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| -rw-r--r-- | r.html.markdown | 311 |
1 files changed, 246 insertions, 65 deletions
diff --git a/r.html.markdown b/r.html.markdown index 0240e8fb..61140be5 100644 --- a/r.html.markdown +++ b/r.html.markdown @@ -16,61 +16,242 @@ R is a statistical computing language. It has lots of good built-in functions fo # Hit COMMAND-ENTER to execute a line + +################################################################### +# Stuff you can do without understanding anything about programming +################################################################### + +data() # Browse pre-loaded data sets +data(rivers) # Lengths of Major North American Rivers +ls() # Notice that "rivers" appears in the workspace +head(rivers) # peek at the dataset +# 735 320 325 392 524 450 +length(rivers) # how many rivers were measured? +# 141 +summary(rivers) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 135.0 310.0 425.0 591.2 680.0 3710.0 +stem(rivers) #stem-and-leaf plot (like a histogram) +# +# The decimal point is 2 digit(s) to the right of the | +# +# 0 | 4 +# 2 | 011223334555566667778888899900001111223333344455555666688888999 +# 4 | 111222333445566779001233344567 +# 6 | 000112233578012234468 +# 8 | 045790018 +# 10 | 04507 +# 12 | 1471 +# 14 | 56 +# 16 | 7 +# 18 | 9 +# 20 | +# 22 | 25 +# 24 | 3 +# 26 | +# 28 | +# 30 | +# 32 | +# 34 | +# 36 | 1 + + +stem(log(rivers)) #Notice that the data are neither normal nor log-normal! Take that, Bell Curve fundamentalists. + +# The decimal point is 1 digit(s) to the left of the | +# +# 48 | 1 +# 50 | +# 52 | 15578 +# 54 | 44571222466689 +# 56 | 023334677000124455789 +# 58 | 00122366666999933445777 +# 60 | 122445567800133459 +# 62 | 112666799035 +# 64 | 00011334581257889 +# 66 | 003683579 +# 68 | 0019156 +# 70 | 079357 +# 72 | 89 +# 74 | 84 +# 76 | 56 +# 78 | 4 +# 80 | +# 82 | 2 + + +hist(rivers, col="#333333", border="white", breaks=25) #play around with these parameters +hist(log(rivers), col="#333333", border="white", breaks=25) #you'll do more plotting later + +#Here's another neat data set that comes pre-loaded. R has tons of these. data() +data(discoveries) +plot(discoveries, col="#333333", lwd=3, xlab="Year", main="Number of important discoveries per year") +plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year", main="Number of important discoveries per year") + + +#rather than leaving the default ordering (by year) we could also sort to see what's typical +sort(discoveries) +# [1] 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 +# [26] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 +# [51] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 +# [76] 4 4 4 4 5 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 8 9 10 12 + +stem(discoveries, scale=2) +# +# The decimal point is at the | +# +# 0 | 000000000 +# 1 | 000000000000 +# 2 | 00000000000000000000000000 +# 3 | 00000000000000000000 +# 4 | 000000000000 +# 5 | 0000000 +# 6 | 000000 +# 7 | 0000 +# 8 | 0 +# 9 | 0 +# 10 | 0 +# 11 | +# 12 | 0 + +max(discoveries) +# 12 + +summary(discoveries) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.0 2.0 3.0 3.1 4.0 12.0 + + + + +#Basic statistical operations don't require any programming knowledge either + +#roll a die a few times +round(runif(7, min=.5, max=6.5)) +# 1 4 6 1 4 6 4 + +#your numbers will differ from mine unless we set the same random.seed(31337) + + +#draw from a standard Gaussian 9 times +rnorm(9) +# [1] 0.07528471 1.03499859 1.34809556 -0.82356087 0.61638975 -1.88757271 +# [7] -0.59975593 0.57629164 1.08455362 + + + + + + + + + ######################### -# The