Skip to contents

Generate Humanitarian Data Story from ggplot

Source: R/generate_plot_story.R
generate_plot_story.Rd

This function takes a ggplot2 object and generates a storytelling narrative focused on humanitarian insights. It uses the {ellmer} package to call a large or small language model from a supported provider.

Usage

generate_plot_story(
  plot,
  max_tokens = 30,
  provider = NULL,
  model = NULL,
  clean_response = TRUE
)

Arguments

plot

A ggplot object from ggplot2.

max_tokens

Maximum number of tokens (approximate) for the narrative (default = 30).

provider

Optional character string specifying the provider. Options include: "openai", "gemini", "anthropic", "ollama", "azure". If NULL, auto-detect from environment keys.

model

Optional character string specifying the model name. For Azure, this is typically the deployment name. If NULL, a default model for the chosen provider will be used.

clean_response

Logical. Whether to clean the response by removing thinking tags and other artifacts (default = TRUE).

Value

A character string containing a storytelling narrative focused on humanitarian data.

Details

If you do not have API keys or need to work offline, simply get ollama and look at top reasoning models - https://ollama.com/search?c=thinking

Setup:

  1. Install {ellmer}: install.packages("ellmer")

  2. Set your API key in your environment. For Azure OpenAI, use the standard OpenAI key variable: Sys.setenv(OPENAI_API_KEY = "<YOUR_AZURE_OPENAI_KEY>")

  3. When using Azure, set provider = "azure" and provide the env variables Sys.setenv(AZURE_OPENAI_ENDPOINT = "<YOUR_AZURE_ENDPOINT>") Sys.setenv(AZURE_OPENAI_API_VERSION = "<YOUR_AZURE_OPENAI_API_VERSION>") The best place to set this is in .Renviron, which you can easily edit by calling usethis::edit_r_environ()

Examples


library(ggplot2)
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
   geom_point() +
    unhcrthemes::theme_unhcr(grid = "Y", axis = "X", axis_title = FALSE) +
   labs(title = "Vehicle Efficiency",
        subtitle = "Fuel consumption vs weight",
        caption = "Source: mtcars dataset")

generate_plot_story(p, provider = "ollama", model = "deepseek-r1")
#> [1] "Key takeaway: Lighter vehicles consume less fuel. From the data: A car weighing only 1.5 tons achieves 30 mph efficiency, over ten points higher than heavier models around 4 tons. This pattern shows that even small weight reductions yield big efficiency winsa powerful message for funders investing to bolster program performance."

story <- generate_plot_story(p, provider = "azure", model = "gpt-4.1-mini", max_tokens = 300)
# To use as subtitle:
p + ggplot2::labs(subtitle = story)