cdesante

###### 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,

cdesante

shade.norm <- function(MU, SD, Lower, Upper, fill.color) {

cord.x <- c(Lower,seq(Lower,Upper,0.01),Upper)

cord.y <- c(0,dnorm(seq(Lower,Upper,0.01), 
mean = MU, sd = SD),0)

y.axis.text <-bquote("Density of N " * " " * "(" *
mu * " " * "=" * " " * .(MU) * ","* " " * sigma *
" " * "=" * " " * .(SD) * ")")

cdesante

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

cdesante

library(ggplot2)
library(foreign)
library(reshape)
ANES <- read.dta("http://www.oberlin.edu/faculty/cdesante/assets/downloads/ANESdemo.dta")

#Add meaningful labels for Religion
ANES$religion[ANES$religion==1] <- "Protestant"
ANES$religion[ANES$religion==2] <- "Catholic"
ANES$religion[ANES$religion==3] <- "Jewish"

cdesante

ANES <- read.csv("http://www.oberlin.edu/faculty/cdesante/assets/downloads/ANES.csv")
head(ANES)
ANES$caseid <- 1:dim(ANES)[1]
head(ANES)
dim(ANES)

cdesante

library(ggplot2)
library(gridExtra)
library(reshape)
anes.year <- melt(ANES, id=c("year"), na.rm=TRUE)
head(anes.year)

cdesante

anes.year.gender <- melt(ANES, id=c("year", "female"), na.rm=TRUE)
head(anes.year.gender)
dim(anes.year.gender)

cdesante

#Truncate Data:
party.and.ideology<- ANES[,c(1, 12, 14)]
head(party.and.ideology)

#Step 1 : Melt
party.and.ideology.year <- melt(party.and.ideology, id=c("year"), na.rm=TRUE)
head(party.and.ideology.year)

#Step 1 : Cast
party.and.ideology.means.over.time <- (cast(party.and.ideology.year,

cdesante

P1 <- ggplot(data = party.and.ideology.means.over.time
              ) + geom_point(aes(x = year, y = pid7))
P1 <- P1  + ylim(0,6) + labs(title="geom_point, P1")
P1

cdesante

head(ANES)
party.and.region <- ANES[,c(1, 12, 16)]
head(party.and.region)
   #Note the additional changes to our id=():
party.and.region.year <- melt(party.and.region, id=c("year", "south"), na.rm=TRUE)
head(party.and.region.year)
   #Note the additional changes to our formula (below):
party.region.time  <- (cast(party.and.region.year, year+south~variable, 
                             mean, na.rm=T) )
party.region.time