From CRAN

install.packages("ihsMW")

Development version

# install.packages("pak")
pak::pak("vituk123/ihsMW")

# or
# install.packages("remotes")
remotes::install_github("vituk123/ihsMW")

Confirm the installation

library(ihsMW)
packageVersion("ihsMW")
#> [1] '1.1.1'

ihsMW requires R 4.1.0 or later, because it uses the native pipe (|>) internally.

Required (installed automatically)

Suggested (install if you need the feature)

Install the ones most people want in one go:

install.packages(c("haven", "survey", "srvyr"))

No internet required

Once installed, ihsMW never makes a network request. The crosswalk, conversion factors and CPI series are all bundled inside the package. This is deliberate: survey work often happens on airgapped or restricted machines, and an analysis that silently depends on a live API is not reproducible.

The problem

The IHS is Malawi’s flagship household survey, run roughly every five years by the National Statistical Office with World Bank LSMS support. It is the evidence base for poverty measurement, food security monitoring and agricultural policy in Malawi.

Working with it raw is painful in specific, repetitive ways:

1. Variable names drift between rounds. The same concept is hhweight in IHS2 and IHS3 but hh_wgt from IHS4 onward. The design stratum is stratum through IHS5 and strata in IHS6. Multiply this across thousands of variables.
2. Missing values are coded as numbers. -99 means “refused”, -98 means “don’t know”. Take a mean without recoding and your average household is younger than zero.
3. Harvest quantities are in whatever unit the household used. Pails, ox-carts, heaps, 50 kg bags — all in the same column. Summing it is meaningless without crop-, unit- and region-specific conversion factors.
4. Money is nominal. Malawi’s price level rose roughly 24-fold between 2004 and 2025. Comparing kwacha across rounds without deflating produces nonsense.
5. The sample is not simple random. It is a stratified two-stage cluster design. Ignore the weights and your estimates are biased; ignore the clustering and your standard errors are far too small.

Every research team solves these five problems again, in their own scripts, with their own undocumented judgement calls. ihsMW solves them once.

What the package does

Thirteen exported functions covering the pipeline from raw file to publication table:

#> Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
#> Use 'xfun::attr2()' instead.
#> See help("Deprecated")
#> Warning in attr(x, "format"): 'xfun::attr()' is deprecated.
#> Use 'xfun::attr2()' instead.
#> See help("Deprecated")

What the package does not do

Being explicit about the boundaries saves you time:

• It does not distribute microdata. World Bank licensing forbids it. You download the files yourself (see §5).
• It does not construct a poverty line or a consumption aggregate. Those are published with each round; the package helps you clean and compare them, not rebuild them.
• It does not adjust for spatial price differences. ihs_deflate() applies a national CPI over time. If you need a Lilongwe-vs-Mzuzu price adjustment, use the spatial price index published with the round’s consumption aggregate.
• It does not link households into a panel across rounds. The IHS panel component has its own ID structure and attrition rules; ihs_panel_ids() tells you which columns to use, but the linking logic is yours.
• It does not make judgement calls for you. When a variable cannot be mapped or a unit cannot be converted, you get NA and a message — never a quietly invented number.

Supported rounds

ihs_panel_ids("all")
#> Panel ID columns across all supported IHS rounds.
#>       role     IHS2     IHS3    IHS4    IHS5    IHS6
#> 1    hh_id  case_id  case_id case_id case_id case_id
#> 2 indiv_id      PID      PID     PID     PID     PID
#> 3    ea_id    ea_id    ea_id   ea_id   ea_id   ea_id
#> 4   strata  stratum  stratum stratum stratum  strata
#> 5   weight hhweight hhweight  hh_wgt  hh_wgt  hh_wgt

Note the two renames that catch people out: the weight column changes at IHS4, and the stratum column changes at IHS6. ihs_svydesign() handles both automatically.

IHS1 (1997/98) is not supported — it is not available in a form the crosswalk can address.

Prefer the Stata distribution

Some IHS CSV exports substitute value labels for numeric codes. In the IHS5 CSV release, for instance, crop_code contains "MAIZE LOCAL" rather than 1, and the harvest unit contains "50 KG BAG" rather than 2. The conversion factor table is keyed on the numeric codes, so unit conversion cannot work on those files.

ihs_convert_units() detects this and stops with an explanatory error rather than handing you a column of NA. The fix is to read the .dta version:

library(haven)
ag <- haven::read_dta("IHS5/ag_mod_i.dta")   # numeric codes, with labels attached

haven also preserves variable labels, which ihs_harmonise() carries through the rename.

