Download as PDF, PPTX
![Exercises
Finding the value in a vector that is closest to a specified value
closest<-function(xv,sv){
xv[which(abs(xv-sv)==min(abs(xv-sv)))]
}
Calculate a trimmed mean of x which ignores both the
smallest and largest values
trimmed.mean <- function (x) {
mean(x[-c(which(x==min(x)),which(x==max(x)))])
}](https://image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-32-2048.jpg)
![lists
person <- list()
person$name <- "Alberto”
person$age <- 37
person$nationality <- "Spain“
class(persona)
[1] "list"
> persona
$name
[1] "Alberto"
$age
[1] 37
$nationality
[1] "Spain"
names(persona)
[1] “name" “age" "nationality"](https://image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-37-2048.jpg)
![Strings
phrase<-"the quick brown fox jumps over the lazy dog"
letras <- table(strsplit(phrase,split=character(0)))
numwords<-1+table(strsplit(phrase,split=character(0)))[1]
words <- unlist(strsplit(phrase,split=" "))
words[grep("o",words)]
"fox" %in% unlist(strsplit(phrase,split=" "))
unlist(strsplit(phrase,,split=" ")) %in% c("fox","dog")](https://image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-38-2048.jpg)
![Strings
nchar(words)
paste(words[1],words[2])
toupper(words)](https://image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-39-2048.jpg)
![Regular expressions
grep("^t", words)
words[grep("^t", words)]
words[grep("s$", words)]
gsub("o","O",words)
regexp()](https://image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-40-2048.jpg)
![Dataframes
lista <- data.frame()
lista[1,1] = "Alberto"
lista[1,2] = 37
lista[2,1] = "Ana"
lista[2,2] = 23
names(lista) <- c("Ana", "Edad")](https://image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-41-2048.jpg)
![Missing values
NA (is.na)
x<-c(1:8,NA)
mean(x)
mean(x,na.rm=T)
which(is.na(x))
as.vector(na.omit(x))
x[!is.na(x)]](https://image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-42-2048.jpg)
![Avoid loops
x<-runif(10000000)
system.time(max(x))
pc<-proc.time()
cmax<-x[1]
for (i in 2:length(x)) {
if(x[i]>cmax) cmax<-x[i]
}
proc.time()-pc](https://image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-48-2048.jpg)
![Exercises
Finding the value in a vector that is closest to a specified value
closest<-function(xv,sv){
xv[which(abs(xv-sv)==min(abs(xv-sv)))]
}
Calculate a trimmed mean of x which ignores both the
smallest and largest values
trimmed.mean <- function (x) {
mean(x[-c(which(x==min(x)),which(x==max(x)))])
}](https://crownmelresort.com/image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-32-2048.jpg)
![lists
person <- list()
person$name <- "Alberto”
person$age <- 37
person$nationality <- "Spain“
class(persona)
[1] "list"
> persona
$name
[1] "Alberto"
$age
[1] 37
$nationality
[1] "Spain"
names(persona)
[1] “name" “age" "nationality"](https://crownmelresort.com/image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-37-2048.jpg)
![Strings
phrase<-"the quick brown fox jumps over the lazy dog"
letras <- table(strsplit(phrase,split=character(0)))
numwords<-1+table(strsplit(phrase,split=character(0)))[1]
words <- unlist(strsplit(phrase,split=" "))
words[grep("o",words)]
"fox" %in% unlist(strsplit(phrase,split=" "))
unlist(strsplit(phrase,,split=" ")) %in% c("fox","dog")](https://crownmelresort.com/image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-38-2048.jpg)
![Strings
nchar(words)
paste(words[1],words[2])
toupper(words)](https://crownmelresort.com/image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-39-2048.jpg)
![Regular expressions
grep("^t", words)
words[grep("^t", words)]
words[grep("s$", words)]
gsub("o","O",words)
regexp()](https://crownmelresort.com/image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-40-2048.jpg)
![Dataframes
lista <- data.frame()
lista[1,1] = "Alberto"
lista[1,2] = 37
lista[2,1] = "Ana"
lista[2,2] = 23
names(lista) <- c("Ana", "Edad")](https://crownmelresort.com/image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-41-2048.jpg)
![Missing values
NA (is.na)
x<-c(1:8,NA)
mean(x)
mean(x,na.rm=T)
which(is.na(x))
as.vector(na.omit(x))
x[!is.na(x)]](https://crownmelresort.com/image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-42-2048.jpg)
![Avoid loops
x<-runif(10000000)
system.time(max(x))
pc<-proc.time()
cmax<-x[1]
for (i in 2:length(x)) {
if(x[i]>cmax) cmax<-x[i]
}
proc.time()-pc](https://crownmelresort.com/image.slidesharecdn.com/parti-introductiontorprogramming-110630064850-phpapp02/75/Introduction-to-R-programming-48-2048.jpg)
Uploaded byAlberto Labarga
Introduction to R programming
R is a free and open-source programming language for statistical analysis and graphics. It allows users to import, clean, transform, visualize and model data. Key features of R include its large collection of statistical and graphical techniques, ability to easily extend its functionality through user-contributed packages, and open-source nature which allows for free use and development. The document provides instructions on installing R, getting started with the R interface and commands, and an overview of common functions and operations for data analysis, visualization and statistics.
