--- language: R contributors: - ["e99n09", "http://github.com/e99n09"] filename: learnr.r --- R is a statistical computing language. It has lots of good built-in functions for uploading and cleaning data sets, running common statistical tests, and making graphs. You can also easily compile it within a LaTeX document. ```python # Comments start with hashtags. # You can't make a multi-line comment per se, # but you can stack multiple comments like so. # Hit COMMAND-ENTER to execute a line ################################################################### # Stuff you can do without understanding anything about programming ################################################################### data() # Browse pre-loaded data sets data(rivers) # Lengths of Major North American Rivers ls() # Notice that "rivers" appears in the workspace head(rivers) # peek at the dataset # 735 320 325 392 524 450 length(rivers) # how many rivers were measured? # 141 summary(rivers) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 135.0 310.0 425.0 591.2 680.0 3710.0 stem(rivers) #stem-and-leaf plot (like a histogram) # # The decimal point is 2 digit(s) to the right of the | # # 0 | 4 # 2 | 011223334555566667778888899900001111223333344455555666688888999 # 4 | 111222333445566779001233344567 # 6 | 000112233578012234468 # 8 | 045790018 # 10 | 04507 # 12 | 1471 # 14 | 56 # 16 | 7 # 18 | 9 # 20 | # 22 | 25 # 24 | 3 # 26 | # 28 | # 30 | # 32 | # 34 | # 36 | 1 stem(log(rivers)) #Notice that the data are neither normal nor log-normal! Take that, Bell Curve fundamentalists. # The decimal point is 1 digit(s) to the left of the | # # 48 | 1 # 50 | # 52 | 15578 # 54 | 44571222466689 # 56 | 023334677000124455789 # 58 | 00122366666999933445777 # 60 | 122445567800133459 # 62 | 112666799035 # 64 | 00011334581257889 # 66 | 003683579 # 68 | 0019156 # 70 | 079357 # 72 | 89 # 74 | 84 # 76 | 56 # 78 | 4 # 80 | # 82 | 2 hist(rivers, col="#333333", border="white", breaks=25) #play around with these parameters hist(log(rivers), col="#333333", border="white", breaks=25) #you'll do more plotting later #Here's another neat data set that comes pre-loaded. R has tons of these. data() data(discoveries) plot(discoveries, col="#333333", lwd=3, xlab="Year", main="Number of important discoveries per year") plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year", main="Number of important discoveries per year") #rather than leaving the default ordering (by year) we could also sort to see what's typical sort(discoveries) # [1] 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 # [26] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 # [51] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 # [76] 4 4 4 4 5 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 8 9 10 12 stem(discoveries, scale=2) # # The decimal point is at the | # # 0 | 000000000 # 1 | 000000000000 # 2 | 00000000000000000000000000 # 3 | 00000000000000000000 # 4 | 000000000000 # 5 | 0000000 # 6 | 000000 # 7 | 0000 # 8 | 0 # 9 | 0 # 10 | 0 # 11 | # 12 | 0 max(discoveries) # 12 summary(discoveries) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 0.0 2.0 3.0 3.1 4.0 12.0 #Basic statistical operations don't require any programming knowledge either #roll a die a few times round(runif(7, min=.5, max=6.5)) # 1 4 6 1 4 6 4 #your numbers will differ from mine unless we set the same random.seed(31337) #draw from a standard Gaussian 9 times rnorm(9) # [1] 0.07528471 1.03499859 1.34809556 -0.82356087 0.61638975 -1.88757271 # [7] -0.59975593 0.57629164 1.08455362 ######################### # Basic programming stuff ######################### # NUMBERS # "numeric" means double-precision floating-point numbers 5 # 5 class(5) # "numeric" 5e4 # 50000 #handy when dealing with large,small,or variable orders of magnitude 6.02e23 # Avogadro's number 1.6e-35 # Planck length # long-storage integers are written with L 5L # 5 class(5L) # "integer" # Try ?class for more information on the class() function # In fact, you can look up the documentation on `xyz` with ?xyz # or see the source for `xyz` by evaluating xyz # Arithmetic 10 + 66 # 76 53.2 - 4 # 49.2 2 * 2.0 # 4 3L / 4 # 