Easily connect to Statistics Canada’s Web Data Service with R. Find and access open economic data (formerly known as CANSIM tables, now identified by Product IDs (PID)) which are accessible as a data frame, directly in the user’s R environment.
For people less comfortable with R and to allow more people to have access to our package, we have also developed a Shiny application.Through the same logic present in our package, researchers can retrieve data from Statistics Canada.
statcanR ExploR is available [here]
The released version of statcanR package is accessible through CRAN and devtools.
install.packages("statcanR")
install.packages("devtools")
devtools::install_github('warint/statcanR')This section presents an example of how to use the statcanR R package
and its functions: statcan_search(), statcan_data(), and
statcan_download_data().
The following example is provided to illustrate how to use the functions. It consists in collecting some descriptive statistics about the Canadian Labour Force at the federal, provincial and industrial levels, on a monthly basis.
To identify a relevant table, the statcan_search() function can be used by using a keyword or set of keywords and specifying the language in which the data will be presented (English or French). Below is an example that reveals the data tables we could be interested in:
library(statcanR)
statcan_search(c("federal","expenditures","objectives"),"eng")Notice that for each corresponding table, the unique table number identifier is also presented. Let's focus the first table out of the two that appear, which contains data on Federal expenditures on science and technology, by socio-economic objectives. Once this table number is identified (‘27-10-0014-01’), the statcan_data() function is easy to use in order to collect the data, as following:
library(statcanR)
mydata <- statcan_data("27-10-0014-01","eng")For the statcan_download_data() function there is no difference on how
to use it, the only difference is that this function allow you to
download the data in a csv file on top of having the data in your
environment.
library(statcanR)
mydata <- statcan_download_data("27-10-0014-01","eng")Tutorial made by Professor Charles Saunders, Director of Master of Financial Economics Program at Western University biography
Thanks!
https://www.youtube.com/embed/z9TDUlgT5lc
This licence is issued on behalf of His Majesty the King in Right of Canada, as represented by the Minister for Statistics Canada (“Statistics Canada”) to you (an individual or a legal entity that you are authorized to represent).
Statistics Canada may modify this licence at any time, and such modifications shall be effective immediately upon posting of the modified licence on the Statistics Canada website. Your use of the Information will be governed by the terms of the licence in force as of the date and time you accessed the Information.
Please refer to the terms of licence before using the Information.
Statistics Canada has a specific procedure regarding the acknowledgment of source :
You shall include and maintain the following notice on all licensed rights of the Information:
Source: Statistics Canada, name of product, reference date. Reproduced and distributed on an "as is" basis with the permission of Statistics Canada.
Where any Information is contained within a Value-added Product, you shall include on such Value-added Product the following notice:
Adapted from Statistics Canada, name of product, reference date. This does not constitute an endorsement by Statistics Canada of this product.
To cite statcanR package in your work:
Warin, T. (2024). Access Statistics Canada’s Open Economic Data for Statistics and Data Science Courses. Technology Innovations in Statistics Education, 15(1). http://dx.doi.org/10.5070/T5.1868 Retrieved from https://escholarship.org/uc/item/9jr7k5hp
@article{warin_access_2024,
title = {Access {Statistics} {Canada}’s {Open} {Economic} {Data} for {Statistics} and {Data} {Science} {Courses}},
volume = {15},
url = {https://escholarship.org/uc/item/9jr7k5hp},
doi = {10.5070/T5.1868},
abstract = {This article is about the two conflicting goals when teaching statistics or data science courses based on real-world data in a business school environment. We propose to look at structured socio-economic data about the Canadian economy. Canada was ranked 8th in 2017 by Open Data Watch (Government of Canada) for its data accessibility policy. Statistics Canada offers several ways to access data across its over 11,000 data tables. We built an R package to ease access to Statistics Canada's open economic data. With this package, we offer students another option to collect data about the Canadian economy.},
language = {en},
number = {1},
urldate = {2024-01-17},
journal = {Technology Innovations in Statistics Education},
author = {Warin, Thierry},
month = jan,
year = {2024},
file = {Full Text PDF:/Users/thierrywarin/Zotero/storage/7LNXFPKL/Warin - 2024 - Access Statistics Canada’s Open Economic Data for .pdf:application/pdf},
}A previous version of this package was developed with Romain Le Duc. This version has benefitted from Thibault Senegas's contribution. The author would like to thank the Center for Interuniversity Research and Analysis of Organizations (CIRANO, Montreal) for its support, as well as Thibault Senegas, Jeremy Schneider, Marine Leroi, Martin Paquette and Romain Le Duc. However, errors and omissions are his.
When you file a bug report, please spend some time making it easy for me to follow and reproduce. The more time you spend on making the bug report coherent, the more time I can dedicate to investigate the bug as opposed to the bug report.
To get started, consider either adding a new example or enhancing the existing documentation.
If you're interested in submitting a Pull Request to include your own functions, please include the following:
- The code for the new function(s), complete with roxygen annotations and sample usage.
- A dedicated section in the relevant vignette that explains how to utilize the new function.
To ensure your changes are compliant, run rhub::check_for_cran() using rhub. After submission, your Pull Request will undergo automated evaluation via GitHub Actions, allowing you to monitor for any issues.