Content from What is a reprex and why is it useful?

Last updated on 2024-08-29 | Edit this page

Estimated time: 17 minutes

Overview

Questions

  • How is the process of getting help in R different from getting help with other things?
  • Why is a minimal reproducible example an important tool for getting help in R?
  • What will we be learning in the rest of the course?

Objectives

  • Recognize what it takes to debug someone else’s code.
  • Define a minimal reproducible example.
  • Describe the general workflow that we will cover in the rest of this lesson.

Welcome and introductions

  • Welcome to “RRRR, I’m Stuck!” We’re glad you’re here! Let’s first take care of a few setup steps. You should have all followed the setup instructions on the workshop website, and you should have both R and RStudio installed.

  • You should also have the following packages installed: {reprex}, {ratdat}, {dplyr}, and {ggplot2}.

  • We have a range of levels of experience here. This workshop assumes that you are researcher in ecology/biology who has some prior experience working with R in RStudio.

  • We won’t be spending a lot of time going over [concepts]. Here’s a handy reference guide to lessen some of the cognitive load… [to be continued.]

  • You don’t have to be an expert. But we also know that even more experienced R coders might be less familiar with how to get unstuck, so we hope this workshop will be useful to you too.

Think, pair, share

When you’re coding in R and you get stuck, what are some things that you do to get help or get unstuck?

Think, pair, share

Think about a time that you helped someone else with their code. What information did you need to know in order to help? (If you have never helped someone else with their code, think about a time that someone helped you–what information did they need to know in order to help?)

Minimal Reproducible Example (aka “reprex”)

"Your code examples should be...
Minimal: Use as little code as possible that still produces the same problem
Complete: Provide all parts someone else needs to reproduce your problem in the question itself
Reproducible: Test the code you're about to provide to make sure it reproduces the problem" - [StackOverflow](https://stackoverflow.com/help/minimal-reproducible-example)
"The goal of a reprex is to package your problematic code in such a way that other people can run it and feel your pain. Then, hopefully, they can provide a solution and put you out of your misery." - [Get help! (Tidyverse)](https://www.tidyverse.org/help/)
"The habit of making little, rigorous, self-contained examples also has the great side effect of making you think more clearly about your programming problems." - [Jenny Bryan](https://posit.co/resources/videos/help-me-help-you-creating-reproducible-examples/)

These steps might seem simple, but they can be challenging to put into practice. In this lesson, we will be guiding you through the process of creating a minimal reproducible example. By the end, you will have a workflow to follow next time you get stuck.

Overview of this lesson

[Visual: screenshot or diagram of someone else’s educational resource where they explain what a minimal reproducible example is (with appropriate credit given of course)] –> use this to motivate how we’re going to be going through each step of that in this lesson.

[Visual: diagram of the general process, with questions along the way]–to draw ourselves ### Understand your code ### Apply “first aid” debugging strategies ### Create minimal reproducible data ### Simplify your code and make it minimal ### Prepare to share your reproducible example with others.

Motivating examples

[Screenshots of real requests for help]

Key Points

  • Mentors and helpers usually need to run your code in order to help debug it.
  • Minimal reproducible examples make it possible for helpers to run your code, which lets them “feel your pain” and figure out what’s wrong.
  • Making a minimal reproducible example helps you understand your own problem and often leads to finding the answer yourself!
  • You can use the {reprex} package to test whether your example is reproducible.

Content from Understanding your code

Last updated on 2024-08-12 | Edit this page

Estimated time: 12 minutes

Overview

Questions

  • TODO

Objectives

  • Describe in general terms what you want your code to do, and what it is currently doing.
  • Break down your code into conceptual steps (pseudocode, or a narrative of what you’re doing)
  • Identify relevant variables/objects

Order the conceptual steps of code

Here is a block of code that creates an example data visualization.

R 

library(dplyr)
library(ggplot2)
fuel_efficient <- mtcars %>%
    filter(mpg > 30) %>%
    select(hp, mpg)
p <- ggplot(fuel_efficient, aes(x = hp, y = mpg))+
    geom_point()
p

Reorder the following conceptual steps so that they accurately reflect what this code is trying to accomplish.

