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329 lines
7.8 KiB
Markdown
329 lines
7.8 KiB
Markdown
---
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language: R
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author: e99n09
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author_url: http://github.com/e99n09
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filename: learnr.r
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---
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R is a statistical computing language.
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```python
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# Comments start with hashtags.
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# You can't make a multi-line comment per se,
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# but you can stack multiple comments like so.
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# Protip: hit COMMAND-ENTER to execute a line
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#########################
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# The absolute basics
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#########################
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# NUMERICS
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# We've got numbers! Behold the "numeric" class
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5 # => [1] 5
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class(5) # => [1] "numeric"
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# Try ?class for more information on the class() function
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# In fact, you can look up the documentation on just about anything with ?
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# Numerics are like doubles. There's no such thing as integers
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5 == 5.0 # => [1] TRUE
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# Because R doesn't distinguish between integers and doubles,
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# R shows the "integer" form instead of the equivalent "double" form
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# whenever it's convenient:
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5.0 # => [1] 5
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# All the normal operations!
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10 + 66 # => [1] 76
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53.2 - 4 # => [1] 49.2
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3.37 * 5.4 # => [1] 18.198
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2 * 2.0 # => [1] 4
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3 / 4 # => [1] 0.75
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2.0 / 2 # => [1] 1
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3 %% 2 # => [1] 1
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4 %% 2 # => [1] 0
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# Finally, we've got not-a-numbers! They're numerics too
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class(NaN) # => [1] "numeric"
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# CHARACTERS
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# We've (sort of) got strings! Behold the "character" class
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"plugh" # => [1] "plugh"
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class("plugh") # "character"
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# There's no difference between strings and characters in R
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# LOGICALS
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# We've got booleans! Behold the "logical" class
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class(TRUE) # => [1] "logical"
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class(FALSE) # => [1] "logical"
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# Behavior is normal
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TRUE == TRUE # => [1] TRUE
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TRUE == FALSE # => [1] FALSE
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FALSE != FALSE # => [1] FALSE
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FALSE != TRUE # => [1] TRUE
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# Missing data (NA) is logical, too
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class(NA) # => [1] "logical"
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# FACTORS
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# The factor class is for categorical data
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# It has an attribute called levels that describes all the possible categories
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factor("dog")
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# =>
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# [1] dog
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# Levels: dog
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# (This will make more sense once we start talking about vectors)
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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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TRUE -> z # this works but is weird
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# We can use coerce variables to different classes
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as.numeric(y) # => [1] 1
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as.character(x) # => [1] "5"
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# LOOPS
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# We've got for loops
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for (i in 1:4) {
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print(i)
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}
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# We've got while loops
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a <- 10
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while (a > 4) {
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cat(a, "...", sep = "")
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a <- a - 1
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}
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# Keep in mind that for and while loops run slowly in R
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# Operations on entire vectors (i.e. a whole row, a whole column)
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# or apply()-type functions (we'll discuss later) are preferred
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# FUNCTIONS
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# Defined like so:
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myFunc <- function(x) {
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x <- x * 4
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x <- x - 1
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return(x)
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}
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# Called like any other R function:
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myFunc(5) # => [1] 19
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#########################
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# Fun with data: vectors, matrices, data frames, and arrays
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#########################
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# ONE-DIMENSIONAL
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# You can vectorize anything, so long as all components have the same type
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vec <- c(4, 5, 6, 7)
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vec # => [1] 4 5 6 7
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# The class of a vector is the class of its components
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class(vec) # => [1] "numeric"
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# If you vectorize items of different classes, weird coercions happen
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c(TRUE, 4) # => [1] 1 4
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c("dog", TRUE, 4) # => [1] "dog" "TRUE" "4"
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# We ask for specific components like so (R starts counting from 1)
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vec[1] # => [1] 4
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# We can also search for the indices of specific components
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which(vec %% 2 == 0)
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# If an index "goes over" you'll get NA:
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vec[6] # => [1] NA
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# You can perform operations on entire vectors or subsets of vectors
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vec * 4 # => [1] 16 20 24 28
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vec[2:3] * 5 # => [1] 25 30
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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
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# =>
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# [,1] [,2]
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# [1,] 1 4
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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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# Ask for the first row
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mat[1,] # => [1] 1 4
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# Perform operation on the first column
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3 * mat[,1] # => [1] 3 6 9
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# Ask for a specific cell
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mat[3,2] # => [1] 6
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# Transpose the whole matrix
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t(mat)
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# =>
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# [,1] [,2] [,3]
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# [1,] 1 2 3
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# [2,] 4 5 6
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# cbind() sticks vectors together column-wise to make a matrix
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mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog"))
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mat2
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# =>
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# [,1] [,2]
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# [1,] "1" "dog"
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# [2,] "2" "cat"
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# [3,] "3" "bird"
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# [4,] "4" "dog"
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class(mat2) # => [1] 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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# 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
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# =>
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# [,1] [,2] [,3] [,4]
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# [1,] 1 2 4 5
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# [2,] 6 7 0 4
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# Aah, everything of the same class. No coercions. Much better.
