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[R/en] Format R code
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Crystal-RainSlide
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r.html.markdown
113
r.html.markdown
@ -91,14 +91,15 @@ stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
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# 82 | 2
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# make a histogram:
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hist(rivers, col="#333333", border="white", breaks=25) # play around with these parameters
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hist(log(rivers), col="#333333", border="white", breaks=25) # you'll do more plotting later
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hist(rivers, col = "#333333", border = "white", breaks = 25)
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hist(log(rivers), col = "#333333", border = "white", breaks = 25)
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# play around with these parameters, you'll do more plotting later
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# Here's another neat data set that comes pre-loaded. R has tons of these.
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data(discoveries)
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plot(discoveries, col="#333333", lwd=3, xlab="Year",
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plot(discoveries, col = "#333333", lwd = 3, xlab = "Year",
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main="Number of important discoveries per year")
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plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year",
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plot(discoveries, col = "#333333", lwd = 3, type = "h", xlab = "Year",
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main="Number of important discoveries per year")
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# Rather than leaving the default ordering (by year),
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@ -109,7 +110,7 @@ sort(discoveries)
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# [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
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# [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
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stem(discoveries, scale=2)
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stem(discoveries, scale = 2)
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#
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# The decimal point is at the |
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#
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@ -134,7 +135,7 @@ summary(discoveries)
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# 0.0 2.0 3.0 3.1 4.0 12.0
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# Roll a die a few times
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round(runif(7, min=.5, max=6.5))
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round(runif(7, min = .5, max = 6.5))
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# 1 4 6 1 4 6 4
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# Your numbers will differ from mine unless we set the same random.seed(31337)
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@ -173,7 +174,7 @@ class(c(4L, 5L, 8L, 3L)) # "integer"
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class(5) # "numeric"
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# Again, everything in R is a vector;
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# you can make a numeric vector with more than one element
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c(3,3,3,2,2,1) # 3 3 3 2 2 1
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c(3, 3, 3, 2, 2, 1) # 3 3 3 2 2 1
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# You can use scientific notation too
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5e4 # 50000
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6.02e23 # Avogadro's number
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@ -197,15 +198,15 @@ class(-Inf) # "numeric"
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class(NaN) # "numeric"
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# You can do arithmetic on two vectors with length greater than 1,
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# so long as the larger vector's length is an integer multiple of the smaller
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c(1,2,3) + c(1,2,3) # 2 4 6
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c(1, 2, 3) + c(1, 2, 3) # 2 4 6
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# Since a single number is a vector of length one, scalars are applied
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# elementwise to vectors
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(4 * c(1,2,3) - 2) / 2 # 1 3 5
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(4 * c(1, 2, 3) - 2) / 2 # 1 3 5
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# Except for scalars, use caution when performing arithmetic on vectors with
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# different lengths. Although it can be done,
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c(1,2,3,1,2,3) * c(1,2) # 1 4 3 2 2 6
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# Matching lengths is better practice and easier to read
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c(1,2,3,1,2,3) * c(1,2,1,2,1,2)
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c(1, 2, 3, 1, 2, 3) * c(1, 2) # 1 4 3 2 2 6
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# Matching lengths is better practice and easier to read most times
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c(1, 2, 3, 1, 2, 3) * c(1, 2, 1, 2, 1, 2) # 1 4 3 2 2 6
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# CHARACTERS
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# There's no difference between strings and characters in R
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@ -215,8 +216,7 @@ class('H') # "character"
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# Those were both character vectors of length 1
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# Here is a longer one:
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c('alef', 'bet', 'gimmel', 'dalet', 'he')
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# =>
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# "alef" "bet" "gimmel" "dalet" "he"
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# => "alef" "bet" "gimmel" "dalet" "he"
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length(c("Call","me","Ishmael")) # 3
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# You can do regex operations on character vectors:
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substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis "
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@ -230,6 +230,7 @@ month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "D
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# LOGICALS
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# In R, a "logical" is a boolean
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class(TRUE) # "logical"
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class(FALSE) # "logical"
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# Their behavior is normal
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@ -245,8 +246,8 @@ TRUE | FALSE # TRUE
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# AND
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TRUE & FALSE # FALSE
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# Applying | and & to vectors returns elementwise logic operations
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c(TRUE,FALSE,FALSE) | c(FALSE,TRUE,FALSE) # TRUE TRUE FALSE
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c(TRUE,FALSE,TRUE) & c(FALSE,TRUE,TRUE) # FALSE FALSE TRUE
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c(TRUE, FALSE, FALSE) | c(FALSE, TRUE, FALSE) # TRUE TRUE FALSE
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c(TRUE, FALSE, TRUE) & c(FALSE, TRUE, TRUE) # FALSE FALSE TRUE
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# You can test if x is TRUE
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isTRUE(TRUE) # TRUE
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# Here we get a logical vector with many elements:
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@ -255,7 +256,7 @@ c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE
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# FACTORS
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# The factor class is for categorical data
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# Factors can be ordered (like childrens' grade levels) or unordered (like colors)
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# Factors can be ordered (like grade levels) or unordered (like colors)
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factor(c("blue", "blue", "green", NA, "blue"))
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# blue blue green <NA> blue
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# Levels: blue green
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@ -273,13 +274,9 @@ levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs"
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# "NULL" is a weird one; use it to "blank out" a vector
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class(NULL) # NULL
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parakeet = c("beak", "feathers", "wings", "eyes")
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parakeet
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# =>
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# [1] "beak" "feathers" "wings" "eyes"
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parakeet # "beak" "feathers" "wings" "eyes"
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parakeet <- NULL
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parakeet
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# =>
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# NULL
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parakeet # NULL
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# TYPE COERCION
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# Type-coercion is when you force a value to take on a different type
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@ -310,8 +307,9 @@ as.numeric("Bilbo")
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# VARIABLES
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# Lots of way to assign stuff:
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x = 5 # this is possible
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y <- "1" # this is preferred
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y <- "1" # this is preferred traditionally
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TRUE -> z # this works but is weird
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# Refer to the Internet for the behaviors and preferences about them.
