Package {pipr}

convert arguments and values of a function to a string to parse into other functions

Description

convert arguments and values of a function to a string to parse into other functions

Usage

args_to_string(il)

Arguments

Value

character

Build request version 2

Description

Build request version 2

Usage

build_request(server, api_version, endpoint, ...)

Arguments

Value

httr2 request

build_request, OLD version

Description

build_request, OLD version

Usage

build_request_old(server, api_version, endpoint, ...)

Arguments

Value

httr2 request

call a table from .pip env

Description

call a table from .pip env

Usage

call_aux(table = NULL)

Arguments

Value

The stored auxiliary table when table is given. With table = NULL, invisibly returns the names of stored tables.

Examples

# List tables already stored in this R session (no network request).
call_aux()
if (interactive()) {
  get_aux("gdp", assign_tb = TRUE, replace = TRUE)
  call_aux("gdp")
}

Convert a grouped-statistics result to a data frame

Description

Convert a grouped-statistics result to a data frame

Usage

change_grouped_stats_to_csv(out)

Arguments

Value

A data frame with one column per decile and no deciles list column.

Examples

change_grouped_stats_to_csv(list(country = "AGO", deciles = c(0.1, 0.2)))

Check internet connection and API status

Description

Check internet connection and API status

Usage

check_api(api_version = "v1", server = NULL)

Arguments

Value

character

Examples

if (interactive()) {
check_api()
}

Datt (1998) grouped data for rural india, 1983

Description

Dataset from Datt (1998) with grouped data for rural India in 1983.

Usage

datt_rural

Format

A data frame with 13 observations on the following 6 variables:

monthly_pc_exp

Welfare range class

mean_monthly_pc_exp

Mean welfare for given welfare range class

percentage_of_persons

Percentage of individuals in given welfare class

L

Cumulative welfare

p

Cumulative population

area

rural

Source

Datt, G. (1998). See get_cp vignette.

Grouped data for urban india, 1983

Description

Dataset from Sarvekshana N26 Vol 9 N 4, created by the author following Datt(1998) methodology with grouped data for urban India in 1983.

Usage

datt_urban

Format

A data frame with 13 observations on the following 6 variables:

monthly_pc_exp

Welfare range class

mean_monthly_pc_exp

Mean welfare for given welfare range class

percentage_of_persons

Percentage of individuals in given welfare class

L

Cumulative welfare

p

Cumulative population

area

urban

Source

Sarvekshana N26 Vol 9 N 4, and Datt, G. (1998) for methodology. See get_cp vignette.

Deletes content of the cache folder

Description

Deletes content of the cache folder

Usage

delete_cache()

Value

Side effect. Deletes files.

Examples

if (interactive()) delete_cache()

Display available auxiliary tables

Description

Display available auxiliary tables

Usage

display_aux(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  simplify = TRUE,
  server = NULL,
  assign_tb = TRUE
)

Arguments

Value

invisible tibble with names of auxiliary tables

Examples

if (interactive()) {
display_aux()
}

Get auxiliary data

Description

get_aux() gets an auxiliary dataset. If no table is specified, it lists the available tables.

Use get_aux("countries") for a table of country names, ISO codes, and associated region codes.

Usage

get_aux(
  table = NULL,
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  simplify = TRUE,
  server = NULL,
  assign_tb = FALSE,
  replace = FALSE
)

get_countries(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_regions(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_cpi(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_dictionary(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_gdp(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_incgrp_coverage(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_interpolated_means(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_hfce(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_pop(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_pop_region(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_ppp(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_region_coverage(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

get_survey_means(
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  server = NULL
)

Arguments

Value

With no table, invisibly returns the available tables (a tibble with a tables column by default). With a selected table, returns its tibble when simplify = TRUE, or a pip_api list when simplify = FALSE. With assign_tb = TRUE or a name, invisibly returns TRUE on successful assignment to .pip; unsuccessful assignment raises an error.

Functions

• get_countries(): Returns a table countries with their full names, ISO codes, and associated region code

• get_regions(): Returns a table regional grouping used for computing aggregate poverty statistics.

• get_cpi(): Returns a table of Consumer Price Index (CPI) values used for poverty and inequality computations. statistics

• get_dictionary(): Returns a data dictionary with a description of all variables available through the PIP API.

• get_gdp(): Returns a table of Growth Domestic Product (GDP) values used for poverty and inequality statistics.

• get_incgrp_coverage(): Returns a table of survey coverage for low and lower-middle income countries. If this coverage is less than 50%, World level aggregate statistics will not be computed.

• get_interpolated_means(): Returns a table of key information and statistics for all years for which poverty and inequality statistics are either available (household survey exists) or extra- / interpolated. Please see get_aux("dictionary") for more information about each variable in this table.

