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### A Pluto.jl notebook ###
# v0.20.5
using Markdown
using InteractiveUtils
# ╔═╡ 1be28f4a-f8b3-464a-af8e-0a37210ba529
using HypertextLiteral, PlutoUI; TableOfContents()
# ╔═╡ f49f4678-9bb0-4669-8143-3d4ece5b8225
using Tidier
# ╔═╡ ceb1cf63-b845-4632-8181-8cacc2ca809e
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
# ╔═╡ ebfa8d00-0f94-11f0-2498-2b147fcb470f
begin
html_string = join(readlines("header.html"), "\n")
HypertextLiteral.@htl("""$(HypertextLiteral.Bypass(html_string))""")
end
# ╔═╡ 3c42954e-ef62-4dd8-9bea-c98025dc3550
md"""
# Reading tabular data from files
On this page, we will explore the different formats, in which we can read and write using TidierFiles.
- CSV files: `read_csv` and `write_csv`
- Tab-separated files: `read_tsv` and `write_tsv`
- JSON files: `read_json` and `write_json`
- Fixed-width files: `read_fwf`
- Excel files: `read_xlsx` and `write_xlsx`
- Google sheets: `read_gsheet` and `write_gsheet`
- Arrow files: `read_arrow` and `write_arrow`
- Parquet files: `read_parquet` and `write_parquet`
- R files (RData and RDS): `read_rdata`
- SAS and SAS export (XPT) files: `read_sas` and `write_sas`
- STATA SAV and POR files: `read_sav` and `write_sav`
Importantly, Tidier provides a consistent interface for reading and writing files across different formats. This means that you can generally use the same arguments when calling the above functions, with a few minor exceptions to handle differences (e.g., to specify which sheet in an Excel file).
## Using `read_file` and `write_file`
Another key implication of the consistent API is that you don't actually need to remember the names of any of the individual file format functions. As long as the file extension matches the file format (e.g., Excel file with an `.xlsx` extension), you can use `read_file` and `write_file`.
!!! info "Make your life easy when reading and writing files"
Unless the file extension does *not* match the format, you should default to using `read_file` and `write_file`.
# Local vs. online files
All of the Tidier file-reading functions operate on both local data and data files on the internet. For the file location, you can provide a string pointing to either a location on your local machine or a file on the internet.
# Working with files vs. databases
The one downside of the `read_*` functions in this package is that they load the entire dataset into memory (in the form of a DataFrame object). When this is not an option, an alternative approach is to load the data into a database or to stream the data using DuckDB, which is supported functionality within TidierDB.
"""
# ╔═╡ 4e85c59d-bfbd-44c2-83fa-a253449d9bd7
md"""
# Loading `Tidier.jl`
We will start off by loading the Tidier meta-package.
"""
# ╔═╡ 80e33f2f-aaa7-4c0a-86e6-6ae546aea3ef
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.
"""
# ╔═╡ 7a51b42e-2d6c-4455-a53a-f48c0887a128
md"""
# Reading a CSV file
One way to read in a CSV file is using the bespoke `read_csv` function.
"""
# ╔═╡ 1b73013a-a5da-4848-88cd-932f85dedfd5
patients_csv = read_csv("data/patients.csv")
# ╔═╡ c1b8f185-79f4-45f5-8c9e-0199056222c8
md"""
But, let's follow the advice we gave you earlier and demonstrate that `read_file` works just as well (and is preferred).
"""
# ╔═╡ 42a84206-d05c-4852-8ab7-de6d7317ccd1
patients = read_file("data/patients.csv")
# ╔═╡ d7f7221e-d744-49a5-b8dd-2f95c91c0af4
md"""
Are they the same? Yes, they are!
"""
# ╔═╡ 894d8e83-8b60-4992-b50d-5895e34e9f2c
isequal(patients, patients_csv)
# ╔═╡ 34e18c7c-16b4-4532-bae2-9de9d02e11c5
md"""
Now, let's save the csv file to some other formats. For demonstration purposes, we will save it to:
- Excel
- Parquet
- Arrow
"""
# ╔═╡ 7a0edd17-d1c7-4027-83e4-69ebc00fdd8f
md"""
# Writing and reading Excel files
Let's use the same `read_file` and `write_file` functions to work with Excel files.
"""
# ╔═╡ b2169a35-9587-47b3-94a5-4c95b5fd215c
write_file(patients, "data/patients_excel.xlsx", overwrite = true)
# ╔═╡ 5624c492-bf75-4877-b51b-743f6e68c4b0
patients_excel = read_file("data/patients_excel.xlsx")
# ╔═╡ 775f12ee-909c-4bfc-833e-f97e5e22dd20
md"""
## Do the CSV and Excel files contain the same content?
Yes.
"""
# ╔═╡ a5a31a6a-a513-42da-974f-96ba6ffc6663
isequal(patients_csv, patients_excel)
# ╔═╡ 8dd4bf0a-b836-4dcd-8bca-54a5965fef5a
md"""
# Writing and reading Parquet files
Let's use the same `read_file` and `write_file` functions to work with Parquet files.
