How can you use R to make data-driven transportation decisions?

1 Install and load packages

Before you can use R to work with transportation data, you need to install and load some packages that provide useful functions and tools. Some of the packages you will need are tidyverse, sf, tmap, leaflet, and shiny. Tidyverse is a collection of packages that help you manipulate, explore, and visualize data in a consistent and efficient way. Sf is a package that supports spatial data analysis and integration with other spatial packages. Tmap and leaflet are packages that allow you to create interactive and static maps with various features and layers. Shiny is a package that enables you to build web applications that display your data and analysis in a user-friendly and interactive way.

2 Import and clean data

The next step is to import and clean your transportation data. You can use various sources and formats of data, such as CSV, Excel, JSON, XML, or API. You can use the readr, readxl, jsonlite, xml2, or httr packages to import your data into R. Once you have your data in R, you need to check for any errors, missing values, outliers, duplicates, or inconsistencies. You can use the dplyr, tidyr, stringr, or lubridate packages to clean and transform your data into a tidy format that is easy to work with.

3 Explore and analyze data

After you have cleaned your data, you can start exploring and analyzing it to gain insights and answer questions. You can use the ggplot2, plotly, or highcharter packages to create various types of charts and graphs that show the distribution, trends, patterns, and relationships in your data. You can also use the sf, sp, or raster packages to perform spatial analysis and operations on your data, such as calculating distances, areas, intersections, buffers, or clusters. You can also use the stats, lmtest, or car packages to conduct statistical tests and models on your data, such as regression, ANOVA, or hypothesis testing.

4 Visualize and communicate data

The final step is to visualize and communicate your data and analysis to your stakeholders and audiences. You can use the tmap, leaflet, or mapview packages to create interactive and static maps that show your spatial data and analysis results. You can also use the shiny, flexdashboard, or rmarkdown packages to build web applications, dashboards, or reports that display your charts, graphs, maps, tables, and text in a coherent and attractive way. You can also use the knitr, kableExtra, or DT packages to export your data and analysis outputs into various formats, such as HTML, PDF, Word, or Excel.

By following these steps, you can use R to make data-driven transportation decisions that can improve your transportation management performance and outcomes. R is a flexible and powerful tool that can help you handle, process, and present transportation data in various ways.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?