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authorMarcel Ribeiro Dantas <ribeirodantasdm@gmail.com>2022-06-27 00:25:19 +0200
committerGitHub <noreply@github.com>2022-06-27 00:25:19 +0200
commit8f28c8021b8ce3cb791861ad19c41e12228d8bcd (patch)
treea3bf73240fc58959405a72b0b4d1d97c15f6322a
parent38a37135cd1c0f8fd2117adec2177813b8239b54 (diff)
parent6162ab53ee855f3ddf8e3d9a4341801d953fc0f9 (diff)
Merge pull request #4342 from Crystal-RainSlide/patch-1
[R/en] Format R code
-rw-r--r--r.html.markdown279
1 files changed, 139 insertions, 140 deletions
diff --git a/r.html.markdown b/r.html.markdown
index e42b1f5e..9b8420fe 100644
--- a/r.html.markdown
+++ b/r.html.markdown
@@ -30,13 +30,13 @@ R is a statistical computing language. It has lots of libraries for uploading an
# R without understanding anything about programming. Do not worry
# about understanding everything the code does. Just enjoy!
-data() # browse pre-loaded data sets
-data(rivers) # get this one: "Lengths of Major North American Rivers"
-ls() # notice that "rivers" now appears in the workspace
-head(rivers) # peek at the data set
+data() # browse pre-loaded data sets
+data(rivers) # get this one: "Lengths of Major North American Rivers"
+ls() # notice that "rivers" now appears in the workspace
+head(rivers) # peek at the data set
# 735 320 325 392 524 450
-length(rivers) # how many rivers were measured?
+length(rivers) # how many rivers were measured?
# 141
summary(rivers) # what are some summary statistics?
# Min. 1st Qu. Median Mean 3rd Qu. Max.
@@ -92,14 +92,15 @@ stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
# 82 | 2
# make a histogram:
-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
+hist(rivers, col = "#333333", border = "white", breaks = 25)
+hist(log(rivers), col = "#333333", border = "white", breaks = 25)
+# play around with these parameters, you'll do more plotting later
# Here's another neat data set that comes pre-loaded. R has tons of these.
data(discoveries)
-plot(discoveries, col="#333333", lwd=3, xlab="Year",
+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",
+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),
@@ -110,7 +111,7 @@ sort(discoveries)
# [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)
+stem(discoveries, scale = 2)
#
# The decimal point is at the |
#
@@ -135,7 +136,7 @@ summary(discoveries)
# 0.0 2.0 3.0 3.1 4.0 12.0
# Roll a die a few times
-round(runif(7, min=.5, max=6.5))
+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)
@@ -158,69 +159,68 @@ rnorm(9)
# INTEGERS
# Long-storage integers are written with L
-5L # 5
-class(5L) # "integer"
+5L # 5
+class(5L) # "integer"
# (Try ?class for more information on the class() function.)
# In R, every single value, like 5L, is considered a vector of length 1
-length(5L) # 1
+length(5L) # 1
# You can have an integer vector with length > 1 too:
-c(4L, 5L, 8L, 3L) # 4 5 8 3
-length(c(4L, 5L, 8L, 3L)) # 4
-class(c(4L, 5L, 8L, 3L)) # "integer"
+c(4L, 5L, 8L, 3L) # 4 5 8 3
+length(c(4L, 5L, 8L, 3L)) # 4
+class(c(4L, 5L, 8L, 3L)) # "integer"
# NUMERICS
# A "numeric" is a double-precision floating-point number
-5 # 5
-class(5) # "numeric"
+5 # 5
+class(5) # "numeric"
# Again, everything in R is a vector;
# you can make a numeric vector with more than one element
-c(3,3,3,2,2,1) # 3 3 3 2 2 1
+c(3, 3, 3, 2, 2, 1) # 3 3 3 2 2 1
# You can use scientific notation too
-5e4 # 50000
-6.02e23 # Avogadro's number
-1.6e-35 # Planck length
+5e4 # 50000
+6.02e23 # Avogadro's number
+1.6e-35 # Planck length
# You can also have infinitely large or small numbers
-class(Inf) # "numeric"
-class(-Inf) # "numeric"
+class(Inf) # "numeric"
+class(-Inf) # "numeric"