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# LOOPS
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# We've got for loops
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@ -341,7 +339,7 @@ if (4 > 3) {
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# FUNCTIONS
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# Defined like so:
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jiggle <- function(x) {
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x = x + rnorm(1, sd=.1) #add in a bit of (controlled) noise
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x = x + rnorm(1, sd=.1) # add in a bit of (controlled) noise
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return(x)
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}
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# Called like any other R function:
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@ -389,7 +387,7 @@ min(vec) # 8
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sum(vec) # 38
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# Some more nice built-ins:
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5:15 # 5 6 7 8 9 10 11 12 13 14 15
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seq(from=0, to=31337, by=1337)
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seq(from = 0, to = 31337, by = 1337)
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# =>
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# [1] 0 1337 2674 4011 5348 6685 8022 9359 10696 12033 13370 14707
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# [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751
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@ -397,7 +395,7 @@ seq(from=0, to=31337, by=1337)
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# TWO-DIMENSIONAL (ALL ONE CLASS)
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# You can make a matrix out of entries all of the same type like so:
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mat <- matrix(nrow = 3, ncol = 2, c(1,2,3,4,5,6))
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mat <- matrix(nrow = 3, ncol = 2, c(1, 2, 3, 4, 5, 6))
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mat
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# =>
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# [,1] [,2]
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@ -405,13 +403,13 @@ mat
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# [2,] 2 5
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# [3,] 3 6
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# Unlike a vector, the class of a matrix is "matrix", no matter what's in it
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class(mat) # => "matrix"
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class(mat) # "matrix"
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# Ask for the first row
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mat[1,] # 1 4
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mat[1, ] # 1 4
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# Perform operation on the first column
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3 * mat[,1] # 3 6 9
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3 * mat[, 1] # 3 6 9
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# Ask for a specific cell
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mat[3,2] # 6
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mat[3, 2] # 6
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# Transpose the whole matrix
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t(mat)
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@ -441,10 +439,10 @@ class(mat2) # matrix
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# Again, note what happened!
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# Because matrices must contain entries all of the same class,
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# everything got converted to the character class
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c(class(mat2[,1]), class(mat2[,2]))
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c(class(mat2[, 1]), class(mat2[, 2]))
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# rbind() sticks vectors together row-wise to make a matrix
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mat3 <- rbind(c(1,2,4,5), c(6,7,0,4))
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mat3 <- rbind(c(1, 2, 4, 5), c(6, 7, 0, 4))
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mat3
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# =>
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# [,1] [,2] [,3] [,4]
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@ -458,9 +456,9 @@ mat3
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# This data structure is so useful for statistical programming,
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# a version of it was added to Python in the package "pandas".
