R Kurzusok

Kurzuskód

rlang

Időtartalma

21 hours (usually 3 days including breaks)

Összefoglaló

R egy nyílt forráskódú programozási nyelv a statisztikai számításokhoz, az adatok elemzéséhez és a grafikákhoz. Az R-t egyre nagyobb számú vezető és adatelemző használja a vállalatokon és az egyetemeken. R a statisztikusok, a mérnökök és a tudósok között is találnak követőket számítógépes programozási készségek nélkül, akik könnyen használhatók. A népszerűsége annak köszönhető, hogy az adatbányászat egyre növekvő mértékben használja a különböző célokat, például a hirdetési árakat, az új gyógyszerek gyorsabb felkutatását vagy a pénzügyi modellek finomhangolását. R sokféle csomagot tartalmaz az adatbányászathoz.

Machine Translated

Kurzusleírás

Day 1

Introduction and preliminaries

  • 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

Simple manipulations; numbers and vectors

  • Vectors and assignment
  • Vector arithmetic
  • Generating regular sequences
  • Logical vectors
  • Missing values
  • Character vectors
  • Index vectors; selecting and modifying subsets of a data set
  • Other types of objects

Objects, their modes and attributes

  • Intrinsic attributes: mode and length
  • Changing the length of an object
  • Getting and setting attributes
  • The class of an object

Ordered and unordered factors

  • A specific example
  • The function tapply() and ragged arrays
  • Ordered factors

Arrays and matrices

  • Arrays
  • Array indexing. Subsections of an array
  • Index matrices
  • The array() function
    • Mixed vector and array arithmetic. The recycling rule
  • The outer product of two arrays
  • Generalized transpose of an array
  • Matrix facilities
    • Matrix multiplication
    • Linear equations and inversion
    • Eigenvalues and eigenvectors
    • Singular value decomposition and determinants
    • Least squares fitting and the QR decomposition
  • Forming partitioned matrices, cbind() and rbind()
  • The concatenation function, (), with arrays
  • Frequency tables from factors

Day 2

Lists and data frames

  • Lists
  • Constructing and modifying lists
    • Concatenating lists
  • Data frames
    • Making data frames
    • attach() and detach()
    • Working with data frames
    • Attaching arbitrary lists
    • Managing the search path

Data manipulation

  • Selecting, subsetting observations and variables          
  • Filtering, grouping
  • Recoding, transformations
  • Aggregation, combining data sets
  • Character manipulation, stringr package

Reading data

  • Txt files
  • CSV files
  • XLS, XLSX files
  • SPSS, SAS, Stata,… and other formats data
  • Exporting data to txt, csv and other formats
  • Accessing data from databases using SQL language

Probability distributions

  • R as a set of statistical tables
  • Examining the distribution of a set of data
  • One- and two-sample tests

Grouping, loops and conditional execution

  • Grouped expressions
  • Control statements
    • Conditional execution: if statements
    • Repetitive execution: for loops, repeat and while

Day 3

Writing your own functions

  • Simple examples
  • Defining new binary operators
  • Named arguments and defaults
  • The '...' argument
  • Assignments within functions
  • More advanced examples
    • Efficiency factors in block designs
    • Dropping all names in a printed array
    • Recursive numerical integration
  • Scope
  • Customizing the environment
  • Classes, generic functions and object orientation

Statistical analysis in R

  • 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)
  • Survival analysis
    • Survival objects in r
    • Kaplan-Meier estimate
    • Confidence bands
    • Cox PH models, constant covariates
    • Cox PH models, time-dependent covariates

Graphical procedures

  • High-level plotting commands
    • The plot() function
    • Displaying multivariate data
    • Display graphics
    • Arguments to high-level plotting functions
  • Basic visualisation graphs
  • Multivariate relations with lattice and ggplot package
  • Using graphics parameters
  • Graphics parameters list

Automated and interactive reporting

  • Combining output from R with text

Creating html, pdf documents

Vélemények

★★★★★
★★★★★

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