recoding-data.jl

### A Pluto.jl notebook ###
# v0.20.5

using Markdown
using InteractiveUtils

# ╔═╡ 52fdd6f8-b472-4224-8b8c-1276f0cdb0b7
 	using HypertextLiteral, PlutoUI; TableOfContents()

# ╔═╡ dee44068-3258-401b-aaf9-a22379817f59
using Tidier

# ╔═╡ d886ef58-5314-4a82-a0b0-f076ba670387
begin
	# If the subfolder "data" does not exist, create it
	if !("data" in readdir())
		mkdir("data")
	end

	# If not already downloaded,
	# Download the MITRE Synthea synthetic data zip file containing multiple .csv files

	if !("data.zip" in readdir("data"))
		download("https://mitre.box.com/shared/static/aw9po06ypfb9hrau4jamtvtz0e5ziucz.zip", "data/data.zip")
	end

	using ZipFile # needed for unzipping files
	zip_file = ZipFile.Reader("data/data.zip")
	
	for file in zip_file.files[2:end]
		seekstart(file)
		file_name = str_replace(file.name, "csv/", "data/")
		write(file_name, read(file))
	end
end

# ╔═╡ 4042d526-18bd-11f0-2d79-8b6ff113de76
begin
	html_string = join(readlines("header.html"), "\n")
	HypertextLiteral.@htl("""$(HypertextLiteral.Bypass(html_string))""")
end

# ╔═╡ a60ab41c-717e-4f6b-9cca-0a4b37fd1020
md"""
# Recoding data

Recoding data refers to the act of converting data from its raw form into one that is analytically relevant and useful. Most commonly, this involves converting a continuous variable into a set of categories based on a set of criteria.
 
For example, it's common that age is represented in datasets as a continuous variable. However, if we want to know how many medications are taken by adults versus by children, we need to first break age into categories. In this case, we may simply need to apply a threshold for age above which we consider someone an adult, and otherwise they are a child. However, sometimes there are multiple criteria that need to be considered across multiple columns in recoding data.

Recoding can also involve lumping together many into a fewer number of categories. Fundamentally, this lesson use of logic to create categories irrespective of the form of the source data.

We will learn about `if_else` and `case_when`, which when used within `@mutate` provide a very flexible and extendable approach to recoding data. We will also begin to introduce some of the convenience functions for handling categorical data (from TidierCats), although this will discussed in more depth in the section on [Working with categorical data](working-with-categorical-data.html).

Before we start, let's load the `Tidier` package and the dataset.
"""

# ╔═╡ fa1ab250-9c22-4049-ac56-ce72b235a269
md"""
# Loading `Tidier.jl`

We will start off by loading the Tidier meta-package.
"""

# ╔═╡ b9770ec7-1345-4ccb-abfe-79c99959f8d7
md"""
# Prelude: downloading the MITRE Synthea Dataset

!!! info "Download the MITRE Synthea dataset"

	This page assumes that you have already downloaded the MITRE Synthea dataset. If you have not done it yet, please go to the [What is Tidier.jl](what-is-tidier-jl.html) page and follow the instruction on downloading the MITRE Synthea Dataset.

After downloading the dataset, we will read in the `data/patients.csv` file into a data frame.
"""

# ╔═╡ 3dd56607-ab8c-477d-8714-1051e5a87eb2
patients = @chain read_file("data/patients.csv") @clean_names

# ╔═╡ b6c3d41f-b7b4-47a5-9ae6-b6fde37a9225
md"""
# Case study: Calculating age categories

While our motivating example at the beginning focused on converting raw age into age categories, real world data remind us that life isn't quite this simple. Real-world health datasets rarely record age as a column. While age on a given date may be provided as a calculated, the raw data typically encode date of birth and death, and they leave it as an exercise to the analyst to calculate the age.

While this seems inconvenient, this is actually preferred because we rarely care about the age as of today's date. Instead, we usually want to know the age in relation to a specific event (e.g., on the day of surgery).

