# Getting started with quallmer

The `quallmer` package helps qualitative researchers leverage the power of large language models for tasks such as coding, annotation, and thematic analysis. It is user-friendly and does not require extensive programming knowledge, making it accessible to researchers from various backgrounds.

Our tutorials provide a brief introduction to the `quallmer` package, which is designed to facilitate the use of large language models (LLMs) for qualitative research tasks. The package relies on the `ellmer` package for LLM interactions, providing a seamless interface for users to work with different LLM providers. For more information on the `ellmer` package and supported LLM interactions, please refer to its documentation here.

## Basic usage

The `quallmer` package is developed for using it in R. Please make sure you have a recent version of R and RStudio installed on your computer. If you are new to R and RStudio, you can find a great and free-of-charge 1.5h introduction to R and RStudio on instats.

To get started with `quallmer`, you first need to install the package from GitHub.

    # If you don't have pak installed yet, uncomment and run the following line:
    # install.packages("pak")
    # Then, install quallmer using pak:
    pak::pak("quallmer/quallmer")

Then, you can load the package and begin using its functions.

    library(quallmer)
    #> Loading required package: ellmer

## The quallmer workflow

The typical quallmer workflow consists of five steps:

1. Define your codebook with `qlm_codebook()`
2. Code your data with `qlm_code()`
3. Replicate with different settings using `qlm_replicate()`
4. Compare results with `qlm_compare()`
5. Document everything with `qlm_trail()`

For a hands-on introduction with code examples, see The quallmer workflow.

## Setting up LLM access

Before using large language models, you need to set up access to an LLM provider:

1. Signing up for an OpenAI API key: Obtain an API key from OpenAI to use models like GPT-4o.

2. Working with an open-source Ollama model: Use open-source models locally with Ollama.

The `quallmer` package supports multiple LLM providers through the `ellmer` package. For more information, see the ellmer documentation.