When discussing performance with colleagues, teaching, sending a bug report or searching for guidance on mailing lists and here on Stack Overflow, a reproducible example is often asked and always helpful.

What are your tips for creating an excellent example? How do you paste data structures from in a text format? What other information should you include?

Are there other tricks in addition to using dput(), dump() or structure()? When should you include library() or require() statements? Which reserved words should one avoid, in addition to c, df, data, etc.?

How does one make a great reproducible example?

  • 34
    I'm confused about the scope of the question. People seem to have jumped on the interpretation of reproducible example in asking questions on SO or R-help (how to "reproduce the error"). What about reproducible R examples in help pages? In package demos? In tutorials / presentations? – baptiste Aug 7 '11 at 2:14
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    @baptiste : The same minus the error. All techniques I explained are used in package help pages, and in tutorials and presentations I give about R – Joris Meys Oct 4 '11 at 15:11
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    The data is sometimes the limiting factor, as the structure may be too complex to simulate. To produce public data from private data: stackoverflow.com/a/10458688/742447 in stackoverflow.com/questions/10454973/… – Etienne Low-Décarie May 11 '12 at 21:41

23 Answers 23


A minimal reproducible example consists of the following items:

  • a minimal dataset, necessary to demonstrate the problem
  • the minimal runnable code necessary to reproduce the error, which can be run on the given dataset
  • the necessary information on the used packages, R version, and system it is run on.
  • in the case of random processes, a seed (set by set.seed()) for reproducibility1

For examples of good minimal reproducible examples, see the help files of the function you are using. In general, all the code given there fulfills the requirements of a minimal reproducible example: data is provided, minimal code is provided, and everything is runnable. Also look at questions on Stack Overflow with lots of upvotes.

Producing a minimal dataset

For most cases, this can be easily done by just providing a vector/data frame with some values. Or you can use one of the built-in datasets, which are provided with most packages.
A comprehensive list of built-in datasets can be seen with library(help = "datasets"). There is a short description to every dataset and more information can be obtained for example with ?mtcars where 'mtcars' is one of the datasets in the list. Other packages might contain additional datasets.

Making a vector is easy. Sometimes it is necessary to add some randomness to it, and there are a whole number of functions to make that. sample() can randomize a vector, or give a random vector with only a few values. letters is a useful vector containing the alphabet. This can be used for making factors.

A few examples :

  • random values : x <- rnorm(10) for normal distribution, x <- runif(10) for uniform distribution, ...
  • a permutation of some values : x <- sample(1:10) for vector 1:10 in random order.
  • a random factor : x <- sample(letters[1:4], 20, replace = TRUE)

For matrices, one can use matrix(), eg :

matrix(1:10, ncol = 2)

Making data frames can be done using data.frame(). One should pay attention to name the entries in the data frame, and to not make it overly complicated.

An example :

Data <- data.frame(
    X = sample(1:10),
    Y = sample(c("yes", "no"), 10, replace = TRUE)

For some questions, specific formats can be needed. For these, one can use any of the provided as.someType functions : as.factor, as.Date, as.xts, ... These in combination with the vector and/or data frame tricks.

Copy your data

If you have some data that would be too difficult to construct using these tips, then you can always make a subset of your original data, using head(), subset() or the indices. Then use dput() to give us something that can be put in R immediately :

> dput(iris[1:4, ]) # first four rows of the iris data set
structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6), Sepal.Width = c(3.5, 
3, 3.2, 3.1), Petal.Length = c(1.4, 1.4, 1.3, 1.5), Petal.Width = c(0.2, 
0.2, 0.2, 0.2), Species = structure(c(1L, 1L, 1L, 1L), .Label = c("setosa", 
"versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length", 
"Sepal.Width", "Petal.Length", "Petal.Width", "Species"), row.names = c(NA, 
4L), class = "data.frame")

If your data frame has a factor with many levels, the dput output can be unwieldy because it will still list all the possible factor levels even if they aren't present in the the subset of your data. To solve this issue, you can use the droplevels() function. Notice below how species is a factor with only one level:

