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grouping-and-combining-verbs.jl
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### A Pluto.jl notebook ###
# v0.20.5
using Markdown
using InteractiveUtils
# ╔═╡ c585065d-cd35-4477-ba87-e773a3679be0
using HypertextLiteral, PlutoUI; TableOfContents()
# ╔═╡ d3f0ab14-e870-41c6-8c51-2f036d74307f
using Tidier
# ╔═╡ c70e1da9-7660-47c9-a402-dd0dcd6e238e
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
# ╔═╡ 4f031ad6-18ba-11f0-3446-b5b9b8418e01
begin
html_string = join(readlines("header.html"), "\n")
HypertextLiteral.@htl("""$(HypertextLiteral.Bypass(html_string))""")
end
# ╔═╡ c35e7828-c429-44fc-ab89-10c153f7e23c
md"""
# Grouping and combining verbs
"""
# ╔═╡ 57f3ea99-a205-4cf4-8785-72c26fa35e62
md"""
# Loading `Tidier.jl`
We will start off by loading the Tidier meta-package.
"""
# ╔═╡ f5ddc893-298a-4061-a917-3253c2501b68
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.
"""
# ╔═╡ 5ebae093-4e31-4630-ad73-1f31e4a3f004
patients = @chain read_file("data/patients.csv") @clean_names
# ╔═╡ 7dd7bcd8-8aca-4ffb-a0a1-5a33feb037ea
md"""
# Grouping to apply data verbs by group
Now that you've learned the 5 "basic" data verbs, and how to chain them together using `@chain`, the last thing to learn before mastering basic exploratory data analysis is grouping, which is achieved using the `@group_by` macro.
All of the above macros respect grouping assignments made by `@group_by`, so if you are running into unusual behavior, it's often because you're data are grouped differently than you expected.
!!! info "Important grouping behavior to know"
In `Tidier.jl`, `@group_by` applies one ore more grouping columns to the data frame, producing a `GroupedDataFrame`.
Using `@summarize` peels off one layer of grouping. Other transformations **do not** remove grouping. If you were grouped by one column, then applying `@summarize` will result in an ungrouped data frame. If you were grouped by two or more columns, then the result will be a grouped data frame with the last layer of grouping removed.
You can always manually ungroup by calling `@ungroup`, which will remove all grouping. Calling `@group_by` on an already-grouped data frame will overwrite the grouping with the new grouping columns.
"""
# ╔═╡ b22377c2-ff5f-45ed-86c1-05d45044b05f
md"""
## Example of `@group_by` followed by `@summarize`
What if we wanted to know the number of people who live in each county? We could use `@group_by` to group the data by county, and `number = n()` to create a new column called `number` containing the number of rows in each group. This is the same `n()` helper function we used to `@slice`.
"""
# ╔═╡ 76b1a4fc-2aa6-4fe4-b77b-64ad59380455
@chain patients begin
@group_by(county)
@summarize(number = n())
end
# ╔═╡ 77694086-11a1-4e39-9f5a-2727ae47835a
md"""
## Sorting results with `@arrange`
You might be bothered by the fact that the numbers are not sorted from highest to lowest. This is easily fixed using `@arrange`. By default, `@arrange` sorts from lowest to highest (or alphabetical order for strings). Surrounding the column with `desc()` indicates it should be sorted in descending order. You can sort by multiple columns in any combination of ascending and descending order.
"""
# ╔═╡ 44c82662-8594-47c4-905d-5a1392faa6e1
@chain patients begin
@group_by(county)
@summarize(number = n())
@arrange(desc(number))
end
# ╔═╡ 61c38292-e1c5-48bd-bc69-8a984cfb376b
md"""
We can also calculate mean expenses using similar syntax.
"""
# ╔═╡ 87f29fce-bee6-4059-a2de-936fe0daffc5
@chain patients begin
@group_by(county)
@summarize(mean_expenses_thousands = mean(healthcare_expenses) / 1_000)
@arrange(desc(mean_expenses_thousands))
end
# ╔═╡ 7e8a9b15-3f87-41c6-a33b-6db2022b8d86
md"""
## Grouping by multiple columns
What if you wanted to group by `county` and `gender`? The same code would work, but because you grouped by two variables and applied `@summarize` once, grouping by `gender` was removed, but the data remains grouped by `county`.