0.75 3 %% 2 # 1 # Weird number types class(NaN) # "numeric" class(Inf) # "numeric" class(-Inf) # "numeric" #used in for example integrate( dnorm(x), 3, Inf ) -- which obviates Z-score tables # but beware, NaN isn't the only weird type... class(NA) # see below class(NULL) # NULL # SIMPLE LISTS c(6, 8, 7, 5, 3, 0, 9) # 6 8 7 5 3 0 9 c('alef', 'bet', 'gimmel', 'dalet', 'he') # "alef" "bet" "gimmel" "dalet" "he" c('Z', 'o', 'r', 'o') == "Zoro" # FALSE FALSE FALSE FALSE #some more nice built-ins 5:15 # 5 6 7 8 9 10 11 12 13 14 15 seq(from=0, to=31337, by=1337) # [1] 0 1337 2674 4011 5348 6685 8022 9359 10696 12033 13370 14707 # [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751 letters # [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" # [20] "t" "u" "v" "w" "x" "y" "z" month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec" # Access the n'th element of a list with list.name[n] or sometimes list.name[[n]] letters[18] # "r" LETTERS[13] # "M" month.name[9] # "September" c(6, 8, 7, 5, 3, 0, 9)[3] # 7 # CHARACTERS # There's no difference between strings and characters in R "Horatio" # "Horatio" class("Horatio") # "character" substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis " gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis." # LOGICALS # booleans class(TRUE) # "logical" class(FALSE) # "logical" # Behavior is normal TRUE == TRUE # TRUE TRUE == FALSE # FALSE FALSE != FALSE # FALSE FALSE != TRUE # TRUE # Missing data (NA) is logical, too class(NA) # "logical" # FACTORS # The factor class is for categorical data # which can be ordered (like childrens' grade levels) # or unordered (like gender) levels(factor(c("female", "male", "male", "female", "NA", "female"))) # "female" "male" "NA" factor(c("female", "female", "male", "NA", "female")) # female female male NA female # Levels: female male NA data(infert) #Infertility after Spontaneous and Induced Abortion levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs" # VARIABLES # Lots of way to assign stuff x = 5 # this is possible y <- "1" # this is preferred TRUE -> z # this works but is weird # We can use coerce variables to different classes as.numeric(y) # 1 as.character(x) # "5" # LOOPS # We've got for loops for (i in 1:4) { print(i) } # We've got while loops a <- 10 while (a > 4) { cat(a, "...", sep = "") a <- a - 1 } # Keep in mind that for and while loops run slowly in R # Operations on entire vectors (i.e. a whole row, a whole column) # or apply()-type functions (we'll discuss later) are preferred # IF/ELSE # Again, pretty standard if (4 > 3) { print("Huzzah! It worked!") } else { print("Noooo! This is blatantly illogical!") } # => # [1] "Huzzah! It worked!" # FUNCTIONS # Defined like so: myFunc <- function(x) { x <- x * 4 x <- x - 1 return(x) } # Called like any other R function: myFunc(5) # 19 ######################### # Fun with data: vectors, matrices, data frames, and arrays ######################### # ONE-DIMENSIONAL # You can vectorize anything, so long as all components have the same type vec <- c(8, 9, 10, 11) vec # 8 9 10 11 # The class of a vector is the class of its components class(vec) # "numeric" # If you vectorize items of different classes, weird coercions happen c(TRUE, 4) # 1 4 c("dog", TRUE, 4) # "dog" "TRUE" "4" # We ask for specific components like so (R starts counting from 1) vec[1] # 8 # We can also search for the indices of specific components, which(vec %% 2 == 0) # 1 3 # or grab just the first or last entry in the vector head(vec, 1) # 8 tail(vec, 1) # 11 # If an index "goes over" you'll get NA: vec[6] # NA # You can find the length of your vector with length() length(vec) # 4 # You can perform operations on entire vectors or subsets of vectors vec * 4 # 16 20 24 28 vec[2:3] * 5 # 25 30 # and there are many built-in functions to summarize vectors mean(vec) # 9.5 var(vec) # 1.666667 sd(vec) # 1.290994 max(vec) # 11 min(vec) # 8 sum(vec) # 38 # TWO-DIMENSIONAL (ALL ONE CLASS) # You can make a matrix out of entries all of the same type like so: mat <- matrix(nrow = 3, ncol = 2, c(1,2,3,4,5,6)) mat # => # [,1] [,2] # [1,] 1 4 # [2,] 2 5 # [3,] 3 6 # Unlike a vector, the