# You might use "Inf", for example, in integrate(dnorm, 3, Inf);
# this obviates Z-score tables.
# BASIC ARITHMETIC
# You can do arithmetic with numbers
# Doing arithmetic on a mix of integers and numerics gives you another 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 numeric gives numeric
-3 %% 2 # 1 # the remainder of two numerics is another 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 numeric gives numeric
+3 %% 2 # 1 # the remainder of two numerics is another numeric
# Illegal arithmetic yields you a "not-a-number":
-0 / 0 # NaN
-class(NaN) # "numeric"
+0 / 0 # NaN
+class(NaN) # "numeric"
# You can do arithmetic on two vectors with length greater than 1,
# so long as the larger vector's length is an integer multiple of the smaller
-c(1,2,3) + c(1,2,3) # 2 4 6
+c(1, 2, 3) + c(1, 2, 3) # 2 4 6
# Since a single number is a vector of length one, scalars are applied
# elementwise to vectors
-(4 * c(1,2,3) - 2) / 2 # 1 3 5
+(4 * c(1, 2, 3) - 2) / 2 # 1 3 5
# Except for scalars, use caution when performing arithmetic on vectors with
# different lengths. Although it can be done,
-c(1,2,3,1,2,3) * c(1,2) # 1 4 3 2 2 6
-# Matching lengths is better practice and easier to read
-c(1,2,3,1,2,3) * c(1,2,1,2,1,2)
+c(1, 2, 3, 1, 2, 3) * c(1, 2) # 1 4 3 2 2 6
+# Matching lengths is better practice and easier to read most times
+c(1, 2, 3, 1, 2, 3) * c(1, 2, 1, 2, 1, 2) # 1 4 3 2 2 6
# CHARACTERS
# There's no difference between strings and characters in R
-"Horatio" # "Horatio"
-class("Horatio") # "character"
-class('H') # "character"
+"Horatio" # "Horatio"
+class("Horatio") # "character"
+class('H') # "character"
# Those were both character vectors of length 1
# Here is a longer one:
c('alef', 'bet', 'gimmel', 'dalet', 'he')
-# =>
-# "alef" "bet" "gimmel" "dalet" "he"
+# => "alef" "bet" "gimmel" "dalet" "he"
length(c("Call","me","Ishmael")) # 3
# You can do regex operations on character vectors:
-substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis "
+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."
# R has several built-in character vectors:
letters
@@ -231,32 +231,33 @@ month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "D
# LOGICALS
# In R, a "logical" is a boolean
-class(TRUE) # "logical"
-class(FALSE) # "logical"
+
+class(TRUE) # "logical"
+class(FALSE) # "logical"
# Their behavior is normal
-TRUE == TRUE # TRUE
-TRUE == FALSE # FALSE
-FALSE != FALSE # FALSE
-FALSE != TRUE # TRUE
+TRUE == TRUE # TRUE
+TRUE == FALSE # FALSE
+FALSE != FALSE # FALSE
+FALSE != TRUE # TRUE
# Missing data (NA) is logical, too
-class(NA) # "logical"
+class(NA) # "logical"
