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Introduction
An R consultant turns a client question into a checked analysis and a usable handoff. The deliverable may be a report, a repeatable data pipeline, a model evaluation, or training; knowing package syntax alone does not define a successful engagement. This guide uses one fictional order-reporting project to connect technical work with scope, acceptance criteria and communication.
Tested with R 4.5.1 and dplyr 1.1.4. Run the code blocks in order. Install the named packages before starting; inputs are supplied below.
Agree on the decision and acceptance criteria
Suppose a client wants weekly revenue by region. Before writing code, agree on the reporting period, order grain, currency, refunds, missing amounts, and who approves the metric. The fictional fixture below represents one row per order, in one currency, after refund treatment has already been agreed.
orders <- data.frame(order_id = 1:5, region = c("East", "East", "West", "West", "West"),
amount = c(100, 150, 80, NA, 20))
stopifnot(!anyNA(orders$order_id), !anyDuplicated(orders$order_id),
is.numeric(orders$amount), !anyNA(orders$region))
print(c(rows = nrow(orders), missing_amounts = sum(is.na(orders$amount))))
# rows 5; missing_amounts 1
Write an acceptance criterion such as: “The report accounts for every source order, reports missing amounts separately, and can be rerun from the supplied input.” These checks are specific enough for a client to review. They do not establish whether the source itself is complete.
Build a small result and reconcile it
Keep known revenue separate from a complete total. An unknown amount is not zero.
report <- orders |>
dplyr::group_by(region) |>
dplyr::summarise(orders = dplyr::n(), missing = sum(is.na(amount)),
known_revenue = sum(amount, na.rm = TRUE),
complete_revenue = if (anyNA(amount)) NA_real_ else sum(amount),
.groups = "drop")
print(as.data.frame(report))
# East: 2 orders, 0 missing, known and complete revenue 250
# West: 3 orders, 1 missing, known revenue 100, complete revenue NA
stopifnot(sum(report$orders) == nrow(orders), sum(report$known_revenue) == 350)
stopifnot(identical(report$complete_revenue, c(250, NA_real_)))
high_value <- orders |> dplyr::filter(amount > 100) |> dplyr::select(order_id, region)
stopifnot(identical(high_value$order_id, 2L))
Filter while the required column still exists, then select the final fields. A selection that removes amount before the filter fails. A useful client-facing explanation here is: “East has 250 of recorded revenue; West has a known subtotal of 100, with one amount requiring resolution.” The complete West total remains unknown.
Test the contract and communicate uncertainty
A good handoff includes failure cases. Duplicate order keys must be investigated rather than silently counted twice.
check_orders <- function(x) {
if (!is.data.frame(x) || !all(c("order_id", "region", "amount") %in% names(x)))
stop("Required order columns are missing")
if (!is.numeric(x$amount) || any(!is.finite(x$amount) & !is.na(x$amount)))
stop("Amounts must be finite numeric values or NA")
if (anyNA(x$order_id) || anyDuplicated(x$order_id) || anyNA(x$region))
stop("Order identifiers and regions need review")
invisible(TRUE)
}
check_orders(orders)
check_orders(orders[FALSE, ])
bad <- rbind(orders, orders[1, ])
stopifnot(inherits(tryCatch(check_orders(bad), error = identity), "error"))
bad <- orders; bad$amount <- as.character(bad$amount)
stopifnot(inherits(tryCatch(check_orders(bad), error = identity), "error"))
Empty input is valid for the validator but should produce a visible “no orders supplied” report status. If negative amounts are allowed, do not describe a partial sum as a lower bound. Investigate missingness and reconcile to an independent source total when available.
Deliver work someone else can maintain
Provide the agreed report, source-to-output script, data dictionary, validation results, setup instructions and tested package versions. Include a short decision log and a list of unresolved data questions. Ask the client to run the handoff in their approved environment before closing the project.
For a first portfolio project, use public or synthetic data and label it accurately. Explain the question, checks and limitations rather than inventing revenue impact. Statistical modeling, forecasting or text sentiment should be separate scoped work with appropriate evaluation; a model fitting without an error does not establish that it answers the client’s question. Set access permissions, confidentiality, ownership, deadlines and change requests with the client before handling real data.
