初めてのtargetsによるワークフロー
Last updated on 2024-03-12 | Edit this page
Overview
Questions
- 解析を整理するためのベストプラクティスは？
-
_targets.Rファイルは何に使うのか？ -
_targets.Rファイルの内容は? - ワークフローはどのように実行出来るのか？
Objectives
- RStudio でプロジェクトを作成する
-
_targets.Rファイルの目的を説明する - 基本的な
_targets.Rファイルを書く -
_targets.Rファイルを使用してワークフローを実行する
プロジェクトの作成
プロジェクトについて
targets
は、分析を整理するために「プロジェクト」という概念を採用しています。あるプロジェクトに必要なファイルはすべて、プロジェクトフォルダという1つのフォルダに収められます。
プロジェクトフォルダには、データ、コード、結果用のフォルダなど、整理のためのサブフォルダも追加可能です。
プロジェクトを使用することによって、別のプロジェクト（解析）から戻って来た際、迷わずに中断したところから再開することができます。 何かをやり始めたら完成するまで他のことに一切手を出さないなら、このような問題は起こりませんが、そのようなことはほとんどありません。 何か別の作業をしてから元のプロジェクトに戻ったとき、自分が何をしていたかを思い出すのは難しいですね（これは「コンテクスト・スイッチング」と呼ばれる現象です）。 標準化されたプロジェクト整理システムを使うことによって、混乱や時間のロスを減らすことができます。 つまり、再現性を高めているのです！
このワークショップではRStudioを使用します。RStudioは「プロジェクト」の概念と相性が良いからです。
RStudio でプロジェクトを作成する
RStudioを使って新しいプロジェクトを開始しましょう。
“File” をクリックし、“New Project” を選択します。
これにより、新規プロジェクトウィザードが開き、プロジェクトの設定に役立つメニューが表示されます。
ウィザードでは、ゼロから新しいプロジェクトを作るので、最初のオプション「新規ディレクトリ」をクリックします。 Click “New Project” in the next menu. In “Directory name”, enter a name that helps you remember the purpose of the project, such as “targets-demo” (follow best practices for naming files and folders). Under “Create project as a subdirectory of…”, click the “Browse” button to select a directory to put the project. We recommend putting it on your Desktop so you can easily find it.
You can leave “Create a git repository” and “Use renv with this project” unchecked, but these are both excellent tools to improve reproducibility, and you should consider learning them and using them in the future, if you don’t already. They can be enabled at any later time, so you don’t need to worry about trying to use them immediately.
Once you work through these steps, your RStudio session should look like this:
Our project now contains a single file, created by RStudio:
targets-demo.Rproj. You should not edit this file by hand.
Its purpose is to tell RStudio that this is a project folder and to
store some RStudio settings (if you use version-control software, it is
OK to commit this file). Also, you can open the project by double
clicking on the .Rproj file in your file explorer (try it
by quitting RStudio then navigating in your file browser to your
Desktop, opening the “targets-demo” folder, and double clicking
targets-demo.Rproj).
OK, now that our project is set up, we are ready to start using
targets!
Create a _targets.R file
Every targets project must include a special file,
called _targets.R in the main project folder (the “project
root”). The _targets.R file includes the specification of
the workflow: directions for R to run your analysis, kind of like a
recipe. By using the _targets.R file, you won’t have to
remember to run specific scripts in a certain order. Instead, R will do
it for you (more reproducibility points)!
Anatomy of a _targets.R file
We will now start to write a _targets.R file.
Fortunately, targets comes with a function to help us do
this.
In the R console, first load the targets package with
library(targets), then run the command
tar_script().
R
library(targets)
tar_script()
Nothing will happen in the console, but in the file viewer, you
should see a new file, _targets.R appear. Open it using the
File menu or by clicking on it.
We can see this default _targets.R file includes three
main parts:
- Loading packages with
library() - Defining a custom function with
function() - Defining a list with
list().
The last part, the list, is the most important part of the
_targets.R file. It defines the steps in the workflow. The
_targets.R file must always end with this list.
Furthermore, each item in the list is a call of the
tar_target() function. The first argument of
tar_target() is name of the target to build, and the second
argument is the command used to build it. Note that the name of the
target is unquoted, that is, it is written without any
surrounding quotation marks.
Set up _targets.R file to run example analysis
Background: non-targets version
We will use this template to start building our analysis of bill
shape in penguins. First though, to get familiar with the functions and
packages we’ll use, let’s run the code like you would in a “normal” R
script without using targets.
Recall that we are using the palmerpenguins R package to
obtain the data. This package actually includes two variations of the
dataset: one is an external CSV file with the raw data, and another is
the cleaned data loaded into R. In real life you are probably have
externally stored raw data, so let’s use the raw penguin
data as the starting point for our analysis too.
The path_to_file() function in
palmerpenguins provides the path to the raw data CSV file
(it is inside the palmerpenguins R package source code that
you downloaded to your computer when you installed the package).
R
library(palmerpenguins)
# Get path to CSV file
penguins_csv_file <- path_to_file("penguins_raw.csv")
penguins_csv_file
OUTPUT
[1] "/home/runner/.local/share/renv/cache/v5/R-4.3/x86_64-pc-linux-gnu/palmerpenguins/0.1.1/6c6861efbc13c1d543749e9c7be4a592/palmerpenguins/extdata/penguins_raw.csv"We will use the tidyverse set of packages for loading
and manipulating the data. We don’t have time to cover all the details
about using tidyverse now, but if you want to learn more
about it, please see the “Manipulating,
analyzing and exporting data with tidyverse” lesson.
Let’s load the data with read_csv().