Format

Any data.frame works, including a tibble or the labelled data frame that haven::read_dta() returns. ihsMW returns the same class it was given.

What each function needs

Column naming

After ihs_harmonise(), columns carry the crosswalk’s harmonised_name. Downstream functions detect standard columns case-insensitively, so HHID and hhid are equivalent. The names each function looks for:

• Weights: hh_wgt, hhweight, weight, panelweight, hh_wgt_adj, hhwght
• Strata: stratum, strata, strataid, strat
• PSU / cluster: ea_id, psu, cluster, clusterid
• Join keys: case_id, hhid, hh_id, HHID, ea_id, PID

If your columns are named differently, pass them explicitly — every auto-detecting function accepts an override.

New columns, never overwritten values

ihsMW never modifies a value in place. Transformations add a suffixed column and leave the original intact, so you can always audit or revert:

Suffixes compose, which is how you tell at a glance what has been done: rexp_cat011_w_real is a winsorized, then deflated, food consumption variable.

ihs_harmonise() is the exception — renaming columns is its entire purpose. It also adds an ihs_round column, which ihs_deflate() later reads.

Audit attributes

Several functions attach a machine-readable record of what they did:

survey_data <- data.frame(
  region   = rep(c("North", "South"), each = 50),
  food_exp = c(rnorm(50, 100, 10), rnorm(50, 500, 50))
)
survey_data$food_exp[1]  <- -99      # a refusal code
survey_data$food_exp[60] <- 99999    # an implausible outlier

cleaned <- ihs_clean(survey_data,
                     winsorize_vars = "food_exp",
                     winsorize_by   = "region")

str(attr(cleaned, "ihs_audit"))
#> List of 6
#>  $ initial_rows       : int 100
#>  $ initial_cols       : int 2
#>  $ missing_conversions:List of 1
#>   ..$ food_exp: int 1
#>  $ winsorized_vars    :List of 1
#>   ..$ food_exp:List of 2
#>   .. ..$ capped_lower: num 2
#>   .. ..$ capped_upper: num 2
#>  $ final_rows         : int 100
#>  $ final_cols         : int 3

The audit tells you 1 value was recoded to NA and how many observations were capped at each bound. Put this in your appendix and reviewers stop asking how you handled outliers.

Attributes available:

8.1 ihs_search() — find a variable

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

Searches the bundled crosswalk. Use it before writing any code, to find out what your variable is called in each round.

res <- ihs_search("household size")
#> Found 1 variable matching "household size".
res[, c("harmonised_name", "label", "ihs2_name", "ihs5_name", "ihs6_name", "n_rounds")]
#> # A tibble: 1 x 6
#>   harmonised_name label          ihs2_name ihs5_name ihs6_name n_rounds
#>   <chr>           <chr>          <chr>     <chr>     <chr>        <dbl>
#> 1 hhsize          Household size hhsize    hhsize    hhsize           5

Restrict to a round to see only variables that exist in it:

nrow(ihs_search("livestock", round = "IHS6"))
#> Found 44 variables matching "livestock".
#> [1] 44

Search a single field to cut noise:

nrow(ihs_search("expenditure", fields = "label"))
#> Found 5 variables matching "expenditure".
#> [1] 5

Returns a tibble with one row per matching variable, one ihs*_name column per round, n_rounds (how many rounds it appears in) and needs_review.

Watch out: keyword is a regular expression, so . and ( are special. Search for "exp", not "exp(".

8.2 ihs_crosswalk_check() — audit cross-round comparability

ihs_crosswalk_check(verbose = TRUE)

Prints a coverage report and returns the full crosswalk. Run it before committing to a pooled multi-round analysis.

cw <- ihs_crosswalk_check(verbose = FALSE)

nrow(cw)                    # total harmonised variables
#> [1] 6482
table(cw$n_rounds_avail)    # how many rounds each appears in
#> 
#>    1    2    3    4    5 
#> 3293  664 1165 1311   49

Read that table carefully. Coverage is not uniform — only a small core of variables appears in all five rounds, and most appear in one or two. Any claim of a “consistent 20-year series” needs to survive this table first.

