R language, an introduction
4 likes1,490 views
The document is a comprehensive introduction to the R programming language, detailing its features, data types, and structures such as vectors, matrices, lists, and data frames. It provides examples of how to create, access, and manipulate these data structures, and includes information on statistical operations, data exploration, and basic graphing techniques. Additionally, it highlights R as a valuable tool for statistical calculations and data analysis.
![Data Structures: Vectors
• A sequence of objects of the same (atomic) data type
• Creation
• x <- b c [ <- is the assignment operator ]
• y <- seq(5, 9, 2) = c(5, 7, 9)
• y <- 5:7 = c(5, 6, 7) [ m:n is equivalent to seq(m, n, 1) ]
• y <- c(1, 4:6) = c(1, 4, 5, 6) [ no nesting / always flattened ]
• z <- rep(1, 3) = c(1, 1, 1)](https://image.slidesharecdn.com/therlanguageanintroduction-130908101945-/85/R-language-an-introduction-5-320.jpg)
![Data Structures: Vectors
• Accessing
• x[1] [ 1-based indexing ]
• x[2:3]
• x[c(2,3)] = x[2:3]
• x[-1] [ Negative subscripts imply exclusion ]
• Naming
• names(x) <- [ Makes equivalent to x[1] ]](https://image.slidesharecdn.com/therlanguageanintroduction-130908101945-/85/R-language-an-introduction-6-320.jpg)
![Data Structures: Vectors
• Operations
• x <- c(5, 6, 7)
• x + 2 = c(7, 8, 9) [ Vectorized operations ]
• x > 5 = c(FALSE, TRUE, TRUE)
• subset(x, x > 5) = c(6, 7)
• which(x > 5) = c(2, 3)
• ifelse(x > 5, NaN, x) = c(5, NaN, NaN)
• sqr <- function (n) { n * n }
• sapply(x,sqr) = c(25 ,36, 49)
• sqr(x) = c(25, 36, 49)](https://image.slidesharecdn.com/therlanguageanintroduction-130908101945-/85/R-language-an-introduction-7-320.jpg)
![Data Structures: Vectors
• Operations
• x <- c(5, 6, 7)
• any(x > 5) = TRUE [ How about all(x > 5)? ]
• sum(c(1, 2, 3, NA), na.rm = TRUE) = 6 [ Why is na.rm required? ]
• sort(c(7, 6, 5)) = c(5, 6, 7)
• order(c(7, 6, 5)) = ???
• subset(x, x > 5) = c(6, 7)
• head(1:100) = ???
• tail(1:100) = ???
• How is x == c(5, 6, 7) different from identical(x, c(5, 6, 7))?
• Try str(x)](https://image.slidesharecdn.com/therlanguageanintroduction-130908101945-/85/R-language-an-introduction-8-320.jpg)
![Data Structures: Matrices
• A two dimensional matrix of objects of the same (atomic) data type
• Creation
• y <- matrix(nrow=2, ncol=3) [ empty matrix ]
• y <- matrix(c(1, 2, 3, 4, 5, 6), nrow=2) =
• y <- matrix(c(1, 2, 3, 4, 5, 6), nrow=2, byrow=T) =
• Accessing
• y[1,2] = 2
• y[,2:3] = [ How about y[1,]? ]
• What’s the difference between y[2,] and y[2,, drop=FALSE]?
