Reproducible reporting with R

3 min read

When managing transportation and logistics, accurate and up-to-date trip metrics are essential for making informed decisions, optimizing routes, and improving customer satisfaction. Manual data processing and reporting quickly becomes a drain on resources, automation saves time and reduces errors.

Many teams start with Excel: formulas, pivot tables, and sometimes VBA macros. Learning VBA can be worthwhile, but if you’re investing time in a programming language for data work, R and Python are the common choices. I personally chose R because it has many libraries tailored to data-analysis pipelines and idioms that are familiar to people used to spreadsheet formulas.

Data collection

The first step is defining the dataset and the features of interest. In our case we exported a sample CSV from a telematics API with trip-level details. Key features include start_time, end_time, distance, and load_weight. Defining these features up front helps identify the KPIs that matter for monitoring operations.

Data Processing

Once the data is loaded into R, you can use R’s data manipulation tools to calculate the metrics needed for the report. The dplyr package makes these calculations clean and expressive.

Here’s a simple example. We compute trip duration (minutes), average speed (km/h):

library(dplyr)
library(lubridate)
 
trip_metrics <- trip_data %>%
  mutate(
    start_time = ymd_hms(start_time),
    end_time   = ymd_hms(end_time),
    duration   = as.numeric(difftime(end_time, start_time, units = "mins")),
    avg_speed  = ifelse(duration > 0, distance / (duration / 60), NA_real_),
  )

Example summaries you might include in the report:

# Totals and averages
total_distance <- sum(trip_metrics$distance, na.rm = TRUE)
average_speed  <- mean(trip_metrics$avg_speed, na.rm = TRUE)
 
total_distance
average_speed

Generating the report

One option is to use R Markdown to create reproducible reports that embed code, output, and explanation in one file. R Markdown supports HTML, PDF and Word outputs, and can be rendered on a schedule (e.g., with cron or RStudio Connect) or on demand.

Another option is R Shiny, which allows you to build interactive dashboards where users can explore data, filter by time periods, and drill down into specific metrics. Shiny apps can be deployed on a server or hosted with services like ShinyApps.io.

A few practical tips:

  • Use knitr::kable() or gt for clean tables.
  • Use ggplot2 for visualizations (time series of distance, heatmaps of routes, speed distributions).
  • Keep code chunks small and annotated so the report is both readable and reproducible.
  • Add a short summary at the top for team members who want the highlights.

Conclusion

Automating metric calculation and report generation with R reduces manual effort, improves consistency, and makes it easier to track performance over time.