# Variables available in every round
head(cw$harmonised_name[cw$n_rounds_avail == 5], 15)
#>  [1] "case_id"     "hhid"        "hhsize"      "plotid"      "poor"       
#>  [6] "region"      "reside"      "rexp_cat01"  "rexp_cat011" "rexp_cat012"
#> [11] "rexp_cat02"  "rexp_cat021" "rexp_cat022" "rexp_cat03"  "rexp_cat031"

c(
  `IHS3+IHS4+IHS5`      = sum(!is.na(cw$ihs3_name) & !is.na(cw$ihs4_name) &
                              !is.na(cw$ihs5_name)),
  `IHS4+IHS5+IHS6`      = sum(!is.na(cw$ihs4_name) & !is.na(cw$ihs5_name) &
                              !is.na(cw$ihs6_name)),
  `all five rounds`     = sum(cw$n_rounds_avail == 5)
)
#>  IHS3+IHS4+IHS5  IHS4+IHS5+IHS6 all five rounds 
#>            1637            2200              49

# Multi-select expansions, with their parent question
exp <- cw[!is.na(cw$ihs6_expansion_of),
          c("harmonised_name", "ihs6_expansion_of")]
head(exp, 5)
#> # A tibble: 5 x 2
#>   harmonised_name ihs6_expansion_of
#>   <chr>           <chr>            
#> 1 ag_d53__0       ag_d53           
#> 2 ag_d53__1       ag_d53           
#> 3 ag_d53__2       ag_d53           
#> 4 ag_d55__0       ag_d55           
#> 5 ag_d55__1       ag_d55
nrow(exp)
#> [1] 342

8.3 ihs_panel_ids() — look up design columns

ihs_panel_ids(round = "IHS5")

ihs_panel_ids("IHS6")
#> Standard ID columns for "IHS6":
#>     hh_id  indiv_id     ea_id    strata    weight 
#> "case_id"     "PID"   "ea_id"  "strata"  "hh_wgt"

Use "all" for the cross-round comparison shown in §3.

8.4 ihs_harmonise() — rename to standard names

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

The foundation of everything else. Renames raw columns to harmonised names using the crosswalk, preserving Stata variable labels, and adds an ihs_round column.

raw <- data.frame(
  case_id = c("a", "b"),
  hhsize  = c(4, 6),
  junk    = c(1, 2)
)

ihs_harmonise(raw, round = "IHS6")
#> Harmonised 2 columns for IHS6.
#>   case_id hhsize ihs_round
#> 1       a      4      IHS6
#> 2       b      6      IHS6

By default, columns absent from the crosswalk are dropped. Keep everything with extra = TRUE:

ihs_harmonise(raw, round = "IHS6", extra = TRUE)
#> Harmonised 2 columns for IHS6.
#>   case_id hhsize junk ihs_round
#> 1       a      4    1      IHS6
#> 2       b      6    2      IHS6

Watch out:

• Matching is case-insensitive, and each raw column is claimed by at most one crosswalk entry.
• A warning that no columns mapped almost always means the wrong round.
• extra = FALSE is destructive. If you need a variable that is not in the crosswalk, use extra = TRUE and open an issue so it can be added.

8.5 ihs_merge() — join modules

ihs_merge(..., by = NULL, type = "left")

hh <- data.frame(case_id = c("A", "B", "C"), hhsize = c(4, 6, 3))
ag <- data.frame(case_id = c("A", "B", "D"), harvest_kg = c(120, 340, 90))

merged <- ihs_merge(hh, ag)
#> Auto-detected join columns: "case_id"
#> Merged 2 data.frames: 3 rows, 3 columns.
merged
#>   case_id hhsize harvest_kg
#> 1       A      4        120
#> 2       B      6        340
#> 3       C      3         NA

type may be "left" (default), "inner" or "full". Row counts at each step are recorded:

attr(merged, "ihs_merge_log")
#> [[1]]
#> [[1]]$step
#> [1] 0
#> 
#> [[1]]$rows
#> [1] 3
#> 
#> 
#> [[2]]
#> [[2]]$step
#> [1] 1
#> 
#> [[2]]$rows_before
#> [1] 3
#> 
#> [[2]]$rows_after
#> [1] 3
#> 
#> [[2]]$rows_right
#> [1] 3

