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806 lines
23 KiB
Markdown
806 lines
23 KiB
Markdown
---
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language: R
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contributors:
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- ["e99n09", "http://github.com/e99n09"]
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- ["isomorphismes", "http://twitter.com/isomorphisms"]
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- ["kalinn", "http://github.com/kalinn"]
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filename: learnr.r
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---
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R is a statistical computing language. It has lots of libraries for uploading and cleaning data sets, running statistical procedures, and making graphs. You can also run `R` commands within a LaTeX document.
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```r
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# Comments start with number symbols.
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# You can't make multi-line comments,
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# but you can stack multiple comments like so.
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# in Windows you can use CTRL-ENTER to execute a line.
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# on Mac it is COMMAND-ENTER
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#############################################################################
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# Stuff you can do without understanding anything about programming
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#############################################################################
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# In this section, we show off some of the cool stuff you can do in
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# R without understanding anything about programming. Do not worry
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# about understanding everything the code does. Just enjoy!
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data() # browse pre-loaded data sets
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data(rivers) # get this one: "Lengths of Major North American Rivers"
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ls() # notice that "rivers" now appears in the workspace
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head(rivers) # peek at the data set
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# 735 320 325 392 524 450
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length(rivers) # how many rivers were measured?
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# 141
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summary(rivers) # what are some summary statistics?
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# Min. 1st Qu. Median Mean 3rd Qu. Max.
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# 135.0 310.0 425.0 591.2 680.0 3710.0
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# make a stem-and-leaf plot (a histogram-like data visualization)
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stem(rivers)
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# The decimal point is 2 digit(s) to the right of the |
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#
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# 0 | 4
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# 2 | 011223334555566667778888899900001111223333344455555666688888999
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# 4 | 111222333445566779001233344567
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# 6 | 000112233578012234468
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# 8 | 045790018
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# 10 | 04507
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# 12 | 1471
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# 14 | 56
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# 16 | 7
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# 18 | 9
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# 20 |
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# 22 | 25
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# 24 | 3
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# 26 |
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# 28 |
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# 30 |
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# 32 |
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# 34 |
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# 36 | 1
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stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
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# Take that, Bell curve fundamentalists.
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# The decimal point is 1 digit(s) to the left of the |
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#
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# 48 | 1
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# 50 |
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# 52 | 15578
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# 54 | 44571222466689
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# 56 | 023334677000124455789
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# 58 | 00122366666999933445777
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# 60 | 122445567800133459
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# 62 | 112666799035
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# 64 | 00011334581257889
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# 66 | 003683579
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# 68 | 0019156
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# 70 | 079357
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# 72 | 89
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# 74 | 84
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# 76 | 56
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# 78 | 4
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# 80 |
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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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# 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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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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main="Number of important discoveries per year")
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# Rather than leaving the default ordering (by year),
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# we could also sort to see what's typical:
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sort(discoveries)
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# [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
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# [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
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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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#
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# The decimal point is at the |
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#
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# 0 | 000000000
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# 1 | 000000000000
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# 2 | 00000000000000000000000000
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# 3 | 00000000000000000000
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# 4 | 000000000000
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# 5 | 0000000
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# 6 | 000000
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# 7 | 0000
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# 8 | 0
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# 9 | 0
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# 10 | 0
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# 11 |
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# 12 | 0
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max(discoveries)
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# 12
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summary(discoveries)
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# Min. 1st Qu. Median Mean 3rd Qu. Max.
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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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# 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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# Draw from a standard Gaussian 9 times
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rnorm(9)
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# [1] 0.07528471 1.03499859 1.34809556 -0.82356087 0.61638975 -1.88757271
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# [7] -0.59975593 0.57629164 1.08455362
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##################################################
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# Data types and basic arithmetic
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##################################################
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# Now for the programming-oriented part of the tutorial.
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# In this section you will meet the important data types of R:
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# integers, numerics, characters, logicals, and factors.
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# There are others, but these are the bare minimum you need to
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# get started.
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# INTEGERS
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# Long-storage integers are written with L
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5L # 5
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class(5L) # "integer"
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# (Try ?class for more information on the class() function.)