The fact that the data frame is grouped is fairly obvious from the way grouped data frames are printed.
"""
# ╔═╡ 8adc0f46-a160-429d-b68b-eae54d3b80cf
@chain patients begin
@group_by(county, gender)
@summarize(number = n())
@arrange(desc(number))
end
# ╔═╡ 83e46102-e3e4-4902-9641-1857e6d2e037
md"""
## Ungrouping
If we want to see more of the data, it can be easier to visualize when the data frame is ungrouped. We can do that using `@ungroup`.
"""
# ╔═╡ ace63f4d-597a-4035-beec-5fccb17d78cf
@chain patients begin
@group_by(county, gender)
@summarize(number = n())
@arrange(desc(number))
@ungroup()
end
# ╔═╡ 59d841a0-7006-43af-9aaf-4a65a879efbc
md"""
# Logging complex data transformations
As you can see, `Tidier.jl` has some nuanced behavior, particularly with respect to grouping. It can be helpful to use some form of logging to detect and prevent errors, especially silent ones.
!!! info "Best practice: set "log" to true!"
TidierData comes with a `"log"` option that can be set to `true` to enable logging. Even though it is set to `false` by default, we recommend that users set it to `true`. For the remainder of this notebook, we will leave it enabled.
"""
# ╔═╡ 7e7e09a6-0f71-410e-a57e-f60fa57d0dee
TidierData_set("log", true)
# ╔═╡ 68f8d011-863f-4388-b058-989ea3f97e74
md"""
Let's repeat the same analysis with logging enabled.
"""
# ╔═╡ 265b461b-78d0-460f-ba9f-9d6959faea35
@chain patients begin
@group_by(county, gender)
@summarize(number = n())
@arrange(desc(number))
@ungroup()
end
# ╔═╡ a7ca9c08-1a0e-4b50-b5d4-3f6f4c6f26c5
md"""
# Creating groups on the fly
What if we wanted to calculate the mean health expenses based on whether someone had died or not (based on missing values for deathdate)?
You might think that you need to first create a `vital_status` column using `@mutate` before you can group by it, but TidierData actually supports the creation of grouping variables on the fly.
"""
# ╔═╡ ab5afb2d-c58a-4794-b992-f4255c5c8455
@chain patients begin
@group_by(vital_status = if_else(ismissing(deathdate), "alive", "dead"))
@summarize(mean_expenses_thousands = mean(healthcare_expenses) / 1_000)
@arrange(desc(mean_expenses_thousands))
end
# ╔═╡ f5a047fe-96dc-47b0-8e0e-bb9b7ac6f9ae
md"""
# `@count` as a shortcut for `@group_by()` + `@summarize` + `@ungroup`
Grouping, summarizing, and ungrouping is such a common pattern that there is a shortcut verb, or "compound verb," to achieve the same goal: `@count`. By default, `@count` will count the number of rows, but if you specify a `wt` argument, it will use that to calculate the summary. As you can see from the log, `@count` is implemented as a wrapper around `@group_by`, `@summarize`, and `@ungroup`.
"""
# ╔═╡ 303bf629-89c6-47c9-854f-2b831331dcf4
@chain patients begin
@count(county)
end
# ╔═╡ b6a74e95-cfd7-4576-923a-43120927b772
md"""
Specifying a `wt` (weight) argument, `@count` produces the same results as our longer bit of code above.
"""
# ╔═╡ 52e17d69-a1bf-44b3-af7a-793f0c7be79b
@chain patients begin
@count(county, wt = mean(healthcare_expenses) / 1_000)
@arrange(desc(n))
end
# ╔═╡ 1d300104-3cb4-4969-8355-3865a8b30041
md"""
# Summary
In this section, we learned how to set groups and do each of these operations separately for each group, using `@group_by` and `@ungroup`.
We also learned about a number of other compound verbs or shortcuts, including
* `@count`
* `@arrange`
And we learned how to chain these operations together using `@chain`.
"""
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
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[compat]
HypertextLiteral = "~0.9.5"
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# ╔═╡ 00000000-0000-0000-0000-000000000002
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