A. Set up a visualization that will show the relationship between horsepower and fuel efficiency
B. Choose only the most fuel efficient cars
C. Display the plot that was just created
D. Load necessary packages
E. Simplify the dataset to show fewer columns
F. Add points to the initial plot
D. Load necessary packages
B. Choose only the most fuel efficient cars
E. Simplify the dataset to show fewer columns
A. Set up a visualization that will show the relationship between horsepower and fuel efficiency
F. Add points to the initial plot
C. Display the plot that was just created

Content from Identify the problem and make a plan

Last updated on 2024-08-12 | Edit this page

Estimated time: 0 minutes

Overview

Questions

  • What do I do when I encounter an error?
  • What do I do when my code outputs something I don’t expect?
  • Why do errors and warnings appear in R?
  • Which areas of code are responsible for errors?
  • How can I fix my code? What other options exist if I can’t fix it?

Objectives

After completing this episode, participants should be able to…

  • decode/describe what an error message is trying to communicate
  • Identify specific lines and/or functions generating the error message
  • Lookup function syntax, use, and examples using R Documentation (?help calls)
  • Describe the general category of error message (e.g. syntax error, semantic errors, package-specific errors, etc.)
  • Describe the output of code you are seeking
  • Identify and quickly fix commonly-encountered R errors
  • Identify which problems are better suited for asking for further help, including online help and reprex

Predict the output from a base R function call

Which of the following results when running the following line of code:

R 

length(5, 6, 7)
  1. 3
  2. Error in length(5, 6, 7) : 3 arguments passed to ‘length’ which requires 1
  3. NULL
  4. 1, 1, 1
  1. Error in length(5, 6, 7) : 3 arguments passed to ‘length’ which requires 1

Content from Minimal Reproducible Data

Last updated on 2024-09-25 | Edit this page

Estimated time: 44 minutes

HELP We need to include but hide the data previously run so that we can continue to use it

R 

# Hide this chunk
library(readr)
library(dplyr)
library(ggplot2)
library(stringr)
library(ratdat)
rodents<-complete_old
krats <- rodents %>%
  filter(genus == "Dipodomys")%>%
  mutate(date = lubridate::ymd(paste(year, month, day, sep = "-")))

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

After completing this episode, participants should be able to…

  • Describe a minimal reproducible dataset
  • List the requirements for a minimal reproducible dataset
  • Identify the important aspects of the dataset that are necessary to reproduce your coding problem
  • Create a dataset from scratch to reproduce a given piece of code
  • Subset an existing dataset to reproduce a given piece of code
  • 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, a seed that will produce the same results each time (e.g., use set.seed())

The first step in creating an MRE is to set up some data for your helper to be able to reproduce your error and play around with the code in order to fix it.

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.

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 were able to give the helper the entire dataset, 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 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?

What needs to be included in a reproducible dataset?

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.
  • it needs to be reproducible. The data you provide must consistently reproduce the output or error with which you are struggling.
  • Can alternatively think of this as “relevant” to the problem. Maybe a silly example but–if you’re struggling with the behavior of a column that’s a factor, it wouldn’t be useful to make a dataset that contains only numeric data, no matter how simple and elegant that dataset might appear to be.
  • It needs to be complete and accessible.

Remember, your helper may not be in the room with you or have access to your computer, therefore the data must be able to stand on its own, it must be complete and free of dependencies. Alternatively, you must ensure your helper has access to the dataset you provide. More on this later.

Callout

Remember that helpers do not have access to files on your computer!

You might be used to always reading data in as 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.

Callout

It can be helpful to simply look at the ?h elp section. Scroll down to where they have examples. 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))

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. Does it work? What happened? Explain.

  1. (A screenshot of our dataset – need to upload images to repo)
  2. sample_data <- read_csv(“/Users/kaija/Desktop/RProjects/ResearchProject/data/sample_dataset.csv”)
  3. dput(complete_old[1:100,])
  4. sample_data <- data.frame(species = species_vector, weight = c(10, 25, 14, 26, 30, 17))
  1. Not reproducible because it is a screenshot.
  2. 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.
  3. 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.”
  4. Not reproducible because we are not given the source for “species_vector.”

Exercise 1 (continued)

For an extra challenge, can you edit the above datasets to make them minimal and reproducible?