# Use | and & for logic operations.
# OR
-TRUE | FALSE # TRUE
+TRUE | FALSE # TRUE
# AND
-TRUE & FALSE # FALSE
+TRUE & FALSE # FALSE
# Applying | and & to vectors returns elementwise logic operations
-c(TRUE,FALSE,FALSE) | c(FALSE,TRUE,FALSE) # TRUE TRUE FALSE
-c(TRUE,FALSE,TRUE) & c(FALSE,TRUE,TRUE) # FALSE FALSE TRUE
+c(TRUE, FALSE, FALSE) | c(FALSE, TRUE, FALSE) # TRUE TRUE FALSE
+c(TRUE, FALSE, TRUE) & c(FALSE, TRUE, TRUE) # FALSE FALSE TRUE
# You can test if x is TRUE
-isTRUE(TRUE) # TRUE
+isTRUE(TRUE) # TRUE
# Here we get a logical vector with many elements:
-c('Z', 'o', 'r', 'r', 'o') == "Zorro" # FALSE FALSE FALSE FALSE FALSE
-c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE
+c('Z', 'o', 'r', 'r', 'o') == "Zorro" # FALSE FALSE FALSE FALSE FALSE
+c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE
# FACTORS
# The factor class is for categorical data
-# Factors can be ordered (like childrens' grade levels) or unordered (like colors)
+# Factors can be ordered (like grade levels) or unordered (like colors)
factor(c("blue", "blue", "green", NA, "blue"))
# blue blue green <NA> blue
# Levels: blue green
@@ -264,31 +265,27 @@ factor(c("blue", "blue", "green", NA, "blue"))
# Note that missing data does not enter the levels
levels(factor(c("green", "green", "blue", NA, "blue"))) # "blue" "green"
# If a factor vector has length 1, its levels will have length 1, too
-length(factor("green")) # 1
+length(factor("green")) # 1
length(levels(factor("green"))) # 1
# Factors are commonly seen in data frames, a data structure we will cover later
-data(infert) # "Infertility after Spontaneous and Induced Abortion"
+data(infert) # "Infertility after Spontaneous and Induced Abortion"
levels(infert$education) # "0-5yrs" "6-11yrs" "12+ yrs"
# NULL
# "NULL" is a weird one; use it to "blank out" a vector
-class(NULL) # NULL
+class(NULL) # NULL
parakeet = c("beak", "feathers", "wings", "eyes")
-parakeet
-# =>
-# [1] "beak" "feathers" "wings" "eyes"
+parakeet # "beak" "feathers" "wings" "eyes"
parakeet <- NULL
-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
+as.character(c(6, 8)) # "6" "8"
+as.logical(c(1,0,1,1)) # TRUE FALSE TRUE TRUE
# 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"
+c(TRUE, 4) # 1 4
+c("dog", TRUE, 4) # "dog" "TRUE" "4"
as.numeric("Bilbo")
# =>
# [1] NA
@@ -310,14 +307,15 @@ as.numeric("Bilbo")
# VARIABLES
# Lots of way to assign stuff:
-x = 5 # this is possible
-y <- "1" # this is preferred
-TRUE -> z # this works but is weird
+x = 5 # this is possible
+y <- "1" # this is preferred traditionally
+TRUE -> z # this works but is weird
+# Refer to the Internet for the behaviors and preferences about them.
# LOOPS
# We've got for loops
for (i in 1:4) {
- print(i)
+ print(i)
}
# We've got while loops
a <- 10
@@ -342,11 +340,11 @@ if (4 > 3) {
# FUNCTIONS
# Defined like so:
jiggle <- function(x) {
- x = x + rnorm(1, sd=.1) #add in a bit of (controlled) noise
+ x = x + rnorm(1, sd=.1) # add in a bit of (controlled) noise
return(x)
}
# Called like any other R function:
-jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
+jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
@@ -358,39 +356,39 @@ jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
# Let's start from the very beginning, and with something you already know: vectors.
vec <- c(8, 9, 10, 11)
-vec # 8 9 10 11
+vec # 8 9 10 11
# We ask for specific elements by subsetting with square brackets
# (Note that R starts counting from 1)
-vec[1] # 8
-letters[18] # "r"
-LETTERS[13] # "M"
-month.name[9] # "September"
-c(6, 8, 7, 5, 3, 0, 9)[3] # 7
+vec[1] # 8
+letters[18] # "r"
+LETTERS[13] # "M"
+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
+which(vec %% 2 == 0) # 1 3
# grab just the first or last few entries in the vector,
-head(vec, 1) # 8
-tail(vec, 2) # 10 11
+head(vec, 1) # 8
+tail(vec, 2) # 10 11