> dput(droplevels(iris[1:4, ]))
0.2, 0.2, 0.2), Species = structure(c(1L, 1L, 1L, 1L), .Label = "setosa",
class = "factor")), .Names = c("Sepal.Length", "Sepal.Width", 
4L), class = "data.frame")

When using dput, you may also want to include only relevant columns:

> dput(mtcars[1:3, c(2, 5, 6)]) # first three rows of columns 2, 5, and 6
structure(list(cyl = c(6, 6, 4), drat = c(3.9, 3.9, 3.85), wt = c(2.62, 
2.875, 2.32)), row.names = c("Mazda RX4", "Mazda RX4 Wag", "Datsun 710"
), class = "data.frame")

One other caveat for dput is that it will not work for keyed data.table objects or for grouped tbl_df (class grouped_df) from dplyr. In these cases you can convert back to a regular data frame before sharing, dput(as.data.frame(my_data)).

Worst case scenario, you can give a text representation that can be read in using the text parameter of read.table :

zz <- "Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa"

Data <- read.table(text=zz, header = TRUE)

Producing minimal code

This should be the easy part but often isn't. What you should not do, is:

  • add all kind of data conversions. Make sure the provided data is already in the correct format (unless that is the problem of course)
  • copy-paste a whole function/chunk of code that gives an error. First, try to locate which lines exactly result in the error. More often than not you'll find out what the problem is yourself.

What you should do, is:

  • add which packages should be used if you use any (using library())
  • if you open connections or create files, add some code to close them or delete the files (using unlink())
  • if you change options, make sure the code contains a statement to revert them back to the original ones. (eg op <- par(mfrow=c(1,2)) ...some code... par(op) )
  • test run your code in a new, empty R session to make sure the code is runnable. People should be able to just copy-paste your data and your code in the console and get exactly the same as you have.

Give extra information

In most cases, just the R version and the operating system will suffice. When conflicts arise with packages, giving the output of sessionInfo() can really help. When talking about connections to other applications (be it through ODBC or anything else), one should also provide version numbers for those, and if possible also the necessary information on the setup.

If you are running R in R Studio using rstudioapi::versionInfo() can be helpful to report your RStudio version.

If you have a problem with a specific package you may want to provide the version of the package by giving the output of packageVersion("name of the package").

1 Note: The output of set.seed() differs between R >3.6.0 and previous versions. Do specify which R version you used for the random process, and don't be surprised if you get slightly different results when following old questions. To get the same result in such cases, you can use the RNGversion()-function before set.seed() (e.g.: RNGversion("3.5.2")).

  • 6
    How do you use dput if the dataframe is very large and the problem is generated by the middle of the dataframe? Is there a way to use dput to reproduce the mid-section of data, say rows 60 through 70? – BgnR Apr 10 '14 at 13:12
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    @BgnR You can extract part of the data frame using indices, eg: tmp <- mydf[50:70,] followed by dput(mydf). If the data frame is really big, try isolating the problem and just submit the few lines that cause the problem. – Joris Meys Apr 11 '14 at 11:27
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    @JorisMeys: Is there a way to tell head or dput to limit data to level N recursively? I'm trying to come up with reproducible example and my data is a list of data frames. So, dput(head(myDataObj)) seems not to be enough, as it generates an output file of 14MB size. – Aleksandr Blekh Aug 4 '14 at 15:51
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    @JorisMeys: Just FYI - posted question in the comment above as a separate question: stackoverflow.com/questions/25127026/…. – Aleksandr Blekh Aug 4 '14 at 20:32
  • 4
    @Konrad The best thing you could do, is link to the file and give the minimal command to read in that file. That'll be less hassle than copy-pasting the output of dput() :) – Joris Meys Jan 3 '16 at 12:57

(Here's my advice from How to write a reproducible example . I've tried to make it short but sweet)

How to write a reproducible example.

You are most likely to get good help with your R problem if you provide a reproducible example. A reproducible example allows someone else to recreate your problem by just copying and pasting R code.

There are four things you need to include to make your example reproducible: required packages, data, code, and a description of your R environment.

  • Packages should be loaded at the top of the script, so it's easy to see which ones the example needs.

