clessnverse/README.Rmd at main · clessn/clessnverse · GitHub

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---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

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

# clessnverse

<!-- badges: start -->
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[![CRAN status](https://www.r-pkg.org/badges/version/wikiverse)](https://CRAN.R-project.org/package=wikiverse)
[![R-CMD-check](https://github.com/clessn/clessnverse/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/clessn/clessnverse/actions/workflows/R-CMD-check.yaml)

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clessnverse contains functions for data domestication, analysis and visualization along with functions specific to the research chair's projects.

## Disclaimer

**July 2023**: clessnverse is no longer under active development.

To avoid breaking dependencies, the package remains available "as is" with no warranty of any kind.

## Installation

To install the latest stable version of this package, run the following line in your R console:

``` r
remotes::install_github("clessn/clessnverse")
```

## Usage

`library("clessnverse")` will load the following packages:

-   [`hublot`](https://github.com/clessn/hublotr), to access clHub
-   [`clessnhub`](https://github.com/clessn/hublotr), to access clessn hub
-   [`locateip`](https://github.com/clessn/locateip), to locate IP addresses using [ip-api.com](https://ip-api.com/)
-   [`wikirest`](https://github.com/clessn/wikirest), to get data from the [Wikimedia REST API](https://wikimedia.org/api/rest_v1/)

## Examples

### Wrangle data

Normalize a continuous variable between 0 and 1

```{r wrangle}
library(clessnverse)

data <- tibble::tibble(a = c(1, 0, 2, 0), b = c(4, 0, 1, 0))

# Base R
sapply(data, normalize_min_max)

# Dplyr
library("dplyr")

data %>%
  mutate(across(c(a, b), normalize_min_max))
```

### Analyse data

```{r analyse}
run_dictionary(
  data.frame(colnames(attitude)),
  text = colnames(attitude),
  dictionary = quanteda::data_dictionary_LSD2015
) %>% head()
```

### Visualise data

```{r visualise}
library("clessnverse")

p  <- ggplot2::ggplot(data = ggplot2::mpg) +
  ggplot2::geom_point(mapping = ggplot2::aes(x = displ, y = cty, colour = class)) +
  ggplot2::labs(title = "Look at this graph!",
                subtitle = "What a great theme, eh?",
                caption = "Data: API Twitter \nCLESSN") +
  ggplot2::xlab("x axis label") +
  ggplot2::ylab("y axis label")

p + theme_clean_light()
p + theme_clean_dark()

p  <- ggplot2::ggplot(data = ggplot2::mpg) +
  ggplot2::geom_point(mapping = ggplot2::aes(x = displ, y = cty, colour = class)) +
  ggplot2::labs(title = "Look at this graph!",
                subtitle = "What a great look, eh?",
                caption = "Data: Twitter API \nCLESSN")

p + scale_discrete_quorum(aesthetics = "colour")
```

## Issues and suggestions

You can submit bugs or suggestions in the Issues tab of this repo. To facilitate problem solving, please include a [minimal reproducible example](https://reprex.tidyverse.org/articles/reprex-dos-and-donts.html) of the issue.