absolute basics +# Basic programming stuff ######################### # NUMBERS -# We've got doubles! Behold the "numeric" class -5 # => [1] 5 -class(5) # => [1] "numeric" -# We've also got integers! They look suspiciously similar, -# but indeed are different -5L # => [1] 5 -class(5L) # => [1] "integer" +# "numeric" means double-precision floating-point numbers +5 # 5 +class(5) # "numeric" +5e4 # 50000 #handy when dealing with large,small,or variable orders of magnitude +6.02e23 # Avogadro's number +1.6e-35 # Planck length + +# long-storage integers are written with L +5L # 5 +class(5L) # "integer" + # Try ?class for more information on the class() function -# In fact, you can look up the documentation on just about anything with ? +# In fact, you can look up the documentation on `xyz` with ?xyz +# or see the source for `xyz` by evaluating xyz + +# Arithmetic +10 + 66 # 76 +53.2 - 4 # 49.2 +2 * 2.0 # 4 +3L / 4 # 0.75 +3 %% 2 # 1 + +# Weird number types +class(NaN) # "numeric" +class(Inf) # "numeric" +class(-Inf) # "numeric" #used in for example integrate( dnorm(x), 3, Inf ) -- which obviates Z-score tables + +# but beware, NaN isn't the only weird type... +class(NA) # see below +class(NULL) # NULL + + +# SIMPLE LISTS +c(6, 8, 7, 5, 3, 0, 9) # 6 8 7 5 3 0 9 +c('alef', 'bet', 'gimmel', 'dalet', 'he') # "alef" "bet" "gimmel" "dalet" "he" +c('Z', 'o', 'r', 'o') == "Zoro" # FALSE FALSE FALSE FALSE + +#some more nice built-ins +5:15 # 5 6 7 8 9 10 11 12 13 14 15 + +seq(from=0, to=31337, by=1337) +# [1] 0 1337 2674 4011 5348 6685 8022 9359 10696 12033 13370 14707 +# [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751 + +letters +# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" +# [20] "t" "u" "v" "w" "x" "y" "z" + +month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec" + + +# Access the n'th element of a list with list.name[n] or sometimes list.name[[n]] +letters[18] # "r" +LETTERS[13] # "M" +month.name[9] # "September" +c(6, 8, 7, 5, 3, 0, 9)[3] # 7 -# All the normal operations! -10 + 66 # => [1] 76 -53.2 - 4 # => [1] 49.2 -2 * 2.0 # => [1] 4 -3L / 4 # => [1] 0.75 -3 %% 2 # => [1] 1 -# Finally, we've got not-a-numbers! They're numerics too -class(NaN) # => [1] "numeric" # CHARACTERS -# We've (sort of) got strings! Behold the "character" class -"plugh" # => [1] "plugh" -class("plugh") # "character" # There's no difference between strings and characters in R +"Horatio" # "Horatio" +class("Horatio") # "character" +substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis " +gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis." + + + # LOGICALS -# We've got booleans! Behold the "logical" class -class(TRUE) # => [1] "logical" -class(FALSE) # => [1] "logical" +# booleans +class(TRUE) # "logical" +class(FALSE) # "logical" # Behavior is normal -TRUE == TRUE # => [1] TRUE -TRUE == FALSE # => [1] FALSE -FALSE != FALSE # => [1] FALSE -FALSE != TRUE # => [1] TRUE +TRUE == TRUE # TRUE +TRUE == FALSE # FALSE +FALSE != FALSE # FALSE +FALSE != TRUE # TRUE # Missing data (NA) is logical, too -class(NA) # => [1] "logical" +class(NA) # "logical" + + # FACTORS # The factor class is for categorical data -# It has an attribute called levels that describes all the possible categories -factor("dog") -# => -# [1] dog -# Levels: dog -# (This will make more sense once we start talking about vectors) +# which can be ordered (like childrens' grade levels) +# or unordered (like gender) +levels(factor(c("female", "male", "male", "female", "NA", "female"))) # "female" "male" "NA" + +factor(c("female", "female", "male", "NA", "female")) +# female female male