1 3 5
2 4 6
1 2 3
4 5 6
2 3
5 6](https://image.slidesharecdn.com/therlanguageanintroduction-130908101945-/85/R-language-an-introduction-9-320.jpg)
![Data Structures: Matrices
• Naming
• rownames() and colnames()
• Operations
• nrow(y) = 2 [ number of rows ]
• ncol(y) = 3 [ number of columns ]
• apply(y, 1, sum) = c(6, 15) [ apply sum to each row ]
• apply(y, 2, sum) = c(5, 7, 9) [ apply sum to each column ]
• t(y) = [ transpose a matrix ]1 4
2 5
3 6](https://image.slidesharecdn.com/therlanguageanintroduction-130908101945-/85/R-language-an-introduction-10-320.jpg)
![Data Structures: Lists
• A sequence of objects (of possibly different data types)
• Creation
• k <- list(c(1, 2, 3),
• l <- [ f1 and f2 are tags ]
• Accessing
• k[2:3]
• k[[2]] [ How about k[2]? ]
• l$f1 = c(1, 2, 3) [ Is it same as l[1] or l[[1]]? ]](https://image.slidesharecdn.com/therlanguageanintroduction-130908101945-/85/R-language-an-introduction-13-320.jpg)
![Data Structures: Data Frames
• A two dimensional matrix of objects where different columns can be of
different types.
• Creation
• x <- data.frame jill
• Accessing
• x$names jill [ How about x[[1]]? ]
• x[1] = ???
• x[c(1,2)] = ???
• x[1,] = ???
• x[,1] = ???](https://image.slidesharecdn.com/therlanguageanintroduction-130908101945-/85/R-language-an-introduction-15-320.jpg)
![Data Structures: Data Frames
• Naming
• rownames() and colnames()
• Operations
• x[x$age > 5,] = data.frame jill ))
• subset(x, age > 5) = ???
• apply(x, 1, sum) = ???
• y <- data.frame(1:3, 5:7)
• apply(y, 1, mean) = ???
• lapply(y, mean) = ???
• sapply(y, mean) = ???
• Try str(y)](https://image.slidesharecdn.com/therlanguageanintroduction-130908101945-/85/R-language-an-introduction-16-320.jpg)
R language, an introduction
- 1. The R Language A Hands-on Introduction Venkatesh-Prasad Ranganath http://about.me/rvprasad
- 2. What is R? • A dynamical typed programming language • http://cran.r-project.org/ • Open source and free • Provides common programming language constructs/features • Multiple programming paradigms • Numerous libraries focused on various data-rich topics • http://cran.r-project.org/web/views/ • Ideal for statistical calculation; lately, the go-to tool for data analysis • Accompanied by RStudio, a simple and powerful IDE • http://rstudio.org
- 3. Data Types (Modes) • Numeric • Character • Logical (TRUE / FALSE) • Complex • Raw (bytes)
- 4. Data Structures • Vectors • Matrices • Arrays • Lists • Data Frames • Factors • Tables
- 5. Data Structures: Vectors • A sequence of objects of the same (atomic) data type • Creation • x <- b c [ <- is the assignment operator ] • y <- seq(5, 9, 2) = c(5, 7, 9) • y <- 5:7 = c(5, 6, 7) [ m:n is equivalent to seq(m, n, 1) ] • y <- c(1, 4:6) = c(1, 4, 5, 6) [ no nesting / always flattened ] • z <- rep(1, 3) = c(1, 1, 1)
- 6. Data Structures: Vectors • Accessing • x[1] [ 1-based indexing ] • x[2:3] • x[c(2,3)] = x[2:3] • x[-1] [ Negative subscripts imply exclusion ] • Naming • names(x) <- [ Makes equivalent to x[1] ]
- 7. Data Structures: Vectors • Operations • x <- c(5, 6, 7) • x + 2 = c(7, 8, 9) [ Vectorized operations ] • x > 5 = c(FALSE, TRUE, TRUE) • subset(x, x > 5) = c(6, 7) • which(x > 5) = c(2, 3) • ifelse(x > 5, NaN, x) = c(5, NaN, NaN) • sqr <- function (n) { n * n } • sapply(x,sqr) = c(25 ,36, 49) • sqr(x) = c(25, 36, 49)
