Minimal Reproducible Data

Last updated on 2024-12-03 | Edit this page

Overview

Questions

What is a minimal reproducible dataset, and why do I need it?
What do I need to include in a minimal reproducible dataset?
How do I create a minimal reproducible dataset?
How do I make my own dataset reproducible?

Objectives

Describe a minimal reproducible dataset
List the requirements for a minimal reproducible dataset
Identify the important aspects of the data that are necessary to reproduce your coding problem
Create a dataset from scratch to reproduce a given example
Subset an existing dataset to reproduce a given example
Share your own dataset in a way that is accessible and reproducible

4.1 What is a minimal reproducible dataset and why do I need it?

Now that we understand some basic errors and how to fix them, let’s look at what to do when we can’t figure out a solution to our coding problem.

This is when you really need to know how to create a minimal reproducible example (MRE) as we talked about in episode 1.

In general, an MRE will need:

A minimal dataset that can reproduce the error (or access to a such a dataset)
Minimal runnable code that can reproduce the error using the minimal dataset
Basic information about the system, R version, and packages being used
In case of random functions (e.g., sample()), a seed that will produce the same results each time (e.g., use set.seed())

The first step in creating an MRE is to create a shareable dataset that your helper can manipulate and use to reproduce your error and fix your issue.

Why? Remember our IT problem? It would be a lot easier for the IT support person to fix your computer if they could actually touch it, see the screen, and click around.

Instructor Note

You can use the next example as well if you choose.

Another example:

You’re knitting a sweater and one of the sleeves looks wonky. You call a friend and ask why it’s messed up. They can’t possibly help without being able to hold the sweater and look at the stitches themselves.

It would be great if we could give the helper our entire computer so they could just take over where we left off, but usually we can’t.

Callout

There are several reasons why you might need to create a separate dataset that is minimal and reproducible instead of trying to use your actual dataset. The original dataset may be:

too large - the Portal dataset is ~35,000 rows with 13 columns and contains data for decades. That’s a lot!
private - your dataset might not be published yet, or maybe you’re studying an endangered species whose locations can’t easily be shared. Another example: many medical datasets cannot be shared publically.
hard to send - on most online forums, you can’t attach supplemental files (more on this later). Even if you are just sending data to a colleague, file paths can get complicated, the data might be too large to attach, etc.
complicated - it would be hard to locate the relevant information. One example to steer away from are needing a ‘data dictionary’ to understand all the meanings of the columns (e.g. what is “plot type” in ratdat?) We don’t our helper to waste valuable time to figure out what everything means.
highly derived/modified from the original file. As an example, you may have already done a bunch of preliminary data wrangling and you don’t want to include all that code when you send the example (see later: the minimal code section), so you need to provide the intermediate dataset directly to your helper.

It’s useful to strip the dataset to its essential parts to identify where exactly the problem area is. A minimal dataset is a dataset that includes the information necessary to run the code, but removes all other unnecessary parts (extra columns/rows, extra context, etc.)

We need minimal reproducible datasets to make it easy/simple/fast for the helper to focus in on the problem at hand and “get their hands dirty” tinkering with the dataset.

4.2 What do I need to include in a minimal reproducible dataset?

Instructor Note

Ask the audience, wait for them to respond

It’s actually all in the name:

it needs to be minimal, which means it only contains the necessary information to run the piece of code with which you are struggling. You can also think of this as being relevant to the problem. Only keep the necessary elements/variables.
it needs to be reproducible. The data you provide must consistently reproduce the output or error with which you are struggling.
For it to truly be reproducible, it also needs to be complete, meaning there are no dependencies that have been omitted, and accessible, which means the helper must have access to the relevant data and code (more on this later).

Remember: your helper may not be in the room with you or have access to your computer and the files that are on it!

You might be used to always uploading data from separate files, but helpers can’t access those files. Even if you sent someone a file, they would need to put it in the right directory, make sure to load it in exactly the same way, make sure their computer can open it, etc. Since the goal is to make everyone’s lives as easy as possible, we need to think about our data in a different way–as a dummy object created in the script itself.

Pro-tip

An example of what minimal reproducible examples look like can also be found in the ?help section, in R Studio. Just scroll all the way down to where there are examples listed. These will usually be minimal and reproducible.

