# Demo 1 install R, R studio, and NodeXL
######################## R basics
# get work directory
getwd()
## [1] "E:/Github/demo1_install_softwares"
setwd("E:/github/ergm/") # modify here to set your work directory
1 + 3 # evaluation
## [1] 4
a = 3 # assignment
a # evaluation
## [1] 3
a = 3 # spacing does not matter
sqrt(a) # use the square root function
## [1] 1.732
b = sqrt(a)
b # use function and save result
## [1] 1.732
help(sqrt) # get specific help for a function
## starting httpd help server ... done
`?`(sqrt # get specific help for a function
)
a == b # Using two equals sign to judge whether a is equivalent to b.
## [1] FALSE
a != b # is a not equal to b?
## [1] TRUE
############################## Vectors and matrices in R
# create a vector by combining values
a = c(1, 3, 5)
a
## [1] 1 3 5
a[2] # select the second element
## [1] 3
b = c("one", "three", "five")
b # also works with strings
## [1] "one" "three" "five"
b[2]
## [1] "three"
a = c(a, a)
a # can apply recursively
## [1] 1 3 5 1 3 5
a = c(a, b)
a # mixing types---what happens?
## [1] "1" "3" "5" "1" "3" "5" "one" "three" "five"
## Sequences and replication
a = seq(from = 1, to = 5, by = 1) # from 1 to 5 the slow way
b = 1:5 # a shortcut!
a == b # all TRUE
## [1] TRUE TRUE TRUE TRUE TRUE
rep(1, times = 5) # a lot of ones
## [1] 1 1 1 1 1
rep(1:5, times = 2) # repeat an entire sequence
## [1] 1 2 3 4 5 1 2 3 4 5
rep(1:5, each = 2) # same, but element-wise
## [1] 1 1 2 2 3 3 4 4 5 5
rep(1:5, times = 5:1) # can vary the count of each element
## [1] 1 1 1 1 1 2 2 2 2 3 3 3 4 4 5
## matrices
a = matrix(data = 1:25, nrow = 5, ncol = 5)
a # create a matrix the 'formal' way
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 6 11 16 21
## [2,] 2 7 12 17 22
## [3,] 3 8 13 18 23
## [4,] 4 9 14 19 24
## [5,] 5 10 15 20 25
a[1, 2] # select a matrix element (two dimensions)
## [1] 6
a[1, ] # just the first row
## [1] 1 6 11 16 21
a[, 2] # can also perform for columns
## [1] 6 7 8 9 10
a[2:3, 3:5] # select submatrices
## [,1] [,2] [,3]
## [1,] 12 17 22
## [2,] 13 18 23
a[-1, ] # get rid of row one
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2 7 12 17 22
## [2,] 3 8 13 18 23
## [3,] 4 9 14 19 24
## [4,] 5 10 15 20 25
# create matrices by combining rows or columns
b = cbind(1:5, 1:5)
b
## [,1] [,2]
## [1,] 1 1
## [2,] 2 2
## [3,] 3 3
## [4,] 4 4
## [5,] 5 5
d = rbind(1:5, 1:5)
d # can perform with rows, too
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 2 3 4 5
## [2,] 1 2 3 4 5
dim(b) # dimensions = number of rows * number of columns
## [1] 5 2
nrow(b) # the number of rows
## [1] 5
ncol(b) # the number of columns
## [1] 2
# Element-wise operations
a = 1:5
a + 1 # addition
## [1] 2 3 4 5 6
a * 2 # multiplication
## [1] 2 4 6 8 10
a/3 # division
## [1] 0.3333 0.6667 1.0000 1.3333 1.6667
a - 4 # subtraction
## [1] -3 -2 -1 0 1
a^5 # the 5th power
## [1] 1 32 243 1024 3125
a + a # also works on pairs of vectors
## [1] 2 4 6 8 10
a * a
## [1] 1 4 9 16 25
log(a)
## [1] 0.0000 0.6931 1.0986 1.3863 1.6094
exp(b)
## [,1] [,2]
## [1,] 2.718 2.718
## [2,] 7.389 7.389
## [3,] 20.086 20.086
## [4,] 54.598 54.598
## [5,] 148.413 148.413
################# Data frames
d = data.frame(income = 1:5, health = c(T, T, T, T, F), name = LETTERS[1:5])
d
## income health name
## 1 1 TRUE A
## 2 2 TRUE B
## 3 3 TRUE C
## 4 4 TRUE D
## 5 5 FALSE E
d[1, 2] # acts a lot like a matrix!
