Visão Geral

Este Curso Introduction to R with Time Series Analysis, R é uma linguagem de programação livre de código aberto para computação estatística, análise de dados e gráficos. O R é usado por um número crescente de gerentes e analistas de dados dentro das corporações e da academia. R possui uma ampla variedade de pacotes para mineração de dados .

Pre-Requisitos

Informações Gerais

Carga horaria: 24h

• Se noturno este curso e ministrado de segunda-feira a sexta-feira das 19h às 23h, total de 6 encontros.
• Se aos sábados este curso e ministrado das 09h às 18h, total de 3 encontros.

Formato de entrega:

• 100% on-line ao vivo via Microsoft Teams, na presença de um instrutor/consultor ativo no mercado e docente em sala de aula. 
• Nota: não é curso gravado (o mesmo acontece em tempo real na presença de um instrutor).
• Apostila + exercícios práticos

Materiais

Conteúdo Programatico

Introduction and preliminaries

1. Making R more friendly, R and available GUIs
2. Rstudio
3. Related software and documentation
4. R and statistics
5. Using R interactively
6. An introductory session
7. Getting help with functions and features
8. R commands, case sensitivity, etc.
9. Recall and correction of previous commands
10. Executing commands from or diverting output to a file
11. Data permanency and removing objects

Simple manipulations; numbers and vectors

1. Vectors and assignment
2. Vector arithmetic
3. Generating regular sequences
4. Logical vectors
5. Missing values
6. Character vectors
7. Index vectors; selecting and modifying subsets of a data set
8. Other types of objects

Objects, their modes and attributes

1. Intrinsic attributes: mode and length
2. Changing the length of an object
3. Getting and setting attributes
4. The class of an object

Arrays and matrices

• Arrays
• Array indexing. Subsections of an array
• Index matrices
• The array() function
• 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

Lists and data frames

1. Lists
2. Constructing and modifying lists
3. Concatenating lists
4. Data frames
5. Making data frames
6. attach() and detach()
7. Working with data frames
8. Attaching arbitrary lists
9. Managing the search path

Data manipulation

1. Selecting, subsetting observations and variables          
2. Filtering, grouping
3. Recoding, transformations
4. Aggregation, combining data sets
5. Character manipulation, stringr package

Reading data

1. Txt files
2. CSV files
3. XLS, XLSX files
4. SPSS, SAS, Stata,… and other formats data
5. Exporting data to txt, csv and other formats
6. Accessing data from databases using SQL language

Probability distributions

1. R as a set of statistical tables
2. Examining the distribution of a set of data
3. One- and two-sample tests

Grouping, loops and conditional execution

1. Grouped expressions
2. Control statements
3. Conditional execution: if statements
4. Repetitive execution: for loops, repeat and while

Writing your own functions

1. Simple examples
2. Defining new binary operators
3. Named arguments and defaults
4. The '...' argument
5. Assignments within functions
6. More advanced examples
7. Efficiency factors in block designs
8. Dropping all names in a printed array
9. Recursive numerical integration
10. Scope
11. Customizing the environment
12. Classes, generic functions and object orientation

Graphical procedures

1. High-level plotting commands
2. The plot() function
3. Displaying multivariate data
4. Display graphics
5. Arguments to high-level plotting functions
6. Basic visualisation graphs
7. Multivariate relations with lattice and ggplot package
8. Using graphics parameters
9. Graphics parameters list

Time series Forecasting

1. Seasonal adjustment
2. Moving average
3. Exponential smoothing
4. Extrapolation
5. Linear prediction
6. Trend estimation
7. Stationarity and ARIMA modelling

Econometric methods (casual methods)

1. Regression analysis
2. Multiple linear regression
3. Multiple non-linear regression
4. Regression validation
5. Forecasting from regression