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# In R, every single value, like 5L, is considered a vector of length 1
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length(5L) # 1
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# You can have an integer vector with length > 1 too:
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c(4L, 5L, 8L, 3L) # 4 5 8 3
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length(c(4L, 5L, 8L, 3L)) # 4
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class(c(4L, 5L, 8L, 3L)) # "integer"
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# NUMERICS
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# A "numeric" is a double-precision floating-point number
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5 # 5
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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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# 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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1.6e-35 # Planck length
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# You can also have infinitely large or small numbers
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class(Inf) # "numeric"
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class(-Inf) # "numeric"
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# You might use "Inf", for example, in integrate(dnorm, 3, Inf);
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# this obviates Z-score tables.
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# BASIC ARITHMETIC
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# You can do arithmetic with numbers
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# Doing arithmetic on a mix of integers and numerics gives you another numeric
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10L + 66L # 76 # integer plus integer gives integer
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53.2 - 4 # 49.2 # numeric minus numeric gives numeric
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2.0 * 2L # 4 # numeric times integer gives numeric
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3L / 4 # 0.75 # integer over numeric gives numeric
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3 %% 2 # 1 # the remainder of two numerics is another numeric
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# Illegal arithmetic yields you a "not-a-number":
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0 / 0 # NaN
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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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# 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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# 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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# CHARACTERS
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# There's no difference between strings and characters in R
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"Horatio" # "Horatio"
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class("Horatio") # "character"
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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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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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gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis."
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# R has several built-in character vectors:
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letters
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# =>
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# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
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# [20] "t" "u" "v" "w" "x" "y" "z"
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month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
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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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TRUE == TRUE # TRUE
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TRUE == FALSE # FALSE
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FALSE != FALSE # FALSE
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FALSE != TRUE # TRUE
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# Missing data (NA) is logical, too
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class(NA) # "logical"
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# Use | and & for logic operations.
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# OR
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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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# 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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c('Z', 'o', 'r', 'r', 'o') == "Zorro" # FALSE FALSE FALSE FALSE FALSE
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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 gender)
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factor(c("female", "female", "male", NA, "female"))
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# female female male <NA> female
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# Levels: female male
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# The "levels" are the values the categorical data can take
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# Note that missing data does not enter the levels
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levels(factor(c("male", "male", "female", NA, "female"))) # "female" "male"
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# If a factor vector has length 1, its levels will have length 1, too
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length(factor("male")) # 1
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length(levels(factor("male"))) # 1
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# Factors are commonly seen in data frames, a data structure we will cover later
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data(infert) # "Infertility after Spontaneous and Induced Abortion"
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levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs"
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# NULL
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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 <- NULL
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parakeet
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# =>
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# 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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as.character(c(6, 8)) # "6" "8"
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as.logical(c(1,0,1,1)) # TRUE FALSE TRUE TRUE
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# If you put elements of different types into a vector, weird coercions happen:
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c(TRUE, 4) # 1 4
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c("dog", TRUE, 4) # "dog" "TRUE" "4"
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as.numeric("Bilbo")
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# =>
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# [1] NA
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# Warning message:
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# NAs introduced by coercion
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# Also note: those were just the basic data types
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# There are many more data types, such as for dates, time series, etc.
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##################################################
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# Variables, loops, if/else
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##################################################
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# A variable is like a box you store a value in for later use.
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# We call this "assigning" the value to the variable.
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# Having variables lets us write loops, functions, and if/else statements
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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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# 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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# IF/ELSE
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# Again, pretty standard
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if (4 > 3) {
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print("4 is greater than 3")
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} else {
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print("4 is not greater than 3")
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}
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# =>
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# [1] "4 is greater than 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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return(x)
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}
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# Called like any other R function:
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jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
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###########################################################################
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# Data structures: Vectors, matrices, data frames, and arrays
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###########################################################################
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# ONE-DIMENSIONAL
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# Let's start from the very beginning, and with something you already know: vectors.