In this document
Powered by AI
Introduction to R as a statistical language, its advantages, installation steps, and finding resources online.
Commands in R, how to use R prompt, install packages, and seek help with functions.
Data maintenance in R, citation in research, and resources for support.
Fundamental concepts of R including code, commands, assignment, and various operators.
Overview of mathematical functions, logical functions, and handling infinite and NaN values.
Creating and manipulating vectors, including plotting functionalities.
Utility functions for data analysis including cumulative calculations and vector operations.
Defining custom functions for calculating geometric and harmonic means.
Set operations, creation and manipulation of matrices and applying the sweep function.
Manipulating lists and strings, including regular expressions for text processing.
Creating data frames, handling missing values, and ensuring data integrity.
Managing dates in R and coercing data types.
Using if statements and loops for program control in R.
Exercises for coding practices and strategies to avoid loops for efficiency.
Help for built-in datasets, accessing variables, using R functions, and data import/export.
More Related Content
Similar to Introduction to R programming
![[1062BPY12001] Data analysis with R / week 2](https://cdn.slidesharecdn.com/ss_thumbnails/dataanalyzer01-180307063046-thumbnail.jpg?width=640&height=640&fit=bounds)
Introduction to R programming
- 1. Quantitative Data Analysis Working with R
- 2. Working with R Whatis R A computer language, with orientation toward statistical applications Advantages Completely free, just download from Internet Many add-on packages for specialized uses Open source
- 3. Getting Started: InstallingR Have Internet connection Go to http://cran.r-project/ R for Windows screen, click “base” Find, click on download R Click Run, OK, or Next for all screens End up with R icon on desktop
- 4. At http://cran.r-project.org/ Haga clic para modificar el estilo de texto del patrón Segundo nivel ● Tercer nivel ● Cuarto nivel ● Quinto nivel
- 5. Downloading Base R Clickon Windows Then in next screen, click on “base” Then screens for Run, OK, or Next And finally “Finish” will put R icon on desktop
- 6. Rgui and RConsolen ending with R prompt (>) Haga clic para modificar el estilo de texto del patrón Segundo nivel ● Tercer nivel ● Cuarto nivel ● Quinto nivel
- 7. The R prompt(>) > This is the “R prompt.” It says R is ready to take your command. Enter these after the prompt, observe output >2+3 >2^3+(5) >6/2+(8+5) >2 ^ 3 + (5)
- 8. Installing Packages and Libraries install.packages("akima") install.packages("chron") install.packages("lme4") install.packages("mcmc") install.packages("odesolve") install.packages("spdep") install.packages("spatstat") install.packages("tree") install.packages("lattice")
- 9. Installing Packages and Libraries
- 10. Installing Packages and Libraries R.version installed.packages() update.packages() setRepositories()
- 11. Help help(mean) ?mean help will notfind a function in a package unless you install it and load it with library help.search(“aspline”) will find functions in packages installed but not loaded apropos("lm")
- 12. Help For help onwhole package: help(package=akima) objects(grep("akima",search())) library(“akima”) my.packages <- search() aki <- grep("akima",my.packages) my.objects <- objects(aki)
- 13. Help example(mean) demo() demo(package = packages(all.available= TRUE)) demo(graphics) vignette(all=TRUE) V <- vignette("sp") print(V) edit(V)
- 14.
