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Merge pull request #4342 from Crystal-RainSlide/patch-1
[R/en] Format R code
This commit is contained in:
Marcel Ribeiro Dantas
GitHub
commit
8f28c8021b
279
r.html.markdown
279
r.html.markdown
@ -30,13 +30,13 @@ R is a statistical computing language. It has lots of libraries for uploading an
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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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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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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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@ -92,14 +92,15 @@ stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
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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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hist(rivers, col = "#333333", border = "white", breaks = 25)
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hist(log(rivers), col = "#333333", border = "white", breaks = 25)
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# play around with these parameters, 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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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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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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@ -110,7 +111,7 @@ sort(discoveries)
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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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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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@ -135,7 +136,7 @@ summary(discoveries)
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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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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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@ -158,69 +159,68 @@ rnorm(9)
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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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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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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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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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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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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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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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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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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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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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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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(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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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 most times
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c(1, 2, 3, 1, 2, 3) * c(1, 2, 1, 2, 1, 2) # 1 4 3 2 2 6
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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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"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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# => "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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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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@ -231,32 +231,33 @@ month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "D
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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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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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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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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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TRUE | FALSE # TRUE
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# AND
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TRUE & FALSE # FALSE
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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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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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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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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 colors)
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# Factors can be ordered (like grade levels) or unordered (like colors)
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factor(c("blue", "blue", "green", NA, "blue"))
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# blue blue green <NA> blue
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# Levels: blue green
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@ -264,31 +265,27 @@ factor(c("blue", "blue", "green", NA, "blue"))
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# Note that missing data does not enter the levels
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levels(factor(c("green", "green", "blue", NA, "blue"))) # "blue" "green"
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# If a factor vector has length 1, its levels will have length 1, too
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length(factor("green")) # 1
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length(factor("green")) # 1
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length(levels(factor("green"))) # 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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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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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 # "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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parakeet # 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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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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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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@ -310,14 +307,15 @@ as.numeric("Bilbo")
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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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x = 5 # this is possible
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y <- "1" # this is preferred traditionally
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TRUE -> z # this works but is weird
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# Refer to the Internet for the behaviors and preferences about them.
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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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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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@ -342,11 +340,11 @@ if (4 > 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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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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jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
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@ -358,39 +356,39 @@ jiggle(5) # 5±ε. After set.seed(2716057), jiggle(5)==5.005043
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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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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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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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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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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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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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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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length(vec) # 4
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# You can perform operations on entire vectors or subsets of vectors
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vec * 4 # 32 36 40 44
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vec[2:3] * 5 # 45 50
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any(vec[2:3] == 8) # FALSE
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vec * 4 # 32 36 40 44
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vec[2:3] * 5 # 45 50
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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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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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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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@ -398,7 +396,7 @@ seq(from=0, to=31337, by=1337)
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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 <- 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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@ -406,13 +404,13 @@ mat
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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" "array"
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class(mat) # "matrix" "array"
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# Ask for the first row
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mat[1,] # 1 4
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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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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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mat[3, 2] # 6
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# Transpose the whole matrix
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t(mat)
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@ -438,14 +436,14 @@ mat2
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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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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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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 <- 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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@ -459,11 +457,11 @@ mat3
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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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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/)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user