So, before we can recode the age into a categories, we first need to calculate age in years.
"""

# ╔═╡ dbbdf3ea-7df9-4990-9ddf-4eb4dcde671a
md"""
## First, let's calculate age

For the sake of this analysis, let's assume that we want to know everyone's age as of the most recent date when this dataset was updated. For simplicity, let's assume that this corresponds to the most recent date recorded in *either* the `birthdate` or `deathdate` columns.
"""

# ╔═╡ 260366bf-35fe-4fb4-81ef-4c0a6eda1e5f
most_recent_birth_date = @chain patients begin
	@slice_max(birthdate, with_ties = false)
	@pull(birthdate) # 1
end

# 1: `@pull` pulls a column from a data frame and returns a vector

# ╔═╡ ef44271c-3df6-43a1-9f1f-8eb49d6b1ca1
most_recent_death_date = @chain patients begin
	@slice_max(deathdate, with_ties = false)
	@pull(deathdate)
end

# ╔═╡ 46cb6a28-88f9-4cfc-935f-324c2dc46622
todays_date = maximum([most_recent_birth_date, most_recent_death_date])

# ╔═╡ 541603d5-71dd-41c9-ac85-0da45ec922d3
md"""
The most recent `birthdate` recorded in the dataset is $most_recent_birth_date. The most recent `deathdate` recorded in the dataset is $most_recent_death_date.

Overall, the `deathdate` is the most recent, so we will assume that the age we are calculating is the latest age as of $todays_date, which we will consider as "today's date." Remember that in real life, we will usually want the age of an index event which may differ for each patient, and not necessarily a universal date for all patients.

## Let's look at the missingness patterns for the `birthdate` and `deathdate` columns.

If we want to calculate age, we want to make sure we have all the data we need, and that there is no missing data.
"""

# ╔═╡ db1f643d-18cf-455c-ba91-8f741b382198
@chain patients begin
	@group_by(birth_date_missing = ismissing(birthdate),
		   death_date_missing = ismissing(deathdate))
	@summarize(n = n())
	@ungroup()
end

# ╔═╡ fee90e72-788e-481d-bbbf-71ce3005b34d
md"""
It looks like all patients have a birth date recorded. On the other hand, 1,000 patients have a missing date of death, with only 163 having a recorded death date.

This leads us to our first complication.

## Age is calculated differently for those are alive vs. dead

### For alive patients

For patients with a missing death date, we want to know their age as of `todays_date` ($todays_date). Let's go ahead and filter our dataset to only those patients with a missing `deathdate`, and then calculate their age as of `todays_date`.

!!! info "Note: we have to interpolate todays_date"

	Because all variables in `Tidier.jl` are assumed to be in a "data frame scope," referring to `todays_date` in our analysis would lead to an error because `Tidier.jl` wouldn't be able to find a colun named `todays_date` in the data frame. Hence, we need to interpolate it in from the parent scope, which requires us to use `@eval` at the start of `@chain` and `$todays_date` to interpolate in its value.
"""

# ╔═╡ 7b62aa0d-0b1b-42a3-a896-31e67cf19500
@eval @chain patients begin # 1
	@filter(ismissing(deathdate)) # 2
	@mutate(age = 
				difftime( # 3
					DateTime($todays_date), # 4
					DateTime(birthdate),
					"days") / 365.25) # 5
	@select(birthdate, deathdate, age)
	@slice_head(n = 10)
end

# 1: Note the use of `@eval` here because we subsequently use `$` for interpolation
# 2: filter to those with a missing death date
# 3: `difftime` comes from the TidierDates.jl package, reimplementing the same function from base R. `difftime` requires `DateTime` types, so the each of the Dates are converted into DateTaimes.
# 4: Interpolate in `todays_date` from the parent scope
# 5: This is an approximateion to convert days to years, where 365.25 accounts for leap years

# ╔═╡ da450461-ffce-42a9-8612-6c32534a2d9e
md"""
### For dead patients

For the 163 patients in the dataset who have died, age can be calculated by taking the difference between the date of birth and death. For these patients, we would not want to know their age as of `todays_date` because today's date is not relevant to their actual age as of today's date.
"""

# ╔═╡ 512f191f-57e2-47a3-a25f-034e96333ef2
@eval @chain patients begin
	@filter(!ismissing(deathdate))
	@mutate(age = 
				difftime(
					DateTime(deathdate),
					DateTime(birthdate),
					"days") / 365.25)
	@select(birthdate, deathdate, age)
	@slice_head(n = 10)
end

# ╔═╡ 8638b0c2-fb9c-4843-baea-1b6e8eb1f61b
md"""
In both of the prior cases, we first filtered the data to those who are alive or dead, and then we calculated their age using different a calculation for each category.