• get_hfce(): Returns a table of Household Final Consumption Expenditure (HFCE) values used for poverty and inequality computations.

• get_pop(): Returns a table of population values used for poverty and inequality computations.

• get_pop_region(): Returns a table of total population by region-year. These values are used for the computation of regional aggregate poverty statistics.

• get_ppp(): Returns a table of Purchasing Power Parity (PPP) values used for poverty and inequality computations.

• get_region_coverage(): Return a table of regional survey coverage: Percentage of available surveys for a specific region-year.

• get_survey_means(): Returns a table of all available surveys and associated key statistics. See get_aux("dictionary") for more information about each variable in this table.

Examples

if (interactive()) {
# Get list of tables
x <- get_aux()

# Get GDP data
df <- get_aux("gdp")

# Get countries
df <- get_aux("countries")

# Display auxiliary tables
get_aux()

# Bind gdp table to "gdp" in .pip env
get_aux("gdp", assign_tb = TRUE)

# Bind gdp table to "new_name" in .pip env
get_aux("gdp", assign_tb = "new_name")

}
if (interactive()) {
# Short hand to get countries
get_aux("countries")
}
if (interactive()) {
# Short hand to get regions
get_aux("regions")
}
if (interactive()) {
# Short hand to get cpi
get_aux("cpi")
}
if (interactive()) {
# Short hand to get dictionary
get_aux("dictionary")
}
if (interactive()) {
# Short hand to get gdp
get_aux("gdp")
}
if (interactive()) {
# Short hand to get incgrp_coverage
get_aux("incgrp_coverage")
}
if (interactive()) {
# Short hand to get interpolated_means
get_aux("interpolated_means")
}
if (interactive()) {
# Short hand to get hfce
get_aux("pce")
}
if (interactive()) {
# Short hand to get pop
get_aux("pop")
}
if (interactive()) {
# Short hand to get pop_region
get_aux("pop_region")
}
if (interactive()) {
# Short hand to get ppp
get_aux("ppp")
}
if (interactive()) {
# Short hand to get region_coverage
get_aux("region_coverage")
}
if (interactive()) {
# Short hand to get survey_means
get_aux("survey_means")
}

Provides some information about cached items

Description

Provides some information about cached items

Usage

get_cache_info()

Value

The result of the cache status message, invisibly (NULL).

Examples

get_cache_info()

Get Country Profiles

Description

Get Country Profiles

Usage

get_cp(
  country = "all",
  povline = 2.15,
  version = NULL,
  ppp_version = 2017,
  release_version = NULL,
  api_version = "v1",
  format = c("arrow", "rds", "json", "csv"),
  simplify = TRUE,
  server = NULL
)

Arguments

Value

A tibble of country profile data when simplify = TRUE, or a pip_api list when simplify = FALSE.

Examples

if (interactive()) {
# One country, all years with default ppp_version = 2017
res <- get_cp(country = "AGO")

# All countries, povline = 1.9
res <- get_cp(povline = 1.9)

# All countries and years with default values
res <- get_cp()
}

Get Country Profiles Key Indicators

Description

Get Country Profiles Key Indicators

Usage

get_cp_ki(
  country = NULL,
  povline = 2.15,
  version = NULL,
  ppp_version = 2017,
  release_version = NULL,
  api_version = "v1",
  simplify = TRUE,
  server = NULL
)

Arguments

Value

An unnested data frame of key indicators when simplify = TRUE, or a pip_api list when simplify = FALSE.

Examples

if (interactive()) {
# One country, all years with default ppp_version = 2017
res <- get_cp_ki(country = "IDN")

# One country, povline = 1.9
res <- get_cp_ki(country = "IDN", povline = 1.9)

}

Get grouped stats

Description

Get grouped stats from the PIP API.

Usage

get_gd(
  cum_welfare,
  cum_population,
  estimate = c("stats", "lorenz", "params"),
  requested_mean = NULL,
  povline = NULL,
  popshare = NULL,
  lorenz = NULL,
  n_bins = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  simplify = TRUE,
  server = NULL
)

Arguments

Value

A data frame when simplify = TRUE, or a pip_api list when simplify = FALSE.

Examples

if (interactive()) {

datt_data <- data.frame(p = c(0.0092, 0.0339, 0.0850, 0.160, 0.2609, 0.4133,
                              0.5497, 0.7196, 0.8196, 0.9174, 0.9570, 0.9751,
                              1),
                       L = c(0.00208, 0.01013, 0.03122, 0.07083, 0.12808,
                             0.23498, 0.34887, 0.51994, 0.64270, 0.79201,
                             0.86966, 0.91277, 1))

# estimate = 'stats': retrieve poverty statistics.
stats <- get_gd(cum_welfare = datt_data$L, cum_population = datt_data$p,
                estimate = "stats",
                requested_mean = 19,
                povline = 2.15)

# estimate = 'lorenz': retrieve Lorenz curve data points for a specified number of bins.

## Best lorenz curve methodolody selected by default:
lorenz <- get_gd(cum_welfare = datt_data$L,
                 cum_population = datt_data$p,
                 estimate = "lorenz",
                 n_bins = 100)  # must be specified, default is NULL.