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vec <- c(8, 9, 10, 11)
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vec # 8 9 10 11
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# We ask for specific elements by subsetting with square brackets
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# (Note that R starts counting from 1)
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vec[1] # 8
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letters[18] # "r"
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LETTERS[13] # "M"
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month.name[9] # "September"
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c(6, 8, 7, 5, 3, 0, 9)[3] # 7
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# We can also search for the indices of specific components,
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which(vec %% 2 == 0) # 1 3
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# grab just the first or last few entries in the vector,
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head(vec, 1) # 8
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tail(vec, 2) # 10 11
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# or figure out if a certain value is in the vector
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any(vec == 10) # TRUE
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# If an index "goes over" you'll get NA:
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vec[6] # NA
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# You can find the length of your vector with length()
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length(vec) # 4
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# You can perform operations on entire vectors or subsets of vectors
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vec * 4 # 16 20 24 28
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vec[2:3] * 5 # 25 30
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any(vec[2:3] == 8) # FALSE
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# and R has many built-in functions to summarize vectors
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mean(vec) # 9.5
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var(vec) # 1.666667
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sd(vec) # 1.290994
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max(vec) # 11
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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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# =>
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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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# 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 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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# Ask for a specific cell
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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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# =>
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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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# Matrix multiplication
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mat %*% t(mat)
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# =>
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# [,1] [,2] [,3]
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# [1,] 17 22 27
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# [2,] 22 29 36
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# [3,] 27 36 45
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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) # 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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# Ah, 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 types, use a data frame
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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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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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# =>
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# name year house
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# 1 Cedric 3 H
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# 2 Fred 2 G
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# 3 George 2 G
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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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class(students$year) # "numeric"
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class(students[,3]) # "factor"
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# find the dimensions
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nrow(students) # 6