What if we wanted to calculate the age for the whole dataset? Here, we encounter our first use of `if_else`, which we will subsequently use for recoding data.

## Conditionally creating a column

To create a age column for both alive and dead patients, we will break this calculation into 2 steps. First, we will create a new column that indicates the date on which we want to calculate the age, which is $todays_date for alive patients and the `deathdate` column for dead patients.

Then, we will use take a difference between that new column and the date of birth to calculate age.

For convenience, we will store the result to a new `patients_age` data frame.
"""

# ╔═╡ 1cb8751d-6b2c-42fd-a836-3777c9670d05
patients_age = 
	@eval @chain patients begin
	@mutate(todays_date = # 1
				if_else(
					ismissing(deathdate),
					$todays_date,
					deathdate)
		   )
	@mutate(age = 
			difftime(
				DateTime(todays_date),
				DateTime(birthdate),
				"days") / 365.25
		   )
	@select(birthdate, deathdate, age, gender)
end

# This creates a column `todays_date` in the `patients` data frame, to which we assign the appropriate date based on whether a patient is alive or dead

# ╔═╡ 1c86df78-10c8-497f-af42-0f67032d4706
md"""
# Recoding using `if_else`

Now that we have a column representing age, we can use the `if_else` function to recode age into two values: "Adult" and "Child".
"""

# ╔═╡ 4650642f-0a14-4d87-b9c2-fcf11d436c9b
@chain patients_age begin
	@mutate(age_category = if_else(age >= 18, "Adult", "Child"))
end

# ╔═╡ b87accbf-005a-4793-b775-6dce03afbc73
md"""
## `if_else` vs. `ifelse`

!!! info "Note: if_else is a more powerful version of ifelse"

	Experienced users may notice that e are using `if_else` rather than the `ifelse` function that is built into Julia. In this instance, `if_else` is not merely an alias but actually refers to a separate function that is bundled with `Tidier.jl`. The main difference between the two functions is that `if_else` includes an optional 4th argument (i.e., `if_else(condition, true, false, missing)`) that describes which value to return if the condition returns a `missing` value.

	While we did not use this optional argument here, it can come in quite handy. Take a look at this example, in which we determine whether a patient a patient died before or after 2020. Since `deathdate` is missing for alive patients, we can optionally assign it a value using `if_else`.
"""

# ╔═╡ a399e93d-1ecd-4282-aee5-3c09e135a443
@chain patients begin
	@mutate(died_since_2020 = 
			if_else(
				deathdate > ymd("2020-01-01"),
				"Died after 2020",
				"Died before 2020",
				"Still alive"))
	@count(died_since_2020)
end

# ╔═╡ d104b2c4-e0bd-42b6-8310-c0582fd770b0
md"""
Returning to our original recoding, let's take a look at how many adults and children we have in the dataset. Remember that `@count(age_category)` is a shortcut for `@group_by(age_category) @summarize(n = n())`.
"""

# ╔═╡ b07dd5e1-db0c-4bfa-81ee-5cff5dd062ea
@chain patients_age begin
	@mutate(age_category = if_else(age >= 18, "Adult", "Child"))
	@count(age_category)
end

# ╔═╡ a8a6c435-0cad-4cc6-bcfd-7c6dc9d62179
md"""
As long as we have only 2 categories to create (or 3 if our condition returns missing values), then `if_else` is the preferred way to recode data. But if we have more categories, then `case_when` is preferred.

# Recoding using `case_when`

The syntax for `case_when` consists of pairs of `condition => value`, where the value is assigned for the first condition that is met for each row. This means that it is perfectly fine for the conditions to overlap, as long as the earliest condition met takes precedence.