R
library(tidyverse)
# Read CSV file into R
penguins_data_raw <- read_csv(penguins_csv_file)
penguins_data_raw
OUTPUT
Rows: 344 Columns: 17
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (9): studyName, Species, Region, Island, Stage, Individual ID, Clutch C...
dbl (7): Sample Number, Culmen Length (mm), Culmen Depth (mm), Flipper Leng...
date (1): Date Egg
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.OUTPUT
# A tibble: 344 × 17
studyName `Sample Number` Species Region Island Stage `Individual ID`
<chr> <dbl> <chr> <chr> <chr> <chr> <chr>
1 PAL0708 1 Adelie Penguin… Anvers Torge… Adul… N1A1
2 PAL0708 2 Adelie Penguin… Anvers Torge… Adul… N1A2
3 PAL0708 3 Adelie Penguin… Anvers Torge… Adul… N2A1
4 PAL0708 4 Adelie Penguin… Anvers Torge… Adul… N2A2
5 PAL0708 5 Adelie Penguin… Anvers Torge… Adul… N3A1
6 PAL0708 6 Adelie Penguin… Anvers Torge… Adul… N3A2
7 PAL0708 7 Adelie Penguin… Anvers Torge… Adul… N4A1
8 PAL0708 8 Adelie Penguin… Anvers Torge… Adul… N4A2
9 PAL0708 9 Adelie Penguin… Anvers Torge… Adul… N5A1
10 PAL0708 10 Adelie Penguin… Anvers Torge… Adul… N5A2
# ℹ 334 more rows
# ℹ 10 more variables: `Clutch Completion` <chr>, `Date Egg` <date>,
# `Culmen Length (mm)` <dbl>, `Culmen Depth (mm)` <dbl>,
# `Flipper Length (mm)` <dbl>, `Body Mass (g)` <dbl>, Sex <chr>,
# `Delta 15 N (o/oo)` <dbl>, `Delta 13 C (o/oo)` <dbl>, Comments <chr>We see the raw data has some awkward column names with spaces (these are hard to type out and can easily lead to mistakes in the code), and far more columns than we need. For the purposes of this analysis, we only need species name, bill length, and bill depth. In the raw data, the rather technical term “culmen” is used to refer to the bill.
Let’s clean up the data to make it easier to use for downstream analyses. We will also remove any rows with missing data, because this could cause errors for some functions later.
R
# Clean up raw data
penguins_data <- penguins_data_raw |>
# Rename columns for easier typing and
# subset to only the columns needed for analysis
select(
species = Species,
bill_length_mm = `Culmen Length (mm)`,
bill_depth_mm = `Culmen Depth (mm)`
) |>
# Delete rows with missing data
remove_missing(na.rm = TRUE)
penguins_data
OUTPUT
# A tibble: 342 × 3
species bill_length_mm bill_depth_mm
<chr> <dbl> <dbl>
1 Adelie Penguin (Pygoscelis adeliae) 39.1 18.7
2 Adelie Penguin (Pygoscelis adeliae) 39.5 17.4
3 Adelie Penguin (Pygoscelis adeliae) 40.3 18
4 Adelie Penguin (Pygoscelis adeliae) 36.7 19.3
5 Adelie Penguin (Pygoscelis adeliae) 39.3 20.6
6 Adelie Penguin (Pygoscelis adeliae) 38.9 17.8
7 Adelie Penguin (Pygoscelis adeliae) 39.2 19.6
8 Adelie Penguin (Pygoscelis adeliae) 34.1 18.1
9 Adelie Penguin (Pygoscelis adeliae) 42 20.2
10 Adelie Penguin (Pygoscelis adeliae) 37.8 17.1
# ℹ 332 more rowsThat’s better!
targets version
What does this look like using targets?
The biggest difference is that we need to put each step of the workflow into the list at the end.
We also define a custom function for the data cleaning step. That is because the list of targets at the end should look like a high-level summary of your analysis. You want to avoid lengthy chunks of code when defining the targets; instead, put that code in the custom functions. The other steps (setting the file path and loading the data) are each just one function call so there’s not much point in putting those into their own custom functions.
Finally, each step in the workflow is defined with the
tar_target() function.
R
library(targets)
library(tidyverse)
library(palmerpenguins)
clean_penguin_data <- function(penguins_data_raw) {
penguins_data_raw |>
select(
species = Species,
bill_length_mm = `Culmen Length (mm)`,
bill_depth_mm = `Culmen Depth (mm)`
) |>
remove_missing(na.rm = TRUE)
}
list(
tar_target(penguins_csv_file, path_to_file("penguins_raw.csv")),
tar_target(penguins_data_raw, read_csv(
penguins_csv_file, show_col_types = FALSE)),
tar_target(penguins_data, clean_penguin_data(penguins_data_raw))
)
I have set show_col_types = FALSE in
read_csv() because we know from the earlier code that the
column types were set correctly by default (character for species and
numeric for bill length and depth), so we don’t need to see the warning
it would otherwise issue.
Run the workflow
Now that we have a workflow, we can run it with the
tar_make() function. Try running it, and you should see
something like this:
R
tar_make()
OUTPUT
• start target penguins_csv_file
• built target penguins_csv_file [0.002 seconds]
• start target penguins_data_raw
• built target penguins_data_raw [0.095 seconds]
• start target penguins_data
• built target penguins_data [0.013 seconds]
• end pipeline [0.213 seconds]Congratulations, you’ve run your first workflow with
targets!
Key Points
- Projects help keep our analyses organized so we can easily re-run them later
- Use the RStudio Project Wizard to create projects
- The
_targets.Rfile is a special file that must be included in alltargetsprojects, and defines the worklow - Use
tar_script()to create a default_targets.Rfile - Use
tar_make()to run the workflow