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Introduction to R Language (Classic)
R language is a programming language evolved from S language, and its origins can be traced back to the 1970s. The S language was originally developed at Bell Labs by Rick Becker, John Chambers, and Allan Wilks. R language has powerful functions in data analysis and statistical modeling, so it is widely used in academia and industry. Functions and objects are important concepts in R language. A function is a reusable piece of code that performs a specific task. R language provides many built-in functions, such as sum() for calculating the sum of elements in a vector and mean() for calculating the average of elements in a vector. Object is a container for storing data, which can be a vector, matrix, data frame, etc. By using functions and objects, we can manipulate and analyze data. Scripting is a way of programming using the R language. A script is a collection of R language codes used to automate specific tasks. Writing scripts can improve the efficiency of data processing and analysis and avoid duplication of work. R language provides rich drawing functions that can be used to visualize data. By using plotting functions, we can draw various types of graphs such as line charts, scatter plots, bar charts, etc. Plotting can help us better understand the distribution and trends of data. Writing functions is an advanced programming skill and is very common in R. A function is a reusable block of code that accepts input parameters and returns an output result. By writing functions, we can break down complex tasks into multiple simple steps, improving code reusability and maintainability. Data saving is one of the common operations in R language. R language provides a variety of data saving methods, such as saving data as text files, Excel files, databases, etc. By saving data, we can easily share and reuse it while also avoiding the risk of data loss. In summary, R language is a powerful programming language suitable for data analysis and statistical modeling. Mastering the basic concepts and skills of R language can help us better process and analyze data, and provide strong support for scientific research and work.
Each distribution has four functions: d (density function), p (distribution function), q (quantile function), r (random number function). For example, the four functions of the normal distribution are dnorm, pnorm, qnorm, and rnorm.
The R language is really powerful. It can complete many different tasks and is very simple to operate. I've covered many of R's features, now it's just left to focus on the specific areas you need to learn.
"Li Dongfeng R Language Tutorial" is an R language learning textbook specially designed for beginners. The entire tutorial is divided into five parts, namely introductory part, data analysis part, data cleaning part, data visualization part and advanced application part. Each part contains multiple chapters, which gradually guide readers from learning the basic knowledge of R language to mastering practical application skills. Whether you are a beginner with no programming experience or a professional who wants to further improve your data analysis skills, you can systematically learn the R language and apply it in actual work through "Li Dongfeng R Language Tutorial".
R is a very professional statistical language. To master it quickly, you must understand basic statistical knowledge. Without this knowledge as a foundation, progress in learning R may be slow. Therefore, before learning R, it is recommended to learn some basic statistical concepts and methods first, so that you can better understand and use R language for statistical analysis.
How to find the configuration file ctex\miktex\dvipdfm\config\dvipdfmx,cfg...
First, we need to convert the TeX source file into a DVI file. You can use tools such as LaTeX and choose the default conversion options. For example, in Windows systems, we can use the program with the path C:\CTeX\texmf\miktex\bin\platex.exe for conversion. Next, we need to convert the DVI file to a pdf file. You can use the dvipdfmx tool for conversion. By executing the corresponding command, we can convert DVI files into pdf files for more convenient viewing and sharing.
This article is to introduce the strategy for clearing the nostalgic cleaning level of "Words Play with Flowers". This level requires players to find 12 chronologically inconsistent places in the map. The clearance guide will explain the specific operation methods in detail to help players who have not yet completed the level pass smoothly. If you are interested in this level, you may wish to continue to pay attention to this site to get more information about "Word Play Flowers" and the nostalgic cleaning level. Thank you for taking the time to read this article.
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