Update 4-minimal-reproducible-data.Rmd

episodes/4-minimal-reproducible-data.Rmd

@@ -66,6 +66,7 @@ It would be a lot easier for the IT support person to fix your computer if they
 ::: instructor
 You can use the next example as well if you choose.
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 ::: solution
 ## 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.
@@ -126,11 +127,9 @@ c(xm, mean(x, trim = 0.10))
 ```
 
 In this case, x is the dummy dataset consisting of just 1 variable. Notice how it was created as part of the example.
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 :::: challenge
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 ### Exercise 1
 
 These datasets are not well suited for use in a reprex.
@@ -185,6 +184,7 @@ B)  does not produce the same problem (NA result with a warning message)--the co
 D)  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).
 E)  uses a dataset that isn't accessible without previous data wrangling code–the object rodents_modified doesn't exist.
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 ## 4.3 How do I create a minimal reproducible dataset?
 
@@ -295,6 +295,7 @@ Try to answer the following questions oon your own and see if you can determine
 1. 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.
 1. NAs are not relevant to our problem
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 ::: instructor
 Maybe we don't need to include the solution and we just walk through it in the following section.
@@ -578,6 +579,7 @@ How might you reproduce this using the mpg dataset?
     xlab("Class")
 ```
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 ## 4.4 Using your own data by creating a minimal subset
 
@@ -640,6 +642,7 @@ D) sample_data <- ____
   C) **?? + what's really the point of this one?**
   D) `sample_data <- data.frame(x = factor(1:3), y = 4:6)`
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 ::: keypoints