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authorAdam Bard <github@adambard.com>2013-08-08 17:40:47 -0700
committerAdam Bard <github@adambard.com>2013-08-08 17:40:47 -0700
commitb86da2e2082e7fd1438d48e12125aeaa6a187b92 (patch)
treeca3065df5c0c3666bd77e8f5e4196c03e27c2f60 /r.html.markdown
parent900f5448eb7d7578d36c559b0c859867478c84ac (diff)
parentfbfca446ff92ab9dec38569cecf250b82ab4aabc (diff)
Merge pull request #180 from isomorphisms/master
things you can do without programming
Diffstat (limited to 'r.html.markdown')
-rw-r--r--r.html.markdown313
1 files changed, 247 insertions, 66 deletions
diff --git a/r.html.markdown b/r.html.markdown
index 3339a07e..dd94072b 100644
--- a/r.html.markdown
+++ b/r.html.markdown
@@ -14,63 +14,244 @@ R is a statistical computing language. It has lots of libraries for uploading an
# You can't make a multi-line comment per se,
# but you can stack multiple comments like so.
-# Hit COMMAND-ENTER to execute a line
+# in Windows, 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)