README.md

In pearsonplc/pearsonverse: A package which stores all pearson-related packages

Overview

pearsonverse is a collection of packages that make it easier to conduct an exploratory data science project in R from start to end. The pearsonverse package itself allows you to easily install and load the core pearsonverse packages in a single command.

Installation

devtools::install_github("pearsonplc/pearsonverse")

Usage

library(pearsonverse) will load the core pearsonverse packages:

• skelpear, for building a project environment and maintaining a reproducibility.
• reppear, for building reproducible reports with a standardised layout.
• vispear, for creating visualisations with a coherent layout.

When you load the pearsonverse library, you'll see that it loads the three packages:

library(pearsonverse)

#> ── Attaching packages ──────────────────────────────── pearsonverse 1.0.0 ──

#> ✔ vispear  1.0.3
#> ✔ reppear  1.1.0
#> ✔ skelpear 1.1.1

Data Science Workflow

1. Load the pearsonverse packages.

library(pearsonverse)

2. Create a new project. It will open a new session.

skelpear::project_create(name = "example_project")

3. In the console, the warning shows up:

"Warning: There is no `packages.dcf` file in your project. 
Please use `snapshot_pkg` function to save your package environment."

The packages.dcf file lists all attached packages used during the project. It is especially useful when there are more people involved in code development and when an identical package environment is critical.

4. Save your package environment.

skelpear::snapshot_pkg()

The function will save your package environment in config/packages.dcf file. Once you push it to a bitbucket repository, anybody can pull it and compare it with the local package envrionment via the compare_snapshot function. You can find more info about these functions in skelpear package introduction.

5. Create a repository on bitbucket and link it to the local project.

6. Now you can start writing your code.

7. Once you're ready to prepare a report with your analysis, use the report_create function.

8. Initialize the report environment.

reppear::report_create(file = "01_report")

By default, the report environment will be stored in reports directory. More info about the structure of report directory here.

9. Once you've finalised your report, prepare index.Rmd file which acts as home page for your report/s.

10. Render all files in the report directory.

reppear::render_all()

11. Then you're ready to publish the whole reports directory on your server.

reppear::publish()

pearsonplc/pearsonverse documentation built on May 30, 2019, 3:45 p.m.