Watch out: columns present in more than one input but not used as join keys get dplyr’s .x/.y suffixes. ihs_merge() warns when this happens, because it silently breaks downstream auto-detection — once hh_wgt becomes hh_wgt.x, ihs_svydesign() can no longer find the survey weight:

a <- data.frame(case_id = c("A", "B"), region = 1:2, x = 1:2)
b <- data.frame(case_id = c("A", "B"), region = 1:2, y = 3:4)

names(suppressWarnings(ihs_merge(a, b)))
#> Auto-detected join columns: "case_id"
#> Merged 2 data.frames: 2 rows, 5 columns.
#> [1] "case_id"  "region.x" "x"        "region.y" "y"

Fix it by dropping the duplicate before merging, or by adding the shared column to by:

names(ihs_merge(a, b, by = c("case_id", "region")))
#> Merged 2 data.frames: 2 rows, 4 columns.
#> [1] "case_id" "region"  "x"       "y"

8.6 ihs_standardize_missing() — recode missing codes

ihs_standardize_missing(data)

Recodes -99, -98, -97, 999, 998 and 997 to NA in numeric columns.

df <- data.frame(age = c(34, -99, 41, 998), village = c("A", "B", "C", "D"))
clean <- ihs_standardize_missing(df)
clean$age
#> [1] 34 NA 41 NA
attr(clean, "ihs_missing_conversions")
#> $age
#> [1] 2

Watch out: 999 is a legitimate value for some variables — a plot area in square metres, a price in kwacha. This is a blanket recode. Check your variables first, and if in doubt pass only the affected columns.

8.7 ihs_winsorize() — cap outliers

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

Caps values below the lower and above the upper percentile, writing the result to a new _w column.

set.seed(42)
df <- data.frame(
  region = rep(c("North", "South"), each = 50),
  cons   = c(rnorm(50, 100, 10), rnorm(50, 500, 50))
)

global    <- ihs_winsorize(df, vars = "cons", probs = c(0.05, 0.95))
by_region <- ihs_winsorize(df, vars = "cons", by = "region",
                           probs = c(0.05, 0.95))

# The raw column is untouched either way
identical(global$cons, df$cons)
#> [1] TRUE

Why stratify. A single national 99th percentile treats the richest rural households as outliers, because the urban distribution sits far above them. Compare the maximum retained in the North under each approach:

c(global    = max(global$cons_w[global$region == "North"]),
  stratified = max(by_region$cons_w[by_region$region == "North"]))
#>     global stratified 
#>   122.8665   117.2254

Stratifying preserves the shape of both distributions. This matters enormously for inequality and poverty measures.

8.8 ihs_clean() — the wrapper

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

Runs ihs_standardize_missing() and then ihs_winsorize(), collecting both audit logs into a single ihs_audit attribute. Use it unless you need finer control.

result <- ihs_clean(df, winsorize_vars = "cons", winsorize_by = "region")
names(attr(result, "ihs_audit"))
#> [1] "initial_rows"        "initial_cols"        "missing_conversions"
#> [4] "winsorized_vars"     "final_rows"          "final_cols"

Watch out: the argument is winsorize_vars, not winsorize_cols, and the grouping argument is winsorize_by, not strata_col. There is no missing_cols argument — missing-code recoding always applies to every numeric column.

8.9 ihs_convert_units() — harvest units to kilograms

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

Converts reported harvest quantities to kilograms using 1,608 official NSO conversion factors, keyed on crop, unit, region and shelling condition.

harvest <- data.frame(
  crop_code = c(1, 1, 1),
  unit_code = c(1, 2, 3),
  quantity  = c(100, 2, 5),
  region    = c(1, 2, 2)
)

ihs_convert_units(harvest, qty_col = "quantity",
                  unit_col = "unit_code", crop_col = "crop_code")
#>   crop_code unit_code quantity region quantity_kg
#> 1         1         1      100      1         100
#> 2         1         2        2      2         100
#> 3         1         3        5      2         450

Two pails of maize is 100 kg; five of the next unit up is 450 kg. Summing the raw quantity column would have given 107 of nothing in particular.

Matching rules, applied in order:

1. exact match on crop, unit, region and condition;
2. same crop, unit and region, falling back through the condition preference order shelled (1) → not applicable (3) → unshelled (2);
3. the same two steps against the Central region, used as the national fallback when a crop-unit pair has no factor for the row’s own region.

