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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))