For example, let’s look at the function mean:

R

?mean

We see examples that can be run directly on the console, with no additional code.

R

x <- c(0:10, 50)
xm <- mean(x)
c(xm, mean(x, trim = 0.10))

OUTPUT

[1] 8.75 5.50

In this case, x is the dummy dataset consisting of just 1 variable. Notice how it was created as part of the example.

Exercise 1

These datasets are not well suited for use in a reprex. For each one, try to reproduce the dataset on your own in R (copy-paste). Does it work? What happened? Explain.

sample_data <- read_csv(“/Users/kaija/Desktop/RProjects/ResearchProject/data/sample_dataset.csv”)
dput(complete_old[1:100,])
sample_data <- data.frame(species = species_vector, weight = c(10, 25, 14, 26, 30, 17))

Show me the solution

Not reproducible because it is a screenshot.
Not reproducible because it is a path to a file that only exists on someone else’s computer and therefore you do not have access to it using that path.
Not minimal, it has far too many columns and probably too many rows. It is also not reproducible because we were not given the source for complete_old.
Not reproducible because we are not given the source for species_vector.

Exercise 2

Let’s say we want to know the average weight of all the species in our rodents dataset. We try to use the following code…

R

mean(rodents$weight)

OUTPUT

[1] NA

…but it returns NA! We don’t know why that is happening and we want to ask for help.

Which of the following represents a minimal reproducible dataset for this code? Can you describe why the other ones are not?

sample_data <- data.frame(month = rep(7:9, each = 2), hindfoot_length = c(10, 25, 14, 26, 30, 17))
sample_data <- data.frame(weight = rnorm(10))
sample_data <- data.frame(weight = c(100, NA, 30, 60, 40, NA))
sample_data <- sample(rodents$weight, 10)
sample_data <- rodents_modified[1:20,]

Show me the solution

The correct answer is C!

does not include the variable of interest (weight).
does not produce the same problem (NA result with a warning message)–the code runs just fine.
minimal and reproducible.
is not reproducible. Sample randomly samples 10 items; sometimes it may include NAs, sometime it may not (not guaranteed to reproduce the error). It can be used if a seed is set (see next section for more info).
uses a dataset that isn’t accessible without previous data wrangling code–the object rodents_modified doesn’t exist.

4.3 How do I create a minimal reproducible dataset?

This is where many often get stuck: how do I recreate the key elements of my dataset in order to reproduce my error? That seems really hard! If you also find this initially overwhelming, don’t worry. We will break it down into smaller steps.

First, there are three approaches to providing a dataset. You can (1) create one from scratch, (2) use a dataset that is already available, (3) copy/recreate your actual dataset in a way that is minimal and reproducible. The approach you choose to take will depend largely on the nature and source of the problem as well as the complexity of your original dataset. Therefore, no matter which approach we take we first need to know which elements of our dataset are necessary:

How many variables do we need?
What data type is each variable?
How many levels and/or observations are necessary?
How many of the values need to be the same/different?
Are there any NAs that could be relevant?

Keep these questions in mind as we move through our examples.

Instructor Note

OR should we start with an example right here where they need to answer those questions?

Let’s start from scratch.

4.3.1 Create a dummy dataset from scratch

There are many ways one can create a dummy dataset from scratch.

You can create vectors using c()

R

vector <- c(1,2,3,4) 
vector

OUTPUT

[1] 1 2 3 4

You can add some randomness by sampling from a vector using sample().

For example you can sample numbers 1 through 10 in a random order

R

x <- sample(1:10)
x

OUTPUT

 [1]  8 10  2  3  1  4  6  5  7  9

Or you can randomly sample from a normal distribution

R

x <- rnorm(10)
x

OUTPUT

 [1] -1.15147204 -0.49372383 -0.77244318 -0.34890722  0.01152801 -0.81740177
 [7]  0.20870688 -0.74824265 -1.09940772  0.08663059

You can also use letters to create factors.