## [1] TRUE
d[, 1] * 5
## [1] 5 10 15 20 25
d[-1, ]
## income health name
## 2 2 TRUE B
## 3 3 TRUE C
## 4 4 TRUE D
## 5 5 FALSE E
d$health # can use dollar sign notation
## [1] TRUE TRUE TRUE TRUE FALSE
d$health[3] = FALSE # making changes
d
## income health name
## 1 1 TRUE A
## 2 2 TRUE B
## 3 3 FALSE C
## 4 4 TRUE D
## 5 5 FALSE E
d[2, 3] # shows factors for string values
## [1] B
## Levels: A B C D E
d$name = LETTERS[1:5] # eliminate evil factors by overwriting
d[2, 3]
## [1] "B"
# avoid to use factors (if you want)
d = data.frame(income = 1:5, health = c(T, T, T, T, F), name = LETTERS[1:5],
stringsAsFactors = FALSE)
d
## income health name
## 1 1 TRUE A
## 2 2 TRUE B
## 3 3 TRUE C
## 4 4 TRUE D
## 5 5 FALSE E
d = as.data.frame(cbind(1:5, 2:6)) # can create from matrices
d
## V1 V2
## 1 1 2
## 2 2 3
## 3 3 4
## 4 4 5
## 5 5 6
is.data.frame(d) # how can we tell it's not a matrix?
## [1] TRUE
is.matrix(d) # the truth comes out
## [1] FALSE
# Finding built-in data sets Many packages have built-in data for testing
# and educational purposes
data() # lists them all
`?`(USArrests # get help on a data set
)
data(USArrests) # load the data set
USArrests # view the object
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
## Connecticut 3.3 110 77 11.1
## Delaware 5.9 238 72 15.8
## Florida 15.4 335 80 31.9
## Georgia 17.4 211 60 25.8
## Hawaii 5.3 46 83 20.2
## Idaho 2.6 120 54 14.2
## Illinois 10.4 249 83 24.0
## Indiana 7.2 113 65 21.0
## Iowa 2.2 56 57 11.3
## Kansas 6.0 115 66 18.0
## Kentucky 9.7 109 52 16.3
## Louisiana 15.4 249 66 22.2
## Maine 2.1 83 51 7.8
## Maryland 11.3 300 67 27.8
## Massachusetts 4.4 149 85 16.3
## Michigan 12.1 255 74 35.1
## Minnesota 2.7 72 66 14.9
## Mississippi 16.1 259 44 17.1
## Missouri 9.0 178 70 28.2
## Montana 6.0 109 53 16.4
## Nebraska 4.3 102 62 16.5
## Nevada 12.2 252 81 46.0
## New Hampshire 2.1 57 56 9.5
## New Jersey 7.4 159 89 18.8
## New Mexico 11.4 285 70 32.1
## New York 11.1 254 86 26.1
## North Carolina 13.0 337 45 16.1
## North Dakota 0.8 45 44 7.3
## Ohio 7.3 120 75 21.4
## Oklahoma 6.6 151 68 20.0
## Oregon 4.9 159 67 29.3
## Pennsylvania 6.3 106 72 14.9
## Rhode Island 3.4 174 87 8.3
## South Carolina 14.4 279 48 22.5
## South Dakota 3.8 86 45 12.8
## Tennessee 13.2 188 59 26.9
## Texas 12.7 201 80 25.5
## Utah 3.2 120 80 22.9
## Vermont 2.2 48 32 11.2
## Virginia 8.5 156 63 20.7
## Washington 4.0 145 73 26.2
## West Virginia 5.7 81 39 9.3
## Wisconsin 2.6 53 66 10.8
## Wyoming 6.8 161 60 15.6
############################# Elementary visualization
## R's workhorse is the ``plot' command
plot(USArrests$Murder, USArrests$UrbanPop) # using dollar sign notation
plot(USArrests$Murder, USArrests$UrbanPop, log = "xy") # log-log scale
## Adding plot title and axis labels
plot(USArrests$Murder, USArrests$Assault, xlab = "Murder", ylab = "Assault",
main = "USArrests")
## Can also add text
plot(USArrests$Murder, USArrests$Assault, xlab = "Murder", ylab = "Assault",
main = "USArrests", type = "n")
text(USArrests$Murder, USArrests$Assault, rownames(USArrests))
## Histograms and boxplots
hist(USArrests$Murder)
boxplot(USArrests)