Let's create an age category where those 75 and older are considered older adults on one extreme, and those below 4 are considered infants. We can start with those of the highest age as conditions, and sequentially lower the age until we get to "infants" as the returned value. It is common practice, but not required, to include a final condition `true`, which assigns the return value to all remaining rows.
"""

# ╔═╡ 7ce1f522-387e-473c-a946-1cbfa3f37ae7
@chain patients_age begin
	@mutate(age_category = 
				case_when(age >= 75  =>  "Older adult",
						  age >= 18  =>  "Adult",
						  age >= 4   =>  "Child",
		   				  true       =>  "Infant")
		   )
	@count(age_category)
end

# ╔═╡ 81163c1a-b2b9-467a-97ce-5501026f7ae4
md"""
If the final condition is not set to `true`, then any rows that do not meet any of the conditions will be assigned a missing value. Here's an example:
"""

# ╔═╡ 32c9a6b5-258c-4dd1-ae41-d3b1bf8056b4
@chain patients_age begin
	@mutate(age_category = 
				case_when(age >= 75  =>  "Older adult",
						  age >= 18  =>  "Adult",
						  age >= 4   =>  "Child",
		   				  age >= 1   =>  "Infant")
		   )
	@count(age_category)
end

# ╔═╡ ab6950fc-a178-4cc7-af1e-9e202f14df5b
md"""
Unlike `if_else`, `case_when` does not have an argument dedicated to what to do if the conditions return a `missing` value. However, you can represent the missingness as a condition within the `case_when` function.
"""

# ╔═╡ b9b69efe-c3fb-4d98-ba9c-22af09264428
@chain patients begin
	@mutate(died_since_2020 = 
			case_when(
				deathdate > ymd("2020-01-01")  =>  "Died after 2020",
				ismissing(deathdate)           =>  "Still alive",
				true                           =>  "Died before 2020")
		   )
	@count(died_since_2020)
end

# ╔═╡ a4e16054-826c-438d-a4d6-67059daf7fd1
md"""
`case_when` is great not only for situations involving 3+ return values, but also for situations involving complex conditions involving multiple columns of data. Here, we created a separate value ofr male and female adults but left the child and infant categories unchanged.
"""

# ╔═╡ 2a7dce86-cb60-4981-b973-820d73587c92
@chain patients_age begin
	@mutate(age_category = 
				case_when(age >= 75 && gender == "F"  =>  "Older adult female",
						  age >= 75 && gender == "M"  =>  "Older adult male",
						  age >= 18 && gender == "F"  =>  "Adult female",
						  age >= 18 && gender == "M"  =>  "Adult male",
						  age >= 4                    =>  "Child",
		   				  true                        =>  "Infant")
		   )
	@count(age_category)
end

# ╔═╡ 8faed932-457a-40d4-95c6-ca7c44f048a9
md"""
!!! info "Disambiguating gender and sex"

	Many health datasets, including this one, confuse sex with gender. Whereaas `male` and `female` refer to biological sex, gender refers to a person's identity, which may be similar or different from a person's biological sex. While we chose to use this synthetic dataset because it is much closer to real-world health datasets in good ways, it is unfortunately also similar to real-world data in bad ways.

	Any time a dataset applies a label to a person, the same issues can arise. For eaxmple, similar issues of categorization and miscategorization can apply to the way in which race and ethnicity are elicited and recorded in health datasets.
"""

# ╔═╡ 5aea8d11-425e-4d66-85b5-12e4649140be
md"""
# Bespoke `TidierCats.jl` functions for recoding variables

If you think of `if_else` and `case_when` as the workhorses of recoding data, `TidierCats.jl` provides a set of bespoke functions for achieving many of the same goals as our two handy go-to functions. Fancy, fancy!

Had we wanted to chop age into categories, we could've achieved this using CategoricalArrays `cut` function (reexported by TidierCats). THe `extend = true` argument ensures that the values higher than the greatest threshold (i.e., those older than 75) are lumped in with the highest category (in this case, "Older adult").

