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### A Pluto.jl notebook ###
# v0.20.5
using Markdown
using InteractiveUtils
# ╔═╡ 51f70061-b9ea-436c-8a2d-90f7aa9f6b50
using HypertextLiteral, PlutoUI; TableOfContents()
# ╔═╡ 6ce7076d-f79a-44da-b7cf-f582e5960f16
using Tidier
# ╔═╡ eff5ec5a-6248-4b21-9955-34a8253fb060
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
# ╔═╡ 2378c980-18d8-11f0-320b-0d370c47816d
begin
html_string = join(readlines("header.html"), "\n")
HypertextLiteral.@htl("""$(HypertextLiteral.Bypass(html_string))""")
end
# ╔═╡ 01e7f4b2-2906-4c46-9086-d56d319b53a1
md"""
# From dataframes to databases
By this lesson, you should already be fairly comfortable in working with data frames, including how to read tabular data from files, how to transform and aggregate data, how to tidy and join data frames, and how to work with a variety of data types. Data frames are the bread-and-butter everyday format for working with tabular data, and for good reason: they are fast (thanks to DataFrames.jl) and they have lots of bells and whistles owing to their being native to Julia.
Unfortunately, working with data frames is not always an option. In large teams, it is often impractical to store data as flat files. When the underlying dataset is frequently updated, different copies of flat files can become out of sync with one another. For this and a variety of other storage and performance-related reasons, datasets are often stored in databases rather than in files.
## How should an analyst work with databases?
Despite the thoughtful design and elegance found within Julia packages for analyzing data frames, the analyst becomes a bit helpless once the source of the data is a table in a database rather than a flat file or series of flat files. The analyst has 2 options to choose from:
### Option 1: Load the entire database table into memory
This is often a reasonable option as long as the data is small enough to fit into the memory of the machine where the analysis is being conducted. On the one hand, memory has gotten cheaper, so this option works more often than not. On the other hand, persistent cloud computing remains expensive, so there are many contexts in which fully instantiating a databse table into memory is not an option.
### Option 2: Learn SQL
This is also a reasonable option. Particularly if you are or plan to professionally practice as a data analyst, you will be expected to know SQL. But what if you analyzed a sample of the data locally and built your entire analytical pipeline in Julia? Do you really need to rewrite substantial chunks of it in SQL?
### TidierDB: the secret 3rd option
No, you don't. This is beacuse there is a secret 3rd option in Julia: use `TidierDB.jl`. TidierDB is automatically re-exported by `Tidier.jl`, and the nice thing about loading `Tidier.jl` with `using Tidier` is that Tidier automatically makes TidierData and TidierDB play nice with each other even though they have a number of macros with overlapping names.
The big idea for TidierDB is that you start by writing code that works on dataframes. Within minimal tweaks, this same code works on databases by writing the SQL queries for you. And that this should work for a multitude of different databases which all have minor differences in their implementation of SQL.
Before we start, let's load `Tidier.jl` and make sure that you have the dataset downloaded (hopefully, you do by now!).
"""
# ╔═╡ b3ddf5af-735f-48d6-bd2d-5fb18267da40
md"""
# Loading `Tidier.jl`
We will start off by loading the Tidier meta-package.
"""
# ╔═╡ 0ed4f375-21c0-4c9c-8cb6-71cb610921b6
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.
"""
# ╔═╡ 0ecf94f5-c983-426e-afd1-b1796acf6897
md"""
# Generating your first SQL query using TidierDB
Let's start by writing our first SQL query in 1, 2, 3...
"""
# ╔═╡ 251a5ff0-b722-4780-9ec4-a64bd777dd65
@chain begin
db_table(connect(duckdb()), "data/patients.csv")
@show_query()
end
# ╔═╡ 9ed13659-5829-4ac0-b7d4-65f07c49ed8a
md"""
Congratulations, you just wrote your first SQL query! Don't worry if you don't understand what is going on in the query above. This is the DuckDB flavor of SQL where you can point DuckDB
"""
# ╔═╡ 48f24dd6-da3d-419f-9790-8935beeb0226
@chain begin
db_table(connect(duckdb()), "data/patients.csv")
@collect()
end
# ╔═╡ 6c7911c1-4652-44ce-8c9f-584fc1e48d59
@chain begin
db_table(connect(duckdb()), "data/patients.csv")
@mutate(birth_date_missing = ismissing(BIRTHDATE), # 1
death_date_missing = ismissing(DEATHDATE))
@group_by(birth_date_missing, death_date_missing)
@summarize(n = n())
@collect()
end
# 1: Creating groups on the fly isn't supported yet in TidierDB, so you first need to create the columns using `@mutate` before accessing the groups in `@group_by`.
# ╔═╡ 42a837e2-c608-49f9-a5e7-f6c63c984508
md"""
Because it parallelizes as much as possible, DuckDB provides no guarantee on the row-order of the values that are returned...