A region column is detected automatically. Without one, every row is priced at Central-region factors and the function says so — applying Central factors to Northern or Southern harvests biases every converted quantity.

There is one exception to the crop-specific rule. Unit code 1 is KILOGRAM, and a kilogram is a kilogram whatever the crop, so quantities already reported in kilograms convert even for crops the factor table does not list — including those introduced in IHS6:

kg <- data.frame(crop_code = c(1, 48), unit_code = c(1, 1), quantity = c(7, 40))
ihs_convert_units(kg, "quantity", "unit_code", "crop_code")
#> No region column found; using Central-region conversion factors for all rows.
#> i Add a `region` column to use region-specific factors.
#>   crop_code unit_code quantity quantity_kg
#> 1         1         1        7           7
#> 2        48         1       40          40

The package verifies this against the bundled table rather than assuming it: if a future NSO release gave the kilogram unit a crop-specific factor, the rule switches itself off.

Unmappable combinations return NA, never a guess, and the function names them:

odd <- data.frame(crop_code = 999, unit_code = 999, quantity = 10)
suppressWarnings(
  ihs_convert_units(odd, "quantity", "unit_code", "crop_code")
)
#> No region column found; using Central-region conversion factors for all rows.
#> i Add a `region` column to use region-specific factors.
#>   crop_code unit_code quantity quantity_kg
#> 1       999       999       10          NA

Set unmapped = "error" to make failures fatal in a production pipeline, or "ignore" to silence the warning once you have understood it.

8.10 ihs_aggregate() — roll up to the household

ihs_aggregate(data, group_col = "case_id")

members <- data.frame(
  case_id   = c("A", "A", "B", "B", "B"),
  income    = c(1000, 500, 200, 300, 250),
  has_radio = c(0, 1, 0, 0, 0),
  district  = c("Lilongwe", "Lilongwe", "Blantyre", "Blantyre", "Blantyre")
)

ihs_aggregate(members, group_col = "case_id")
#> i Aggregating data by `case_id`...
#> # A tibble: 2 x 4
#>   case_id income has_radio district
#>   <chr>    <dbl>     <dbl> <chr>   
#> 1 A         1500         1 Lilongwe
#> 2 B          750         0 Blantyre

Rules. Continuous numeric columns are summed; 0/1 columns become a logical OR; logical columns become any(); character and factor columns take their single distinct value, or the distinct values joined with " | "; anything else is dropped with a warning.

Watch out: summing is right for quantities and expenditures and wrong for ages, prices and rates. Select your columns before calling, or aggregate those yourself.

8.11 ihs_deflate() — nominal to real

ihs_deflate(data, value_cols, round = NULL, base_year = 2019)

Converts nominal kwacha to constant-price kwacha so figures from different rounds can be compared.

pooled <- data.frame(
  food_exp  = c(1000, 1000, 1000, 1000, 1000),
  ihs_round = c("IHS2", "IHS3", "IHS4", "IHS5", "IHS6")
)

ihs_deflate(pooled, value_cols = "food_exp")
#> Auto-detected round(s) from `ihs_round`: "IHS2", "IHS3", "IHS4", "IHS5", and
#> "IHS6"
#> ! CPI for 2024 are provisional and may be revised.
#> i Bundled series retrieved 2026-07-29. Rebuild with 'data-raw/02_build_cpi.R'
#>   to refresh.
#> Deflated `food_exp` across 5 round(s) to base year 2019.
#>   food_exp ihs_round food_exp_real
#> 1     1000      IHS2     7521.0590
#> 2     1000      IHS3     4183.4003
#> 3     1000      IHS4     1371.4787
#> 4     1000      IHS5     1000.0000
#> 5     1000      IHS6      408.9428

1,000 kwacha in 2004 bought what roughly 7,500 bought in 2019; 1,000 kwacha in 2024 bought about 409-worth. Comparing the nominal column across rounds would have reversed the story entirely.