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ncol(students) # 3
|
||
dim(students) # 6 3
|
||
# The data.frame() function converts character vectors to factor vectors
|
||
# by default; turn this off by setting stringsAsFactors = FALSE when
|
||
# you create the data.frame
|
||
?data.frame
|
||
|
||
# There are many twisty ways to subset data frames, all subtly unalike
|
||
students$year # 3 2 2 1 0 -1
|
||
students[,2] # 3 2 2 1 0 -1
|
||
students[,"year"] # 3 2 2 1 0 -1
|
||
|
||
# An augmented version of the data.frame structure is the data.table
|
||
# If you're working with huge or panel data, or need to merge a few data
|
||
# sets, data.table can be a good choice. Here's a whirlwind tour:
|
||
install.packages("data.table") # download the package from CRAN
|
||
require(data.table) # load it
|
||
students <- as.data.table(students)
|
||
students # note the slightly different print-out
|
||
# =>
|
||
# name year house
|
||
# 1: Cedric 3 H
|
||
# 2: Fred 2 G
|
||
# 3: George 2 G
|
||
# 4: Cho 1 R
|
||
# 5: Draco 0 S
|
||
# 6: Ginny -1 G
|
||
students[name=="Ginny"] # get rows with name == "Ginny"
|
||
# =>
|
||
# name year house
|
||
# 1: Ginny -1 G
|
||
students[year==2] # get rows with year == 2
|
||
# =>
|
||
# name year house
|
||
# 1: Fred 2 G
|
||
# 2: George 2 G
|
||
# data.table makes merging two data sets easy
|
||
# let's make another data.table to merge with students
|
||
founders <- data.table(house=c("G","H","R","S"),
|
||
founder=c("Godric","Helga","Rowena","Salazar"))
|
||
founders
|
||
# =>
|
||
# house founder
|
||
# 1: G Godric
|
||
# 2: H Helga
|
||
# 3: R Rowena
|
||
# 4: S Salazar
|
||
setkey(students, house)
|
||
setkey(founders, house)
|
||
students <- founders[students] # merge the two data sets by matching "house"
|
||
setnames(students, c("house","houseFounderName","studentName","year"))
|
||
students[,order(c("name","year","house","houseFounderName")), with=F]
|
||
# =>
|
||
# studentName year house houseFounderName
|
||
# 1: Fred 2 G Godric
|
||
# 2: George 2 G Godric
|
||
# 3: Ginny -1 G Godric
|
||
# 4: Cedric 3 H Helga
|
||
# 5: Cho 1 R Rowena
|
||
# 6: Draco 0 S Salazar
|
||
|
||
# data.table makes summary tables easy
|
||
students[,sum(year),by=house]
|
||
# =>
|
||
# house V1
|
||
# 1: G 3
|
||
# 2: H 3
|
||
# 3: R 1
|
||
# 4: S 0
|
||
|
||
# To drop a column from a data.frame or data.table,
|
||
# assign it the NULL value
|
||
students$houseFounderName <- NULL
|
||
students
|
||
# =>
|
||
# studentName year house
|
||
# 1: Fred 2 G
|
||
# 2: George 2 G
|
||
# 3: Ginny -1 G
|
||
# 4: Cedric 3 H
|
||
# 5: Cho 1 R
|
||
# 6: Draco 0 S
|
||
|
||
# Drop a row by subsetting
|
||
# Using data.table:
|
||
students[studentName != "Draco"]
|
||
# =>
|
||
# house studentName year
|
||
# 1: G Fred 2
|
||
# 2: G George 2
|
||
# 3: G Ginny -1
|
||
# 4: H Cedric 3
|
||
# 5: R Cho 1
|
||
# Using data.frame:
|
||
students <- as.data.frame(students)
|
||
students[students$house != "G",]
|
||
# =>
|
||
# house houseFounderName studentName year
|
||
# 4 H Helga Cedric 3
|
||
# 5 R Rowena Cho 1
|
||
# 6 S Salazar Draco 0
|
||
|
||
# MULTI-DIMENSIONAL (ALL ELEMENTS OF ONE TYPE)
|
||
|
||
# Arrays creates n-dimensional tables
|
||
# All elements must be of the same type
|
||
# You can make a two-dimensional table (sort of like a matrix)
|
||
array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
|
||
# =>
|
||
# [,1] [,2] [,3] [,4]
|
||
# [1,] 1 4 8 3
|
||
# [2,] 2 5 9 6
|
||
# You can use array to make three-dimensional matrices too
|
||
array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
|
||
# =>
|
||
# , , 1
|
||
#
|
||
# [,1] [,2]
|
||
# [1,] 2 8
|
||
# [2,] 300 9
|
||
# [3,] 4 0
|
||
#
|
||
# , , 2
|
||
#
|
||
# [,1] [,2]
|
||
# [1,] 5 66
|
||
# [2,] 60 7
|
||
# [3,] 0 847
|
||
|
||
# LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES)
|
||
|
||
# Finally, R has lists (of vectors)
|
||
list1 <- list(time = 1:40)
|
||
list1$price = c(rnorm(40,.5*list1$time,4)) # random
|
||
list1
|
||
# You can get items in the list like so
|
||
list1$time # one way
|
||
list1[["time"]] # another way
|
||
list1[[1]] # yet another way
|
||
# =>
|
||
# [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
|
||
# [34] 34 35 36 37 38 39 40
|
||
# You can subset list items like any other vector
|
||
list1$price[4]
|
||
|
||
# Lists are not the most efficient data structure to work with in R;
|
||
# unless you have a very good reason, you should stick to data.frames
|
||
# Lists are often returned by functions that perform linear regressions
|
||
|
||
##################################################
|
||
# 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, jiggle)
|
||
# =>
|
||
# [,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
|
||
# (but it could just as easily be a file on your own computer)
|
||
require(RCurl)
|
||
pets <- read.csv(textConnection(getURL("https://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