"""
# ╔═╡ c2c45272-96c7-4ba4-ad79-e1c6cfcad9c1
write_file(patients, "data/patients_parquet.parquet")
# ╔═╡ 2462f9a4-d248-43cc-8e4b-7caf6edf134e
patients_parquet = read_file("data/patients_parquet.parquet")
# ╔═╡ 5c3bc77e-80ae-4e85-be12-cfc15e7fcb5a
md"""
## Do the CSV and Parquent files contain the same content?
Yes.
"""
# ╔═╡ 2a42f468-739b-41a1-94dc-62cefb24ed37
isequal(patients_csv, patients_parquet)
# ╔═╡ c8e03aa1-e1f5-4c73-8c40-abbbe8544e91
md"""
# Writing and reading Arrow files
Let's use the same `read_file` and `write_file` functions to work with Arrow files.
"""
# ╔═╡ 1674560b-9576-4e6c-ae28-cc5af8250b96
write_file(patients, "data/patients_arrow.arrow")
# ╔═╡ 5a413c6e-6bab-464b-ad80-b4a265e064de
patients_arrow = read_file("data/patients_arrow.arrow")
# ╔═╡ df3c2f95-8e1a-48bb-b219-26e9261ea1f5
md"""
## Do the CSV and Arrow files contain the same content?
Yes, again.
"""
# ╔═╡ eb1af275-28e3-468d-abfd-800adde63887
isequal(patients_csv, patients_arrow)
# ╔═╡ 93f563aa-4541-45d2-a739-9548c1577fb8
md"""
# Which file format should we use?
Hopefully, you can see that the goal of TidierFiles is that it's completely up to you.
"""
# ╔═╡ 9441ff3e-d485-4f8d-ab69-c064c90d4f95
DataFrame(
CSV = filesize("data/patients.csv") / 1000,
Excel = filesize("data/patients_excel.xlsx") / 1000,
Parquet = filesize("data/patients_parquet.parquet") / 1000,
Arrow = filesize("data/patients_arrow.arrow") / 1000,
)
# ╔═╡ 6fe58ade-a353-465e-9d4e-867476db7527
md"""
# Reading files using DuckDB
On rare occasions, native file Julia I/O functions (which are wrapped by TidierFiles) may be slow or may not be able to read in certain data files. In case you run into any issues with reading files with TidierFiles, an alternative option supported by TidierDB is to read files in using DuckDB's file I/O utilities.
The syntax for reading files with TidierDB is slightly different. Rather than using `read_file`, you will use `db_table(connect(duckdb()), "file_path_here")`.
DuckDB's file utilities currently support:
* CSV and TSV files
* Parquet files
* JSON files
* Iceberg table
* Delta
* S3 tables from AWS or Google Cloud
* Google Sheets spreadsheet link
The DuckDB approach has two advantages over reading in files using `read_file`:
* You can work with larger-than-memory files, because any data transformation/aggregation macros called prior to `@collect` are translated into SQL before the dataset is loaded in memory.
* You have the option to stream in data using `@collect(stream = true)`
"""
# ╔═╡ 1ba23656-5fa6-4c10-995f-074a3ab05250
patients_duckdb = @chain begin
db_table(connect(duckdb()), "data/patients.csv")
@collect()
end
# ╔═╡ fc4a3e6b-9ee2-4f25-92b9-fac6f33f909b
md"""
## Do the CSV files read using TidierFiles and TidierDB contain the same content?
Technically, no.
"""
# ╔═╡ c19f4cfc-b5b3-42bc-809c-a3b5ec1347f9
isequal(patients_csv, patients_duckdb)
# ╔═╡ 7e2af5a4-de58-411f-b7bf-d6388356229a
md"""
But if you look carefully, the main differences between the two files have to do with data types, which can be harmonized. It's important to be aware of this difference when using DuckDB for file I/O in `Tidier.jl`.
"""
# ╔═╡ 7ad84940-3a91-4129-b584-f9397e954b32
@glimpse(patients_duckdb)
# ╔═╡ 3e83d7ea-a8df-4fc9-a667-bf76e5f31c13
@glimpse(patients_csv)
# ╔═╡ 5f5dd027-9853-44ec-ae60-87a639257c7c
md"""
# Writing files using DuckDB
`Tidier.jl` (via TidierDB) not only supports reading files using DuckDB - it also supports writing files using DuckDB. If you want to use DuckDB's file I/O utilities to write files, make sure to call `write_file` without calling `@collect`, because `@collect` will load the result into a data frame, after which subsequent `write_file` calls will invoke the TidierFiles I/O functions.
"""
# ╔═╡ cdcee377-8e6e-442c-b607-83087c8069eb
@chain begin
db_table(connect(duckdb()), "data/patients.csv")
write_file("data/patients_duckdb.parquet")
end
# ╔═╡ 95f53d80-a32d-4fb3-babb-2c892aa29e68
md"""
# Summary
In this lesson, you've learned how to read in tabular data using `read_file`, how to write tabular data (using `write_file`), and how to use DuckDB's file I/O utilities using TidierDB's `db_table(connect(duckdb()), "file_path_here")` syntax.
"""
# ╔═╡ 00000000-0000-0000-0000-000000000001
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# ╔═╡ 00000000-0000-0000-0000-000000000002
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