  • The easiest way to include data in an email or Stack Overflow question is to use dput() to generate the R code to recreate it. For example, to recreate the mtcars dataset in R, I'd perform the following steps:

    1. Run dput(mtcars) in R
    2. Copy the output

    • make sure you've used spaces and your variable names are concise, but informative

    • use comments to indicate where your problem lies

    • do your best to remove everything that is not related to the problem.
      The shorter your code is, the easier it is to understand.

  • Include the output of sessionInfo() in a comment in your code. This summarises your R environment and makes it easy to check if you're using an out-of-date package.

You can check you have actually made a reproducible example by starting up a fresh R session and pasting your script in.

Before putting all of your code in an email, consider putting it on Gist github . It will give your code nice syntax highlighting, and you don't have to worry about anything getting mangled by the email system.

  • 24
    reprex in tidyverse is a good package for producing minimal, reproducible example: github.com/tidyverse/reprex – mt1022 Jun 1 '17 at 7:24
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    I routinely receive emails with code in them. I even receive emails with attached word documents that contain code. Sometimes I even get emails with attached word documents that contain SCREENSHOTS of code. – hadley Mar 4 '18 at 14:46

Personally, I prefer "one" liners. Something along the lines:

my.df <- data.frame(col1 = sample(c(1,2), 10, replace = TRUE),
        col2 = as.factor(sample(10)), col3 = letters[1:10],
        col4 = sample(c(TRUE, FALSE), 10, replace = TRUE))
my.list <- list(list1 = my.df, list2 = my.df[3], list3 = letters)

The data structure should mimic the idea of writer's problem and not the exact verbatim structure. I really appreciate it when variables don't overwrite my own variables or god forbid, functions (like df).

Alternatively, one could cut a few corners and point to a pre-existing data set, something like:

ord <- metaMDS(varespec)

Don't forget to mention any special packages you might be using.

If you're trying to demonstrate something on larger objects, you can try

my.df2 <- data.frame(a = sample(10e6), b = sample(letters, 10e6, replace = TRUE))

If you're working with spatial data via the raster package, you can generate some random data. A lot of examples can be found in the package vignette, but here's a small nugget.

r1 <- r2 <- r3 <- raster(nrow=10, ncol=10)
values(r1) <- runif(ncell(r1))
values(r2) <- runif(ncell(r2))
values(r3) <- runif(ncell(r3))
s <- stack(r1, r2, r3)

If you're in need of some spatial object as implemented in sp, you can get some datasets via external files (like ESRI shapefile) in "spatial" packages (see the Spatial view in Task Views).

ogrInfo(dsn=dsn, layer="cities")
cities <- readOGR(dsn=dsn, layer="cities")
  • 1
    IMHO, when using sample or runif it's prudent to set.seed. At least, this the suggestion I've received when producing examples relaying on sampling or random number generation. – Konrad Jun 25 '18 at 16:16
  • 1
    @Konrad I agree, but this may depend. If you're just trying to generate some numbers a seed may not be needed but if you're trying to understand something specific where fixed numbers are needed, a seed would be mandatory. – Roman Luštrik Jun 26 '18 at 11:31
  • 1
    It's always better with a seed imo, it makes it easier to compare own solution to expected output, to compare solutions between themselves, and this way users that don't know (and don't need to know) functions like runif or sample are not confused that they can't get the same data. – Moody_Mudskipper Aug 10 '18 at 8:51
  • 2
    @mikey have you looked at usmap package? – Roman Luštrik Aug 13 '19 at 17:42
  • 2
    @mikey the package tigris downloads shapefiles from the Census Bureau in a variety of formats – camille Nov 7 '19 at 17:15

Inspired by this very post, I now use a handy function
reproduce(<mydata>) when I need to post to StackOverflow.


If myData is the name of your object to reproduce, run the following in R:




This function is an intelligent wrapper to dput and does the following:

  • automatically samples a large data set (based on size and class. Sample size can be adjusted)
  • creates a dput output
  • allows you to specify which columns to export
  • appends to the front of it objName <- ... so that it can be easily copy+pasted, but...
  • If working on a mac, the output is automagically copied to the clipboard, so that you can simply run it and then paste to your question.

