Package 'africamonitor'

Title: Africa Macroeconomic Monitor Database API
Description: An R API providing access to a relational database with macroeconomic data for Africa. The database contains >700 macroeconomic time series from mostly international sources, grouped into 50 macroeconomic and development-related topics. Series are carefully selected on the basis of data coverage for Africa, frequency, and relevance to the macro-development context. The project is part of the 'Kiel Institute Africa Initiative' <https://www.ifw-kiel.de/institute/initiatives/kiel-institute-africa-initiative/>, which, amongst other things, aims to develop a parsimonious database with highly relevant indicators to monitor macroeconomic developments in Africa, accessible through a fast API and a web-based platform at <https://africamonitor.ifw-kiel.de/>. The database is maintained at the Kiel Institute for the World Economy <https://www.ifw-kiel.de/>.
Authors: Sebastian Krantz [aut, cre]
Maintainer: Sebastian Krantz <[email protected]>
License: GPL-3
Version: 0.2.4
Built: 2024-10-29 04:31:26 UTC
Source: https://github.com/cran/africamonitor

Help Index

Africa Macroeconomic Monitor Database API

Description

An R API providing access to a relational database with macroeconomic data for Africa. The database is maintained at the Kiel Institute for the World Economy.

Functions

Functions and data providing information about the available data

am_sources()
am_series()
am_countries
am_countries_wld
am_entities

Function to retrieve the data from the database

am_data()

Functions to reshape data and add temporal identifiers

am_pivot_wider()
am_pivot_longer()
am_expand_date()

Helper functions to convert inputs to R dates

am_as_date()

Global Macros with core ID variables in the database

.AMID
.AMT

Global Identifier Macros

Description

The macro .AMID contains the string c("ISO3", "Series") denoting the names of ID variables that identify the cross-sectional dimension in the database.

The macro .AMT contains the string c("Date", "Year", "Quarter", "FY", "QFY", "Month", "Day") denoting temporal identifiers generated by am_expand_date. The "Date" variable is sufficient to uniquely identify a point in time in the database.

Each value in the database is uniquely identified by ISO3, Series and Date.

Usage

.AMID
.AMT

See Also

africamonitor

Examples

.AMID
.AMT

Coerce Vectors to Dates

Description

This function coerces date strings i.e. "YYYY-MM-DD" or "YYYY-MM", years e.g. 2015 (numeric or character), year-quarters e.g. "2015Q1" or "2015-Q1", year-months e.g. "2015M01" or "2015-M01", fiscal years e.g. "1997/98" or numeric values representing dates (e.g. previously imported Excel date) to a regular R date.

Usage

am_as_date(x, end = FALSE, origin = "1899-12-30")

Arguments

x

a character date string "YYYY-MM-DD" or "YYYY-MM", year-quarter "YYYYQN" or "YYYY-QN", year-month "YYYYMNN" or "YYYY-MNN", fiscal year "YYYY/YY" or calendar year YYYY (numeric or character), or a numeric value corresponding to a date that can be passed to as.Date.numeric.
end

logical. TRUE replaces missing time information with a period end-date which is the last day of the period. FALSE replaces missing month and day information with "-01", so the year date is the 1st of January, the fiscal year date the 1st of July, and for months / quarters the 1st day of the month / quarter.
origin

a date or date-string that can be used as reference for converting numeric values to dates. The default corresponds to dates generated in Excel for Windows. See as.Date.numeric.

Value

A Date vector.

See Also

am_expand_date, africamonitor

Examples

am_as_date("2011-05")
am_as_date(2011)
am_as_date("2011/12")
am_as_date("2011/12", end = TRUE)
am_as_date("2011Q1")
am_as_date("2011Q1", end = TRUE)

Dataset of Countries in the Database

Description

am_countries is a data.frame containing standardized codes of 55 African countries (including Western Sahara) according to various classifications and regional aggregations. am_countries_wld provides the same information for 195 countries, which includes the 193 UN Member States, Western Sahara and Taiwan. The API generally provides data for all 195 countries, but by default only requests data for Africa. Note that the API (am_data) only supports "ISO3" character codes.

Usage

am_countries

am_countries_wld

Format

A data frame with 55 (wld = 195) rows and 9 variables (sorted by Country):

Country

Short Country Name

Country_ISO

ISO Standardized Country Name

ISO2

ISO 2-Character Country Code

ISO3

ISO 3-Character Country Code

ISO3N

ISO Numeric Country Code

IMF

IMF Numeric Country Code

Region

2-Region Classification (UN except for Sudan)

Region_Detailed

5-Region Classification (Former World Bank)

Currency

Main Official Currency

An object of class data.table (inherits from data.frame) with 195 rows and 9 columns.

Source

countrycode R package (with some modification of regional aggregates).

See Also

am_entities, am_sources, am_series, africamonitor

Examples

head(am_countries)

Retrieve Data from the Database

Description

This is the main function of the package to retrieve data from the database.