class of a matrix is "matrix", no matter what's in it class(mat) # => "matrix" # Ask for the first row mat[1,] # 1 4 # Perform operation on the first column 3 * mat[,1] # 3 6 9 # Ask for a specific cell mat[3,2] # 6 # Transpose the whole matrix t(mat) # => # [,1] [,2] [,3] # [1,] 1 2 3 # [2,] 4 5 6 # cbind() sticks vectors together column-wise to make a matrix mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog")) mat2 # => # [,1] [,2] # [1,] "1" "dog" # [2,] "2" "cat" # [3,] "3" "bird" # [4,] "4" "dog" class(mat2) # matrix # Again, note what happened! # Because matrices must contain entries all of the same class, # everything got converted to the character class c(class(mat2[,1]), class(mat2[,2])) # rbind() sticks vectors together row-wise to make a matrix mat3 <- rbind(c(1,2,4,5), c(6,7,0,4)) mat3 # => # [,1] [,2] [,3] [,4] # [1,] 1 2 4 5 # [2,] 6 7 0 4 # Aah, everything of the same class. No coercions. Much better. # TWO-DIMENSIONAL (DIFFERENT CLASSES) # For columns of different classes, use the data frame dat <- data.frame(c(5,2,1,4), c("dog", "cat", "bird", "dog")) names(dat) <- c("number", "species") # name the columns class(dat) # "data.frame" dat # => # number species # 1 5 dog # 2 2 cat # 3 1 bird # 4 4 dog class(dat$number) # "numeric" class(dat[,2]) # "factor" # The data.frame() function converts character vectors to factor vectors # There are many twisty ways to subset data frames, all subtly unalike dat$number # 5 2 1 4 dat[,1] # 5 2 1 4 dat[,"number"] # 5 2 1 4 # MULTI-DIMENSIONAL (ALL OF ONE CLASS) # Arrays creates n-dimensional tables # You can make a two-dimensional table (sort of like a matrix) array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4)) # => # [,1] [,2] [,3] [,4] # [1,] 1 4 8 3 # [2,] 2 5 9 6 # You can use array to make three-dimensional matrices too array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2)) # => # , , 1 # # [,1] [,2] # [1,] 1 4 # [2,] 2 5 # # , , 2 # # [,1] [,2] # [1,] 8 1 # [2,] 9 2 # LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES) # Finally, R has lists (of vectors) list1 <- list(time = 1:40) list1$price = c(rnorm(40,.5*list1$time,4)) # random list1 # You can get items in the list like so list1$time # You can subset list items like vectors list1$price[4] ######################### # The apply() family of functions ######################### # Remember mat? mat # => # [,1] [,2] # [1,] 1 4 # [2,] 2 5 # [3,] 3 6 # Use apply(X, MARGIN, FUN) to apply function FUN to a matrix X # over rows (MAR = 1) or columns (MAR = 2) # That is, R does FUN to each row (or column) of X, much faster than a # for or while loop would do apply(mat, MAR = 2, myFunc) # => # [,1] [,2] # [1,] 3 15 # [2,] 7 19 # [3,] 11 23 # Other functions: ?lapply, ?sapply # Don't feel too intimidated; everyone agrees they are rather confusing # The plyr package aims to replace (and improve upon!) the *apply() family. install.packages("plyr") require(plyr) ?plyr ######################### # Loading data ######################### # "pets.csv" is a file on the internet pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv") pets head(pets, 2) # first two rows tail(pets, 1) # last row # To save a data frame or matrix as a .csv file write.csv(pets, "pets2.csv") # to make a new .csv file # set working directory with setwd(), look it up with getwd() # Try ?read.csv and ?write.csv for more information ######################### # Plots ######################### # Scatterplots! plot(list1$time, list1$price, main = "fake data") # Regressions! linearModel <- lm(price ~ time, data = list1) linearModel # outputs result of regression # Plot regression line on existing plot abline(linearModel, col = "red") # Get a variety of nice diagnostics plot(linearModel) # Histograms! hist(rpois(n = 10000, lambda = 5), col = "thistle") # Barplots! barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow")) # Try the ggplot2 package for more and better graphics install.packages("ggplot2") require(ggplot2) ?ggplot2 ``` ## How do I get R? * Get R and the R GUI from [http://www.r-project.org/](http://www.r-project.org/) * [RStudio](http://www.rstudio.com/ide/) is another GUI