RDirectory/Interactive Script.R at master · harshadindigal/RDirectory · GitHub

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library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)

set.seed(955)

dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))


p <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1)      # Use hollow circles
p
ggplotly(p)


p <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth(method=lm)   # Add linear regression line
ggplotly(p)
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()            # Add a loess smoothed fit curve with confidence region
ggplotly(p)

n <- 20
x1 <- rnorm(n); x2 <- rnorm(n)
y1 <- 2 * x1 + rnorm(n)
y2 <- 3 * x2 + (2 + rnorm(n))
A <- as.factor(rep(c(1, 2), each = n))
df <- data.frame(x = c(x1, x2), y = c(y1, y2), A = A)
fm <- lm(y ~ x + A, data = df)

p <- ggplot(data = cbind(df, pred = predict(fm)), aes(x = x, y = y, color = A))
p <- p + geom_point() + geom_line(aes(y = pred))
ggplotly(p)


dfGamma = data.frame(nu75 = rgamma(100, 0.75),
                     nu1 = rgamma(100, 1),
                     nu2 = rgamma(100, 2))

dfGamma = stack(dfGamma)

p <- ggplot(dfGamma, aes(x = values)) +
  stat_density(aes(group = ind, color = ind),position="identity",geom="line")
ggplotly(p)


dim1 <- c(rnorm(100, mean=1), rnorm(100, mean=4))
dim2 <- rnorm(200, mean=1)
cat <- factor(c(rep("a", 100), rep("b", 100)))
mydf <- data.frame(cbind(dim2, dim1, cat))
p <- ggplot(data=mydf, aes(x=dim1, y=dim2, colour=as.factor(cat))) +
  geom_point() +
  stat_density(aes(x=dim1, y=(-2+(..scaled..))),
               position="identity", geom="line")

stuff <- ggplot_build(p)
xrange <- stuff[[2]]$ranges[[1]]$x.range  # extract the x range to make the
# new densities align with y-axis

## Get densities of dim2
ds <- do.call(rbind, lapply(unique(mydf$cat), function(lev) {
  dens <- with(mydf, density(dim2[cat==lev]))
  data.frame(x=dens$y+xrange[1], y=dens$x, cat=lev)
}))

p <- p + geom_path(data=ds, aes(x=x, y=y, color=factor(cat)))

ggplotly(p)


dd<-data.frame(matrix(rnorm(144, mean=2, sd=2),72,2),c(rep("A",24),rep("B",24),rep("C",24)))
colnames(dd) <- c("x_value", "Predicted_value",  "State_CD")

dd <- data.frame(
  predicted = rnorm(72, mean = 2, sd = 2),
  state = rep(c("A", "B", "C"), each = 24)
)

grid <- with(dd, seq(min(predicted), max(predicted), length = 100))
normaldens <- ddply(dd, "state", function(df) {
  data.frame(
    predicted = grid,
    density = dnorm(grid, mean(df$predicted), sd(df$predicted))
  )
})

p <- ggplot(dd, aes(predicted))  +
  geom_density() +
  geom_line(aes(y = density), data = normaldens, colour = "red") +
  facet_wrap(~ state)
ggplotly(p)


df <- data.frame(x <- rchisq(1000, 10, 10),
                 y <- rnorm(1000))

p <- ggplot(df, aes(x, y)) +
  geom_point(alpha = 0.5) +
  geom_density_2d() +
  theme(panel.background = element_rect(fill = '#ffffff'))

ggplotly(p)