Usage

am_data(
  ctry = africamonitor::am_countries$ISO3,
  series = NULL,
  from = NULL,
  to = NULL,
  labels = TRUE,
  wide = TRUE,
  expand.date = FALSE,
  drop.1iso3c = TRUE,
  ordered = TRUE,
  return.query = FALSE,
  ...
)

Arguments

ctry

character. (Optional) the ISO3 code of countries (see am_countries). Default is to load data for all African countries. Putting NULL gets data for all countries (codes available in am_countries_wld).
series

character. (Optional) codes of series matching the 'Series' column of the series table (retrieved using am_series()).
from

set the start time of the data retrieved by either supplying a start date, a date-string of the form "YYYY-MM-DD" or "YYYY-MM", year-quarters of the form "YYYYQN" or "YYYY-QN", a numeric year YYYY (numeric or character), or a fiscal year of the form "YYYY/YY". These expressions are converted to a regular date by am_as_date.
to

same as from: to set the time period until which data is retrieved. For expressions that are not full "YYYY-MM-DD" dates, the last day of the period is chosen.
labels

logical. TRUE will also return labels (series descriptions) along with the series codes.
wide

logical. TRUE calls am_pivot_wider on the result. FALSE returns the data in a long format without missing values (suitable for ggplot2).
expand.date

logical. TRUE will call am_expand_date on the result.
drop.1iso3c

logical. If only one country is selected through ctry, TRUE will drop the 'ISO3' column in the output.
ordered

logical. TRUE orders the result by 'Date' and, if labels = TRUE, by series, maintaining a fixed column-order of series. FALSE returns the result in a random order, to the benefit of faster query execution.
return.query

logical. TRUE will not query the database but instead return the constructed SQL query as a character string (for debugging purposes).
...

further arguments passed to am_pivot_wider (if wide = TRUE) or am_expand_date (if expand.date = TRUE).

Details

If labels = FALSE, the series table is not joined to the data table, and ordered = TRUE will order series retrieved in alphabetic order. If labels = TRUE data is ordered by series and date, preserving the order of columns in the dataset. If multiple countries are received they are ordered alphabetically according to the 'ISO3' column.

Series at different frequencies can be queried, but, if wide = TRUE, this will result in missing values for all but the first observations per period in the lower frequency series.

Value

A data.table with the result of the query.

See Also

am_pivot_wider, am_expand_date, africamonitor

Examples

# Return all indicators for Kenya from 2000
am_data("KEN", from = 2000)

# Return all indicators for Kenya from 2000 in long format
am_data("KEN", from = 2000, wide = FALSE)

# Return with date expanded
am_data("KEN", from = 2000, expand.date = TRUE)

# Same thing in multiple steps (with additional customization options):
am_data("KEN", from = 2000, wide = FALSE) |> am_pivot_wider() |> am_expand_date()

# Getting only GDP growth
am_data("KEN", "NGDP_RPCH", from = 2000)

# Getting GDP growth for all countries
am_data(series = "NGDP_RPCH", from = 2000)

# Reshaping to wider format
am_data(series = "NGDP_RPCH", from = 2000) |>
  am_pivot_wider(id_cols = "Date",
                 names_from = "ISO3",
                 values_from = "NGDP_RPCH")

# Getting growth and inflation for the EAC countries (all available years)
am_data(ctry = c("UGA", "KEN", "TZA", "RWA", "BDI", "SSD"),
        series = c("NGDP_RPCH", "PCPIPCH"))

Dataset of African Economic and Regional Entities

Description

A dataset mapping African countries to various economic and regional entities.

Usage

am_entities

Format

A data frame with 54 rows (one for each country, excluding Western Sahara) and 27 variables, of which 6 are country identifiers and the remaining 21 are logical variables indicating country membership to various economic and regional entities.

Source

Own compilation.

See Also

am_countries, am_sources, am_series, africamonitor

Examples

head(am_entities)

Generate Temporal Identifiers from a Date Column

Description

This function expands a date column and generates additional temporal identifiers from it (such as the year, month, quarter, fiscal year etc.).

Usage

am_expand_date(
  x,
  gen = c("Year", "Quarter", "Month"),
  origin = "1899-12-30",
  keep.date = TRUE,
  remove.missing.date = TRUE,
  sort = TRUE,
  as.factor = TRUE,
  name = "Date",
  ...
)

Arguments

x

either a vector of class 'Date', or coercible to date using as.Date, or a data frame / list containing with a date-column called name.
gen

character. A vector of identifiers to generate from x. The possible identifiers are found in .AMT.
origin

character / Date. Passed to as.Date: for converting numeric x to date.
keep.date

logical. TRUE will keep the date variable in the resulting dataset, FALSE will remove the date variable in favor of the generated identifiers.
remove.missing.date

logical. TRUE will remove missing values in x. If x is a dataset, rows missing the date variable will be removed.
sort

logical. TRUE will sort the data by the date column.
as.factor

TRUE will generate quarters, fiscal years and months ('Quarter', 'FY', 'QFY', 'Month') as factor variables. It is also possible to use as.factor = "ordered" to generate ordered factors. FALSE will generate fiscal years as character and quarters and months as integer variables.
name

character. The name of the date variable to expand.
...

not used.