## `cut` for cutting continuous values into categories
"""

# ╔═╡ d1e255ae-4f4c-409a-a18b-508f6750deaf
@chain patients_age begin
	@mutate(age_category =
				~cut( # 1
					age,
					[0, 4, 18, 75],
					labels = ["Infant", "Child", "Adult", "Older adult"],
					extend = true))
	@count(age_category)
end

# 1: Note that we had to prefix `cut` with `~` because `cut` operates on the entire column (and not rowwise on each row), so we do not want it to be vectorized. If you omit the tilde, it will produce an error. Once we update `TidierData` to include `cut` on the list of functions not to vectorize, the `~` will no longer be required, and we will update this code.

# ╔═╡ 2c25e3d3-2861-4c26-80df-e10d7e78a941
md"""
In this case, we get the same exact numbers as what we calculated using `case_when`, but any time you use a bespoke function, you have to be wary as to how threhsolds are applied. If you read the documentation for `cut`, you'll see that the left side of the interval is is inclusive, but the right side is not. In this case, it produces the same logic as what we had coded in `case_when` because we had made the left side inclusive (e.g., `age >= 18`).

This is noticeable if you remove the `labels` keyword argument and let `cut` produce an automatic label. With intervals, square brackets `[]` are considered inclusive, and parentheses `()` are considered exclusive.
"""

# ╔═╡ a385861d-7036-49f0-a4c0-fbbfe04d52c5
@chain patients_age begin
	@mutate(age_category =
				~cut(
					age,
					[0, 4, 18, 75],
					# labels = ["Infant", "Child", "Adult", "Older adult"],
					extend = true))
	@count(age_category)
end

# ╔═╡ 066d4e1c-4e89-41ea-a3e1-8905b8e54d55
md"""
Had we wanted to code `age > 18` instead of `age >= 18`, then `cut` would not have been helpful. Thus, my personal preference is to rely on `case_when` rather than `cut`.

Another subtle difference you'll notice if you hover over or tap on the results table produced by TidierCats functions (like `cut`) is that TidierCats functions always return a `CategoricalValue` type, whereas `case_when` returned a string.
"""

# ╔═╡ a46051c1-301d-4744-880a-8c378e69ca2f
md"""
## A brief taste of `cat_*` functions

TidierCats provides a number of functions beginning with `cat_` which make it possible to write concise code. We will dive deeper into the TidierCats packate in the lesson on [Working with categorical data](working-with-categorical-data.html). Here, we'll give you a brief taste of how `Tidier.jl` makes it easy to work with categorical variables.


Let's say you have a categorical variable `age_category`, and now you want to lump any categories with fewer than 10% of the values into a new "Other" category. While this is totally achievable using `case_when`, it would require a bit of thought and work to calculate the proportions before recoding the data.

With `cat_lump_prop`, we can achieve this in a single concise line of code:
"""

# ╔═╡ 023f1d53-267d-4abb-8b86-c8639a259317
@chain patients_age begin
	@mutate(age_category =
				~cut(
					age,
					[0, 4, 18, 75],
					labels = ["Infant", "Child", "Adult", "Older adult"],
					extend = true))
	@mutate(age_category = cat_lump_prop(age_category, 0.10))
	@count(age_category)
end

# ╔═╡ 7f8e63ab-0110-4ff3-b1b6-8f412623cd83
md"""
!!! info "Use caution when lumping categories"

	Just because you *can* lump together categories doesn't mean that you *should*. In this case, you'll notice that infants got lumped with older adults into an "Other" category. We would pretty much never want to lump these two groups together for analytical purposes, so use the TidierCats functions with a healthy dose of caution. With great power comes great responsibility.
"""

# ╔═╡ bf44ca88-2e8b-44af-99af-f68a68d64191
md"""
# Summary

In this lesson, we learned about the two main workhorse functions for recoding data: `if_else` and `case_when`. We also dipped our toes into some of the bespoke TidierCats functions that can help with both recoding data and handling categorical variables more broadly.

