Gantt charts with R

How do I create a Gantt chart in R?

I'm looking for something sophisticated (looking more or less like this):

P.S. I could live without the dependency arrows.

There are now a few elegant ways to generate a Gantt chart in R.

Using Candela

library(candela)

data <- list(
    list(name='Do this', level=1, start=0, end=5),
    list(name='This part 1', level=2, start=0, end=3),
    list(name='This part 2', level=2, start=3, end=5),
    list(name='Then that', level=1, start=5, end=15),
    list(name='That part 1', level=2, start=5, end=10),
    list(name='That part 2', level=2, start=10, end=15))

candela('GanttChart',
    data=data, label='name',
    start='start', end='end', level='level',
    width=700, height=200)

Using DiagrammeR

library(DiagrammeR)

mermaid("
gantt
dateFormat  YYYY-MM-DD
title A Very Nice Gantt Diagram

section Basic Tasks
This is completed             :done,          first_1,    2014-01-06, 2014-01-08
This is active                :active,        first_2,    2014-01-09, 3d
Do this later                 :               first_3,    after first_2, 5d
Do this after that            :               first_4,    after first_3, 5d

section Important Things
Completed, critical task      :crit, done,    import_1,   2014-01-06,24h
Also done, also critical      :crit, done,    import_2,   after import_1, 2d
Doing this important task now :crit, active,  import_3,   after import_2, 3d
Next critical task            :crit,          import_4,   after import_3, 5d

section The Extras
First extras                  :active,        extras_1,   after import_4,  3d
Second helping                :               extras_2,   after extras_1, 20h
More of the extras            :               extras_3,   after extras_1, 48h
")

Find this example and many more on DiagrammeR GitHub

---

If your data is stored in a data.frame, you can create the string to pass to mermaid() by converting it to the proper format.

Consider the following:

df <- data.frame(task = c("task1", "task2", "task3"),
                 status = c("done", "active", "crit"),
                 pos = c("first_1", "first_2", "first_3"),
                 start = c("2014-01-06", "2014-01-09", "after first_2"),
                 end = c("2014-01-08", "3d", "5d"))

#   task status     pos         start        end
#1 task1   done first_1    2014-01-06 2014-01-08
#2 task2 active first_2    2014-01-09         3d
#3 task3   crit first_3 after first_2         5d

Using dplyr and tidyr (or any of your favorite data wrangling ressources):

library(tidyr)
library(dplyr)

mermaid(
  paste0(
    # mermaid "header", each component separated with "\n" (line break)
    "gantt", "\n", 
    "dateFormat  YYYY-MM-DD", "\n", 
    "title A Very Nice Gantt Diagram", "\n",
    # unite the first two columns (task & status) and separate them with ":"
    # then, unite the other columns and separate them with ","
    # this will create the required mermaid "body"
    paste(df %>%
            unite(i, task, status, sep = ":") %>%
            unite(j, i, pos, start, end, sep = ",") %>%
            .$j, 
          collapse = "\n"
    ), "\n"
  )
)

As per mentioned by @GeorgeDontas in the comments, there is a little hack that could allow to change the labels of the x axis to dates instead of 'w.01, w.02'.

Assuming you saved the above mermaid graph in m, do:

m$x$config = list(ganttConfig = list(
  axisFormatter = list(list(
    "%b %d, %Y" 
    ,htmlwidgets::JS(
      'function(d){ return d.getDay() == 1 }' 
    )
  ))
))

Which gives:

---

Using timevis

From the timevis GitHub:

> timevis lets you create rich and fully interactive timeline > visualizations in R. Timelines can be included in Shiny apps and R > markdown documents, or viewed from the R console and RStudio Viewer.

library(timevis)

data <- data.frame(
  id      = 1:4,
  content = c("Item one"  , "Item two"  ,"Ranged item", "Item four"),
  start   = c("2016-01-10", "2016-01-11", "2016-01-20", "2016-02-14 15:00:00"),
  end     = c(NA          ,           NA, "2016-02-04", NA)
)

timevis(data)

Which gives:

---

Using plotly

I stumbled upon this post providing another method using plotly. Here's an example:

library(plotly)

df <- read.csv("https://cdn.rawgit.com/plotly/datasets/master/GanttChart-updated.csv", 
               stringsAsFactors = F)

df$Start  <- as.Date(df$Start, format = "%m/%d/%Y")
client    <- "Sample Client"
cols      <- RColorBrewer::brewer.pal(length(unique(df$Resource)), name = "Set3")
df$color  <- factor(df$Resource, labels = cols)

p <- plot_ly()
for(i in 1:(nrow(df) - 1)){
  p <- add_trace(p,
                 x = c(df$Start[i], df$Start[i] + df$Duration[i]), 
                 y = c(i, i), 
                 mode = "lines",
                 line = list(color = df$color[i], width = 20),
                 showlegend = F,
                 hoverinfo = "text",
                 text = paste("Task: ", df$Task[i], "<br>",
                              "Duration: ", df$Duration[i], "days<br>",
                              "Resource: ", df$Resource[i]),
                 evaluate = T
  )
}

p

Which gives:

You can then add additional information and annotations, customize fonts and colors, etc. (see blog post for details)

A simple ggplot2 gantt chart.

First, we create some data.

library(reshape2)
library(ggplot2)

tasks <- c("Review literature", "Mung data", "Stats analysis", "Write Report")
dfr <- data.frame(
  name        = factor(tasks, levels = tasks),
  start.date  = as.Date(c("2010-08-24", "2010-10-01", "2010-11-01", "2011-02-14")),
  end.date    = as.Date(c("2010-10-31", "2010-12-14", "2011-02-28", "2011-04-30")),
  is.critical = c(TRUE, FALSE, FALSE, TRUE)
)
mdfr <- melt(dfr, measure.vars = c("start.date", "end.date"))

Now draw the plot.

ggplot(mdfr, aes(value, name, colour = is.critical)) + 
  geom_line(size = 6) +
  xlab(NULL) + 
  ylab(NULL)

Very old question, I know, but perhaps worth leaving here that - unsatisfied with the answers I found to this question - a few months ago I made a basic package for making ggplot2-based Gantt charts: ganttrify (more details in the package's readme).

Example output:

Consider to use the package projmanr (version 0.1.0 released on CRAN on 23 Aug 2017).

library(projmanr)

# Use raw example data
(data <- taskdata1)

taskdata1:

  id name duration pred
1  1   T1        3     
2  2   T2        4    1
3  3   T3        2    1
4  4   T4        5    2
5  5   T5        1    3
6  6   T6        2    3
7  7   T7        4 4,5 
8  8   T8        3  6,7

Now start to prepare gantt:

# Create a gantt chart using the raw data
gantt(data)

# Create a second gantt chart using the processed data
res <- critical_path(data)
gantt(res)

# Use raw example data
data <- taskdata1
# Create a network diagram chart using the raw data
network_diagram(data)

# Create a second network diagram using the processed data
res <- critical_path(data)
network_diagram(res)

Try this:

install.packages("plotrix")
library(plotrix)
?gantt.chart

Package plan supports the creation of burndown charts and gantt diagrams and contains a plot.gantt function. See this R Graphical Manual page

See also how to make one in R using Plotly’s R API GANTT CHARTS IN R USING PLOTLY.

You can do it with the GoogleVis package:

datTL <- data.frame(Position=c(rep("President", 3), rep("Vice", 3)),
                    Name=c("Washington", "Adams", "Jefferson",
                           "Adams", "Jefferson", "Burr"),
                    start=as.Date(x=rep(c("1789-03-29", "1797-02-03", 
                                          "1801-02-03"),2)),
                    end=as.Date(x=rep(c("1797-02-03", "1801-02-03", 
                                        "1809-02-03"),2)))

Timeline <- gvisTimeline(data=datTL, 
                         rowlabel="Name",
                         barlabel="Position",
                         start="start", 
                         end="end",
                         options=list(timeline="{groupByRowLabel:false}",
                                      backgroundColor='#ffd', 
                                      height=350,
                                      colors="['#cbb69d', '#603913', '#c69c6e']"))
plot(Timeline)

Source: https://cran.r-project.org/web/packages/googleVis/vignettes/googleVis_examples.html

For me, Gvistimeline was the best tool to do this, but its required online connection was not useful to me. Thus I created a package called vistime that uses plotly (similar to the answer of @Steven Beaupré), so you can zoom in etc.:

https://github.com/shosaco/vistime

> vistime: Create interactive timelines or Gantt charts using plotly.js. The > charts can be included in Shiny apps and manipulated via > plotly_build().

install.packages("vistime")    
library("vistime")  

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

vistime(dat, events="Position", groups="Name", title="Presidents of the USA")

I used and modified the above example from Richie, worked like a charm. Modified version to show how his model could translate into ingesting CSV data rather than manually provided text items.

