minimal reproducible example r

dput (iris [1:4, ]) # first four rows of the iris data set. Example: reproduce(DF, cols=c(1:3, 17, 23), lines.out=7) yields: The most important point is: Make a small piece of code that we can run to see what the problem is. Reproducible code is the key to get help. raised an issue against the When contacting someone (this applies to anyone: a professor, colleague, the author of a package, etc.) For example, to recreate the mtcars dataset in R, I'd perform the following steps: Run dput (mtcars) in R Copy the output In my reproducible script, type mtcars <- then paste. If simulated, you may need to set the seed via set.seed () if the error appears only intermittently. Although compiling numpy from source did fix our issue, it currently opted to build the numpy package from source at image build-time, This means course content in a main branch should never fail our checks. About. If you have a problem with a specific package you may want to provide version of the package by giving the output of packageVersion("name of the package"). The analysis component is based on R using both custom R programs as well as existing R/Bioconductor packages ( Figure 1A). What is the meaning of to fight a Catch-22 is to accept it? the real culprits were the incompatible BLAS (Basic Linear Algebra future change to the BLAS libraries used by the rocker image series or nested) ID codes (A01-B01, A01-B02, A02-B01, A02-B02, A03-B01 ), When you ask a question we (JD and/or BB) will often say Can you send me a (minimal) reproducible example? (these are sometimes called reprexes or abbreviated MWE (minimal working example) or MRE (ditto, reproducible)). To learn more about vetiver, see: the documentation at https://vetiver.rstudio.com/ the Python package at https://rstudio.github.io/vetiver-python/ You can use vetiver with: a tidymodels workflow caret mlr3 XGBoost ranger Something along the lines: The data structure should mimic the idea of the writer's problem and not the exact verbatim structure. To get the same result in such cases, you can use the RNGversion()-function before set.seed() (e.g. copy-paste a whole script that gives an error somewhere. Main requirements Use the smallest, simplest, most built-in data possible. Python, Stan, TensorFlow, and others. Here are some examples of good questions: In both cases, the user's problems are almost certainly not with the simple examples they provide. training on Docker, Making data frames can be done using data.frame(). To install quickly, use: 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: The original post referred to the now retired r-fiddle service from datacamp. 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: DF is about 100 x 102. 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 the tidyverse. Simply type e.g. git clone csgroen/blog_example) Mount the repo into the docker image and run: Can we remove all unnecessary files, such as presentation slides? Press J to jump to the feed. A minimal reproducible example After all of our simplifications, we arrived at a minimal reproducible example with the Dockerfile: FROM rocker/r-ver:latest RUN apt update && apt install -y python3 python3-dev python3-venv RUN install2.r --error reticulate COPY test.R /root/ and associated R script: 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 data set that your problem manifests itself on. Subprograms) libraries being used by R and numpy! First, you can use R to generate your own random data, and post the code in your question. To solve this issue, you can use the droplevels() function. the container used a large number of internal Jumping Rivers R data.tables setorder() was ~14x faster than the fastest of other methods (dplyr), while taking just 0.4GB extra memory. The reprex package will save effort for you and others who want to help. Reducing the code to the bare minimum necessary to convey the problem makes the question easier to ask, and inherently easier to answer. Basically, a minimal reproducible example (MRE) should enable others to exactly reproduce your issue on their machines. Are there other tricks in addition to using dput(), dump() or structure()? c++. Quantum Teleportation with mixed shared state. make sure youve used spaces and your variable names are concise, but informative. command. The file should contain the following three sections: Packages to be loaded . Find centralized, trusted content and collaborate around the technologies you use most. More often than not youll find out what the problem is yourself. Which reserved words should one avoid, in addition to c, df, data, etc.? Everything went dark and you cannot check the cables on the back of the computer because the lights are off due to the power outage. CoronaVirus_Disease_2019_prevalence: Who should be inspected? This summarises your R For more information on how to debug your program so that you can create a minimal example, Eric Lippert has written a fantastic blog post on the subject: How to debug small programs. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. Minimal reproducible example consists of the following items: A minimal dataset which is necessary to reproduce the error. How to share a dataframe in stack overflow, create copy paste example from dataframe or matrix in r, How to join (merge) data frames (inner, outer, left, right). People want to help you, but you have to give them an example that they can work with on their own computer. The computer sits around on the couch all day eating chips and watching talk shows. Spend a little bit of time ensuring that your code is easy for others to read: make sure you've used spaces and your variable names are concise, but informative Why did The Bahamas vote against the UN resolution for Ukraine reparations? difficult to debug as. Feel free to use the code and adapt it to you. Also, make sure that you identified where the problem is yourself. A reproducible example allows someone else to recreate your problem by just copying and pasting R code. It should consist of a single R script file that can be run without error. 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. The provided reproduction is a minimal reproducible example of the bug. Looking at the examples in the help files of the used functions is often helpful. Required packages. Yes: a simpler Rmd file Yes. from others. ---
title: "How to make a great R reproducible example?"
author: "Stone_Hou"
date: "2017年7月16日"
output:
  html_notebook:
    theme: readable
    toc: yes
    toc_depth: 4
---

