A library for creating time-based charts, like Gantt or timelines. Possible outputs include ggplots, plotly graphs, Highcharts or data.frames. Results can be used in the RStudio viewer pane, in R Markdown documents or in Shiny apps. In the interactive outputs created by vistime() and hc_vistime() you can interact with the plot using mouse hover or zoom. Timelines and their components can afterwards be manipulated using ggplot::theme(), plotly_build() or hc_*functions (for gg_vistime(), vistime() or hc_vistime(), respectively). When choosing the data.frame output, you can use your own plotting engine for visualizing the graph.

If you find vistime useful, please consider supporting its development:

Feedback welcome: [email protected]

1. Installation
2. Main functionality
3. Real-life example
4. Usage and documentation

To install the package from CRAN, type the following in your R console:

install.packages("vistime")

This package vistime provides four main functions, the first three allow you to draw a timeline with Plotly, Highcharts or ggplot2, the last one outputs the pure optimized data frame ready for plotting.

timeline_data <- data.frame(event = c("Event 1", "Event 2"),
                            start = c("2020-06-06", "2020-10-01"), 
                            end   = c("2020-10-01", "2020-12-31"),
                            group = "My Events")
                            
vistime(timeline_data)

timeline_data <- data.frame(event = c("Event 1", "Event 2"),
                            start = c("2020-06-06", "2020-10-01"), 
                            end   = c("2020-10-01", "2020-12-31"),
                            group = "My Events")
                            
hc_vistime(timeline_data)

This is facilitated by the highcharter package, so, this package needs to be installed before attempting to produce any hc_vistime() output.

timeline_data <- data.frame(event = c("Event 1", "Event 2"),
                            start = c("2020-06-06", "2020-10-01"), 
                            end   = c("2020-10-01", "2020-12-31"),
                            group = "My Events")
                            
gg_vistime(timeline_data)

timeline_data <- data.frame(event = c("Event 1", "Event 2"),
                            start = c("2020-06-06", "2020-10-01"), 
                            end   = c("2020-10-01", "2020-12-31"),
                            group = "My Events")
                            
vistime_data(timeline_data)

#>     event      start        end     group                                      tooltip      col subplot   y
#> 1 Event 1 2020-06-06 2020-10-01 My Events  from <b>2020-06-06</b> to <b>2020-10-01</b>  #8DD3C7       1   1
#> 2 Event 2 2020-10-01 2020-12-31 My Events  from <b>2020-10-01</b> to <b>2020-12-31</b>  #FFFFB3       1   1

You want to use this for the intelligent y-axis assignment depending on overlapping of events (this can be disabled with optimize_y = FALSE).

During COVID-19 2020, @wlhamilton used gg_vistime() for visualizing patient ward movements as timelines in order to investigate possible hospital acquired infections. See his github for the code.

There is a vignette for each of the three functions of the package where they are explained in detail:

• vistime() for interactive Plotly output: Link to manual
• gg_vistime() for static ggplot2 output: Link to manual
• hc_vistime() for interactive Highcharts output: Link to manual