- 15. Maintenance getwd() setwd() source("myprogram.R ") save(list =ls(all=TRUE), file= "all.Rdata") load("all.Rdata") save.image() savehistory()
- 16. To cite useof R To cite the use of R for statistical work, R documentation recommends the following: R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/. Get the latest citation by typing citation ( ) at the prompt.
- 17. Email Support Lists http://r-project.orgunder "mailing lists" r-help is the most general one Before posting, read: http://www.R-project.org/postingguide.html Send the smallest possible example of your problem (generated data is handy) sessionInfo() will list your computer & R details to cut/paste to your question
- 18.
- 19.
- 20.
- 21. Mathematical operators + -*/ ^ arithmetic > >= < <= == != relational ! & logical $ list indexing (the ‘element name’ operator) : create a sequence ~ model formulae
- 22. Logical operators ! logicalNOT & logical AND | logical OR < less than <= less than or equal to > greater than >= greater than or equal to == logical equals (double =) != not equal && AND with IF || OR with IF xor(x,y) exclusive OR isTRUE(x) an abbreviation of identical(TRUE,x) all(x) any(x)
- 23. Mathematical functions log(x) logto base e of x exp(x) antilog of x ex log(x,n) log to base n of x log10(x) log to base 10 of x sqrt(x) square root of x factorial(x) x! choose(n,x) binomial coefficients n!/(x! n−x!) gamma(x) x, for real x x−1!, for integer x lgamma(x) natural log of x
- 24. Mathematical functions floor(x) greatestinteger <x ceiling(x) smallest integer >x trunc(x) round(x, digits=0) round the value of x to an integer abs(x) the absolute value of x, ignoring the minus sign if there is one signif(x, digits=6) give x to 6 digits in scientific notation
- 25. Trigonometrical functions cos(x) cosineof x in radians sin(x) sine of x in radians tan(x) tangent of x in radians acos(x), asin(x), atan(x) inverse trigonometric transformations of real or complex numbers acosh(x), asinh(x), atanh(x) inverse hyperbolic trigonometric transformations of real or complex numbers
- 26. Infinity and Thingsthat Are Not a Number Inf (is.finite,is.infinite) 3/0 2 / Inf exp(-Inf) (0:3)^Inf NaN (is.nan) 0/0
- 27. Vectors a <- c(1,2,3,4,5) a<- 1:5 a <- scan() a <- seq(1,10,2) b <- 1:4 a <- seq(1,10,along=b) x <- runif(10) which(a == 2)
- 28.
- 29. Vector functions max(x) maximumvalue in x min(x) minimum value in x sum(x) total of all the values in x sort(x) a sorted version of x rank(x) vector of the ranks of the values in x order(x) an integer vector containing the permutation to sort x into ascending order range(x) vector of minx and maxx
- 30. More functions cumsum(x) vectorcontaining the sum of all of the elements up to that point cumprod(x) vector containing the product of all of the elements up to that point cummax(x) vector of non-decreasing numbers which are the cumulative maxima of the values in x up to that point cummin(x) vector of non-increasing numbers which are the cumulative minima of the values in x up to that point pmax(x,y,z) vector, of length equal to the longest of x y or z, containing the maximum of x y or z for the ith position in eachpmin(x,y,z) vector, of length equal to the longest of x y or z, containing the minimum of x y or z for the ith position in each rowSums(x) row totals of dataframe or matrix x colSums(x) column totals of dataframe or matrix x
- 31. functions Geometric mean (p.49) geometric<-function(x) exp(mean(log(x))) Harmonic mean (p.51) harmonic<-function (x) 1/mean(1/x)
- 32. Exercises Finding the valuein a vector that is closest to a specified value closest<-function(xv,sv){ xv[which(abs(xv-sv)==min(abs(xv-sv)))] } Calculate a trimmed mean of x which ignores both the smallest and largest values trimmed.mean <- function (x) { mean(x[-c(which(x==min(x)),which(x==max(x)))]) }
- 33.
