1 / 76

An Introduction to

An Introduction to. Web resources. R home page: http://www.r-project.org/ R Archive: http://cran.r-project.org/ R FAQ ( frequently asked questions about R ): http://cran.r-project.org/doc/FAQ/R-FAQ.html R manuals: http://cran.r-project.org/manuals.html. The R environment.

megara
Download Presentation

An Introduction to

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. An Introduction to

  2. Web resources • R home page: http://www.r-project.org/ • R Archive: http://cran.r-project.org/ • R FAQ (frequently asked questions about R): http://cran.r-project.org/doc/FAQ/R-FAQ.html • R manuals: http://cran.r-project.org/manuals.html

  3. The R environment • R command window (console) or Graphical User Interface (GUI) • Used for entering commands, data manipulations, analyses, graphing • Output: results of analyses, queries, etc. are written here • Toggle through previous commands by using the up and down arrow keys

  4. The R environment • The R workspace • Current working environment • Comprised primarily of variables, datasets, functions

  5. The R environment • R scripts • A text file containing commands that you would enter on the command line of R • To place a comment in a R script, use a hash mark (#) at the beginning of the line

  6. Some tips for getting started in R • Create a new folder on your hard drive for your current R session • Open R and set the working directory to that folder • Save the workspace with a descriptive name and date • Open a new script and save the script with a descriptive name and date

  7. Executing simple commands • The assignment operator <- • x <- 5assigns the value of 5 to the variable x • y <- 2*xassigns the value of 2 times x (10 in this case) to the variable y • r <- 4 • area.circle <- pi*r^2 • NOTE: R is case-sensitive (y ≠ Y)

  8. R object types • Vector • Matrix • Array • Data frame • Function • List

  9. Vectors and arrays • Vector: a one-dimensional array, all elements of a vector must be of the same type (numerical, character, etc) • Matrix: a two-dimensional array with rows and columns • Array: as a matrix, but of arbitrary dimension

  10. Entering data into R • Vectors: • The “c” command • combine or concatenate data • Data can be character or numeric v1 <- c(12, 5, 6, 8, 24) v2 <- c("Yellow perch", "Largemouth bass", "Rainbow trout", "Lake whitefish“)

  11. Entering data into R • Vectors: • Sequences of numbers c() seq() years<-c(1990:2007) x<-seq(0,100,10) x<-seq(0, 200, length=100)

  12. Entering data into R • Arrays: array() matrix() m1<-array(1:20, dim=c(4,5)) m2<-matrix(1:20, ncol=5, nrow=4)

  13. Entering data into R • Arrays: • Combine vectors as columns or rows cbind() rbind() Matrix1 <- cbind(v1, v2) Matrix2 <- rbind(v1, v2)

  14. Data frames • A data frame is a list of variables of the same length with unique row names • A collection of variables which share many of the properties of matrices and of lists • Used as the fundamental data structure by most of R's modeling software

  15. Data frames • Convert vectors or matrices into a data frame data.frame() df1<-data.frame(v1, v2) df2<-data.frame(matrix1)

  16. Data frames • Editing data frames in spreadsheet-like view edit() df2<-edit(df1)

  17. Let’s go to R and enter some vectors, arrays, and data frames Script : QFC R short course R object types_1_vectors and arrays.R

  18. Placing variables in the R search path • When variables in a data frame are used in R, the data frame name followed by a $ sign and then the variable name is required query1<-df1$v3 > 20

  19. Placing variables in the R search path • Alternatively, the attach() function can be used attach(df1) query1 <- v3 > 20 detach(df1)

  20. Accessing data from an array, vector, or data frame • Subscripts are used to extract data from objects in R • Subscripts appear in square brackets and reference rows and columns, respectively

  21. Subscripts df1 df[3,5] df[,3] df[5,] df[2:5,]

  22. Queries in R: Common logical arguments

  23. Queries in R • The use of logical tests query1<-df1$v3 > 20 df1[query1,] query2<-df1$v3 > 20 & df1$v4 < 30 (&=and) query2<-df1$v3 > 20 | df1$v4 < 30 (| = or)

  24. Queries in R Script : QFC R short course R object types_2_query arrays data frames.R

  25. Exercise 1

  26. Importing data from Excel(or other database management programs) • Export as text file (.txt) • Tips • Avoid spaces in variable and character names, use a period (e.g., fish.weight, not fish weight and Round.lake not Round lake) • Replace missing data with “NA” • See Excel example (MI STORET data RAW.xls)

  27. Importing data from Excel • read.table() data.frame.name <- read.table(“file path”, na.strings=”NA”, header=TRUE) df1<-read.table("C:\\R\\Example\\datafile1.txt", na.strings="NA", header=TRUE) Note the use of \\ instead of \ in path name

  28. Importing data from Excel • If your working directory is set, R will automatically look for the data text file there. • So, the read.table syntax can be simplified by excluding the file path name: • read.table(“data.txt”, na.strings=“NA”, header=T)

  29. Exporting data from R write.table() write.table(df, file = "Path Name\\file_name.csv", sep = ",", col.names = NA)

  30. Introduction to R functions • R has many built-in functions and many more that can be downloaded from CRAN sites (Comprehensive R Archive Network) • User-defined functions can also be created

  31. The R base package

  32. Introduction to R functions • Common functions names(): obtain variable names of a df summary(): summary of all variables in a df mean(): Mean var(): Variance sd(): standard deviation Script: QFC R short course R functions_1.R

  33. Introduction to R functions, cont head(): print first few rows of data frame sapply() and tapply(): column-wise summaries levels(): obtain levels of a character variable by(): produce summaries by group

  34. Introduction to R functions, cont tapply(variable, list(group1, group2), mean) Applies function to each element in ragged arrays sapply(variable, FUN=) Applies a function to elements in a list by(data, INDICES, FUN)

  35. Introduction to R loops Basin syntax: for (i in 1:n){ some code } *Excel example Script:QFC R short course R functions_2.R

  36. User-defined functions Function name <- function(x){ argument } Script: QFC R short course R user defined functions_3.R

  37. Exercise 2 (Part 1)

  38. R functions part 2: subset data • subset() function sub<- subset(data frame, criteria) sub1<-subset(fish, no.fish > 50) sub2<-subset(fish, no.fish>50 & position=="Below")

  39. R functions part 2: subset data • Select specific columns sub3<-subset(fish, select=c(stream, site, no.fish)) Script: QFC R short course R subset_4.R

  40. Exercise 2 (Part 2)

  41. Introduction to basic graphing http://addictedtor.free.fr/graphiques/

  42. Graphing basics Plotting commands • High-level functions: Create a new plot on the graphics device • Low-level functions: Add more information to an already existing plot, such as extra points, lines, and labels • Interactive graphing functions: Allow you to interactively add information to a graph

  43. Common high-level functions • plot(): A generic function that produces a type of plot that is dependent on the type of the first arguement • hist(): Creates a histogram of frequencies • barplot(): Creates a histogram of values • boxplot(): Creates a boxplot • pairs(): Creates a scatter plot matrix

  44. Common high-level functions plot() plot(x) plot(x,y) : scatter plot plot(y~x) : scatter plot plot(group, x) : box plot

  45. Common high-level functions hist(x) boxplot(x~group) pairs(z) pairs(df1[,3:7])

  46. Common high-level functions Script: QFC R course Graphing Basics_1.R

  47. Exercise 3

  48. END OF DAY 1

  49. Lower-level graphing functions

More Related