https://github.com/brightdaniel/orderdataprocessing
Analysing Dataset with R Programming
https://github.com/brightdaniel/orderdataprocessing
Last synced: over 1 year ago
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Analysing Dataset with R Programming
- Host: GitHub
- URL: https://github.com/brightdaniel/orderdataprocessing
- Owner: BrightDaniel
- Created: 2024-07-08T09:19:16.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-08T09:58:53.000Z (almost 2 years ago)
- Last Synced: 2025-01-09T08:16:37.542Z (over 1 year ago)
- Language: R
- Size: 117 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# OrderDataProcessing
This repository contains R scripts for processing the `OrderData` dataset and practicing R programming techniques.
## Project Overview
The project is divided into two main tasks:
### Task 1: Processing the Dataset
1. **Vector Operations**: Store a column from the dataset in a vector and determine its attributes.
2. **List Operations**: Create a list containing multiple columns and determine its attributes.
3. **Data Frame Operations**: Create a data frame with the first 4 columns and 30 rows from the dataset and determine its attributes.
### Task 2: Practicing R Programming
1. **R Operators**: Perform operations on vectors.
2. **Conditional Statements**: Use conditional statements to control the flow of the script.
3. **Functions**: Create and use functions in R.
4. **Handling Missing Values**: Remove missing/NA values from data.
## Files
- `OrderData.r`: R script for processing the `OrderData` dataset.
- `R_programming_practise.r`: R script demonstrating R operators, conditional statements, functions, and handling missing values.
- `updated_data.csv`: The cleaned dataset.
## Getting Started
### Prerequisites
Ensure you have R and RStudio installed on your machine. You will also need the following R packages:
```R
install.packages("tidyverse")
install.packages("here")
install.packages("skimr")
install.packages("janitor")
```
### Running the Scripts
1. Clone this repository to your local machine:
```sh
git clone https://github.com//OrderDataProcessing.git
cd OrderDataProcessing
```
2. Open RStudio and set the working directory to the repository folder:
```R
setwd("")
```
3. Run the `OrderData.r` script to process the dataset:
```R
source("OrderData.r")
```
4. Run the `R_programming_practise.r` script to practice R programming techniques:
```R
source("R_programming_practise.r")
```
## Description of Concepts
### Vector
- A one-dimensional data structure that stores elements of the same type.
- Example: `OrderData_vector` stores the `units` column.
### List
- A versatile data structure that can contain elements of different types.
- Example: `OrderData_list` stores multiple columns (`order_date`, `region`, `rep`, `item`).
### Data Frame
- A two-dimensional, table-like data structure with rows and columns.
- Example: `OrderData_dataframe` is a subset of the original data frame with the first 4 columns and 30 rows.
### Attributes/Properties
- **Type**: Indicates the data type of the structure (`vector`, `list`, `data.frame`).
- **Length**: The number of elements or components in the structure.
- **Names**: Column names for data frames and lists.
- **Row names**: Row identifiers for data frames.
## Additional Practice
The `R_programming_practise.r` script demonstrates:
- **R Operators**: Performing operations on vectors.
- **Conditional Statements**: Using `if` statements to control the flow.
- **Functions**: Creating and using functions.
- **Handling Missing Values**: Removing rows with NA values.
## Resources
- [R Manuals](https://cran.r-project.org/doc/manuals/r-release/R-intro.html)
- [W3Schools R Tutorials](https://www.w3schools.in/r/)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Thanks to the R community for the excellent resources and documentation.