"""
# ╔═╡ dd23549b-0f61-4f6c-84b8-b1fec574abb7
@eval @chain begin
db_table(connect(duckdb()), "data/patients.csv")
@filter(!ismissing(deathdate))
@mutate(age = datediff("Days", birthdate, deathdate) / 365.25)
@select(birthdate, deathdate, age)
@collect()
end
# ╔═╡ e3e9d08e-30f7-45d8-bb36-be9531d9beeb
@chain begin
db_table(connect(duckdb()), "data/patients.csv")
@summarize(max_birthdate = maximum(birthdate),
max_deathdate = maximum(deathdate))
@collect()
end
# ╔═╡ 5c7e0133-a100-4493-a6ec-a2316fd1e3e7
@chain begin
db_table(connect(duckdb()), "data/patients.csv")
@summarize(max_birthdate = maximum(birthdate),
max_deathdate = maximum(deathdate))
@collect()
@pivot_longer(cols = everything())
end
# ╔═╡ 1f9d1d3d-6132-4cfe-a8ab-132aba378f96
todays_date = @chain begin
patients
@summarize(max_birthdate = maximum(birthdate),
max_deathdate = maximum(deathdate))
@collect()
@pivot_longer(cols = everything())
@filter(value == maximum(value))
@pull(value)
_[1] # 1
"$(year(_))-$(month(_))-$(day(_))" # 2
end
# 1: pulls the first value of the dates vector, so that it is a scalar
# 2: uses string interpolation to return the date in a y-m-d format
# ╔═╡ 5b334589-a36e-4051-9478-7bb61e9508d4
@eval @chain begin
db_table(connect(duckdb()), "data/patients.csv")
@mutate(todays_date = # 1
if_else(
ismissing(deathdate),
$todays_date,
deathdate)
)
@mutate(age =
datediff("Days",
birthdate,
todays_date) / 365.25
)
@select(birthdate, deathdate, age, gender)
@collect()
end
# ╔═╡ 72ef8e43-8269-4389-93f6-d5388235b2d8
patients_age = @eval @chain begin
db_table(connect(duckdb()), "data/patients.csv")
@mutate(todays_date = # 1
if_else(
ismissing(deathdate),
$todays_date,
deathdate)
)
@mutate(age =
datediff("Days",
birthdate,
todays_date) / 365.25
)
@select(birthdate, deathdate, age, gender)
end
# ╔═╡ 005330a2-e7b1-4381-9d50-453ba1d2ce4e
@chain patients_age begin
@mutate(age_category = if_else(age >= 18, "Adult", "Child"))
@count(age_category)
@collect()
end
# ╔═╡ 5d2390fb-c618-4d19-963b-9b2601021c80
@chain patients_age begin
@mutate(age_category =
case_when(age >= 75 => "Older adult",
age >= 18 => "Adult",
age >= 4 => "Child",
true => "Infant"))
@count(age_category)
@collect()
end
# ╔═╡ c8ccb7ec-ae75-4dbb-9448-0bfe853b5268
@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)
@collect()
end
# ╔═╡ 23fc7bdb-2c05-40d0-9507-466d2346f99f
patients = db_table(connect(duckdb()), "data/patients.csv");
# ╔═╡ b1dba3b5-1a98-4d25-ad43-c5d9acf6a82e
meds = db_table(connect(duckdb()), "data/medications.csv");
# ╔═╡ 215ed9ca-6698-4f00-ac25-04fc08470981
@chain patients @collect()
# ╔═╡ 2b7d151e-af7c-49ab-9b64-6b3d922c9312
@chain meds @collect()
# ╔═╡ 917616d8-bc68-450f-bb72-0571985643af
@chain meds begin
@filter(!ismissing(start), ismissing(stop)) # med is still active
@count(patient) # count unique rows at the patient level, save result to `n`
@arrange(desc(n)) # arrange in descending order of count
@collect()
end
# ╔═╡ b256e86c-c091-4125-bd33-867908b19b0a
meds_clean_count =
@chain meds begin
@filter(!ismissing(start), ismissing(stop)) # med is still active
@count(patient) # count unique rows at the patient level, save result to `n`
@arrange(desc(n)) # arrange in descending order of count
end
# ╔═╡ 662ee3aa-1239-419d-b0a4-516ef8b47a15
@chain begin
@left_join(patients, meds_clean_count, id = patient)
@mutate(num_meds = replace_missing(n, 0))
@summarize(across(num_meds, (mean, minimum, maximum)))
@collect()
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
HypertextLiteral = "ac1192a8-f4b3-4bfe-ba22-af5b92cd3ab2"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
Tidier = "f0413319-3358-4bb0-8e7c-0c83523a93bd"
ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
[compat]
HypertextLiteral = "~0.9.5"
PlutoUI = "~0.7.62"
Tidier = "~1.6.1"
ZipFile = "~0.10.1"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
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weakdeps = ["ChainRulesCore", "Test"]
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AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
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git-tree-sha1 = "6e1d2a35f2f90a4bc7c2ed98079b2ba09c35b83a"
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