## Specify lorenz curve methodology:
### Beta Lorenz ("lb")
lorenz_lb <- get_gd(cum_welfare = datt_data$L,
                 cum_population = datt_data$p,
                 estimate = "lorenz",
                 lorenz = "lb",
                 n_bins = 100)

### Quadratic Lorenz ("lq")
lorenz_lq <- get_gd(cum_welfare = datt_data$L,
                 cum_population = datt_data$p,
                 estimate = "lorenz",
                 lorenz = "lq",
                 n_bins = 100)

# estimate = 'params': retrieve regression parameters used for the lorenz curve estimation.
params <- get_gd(cum_welfare = datt_data$L,
                 cum_population = datt_data$p,
                 estimate = "params")
}

Get PIP info

Description

Get information about the API.

Usage

get_pip_info(api_version = "v1", server = NULL)

Arguments

Value

list

Examples

if (interactive()) {
get_pip_info()
}

Get poverty and inequality statistics

Description

Get poverty and inequality statistics

Usage

get_stats(
  country = "all",
  year = "all",
  povline = NULL,
  popshare = NULL,
  fill_gaps = FALSE,
  nowcast = FALSE,
  subgroup = NULL,
  welfare_type = c("all", "income", "consumption"),
  reporting_level = c("all", "national", "urban", "rural"),
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("arrow", "rds", "json", "csv"),
  simplify = TRUE,
  server = NULL
)

get_wb(
  year = "all",
  povline = NULL,
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  simplify = TRUE,
  server = NULL
)

get_agg(
  year = "all",
  povline = NULL,
  version = NULL,
  ppp_version = NULL,
  release_version = NULL,
  aggregate = NULL,
  api_version = "v1",
  format = c("rds", "json", "csv"),
  simplify = TRUE,
  server = NULL
)

Arguments

Value

A tibble of statistics when simplify = TRUE, or a pip_api list when simplify = FALSE.

Examples

if (interactive()) {
# One country-year
res <- get_stats(country = "AGO", year = 2000)

# All years for a specific country
res <- get_stats(country = "AGO", year = "all")

# All countries and years
res <- get_stats(country = "all", year = "all")

# All countries and years w/ alternative poverty line
res <- get_stats(country = "all", year = "all", povline = 3.2)

# Fill gaps for years without available survey data
res <- get_stats(country = "all", year = "all", fill_gaps = TRUE)

# Proportion living below the poverty line
res <- get_stats(country = "all", year = "all", popshare = .4)

# World Bank global and regional aggregates
res <- get_stats("all", year = "all", subgroup = "wb_regions")

# Short hand to get WB global/regional stats
res <- get_wb()

# Short hand to get fcv stats
res <- get_agg(aggregate = "fcv")

# Custom aggregates
res <- get_stats(c("ARG", "BRA"), year = "all", subgroup = "none")
}

Get versions

Description

Get available data versions.

Usage

get_versions(api_version = "v1", server = NULL, simplify = TRUE)

Arguments

Value

tibble or list

Examples

if (interactive()) {
get_versions()
}

parse_error_body

Description

Helper function to parse error messages generated by the PIP API

Usage

parse_error_body(resp)

Arguments

Value

character

parse_response

Description

parse_response

Usage

parse_response(res, simplify)

Arguments

Value

If simplify = TRUE, it returns a tibble with the requested content. If simplify = FALSE, it returns a list of class "pip_api" with information about the PIP API query

pip_is_transient

Description

Helper function to determine if an error is due to the number of requests going over the rate limit

Usage

pip_is_transient(resp)

Arguments

Value

logical

rename columns in dataframe

Description

rename columns in dataframe

Usage

rename_cols(df, oldnames, newnames)

Arguments

Value

data frame with new names

retry_after

Description

Helper function to determine how much time to wait before a new query can be sent

Usage

retry_after(resp)

Arguments

Value

numeric

whether or not to run cli or just to show code

Description

whether or not to run cli or just to show code

Usage

run_cli()

Value

logical

Set auxiliary table in .pip environment for later call

Description

Set auxiliary table in .pip environment for later call

Usage

set_aux(table, value, replace = FALSE)

Arguments

Value

Invisible TRUE if set correctly. FALSE otherwise

Unnest the key indicators

Description

Unnest the key indicators

Usage

unnest_ki(out)

Arguments

Value

data frame, unnested.

Functions

• unnest_ki(): takes the simplified output from cp-key-indicators endpoint and unnests it.