# How to make a great R reproducible example

> [how-to-make-a-great-r-reproducible-example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example)

When discussing performance with colleagues, teaching, sending a bug report or searching for guidance on mailing lists and here on SO, 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 r 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 r reproducible example?

## Answer 1
A minimal reproducible example consists of the following items:

* a minimal dataset, necessary to reproduce the error

* 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 reproducibility

Looking at the examples in the help files of the used functions is often helpful. 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.

### 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 :
```{r data.frame ex1}
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 eg `head()`, `subset()` or the `indices`. Then use eg. `dput()` to give us something that can be put in R immediately :

```{r dput ex1}
dput(head(iris,4))
# 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")
```

when data set is large
```{r dput ex2}
tmp <- mydf[50:70,]
dput(tmp)
```


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:

```{r droplevels}
dput(droplevels(head(iris, 4)))
dput(droplevels(head(mydata)))
# 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 = "setosa",
# class = "factor")), .Names = c("Sepal.Length", "Sepal.Width", 
# "Petal.Length", "Petal.Width", "Species"), row.names = c(NA, 
# 4L), 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` :

```{r 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.

* if you open connections or make 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 version of the package by giving the output of `packageVersion("name of the package")`.


## Answer 2

> [http://adv-r.had.co.nz/Reproducibility.html](http://adv-r.had.co.nz/Reproducibility.html)

### 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.

1. *Packages* should be loaded at the top of the script, so it's easy to see which ones the example needs.

2. 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:

* Run `dput(mtcars)` in R

* Copy the output

* In my reproducible script, type `mtcars <-` then paste.

3. Spend a little bit of time ensuring that your code is easy for others to read:

  + 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.

4. 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 [http://gist.github.com/](http://gist.github.com/). It will give your code nice syntax highlighting, and you don't have to worry about anything getting mangled by the email system.


## Get your data

### Data Namming and Generated Data
```{r data frame list}
my.dt <- data.frame(
    Z = sample(LETTERS,10),
    X = sample(1:10),
    Y = sample(c("yes", "no"), 10, replace = TRUE)
)

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)

my.df2 <- data.frame(a = sample(10e6), b = sample(letters, 10e6, replace = TRUE))

my.df3 <- read.table(header=T, 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
");my.df3

```

### From clipboard-Excel and txt data

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:

```{r clipboard Excel data}
# copy to clipboard
read.table("clipboard", header=TRUE)

# Excel copy + clipboard
dput(read.table("clipboard",sep="\t",header=TRUE))
```

for data in a txt file:
```{r clipboard txt data}
# txt copy + clipboard
dput(read.table("clipboard",sep="",header=TRUE))
```
You can change the sep in the latter if necessary. This will only work if your data is in the clipboard of course.

### input your data manually
```{r input your data manually by fix function}
# You can define your structure firstly.For example
mydata <- data.frame(
  a=character(0), 
  b=numeric(0),  
  c=numeric(0), 
  d=numeric(0))

# input your data manually
fix(mydata)

#then dput mydata
dput(mydata)
```

### built-in data sets

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)

### 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 data set that your problem manifests itself on. 

First, you can use R to generate your own random data, and post the code in your question.

The following R code generates a small sample data.frame
with variables for id, gender, and age. The set.seed function makes sure that the random values sampled will be identical, no matter who runs the code. For example

