Send Reports and Emails with Ease Using R and GitHub

Introduction

Automating daily email reports can save time and effort. With the Blastula package in R and GitHub, you can schedule and send emails seamlessly—no need for task schedulers (that require your laptop to stay on), paid cloud services, or Office365 accounts. Just code your analysis, set the timer, and let the process run automatically.

Let us begin with installing and loading the required package

install.packages("blastula")
library(blastula)

Step 1: Generating a password file

Creating a password file in Blastula secures your credentials by keeping them hidden from the code, reducing the risk of accidental exposure.

create_smtp_creds_file(
  file = "my_password_file",
  user = "sender@gmail.com",
  provider = "gmail",  # Can be "outlook", "office365", "yahoo", or a custom SMTP
  host = "smtp.gmail.com",
  port = 587,
  use_ssl = TRUE
)

Note: The code should be executed once to generate the password file. Upon execution, it will create a file in your current working directory with the name specified in the “file” argument. /

Step 2: R code for composing an Email message

Below code can be a part of the same R Analysis code (e.g. My_analysis.R)

# A regular data frame must be converted into HTML format to embed it properly in the email body.
library(knitr)
library(kableExtra)

html_table <- kable(
  Analysis_df,      # Data frame to be converted into HTML
  format = "html",  # Specify HTML format for the output
  table.attr = "style='width:50%; border:1px solid black;'",  # Set table styling
  align = "c"       # Center align the table content
) %>%
  kable_styling()   # Apply additional styling for enhanced appearance


# Define the email body with HTML content
email_body <- paste0(
  "<p>Dear Recipient,</p>",  # Salutation
  "<p>Please find the requested analysis table below:</p>",  # Introduction
  html_table,           # Embed the HTML table generated from the data frame. Table will appear in the email body
  "<br><br>",           # Add spacing between the table and closing remarks
  "Best regards,<br>",  # Closing phrase
  "<b>XYZ Team</b>"     # Signature with bold styling
)

# Compose the email with the defined body using markdown for rendering
email <- compose_email(
  body = md(email_body)  # Convert the email body into markdown format for compatibility
) %>%
  add_attachment(
    file = "C:/My_Path_to_file",      # Path to the Excel file
    filename = "Previous_Analysis_Table.xlsx"  # Any file that needs to be sent as an attachment
  )

Step 3: R code to send the email

email %>%
  smtp_send(
    to = c("receiver1@domain.com", "receiver2@domain.com"),
    from = "sender@gmail.com",
    subject = paste("Daily analysis report", sys_date),
    credentials = creds_file("D:/Mention path to your password file")  # Importing password file
  )

Step 4: Automating the process to send email with Github

Create a private repository for securing your password file.

1. Create a Private Repository - Log in to your GitHub account.

• Click the + icon in the top-right corner and select New repository.

• Fill in the repository name and description.

• Select Private under Repository visibility.

• Click Create repository.

• Once repository is created, upload the Analysis R code file(e.g. My_analysis.R), Password file created in Step 1 (my_password_file), any other supporting files

2. Enable Workflow Permissions

• Go to your repository’s Settings.
• Navigate to Actions in the left-hand menu.
• Scroll to Workflow permissions.
• Select Read and write permissions.
• Click Save.

3. Create a folder named .github/workflows

• Add a workflow YAML file (e.g., main.yml)
• Note: The .github/workflows folder is necessary because it is where GitHub Actions looks for workflow YAML files that define automated tasks, such as CI/CD pipelines, to execute for your repository.

# Example of Yaml file store on private repository e.g. Email_from_R/.github/workflows/schedule-email.yml

# Workflow name
name: Send email using R and blastula

# Trigger the workflow on a specific schedule (every weekday at 2:50 AM )
on:
  schedule:
    - cron: '50 2 * * 1-5'

jobs:
  render:
    runs-on: windows-latest  # Set the environment where the job will run

    steps:
      - uses: actions/checkout@v2  # Checkout the repository

      - name: Set up R
        uses: r-lib/actions/setup-r@v2  # Set up R environment

      - name: Install dependencies
        run: |  # Run multiple commands
          install.packages(c("blastula", "quantmod", "dplyr", "purrr", "tidyr", "lubridate", "kableExtra"), type = "win.binary") # Required libraries for your analaysis R code
        shell: Rscript {0}  # Use Rscript to run the command

      - name: Run email script
        run: Rscript My_analysis.R  # Execute the R script for sending emails

      - name: Commit files
        run: |  # Run multiple commands
          git config --local user.name github-actions
          git config --local user.email "actions@github.com"
          git commit -am "commit on $(date)"        # Commit with a message including the current date
          git push origin main                      # Push changes to the main branch
        env:
          REPO_KEY: ${{ secrets.GITHUB_TOKEN }}     # Access GitHub token from secrets
          username: github-actions                  # Set up commit user

Conclusion:

Using Blastula with R and GitHub streamlines email automation efficiently and cost-effectively. This setup eliminates dependencies on additional software or paid services, making it a powerful tool for automating your daily workflows.