The source is available here:


# sample data
DF <- data.frame(id=rep(LETTERS, each=4)[1:100], replicate(100, sample(1001, 100)), Class=sample(c("Yes", "No"), 100, TRUE))

DF is about 100 x 102. I want to sample 10 rows, and a few specific columns

reproduce(DF, cols=c("id", "X1", "X73", "Class"))  # I could also specify the column number. 

Gives the following output:

This is what the sample looks like: 

    id  X1 X73 Class
1    A 266 960   Yes
2    A 373 315    No            Notice the selection split 
3    A 573 208    No           (which can be turned off)
4    A 907 850   Yes
5    B 202  46   Yes         
6    B 895 969   Yes   <~~~ 70 % of selection is from the top rows
7    B 940 928    No
98   Y 371 171   Yes          
99   Y 733 364   Yes   <~~~ 30 % of selection is from the bottom rows.  
100  Y 546 641    No        

         Copy+Paste this part. (If on a Mac, it is already copied!)

 DF <- structure(list(id = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 25L, 25L, 25L), .Label = c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y"), class = "factor"), X1 = c(266L, 373L, 573L, 907L, 202L, 895L, 940L, 371L, 733L, 546L), X73 = c(960L, 315L, 208L, 850L, 46L, 969L, 928L, 171L, 364L, 641L), Class = structure(c(2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L), .Label = c("No", "Yes"), class = "factor")), .Names = c("id", "X1", "X73", "Class"), class = "data.frame", row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 98L, 99L, 100L)) 


Notice also that the entirety of the output is in a nice single, long line, not a tall paragraph of chopped up lines. This makes it easier to read on SO questions posts and also easier to copy+paste.

Update Oct 2013:

You can now specify how many lines of text output will take up (ie, what you will paste into StackOverflow). Use the lines.out=n argument for this. Example:

reproduce(DF, cols=c(1:3, 17, 23), lines.out=7) yields:


      X1 = c(809L, 81L, 862L,747L, 224L, 721L, 310L, 53L, 853L, 642L),
      X2 = c(926L, 409L,825L, 702L, 803L, 63L, 319L, 941L, 598L, 830L),
      X16 = c(447L,164L, 8L, 775L, 471L, 196L, 30L, 420L, 47L, 327L),
      X22 = c(335L,164L, 503L, 407L, 662L, 139L, 111L, 721L, 340L, 178L)), .Names = c("id","X1",
      "X2", "X16", "X22"), class = "data.frame", row.names = c(1L,2L, 3L, 4L, 5L, 6L, 7L, 98L, 99L, 100L))


Here is a good guide.

The most important point is: Just make sure that you make a small piece of code that we can run to see what the problem is. A useful function for this is dput(), but if you have very large data, you might want to make a small sample dataset or only use the first 10 lines or so.


Also make sure that you identified where the problem is yourself. The example should not be an entire R script with "On line 200 there is an error". If you use the debugging tools in R (I love browser()) and Google you should be able to really identify where the problem is and reproduce a trivial example in which the same thing goes wrong.


The R-help mailing list has a posting guide which covers both asking and answering questions, including an example of generating data:


If I have a matrix x as follows:

  > x
    x y
  A 1 5
  B 2 6
  C 3 7
  D 4 8

how can I turn it into a dataframe with 8 rows, and three columns named 'row', 'col', and 'value', which have the dimension names as the values of 'row' and 'col', like this:

  > x.df
     row col value
  1    A   x      1

(To which the answer might be:



The word small is especially important. You should be aiming for a minimal reproducible example, which means that the data and the code should be as simple as possible to explain the problem.

EDIT: Pretty code is easier to read than ugly code. Use a style guide.


Since R.2.14 (I guess) you can feed your data text representation directly to read.table:

 df <- read.table(header=TRUE, 
  text="Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
  • 3
    @sebastian-c how is that good for making reproducible example?? :) – TMS May 24 '14 at 8:43
  • @TMS Giving it serious thought, if the asker has supplied the data and the problem is small (but might have a few solutions), then it might be faster and you can still follow all the steps. – sebastian-c May 26 '14 at 17:52

Sometimes the problem really isn't reproducible with a smaller piece of data, no matter how hard you try, and doesn't happen with synthetic data (although it's useful to show how you produced synthetic data sets that did not reproduce the problem, because it rules out some hypotheses).