# or figure out if a certain value is in the vector
-any(vec == 10) # TRUE
+any(vec == 10) # TRUE
# If an index "goes over" you'll get NA:
-vec[6] # NA
+vec[6] # NA
# You can find the length of your vector with length()
-length(vec) # 4
+length(vec) # 4
# You can perform operations on entire vectors or subsets of vectors
-vec * 4 # 32 36 40 44
-vec[2:3] * 5 # 45 50
-any(vec[2:3] == 8) # FALSE
+vec * 4 # 32 36 40 44
+vec[2:3] * 5 # 45 50
+any(vec[2:3] == 8) # FALSE
# and R has many built-in functions to summarize vectors
-mean(vec) # 9.5
-var(vec) # 1.666667
-sd(vec) # 1.290994
-max(vec) # 11
-min(vec) # 8
-sum(vec) # 38
+mean(vec) # 9.5
+var(vec) # 1.666667
+sd(vec) # 1.290994
+max(vec) # 11
+min(vec) # 8
+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)
+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
@@ -398,7 +396,7 @@ seq(from=0, to=31337, by=1337)
# TWO-DIMENSIONAL (ALL ONE CLASS)
# You can make a matrix out of entries all of the same type like so:
-mat <- matrix(nrow = 3, ncol = 2, c(1,2,3,4,5,6))
+mat <- matrix(nrow = 3, ncol = 2, c(1, 2, 3, 4, 5, 6))
mat
# =>
# [,1] [,2]
@@ -406,13 +404,13 @@ mat
# [2,] 2 5
# [3,] 3 6
# Unlike a vector, the class of a matrix is "matrix", no matter what's in it
-class(mat) # => "matrix" "array"
+class(mat) # "matrix" "array"
# Ask for the first row
-mat[1,] # 1 4
+mat[1, ] # 1 4
# Perform operation on the first column
-3 * mat[,1] # 3 6 9
+3 * mat[, 1] # 3 6 9
# Ask for a specific cell
-mat[3,2] # 6
+mat[3, 2] # 6
# Transpose the whole matrix
t(mat)
@@ -438,14 +436,14 @@ mat2
# [2,] "2" "cat"
# [3,] "3" "bird"
# [4,] "4" "dog"
-class(mat2) # 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
-c(class(mat2[,1]), class(mat2[,2]))
+c(class(mat2[, 1]), class(mat2[, 2]))
# rbind() sticks vectors together row-wise to make a matrix
-mat3 <- rbind(c(1,2,4,5), c(6,7,0,4))
+mat3 <- rbind(c(1, 2, 4, 5), c(6, 7, 0, 4))
mat3
# =>
# [,1] [,2] [,3] [,4]
@@ -459,11 +457,11 @@ mat3
# This data structure is so useful for statistical programming,
# a version of it was added to Python in the package "pandas".
-students <- data.frame(c("Cedric","Fred","George","Cho","Draco","Ginny"),
- c(3,2,2,1,0,-1),
- c("H", "G", "G", "R", "S", "G"))
+students <- data.frame(c("Cedric", "Fred", "George", "Cho", "Draco", "Ginny"),
+ c( 3, 2, 2, 1, 0, -1),
+ c( "H", "G", "G", "R", "S", "G"))
names(students) <- c("name", "year", "house") # name the columns
-class(students) # "data.frame"
+class(students) # "data.frame"
students
# =>
# name year house
@@ -473,12 +471,12 @@ students
# 4 Cho 1 R
# 5 Draco 0 S
# 6 Ginny -1 G
-class(students$year) # "numeric"
-class(students[,3]) # "factor"
+class(students$year) # "numeric"
+class(students[,3]) # "factor"
# find the dimensions
-nrow(students) # 6
-ncol(students) # 3
-dim(students) # 6 3
+nrow(students) # 6
+ncol(students) # 3
+dim(students) # 6 3
# The data.frame() function used to convert character vectors to factor
# vectors by default; This has changed in R 4.0.0. If your R version is
# older, turn this off by setting stringsAsFactors = FALSE when you
@@ -486,9 +484,9 @@ dim(students) # 6 3
?data.frame
# There are many twisty ways to subset data frames, all subtly unalike
-students$year # 3 2 2 1 0 -1
-students[,2] # 3 2 2 1 0 -1
-students[,"year"] # 3 2 2 1 0 -1
+students$year # 3 2 2 1 0 -1
+students[, 2] # 3 2 2 1 0 -1
+students[, "year"] # 3 2 2 1 0 -1
# An augmented version of the data.frame structure is the data.table
# If you're working with huge or panel data, or need to merge a few data
@@ -505,19 +503,19 @@ students # note the slightly different print-out
# 4: Cho 1 R
# 5: Draco 0 S
# 6: Ginny -1 G
-students[name=="Ginny"] # get rows with name == "Ginny"
+students[name == "Ginny"] # get rows with name == "Ginny"
# =>
# name year house
# 1: Ginny -1 G
-students[year==2] # get rows with year == 2