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students <- data.frame(c("Cedric","Fred","George","Cho","Draco","Ginny"),
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c(3,2,2,1,0,-1),
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c("H", "G", "G", "R", "S", "G"))
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students <- data.frame(c("Cedric", "Fred", "George", "Cho", "Draco", "Ginny"),
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c( 3, 2, 2, 1, 0, -1),
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c( "H", "G", "G", "R", "S", "G"))
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names(students) <- c("name", "year", "house") # name the columns
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class(students) # "data.frame"
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students
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@ -485,8 +483,8 @@ dim(students) # 6 3
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# There are many twisty ways to subset data frames, all subtly unalike
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students$year # 3 2 2 1 0 -1
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students[,2] # 3 2 2 1 0 -1
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students[,"year"] # 3 2 2 1 0 -1
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students[, 2] # 3 2 2 1 0 -1
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students[, "year"] # 3 2 2 1 0 -1
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# An augmented version of the data.frame structure is the data.table
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# If you're working with huge or panel data, or need to merge a few data
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@ -503,19 +501,19 @@ students # note the slightly different print-out
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# 4: Cho 1 R
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# 5: Draco 0 S
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# 6: Ginny -1 G
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students[name=="Ginny"] # get rows with name == "Ginny"
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students[name == "Ginny"] # get rows with name == "Ginny"
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# =>
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# name year house
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# 1: Ginny -1 G
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students[year==2] # get rows with year == 2
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students[year == 2] # get rows with year == 2
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# =>
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# name year house
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# 1: Fred 2 G
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# 2: George 2 G
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# data.table makes merging two data sets easy
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# let's make another data.table to merge with students
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founders <- data.table(house=c("G","H","R","S"),
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founder=c("Godric","Helga","Rowena","Salazar"))
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founders <- data.table(house = c("G" , "H" , "R" , "S"),
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founder = c("Godric", "Helga", "Rowena", "Salazar"))
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founders
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# =>
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# house founder
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@ -526,8 +524,8 @@ founders
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setkey(students, house)
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setkey(founders, house)
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students <- founders[students] # merge the two data sets by matching "house"
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setnames(students, c("house","houseFounderName","studentName","year"))
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students[,order(c("name","year","house","houseFounderName")), with=F]
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setnames(students, c("house", "houseFounderName", "studentName", "year"))
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students[, order(c("name", "year", "house", "houseFounderName")), with = F]
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# =>
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# studentName year house houseFounderName
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# 1: Fred 2 G Godric
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@ -538,7 +536,7 @@ students[,order(c("name","year","house","houseFounderName")), with=F]
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# 6: Draco 0 S Salazar
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# data.table makes summary tables easy
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students[,sum(year),by=house]
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students[, sum(year), by = house]
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# =>
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# house V1
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# 1: G 3
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@ -571,7 +569,7 @@ students[studentName != "Draco"]
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# 5: R Cho 1
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# Using data.frame:
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students <- as.data.frame(students)
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students[students$house != "G",]
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students[students$house != "G", ]
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# =>
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# house houseFounderName studentName year
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# 4 H Helga Cedric 3
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@ -583,13 +581,13 @@ students[students$house != "G",]
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# Arrays creates n-dimensional tables
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# All elements must be of the same type
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# You can make a two-dimensional table (sort of like a matrix)
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array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
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array(c(c(1, 2, 4, 5), c(8, 9, 3, 6)), dim = c(2, 4))
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# =>
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# [,1] [,2] [,3] [,4]
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# [1,] 1 4 8 3
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# [2,] 2 5 9 6
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# You can use array to make three-dimensional matrices too
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array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
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array(c(c(c(2, 300, 4), c(8, 9, 0)), c(c(5, 60, 0), c(66, 7, 847))), dim = c(3, 2, 2))
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# =>
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# , , 1
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#
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@ -609,7 +607,7 @@ array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
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# Finally, R has lists (of vectors)
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list1 <- list(time = 1:40)
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list1$price = c(rnorm(40,.5*list1$time,4)) # random
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list1$price = c(rnorm(40, .5*list1$time, 4)) # random
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list1
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# You can get items in the list like so
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list1$time # one way
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@ -719,7 +717,7 @@ summary(linearModel)$coefficients # another way to extract results
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# Estimate Std. Error t value Pr(>|t|)
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# (Intercept) 0.1452662 1.50084246 0.09678975 9.234021e-01
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# time 0.4943490 0.06379348 7.74920901 2.440008e-09
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summary(linearModel)$coefficients[,4] # the p-values
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summary(linearModel)$coefficients[, 4] # the p-values
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# =>
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# (Intercept) time
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# 9.234021e-01 2.440008e-09
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@ -728,8 +726,7 @@ summary(linearModel)$coefficients[,4] # the p-values
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# Logistic regression
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set.seed(1)
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list1$success = rbinom(length(list1$time), 1, .5) # random binary
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glModel <- glm(success ~ time, data = list1,
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family=binomial(link="logit"))
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glModel <- glm(success ~ time, data = list1, family=binomial(link="logit"))
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glModel # outputs result of logistic regression
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# =>
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# Call: glm(formula = success ~ time,
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@ -745,8 +742,10 @@ glModel # outputs result of logistic regression
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summary(glModel) # more verbose output from the regression
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# =>
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# Call:
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# glm(formula = success ~ time,
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# family = binomial(link = "logit"), data = list1)
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# glm(
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# formula = success ~ time,
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# family = binomial(link = "logit"),
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# data = list1)
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# Deviance Residuals:
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# Min 1Q Median 3Q Max
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@ -780,7 +779,7 @@ plot(linearModel)
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# Histograms!
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hist(rpois(n = 10000, lambda = 5), col = "thistle")
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# Barplots!
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barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))
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barplot(c(1, 4, 5, 1, 2), names.arg = c("red", "blue", "purple", "green", "yellow"))
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# GGPLOT2
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# But these are not even the prettiest of R's plots
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@ -788,10 +787,10 @@ barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))
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install.packages("ggplot2")
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require(ggplot2)
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?ggplot2
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pp <- ggplot(students, aes(x=house))
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pp <- ggplot(students, aes(x = house))
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pp + geom_bar()
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ll <- as.data.table(list1)
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pp <- ggplot(ll, aes(x=time,price))
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pp <- ggplot(ll, aes(x = time, price))
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pp + geom_point()
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# ggplot2 has excellent documentation (available http://docs.ggplot2.org/current/)
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