  • Posting the data to the web somewhere and providing a URL may be necessary.
  • If the data can't be released to the public at large but could be shared at all, then you may be able to offer to e-mail it to interested parties (although this will cut down the number of people who will bother to work on it).
  • I haven't actually seen this done, because people who can't release their data are sensitive about releasing it any form, but it would seem plausible that in some cases one could still post data if it were sufficiently anonymized/scrambled/corrupted slightly in some way.

If you can't do either of these then you probably need to hire a consultant to solve your problem ...

edit: Two useful SO questions for anonymization/scrambling:


The answers so far are obviously great for the reproducibility part. This is merely to clarify that a reproducible example cannot and should not be the sole component of a question. Don't forget to explain what you want it to look like and the contours of your problem, not just how you have attempted to get there so far. Code is not enough; you need words also.

Here's a reproducible example of what to avoid doing (drawn from a real example, names changed to protect the innocent):

The following is sample data and part of function I have trouble with.

code (40 or so lines of it)

How can I achieve this ?


I have a very easy and efficient way to make a R example that has not been mentioned above. You can define your structure firstly. For example,

mydata <- data.frame(a=character(0), b=numeric(0),  c=numeric(0), d=numeric(0))


When you execute 'fix' command, you will get this pop-up box

Then you can input your data manually. This is efficient for smaller examples rather than big ones.

  • 18
    ...then dput(mydata) – GSee Mar 8 '14 at 16:46
  • What is your frontend? It would be nice to have a complete answer. Etc make a data which you can directly loop like for (d in data) {...}. – Léo Léopold Hertz 준영 Oct 30 '16 at 7:38

To quickly create a dput of your data you can just copy (a piece of) the data to your clipboard and run the following in R:

for data in Excel:


for data in a txt file:


You can change the sep in the latter if necessary. This will only work if your data is in the clipboard of course.



Your main objective in crafting your questions should be to make it as easy as possible for readers to understand and reproduce your problem on their systems. To do so:

  1. Provide input data
  2. Provide expected output
  3. Explain your problem succinctly
    • if you have over 20 lines of text + code you can probably go back and simplify
    • simplify your code as much as possible while preserving the problem/error

This does take some work but seems like a fair trade-off since you are asking others to do work for you.

Providing Data:

Built-in Data Sets

The best option by far is to rely on built-in datasets. This makes it very easy for others to work on your problem. Type data() at the R prompt to see what data is available to you. Some classic examples:

  • iris
  • mtcars
  • ggplot2::diamonds (external package, but almost everyone has it)

See this SO QA for how to find data sets suitable for your problem.

If you are able to rephrase your problem to use the built-in datasets you are much more likely to get good answers (and upvotes).

Self Generated Data

If your problem is very specific to a type of data that is not represented in the existing data sets, then provide the R code that generates the smallest possible dataset that your problem manifests itself on. For example

set.seed(1)  # important to make random data reproducible
myData <- data.frame(a=sample(letters[1:5], 20, rep=T), b=runif(20))

Now someone trying to answer my question can copy/paste those two lines and start working on the problem immediately.


As a last resort, you can use dput to transform a data object to R code (e.g. dput(myData)). I say as a "last resort" because the output of dput is often fairly unwieldy, annoying to copy-paste, and obscures the rest of your question.

Provide Expected Output:

Someone once said:

A picture of expected output is worth 1000 words

-- a very wise person

If you can add something like "I expected to get this result":

   cyl   mean.hp
1:   6 122.28571
2:   4  82.63636
3:   8 209.21429

to your question, people are much more likely to quickly understand what you are trying to do. If your expected result is large and unwieldy, then you probably haven't thought enough about how to simplify your problem (see next).