```{r Self Generated Data}
set.seed(3)

sampleData <- data.frame(id = 1:10, 
                         gender = sample(c("Male", "Female"), 10, replace = TRUE),
                         age = rnorm(10, 40, 10))
summary(sampleData)


set.seed(1)  # important to make random data reproducible
myData <- data.frame(a=sample(letters[1:5], 20, rep=T), b=runif(20))

# set.seed(100)
my.dm <- matrix(rnorm(20),nrow=20,ncol=5);my.dm
class(my.dm)
# this shows the type of the data you have 
dim(my.dm)
# this shows the dimension of your data

#found based on the following 
typeof(my.dm) #what it is.
length(my.dm) #how many elements it contains.
attributes(my.dm) #additional arbitrary metadata.

#If you cannot share your original data, you can str it and give an idea about the structure of your data
head(str(my.dm))
```

### Sample Large Dataset

```{r Sample Large Dataset}
## we will generate a data.frame for this example, but
## this object represents your "real" data
largeData <- data.frame(id = 1:1000, age = rnorm(1000, 40, 10))

## posting the dput output of a data.frame with 1000 observations
## is probably not necessary, so we will take a small subset 
sampleData <- largeData[sample(nrow(largeData), 10), ]

## use dput to write out a text representation of the R object
dput(sampleData)
```


## Unit Test

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
```{r testthat}
# import package in a safe way
if(!suppressWarnings(require('testthat'))) {
  install.packages('testthat')
  require('testthat')
}

y <- c(10.5)

# code defining x and y
if (y >= 10) {
    expect_equal(x, 1.23)
} else {
    expect_equal(x, 3.21)
}
```

It 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

## How do I replace NA values with zeros in an R dataframe?

```{r}
my.dm <- matrix(sample(c(NA, 1:10), 100, replace = TRUE), 10)
my.df <- as.data.frame(my.dm);my.df
#    V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
# 1   4  3 NA  3  7  6  6 10  6   5
# 2   9  8  9  5 10 NA  2  1  7   2
# 3   1  1  6  3  6 NA  1  4  1   6
# 4  NA  4 NA  7 10  2 NA  4  1   8
# 5   1  2  4 NA  2  6  2  6  7   4
# 6  NA  3 NA NA 10  2  1 10  8   4
# 7   4  4  9 10  9  8  9  4 10  NA
# 8   5  8  3  2  1  4  5  9  4   7
# 9   3  9 10  1  9  9 10  5  3   3
# 10  4  2  2  5 NA  9  7  2  5   5

my.df[is.na(my.df)] <- 0; my.df

# For a single vector:
x <- c(1,2,NA,4,5)
x[is.na(x)] <- 0

# dplyr way ifelse
require(dplyr)
my.df <- my.df %>%
      mutate(colname = ifelse(is.na(colname),0,colname))

#function way
na_2_zero <- function (x) {
    x[is.na(x)] <- 0
    return(x)
}
#na_2_zero 
na_2_zero(some.vector)

#csv
write.csv(data,"data.csv",na="0")

# dplyr mutate_all()
library(tidyverse)
#The *microbenchmark* package provides an easy way to run a substantial number of trials
# import package in a safe way
if(!suppressWarnings(require('tidyverse', 'microbenchmark'))) {
  install.packages('tidyverse', 'microbenchmark')
  require('tidyverse', 'microbenchmark')
}
library(microbenchmark)

# Numerics replaced with numerics
set.seed(24)
dfN <- as.data.frame(matrix(sample(as.numeric(c(NA, 1:5)), 1e6 * 12, replace=TRUE),
                            dimnames = list(NULL, paste0("var", 1:12)), 
                            ncol=12))

opN<- microbenchmark(
    baseR_replace    = local(dfN %>% replace(., is.na(.), 0)),
    subsetReassign   = local(dfN[is.na(dfN)] <- 0),
    mut_at_replace   = local(dfN %>% mutate_at(funs(replace(., is.na(.), 0)), .vars = c(1:12))),
    mut_all_coalesce = local(dfN %>% mutate_all(funs(coalesce(., 0)))),
    mut_all_replace  = local(dfN %>% mutate_all(funs(replace(., is.na(.), 0)))),
    replace_na       = local(dfN %>% replace_na(list(var1 = 0, var2 = 0, var3 = 0, var4 = 0, var5 = 0, var6 = 0, var7 = 0, var8 = 0, var9 = 0, var10 = 0, var11 = 0, var12 = 0))),
    times = 1000L
)

print(opN) #standard data frame of the output
boxplot(opN)
```

This simple function extracted from Datacamp could help:

