p <- ggplot(data=diamonds, aes(x=carat,y=price,colour=cut)) #init. plot, specifying data and aes
p <- p + layer(geom="point") # add a layer with points geom
p #render plot

p <- ggvis(diamonds, x=~carat, y=~price, fill=~cut)
p <- p %>% layer_points()
p #render plot

p <- ggplot(diamonds, aes(x=carat)) #init
p <- p + layer(
  geom="bar",
  geom_params=list(fill="steelblue"),
  stat="bin",
  stat_params=list(binwidth=.25)
)
p #render the plot

p <- ggvis(diamonds, x=~carat)
p <- p %>% layer_histograms(fill:="steelblue", binwidth=.25)
p

#specify the geom, using default stat
ggplot(diamonds, aes(x=carat)) + geom_histogram(binwidth=.25, fill="steelblue")

#specify the stat, using default geom
ggplot(diamonds, aes(x=carat)) + stat_bin(binwidth=.25, fill="steelblue") + geom_density()

diamonds %>% ggvis(~carat) %>% layer_histograms(binwidth=.25, fill:="steelblue")

p2 <- ggplot(data=hw) #initialize

p2 <- p2 + aes(x=height, y=health) #specify a mapping
p2 + geom_point() #render

p2 <- p2 + aes(x=weight, y=health) #change mapping 
p2 + geom_point() 

#add another aesthetic (colour)
p2 + geom_point(aes(colour=height)) 

#we can also remove aesthetics
p2 + geom_point(aes(colour=NULL)) 

#instead of mapping aesthetics to a variable, we can set them to a constant
p2 + geom_point(colour="darkblue") #set col to darkblue

#This is different from an aesthetic mapping:
p2 + geom_point(aes(colour="darkblue"))  #create a new variable called "darkblue", and map it to color

p2 <- hw

p2 <- hw %>% ggvis() %>% add_props(x=~height, y=~health)
p2 #render

p2 <- p2 %>% add_props(x=~weight, y=~health) # change mapping
p2

#add another aesthetic (colour)
p2 %>% layer_points(fill=~height)

#there is not currently a way to remove a mapping

#instead of mapping aesthetics to a variable, we can set them to a constant
p2 %>% layer_points(fill:="darkblue") #set col to darkblue

#incorrectly mapping an aesthetic to a scalar is not possible in ggvis.

data(package='effects', 'Arrests')

head(Arrests)

dat <- data.frame(colour= factor(c("Black", "White"), levels=c("Black","White")), Percent_Released = c(0.74, 0.85))

# basic bar graph
ggplot(dat, aes(x=colour, y=Percent_Released)) + geom_bar(stat="identity")

# Fill different fill colors.
ggplot(dat, aes(x=colour, y=Percent_Released)) + geom_bar(aes(fill=colour), stat="identity")

# Add a black outline
ggplot(dat, aes(x=colour, y=Percent_Released, fill=colour)) + geom_bar(colour="black", stat="identity")

# Removing the legend
ggplot(dat, aes(x=colour, y=Percent_Released, fill=colour)) +
 geom_bar(colour="black", stat="identity") +
 guides(fill=FALSE)

data(package='effects', 'Arrests')

head(Arrests)

dat <- data.frame(count=c(1L,2L), colour=c("Black", "White"), Percent_Released = c(0.74, 0.85))

# basic bar graph
dat %>% ggvis(x=~count, y=~Percent_Released) %>% layer_bars()

# Fill different fill colors.
dat %>%
  ggvis(x=~count, y=~Percent_Released, fill:=~colour) %>%
  group_by(colour)  %>%
  layer_bars()

# ggvis has a black outline by default

# Removing the legend broken right now, see issue #229
dat %>% group_by(colour) %>%
  ggvis(x=~count, y=~Percent_Released, fill=~colour) %>%
  layer_bars() %>%
  hide_legend("fill")

# Overlaid histograms
ggplot(Arrests, aes(x=checks, fill=released)) + geom_histogram(binwidth=1, alpha=.5, position="identity")

# conclusions from this plot: if you have more checks, you are much less likely to be released

# Overlaid histograms
Arrests %>%
  group_by(released) %>%
  ggvis(x=~checks, fill=~released) %>%
  layer_histograms(opacity:=1/2, stack=FALSE)

#specify the theme
p <- ggplot(data=Arrests) + theme(plot.title=element_text(lineheight=.8, face="bold"))

p + geom_boxplot(mapping=aes(x=colour, y=unclass(checks))) + ggtitle("Prior Police Checks by Race") 

p + geom_boxplot(mapping=aes(x=released, y=unclass(checks))) + ggtitle("Prior Police Checks by Released (Yes/No)")

# faceting
p + facet_wrap(~released) +
  geom_boxplot(mapping=aes(x=colour, y=unclass(checks), color=colour)) +
  ggtitle("Prior Police Checks by Race and Released (Yes/No)")

#specify the theme
Arrests %>% ggvis(x=~colour, y=~checks) %>% layer_boxplots()

Arrests %>% ggvis(x=~released, y=~checks) %>% layer_boxplots()

# Basic histogram from the vector "rating". Each bin is .5 wide.

df <- data.frame(cond = factor( rep(c("A","B"), each=200) ), rating = c(rnorm(200),rnorm(200, mean=.5)))
###simulate data from mixed dist. 0.5N(0,1) + 0.5N(0.5,1)

ggplot(df, aes(x=rating)) + geom_histogram(binwidth=.5)

# Draw with black outline, white fill
ggplot(df, aes(x=rating)) + geom_histogram(binwidth=.5, colour="black", fill="white")

# Density curve
ggplot(df, aes(x=rating)) + geom_density()

# Histogram overlaid with kernel density curve
ggplot(df, aes(x=rating)) + geom_histogram(aes(y=..density..),      
# Histogram with density instead of count on y-axis
binwidth=.5,
colour="black", fill="white") +
geom_density(alpha=.2, fill="pink")  # Overlay with transparent density plot

# Basic histogram from the vector "rating". Each bin is .5 wide.

df <- data.frame(cond = factor( rep(c("A","B"), each=200) ), rating = c(rnorm(200),rnorm(200, mean=.5)))
###simulate data from mixed dist. 0.5N(0,1) + 0.5N(0.5,1)

df %>% ggvis(x=~rating) %>% layer_histograms(binwidth=.5)

# Draw with black outline, white fill
df %>% ggvis(x=~rating) %>% layer_histograms(binwidth=.5, fill:="white")

# Density curve
df %>% ggvis(x=~rating) %>% layer_densities()

# It is not currently possible to have a density and histogram on the same plot using ggvis