Because ihs_harmonise() adds ihs_round, pooled data deflates in one call with no arguments. For a single-round frame, name the round:

ihs_deflate(data.frame(v = 1000), value_cols = "v", round = "IHS6")
#> ! CPI for 2024 are provisional and may be revised.
#> i Bundled series retrieved 2026-07-29. Rebuild with 'data-raw/02_build_cpi.R'
#>   to refresh.
#> `v`: deflation factor = 0.4089 (IHS6 -> 2019)
#>      v   v_real
#> 1 1000 408.9428

The CPI series. World Bank WDI FP.CPI.TOTL for Malawi, rebased to 2019 = 100, covering 2004–2025. Rounds map to their survey midpoint year: IHS2 → 2004, IHS3 → 2010, IHS4 → 2016, IHS5 → 2019, IHS6 → 2024.

The table records where each figure came from and whether it is still liable to revision:

cpi <- read.csv(system.file("extdata", "mw_cpi_annual.csv", package = "ihsMW"))
tail(cpi[, c("year", "cpi_index", "retrieved", "provisional")], 3)
#>    year cpi_index  retrieved provisional
#> 20 2023   185.001 2026-07-29       FALSE
#> 21 2024   244.533 2026-07-29        TRUE
#> 22 2025   313.908 2026-07-29        TRUE

The World Bank revises recent observations as national statistics are finalised, so the two most recent years are marked provisional. ihs_deflate() says so whenever a round you are deflating depends on one — as IHS6 (2024) currently does. These are the best figures available; just be aware they can move, and record which vintage you used. Refresh with data-raw/02_build_cpi.R.

Watch out: this is a national deflator over time. It does not adjust for price differences between districts or between urban and rural areas. If your analysis is sensitive to those, each round’s consumption aggregate publishes its own spatial index — IHS6 ships spatial_indexL (regional price level) and price_indexL (combined spatial-temporal) — and you should divide by that rather than relying on this function alone.

8.12 ihs_svydesign() — build the survey design

ihs_svydesign(data, weight_col = NULL, strata_col = NULL, psu_col = NULL)

Wraps survey::svydesign(), detecting the weight, stratum and PSU columns.

hh <- data.frame(
  case_id  = 1:20,
  ea_id    = rep(1:5, each = 4),
  strata   = rep(c("urban", "rural"), each = 10),
  hh_wgt   = runif(20, 0.5, 3),
  food_exp = rnorm(20, 5000, 1000)
)

dsgn <- ihs_svydesign(hh)
#> Auto-detected weight column: `hh_wgt`
#> Auto-detected strata column: `strata`
#> Auto-detected PSU column: `ea_id`
#> Creating survey design:
#> * Weights: `hh_wgt`
#> * Strata: `strata`
#> * PSU: `ea_id`
survey::svymean(~food_exp, dsgn, na.rm = TRUE)
#>          mean     SE
#> food_exp 4570 166.78

The same call works on every round: case-insensitive matching handles IHS2/IHS3 hhweight, IHS4+ hh_wgt, IHS2–IHS5 stratum and IHS6 strata.

If strata or PSU are missing the function degrades gracefully — to a clustered design, then to a simple weighted one — and tells you which it built. Read that message: a simple weighted design gives correct point estimates but standard errors that are too small.

8.13 ihs_report() — summary table

ihs_report(data, vars = NULL, by = NULL, weights = NULL)

hh <- data.frame(
  hhsize   = c(4, 6, 3, 5, 7, 2),
  food_exp = c(1200, 3400, 900, 2100, 4500, 700),
  region   = c("North", "North", "Central", "Central", "South", "South"),
  hh_wgt   = c(1.2, 0.8, 1.5, 1.1, 0.9, 1.3)
)

ihs_report(hh, vars = c("hhsize", "food_exp"))
#> Summary statistics for 2 variables.
#>   variable n     mean        sd median min  max pct_missing
#> 1   hhsize 6    4.500    1.8708    4.5   2    7           0
#> 2 food_exp 6 2133.333 1526.6521 1650.0 700 4500           0

Group and weight it:

ihs_report(hh, vars = "food_exp", by = "region", weights = "hh_wgt")
#> Summary statistics for 1 variable across 3 groups.
#>    region variable n     mean        sd median  min  max pct_missing
#> 1   North food_exp 2 2080.000 1077.7755   2300 1200 3400           0
#> 2 Central food_exp 2 1407.692  592.8569   1500  900 2100           0
#> 3   South food_exp 2 2254.546 1868.3311   2600  700 4500           0

With vars = NULL every numeric column is summarised, with weight-like columns excluded automatically.

Watch out: only the mean and SD are weighted. n, median, min, max and pct_missing describe the sample, not the population. For weighted quantiles or design-correct standard errors, use ihs_svydesign() and the survey package.