Value

A data.table containing the computed identifiers as columns. See Examples.

See Also

am_as_date, africamonitor

Examples

# First a basic example
x <- seq.Date(as.Date("1999-01-01"), as.Date("2000-01-01"), by = "month")
am_expand_date(x)
am_expand_date(x, gen = .AMT[-1L], keep.date = FALSE)

# Now using the API
am_expand_date(am_data("KEN"))

# Same thing
am_data("KEN", expand.date = TRUE)

Reshape Column-Based Data to Long Format

Description

This function automatically reshapes wide (column-based) data into a long format akin to the format of the raw data coming from the database (am_data(..., wide = FALSE)). It can also be used as a general purpose reshaping command - with an additional capability to handle variable labels.

Usage

am_pivot_longer(
  data,
  id_cols = intersect(c("ISO3", .AMT), names(data)),
  to_value = setdiff(names(data), id_cols),
  variable_name = "Series",
  value_name = "Value",
  label_name = "Label",
  na.rm = TRUE,
  variable.factor = TRUE,
  label.factor = TRUE,
  ...
)

Arguments

data

a wide format data frame where all series have their own column.
id_cols

character. Temporal identifiers of the data. By default all variables in .AMT and "ISO3" are selected.
to_value

character. The names of all series to be stacked into the long format data frame.
variable_name

character. The name of the variable to store the names of the series.
value_name

character. The name of the variable to store the data values.
label_name

character. The name of the variable to store the series labels.
na.rm

logical. TRUE will remove all missing values from the long data frame.
variable.factor, label.factor

logical. TRUE will code the "Series" and "Label" columns as factors, which is more memory efficient.
...

further arguments passed to melt.

Value

A data.table with the reshaped data.

See Also

am_pivot_wider, africamonitor

Examples

# Return all indicators for Kenya and Nigeria from the year 2000 onwards
data <- am_data(c("KEN", "NGA"), from = 2000)
am_pivot_longer(data)

Reshape Long API Data to Column-Based Format

Description

This function automatically reshapes long (stacked) raw data from the API (am_data(..., wide = FALSE)) to a wide format where each variable has its own column. It can also be used as a general purpose reshaping command - with an additional capability to handle variable labels.

Usage

am_pivot_wider(
  data,
  id_cols = intersect(c("ISO3", .AMT), names(data)),
  names_from = "Series",
  values_from = "Value",
  labels_from = if (any(names(data) == "Label")) "Label" else NULL,
  expand.date = FALSE,
  ...
)

Arguments

data

raw data from the API: A long format data frame where all values are stacked in a value column.
id_cols

character. Temporal identifiers of the data. By default all variables in .AMT and "ISO3" are selected.
names_from

character. The column containing the series codes. These will become the names of the new columns in the wider data format.
values_from

character. The column containing the data values.
labels_from

character. The column containing the labels describing the series.
expand.date

logical. TRUE will call am_expand_date on the data after reshaping.
...

further arguments passed to pivot or am_expand_date.

Value

A data.table with the reshaped data.

See Also

am_pivot_longer, africamonitor

Examples

# Return all indicators for Kenya and Nigeria from the year 2000 onwards
am_pivot_wider(am_data(c("KEN", "NGA"), from = 2000, wide = FALSE))

Retrieve Series Table

Description

This function pulls information about the data series available in the database.

Usage

am_series(
  dsid = NULL,
  source.info = TRUE,
  ordered = TRUE,
  return.query = FALSE
)

Arguments

dsid

character. (Optional) id's of datasources matching the 'DSID' column of the data sources table (retrieved using am_sources()) for which series information is to be returned.
source.info

logical. TRUE returns additional information from the data sources table (the source, the frequency of the data and when it was last updated).
ordered

logical. TRUE returns the series in a fixed order, while FALSE returns the result in a random order, to the benefit of faster query execution.
return.query

logical. TRUE will not query the database but instead return the constructed SQL query as a character string (for debugging purposes).

Details

The series table gives information about all of the time series in the database. Each series is given a unique code, and has a label describing the series. Further information recorded are the minimum and maximum time coverage, and (optionally) a separate series source and url. The default source.info = TRUE adds the source, the frequency of the data (homogeneous within source), and the date when the source was last updated.

Value

A data.table with information about the available series in the database.

See Also

am_countries, am_sources, am_data, africamonitor

Examples

# By default returns all series with additional information
am_series()

# Raw series table
am_series(source.info = FALSE)

# Only series in the WEO
am_series("IMF_WEO")

Retrieve Data Sources Table

Description

This function retrieves a table with information about the sources of data in the database, and when data from different sources was updated.

Usage

am_sources(ordered = TRUE)

Arguments

ordered

logical. TRUE orders the result in the order data was entered into the database, while FALSE returns the result in a random order, to the benefit of faster query execution.

Details

The data source table gives information about the various sources / providers of data in this database, including the source website, frequency and time coverage of data, a description of the source, when data from the source was updated and the way data is accessed from the source.

Value

A data.table with information about the sources of data in the database.

See Also

am_countries, am_series, africamonitor

Examples

am_sources()