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{scatter}

This package provides functions for quickly calculating measures of model prediction accuracy and creating enhanced scatterplots. The scatterplots can include text summarizing the agreement metrics (e.g., R², RMSE, MAPE) between two plotted variables, with support for grouped data and faceting. Agreement metrics are sourced from the {yardstick} package and can be further customized by the user.

Why?

Scatterplots are one of the most frequently used visualizations in my daily work. While creating a scatterplot is straightforward, I wanted a version that would meet these specific needs:

  • Ensure the plot is always square.
  • Include a 1:1 reference line.
  • Display “predicted” values on the x-axis.
  • Provide an optional text panel showing agreement statistics, allowing an easy customization of the metrics included.
  • Seamlessly handle grouped data, creating faceted plots with minimal effort.

Installation 

devtools::install_github("ptompalski/scatter")
library(scatter)

Examples

Simple scatterplot with R² and RMSE

library(dplyr)
library(scatter)

# some fake data
df <- 
tibble(
  truth = c(rnorm(150, 10, 2)),
  estimate = truth + rnorm(150,0, 1),
  group = rep(c("A", "B", "C"), each = 50),          
  group2 = rep(c("D1", "D2"), each = 75),          
)

scatter(df, truth, estimate, metrics=list(rsq,rmse))

Scatterplot for grouped data with agreement metrics

df %>%
  group_by(group) %>%
  scatter(truth, estimate, metrics=list(rsq,rmse,mape,msd))

Scatterplot for grouped data with agreement metrics positioned outside the plots.

df %>%
  group_by(group) %>%
  scatter(truth, estimate, metrics=list(rsq,rmse), metrics_position = "outside")