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# TWO-DIMENSIONAL (DIFFERENT CLASSES)
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# For columns of different classes, use the data frame
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dat <- data.frame(c(5,2,1,4), c("dog", "cat", "bird", "dog"))
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names(dat) <- c("number", "species") # name the columns
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class(dat) # => [1] "data.frame"
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dat
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# =>
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# number species
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# 1 5 dog
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# 2 2 cat
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# 3 1 bird
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# 4 4 dog
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class(dat$number) # => [1] "numeric"
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class(dat[,2]) # => [1] "factor"
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# The data.frame() function converts character vectors to factor vectors
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# There are many twisty ways to subset data frames, all subtly unalike
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dat$number # => [1] 5 2 1 4
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dat[,1] # => [1] 5 2 1 4
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dat[,"number"] # => [1] 5 2 1 4
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# MULTI-DIMENSIONAL (ALL OF ONE CLASS)
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# Arrays creates n-dimensional tables
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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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# =>
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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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# =>
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# , , 1
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#
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# [,1] [,2]
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# [1,] 1 4
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# [2,] 2 5
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#
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# , , 2
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#
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# [,1] [,2]
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# [1,] 8 1
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# [2,] 9 2
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# LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES)
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# Finally, R has lists (of vectors)
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list1 <- list(time = 1:40, 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
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# You can subset list items like vectors
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list1$price[4]
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#########################
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# The apply() family of functions
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#########################
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# Remember mat?
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mat
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# =>
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# [,1] [,2]
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# [1,] 1 4
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# [2,] 2 5
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# [3,] 3 6
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# Use apply(X, MARGIN, FUN) to apply function FUN to a matrix X
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# over rows (MAR = 1) or columns (MAR = 2)
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# That is, R does FUN to each row (or column) of X, much faster than a
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# for or while loop would do
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apply(mat, MAR = 2, myFunc)
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# =>
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# [,1] [,2]
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# [1,] 3 15
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# [2,] 7 19
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# [3,] 11 23
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# Other functions: ?lapply, ?sapply
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# Don't feel too intimidated; everyone agrees they are rather confusing
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# The plyr package aims to replace (and improve upon!) the *apply() family.
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install.packages("plyr")
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require(plyr)
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?plyr
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#########################
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# Loading data
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#########################
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# "pets.csv" is a file on the internet
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pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
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pets
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head(pets, 2) # first two rows
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tail(pets, 1) # last row
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# To save a data frame or matrix as a .csv file
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write.csv(pets, "pets2.csv") # to make a new .csv file
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# set working directory with setwd(), look it up with getwd()
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# Try ?read.csv and ?write.csv for more information
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#########################
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# Plots
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#########################
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# Scatterplots!
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plot(list1$time, list1$price, main = "fake data")
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# Fit a linear model
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myLm <- lm(price ~ time, data = list1)
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myLm # outputs result of regression
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# Plot regression line on existing plot
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abline(myLm, col = "red")
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# Get a variety of nice diagnostics
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plot(myLm)
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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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# Try the ggplot2 package for more and better graphics
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install.packages("ggplot2")
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require(ggplot2)
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?ggplot2
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```
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