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X <- 1:5
Y <- c(2, 5, 2.4, 3.67, 0.49)
qplot(X, weight=Y, geom="bar", ymin=0, xlab="X axis abel", ylab="Y axis label"
, main="MAIN LABEL")+ scale_y_continuous(limits=c(0, 5),breaks=c(1,2,3,4,5),labels=c("Strongly Disagree",
"Disagree","Neutral","Agree","Strongly Agree")) + theme_bw()

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library(ggplot2)

FEM <- c(rep(1, 100), rep(0, 100))
Gender <- c(rep("Female", 100), rep("Male", 100))
pid7 <- sample(0:6, 200, replace=T)

Bfem <- rnorm(200, 1.94, 0.37)
Bpid <- rnorm(200, -1.45, 0.82)
Binter <- rnorm(200, 0.31, .11)
B0 <- rnorm(200, 74.2, .29)

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#by Christopher DeSante#
#Load ggplot2 #
library(ggplot2)
 ### X-axis: ###
anes.years <- c(1988, 1990, 1992, 1994, 2000, 2004, 2008,2016)

## Some DVs ##

mum1<-rnorm(8, 8,1)
mum2<-rnorm(8, 8,1)

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#Set the working directory, plots will be saved locally.
library(ggplot2)
X <- rnorm(100)
Y <- rnorm(100)
Z <- rnorm(100)
A <- rnorm(100)
B <- rnorm(100)

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#The birthday Problem.

#Simple computation:
N <- 1:50
pshare <- 1- exp(-(N*(N-1)/2)/365)
cbind(N, pshare)

#just want one value:
bday <- function(X){
N <- X

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Run from the command prompt in Windows:

attrib +h *.bak /s
attrib +h *.blg /s 
attrib +h *.aux /s 

you can hide system files so all you see locally is the .tex and .pdf

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#This code produces a faceted plot of dyad-level data with asymmetry in the variable of interest across dyads.

MYDATA <- matrix(rnorm(16*16), 16, 16)
diag(MYDATA) <- rep(NA, 16)
MYDATA
colnames(MYDATA) <- rownames(MYDATA) <- LETTERS[1:16]
MYDATA
sender.row <- c()
receiver.col <- c()
value.to.plot <-c()

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#R as a giant awesome calculator:
4+5
exp(5)
log(10)
log(exp(6))
log(10, base=10)
sqrt(64)
pi^2
sqrt(pi^2)
pi

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###### Ordered Categorical DV: #####
#This uses "importance of aid to blacks" as a DV and race of R as the indicator IV.#
     library(MASS)
     white <- c(1,1,0,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,
1,1,0,1,1,1,1,1,1,1,1,1,0,0,0,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,1,1,1,0,1,1,1,0,1,1,
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,0,1,1,1,1,0,1,0,0,1,0,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,
1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,0,0,1,1,1,1,1,1,1,0,1,0,0,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,
0,1,1,0,1,1,1,1,1,1,0,0,1,1,0,1,0,1,1,1,1,1,0,1,1,0,1,1,1,1,1,0,1,1,1,0,1,0,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,
1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,

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is.R()
IQ <- round(rnorm(1000, 100, 15))
#IQ
plot(IQ)
plot(density(IQ), col="red")