Exercise 2

Here is a piece of code that is throwing me a simple error (it is returning NA):

R 

mean(rodents$weight)

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

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

The correct answer is C!

  1. does not include the variable of interest (weight).
  2. does not produce the same problem (NA result with a warning message)–the code runs just fine.
  3. 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).
  4. 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 I often get stuck: how do I recreate the key elements on my dataset in order to reproduce my error?? That seems really hard! If you are like me and find this initially overwhelming, don’t worry. We will break it down into smaller steps. First, there are two approaches to providing a dataset. You can create one from scratch or you can use an already available dataset.

We then need to know which elements of your dataset are necessary. - How many variables? - What data type is each variable? - How many levels or observations are necessary? - How many of the values need to be the same/different?

4.3.1 Create a dummy dataset from scratch

You can create vectors using

R 

  vector <- c(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)

Or you can use a random normal distribution

R 

    x <- rnorm(10)

You can also use letters to create factors.

R 

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

You can create a dataframe using data.frame (or tibble in the dplyr package).

R 

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

Make sure you name your variables and keep it simple.

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 you 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.

What variables would we need to reproduce this figure?

A variable for species, how many? 4. Let’s call them A, B, C, and D.

R 

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

We then need a variable for plot type, we have 5 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 we need a variable for date. The specifics don’t matter here, so let’s just call it days and make it 1-10.

R 

days <- c(1:10)

Great! Now we have all of our variables, let’s go sampling–we can simulate the data collected each day by using sample().

We need to sample each plot for 10 days and presumably find a certain number of each species in each plot. Let’s pretend we find a total of 100 species, this mean we need to sample the vector species 100 times. Since we want species to repeat, we will also set replace to T. We then need to sample the plots for the same number of times, so that each species sample is associated with either P1 or P2. Lastly, we want to add each day we sampled.

R 

sample_data <- data.frame(
  Species = sample(species, 100, replace=T),
  Plot = sample(plot.type, 100, replace=T),
  Day = day
)

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

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, 100, replace=T),
  Plot = sample(plot.type, 100, replace=T),
  Day = day
)
sample_data

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")

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'), 100, replace = T),
days = 1:2
)

sample_data2 %>%
  ggplot(aes(x=days)) +
  geom_histogram(alpha=0.6)+
  facet_wrap(~species)+ 
  theme_bw()

which is even more simplistic than the one before but still contains the elemnts we are interested–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 3 (10 minutes)

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

A. One categorical variable with 5 levels. One continuous variable. B. One continuous variable normally distributed C. First name, last 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(), indexing with [] (e.g., iris[1:4,]). Alternatively, you can use tidyverse functions like select(), and filter(). 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 what 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)
glimpse(data)

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

R 

length(unique(data$model)) 
length(unique(data$manufacturer))

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 out 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")

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")

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!

Challenge

For each of the following, identify which data are necessary to create a minimal reproducible dataset using mpg. A) We want to know how the highway mpg has changed over the years
B) We need a list of all “types” of cars and their fuel type for each manufacturer
C) 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.

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)

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")

Challenge

How might you reproduce this using the mpg dataset?

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)

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”)

# 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)

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”) ```

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)

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) 
# 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))

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…) ::: challenge Excercise:** 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. A) 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?

  1. bodyweight <- c(12, 13, 14, , ) max(bodyweight) [1] NA

  2. 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

  3. sample_data <- ____ dim(sample_data) NULL :::
    ::: solution

  4. “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))

  5. one of the blanks must be an NA

  6. ?? + what’s really the point of this one?

  7. 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 data by…

Content from Minimal Reproducible Code

Last updated on 2024-08-12 | Edit this page

Estimated time: 12 minutes

Overview

Questions

  • To do

Objectives

  • To do

Idea: take some of the same exercises from the understanding your code section and use those to get people to break down code into minimal chunks. Emphasize that it’s not always going to be linear (

Content from Asking your question

Last updated on 2024-08-12 | Edit this page

Estimated time: 12 minutes

Overview

Questions

  • How can I easily share a reproducible example with a mentor or helper, or online?
  • How do I ask a good question?

Objectives

  • To do

Key Points

  • The {reprex} package makes it easy to format and share your reproducible examples.