NOTE: Richie's answer was missing indication that 2 packages ( reshape and ggplot2 ) are needed for the above/below code to work.

rawschedule <- read.csv("sample.csv", header = TRUE) #modify the "sample.csv" to be the name of your file target. - Make sure you have headers of: Task, Start, Finish, Critical OR modify the below to reflect column count.
tasks <- c(t(rawschedule["Task"]))
dfr <- data.frame(
name        = factor(tasks, levels = tasks),
start.date  = c(rawschedule["Start"]),
end.date    = c(rawschedule["Finish"]),
is.critical = c(rawschedule["Critical"]))
mdfr <- melt(dfr, measure.vars = c("Start", "Finish"))


#generates the plot
ggplot(mdfr, aes(as.Date(value, "%m/%d/%Y"), name, colour = Critical)) + 
geom_line(size = 6) +
xlab("Duration") + ylab("Tasks") +
theme_bw()

Found the geom_segment in ggplot is great. From the previous solutions use the data but no need to melt.

library(ggplot2)

tasks <- c("Review literature", "Mung data", "Stats analysis", "Write Report")
dfr <- data.frame(
  name        = factor(tasks, levels = tasks),
  start.date  = as.Date(c("2010-08-24", "2010-10-01", "2010-11-01", "2011-02-14")),
  end.date    = as.Date(c("2010-10-31", "2010-12-14", "2011-02-28", "2011-04-30")),
  is.critical = c(TRUE, FALSE, FALSE, TRUE)
)

ggplot(dfr, aes(x =start.date, xend= end.date, y=name, yend = name, color=is.critical)) +
  geom_segment(size = 6) +
  xlab(NULL) + ylab(NULL)

GantPlot

Library PlotPrjNetworks provides useful Networking Tools for Project Management.

library(PlotPrjNetworks)
project1=data.frame(
task=c("Market Research","Concept Development","Viability Test",
"Preliminary Design","Process Design","Prototyping","Market Testing","Final Design",
"Launching"),
start=c("2015-07-05","2015-07-05","2015-08-05","2015-10-05","2015-10-05","2016-02-18",
"2016-03-18","2016-05-18","2016-07-18"),
end=c("2015-08-05","2015-08-05","2015-10-05","2016-01-05","2016-02-18","2016-03-18",
"2016-05-18","2016-07-18","2016-09-18"))
project2=data.frame(
from=c(1,2,3,4,5,6,7,8),
to=c(2,3,4,5,6,7,8,9),
type=c("SS","FS","FS","SS","FS","FS","FS","FS"),
delay=c(7,7,7,8,10,10,10,10))
GanttChart(project1,project2)

I would like to improve the ggplot-Answer with several bars for each task.

First generate some data (dfrP is the data.frame of the other answer, dfrR is some other data.frame with realisation dates and mdfr is a merge fitting to the following ggplot()-statement):

library(reshape2)
tasks <- c("Review literature", "Mung data", "Stats analysis", "Write Report")
dfrP <- data.frame(
  name        = factor(tasks, levels = tasks),
  start.date  = as.Date(c("2010-08-24", "2010-10-01", "2010-11-01", "2011-02-14")),
  end.date    = as.Date(c("2010-10-31", "2010-12-14", "2011-02-28", "2011-04-30")),
  is.critical = c(TRUE, FALSE, FALSE, TRUE)
)
dfrR <- data.frame(
  name        = factor(tasks, levels = tasks),
  start.date  = as.Date(c("2010-08-22", "2010-10-10", "2010-11-01", NA)),
  end.date    = as.Date(c("2010-11-03", "2010-12-22", "2011-02-24", NA)),
  is.critical = c(TRUE, FALSE, FALSE,TRUE)
)
mdfr <- merge(data.frame(type="Plan", melt(dfrP, measure.vars = c("start.date", "end.date"))),
  data.frame(type="Real", melt(dfrR, measure.vars = c("start.date", "end.date"))), all=T)

Now plot this data using facets for the task name:

library(ggplot2)
ggplot(mdfr, aes(x=value, y=type, color=is.critical))+
  geom_line(size=6)+
  facet_grid(name ~ .) +
  scale_y_discrete(limits=c("Real", "Plan")) +
  xlab(NULL) + ylab(NULL)

Without the is.critical-information you could also use Plan/Real as color (which I would prefere), but I wanted to use the data.frame of the other answer to make it better comparable.

You could take a look at this post. This uses R and ggplot.

https://dwh-businessintelligence.blogspot.nl/2016/05/what-if-for-project-management.html