# vistime - Pretty Timelines

#### Sandro Raabe

#### 2020-07-24

A library for creating time-based charts, like Gantt or timelines. Possible outputs include `ggplot`s, `plotly` graphs, `Highchart`s or `data.frame`s. Results can be used in the RStudio viewer pane, in R Markdown documents or in Shiny apps. In the interactive outputs created by `Plotly.js` and `Highcharts.js` 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: sa.ra.online@posteo.de

## 1. Main functionality

This package `vistime` provides three main functions:

### 1) `vistime()` to produce interactive `Plotly` charts:

    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")
    vistime(timeline_data)

### 2) `hc_vistime()` to produce interactive `Highcharts` charts:

    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)

### 3) `gg_vistime()` to produce static `ggplot` 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)

### 3) `vistime_data()`, for pure `data.frame` output that you can use with the plotting engine of your choice:

    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`).

## 2. Installation

To install the package from CRAN (v1.0.0), 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. You can also tweak the y positions, linewidth, title, label visibility and number of lines in the background.

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

    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)

    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)

    vistime_data(data, col.event = "event", col.start = "start", col.end = "end", col.group = "group", col.color = "color",
                 col.fontcolor = "fontcolor", col.tooltip = "tooltip", optimize_y = TRUE)

## 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.
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.
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

- `vistime` returns an object of class `plotly` and `htmlwidget`
- `hc_vistime` returns an object of class `highchart` and `htmlwidget`
- `gg_vistime` returns an object of class `gg` and `ggplot`
- `vistime_data` returns an object of class `data.frame`

## 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"))

    vistime(pres, col.event = "Position", col.group = "Name", title = "Presidents of the USA")      # the Plotly version

    # hc_vistime(pres, col.event = "Position", col.group = "Name", title = "Presidents of the USA") # Alternative for Highcharts
    # gg_vistime(pres, col.event = "Position", col.group = "Name", title = "Presidents of the USA") # Alternative for ggplot2

### 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")

    vistime(data)       # the Plotly version

    # hc_vistime(data)  # Alternative for Highcharts
    # gg_vistime(data)  # Alternative for ggplot2

### 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")

    vistime(data, optimize_y = TRUE, linewidth = 25)

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

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

## 7. Export of vistime as PDF or PNG

Once created, you can use `plotly::export()` for saving your vistime chart (the plotly version) as PDF, PNG or JPEG:

    # webshot::install_phantomjs()
    chart <- vistime(pres, col.event = "Position")
    plotly::export(chart, file = "presidents.pdf")

Note that export requires the `webshot` package and additional arguments like width or height can be used (`?webshot` for the details). You can also download the plot as PNG by using the toolbar on the upper right side of the generated plot.

## 8. Usage in Shiny apps

- `vistime()` objects can be integrated into Shiny via `plotlyOutput()` and `renderPlotly()`
- `hc_vistime()` objects can be integrated into Shiny via `highchartOutput()` and `renderHighchart()`
- `gg_vistime()` objects can be integrated into Shiny via `plotOutput()` and `renderPlot()`

    library(shiny)
    library(plotly)
    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 = plotlyOutput("myVistime"),
      server = function(input, output) {
        output$myVistime <- renderPlotly({
          vistime(pres, col.event = "Position", col.group = "Name")
        })
      }
    )

## 8. Customization

### Using `ggplot2::theme()` for `gg_vistime` charts

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))

### Using `plotly::layout()` for `vistime` charts

    library(plotly)
    p2 <- vistime(data, optimize_y = T, col.group = "event", title = "Plotly customization example")
    p2 %>% layout(xaxis=list(fixedrange=TRUE, tickfont=list(size=30, color="violet")),
                  yaxis=list(fixedrange=TRUE, tickfont=list(size=30, color="red"), tickangle=30,
                             mirror = FALSE, range = c(0.7, 3.5), showgrid = T),
                  plot_bgcolor = "lightgreen")

See `?plotly::layout` and the official Plotly API reference for details.

### Using `highcharter::hc_*()` for `hc_vistime` charts

    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)

See `?hc_xAxis`, `?hc_chart` and the official Highcharts API reference for details.

### Using `plotly::plotly_build()` for `vistime` charts

The function `plotly_build()` from package `plotly` turns your plot into a list. You can then use the function `str` to explore the structure of your plot. You can even manipulate all the elements there.

The key is to first create a simple Plotly example yourself, turning it into a list (using `plotly_build()`) and exploring the resulting list regarding the naming of the relevant attributes. Then manipulate or create them in your vistime example accordingly. Below are some examples of common solutions.

#### Changing x-axis tick font size

The following example creates the presidents example and manipulates the font size of the x axis ticks:

    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"))

    p <- vistime(pres, col.event = "Position", col.group = "Name", title = "Presidents of the USA")

    # step 1: transform into a list
    library(plotly)
    pp <- plotly_build(p)

    # step 2: change the font size
    pp$x$layout$xaxis$tickfont <- list(size = 28)

    pp

#### Changing y-axis tick font size

We need to change the font size of the y-axis:

    pp$x$layout[["yaxis"]]$tickfont <- list(size = 28)
    pp

#### Changing events font size

The following example creates the presidents example and manipulates the font size of the events:

    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"))

    p <- vistime(pres, col.event = "Position", col.group = "Name", title = "Presidents of the USA", linewidth=30)

    # step 1: transform into a list
    library(plotly)
    pp <- plotly_build(p)

    # step 2: loop over pp$x$data, and change the font size of all text elements to 28
    for(i in seq_along(pp$x$data)){
        if(pp$x$data[[i]]$mode == "text") pp$x$data[[i]]$textfont$size <- 28
    }

    #' # or, using purrr:
    #' text_idx <- which(purrr::map_chr(pp$x$data, "mode") == "text")
    #' for(i in text_idx) pp$x$data[[i]]$textfont$size <- 28
    #' pp

    pp

#### Changing marker size

The following example a simple example using markers and manipulates the size of the markers:

    dat <- data.frame(event = 1:4, start = c("2019-01-01", "2019-01-10"))

    p <- vistime(dat)

    # step 1: transform into a list
    library(plotly)
    pp <- plotly_build(p)

    # step 2: loop over pp$x$data, and change the marker size of all text elements to 50px
    for(i in seq_along(pp$x$data)){
        if(pp$x$data[[i]]$mode == "markers") pp$x$data[[i]]$marker$size <- 20
    }

    # or, using purrr:
    # marker_idx <- which(purrr::map_chr(pp$x$data, "mode") == "markers")
    # for(i in marker_idx) pp$x$data[[i]]$marker$size <- 20
    # pp

    pp