NA female +# Levels: female male NA + +data(infert) #Infertility after Spontaneous and Induced Abortion +levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs" + + # VARIABLES @@ -80,8 +261,8 @@ y <- "1" # this is preferred TRUE -> z # this works but is weird # We can use coerce variables to different classes -as.numeric(y) # => [1] 1 -as.character(x) # => [1] "5" +as.numeric(y) # 1 +as.character(x) # "5" # LOOPS @@ -122,7 +303,7 @@ myFunc <- function(x) { } # Called like any other R function: -myFunc(5) # => [1] 19 +myFunc(5) # 19 ######################### # Fun with data: vectors, matrices, data frames, and arrays @@ -132,35 +313,35 @@ myFunc(5) # => [1] 19 # You can vectorize anything, so long as all components have the same type vec <- c(8, 9, 10, 11) -vec # => [1] 8 9 10 11 +vec # 8 9 10 11 # The class of a vector is the class of its components -class(vec) # => [1] "numeric" +class(vec) # "numeric" # If you vectorize items of different classes, weird coercions happen -c(TRUE, 4) # => [1] 1 4 -c("dog", TRUE, 4) # => [1] "dog" "TRUE" "4" +c(TRUE, 4) # 1 4 +c("dog", TRUE, 4) # "dog" "TRUE" "4" # We ask for specific components like so (R starts counting from 1) -vec[1] # => [1] 8 +vec[1] # 8 # We can also search for the indices of specific components, -which(vec %% 2 == 0) # => [1] 1 3 +which(vec %% 2 == 0) # 1 3 # or grab just the first or last entry in the vector -head(vec, 1) # => [1] 8 -tail(vec, 1) # => [1] 11 +head(vec, 1) # 8 +tail(vec, 1) # 11 # If an index "goes over" you'll get NA: -vec[6] # => [1] NA +vec[6] # NA # You can find the length of your vector with length() -length(vec) # => [1] 4 +length(vec) # 4 # You can perform operations on entire vectors or subsets of vectors -vec * 4 # => [1] 16 20 24 28 -vec[2:3] * 5 # => [1] 25 30 +vec * 4 # 16 20 24 28 +vec[2:3] * 5 # 25 30 # and there are many built-in functions to summarize vectors -mean(vec) # => [1] 9.5 -var(vec) # => [1] 1.666667 -sd(vec) # => [1] 1.290994 -max(vec) # => [1] 11 -min(vec) # => [1] 8 -sum(vec) # => [1] 38 +mean(vec) # 9.5 +var(vec) # 1.666667 +sd(vec) # 1.290994 +max(vec) # 11 +min(vec) # 8 +sum(vec) # 38 # TWO-DIMENSIONAL (ALL ONE CLASS) @@ -175,11 +356,11 @@ mat # Unlike a vector, the class of a matrix is "matrix", no matter what's in it class(mat) # => "matrix" # Ask for the first row -mat[1,] # => [1] 1 4 +mat[1,] # 1 4 # Perform operation on the first column -3 * mat[,1] # => [1] 3 6 9 +3 * mat[,1] # 3 6 9 # Ask for a specific cell -mat[3,2] # => [1] 6 +mat[3,2] # 6 # Transpose the whole matrix t(mat) # => @@ -196,7 +377,7 @@ mat2 # [2,] "2" "cat" # [3,] "3" "bird" # [4,] "4" "dog" -class(mat2) # => [1] matrix +class(mat2) # matrix # Again, note what happened! # Because matrices must contain entries all of the same class, # everything got converted to the character class @@ -216,7 +397,7 @@ mat3 # For columns of different classes, use the data frame dat <- data.frame(c(5,2,1,4), c("dog", "cat", "bird", "dog")) names(dat) <- c("number", "species") # name the columns -class(dat) # => [1] "data.frame" +class(dat) # "data.frame" dat # => # number species @@ -224,14 +405,14 @@ dat # 2 2 cat # 3 1 bird # 4 4 dog -class(dat$number) # => [1] "numeric" -class(dat[,2]) # => [1] "factor" +class(dat$number) # "numeric" +class(dat[,2]) # "factor" # The data.frame() function converts character vectors to factor vectors # There are many twisty ways to subset data frames, all subtly unalike -dat$number # => [1] 5 2 1 4 -dat[,1] # => [1] 5 2 1 4 -dat[,"number"] # => [1] 5 2 1 4 +dat$number # 5 2 1 4 +dat[,1] # 5 2 1 4 +dat[,"number"] # 5 2 1 4 # MULTI-DIMENSIONAL (ALL OF ONE CLASS) |