課程代碼

rintrob

課程時長

28 時間：同常來說包括休息是 4天

概觀

R是一種用於統計計算，數據分析和圖形的開源免費編程語言。 R被企業和學術界內越來越多的經理和數據分析師使用。 R還發現沒有計算機編程技能的統計學家，工程師和科學家的追隨者都很容易使用。它的受歡迎程度是由於越來越多地使用數據挖掘來實現各種目標，例如設定廣告價格，更快地找到新藥或微調財務模型。 R有各種各樣的數據挖掘包。

Machine Translated

課程簡介

I. Introduction and preliminaries

1. Overview

Making R more friendly, R and available GUIs
Rstudio
Related software and documentation
R and statistics
Using R interactively
An introductory session
Getting help with functions and features
R commands, case sensitivity, etc.
Recall and correction of previous commands
Executing commands from or diverting output to a file
Data permanency and removing objects
Good programming practice: Self-contained scripts, good readability e.g. structured scripts, documentation, markdown
installing packages; CRAN and Bioconductor

2. Reading data

Txt files (read.delim)
CSV files

3. Simple manipulations; numbers and vectors + arrays

Vectors and assignment
Vector arithmetic
Generating regular sequences
Logical vectors
Missing values
Character vectors
Index vectors; selecting and modifying subsets of a data set
- Arrays
Array indexing. Subsections of an array
Index matrices
The array() function + simple operations on arrays e.g. multiplication, transposition
Other types of objects

4. Lists and data frames

Lists
Constructing and modifying lists
- Concatenating lists
Data frames
- Making data frames
- Working with data frames
- Attaching arbitrary lists
- Managing the search path

5. Data manipulation

Selecting, subsetting observations and variables
Filtering, grouping
Recoding, transformations
Aggregation, combining data sets
Forming partitioned matrices, cbind() and rbind()
The concatenation function, (), with arrays
Character manipulation, stringr package
short intro into grep and regexpr

6. More on Reading data

XLS, XLSX files
readr and readxl packages
SPSS, SAS, Stata,… and other formats data
Exporting data to txt, csv and other formats

6. Grouping, loops and conditional execution

Grouped expressions
Control statements
Conditional execution: if statements
Repetitive execution: for loops, repeat and while
intro into apply, lapply, sapply, tapply

7. Functions

Creating functions
Optional arguments and default values
Variable number of arguments
Scope and its consequences

8. Simple graphics in R

Creating a Graph
Density Plots
Dot Plots
Bar Plots
Line Charts
Pie Charts
Boxplots
Scatter Plots
Combining Plots

II. Statistical analysis in R

1. Probability distributions

R as a set of statistical tables
Examining the distribution of a set of data

2. Testing of Hypotheses

Tests about a Population Mean
Likelihood Ratio Test
One- and two-sample tests
Chi-Square Goodness-of-Fit Test
Kolmogorov-Smirnov One-Sample Statistic
Wilcoxon Signed-Rank Test
Two-Sample Test
Wilcoxon Rank Sum Test
Mann-Whitney Test
Kolmogorov-Smirnov Test

3. Multiple Testing of Hypotheses

Type I Error and FDR
ROC curves and AUC
Multiple Testing Procedures (BH, Bonferroni etc.)

4. Linear regression models

Generic functions for extracting model information
Updating fitted models
Generalized linear models
- Families
- The glm() function
Classification
- Logistic Regression
- Linear Discriminant Analysis
Unsupervised learning
- Principal Components Analysis
- Clustering Methods(k-means, hierarchical clustering, k-medoids)

5. Survival analysis (survival package)

Survival objects in r
Kaplan-Meier estimate, log-rank test, parametric regression
Confidence bands
Censored (interval censored) data analysis
Cox PH models, constant covariates
Cox PH models, time-dependent covariates
Simulation: Model comparison (Comparing regression models)

6. Analysis of Variance

One-Way ANOVA
Two-Way Classification of ANOVA
MANOVA

III. Worked problems in bioinformatics

Short introduction to limma package
Microarray data analysis workflow
Data download from GEO: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
Data processing (QC, normalisation, differential expression)
Volcano plot
Custering examples + heatmaps

客戶評論

★★★★★

培訓師知識淵博，包括我感興趣的領域。

Mohamed Salama

課程:Data Mining & Machine Learning with R

Machine Translated

非常適合需求。

Yashan Wang

課程:Data Mining with R

Machine Translated

我真的很喜歡與Gunner一起工作1：1。

Bryant Ives

課程:Introduction to R

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我喜歡深度機器學習的新見解。

Josip Arneric

課程:Neural Network in R

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我們獲得了一些關於NN的知識，對我來說最有趣的是現在流行的新型NN。

Tea Poklepovic

課程:Neural Network in R

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我最喜歡R :)））中的圖表。

Faculty of Economics and Business Zagreb

課程:Neural Network in R

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靈活友好的風格。準確學習什麼對我有用和相關。

Jenny Tickner

課程:Advanced R

Machine Translated

我喜歡提供帶有示例練習的Excel表格。這意味著，如果泰米爾語被阻止幫助其他人，我可以繼續使用下一部分。

Luke Pontin