R

x <- sample(letters[1:4], 20, replace=T)
x

OUTPUT

 [1] "b" "c" "b" "c" "c" "a" "d" "a" "c" "a" "c" "b" "b" "c" "b" "a" "a" "b" "b"
[20] "c"

Remember that a data frame is just a collection of vectors. You can create a data frame using data.frame (or tibble in the dplyr package). You can then create a vector for each variable.

R

data <- data.frame (x = sample(letters[1:3], 20, replace=T), 
                    y = rnorm(1:20))
head(data)

OUTPUT

  x           y
1 c -2.29437118
2 c -0.06235243
3 c  0.42491846
4 b -1.26774180
5 c -0.98678492
6 a -0.48549256

However, when sampling at random you must remember to set.seed() before sending it to someone to make sure you both get the same numbers!

Callout

For more handy functions for creating data frames and variables, see the cheatsheet. For some questions, specific formats can be needed. For these, one can use any of the provided as.someType functions: as.factor, as.integer, as.numeric, as.character, as.Date, as.xts.

Let’s come back to our kangaroo rats example.

Since we will be working with the same dataset this year, we want to know how many kangaroo rats of each species were found in each plot type in past years so that we can better estimate what sample size we can expect.

Here is the code we use:

R

krats %>%
  ggplot(aes(x = date, fill = plot_type)) +
  geom_histogram(alpha=0.6)+
  facet_wrap(~species)+ 
  theme_bw()+
  scale_fill_viridis_d(option = "plasma")+
  geom_vline(aes(xintercept = lubridate::ymd("1988-01-01")), col = "red")

Now let’s say we saw this and decided we wanted to get rid of “sp.” but didn’t know how. We want to ask someone online but we first need to create a minimal reproducible example. Remember our questions from earlier…

Excercise 3

Try to answer the following questions oon your own and see if you can determine what we need to include in our minimal reproducible dataset:

How many variables do we need?
What data type is each variable?
How many levels and/or observations are necessary?
How many of the values need to be the same/different?
Are there any NAs that could be relevant?

Show me the solution

We will need 3 variables to represent species, plot type, and date.
Two of our variables will need to be categorical (factors) and one of them continuous.
To reproduce the figure, we can use 2-4 levels for one factors (species), and maybe 2 levels for the other factor (plot type) to keep it minimal. Our continuous variable could range 1 to 10 (date). We don’t need too many observations, but we do have 2 categories, one with 4 levels. Let’s make it an even 100.
NAs are not relevant to our problem

Instructor Note

Maybe we don’t need to include the solution and we just walk through it in the following section.

What variables would we need to reproduce this figure?

We will need 3 variables to represent species, plot type, and date.

What data type is each variable?

Two of our variables (species and plot type) will need to be categorical (factors) and one of them continuous (date).

How many levels and/or observations are necessary?

For species, our original figure has 4 levels. We could reduce this to 2, but let’s keep it at 4. Let’s call these species A, B, C, and D.

R

species <- c('A','B','C','D')

For plot type, our original figure has 5 levles, but we could cut it down to 2. Let’s call them P1 and P2. In reality, we probably don’t even need this for this question, but for the sake of practicing let’s add it in.

R

plot.type <- c('P1','P2')

Lastly, date is our continuous variable. To mimic our original figure, we probably want it long enough to show multiple bars along the x axis, but we still want to keep it minimal. Let’s just call it days and make it 1-10.

R

days <- c(1:10)

Great! Now we have all of our variables, we need to go sampling. How many observations do we need? Again, we want enough to show a similar graph, but also keep it minimal. We need to sample each plot for 10 days, and each plot should give us a varying number of species, of which we have 4. Let’s say we find 20 individuals.

We can simulate the data collected each day by using sample() and specifying our number of observations (we need to sample 20 times). Since we want species and plots to repeat, we will also set replace to T.

All together we get:

R

sample_data <- data.frame(
  Day = days,
  Plot = sample(plot.type, 20, replace=T),
  Species = sample(species, 20, replace=T)
)
sample_data

OUTPUT

   Day Plot Species
1    1   P2       B
2    2   P1       A
3    3   P1       D
4    4   P1       D
5    5   P2       D
6    6   P1       D
7    7   P1       D
8    8   P1       C
9    9   P1       C
10  10   P2       A
11   1   P2       A
12   2   P1       C
13   3   P2       C
14   4   P1       C
15   5   P1       C
16   6   P1       D
17   7   P1       A
18   8   P1       D
19   9   P1       D
20  10   P1       B

Great! Now we have a sample data set that is minimal, but is is reproducible?