Explain Your Problem Succinctly

The main thing to do is to simplify your problem as much as possible before you ask your question. Re-framing the problem to work with the built-in datasets will help a lot in this regard. You will also often find that just by going through the process of simplification you will answer your own problem.

Here are some examples of good questions:

In both cases, the user's problems are almost certainly not with the simple examples they provide. Rather they abstracted the nature of their problem and applied it to a simple data set to ask their question.

Why Yet Another Answer To This Question?

This answer focuses on what I think is the best practice: use built-in data sets and provide what you expect as a result in a minimal form. The most prominent answers focus on other aspects. I don't expect this answer to rising to any prominence; this is here solely so that I can link to it in comments to newbie questions.


Reproducible code is key to get help. However, there are many users that might be skeptical of pasting even a chunk of their data. For instance, they could be working with sensitive data or on an original data collected to use in a research paper. For any reason, I thought it would be nice to have a handy function for "deforming" my data before pasting it publicly. The anonymize function from the package SciencesPo is very silly, but for me it works nicely with dput function.


dt <- data.frame(
    Z = sample(LETTERS,10),
    X = sample(1:10),

> dt
   Z  X   Y
1  D  8  no
2  T  1 yes
3  J  7  no
4  K  6  no
5  U  2  no
6  A 10 yes
7  Y  5  no
8  M  9 yes
9  X  4 yes
10 Z  3  no

Then I anonymize it:

> anonymize(dt)
     Z    X  Y
1   b2  2.5 c1
2   b6 -4.5 c2
3   b3  1.5 c1
4   b4  0.5 c1
5   b7 -3.5 c1
6   b1  4.5 c2
7   b9 -0.5 c1
8   b5  3.5 c2
9   b8 -1.5 c2
10 b10 -2.5 c1

One may also want to sample few variables instead of the whole data before apply anonymization and dput command.

    # sample two variables without replacement
> anonymize(sample.df(dt,5,vars=c("Y","X")))
   Y    X
1 a1 -0.4
2 a1  0.6
3 a2 -2.4
4 a1 -1.4
5 a2  3.6

Often you need some data for an example, however, you don't want to post your exact data. To use some existing data.frame in established library, use data command to import it.



and then do the problem

your problem demostrated on the mtcars data set
| improve this answer | |
  • 13
    Many built-in data sets (like popular mtcars and iris datasets) don't actually need the data call to be used. – Gregor Thomas Jul 17 '13 at 19:17

If you have large dataset which cannot be easily put to the script using dput(), post your data to pastebin and load them using read.table:

d <- read.table("http://pastebin.com/raw.php?i=m1ZJuKLH")

Inspired by @Henrik.


I am developing the wakefield package to address this need to quickly share reproducible data, sometimes dput works fine for smaller data sets but many of the problems we deal with are much larger, sharing such a large data set via dput is impractical.


wakefield allows the user to share minimal code to reproduce data. The user sets n (number of rows) and specifies any number of preset variable functions (there are currently 70) that mimic real if data (things like gender, age, income etc.)


Currently (2015-06-11), wakefield is a GitHub package but will go to CRAN eventually after unit tests are written. To install quickly, use:



Here is an example:

    n = 500,

This produces:

    ID  Race Age    Sex     Hour  IQ Height  Died
1  001 White  33   Male 00:00:00 104     74  TRUE
2  002 White  24   Male 00:00:00  78     69 FALSE
3  003 Asian  34 Female 00:00:00 113     66  TRUE
4  004 White  22   Male 00:00:00 124     73  TRUE
5  005 White  25 Female 00:00:00  95     72  TRUE
6  006 White  26 Female 00:00:00 104     69  TRUE
7  007 Black  30 Female 00:00:00 111     71 FALSE
8  008 Black  29 Female 00:00:00 100     64  TRUE
9  009 Asian  25   Male 00:30:00 106     70 FALSE
10 010 White  27   Male 00:30:00 121     68 FALSE
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If you have one or more factor variable(s) in your data that you want to make reproducible with dput(head(mydata)), consider adding droplevels to it, so that levels of factors that are not present in the minimized data set are not included in your dput output, in order to make the example minimal:


I wonder if an http://old.r-fiddle.org/ link could be a very neat way of sharing a problem. It receives a unique ID like and one could even think about embedding it in SO.