+students[year == 2] # get rows with year == 2
# =>
# name year house
# 1: Fred 2 G
# 2: George 2 G
# data.table makes merging two data sets easy
# let's make another data.table to merge with students
-founders <- data.table(house=c("G","H","R","S"),
- founder=c("Godric","Helga","Rowena","Salazar"))
+founders <- data.table(house = c("G" , "H" , "R" , "S"),
+ founder = c("Godric", "Helga", "Rowena", "Salazar"))
founders
# =>
# house founder
@@ -528,8 +526,8 @@ founders
setkey(students, house)
setkey(founders, house)
students <- founders[students] # merge the two data sets by matching "house"
-setnames(students, c("house","houseFounderName","studentName","year"))
-students[,order(c("name","year","house","houseFounderName")), with=F]
+setnames(students, c("house", "houseFounderName", "studentName", "year"))
+students[, order(c("name", "year", "house", "houseFounderName")), with = F]
# =>
# studentName year house houseFounderName
# 1: Fred 2 G Godric
@@ -540,7 +538,7 @@ students[,order(c("name","year","house","houseFounderName")), with=F]
# 6: Draco 0 S Salazar
# data.table makes summary tables easy
-students[,sum(year),by=house]
+students[, sum(year), by = house]
# =>
# house V1
# 1: G 3
@@ -573,7 +571,7 @@ students[studentName != "Draco"]
# 5: R Cho 1
# Using data.frame:
students <- as.data.frame(students)
-students[students$house != "G",]
+students[students$house != "G", ]
# =>
# house houseFounderName studentName year
# 4 H Helga Cedric 3
@@ -585,13 +583,13 @@ students[students$house != "G",]
# 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))
+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))
+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
#
@@ -611,7 +609,7 @@ 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))
# Finally, R has lists (of vectors)
list1 <- list(time = 1:40)
-list1$price = c(rnorm(40,.5*list1$time,4)) # random
+list1$price = c(rnorm(40, .5*list1$time, 4)) # random
list1
# You can get items in the list like so
list1$time # one way
@@ -684,7 +682,7 @@ write.csv(pets, "pets2.csv") # to make a new .csv file
#########################
# Linear regression!
-linearModel <- lm(price ~ time, data = list1)
+linearModel <- lm(price ~ time, data = list1)
linearModel # outputs result of regression
# =>
# Call:
@@ -721,7 +719,7 @@ summary(linearModel)$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)$coefficients[,4] # the p-values
+summary(linearModel)$coefficients[, 4] # the p-values
# =>
# (Intercept) time
# 9.234021e-01 2.440008e-09
@@ -730,8 +728,7 @@ summary(linearModel)$coefficients[,4] # the p-values
# Logistic regression
set.seed(1)
list1$success = rbinom(length(list1$time), 1, .5) # random binary
-glModel <- glm(success ~ time, data = list1,
- family=binomial(link="logit"))
+glModel <- glm(success ~ time, data = list1, family=binomial(link="logit"))
glModel # outputs result of logistic regression
# =>
# Call: glm(formula = success ~ time,
@@ -747,8 +744,10 @@ glModel # outputs result of logistic regression
summary(glModel) # more verbose output from the regression
# =>
# Call:
-# glm(formula = success ~ time,
-# family = binomial(link = "logit"), data = list1)
+# glm(
+# formula = success ~ time,
+# family = binomial(link = "logit"),
+# data = list1)
# Deviance Residuals:
# Min 1Q Median 3Q Max
@@ -782,7 +781,7 @@ 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"))
+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
@@ -790,10 +789,10 @@ barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))
install.packages("ggplot2")
require(ggplot2)
?ggplot2
-pp <- ggplot(students, aes(x=house))
+pp <- ggplot(students, aes(x = house))
pp + geom_bar()
ll <- as.data.table(list1)
-pp <- ggplot(ll, aes(x=time,price))
+pp <- ggplot(ll, aes(x = time, price))
pp + geom_point()
# ggplot2 has excellent documentation (available http://docs.ggplot2.org/current/)