We will return to some of these advanced capabilities in the [Working with categorical data](working-with-categorical-data.html) section.
"""

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uuid = "06e1c1a7-607b-532d-9fad-de7d9aa2abac"
version = "0.5.0"

[[deps.TimeZones]]
deps = ["Artifacts", "Dates", "Downloads", "InlineStrings", "Mocking", "Printf", "Scratch", "TZJData", "Unicode", "p7zip_jll"]
git-tree-sha1 = "2c705e96825b66c4a3f25031a683c06518256dd3"
uuid = "f269a46b-ccf7-5d73-abea-4c690281aa53"
version = "1.21.3"
weakdeps = ["RecipesBase"]

    [deps.TimeZones.extensions]
    TimeZonesRecipesBaseExt = "RecipesBase"

[[deps.TranscodingStreams]]
git-tree-sha1 = "0c45878dcfdcfa8480052b6ab162cdd138781742"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.11.3"

[[deps.Transducers]]
deps = ["Accessors", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "ConstructionBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "SplittablesBase", "Tables"]
git-tree-sha1 = "7deeab4ff96b85c5f72c824cae53a1398da3d1cb"
uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
version = "0.4.84"

    [deps.Transducers.extensions]
    TransducersAdaptExt = "Adapt"
    TransducersBlockArraysExt = "BlockArrays"
    TransducersDataFramesExt = "DataFrames"
    TransducersLazyArraysExt = "LazyArrays"
    TransducersOnlineStatsBaseExt = "OnlineStatsBase"
    TransducersReferenceablesExt = "Referenceables"

    [deps.Transducers.weakdeps]
    Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
    BlockArrays = "8e7c35d0-a365-5155-bbbb-fb81a777f24e"
    DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
    LazyArrays = "5078a376-72f3-5289-bfd5-ec5146d43c02"
    OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338"
    Referenceables = "42d2dcc6-99eb-4e98-b66c-637b7d73030e"

[[deps.Tricks]]
git-tree-sha1 = "6cae795a5a9313bbb4f60683f7263318fc7d1505"
uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775"
version = "0.1.10"

[[deps.TriplotBase]]
git-tree-sha1 = "4d4ed7f294cda19382ff7de4c137d24d16adc89b"
uuid = "981d1d27-644d-49a2-9326-4793e63143c3"
version = "0.1.0"

[[deps.URIs]]
git-tree-sha1 = "cbbebadbcc76c5ca1cc4b4f3b0614b3e603b5000"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.5.2"

[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
version = "1.11.0"

[[deps.UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
version = "1.11.0"

[[deps.UnicodeFun]]
deps = ["REPL"]
git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf"
uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1"
version = "0.4.1"

[[deps.Unitful]]
deps = ["Dates", "LinearAlgebra", "Random"]
git-tree-sha1 = "c0667a8e676c53d390a09dc6870b3d8d6650e2bf"
uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
version = "1.22.0"
weakdeps = ["ConstructionBase", "InverseFunctions"]

    [deps.Unitful.extensions]
    ConstructionBaseUnitfulExt = "ConstructionBase"
    InverseFunctionsUnitfulExt = "InverseFunctions"

[[deps.UnsafeArrays]]
git-tree-sha1 = "d32529bcc1dcef5ff8cf5b6df9c5862c224fa264"
uuid = "c4a57d5a-5b31-53a6-b365-19f8c011fbd6"
version = "1.0.7"

[[deps.VectorizationBase]]
deps = ["ArrayInterface", "CPUSummary", "HostCPUFeatures", "IfElse", "LayoutPointers", "Libdl", "LinearAlgebra", "SIMDTypes", "Static", "StaticArrayInterface"]
git-tree-sha1 = "4ab62a49f1d8d9548a1c8d1a75e5f55cf196f64e"
uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
version = "0.21.71"

[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.2"

[[deps.WebP]]
deps = ["CEnum", "ColorTypes", "FileIO", "FixedPointNumbers", "ImageCore", "libwebp_jll"]
git-tree-sha1 = "aa1ca3c47f119fbdae8770c29820e5e6119b83f2"
uuid = "e3aaa7dc-3e4b-44e0-be63-ffb868ccd7c1"
version = "0.1.3"

[[deps.WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "c1a7aa6219628fcd757dede0ca95e245c5cd9511"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "1.0.0"

[[deps.WorkerUtilities]]
git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
version = "1.6.1"

