Course Code

rintrob

Duration

28 hours (usually 4 days including breaks)

Overview

R은 통계 컴퓨팅, 데이터 분석 및 그래픽을위한 오픈 소스 무료 프로그래밍 언어입니다. R은 기업 및 학계 내에서 점점 더 많은 관리자 및 데이터 분석가가 사용합니다. R은 컴퓨터 프로그래밍 기술 없이도 통계학 자, 엔지니어 및 과학자 중 추종자를 쉽게 찾을 수 있음을 발견했습니다. 그 인기는 광고 가격 설정, 신약의 신속 검색 또는 재무 모델 미세 조정과 같은 다양한 목표에 대한 데이터 마이닝의 사용 증가 때문입니다. R에는 데이터 마이닝을위한 다양한 패키지가 있습니다.

Machine Translated

Course Outline

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

회원 평가

★★★★★

트레이너는 매우 지식이 많았고 내가 관심이있는 영역을 포함했습니다.

Mohamed Salama

Course:Data Mining & Machine Learning with R

Machine Translated

필요에 매우 맞춘

Yashan Wang

Course:Data Mining with R

Machine Translated

Gunnar와 1 : 1로 작업하십시오.

Bryant Ives

Course:Introduction to R

Machine Translated

깊은 기계 학습에 대한 새로운 통찰력

Josip Arneric

Course:Neural Network in R

Machine Translated

우리는 일반적으로 NN에 대한 지식을 얻었으며 요즘 인기있는 새로운 유형의 NN이 나에게 가장 흥미로운 것은 무엇입니까?

Tea Poklepovic

Course:Neural Network in R

Machine Translated

그래프는 R :)))

Faculty of Economics and Business Zagreb

Course:Neural Network in R

Machine Translated

유연하고 친절한 스타일. 나를 위해 유용하고 관련성이 높은 것을 정확하게 배웁니다

Jenny Tickner

Course:Advanced R

Machine Translated

나는 예제를 가지고 연습을하는 엑셀 시트를 즐겼다. 이것은 카밀이 다른 사람들을 돕기 위해 붙잡 혔을 때, 나는 다음 부분으로 갈라 갈 수 있다는 것을 의미했다

Luke Pontin