Static timeline plots with gg

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1. Basic example

gg_vistime() produces ggplot2 charts. For interactive Plotly output, see vistime(), for interactive Highcharts output, see hc_vistime().

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)

2. Installation

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

install.packages("vistime")

3. Usage and default arguments

The simplest way to create a timeline is by providing a data frame with event and start columns. If your columns are named otherwise, you need to tell the function using the col. arguments. You can also tweak the y positions, linewidth, title, label visibility and number of lines in the background.

gg_vistime(data, 
           col.event = "event", 
           col.start = "start", 
           col.end = "end", 
           col.group = "group", 
           col.color = "color", 
           col.fontcolor = "fontcolor",
           optimize_y = TRUE, 
           linewidth = NULL, 
           title = NULL, 
           show_labels = TRUE,
           background_lines = NULL)

4. Arguments

parameter	optional?	data type	explanation
data	mandatory	data.frame	data.frame that contains the data to be visualized
col.event	optional	character	the column name in data that contains event names. Default: event
col.start	optional	character	the column name in data that contains start dates. Default: start
col.end	optional	character	the column name in data that contains end dates. Default: end
col.group	optional	character	the column name in data to be used for grouping. Default: group
col.color	optional	character	the column name in data that contains colors for events. Default: color, if not present, colors are chosen via RColorBrewer.
col.fontcolor	optional	character	the column name in data that contains the font color for event labels. Default: fontcolor, if not present, color will be black.
optimize_y	optional	logical	distribute events on y-axis by smart heuristic (default) or use order of input data.
linewidth	optional	numeric	override the calculated linewidth for events. Default: heuristic value.
title	optional	character	the title to be shown on top of the timeline. Default: empty.
show_labels	optional	logical	choose whether or not event labels shall be visible. Default: `TRUE`.
background_lines	optional	integer	the number of vertical lines to draw in the background to demonstrate structure. Default: 10.

5. Value

gg_vistime returns an object of class gg and ggplot

6. Examples

Ex. 1: Presidents

pres <- data.frame(Position = rep(c("President", "Vice"), each = 3),
                   Name = c("Washington", rep(c("Adams", "Jefferson"), 2), "Burr"),
                   start = c("1789-03-29", "1797-02-03", "1801-02-03"),
                   end = c("1797-02-03", "1801-02-03", "1809-02-03"),
                   color = c('#cbb69d', '#603913', '#c69c6e'),
                   fontcolor = c("black", "white", "black"))
                  
gg_vistime(pres, col.event = "Position", col.group = "Name", title = "Presidents of the USA")

Ex. 2: Project Planning

data <- read.csv(text="event,group,start,end,color
                       Phase 1,Project,2016-12-22,2016-12-23,#c8e6c9
                       Phase 2,Project,2016-12-23,2016-12-29,#a5d6a7
                       Phase 3,Project,2016-12-29,2017-01-06,#fb8c00
                       Phase 4,Project,2017-01-06,2017-02-02,#DD4B39
                       Room 334,Team 1,2016-12-22,2016-12-28,#DEEBF7
                       Room 335,Team 1,2016-12-28,2017-01-05,#C6DBEF
                       Room 335,Team 1,2017-01-05,2017-01-23,#9ECAE1
                       Group 1,Team 2,2016-12-22,2016-12-28,#E5F5E0
                       Group 2,Team 2,2016-12-28,2017-01-23,#C7E9C0
                       3-200,category 1,2016-12-25,2016-12-25,#1565c0
                       3-330,category 1,2016-12-25,2016-12-25,#1565c0
                       3-223,category 1,2016-12-28,2016-12-28,#1565c0
                       3-225,category 1,2016-12-28,2016-12-28,#1565c0
                       3-226,category 1,2016-12-28,2016-12-28,#1565c0
                       3-226,category 1,2017-01-19,2017-01-19,#1565c0
                       3-330,category 1,2017-01-19,2017-01-19,#1565c0
                       1-217.0,category 2,2016-12-27,2016-12-27,#90caf9
                       4-399.7,moon rising,2017-01-13,2017-01-13,#f44336
                       8-831.0,sundowner drink,2017-01-17,2017-01-17,#8d6e63
                       9-984.1,birthday party,2016-12-22,2016-12-22,#90a4ae
                       F01.9,Meetings,2016-12-26,2016-12-26,#e8a735
                       Z71,Meetings,2017-01-12,2017-01-12,#e8a735
                       B95.7,Meetings,2017-01-15,2017-01-15,#e8a735
                       T82.7,Meetings,2017-01-15,2017-01-15,#e8a735")
                           
gg_vistime(data)

Ex. 3: Gantt Charts

The argument optimize_y can be used to change the look of the timeline. TRUE (the default) will find a nice heuristic to save y-space, distributing the events:

data <- read.csv(text="event,start,end
                       Phase 1,2020-12-15,2020-12-24
                       Phase 2,2020-12-23,2020-12-29
                       Phase 3,2020-12-28,2021-01-06
                       Phase 4,2021-01-06,2021-02-02")
        
gg_vistime(data, optimize_y = TRUE, linewidth = 25)

FALSE will plot events as-is, not saving any space:

gg_vistime(data, optimize_y = FALSE, linewidth = 25)

7. Export of vistime as PNG

Once created, you can use ggplot2::ggsave() for saving your vistime chart as PNG:

chart <- vistime(pres, col.event = "Position")
ggplot2::ggsave("presidents.png", timeline)

8. Usage in Shiny apps

gg_vistime() objects can be integrated into Shiny via plotOutput() and renderPlot()

library(vistime)

pres <- data.frame(Position = rep(c("President", "Vice"), each = 3),
                   Name = c("Washington", rep(c("Adams", "Jefferson"), 2), "Burr"),
                   start = c("1789-03-29", "1797-02-03", "1801-02-03"),
                   end = c("1797-02-03", "1801-02-03", "1809-02-03"),
                   color = c('#cbb69d', '#603913', '#c69c6e'),
                   fontcolor = c("black", "white", "black"))

shinyApp(
  ui = plotOutput("myVistime"),
  server = function(input, output) {
    output$myVistime <- renderPlot({
      vistime(pres, col.event = "Position", col.group = "Name")
    })
  }
)

9. Customization

Since every gg_vistime() output is a ggplot object, you can customize and override literally everything:

library(vistime)
data <- read.csv(text="event,start,end
                       Phase 1,2020-12-15,2020-12-24
                       Phase 2,2020-12-23,2020-12-29
                       Phase 3,2020-12-28,2021-01-06
                       Phase 4,2021-01-06,2021-02-02")
        
p <- gg_vistime(data, optimize_y = T, col.group = "event", title = "ggplot customization example")

library(ggplot2)
p + ggplot2::theme(
      plot.title = element_text(hjust = 0, size=10),
      axis.text.x = element_text(size = 10, color = "violet"),
      axis.text.y = element_text(size = 10, color = "red", angle = 30),
      panel.border = element_rect(linetype = "dashed", fill=NA),
      panel.background = element_rect(fill = 'green')) +
    coord_cartesian(ylim = c(0.7, 3.5))

Static timeline plots with gg_vistime()

April 2021