[[deps.XLSX]]
deps = ["Artifacts", "Dates", "EzXML", "Printf", "Tables", "ZipArchives", "ZipFile"]
git-tree-sha1 = "7fca49e6dbb35b7b7471956c2a9d3d921360a00f"
uuid = "fdbf4ff8-1666-58a4-91e7-1b58723a45e0"
version = "0.10.4"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
git-tree-sha1 = "b8b243e47228b4a3877f1dd6aee0c5d56db7fcf4"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.13.6+1"

[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "7d1671acbe47ac88e981868a078bd6b4e27c5191"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.42+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "9dafcee1d24c4f024e7edc92603cedba72118283"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.8.6+3"

[[deps.Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "e9216fdcd8514b7072b43653874fd688e4c6c003"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.12+0"

[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "89799ae67c17caa5b3b5a19b8469eeee474377db"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.5+0"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
git-tree-sha1 = "d7155fea91a4123ef59f42c4afb5ab3b4ca95058"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.6+3"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
git-tree-sha1 = "a490c6212a0e90d2d55111ac956f7c4fa9c277a6"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.11+1"

[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "c57201109a9e4c0585b208bb408bc41d205ac4e9"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.2+0"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "1a74296303b6524a0472a8cb12d3d87a78eb3612"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.17.0+3"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "6dba04dbfb72ae3ebe5418ba33d087ba8aa8cb00"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.5.1+0"

[[deps.ZipArchives]]
deps = ["ArgCheck", "CodecInflate64", "CodecZlib", "InputBuffers", "PrecompileTools", "TranscodingStreams", "Zlib_jll"]
git-tree-sha1 = "7238679c261680bdbde59eb7bc04673368d726a3"
uuid = "49080126-0e18-4c2a-b176-c102e4b3760c"
version = "2.4.1"

[[deps.ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"]
git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a"
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
version = "0.10.1"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+1"

[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "446b23e73536f84e8037f5dce465e92275f6a308"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.7+1"

[[deps.isoband_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "51b5eeb3f98367157a7a12a1fb0aa5328946c03c"
uuid = "9a68df92-36a6-505f-a73e-abb412b6bfb4"
version = "0.2.3+0"

[[deps.libaom_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "522c1df09d05a71785765d19c9524661234738e9"
uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b"
version = "3.11.0+0"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Zlib_jll"]
git-tree-sha1 = "e17c115d55c5fbb7e52ebedb427a0dca79d4484e"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.2+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.11.0+0"

[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "8a22cf860a7d27e4f3498a0fe0811a7957badb38"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.3+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"]
git-tree-sha1 = "068dfe202b0a05b8332f1e8e6b4080684b9c7700"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.47+0"

[[deps.libsixel_jll]]
deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "Libdl", "libpng_jll"]
git-tree-sha1 = "c1733e347283df07689d71d61e14be986e49e47a"
uuid = "075b6546-f08a-558a-be8f-8157d0f608a5"
version = "1.10.5+0"

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "490376214c4721cdaca654041f635213c6165cb3"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+2"

[[deps.libwebp_jll]]
deps = ["Artifacts", "Giflib_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libglvnd_jll", "Libtiff_jll", "libpng_jll"]
git-tree-sha1 = "ccbb625a89ec6195856a50aa2b668a5c08712c94"
uuid = "c5f90fcd-3b7e-5836-afba-fc50a0988cb2"
version = "1.4.0+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.59.0+0"

[[deps.oneTBB_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "d5a767a3bb77135a99e433afe0eb14cd7f6914c3"
uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e"
version = "2022.0.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+2"

[[deps.snappy_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "ca88363dd41d2547f52118287dd34dbbc14f3eb7"
uuid = "fe1e1685-f7be-5f59-ac9f-4ca204017dfd"
version = "1.2.3+0"

[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "14cc7083fc6dff3cc44f2bc435ee96d06ed79aa7"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "10164.0.1+0"

[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "dcc541bb19ed5b0ede95581fb2e41ecf179527d2"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.6.0+0"
"""

# ╔═╡ Cell order:
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# ╟─066d4e1c-4e89-41ea-a3e1-8905b8e54d55
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# ╠═023f1d53-267d-4abb-8b86-c8639a259317
# ╟─7f8e63ab-0110-4ff3-b1b6-8f412623cd83
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