ID de curs

rintrob

Durata

28 ore (usually 4 days including breaks)

Sinoptic

R este un limbaj de programare liber-sursă pentru calculul statistic, analiza datelor și grafica. R este utilizat de un număr tot mai mare de manageri și analiști de date din cadrul corporațiilor și al mediului academic. R a găsit, de asemenea, adepți printre statisticienii, inginerii și oamenii de știință, fără abilități de programare pe calculator, care le găsesc ușor de folosit. Popularitatea sa se datorează utilizării din ce în ce mai mari a activităților de exploatare a datelor pentru diverse scopuri, cum ar fi stabilirea prețurilor anunțurilor, găsirea de noi medicamente mai rapid sau ajustarea modelelor financiare. R are o mare varietate de pachete de date mining.

Machine Translated

Schița de curs

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

Mărturii

★★★★★

Formatorul a fost atât de informat și a inclus zonele în care mă interesează.

Mohamed Salama

Curs:Data Mining & Machine Learning with R

Machine Translated

Foarte adaptate nevoilor.

Yashan Wang

Curs:Data Mining with R

Machine Translated

M-am bucurat cu adevărat să lucrez la 1: 1 cu Gunner.

Bryant Ives

Curs:Introduction to R

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Mi-au plăcut noile cunoștințe despre învățarea mașinilor adânci.

Josip Arneric

Curs:Neural Network in R

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Am câștigat câteva cunoștințe despre NN în general, și ceea ce a fost cel mai interesant pentru mine au fost noile tipuri de NN care sunt populare în zilele noastre.

Tea Poklepovic

Curs:Neural Network in R

Machine Translated

M-am bucurat cel mai mult de graficele din R :))).

Faculty of Economics and Business Zagreb

Curs:Neural Network in R

Machine Translated

Stilul flexibil și prietenos. Aflați exact ce a fost util și relevant pentru mine.

Jenny Tickner

Curs:Advanced R

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Mi-a plăcut foile Excel oferind exerciții cu exemple. Aceasta a însemnat că, dacă Tamil ar fi fost răzbunat cu ajutorul altor persoane, aș putea să mă descurc cu următoarele părți.

Luke Pontin