- 34.
- 35.
- 36.
- 37. lists person <- list() person$name<- "Alberto” person$age <- 37 person$nationality <- "Spain“ class(persona) [1] "list" > persona $name [1] "Alberto" $age [1] 37 $nationality [1] "Spain" names(persona) [1] “name" “age" "nationality"
- 38. Strings phrase<-"the quick brownfox jumps over the lazy dog" letras <- table(strsplit(phrase,split=character(0))) numwords<-1+table(strsplit(phrase,split=character(0)))[1] words <- unlist(strsplit(phrase,split=" ")) words[grep("o",words)] "fox" %in% unlist(strsplit(phrase,split=" ")) unlist(strsplit(phrase,,split=" ")) %in% c("fox","dog")
- 39.
- 40. Regular expressions grep("^t", words) words[grep("^t",words)] words[grep("s$", words)] gsub("o","O",words) regexp()
- 41. Dataframes lista <- data.frame() lista[1,1]= "Alberto" lista[1,2] = 37 lista[2,1] = "Ana" lista[2,2] = 23 names(lista) <- c("Ana", "Edad")
- 42.
- 43. Dates and Timesin R date() date<- as.POSIXlt(Sys.time()) unlist(unclass(date)) difftime() excel.dates <- c("27/02/2004", "27/02/2005", "14/01/2003“,"28/06/2005", "01/01/1999") strptime(excel.dates,format="%d/%m/%Y")
- 44.
- 45. if if (y >0) print(1) else print (-1) z <- ifelse (y < 0, -1, 1)
- 46. Loops and Repeats for(i in 1:10) print(i^2) t = 1 while(t<=10) { print(i^2) i <- i + 1 } t = 1 repeat { if (i > 10)break print(i^2) i <- i + 1 }
- 47. Exercise Compute the Fibonacciseries 1, 1, 2, 3, 5, 8 fibonacci<-function(n) { a<-1 b<-0 while(n>0) {swap<-a a<-a+b b<-swap n<-n-1 } b }
- 48. Avoid loops x<-runif(10000000) system.time(max(x)) pc<-proc.time() cmax<-x[1] for (iin 2:length(x)) { if(x[i]>cmax) cmax<-x[i] } proc.time()-pc
- 49. switch central<-function(y, measure) { switch(measure, Mean = mean(y), Geometric = exp(mean(log(y))), Harmonic = 1/mean(1/y), Median = median(y), stop("Measure not included")) }
- 50.
- 51. Help for Datasets Tolist built-in datasets: data() data(package = .packages(all.available = TRUE)) data(swiss) For help on a dataset: help(swiss) “Standardized fertility measure and socio-economic indicators for each of 47 French-speaking provinces of Switzerland at about 1888.”
- 52. The attach Command Toaccess individual variables, do this: > attach(swiss) Now try: > mean(Fertility) > detach(swiss)
- 53. Using R Functions:Simple Stuff rownames(swiss) colnames(swiss) • summary(swiss) Applying functions mean(swiss$Fertility) sd(swiss$Fertility) apply(swiss,2,max)
- 54.
- 55. Working with yourdataset fix(swiss) hist(Agriculture) plot(Catholic,Fertility)
- 56. Working with yourown datasets write.table(swiss, "swiss.txt") swiss2 <- read.table("swiss.txt") data<- read.table(file.choose(),header=T) readLines()
- 57. Reading data fromfiles read.table(file) reads a file in table format and creates a data frame from it; the default separator sep="" is any whitespace; use header=TRUE to read the first line as a header of column names; use as.is=TRUE to prevent character vectors from being converted to factors; use comment.char="" to prevent "#" from being interpreted as a comment; use skip=n to skip n lines before reading data; see the help for options on row naming, NA treatment, and others read.csv("filename", header=TRUE) id. but with defaults set for reading comma-delimited files read.delim("filename", header=TRUE) id. but with defaults set for reading tab-delimited files read.fwf(file,widths) read a table of f ixed width f ormatted data into a ’data.frame’; widths is an integer vector, giving the widths of the fixed-width fields
- 58.