Package 'ihsMW'

Smart Aggregation to Household Level

Description

Automatically detects variable types and applies sensible aggregations (e.g., 'sum' for continuous quantities, 'max' or logical OR for dummies). Throws warnings for ambiguous columns rather than failing silently.

Usage

ihs_aggregate(data, group_col = "case_id")

Arguments

data	A data.frame at the individual or plot level
group_col	The column name identifying the household (e.g., "case_id" or "y4_hhid")

Value

A data.frame aggregated to the household level

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Clean and Harmonise IHS Data

Description

This wrapper function applies standard cleaning procedures to Malawi IHS data. It handles missing value conversions, winsorization of continuous variables, and returns an audit log of all transformations applied.

Usage

ihs_clean(
  data,
  winsorize_vars = NULL,
  winsorize_by = NULL,
  probs = c(0.01, 0.99)
)

Arguments

data	A data.frame (typically loaded from a '.dta' file)
winsorize_vars	Character vector of continuous variables to winsorize (e.g., consumption, harvest)
winsorize_by	Optional character string of a grouping variable (e.g., region) for stratified winsorization
probs	Numeric vector of length 2 specifying the lower and upper quantiles for winsorization. Default is 'c(0.01, 0.99)'.

Value

A data.frame with cleaning applied. The returned object has an 'ihs_audit' attribute containing a log of modifications.

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Convert Agricultural Units to Kilograms

Description

Converts reported harvest units (e.g., Pails, Oxcarts, Heaps) into standard kilograms using official NSO crop-specific conversion factors.

Usage

ihs_convert_units(data, qty_col, unit_col, crop_col, unmapped = "warn")

Arguments

data	A data.frame
qty_col	The name of the column containing the quantity
unit_col	The name of the column containing the unit code or name
crop_col	The name of the column containing the crop code
unmapped	Action to take when a unit cannot be mapped: '"warn"' (default), '"error"', or '"ignore"'.

Value

A data.frame with a new qty_col_kg column.

---

Check the comparability of variables across IHS rounds

Description

Evaluates the completeness and comparability of variables across the available IHS rounds (IHS2, IHS3, IHS4, IHS5) using the bundled crosswalk.

Usage

ihs_crosswalk_check(verbose = TRUE)

Arguments

verbose

Logical. If TRUE (default), prints a summary report to the console using cli.

Value

A tibble containing the full crosswalk. If verbose is TRUE, also prints a summary.

Examples

## Not run: 
  # Check the crosswalk and print a report
  cw <- ihs_crosswalk_check()

## End(Not run)

---

Harmonise Raw IHS Data

Description

Takes a raw data.frame loaded from a Malawi IHS survey round (e.g. from a '.dta' file) and renames its columns to the standard harmonised variable names defined in the crosswalk.

Usage

ihs_harmonise(data, round = "IHS5", extra = FALSE)

Arguments

data	A data.frame, typically read from a '.dta' file using haven::read_dta.
round	A character string specifying the IHS round (e.g., "IHS5", "IHS4").
extra	Logical. If FALSE (default), drops columns that are not in the harmonisation crosswalk or standard ID columns. If TRUE, keeps all original columns.

Value

A data.frame with columns renamed to standard 'harmonised_name's where applicable.

---

Search across all IHS rounds for variables manually mapped

Description

Searches the manual harmonisation crosswalk bundled within ihsMW for specific variables.

Usage

ihs_search(keyword, round = NULL, fields = c("name", "label", "module"))

Arguments

keyword	A single search string to find (case-insensitive).
round	Limits search to a specific round. Valid inputs are "IHS2", "IHS3", "IHS4", "IHS5". Defaults to NULL (all rounds).
fields	A character vector of fields to include in the search. Valid fields are "name", "label", and "module".

Value

A tibble with cross-round harmonised search results.

Examples

ihs_search("consumption")
ihs_search("expenditure", round = "IHS5")
ihs_search("age", fields = c("name", "label"))

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Standardize Survey Missing Codes

Description

Converts common negative missing codes (like -99 for "Refused" or -98 for "Don't Know") into standard R 'NA' values to prevent them from skewing numeric calculations.

Usage

ihs_standardize_missing(data)

Arguments

data	A data.frame

Value

A data.frame with missing values standardized

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Winsorize Continuous Variables

Description

Caps extreme outliers at specified percentiles. Crucially, this function allows for stratified winsorization (e.g., by region) to avoid over-trimming poor/rich areas, and it creates new '_w' suffixed columns to preserve raw data provenance.

Usage

ihs_winsorize(data, vars, by = NULL, probs = c(0.01, 0.99))

Arguments

data	A data.frame
vars	Character vector of column names to winsorize
by	Optional grouping variable name (e.g., "region") for stratified thresholds
probs	Numeric vector of lower and upper quantiles. Default 'c(0.01, 0.99)'

Value

A data.frame with new '*_w' columns added.

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