Instructor Note

Give them time to think about it and answer the question.

It isn’t! Why?

Remember: sample() creates a random dataset! This will not be consistently reproducible. In order to make this example fully reproducible we should first set.seed().

R

set.seed(1)
sample_data <- data.frame(
  Species = sample(species, 20, replace=T),
  Plot = sample(plot.type, 20, replace=T),
  Day = days
)
head(sample_data)

OUTPUT

  Species Plot Day
1       A   P1   1
2       D   P1   2
3       C   P1   3
4       A   P1   4
5       B   P1   5
6       A   P1   6

Now we have our minimal reproducible example! But are we sure it reproduces what we are trying to reproduce? Let’s test it out.

R

sample_data %>%
  ggplot(aes(x = Day, fill = Plot)) +
  geom_histogram(alpha=0.6)+
  facet_wrap(~Species)+ 
  theme_bw()+
  scale_fill_viridis_d(option = "plasma")

OUTPUT

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Yes!

It is certainly simplified, but it has the elements we want it to have. And now we can ask how to get rid of “C”.

Given that this was a very simple question, we could have simplified this example even further; we could have used 2 species and even just 2 days, in which case a simple solution could be

R

sample_data2 <- data.frame(
species = sample(c('A','B'), 6, replace = T),
days = 1:2
)

sample_data2 %>%
  ggplot(aes(x=days)) +
  geom_histogram()+
  facet_wrap(~species)

OUTPUT

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

which is even more simplistic than the one before but still contains the elements we are interested in–we have a set of “species” separated into facets and we want to get rid of one of them. In reality, had we realized that we needed to get rid of the rows with “sp.” in them, we could have ignored the figure entirely and posed the question about the data alone. E.g., “how do I remove rows that contain a specific name?” Then give just the example dataset we created.

Exercise 4 (10 minutes) – optional?

Now practice doing it yourself. Create a data frame with:

A. One categorical variable with 2 levels and one continuous variable. B. One continuous variable that is normally distributed. C. Name, sex, age, and treatment type.

4.3.2 Create a dataset using an existing dataset

If you don’t want to create a dataset from scratch, maybe because you have too many variables or it’s a more complicated structure and you are not sure where the error is, you can subset from an existing dataset. Useful functions for subsetting a dataset include subset(), head(), tail(), and indexing with [] (e.g., iris[1:4,]). Alternatively, you can use tidyverse functions like select(), and filter() from the tidyverse. You can also use the same sample() functions we covered earlier.

A list of readily available datasets can be found using library(help="datasets"). You can then use ? in front of the dataset name to get more information about the contents of the dataset.

When working with a built-in dataset you still have to edit your code to fit the new data, but it is probably faster than building a large dataset from scratch, and it gets easier with practice!

Let’s keep using our previous example, how can we reproduce that figure using the existing dataset mpg. First, let’s interrogate this dataset to see what we are working with.

R

?mpg

Which variable from mpg do you think we could use to replace our variables? Remember: we need one for species, one for plot type, and one for date.

There are certainly multiple options! Let’s go with model for species, manufacturer for plot type, and year for date.

R

data <- mpg %>% select(model, manufacturer, year)
dim(data)

OUTPUT

[1] 234   3

R

glimpse(data)

OUTPUT

Rows: 234
Columns: 3
$ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "…
$ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "…
$ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200…

We only need 4 species, and 5 plots. How many do we have here?

R

length(unique(data$model))

OUTPUT

[1] 38

R

length(unique(data$manufacturer))

OUTPUT

[1] 15

Certainly more than we need. Then let’s simplify.

R

set.seed(1)
data <- data %>%
    filter(model %in% sample(model, 4, replace = F))

Cool, now we have just 4 models. BUT we also only have 2 years… so maybe year wasn’t the best choice afterall, let’s change it to hwy

R

data <- mpg %>% select(model, manufacturer, hwy) %>%
  filter(model %in% sample(model, 4, replace = F))

Now we can try our plot

R

data %>%
  ggplot(aes(x = hwy, fill = manufacturer)) +
  geom_histogram(alpha=0.6)+
  facet_wrap(~model)+ 
  theme_bw()+
  scale_fill_viridis_d(option = "plasma")

OUTPUT

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Do you think that works?

