knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(vistime) library(highcharter) hc_vistime <- function(...) hc_size(vistime::hc_vistime(...), width=700, height=150)
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library(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") hc_vistime(timeline_data)
To install vistime
package from CRAN, type the following in your R console:
install.packages("vistime")
For interactive hc_vistime()
plots, you need to install the highcharter
package. This package is free for non-commercial and non-governmental use:
install.packages("highcharter")
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. You can also tweak the y positions, title and label visibility.
hc_vistime(data, col.event = "event", col.start = "start", col.end = "end", col.group = "group", col.color = "color", optimize_y = TRUE, title = NULL, show_labels = TRUE)
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.tooltip | optional | character | the column name in data that contains the mouseover tooltips for the events. Default: tooltip, if not present, then tooltips are build from event name and date. Basic HTML is allowed.
optimize_y | optional | logical | distribute events on y-axis by smart heuristic (default) or use order of input data.
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
.
hc_vistime
returns an object of class highchart
and htmlwidget
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")) hc_vistime(pres, col.event = "Position", col.group = "Name", title = "Presidents of the USA") %>% hc_size(width = 700, height = 300)
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")) hc_vistime(pres, col.event = "Position", col.group = "Name", title = "Presidents of the USA") %>% hc_size(width = 700, height = 300)
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") hc_vistime(data)
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") hc_vistime(data) %>% hc_size(width = 700, height = 500)
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") hc_vistime(data, optimize_y = TRUE)
FALSE
will plot events as-is, not saving any space:
hc_vistime(data, optimize_y = FALSE)
hc_vistime()
objects can be integrated into Shiny via highchartOutput()
and renderHighchart()
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 = highcharter::highchartOutput("myVistime"), server = function(input, output) { output$myVistime <- highcharter::renderHighchart({ vistime(pres, col.event = "Position", col.group = "Name") }) } )
Since every hc_vistime()
output is a highchart
object, you can customize and override literally everything using its functions. See ?hc_xAxis
, ?hc_chart
etc. and the official Highcharts API reference for details.
library(highcharter) p3 <- hc_vistime(data, optimize_y = T, col.group = "event", title = "Highcharts customization example") p3 %>% hc_title(style = list(fontSize = 30)) %>% hc_yAxis(labels = list(style = list(fontSize=30, color="violet"))) %>% hc_xAxis(labels = list(style = list(fontSize=30, color="red"), rotation=30)) %>% hc_chart(backgroundColor = "lightgreen") %>% hc_size(width = 700, height = 300)
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