Getting started with statcanR

Overview

statcanR connects R to Statistics Canada’s Web Data Service (WDS). This vignette walks through a complete workflow: describing the data you need, choosing a table, understanding its identifier, downloading it, inspecting the result, and optionally saving a CSV copy.

The package supports four common tasks:

Find likely tables from an ordinary-language description.
Search for exact keywords in the official table catalogue.
Download a complete table in English or French into R.
Save the downloaded table as a CSV file.

The four public functions have distinct purposes:

Function	Use it when…	Result
`statcan_find()`	You can describe the subject, geography, or period you need	A ranked data frame of likely tables and reasons for each match
`statcan_search()`	You know words that occur in the official table title	An interactive table of exact keyword matches
`statcan_data()`	You want the complete table in R	A data frame
`statcan_download_data()`	You want the data frame and a CSV copy	A data frame with the saved file path attached

The download functions retrieve a complete table, not a filtered selection of observations. A Statistics Canada table can be large. It is therefore useful to identify the right table before starting the download.

Two concepts to know first

Table number and Product ID

Statistics Canada displays table numbers such as 10-10-0001-01. The first eight digits form the WDS Product ID (PID), 10100001; the final 01 identifies the displayed view. statcanR accepts either the displayed table number or the eight-digit PID:

table_data <- statcan_data("10-10-0001-01", "eng")
table_data <- statcan_data("10100001", "eng")

These two calls request the same table.

Language

Use lang = "eng" for an English table and lang = "fra" for a French table. The language controls the table contents and labels returned by Statistics Canada; it is not a translation performed by statcanR.

Install or upgrade

The command used for a first installation also upgrades an older CRAN installation:

install.packages("statcanR")

Then load the package:

library(statcanR)

If the package was loaded while you upgraded it, restart the R session before calling library(statcanR) again. Check which version R will use with:

packageVersion("statcanR")

Version 0.3.0 preserves the familiar calls from earlier releases. In particular, code that supplies a table number and a language to statcan_data() or statcan_download_data() continues to work.

Step 1: describe the table you need

Use statcan_find() when you know what data you want but do not know the official title or table number. Write a short request containing as much of the subject, Canadian geography, and period as you know:

matches <- statcan_find(
  "R&D expenditures in Quebec since 2020",
  lang = "eng",
  n = 5
)

matches[, c("title", "id", "score", "match_reason")]

This request is interpreted as:

a subject: research and development expenditures;
a geography: Quebec; and
a date requirement: the table must include 2020.

The result is an ordinary data frame. rank orders the candidates, score summarizes the strength of each match, and match_reason states which clues were confirmed. The score helps with discovery; it is not a measure of data quality. Read the candidate titles because two tables can represent different valid interpretations of the same short request.

When a province or territory appears in the query, statcan_find() checks WDS metadata to confirm that the table includes it. start_date and end_date describe the coverage of the whole table. These checks help choose a table; they do not select rows. The download functions still retrieve the complete table, after which you filter its geography and reference-period columns.

French requests and French catalogue titles are supported too:

matches_fr <- statcan_find(
  "Dépenses de R-D au Québec depuis 2020",
  lang = "fra"
)

The parser recognizes common wording and abbreviations, but it is deliberately simple and predictable. A concise request generally works better than a long question. If the results are too broad, add a more specific subject term. If there are no results, remove a detail or try an official keyword.

Search exact title keywords

Use statcan_search() when you know words that occur in the official title. It searches titles without regard to letter case:

statcan_search(
  c("federal", "expenditures", "objectives"),
  lang = "eng"
)

The result is an interactive table displayed in the RStudio Viewer or a browser. The most important columns are:

title: the official table title;
id: the table number to pass to a download function;
release_date: the catalogue release date; and
lang: the language searched.

