Intelligent Project Management Dashboards with R Shiny and Plotly

ByJordan Brown

Project management is a complex discipline that requires juggling multiple priorities, stakeholders, resources and timelines. Keeping all the balls in the air can be a challenge, especially as the scale and complexity of projects grow. That‘s where having a robust set of tools and processes becomes critical.

In recent years, data-driven approaches to project management have gained traction as a way to surface insights, optimize decision making and automate repetitive tasks. Building custom analytical dashboards with tools like R Shiny and plotly is one way to harness the power of data without relying on rigid, off-the-shelf software.

Why Use R Shiny for Project Management?

Traditionally, project management software has focused on Gantt charts, resource allocation, task lists and timesheets. Tools like Microsoft Project, Jira and Asana are widely used for keeping track of progress and deliverables. However, these tools can be inflexible and limit the ability to do custom analysis and reporting.

Using a data science platform like R, and particular the Shiny framework for interactive web apps, opens up a whole new realm of possibilities. With R, you can:

  • Connect to a wide variety of data sources, from CSV files to databases to web APIs
  • Manipulate, transform and merge datasets with packages like dplyr and data.table
  • Create static and interactive visualizations with ggplot2 and plotly
  • Train and deploy machine learning models for prediction and optimization
  • Generate automated reports and alerts with R Markdown and scheduled scripts
  • Build interactive dashboards and decision support tools with Shiny

In other words, R provides an end-to-end toolkit for data-driven project management. Shiny apps enable building bespoke interfaces and workflows powered by R code and visualizations. Some common use cases include:

  • Executive dashboards with high-level KPIs and drill-downs
  • Project and portfolio status reports
  • Planning and forecasting tools for budgets, resources and timelines
  • Anomaly detection and early warning systems for at-risk projects
  • Scenario modeling and what-if analysis
  • Benchmark analysis and historical comparisons

Of course, Shiny is not a silver bullet. It requires R programming skills and a fair bit of development effort. But for organizations already using R for data science and statistical analysis, it can be a natural fit.

Building a Project Management Dashboard in Shiny

Let‘s walk through an example of building a Shiny dashboard for managing projects and tasks. We‘ll use a combination of static and interactive visualizations from the plotly package to surface insights.

Here‘s the full code for the app – we‘ll break down the key components here.

The app has three main views – a high-level dashboard, a projects report and a tasks report. Here‘s what the dashboard looks like:

Example Project Management Dashboard

The key features include:

  • Value boxes with summary totals for projects, tasks and % complete
  • An interactive pie chart showing project status breakdown
  • An interactive bar chart of tasks per person
  • Gantt chart showing project timelines
  • Interactive data tables for drilling down into project and task details

Starting with the UI code, we use the shinydashboard package for a multi-page dashboard layout:

library(shiny)
library(shinydashboard)
library(plotly)

header <- dashboardHeader(title = "Project Management Dashboard")

sidebar <- dashboardSidebar(
  sidebarMenu(
    menuItem("Dashboard", tabName = "dashboard", icon = icon("dashboard")),
    menuItem("Projects", tabName = "projects", icon = icon("folder")),
    menuItem("Tasks", tabName = "tasks", icon = icon("tasks")),
  )
)

body <- dashboardBody(
  tabItems(
    # Dashboard tab
    tabItem(tabName = "dashboard",
      fluidRow(
        valueBoxOutput("projectsBox", width = 4),
        valueBoxOutput("tasksBox", width = 4),
        valueBoxOutput("completedBox", width = 4)
      ),
      fluidRow(
        box(plotlyOutput("statusPlot"), title = "Project Status", width = 6),
        box(plotlyOutput("teamTasksPlot"), title = "Tasks per Person", width = 6)
      ),
      fluidRow(
        box(plotlyOutput("projectGantt"), title = "Project Timelines", width = 12)    
      )
    ),

    # Projects tab
    tabItem(tabName = "projects",
      fluidRow(
        box(dataTableOutput("projectsTable"), width = 12)
      )
    ),

    # Tasks tab 
    tabItem(tabName = "tasks", 
      fluidRow(
        box(dataTableOutput("tasksTable"), width = 12)
      )  
    )
  )
)

ui <- dashboardPage(header, sidebar, body)

This sets up the page with a header, sidebar menu and three tabbed views for the main dashboard content, project details and task details.

In the dashboard view, we have valueBoxOutputs for summary stats, plotlyOutputs for the charts, and a dataTableOutput for listing out the data. The projects and tasks views just have dataTableOutputs for now.

