ces

https://github.com/laurenceomfoisy/ces

Repository

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# ces: Canadian Election Study Data Package




The `ces` package provides easy access to Canadian Election Study (CES) datasets for analysis in R. The Canadian Election Study has been conducted during federal elections since 1965, providing valuable data for political scientists, researchers, and students studying Canadian politics and voting behavior.

## Installation

You can install the released version of ces from CRAN with:

```r
install.packages("ces")
```

And the development version from GitHub with:

```r
# install.packages("devtools")
devtools::install_github("laurenceomfoisy/ces")
```

## Features

- Easy access to CES datasets from various years (1965-2021)
- Consistent data format across years
- Simple filtering and subsetting functions
- Automatic generation of variable codebooks
- Complete preservation of variable metadata (labels, value labels)
- Metadata examination tools
- Export capabilities for sharing dataset documentation
- Official PDF codebook downloads for all datasets

## Basic Usage

```{r eval=FALSE}
library(ces)

# See available datasets
list_ces_datasets(details = TRUE)

# Download 2019 CES data
ces_2019 <- get_ces("2019")

# Create a codebook
codebook <- create_codebook(ces_2019)
head(codebook)

# Get subset of variables about voting behavior
voting_data <- get_ces_subset("2019", variables = c("vote_choice", "turnout"))

# Download the official codebook PDF
download_pdf_codebook("2019")

# Download a single dataset
download_ces_dataset("2019", path = "~/CES_data")

# Download all datasets at once
download_all_ces_datasets(path = "~/CES_data")
```

## Example Analysis

```{r eval=FALSE}
library(ces)
library(dplyr)
library(ggplot2)

# Get data
ces_data <- get_ces("2019")

# Create codebook to find variables
codebook <- create_codebook(ces_data)

# Find variables related to leader evaluations
leader_vars <- codebook %>%
  filter(grepl("leader|trudeau|scheer", question, ignore.case = TRUE)) %>%
  pull(variable)

# Get subset with these variables
leader_data <- get_ces_subset("2019", variables = leader_vars)

# Example plot (depending on actual variable names)
if ("leader_rating_trudeau" %in% names(leader_data) && 
    "leader_rating_scheer" %in% names(leader_data)) {
  
  # Basic comparison plot
  leader_data %>%
    select(leader_rating_trudeau, leader_rating_scheer) %>%
    gather(key = "leader", value = "rating") %>%
    ggplot(aes(x = rating, fill = leader)) +
    geom_density(alpha = 0.5) +
    labs(title = "Leader Rating Distributions",
         x = "Rating", y = "Density") +
    theme_minimal()
}
```

## Available Datasets

The package currently provides access to CES datasets from 1965 to 2021:

```{r eval=FALSE}
# List all datasets
list_ces_datasets()

# Get detailed information
list_ces_datasets(details = TRUE)
```

## Working with Codebooks

CES datasets contain many variables with complex coding. The `create_codebook()` function helps you understand these variables:

```{r eval=FALSE}
# Create and explore a codebook
ces_data <- get_ces("2019")
codebook <- create_codebook(ces_data)

# Find all variables related to voting
voting_vars <- dplyr::filter(codebook, grepl("vote|ballot", question, ignore.case = TRUE))

# Export the codebook
export_codebook(codebook, "ces_2019_codebook.csv")
```

## Acknowledgments and Disclaimers

### Data Sources
This package accesses Canadian Election Study data from multiple sources:

- **[Borealis Data repository](https://borealisdata.ca/)** - the primary institutional repository hosting most CES datasets
- **[Canadian Election Study website](https://ces-eec.arts.ubc.ca/)** - the official CES project website hosting additional datasets

We gratefully acknowledge both Borealis Data and the Canadian Election Study team for maintaining and providing access to these valuable datasets.

The package author is not affiliated with the Canadian Election Study, Borealis Data, or the University of British Columbia. Users of this package should properly cite the original Canadian Election Study data in their research publications.

### Package Development
This package was developed with assistance from Claude Sonnet 3.7, an AI assistant by Anthropic. Claude helped with structuring the package, writing documentation, and implementing the core functionality. The collaboration between the human author and AI demonstrates how these tools can be used to create helpful resources for the research community.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Citation

If you use this package in your research, please cite it:

```{r eval=FALSE}
citation("ces")
```

Additionally, please cite the original Canadian Election Study data according to their citation guidelines.

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