- 8. Data Structures: Vectors • Operations • x <- c(5, 6, 7) • any(x > 5) = TRUE [ How about all(x > 5)? ] • sum(c(1, 2, 3, NA), na.rm = TRUE) = 6 [ Why is na.rm required? ] • sort(c(7, 6, 5)) = c(5, 6, 7) • order(c(7, 6, 5)) = ??? • subset(x, x > 5) = c(6, 7) • head(1:100) = ??? • tail(1:100) = ??? • How is x == c(5, 6, 7) different from identical(x, c(5, 6, 7))? • Try str(x)
- 9. Data Structures: Matrices • A two dimensional matrix of objects of the same (atomic) data type • Creation • y <- matrix(nrow=2, ncol=3) [ empty matrix ] • y <- matrix(c(1, 2, 3, 4, 5, 6), nrow=2) = • y <- matrix(c(1, 2, 3, 4, 5, 6), nrow=2, byrow=T) = • Accessing • y[1,2] = 2 • y[,2:3] = [ How about y[1,]? ] • What’s the difference between y[2,] and y[2,, drop=FALSE]? 1 3 5 2 4 6 1 2 3 4 5 6 2 3 5 6
- 10. Data Structures: Matrices • Naming • rownames() and colnames() • Operations • nrow(y) = 2 [ number of rows ] • ncol(y) = 3 [ number of columns ] • apply(y, 1, sum) = c(6, 15) [ apply sum to each row ] • apply(y, 2, sum) = c(5, 7, 9) [ apply sum to each column ] • t(y) = [ transpose a matrix ]1 4 2 5 3 6
- 11. Data Structures: Matrices • Operations • rbind(y, c(7, 8, 9)) = • cbind(y, c(7, 8)) = • Try str(y) 1 2 3 4 5 6 7 8 9 1 2 3 7 4 5 6 8
- 12. Data Structures: Matrices • What will this yield? m <- matrix(nrow=4, ncol=4) m <- ifelse(row(m) == col(m), 1, 0.3)
- 13. Data Structures: Lists • A sequence of objects (of possibly different data types) • Creation • k <- list(c(1, 2, 3), • l <- [ f1 and f2 are tags ] • Accessing • k[2:3] • k[[2]] [ How about k[2]? ] • l$f1 = c(1, 2, 3) [ Is it same as l[1] or l[[1]]? ]
- 14. Data Structures: Lists • Naming • names(k) <- • Operations • lapply(list(1:2, 9:10), sum) = list(3, 19) • sapply(list(1:2, 9:10), sum) = c(3, 19) • l$f1 <- NULL = ??? • str(l) = ???
- 15. Data Structures: Data Frames • A two dimensional matrix of objects where different columns can be of different types. • Creation • x <- data.frame jill • Accessing • x$names jill [ How about x[[1]]? ] • x[1] = ??? • x[c(1,2)] = ??? • x[1,] = ??? • x[,1] = ???
- 16. Data Structures: Data Frames • Naming • rownames() and colnames() • Operations • x[x$age > 5,] = data.frame jill )) • subset(x, age > 5) = ??? • apply(x, 1, sum) = ??? • y <- data.frame(1:3, 5:7) • apply(y, 1, mean) = ??? • lapply(y, mean) = ??? • sapply(y, mean) = ??? • Try str(y)
- 17. Factors (and Tables) • Type for categorical/nominal values. • Example • xf <- factor(c(1:3, 2, 4:5)) • Try xf and str(xf) • Operations • table(xf) = ??? • with(mtcars, split(mpg, cyl)) = ??? • with(mtcars, tapply(mpg, cyl, mean)) = ??? • by(mtcars, mtcars$cyl, function(m) { median(m$mpg) } = ??? • aggregate(mtcars, list(mtcars$cyl), median) = ??? • You can use cut to bin values and create factors. Try it.
- 18. Basic Graphs • with(mtcars, boxplot(mpg)) • hist(mtcars$mpg) • with(mtcars, plot(hp, mpg)) • dotchart(VADeaths) • Try plot(aggregate(mtcars, list(mtcars$cyl), median)) You can get the list of datasets via ls package.datasets
- 19. Stats 101 using R • mean • median • What about mode? • fivenum • quantile • sd • var • cov • cor
- 20. Data Exploration using R Let’s get out hands dirty!!