Kurskode

rintrob

Varighet

28 timer (vanligvis 4 dag inkludert pauser)

Oversikt

R er et gratis programmeringsspråk med åpen kildekode for statistisk databehandling, dataanalyse og grafikk. R brukes av et økende antall ledere og dataanalytikere i selskaper og akademia. R har også funnet tilhengere blant statistikere, ingeniører og forskere uten dataprogrammeringsevner som synes det er enkelt å bruke. Dens popularitet skyldes den økende bruken av data mining for forskjellige mål, for eksempel angitte annonsepriser, finne nye medisiner raskere eller finjustere økonomiske modeller. R har et bredt utvalg av pakker for data mining.

Machine Translated

Kursplan

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

Testimonials

★★★★★

Treneren var så kunnskapsrik og inkluderte områder jeg var interessert i

Mohamed Salama

Kurs:Data Mining & Machine Learning with R

Machine Translated

veldig skreddersydd etter behov

Yashan Wang

Kurs:Data Mining with R

Machine Translated

Jobber med 1: 1 med Gunnar.

Bryant Ives

Kurs:Introduction to R

Machine Translated

ny innsikt i dyp maskinlæring

Josip Arneric

Kurs:Neural Network in R

Machine Translated

Vi fikk litt kunnskap om NN generelt, og det som var det mest interessante for meg var de nye typene NN som er populære nå for tiden.

Tea Poklepovic

Kurs:Neural Network in R

Machine Translated

Grafer i R :)))

Faculty of Economics and Business Zagreb

Kurs:Neural Network in R

Machine Translated

Den fleksible og vennlige stilen. Lær nøyaktig hva som var nyttig og relevant for meg

Jenny Tickner

Kurs:Advanced R

Machine Translated

Jeg likte Excel arkene som ga øvelsene med eksempler. Dette betydde at hvis Kamil ble holdt opp med å hjelpe andre mennesker, kunne jeg slå meg løs med de neste delene.

Luke Pontin