To accomplish your goal you'll need to familiarize yourself with several concepts and R packages. Here’s a structured guide to help you learn how to accomplish this:
1. **Learn the Basics of R Programming**
- **Online Resources**:
- [R for Data Science](https://r4ds.had.co.nz/) by Hadley Wickham
- [DataCamp](https://www.datacamp.com/courses/tech:r) offers various interactive R courses.
2. **Understanding Data Sources**
- Learn how to connect to different data sources like APIs, databases, or CSV/Excel files.
- **Key Concepts**:
- **APIs**: Understand RESTful APIs and how to make GET requests to retrieve data.
- **Databases**: Learn SQL basics for querying data from databases.
3. **Key R Packages**
- **`httr`**: For making HTTP requests to APIs to download data.
- **`readr`**: For reading CSV files.
- **`readxl`**: For reading Excel files.
- **`dplyr`**: For data manipulation and combining datasets.
- **`lubridate`**: For handling date and time columns.
- **`openxlsx`**: For writing data to Excel files.
4. **Data Manipulation and Combination**
- Familiarize yourself with data manipulation techniques in R.
- **Key Functions**:
- `left_join()`, `right_join()`, `full_join()`: To combine datasets based on a common time column.
- `bind_rows()`: To combine data frames vertically.
5. **Writing Data to CSV or Excel**
- Learn how to save data frames to CSV or Excel formats using:
- `write.csv()`: For saving data frames as CSV files.
- `write.xlsx()` from the `openxlsx` package: For saving data frames as Excel files.
6. **Building the Function**
- **Conceptualize Your Function**:
- Create parameters for the function:
- Data sources (URLs, file paths, etc.)
- Time column to combine datasets
- Output file path
- Implement logic to:
- Connect to each data source and download the data.
- Combine the datasets based on the specified time column.
- Save the combined dataset to a CSV or Excel file.
7. **Example Learning Materials**
- **Books**:
- "R for Data Science" by Hadley Wickham
- **Courses**:
- [Coursera R Programming](https://www.coursera.org/learn/r-programming)
- **YouTube Tutorials**:
- Search for "R data manipulation" and "R API requests" for visual and practical examples.
8. **Practice and Experimentation**
- Try building small scripts that connect to various data sources.
- Combine datasets manually first to understand the logic before automating it with a function.
Practice and experimentation will be key to mastering these concepts!