Workflow A — one round, weighted poverty statistics

library(ihsMW)
library(haven)
library(survey)

# 1. Read
demog <- haven::read_dta("IHS6/hh_mod_a_filt.dta")
cons  <- haven::read_dta("IHS6/ihs6_consumptionaggregates.dta")

# 2. Harmonise (adds `ihs_round`)
demog_h <- ihs_harmonise(demog, round = "IHS6")
cons_h  <- ihs_harmonise(cons,  round = "IHS6")

# 3. Merge, dropping columns that appear in both to avoid .x/.y suffixes
dupes  <- c("region", "district", "hhsize", "hh_wgt", "ihs_round")
merged <- ihs_merge(demog_h, cons_h[, setdiff(names(cons_h), dupes)])

# 4. Clean: recode missing codes, winsorize within region
clean <- ihs_clean(merged,
                   winsorize_vars = "rexp_cat011",
                   winsorize_by   = "region")

# 5. Deflate to 2019 prices
real <- ihs_deflate(clean, value_cols = "rexp_cat011_w")

# 6. Survey design
dsgn <- ihs_svydesign(real)

# 7. Report
ihs_report(real,
           vars    = c("hhsize", "rexp_cat011_w_real"),
           by      = "region",
           weights = "hh_wgt")

# Design-correct national mean with a standard error
survey::svymean(~rexp_cat011_w_real, dsgn, na.rm = TRUE)

# Document what cleaning did, for your appendix
str(attr(clean, "ihs_audit"))

Workflow B — pooling IHS4, IHS5 and IHS6

library(ihsMW)
library(haven)
library(dplyr)

read_round <- function(path, round) {
  haven::read_dta(path) |> ihs_harmonise(round = round)
}

ihs4 <- read_round("IHS4/ihs4_consumption_aggregate.dta", "IHS4")
ihs5 <- read_round("IHS5/ihs5_consumption_aggregate.dta", "IHS5")
ihs6 <- read_round("IHS6/ihs6_consumptionaggregates.dta", "IHS6")

# Check comparability BEFORE pooling
cw <- ihs_crosswalk_check(verbose = FALSE)
comparable <- cw$harmonised_name[
  !is.na(cw$ihs4_name) & !is.na(cw$ihs5_name) & !is.na(cw$ihs6_name)
]

keep <- intersect(comparable, Reduce(intersect, list(names(ihs4), names(ihs5), names(ihs6))))
keep <- union(keep, "ihs_round")

pooled <- bind_rows(ihs4[, keep], ihs5[, keep], ihs6[, keep])

# One call deflates every round correctly, using `ihs_round`
pooled <- ihs_deflate(pooled, value_cols = "rexp_cat011")

# Real food consumption by round, on a common 2019 basis
ihs_report(pooled, vars = "rexp_cat011_real", by = "ihs_round",
           weights = "hh_wgt")

The comparable step is the important one. Pooling variables that do not actually exist in all three rounds produces a series with silent structural breaks.

Workflow C — agricultural production per household

library(ihsMW)
library(haven)

# Plot-level harvest, and the household roster for region
harvest <- haven::read_dta("IHS6/ag_mod_g.dta")
roster  <- haven::read_dta("IHS6/hh_mod_a_filt.dta")

# Attach region so region-specific conversion factors are used
harvest <- ihs_merge(harvest, roster[, c("case_id", "region")], by = "case_id")

# Convert mixed units to kilograms
harvest_kg <- ihs_convert_units(
  harvest,
  qty_col  = "ag_g13a",
  unit_col = "ag_g13b",
  crop_col = "crop_code",
  unmapped = "warn"
)

# Sum to household level
hh_production <- ihs_aggregate(
  harvest_kg[, c("case_id", "ag_g13a_kg")],
  group_col = "case_id"
)

head(hh_production)

Note the merge in step two: without a region column, every row is converted at Central-region factors.

Diagnosing anything else

# What do I actually have?
names(data)
str(data[, 1:10])

# Did harmonisation do anything?
sum(names(data) %in% ihs_crosswalk_check(verbose = FALSE)$harmonised_name)

# Where did my rows go?
attr(merged, "ihs_merge_log")

# What did cleaning change?
str(attr(cleaned, "ihs_audit"))