|
||
|
||
|
||
|
||
#########################
|
||
# Statistical Analysis
|
||
#########################
|
||
|
||
# Linear regression!
|
||
linearModel <- lm(price ~ time, data = list1)
|
||
linearModel # outputs result of regression
|
||
# =>
|
||
# Call:
|
||
# lm(formula = price ~ time, data = list1)
|
||
#
|
||
# Coefficients:
|
||
# (Intercept) time
|
||
# 0.1453 0.4943
|
||
summary(linearModel) # more verbose output from the regression
|
||
# =>
|
||
# Call:
|
||
# lm(formula = price ~ time, data = list1)
|
||
#
|
||
# Residuals:
|
||
# Min 1Q Median 3Q Max
|
||
# -8.3134 -3.0131 -0.3606 2.8016 10.3992
|
||
#
|
||
# Coefficients:
|
||
# Estimate Std. Error t value Pr(>|t|)
|
||
# (Intercept) 0.14527 1.50084 0.097 0.923
|
||
# time 0.49435 0.06379 7.749 2.44e-09 ***
|
||
# ---
|
||
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
|
||
#
|
||
# Residual standard error: 4.657 on 38 degrees of freedom
|
||
# Multiple R-squared: 0.6124, Adjusted R-squared: 0.6022
|
||
# F-statistic: 60.05 on 1 and 38 DF, p-value: 2.44e-09
|
||
coef(linearModel) # extract estimated parameters
|
||
# =>
|
||
# (Intercept) time
|
||
# 0.1452662 0.4943490
|
||
summary(linearModel)$coefficients # another way to extract results
|
||
# =>
|
||
# Estimate Std. Error t value Pr(>|t|)
|
||
# (Intercept) 0.1452662 1.50084246 0.09678975 9.234021e-01
|
||
# time 0.4943490 0.06379348 7.74920901 2.440008e-09
|
||
summary(linearModel)$coefficients[,4] # the p-values
|
||
# =>
|
||
# (Intercept) time
|
||
# 9.234021e-01 2.440008e-09
|
||
|
||
# GENERAL LINEAR MODELS
|
||
# Logistic regression
|
||
set.seed(1)
|
||
list1$success = rbinom(length(list1$time), 1, .5) # random binary
|
||
glModel <- glm(success ~ time, data = list1,
|
||
family=binomial(link="logit"))
|
||
glModel # outputs result of logistic regression
|
||
# =>
|
||
# Call: glm(formula = success ~ time,
|
||
# family = binomial(link = "logit"), data = list1)
|
||
#
|
||
# Coefficients:
|
||
# (Intercept) time
|
||
# 0.17018 -0.01321
|
||
#
|
||
# Degrees of Freedom: 39 Total (i.e. Null); 38 Residual
|
||
# Null Deviance: 55.35
|
||
# Residual Deviance: 55.12 AIC: 59.12
|
||
summary(glModel) # more verbose output from the regression
|
||
# =>
|
||
# Call:
|
||
# glm(formula = success ~ time,
|
||
# family = binomial(link = "logit"), data = list1)
|
||
|
||
# Deviance Residuals:
|
||
# Min 1Q Median 3Q Max
|
||
# -1.245 -1.118 -1.035 1.202 1.327
|
||
#
|
||
# Coefficients:
|
||
# Estimate Std. Error z value Pr(>|z|)
|
||
# (Intercept) 0.17018 0.64621 0.263 0.792
|
||
# time -0.01321 0.02757 -0.479 0.632
|
||
#
|
||
# (Dispersion parameter for binomial family taken to be 1)
|
||
#
|
||
# Null deviance: 55.352 on 39 degrees of freedom
|
||
# Residual deviance: 55.121 on 38 degrees of freedom
|
||
# AIC: 59.121
|
||
#
|
||
# Number of Fisher Scoring iterations: 3
|
||
|
||
|
||
#########################
|
||
# Plots
|
||
#########################
|
||
|
||
# BUILT-IN PLOTTING FUNCTIONS
|
||
# Scatterplots!
|
||
plot(list1$time, list1$price, main = "fake data")
|
||
# 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"))
|
||
|
||
# GGPLOT2
|
||
# But these are not even the prettiest of R's plots
|
||
# Try the ggplot2 package for more and better graphics
|
||
install.packages("ggplot2")
|
||
require(ggplot2)
|
||
?ggplot2
|
||
pp <- ggplot(students, aes(x=house))
|
||
pp + geom_bar()
|
||
ll <- as.data.table(list1)
|
||
pp <- ggplot(ll, aes(x=time,price))
|
||
pp + geom_point()
|
||
# ggplot2 has excellent documentation (available http://docs.ggplot2.org/current/)
|
||
|
||
|
||
|
||
```
|
||
|
||
## 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
|