It turns out that maybe manufacturer was not the best representation for plot, since we do need each car model to appear in each “plot”. What would all cars have?

Let’s change model to manufacturer, and let’s add class.

R

set.seed(1)
data2 <- mpg %>% select(manufacturer, class, hwy) %>%
  filter(manufacturer %in% sample(manufacturer, 4, replace = F))
  
data2 %>% 
  ggplot(aes(x = hwy, fill = class)) +
  geom_histogram(alpha=0.6)+
  facet_wrap(~manufacturer)+ 
  theme_bw()+
  scale_fill_viridis_d(option = "plasma")

OUTPUT

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

That’s more like it! You can keep playing around with it or you can give it more thought apriori, but either way you get the idea. While what we get is not an exact replica, it’s an analogy. The important thing is that we created a figure whose basic elements/structure or “key features” remain intact–namely, the number and type of variables and categories.

Now it is your turn!

Excercise 4

For each of the following, identify which data are necessary to create a minimal reproducible dataset using mpg.

We want to know how the highway mpg has changed over the years
We need a list of all “types” of cars and their fuel type for each manufacturer
We want to compare the average city mpg for a compact car from each manufacturer

OR change the above challenge to be about ratdat

OR move to…

Now that we know how many of each species were captured over the years, we want to know how many of each species you might expect to catch per day.

Let’s practice how we would do this with our data.

Instructor Note

Let the students try it out and discuss outloud

We end up with the following code:

R

krats_per_day <- krats %>%
  group_by(date, year, species) %>%
  summarize(n = n()) %>%
  group_by(species)

OUTPUT

`summarise()` has grouped output by 'date', 'year'. You can override using the
`.groups` argument.

R

krats_per_day %>%
  ggplot(aes(x = species, y = n))+
  geom_boxplot(outlier.shape = NA)+
  geom_jitter(width = 0.2, alpha = 0.2)+
  theme_classic()+
  ylab("Number per day")+
  xlab("Species")

Excercise 5

How might you reproduce this using the mpg dataset?

Show me the solution

Substitute krats with cars, species with class, date with year. The question becomes, how many cars of each class are produced per year?

R

  set.seed(1)
    cars_per_y <- mpg %>%
    filter(class %in% sample(class, 4, replace=F)) %>%
    group_by(class, year) %>%
    summarize(n=n()) %>%
    group_by(class)

OUTPUT

`summarise()` has grouped output by 'class'. You can override using the
`.groups` argument.

R

  cars_per_y %>%
    ggplot(aes(x = class, y = n))+
    geom_boxplot(outlier.shape = NA)+
    geom_jitter(width = 0.2, alpha=0.2)+
    theme_classic()+
    ylab("Cars per year")+
    xlab("Class")

R

  # this is only giving us 3 classes even though we asked for 4, why?
  
  # Because it is sampling from the column "class" which has many of the same class. 
  # Therefore, we need to specify that we want to sample from within the unique values in "class".
  
  cars_per_y <- mpg %>%
    filter(class %in% sample(unique(mpg$class), 4, replace=F)) %>%
    group_by(class, year) %>%
    summarize(n=n()) %>%
    group_by(class)

OUTPUT

`summarise()` has grouped output by 'class'. You can override using the
`.groups` argument.

R

  cars_per_y %>%
    ggplot(aes(x = class, y = n))+
    geom_boxplot(outlier.shape = NA)+
    geom_jitter(width = 0.2, alpha=0.2)+
    theme_classic()+
    ylab("Cars per year")+
    xlab("Class")

4.4 Using your own data by creating a minimal subset

Perhaps you are now thinking that if you can use a subset of an existing dataset, wouldn’t it be easier to just subset my own data to make it minimal? You are not wrong. There are cases when you can subset your own data in the same way you would subset an existing dataset to make a minimal dataset, the key is to then make it reproducible. That’s when we use the function dput, which essentially takes your dataframe and give you code to reproduce it!

