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| -rw-r--r-- | r.html.markdown | 93 |
1 files changed, 69 insertions, 24 deletions
diff --git a/r.html.markdown b/r.html.markdown index dfc945c1..cd09e8da 100644 --- a/r.html.markdown +++ b/r.html.markdown @@ -188,7 +188,7 @@ class(-Inf) # "numeric" 10L + 66L # 76 # integer plus integer gives integer 53.2 - 4 # 49.2 # numeric minus numeric gives numeric 2.0 * 2L # 4 # numeric times integer gives numeric -3L / 4 # 0.75 # integer over integer gives numeric +3L / 4 # 0.75 # integer over numeric gives numeric 3 %% 2 # 1 # the remainder of two numerics is another numeric # Illegal arithmetic yeilds you a "not-a-number": 0 / 0 # NaN @@ -241,27 +241,29 @@ factor(c("female", "female", "male", "NA", "female")) # Levels: female male NA # The "levels" are the values the categorical data can take levels(factor(c("male", "male", "female", "NA", "female"))) # "female" "male" "NA" -# If a factor has length 1, its levels will have length 1, too +# If a factor vector has length 1, its levels will have length 1, too length(factor("male")) # 1 length(levels(factor("male"))) # 1 # Factors are commonly seen in data frames, a data structure we will cover later -# in this tutorial: data(infert) # "Infertility after Spontaneous and Induced Abortion" levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs" -# WEIRD TYPES -# A quick summary of some of the weirder types in R -class(Inf) # "numeric" -class(-Inf) # "numeric" -class(NaN) # "numeric" -class(NA) # "logical" +# NULL +# "NULL" is a weird one; use it to "blank out" a vector class(NULL) # NULL +parakeet +# => +# [1] "beak" "feathers" "wings" "eyes" +parakeet <- NULL +parakeet +# => +# NULL # TYPE COERCION # Type-coercion is when you force a value to take on a different type as.character(c(6, 8)) # "6" "8" as.logical(c(1,0,1,1)) # TRUE FALSE TRUE TRUE -# If you put elements of different classes into a vector, weird coercions happen: +# If you put elements of different types into a vector, weird coercions happen: c(TRUE, 4) # 1 4 c("dog", TRUE, 4) # "dog" "TRUE" "4" as.numeric("Bilbo") @@ -332,8 +334,6 @@ jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043 # ONE-DIMENSIONAL # Let's start from the very beginning, and with something you already know: vectors. -# As explained above, every single element in R is already a vector -# Make sure the elements of long vectors all have the same type vec <- c(8, 9, 10, 11) vec # 8 9 10 11 # We ask for specific elements by subsetting with square brackets @@ -345,9 +345,9 @@ month.name[9] # "September" c(6, 8, 7, 5, 3, 0, 9)[3] # 7 # We can also search for the indices of specific components, which(vec %% 2 == 0) # 1 3 -# grab just the first or last entry in the vector, +# grab just the first or last few entries in the vector, head(vec, 1) # 8 -tail(vec, 1) # 11 +tail(vec, w) # 10 11 # or figure out if a certain value is in the vector any(vec == 10) # TRUE # If an index "goes over" you'll get NA: @@ -358,7 +358,7 @@ length(vec) # 4 vec * 4 # 16 20 24 28 vec[2:3] * 5 # 25 30 any(vec[2:3] == 8) # FALSE -# and there are many built-in functions to summarize vectors +# and R has many built-in functions to summarize vectors mean(vec) # 9.5 var(vec) # 1.666667 sd(vec) # 1.290994 @@ -368,6 +368,7 @@ sum(vec) # 38 # 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 @@ -427,11 +428,11 @@ mat3 # [,1] [,2] [,3] [,4] # [1,] 1 2 4 5 # [2,] 6 7 0 4 -# Aah, everything of the same class. No coercions. Much better. +# Ah, everything of the same class. No coercions. Much better. # TWO-DIMENSIONAL (DIFFERENT CLASSES) -# For columns of different classes, use the data frame +# For columns of different types, use a data frame # This data structure is so useful for statistical programming, # a version of it was added to Python in the package "pandas". @@ -465,11 +466,11 @@ students$year # 3 2 2 1 0 -1 students[,2] # 3 2 2 1 0 -1 students[,"year"] # 3 2 2 1 0 -1 -# A popular replacement for the data.frame structure is the data.table +# 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") -require(data.table) +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 # => @@ -480,15 +481,17 @@ students # note the slightly different print-out # 4: Cho 1 R # 5: Draco 0 S # 6: Ginny -1 G -students[name=="Ginny"] +students[name=="Ginny"] # get rows with name == "Ginny" # => # name year house # 1: Ginny -1 G -students[year==2] +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 @@ -500,7 +503,7 @@ founders # 4: S Salazar setkey(students, house) setkey(founders, house) -students <- founders[students] # merge the two data sets +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] # => @@ -512,9 +515,51 @@ students[,order(c("name","year","house","houseFounderName")), with=F] # 5: Cho 1 R Rowena # 6: Draco 0 S Salazar -# MULTI-DIMENSIONAL (ALL OF ONE CLASS) +# 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)) # => |