wDCPlot with mtcars

part10.R

# We don't want a numeric vector. We want a data.frame slice.
head(mtcars["mpg"])

part11.R

# Did we do it right? What's the type/class?
class(mtcars["mpg"])

part12.R

# How do I get more than one column?
head(mtcars[c("mpg","hp")])

part13.R

# How do we know the row names?
row.names(mtcars)

part14.R

# How do we access a specific value?
mtcars["Mazda RX4", "cyl"] 

# OR
mtcars[1,2]

part15.R

# How do I access a row?
mtcars["Mazda RX4", ] 

# Also the same as ...
mtcars[1, ]

part16.R

# Vehicles with automatic transmission?
head(mtcars[mtcars$am==0,])

part17.R

# mileage for automatic transmission vehicles
mtcars[mtcars$am==0,]$mpg

part18.R

# Let's know our data better
summary(mtcars)

part19.R

# We don't want to keep typing mtcars$... so we use attach
attach(mtcars)

# You can now directly refer mtcars$mpg as mpg
identical(mtcars$mpg, mpg)

part2.R

# Let's load the popular mtcars dataset. It comes pre-bundled with R
data(mtcars)

part20.R

# Let's summarize our data
table(cyl)

part21.R

# Plotting is easy
barplot(table(cyl))

part22.R

# OR a histogram
hist(mpg)

part23.R

# OR a boxplot
boxplot(mpg)

part25.R

# Looks like mpg are pretty low
mean(mpg)

part26.R

# Mean of mileage for 4 cylinder cars
mean(mpg[cyl==4])

part27.R

# vs 8 cylinder cars
mean(mpg[cyl==8])

part28.R

# Is that because of a higher number of cylinders?
plot(cyl, mpg)

part29.R

# Let's fit a regression line
lm(mpg~cyl+hp)

# Pearson correlation, is that a good fit?
cor(mpg,cyl)^2
cor(mpg, hp)^2

part30.R

# Let's plot based on number of cylinders
plot(hp,mpg,pch=19, col=cyl)
# add a legend
legend(250, 30, pch=19, col=c(4,6,8), legend=c("4 cylinders","6 cylinders","8 cylinders"))  

part31.R

#Now we do the cool stuff

part33.md

wdcplot(mtcars, 
dimensions(..index.., mpg, wt, cyl),
groups(mpgGroup = group(mpg, bin(2)),
       wtGroup = group(wt, group = bin(0.5)),
       cylinders = group(cyl)),
charts(SWvL = bubble('Miles Per Gallon vs. Weight',
                     dimension = ..index..,
                     color = cyl,  
                     x = wt,
                     y = mpg,
                     r = ..selected.. * 3,
                     label = NULL),
       cyl = pie("Cylinders",group = cylinders),
       PW = bar('Miles Per Gallon', group = mpgGroup, x.domain = c(10.4,33.9)),
       PL = bar('Weight', group = wtGroup, x.domain = c(1.5,5.5))
))

part34.R

detach(mtcars)

part4.R

# What does the data look like?
head(mtcars)

part5.R

# Looks like mtcars is a data frame
class(mtcars)

part6.R

# So the structure is ...
str(mtcars)

part7.R

# What are the total number of rows?
nrow(mtcars)

part8.R

# How do we access a column?
head(mtcars$mpg)

part9.R

# What's the type/class?
class(mtcars$mpg)

# OR
head(mtcars[,1])

# OR
head(mtcars[, "mpg"])

scratch.R

# scratch file