When several keywords are supplied, all of them must occur in the title. This makes searches precise, but it can also produce no matches. If that happens, remove one keyword or use a broader term:

statcan_search("expenditures", lang = "eng")

Search French titles by using lang = "fra":

statcan_search(c("dépenses", "fédérales"), lang = "fra")

The catalogue is cached for 24 hours in R’s platform-appropriate user cache directory. statcan_find() caches the table metadata used for geography checks for seven days. These caches make repeated searches faster and avoid unnecessary requests to Statistics Canada. Set refresh = TRUE only when you specifically need fresh information:

statcan_find(
  "population in Alberta since 2021",
  lang = "eng",
  refresh = TRUE
)

If WDS is temporarily unavailable, catalogue searches use the most recent valid cache and issue a warning. If geography metadata cannot be refreshed, statcan_find() uses valid cached metadata where possible. Candidates whose geography could not be checked have geography_match = NA; the match explanation makes that uncertainty explicit.

Step 2: download a complete table

After choosing an identifier, pass it and the desired language to statcan_data(). This example uses a relatively small table that is convenient for learning:

table_data <- statcan_data("10-10-0001-01", lang = "eng")

The function downloads and unpacks the current full-table CSV archive, then returns a data frame. Start by examining its dimensions, names, and first observations:

dim(table_data)
names(table_data)
head(table_data)

The exact columns depend on the table selected. statcanR applies a few consistent rules:

the first column is named REF_DATE;
annual, monthly, daily, and fiscal reference periods are converted to Date values when they can be interpreted safely;
coordinate columns remain character values so leading zeros and compound coordinates are not lost; and
INDICATOR contains the official table title read from its metadata.

For example, you can select observations from 2020 onward with ordinary R subsetting:

recent_data <- table_data[
  !is.na(table_data$REF_DATE) &
    table_data$REF_DATE >= as.Date("2020-01-01"),
]

To download the French version of the table, change the language:

table_fr <- statcan_data("10-10-0001-01", lang = "fra")

Most source column names remain in the selected language, so do not assume that every English column name has an identical French equivalent.

Step 3: save a CSV copy when needed

If you only need to analyse the data in the current R session, statcan_data() is sufficient. Use statcan_download_data() when you also need a CSV file.

Existing two-argument calls save the file in the current working directory:

table_data <- statcan_download_data("10-10-0001-01", "eng")
getwd()

This creates statcan_10100001_eng.csv. To keep project files organized, create a dedicated directory and pass it through path:

output_dir <- file.path(tempdir(), "statcanR-data")
dir.create(output_dir, recursive = TRUE, showWarnings = FALSE)

table_data <- statcan_download_data(
  "10-10-0001-01",
  "eng",
  path = output_dir
)

attr(table_data, "statcan_file")

The output directory must already exist. The function returns the same data frame as statcan_data() and stores the exact CSV path in its statcan_file attribute. The CSV uses UTF-8 encoding, excludes R row names, and writes missing values as empty fields.

Compatibility with earlier statcanR scripts

The update does not require you to rewrite established calls:

# This familiar two-argument form remains valid.
table_data <- statcan_data("10-10-0001-01", "eng")

# This also remains valid and saves into the working directory.
table_data <- statcan_download_data("10-10-0001-01", "eng")

The optional path argument extends statcan_download_data() without changing the meaning of the original arguments. Both hyphenated table numbers and eight-digit PIDs are accepted.

Troubleshooting

The package validates inputs before downloading and reports network or service problems explicitly. Common issues include:

Message or symptom	What to check
No natural-language results	Keep a clear subject, but remove a geography or date constraint; then inspect broader candidates
No exact keyword results	Try fewer keywords, check the selected language, or use a broader official term
Invalid `tableNumber`	Use a displayed number such as `10-10-0001-01` or an eight-digit PID such as `10100001`
Invalid `lang`	Use exactly `"eng"` or `"fra"`
Output directory does not exist	Create the directory before supplying it through `path`
WDS is unavailable	Check the internet connection and try again later; catalogue search may use a valid cache
Download takes a long time	The function retrieves the complete table, which may be large

Network failures, invalid tables, unexpected API responses, and malformed archives stop with messages that identify the affected Product ID. Temporary files created by a call are removed when it finishes; other files in the R session’s temporary directory are left untouched.