```{r replace_missings}
replace_missings <- function(x, replacement) {
  is_miss <- is.na(x)
  x[is_miss] <- replacement

  message(sum(is_miss), " missings replaced by the value ", replacement)
  x
}

my.dm <- matrix(sample(c(NA, 1:10), 100, replace = TRUE), 10)
my.df <- as.data.frame(my.dm);my.df
replace_missings(my.df, replacement = 0)

```

## How to sort a dataframe by column(s)?

```{r sort a dataframe by column}
dd <- data.frame(b = factor(c("Hi", "Med", "Hi", "Low"), 
      levels = c("Low", "Med", "Hi"), ordered = TRUE),
      x = c("A", "D", "A", "C"), y = c(8, 3, 9, 9),
      z = c(1, 1, 1, 2));dd

#     b x y z
# 1  Hi A 8 1
# 2 Med D 3 1
# 3  Hi A 9 1
# 4 Low C 9 2

dd[with(dd, order(-z, b)), ]
#     b x y z
# 4 Low C 9 2
# 2 Med D 3 1
# 1  Hi A 8 1
# 3  Hi A 9 1
dd[ order(-dd[,4], dd[,1]), ]
#     b x y z
# 4 Low C 9 2
# 2 Med D 3 1
# 1  Hi A 8 1
# 3  Hi A 9 1

################
## The data.frame way
dd[with(dd, order(-z, b)), ]

## The data.table way: (7 fewer characters, but that's not the important bit)
dd[order(-z, b)]

###################
# dplyr way
library(dplyr)
# sort mtcars by mpg, ascending... use desc(mpg) for descending
arrange(mtcars, mpg)
# sort mtcars first by mpg, then by cyl, then by wt)
arrange(mtcars , mpg, cyl, wt)


###############
require(plyr)
require(doBy)
require(data.table)
require(dplyr)
require(taRifx)

set.seed(45L)
dat = data.frame(b = as.factor(sample(c("Hi", "Med", "Low"), 1e8, TRUE)),
                 x = sample(c("A", "D", "C"), 1e8, TRUE),
                 y = sample(100, 1e8, TRUE),
                 z = sample(5, 1e8, TRUE), 
                 stringsAsFactors = FALSE)
# Benchmarks:

# The timings reported are from running system.time(...) on these functions shown below. The timings are tabulated below (in the order of slowest to fastest).

orderBy( ~ -z + b, data = dat)     ## doBy
plyr::arrange(dat, desc(z), b)     ## plyr
arrange(dat, desc(z), b)           ## dplyr
sort(dat, f = ~ -z + b)            ## taRifx
dat[with(dat, order(-z, b)), ]     ## base R

# convert to data.table, by reference
setDT(dat)

dat[order(-z, b)]                  ## data.table, base R like syntax
setorder(dat, -z, b)               ## data.table, using setorder()
                                   ## setorder() now also works with data.frames 

# R-session memory usage (BEFORE) = ~2GB (size of 'dat')
# ------------------------------------------------------------
# Package      function    Time (s)  Peak memory   Memory used
# ------------------------------------------------------------
# doBy          orderBy      409.7        6.7 GB        4.7 GB
# taRifx           sort      400.8        6.7 GB        4.7 GB
# plyr          arrange      318.8        5.6 GB        3.6 GB 
# base R          order      299.0        5.6 GB        3.6 GB
# dplyr         arrange       62.7        4.2 GB        2.2 GB
# ------------------------------------------------------------
# data.table      order        6.2        4.2 GB        2.2 GB
# data.table   setorder        4.5        2.4 GB        0.4 GB
# ------------------------------------------------------------


```

`data.table`'s `DT[order(...)]` syntax was ~10x faster than the fastest of other methods (`dplyr`), while consuming the same amount of memory as dplyr.

data.table's `setorder()` was ~14x faster than the fastest of other methods (`dplyr`), while taking just 0.4GB extra memory. dat is now in the order we require (as it is updated by reference).



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