school_attendance,
       .cutoff = attending_school == 2 & age %in% c(5:24),
       .collapse = TRUE
      )

    deprivation_profile$cooking_fuel <- df_household |>
      define_deprivation(
       .indicator = cooking_fuel,
       .cutoff = cooking_fuel %in% c(4:6, 9)
      )

    deprivation_profile$sanitation <- df_household |>
      define_deprivation(
       .indicator = sanitation,
       .cutoff = toilet > 1
      )

    deprivation_profile$drinking_water <- df_household |>
      define_deprivation(
       .indicator = drinking_water,
       .cutoff = drinking_water == 2
      )

    deprivation_profile$electricity <- df_household |>
      define_deprivation(
       .indicator = electricity,
       .cutoff = electricity == 2
      )

    deprivation_profile$housing <- df_household |>
      define_deprivation(
       .indicator = housing,
       .cutoff = roof %in% c(5, 7, 9) |
         walls %in% c(5, 8, 9, 99) == 2 |
         floor %in% c(5, 6, 9)
      )

    deprivation_profile$assets <- df_household |>
      dplyr::mutate_at(
        dplyr::vars(dplyr::starts_with('asset_')),
        ~ dplyr::if_else(. > 0, 1L, 0L)
      ) |>
      dplyr::mutate(
       asset_phone = dplyr::if_else(
         (asset_telephone + asset_mobile_phone) > 0,
         1L,
         0L
       )
      ) |>
      dplyr::mutate(
       with_hh_conveniences = (
         asset_tv + asset_phone + asset_computer +
           asset_animal_cart + asset_bicycle +
           asset_motorcycle + asset_refrigerator) > 1,
       with_mobility_assets = (asset_car + asset_truck) > 0
      ) |>
      define_deprivation(
       .indicator = assets,
       .cutoff = !(with_hh_conveniences & with_mobility_assets)
      )

    # ----------------------------------
    # Compute the MPI
    mpi_result <- df_household |>
      compute_mpi(deprivation_profile)

    # ----------------------------------
    # You may also save your output into an Excel file

    # Formatted output
    # save_mpi(mpi_result, .filename = 'MPI Sample Output', .include_specs = T)

    # Not formatted
    save_mpi(
      mpi_result,
      .filename = 'MPI Sample Output (no format)',
      .formatted_output = FALSE,
      .include_specs = TRUE
    )