Now let‘s look at the server code for rendering these outputs. First, we‘ll read in some dummy data from CSV files:

projects <- read.csv("projects.csv")
tasks <- read.csv("tasks.csv")

Then, we define the output rendering functions:

server <- function(input, output) { 

  # Value boxes
  output$projectsBox <- renderValueBox({
    valueBox(nrow(projects), "Active Projects", icon = icon("folder"), color = "blue")
  })

  output$tasksBox <- renderValueBox({
    valueBox(nrow(tasks), "Total Tasks", icon = icon("tasks"), color = "green")
  })

  output$completedBox <- renderValueBox({
    pct_complete <- round(100*sum(tasks$complete)/nrow(tasks),0)
    valueBox(paste0(pct_complete,"%"), "Tasks Completed", icon = icon("check"), color = "yellow")
  })

  # Dashboard view plots 
  output$statusPlot <- renderPlotly({
    status_count <- projects %>% group_by(status) %>% summarise(count=n())
    plot_ly(status_count, labels = ~status, values = ~count, type = "pie")
  })

  output$teamTasksPlot <- renderPlotly({
    team_tasks <- tasks %>% group_by(assigned_to) %>% summarise(total=n())  
    plot_ly(team_tasks, x = ~assigned_to, y = ~total, type = "bar") %>%
      layout(title = "Tasks per Person", xaxis = list(title = "Assigned To"), yaxis = list(title = "Task Count"))
  })  

  output$projectGantt <- renderPlotly({
    p_gantt <- plot_ly(
      x = c(projects$start_date, projects$end_date),
      y = projects$name,
      type = "scatter", mode = "lines",
      line = list(color = projects$status,width = 25)) %>%
        layout(title = "Project Timelines", xaxis = list(title = "Date"), yaxis = list(title = "Project"))
    p_gantt
  })

  # Data tables  
  output$projectsTable <- renderDataTable(projects %>% select(-c(id)))
  output$tasksTable <- renderDataTable(tasks)
}

For the value boxes, we calculate summary statistics from the projects and tasks data frames. The status pie chart uses dplyr to count projects by status category and feeds that to plot_ly. The tasks per person bar chart groups and counts tasks assigned to each person.

The Gantt chart is a simple plot_ly scatter plot with start/end dates on X axis and project name on Y axis. We use a line mode with custom colors and width to mimic Gantt styling.

Finally, for the data tables, we use renderDataTable to just display the full data frames, minus any ID columns.

Advanced Analytics for Project Management

The Shiny dashboard provides a high-level view of project and task status, but we can go further by leveraging predictive modeling and AI techniques in R. Some potential applications include:

  1. Forecasting project finish dates: Using historical project data, we can train a machine learning model to predict the likelihood of on-time completion based on factors like task completion rate, team velocity, scope changes and blockers. This could be implemented as an R function that gets called with current project stats to generate a forecast.

  2. Predicting budget overruns: Similarly, a model could be trained on past project financial data to estimate the risk of going over budget based on burn rate, percentage completion, change requests, etc. This could feed into a dashboard widget showing budget risk level and estimated overrun amount.

  3. Identifying at-risk tasks: Applying anomaly detection algorithms to task-level data could surface potential problems before they impact the critical path. Red flags might include overdue tasks, low completion rate, too many open bugs, or sentiment analysis of comments indicating blockers or confusion. Integrating this into the dashboard could be an early warning system for PMs.

  4. Analyzing unstructured project data: Using natural language processing techniques like topic modeling, named entity extraction and summarization on project notes, comments and documents could provide insights not captured in standard structured data. This might surface themes and trends helpful for retrospectives and process improvement.

  5. Optimizing resource allocation: Could we use constraint optimization techniques to intelligently map tasks to team members based on skills, availability, priority and dependencies? An automated scheduling tool could save PMs hours of manual planning.

  6. Recommending process improvements: By combining project outcomes data with data on methodology, tools, and practices, we may be able to identify what factors lead to more successful projects. Statistical learning approaches like CART and random forests are useful for feature selection and deriving business rules.

The data science and AI possibilities are endless, but some challenges to consider include data quality, algorithm interpretability, potential bias in training data, and change management for process changes. It‘s important to start small, establish baseline models, and continuously monitor and refine.

Conclusion

R Shiny is a powerful framework for building custom, data-driven project management dashboards. With interactive visualization libraries like plotly, we can create intuitive and insightful interfaces for exploring project and task data.

Layering on advanced analytics and machine learning opens up even more opportunities to optimize processes, predict risks and prescribe actions. Project management is ripe for disruption through data science and AI.

However, tools are only part of the solution. Effective project management still requires experienced leaders, clear communication and sound methodology. Dashboards and models should augment rather than replace human expertise and judgment.

The future of project management likely will be a human-AI collaboration – blending the best of both to achieve better outcomes. R Shiny can be an important piece of that toolkit.

Additional Resources

To learn more about using R Shiny for project management and data science, check out these resources:

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