mentals-of-R-Programming-and-Statistical-Analysis
所属分类：模式识别(视觉/语音等)
开发工具：R
文件大小：33565KB
下载次数：0
上传日期：2021-01-19 13:07:43
上 传 者：
sh-1993
说明： R编程与统计分析基础，R编程与统计学分析基础[视频]，Packt出版
(Fundamentals-of-R-Programming-and-Statistical-Analysis,Fundamentals of R Programming and Statistical Analysis [video], published by Packt)
文件列表:
LICENSE (1062, 2021-01-19)
Section 1 (0, 2021-01-19)
Section 1\1.2.R (2758, 2021-01-19)
Section 1\1.3.R (4990, 2021-01-19)
Section 1\1.4.R (1909, 2021-01-19)
Section 1\1.5.R (3872, 2021-01-19)
Section 1\1.6.R (4042, 2021-01-19)
Section 1\Example1.6_TumorMeasurements.csv (239, 2021-01-19)
Section 1\Example1.6_TumorMeasurements.txt (239, 2021-01-19)
Section 1\Example1.6_TumorMeasurements.xlsx (36118, 2021-01-19)
Section 1\example1-5_exprsMatrix.txt (5706, 2021-01-19)
Section 1\exampleData.RData (134, 2021-01-19)
Section 1\savedata_file.csv (172, 2021-01-19)
Section 1\savedata_file.txt (146, 2021-01-19)
Section 1\x.rds (48, 2021-01-19)
Section 1\y.rds (95, 2021-01-19)
Section 2 (0, 2021-01-19)
Section 2\2.1.R (2300, 2021-01-19)
Section 2\2.2.R (3041, 2021-01-19)
Section 2\2.3.R (3049, 2021-01-19)
Section 2\2.4.R (2396, 2021-01-19)
Section 2\2.5.R (2511, 2021-01-19)
Section 2\Day20_measurements_2.4.txt (175, 2021-01-19)
Section 2\Day60_measurements_2.4.txt (185, 2021-01-19)
Section 2\example2_1exprsdata.txt (4025, 2021-01-19)
Section 2\tumorVolume_2_5.rda (286, 2021-01-19)
Section 3 (0, 2021-01-19)
Section 3\3.1.R (2567, 2021-01-19)
Section 3\3.2.R (3348, 2021-01-19)
Section 3\3.3.R (5676, 2021-01-19)
Section 3\3.4.R (2954, 2021-01-19)
Section 3\3.5.R (1961, 2021-01-19)
Section 3\exampleGEmatrix.rds (2449, 2021-01-19)
Section 3\exampleLogFCdata.rds (457208, 2021-01-19)
Section 3\string_interactions.tsv (22638, 2021-01-19)
... ...
# Fundamentals of R Programming and Statistical Analysis [Video]
This is the code repository for [Fundamentals of R Programming and Statistical Analysis [Video]](https://www.packtpub.com/big-data-and-business-intelligence/fundamentals-r-programming-and-statistical-analysis-video?utm_source=github&utm_medium=repository&utm_campaign=9781782175247), published by [Packt](https://www.packtpub.com/?utm_source=github). It contains all the supporting project files necessary to work through the video course from start to finish.
## About the Video Course
The R language is widely used among statisticians and data miners to develop statistical software and data analysis.
In this course, we’ll start by diving into the different types of R data structures and you’ll learn how the R programming language handles data. Then we’ll look in-depth at manipulating different datasets in R. After that, we’ll dive into data visualization with R, using basic plots, heat maps, and networks. We’ll explore the different flow control loops of the R programming language, and you’ll learn how to debug your code.
In the second half of the course, you’ll get hands-on working with the various statistical methods in R programming. You’ll find out how to work with different probability distributions, various types of hypothesis testing, and statistical analysis with the R programming language.
By the end of this video course, you will be well-versed in the basics of R programming and the various concepts of statistical data analysis with R.
What You Will Learn
- Improve your understanding of descriptive statistics and apply them over a dataset.
- Learn how to deal with missing data and outliers to resolve data inconsistencies.
- Explore various visualization techniques for bivariate and multivariate analysis.
- Enhance your programming skills and master data exploration and visualization in Python.
- Learn multidimensional analysis and reduction techniques.
- Master advanced visualization techniques (such as heatmaps) for better analysis and rapidly broaden your understanding
## Instructions and Navigation
### Assumed Knowledge
To fully benefit from the coverage included in this course, you will need:
This course is for those who are interested in using R for scientific computing and bioinformatics. For users who want to use R to perform basic and advanced scientific computing that are in turn needed to perform commonly needed scientific data tasks such as looking for statistically significant changes between groups and statistical modeling to look for relationships in the data. Basically, this course is for people who are keen in learning how to analyze scientific data, that is, look for differentially expressed genes and gene set enrichment analysis etc
### Technical Requirements
This course has the following software requirements:
Basic R and graphs
## Related Products
* [Exploratory Data Analysis with Pandas and Python 3.x [Video]](https://www.packtpub.com/application-development/exploratory-data-analysis-pandas-and-python-3x-video?utm_source=github&utm_medium=repository&utm_campaign=9781789959116)
* [Fundamentals of R Programming and Statistical Analysis [Video]](https://www.packtpub.com/big-data-and-business-intelligence/fundamentals-r-programming-and-statistical-analysis-video?utm_source=github&utm_medium=repository&utm_campaign=9781782175247)
* [Fundamentals of R Programming and Statistical Analysis [Video]](https://www.packtpub.com/big-data-and-business-intelligence/fundamentals-r-programming-and-statistical-analysis-video?utm_source=github&utm_medium=repository&utm_campaign=9781782175247)
近期下载者：
相关文件：
收藏者：