knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of {canlang} is to easily share language data collected in the 2016 Canadian census. This data was retreived from the 2016 Canadian census data set using the {cancensus} R package.
This package contains three data sets:
can_lang
: Contains the counts of the total number of Canadians that report each language as their mother tongue, which language they speak most often at home, which language they use most often at work, and which language they have knowledge for.
region_lang
: For each census division, it contains the counts of how many Canadians report each language as their mother tongue, which language they speak most often at home, which language they use most often at work, and which language they have knowledge for.
region_data
: For each census division, it contains the statistics for number of households, land area, population and number of dwellings.
You can install the development version from GitHub with:
# install.packages("devtools") devtools::install_github("ttimbers/canlang")
can_lang
The data set can_lang
contains the counts of the total number of Canadians that report each language as their mother tongue, which language they speak most often at home, which language they use most often at work, and which language they have knowledge for. This data was recorded in the 2016 Census:
library(canlang) head(can_lang)
library(ggplot2) ggplot2::ggplot(data = can_lang, aes(x = most_at_home, y = mother_tongue, colour = category, shape = category)) + geom_point(alpha = 0.7) + scale_color_manual(values = c("blue3","red3","black")) + scale_y_log10(name = "Number of Canadians reporting the \n language as their mother tongue", labels = scales::comma) + scale_x_log10(name = "Number of Canadians speaking the language \n as their primary language at home", labels = scales::comma) + annotation_logticks() + theme_bw()
region_lang
For each census metropolitan area (CMA), the data set region_lang
contains the counts of how many Canadians report each language as their mother tongue, which language they speak most often at home, which language they use most often at work, and which language they have knowledge for.
library(canlang) library(dplyr) region_lang %>% filter(region == "Vancouver") %>% arrange(desc(mother_tongue)) %>% head()
region_data
For each census metropolitan area (CMA), the data set region_data
contains the statistics for number of households, land area, population and number of dwellings.
library(canlang) library(dplyr) region_data %>% arrange(desc(population)) %>% head()
We have included several different plain text files, an excel files and a SQLite database file in this repo to be used for practice importing from these filetypes. Specifically, they are:
can_lang.csv
: the same dataset available via canlang::can_lang
stored as a vanilla .csv
file.can_lang-meta-data.csv
: the same dataset available via canlang::can_lang
stored as a vanilla .csv
file with two rows of metadata that should be skipped.can_lang.tsv
: the same dataset available via canlang::can_lang
stored as a .tsv
(tab separated) file and has no column names.can_lang.xlsx
: the same dataset available via canlang::can_lang
stored as a .xlsx
file. Can be read in using the {readxl} package.can_lang.db
: the same dataset available via canlang::can_lang
stored as a SQLite database (.db
) file. Can be read in using the {RSQLite} package.vancouver_lang.csv
& calgary_lang.csv
: data for Vancouver, BC and Calgary, AB, respectively, stored as a vanilla .csv
file.victoria_lang.csv
: data for Victoria, BC stored as a vanilla .tsv
file.kelowna_lang.csv
: data for Kelowna, BC stored as a .csv
file (csv2 flavour) with metadata in the header and footer that should be skipped.abbotsford_lang.xlsx
: data for Abbotsford, BC stored as a .xlsx
file where sheet 1 is the column names, and sheet 2 is the data with no column names. Can be read in using the {readxl} package.edmonton_lang.xlsx
: data for Edmonton, AB stored as a .xlsx
file where all the data is in sheet 1.The data-raw
directory contains the the scripts necessary to create everything in this package, including the R data objects and the plain text, excel and SQLite database files.
Data originally published in:
Package development resources:
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