R Data Analysis Tutorial

To perform data analysis using R language, first you need to install R language and RStudio. Then, proceed with the following steps for data analysis:

1. Import data: Use the read.csv() or read.table() function to import a dataset, for example: data <- read.csv("data.csv").
2. Data cleaning involves preliminary cleaning of the dataset, such as removing duplicates and handling missing values. Functions like duplicated() and na.omit() can be used for this purpose.
3. Exploratory Data Analysis (EDA): Utilizing a variety of statistical charts and descriptive statistics to understand the basic characteristics of a dataset. R offers many plotting functions, such as plot(), hist(), boxplot(), etc.
4. Data transformation and processing: Perform data transformation and processing according to analytical needs. Functions such as subset(), filter() from the dplyr package, and mutate() can be utilized.
5. Statistical analysis: Choose appropriate statistical methods based on the analysis purpose, such as descriptive statistics, hypothesis testing, linear regression, cluster analysis, etc. R provides a variety of statistical functions and packages, such as summary(), t.test(), lm(), kmeans(), etc.
6. Visualization of results: Using plotting functions to visualize the analysis results, making them easier to understand and interpret. Functions such as plot() or functions in the ggplot2 package can be used.
7. Report and share: Write a report or present the analysis results based on data analysis. Use tools like RMarkdown to combine code and text to generate the report or presentation slides.

It is important to note that the data analysis process is iterative, and may require multiple rounds of data cleaning, exploratory data analysis, and model building steps to obtain accurate and reliable analysis results. Additionally, being flexible in applying various functions and packages of the R language can make data analysis more efficient and convenient.