Please do not paste your console outputs like this:

If I have a matrix x as follows:
> x
  x y
A 1 5
B 2 6
C 3 7
D 4 8

How can I turn it into a dataframe with 8 rows, and three
columns named `row`, `col`, and `value`, which have the
dimension names as the values of `row` and `col`, like this:
> x.df
    row col value
1    A   x      1
(To which the answer might be:

We can not copy-paste it directly.

To make questions and answers properly reproducible, try to remove + & > before posting it and put # for outputs and comments like this:

#If I have a matrix x as follows:
#  x y
#A 1 5
#B 2 6
#C 3 7
#D 4 8

# How can I turn it into a dataframe with 8 rows, and three

#    row col value
#1    A   x      1
#To which the answer might be:


One more thing, if you have used any function from certain package, mention that library.

  • 2
    do you remove the > and add the # manually or is there an automatic way to do that? – BCArg Jul 7 '17 at 8:22
  • 3
    @BCArg I remove > manually. But, for addition of #, I use Ctrl+Shift+C shortcut in RStudio editor. – user2100721 Jul 22 '17 at 15:15

You can do this using reprex.

As mt1022 noted, "... good package for producing minimal, reproducible example is "reprex" from tidyverse".

According to Tidyverse:

The goal of "reprex" is to package your problematic code in such a way that other people can run it and feel your pain.

An example is given on tidyverse web site.

y <- 1:4

I think this is the simplest way to create a reproducible example.


Apart of all above answers which I found very interesting, it could sometimes be very easy as it is discussed here :- HOW TO MAKE A MINIMAL REPRODUCIBLE EXAMPLE TO GET HELP WITH R

There are many ways to make a random vector Create a 100 number vector with random values in R rounded to 2 decimals or random matrix in R

mydf1<- matrix(rnorm(20),nrow=20,ncol=5)

Note that sometimes it is very difficult to share a given data because of various reasons such as dimension etc. However, all above answers are great and very important to think and use when one wants to make a reproducible data example. But note that in order to make a data as representative as the original (in case the OP cannot share the original data), it is good to add some information with the data example as (if we call the data mydf1)

# this shows the type of the data you have 
# this shows the dimension of your data

Moreover, one should know the type, length and attributes of a data which can be Data structures

#found based on the following 
typeof(mydf1), what it is.
length(mydf1), how many elements it contains.
attributes(mydf1), additional arbitrary metadata.

#If you cannot share your original data, you can str it and give an idea about the structure of your data

Here are some of my suggestions:

  • Try to use default R datasets
  • If you have your own dataset, include them with dput, so others can help you more easily
  • Do not use install.package() unless it is really necessary, people will understand if you just use require or library
  • Try to be concise,

    • Have some dataset
    • Try to describe the output you need as simply as possible
    • Do it yourself before you ask the question
  • It is easy to upload an image, so upload plots if you have
  • Also include any errors you may have

All these are part of a reproducible example.

  • 1
    You haven't really added anything of substance here. dput() has been mentioned previously, and much of this is just reiterating standard SO guidelines. – Rich Scriven Apr 9 '16 at 19:04
  • 1
    I was having problem with install.package function included in the example which is not really necessary (in my opinion). Further, using default R dataset would make the reproducible easier. The SO guidelines has not talked anything about these topics specifically. Further, It was meant to give my opinion and these are the one which I have encountered most. – TheRimalaya Apr 9 '16 at 19:17

It's a good idea to use functions from the testthat package to show what you expect to occur. Thus, other people can alter your code until it runs without error. This eases the burden of those who would like to help you, because it means they don't have to decode your textual description. For example

# code defining x and y
if (y >= 10) {
    expect_equal(x, 1.23)
} else {
    expect_equal(x, 3.21)

is clearer than "I think x would come out to be 1.23 for y equal to or exceeding 10, and 3.21 otherwise, but I got neither result". Even in this silly example, I think the code is clearer than the words. Using testthat lets your helper focus on the code, which saves time, and it provides a way for them to know they have solved your problem, before they post it

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