For example, using our previous data2

R

dput(cars_per_y)

OUTPUT

structure(list(class = c("midsize", "midsize", "pickup", "pickup",
"subcompact", "subcompact", "suv", "suv"), year = c(1999L, 2008L,
1999L, 2008L, 1999L, 2008L, 1999L, 2008L), n = c(20L, 21L, 16L,
17L, 19L, 16L, 29L, 33L)), class = c("grouped_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -8L), groups = structure(list(
    class = c("midsize", "pickup", "subcompact", "suv"), .rows = structure(list(
        1:2, 3:4, 5:6, 7:8), ptype = integer(0), class = c("vctrs_list_of",
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -4L), .drop = TRUE))

As you can see, even with our minimal dataset, it is still quite a chunk of code. What if you tried putting in krats_per_day? It is clear that either way you will still need to considerably minimize your data. Even then, it will often be simpler to provide an existing dataset or provide one from scratch. Furthermore, often we are able to discover the source of our error or solve our own problem when we have to go through the process of breaking it down into its essential components!

Nevertheless, it remains an option for when your data appears too complex or you are not quite sure where your error lies and therefore are not sure what minimal components are needed to reproduce the example.

Callout

What about NAs? If your data has NAs and they may be causing the problem, it is important to include them in your MR dataset. You can find where there are NAs in your dataset by using is.na, for example: is.na(krats$weight). This will return a logical vector or TRUE if the cell contains an NA and FALSE if not. The simplest way to include NAs in your dummy dataset is to directly include it in vectors: x <- c(1,2,3,NA). You can also subset a dataset that already contains NAs, or change some of the values to NAs using mutate(ifelse()) or substitute all the values in a column by sampling from within a vector that contains NAs.

One important thing to note when subsetting a dataset with NAs is that subsetting methods that use a condition to match rows won’t necessarily match NA values in the way you expect. For example

R

test <- data.frame(x = c(NA, NA, 3, 1), y = rnorm(4))
test %>% filter(x != 3)

OUTPUT

  x         y
1 1 0.7635935

R

# you might expect that the NA values would be included, since “NA is not equal to 3”. But actually, the expression NA != 3 evaluates to NA, not TRUE. So the NA rows will be dropped!
# Instead you should use is.na() to match NAs
test %>% filter(x != 3 | is.na(x))

OUTPUT

   x            y
1 NA -0.294720447
2 NA -0.005767173
3  1  0.763593461

Here are some more practice exercises if you wish to test your knowledge

(I copied these from excercise 6 in the google doc… but I’m not sure that they are getting at the point of the lesson…)

Excercise 6

Each of the following examples needs your help to create a dataset that will correctly reproduce the given result and/or warning message when the code is run. Fix the dataset shown or fill in the blanks so it reproduces the problem.

set.seed(1) sample_data <- data.frame(fruit = rep(c(“apple”, “banana”), 6), weight = rnorm(12)) ggplot(sample_data, aes(x = fruit, y = weight)) + geom_boxplot() HELP: how do I insert an image from clipboard?? Is it even possible?
bodyweight <- c(12, 13, 14, , ) max(bodyweight) [1] NA
sample_data <- data.frame(x = 1:3, y = 4:6) mean(sample_data$x) [1] NA Warning message: In mean.default(sample_data$x): argument is not numeric or logical: returning NA
sample_data <- ____ dim(sample_data) NULL

Show me the solution

“fruit” needs to be a factor and the order of the levels must be specified: sample_data <- data.frame(fruit = factor(rep(c("apple", "banana"), 6), levels = c("banana", "apple")), weight = rnorm(12))
one of the blanks must be an NA
?? + what’s really the point of this one?
sample_data <- data.frame(x = factor(1:3), y = 4:6)

Key Points

A minimal reproducible dataset contains (a) the minimum number of lines, variables, and categories, in the correct format, to reproduce a certain problem; and (b) it must be fully reproducible, meaning that someone else can reproduce the same problem using only the information provided.
You can create a dataset from scratch using as.data.frame, you can use available datasets like iris or you can use a subset of your own dataset
You can share your own data by first subsetting it into its minimal components and then using dput() to create it via reproducible code