7 Creating a Dashboard - Part II

The Dashboard on this blog

Data Source

7 Creating a Dashboard - Part 1

Basic Column Chart Showing All Data

Data Source

3 Specifying Range of Data and Selecting Columns : Google Charts with Google Docs data

Creating a Google Chart using data pulled from Google Docs

Basic Google Chart

The first Pi-chart  and here i can put some text

Sample Codes for illustration of ggplot2

## Sample Codes for illustration of ggplot2
## Susheel Shukla 09-01-2017

### install & load ggplot library
install.package("ggplot2")
library("ggplot2")

### show info about the data
head(diamonds)
head(mtcars)



### comparison qplot vs ggplot                                                      

# qplot histogram
qplot(clarity, data=diamonds, fill=cut, geom="bar")                          

# ggplot histogram -> same output
ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar()

P.S. - both qplot and ggplot having same graph (as shown here)

### how to use qplot                                     

# scatterplot

qplot(wt, mpg, data=mtcars)

# transform input data with functions

qplot(log(wt), mpg - 10, data=mtcars)

# add aesthetic mapping (hint: how does mapping work)

qplot(wt, mpg, data=mtcars, color=qsec)

# change size of points (hint: color/colour, hint: set aesthetic/mapping)

qplot(wt, mpg, data=mtcars, color=qsec, size=3)

qplot(wt, mpg, data=mtcars, colour=qsec, size=I(3))  #aesthetics can be set to a constant value instead of mapping

# use alpha blending

qplot(wt, mpg, data=mtcars, alpha=qsec)        # values between 0 (transparent) and 1 (opaque)

# continuous scale vs. discrete scale

head(mtcars)

qplot(wt, mpg, data=mtcars, colour=cyl)

levels(mtcars$cyl)

qplot(wt, mpg, data=mtcars, colour=factor(cyl))

# use different aesthetic mappings

qplot(wt, mpg, data=mtcars, shape=factor(cyl))

qplot(wt, mpg, data=mtcars, size=qsec)

# combine mappings (hint: hollow points, geom-concept, legend combination)

qplot(wt, mpg, data=mtcars, size=qsec, color=factor(carb))

qplot(wt, mpg, data=mtcars, size=qsec, color=factor(carb), shape=I(1))

qplot(wt, mpg, data=mtcars, size=qsec, shape=factor(cyl), geom="point")

qplot(wt, mpg, data=mtcars, size=factor(cyl), geom="point")

# bar-plot

qplot(factor(cyl), data=mtcars, geom="bar")

# flip plot by 90°

qplot(factor(cyl), data=mtcars, geom="bar") + coord_flip() 

# difference between fill/color bars

qplot(factor(cyl), data=mtcars, geom="bar", fill=factor(cyl))

qplot(factor(cyl), data=mtcars, geom="bar", colour=factor(cyl))

                             

# fill by variable

qplot(factor(cyl), data=mtcars, geom="bar", fill=factor(gear))

# use different display of bars (stacked, dodged, identity)

head(diamonds)

qplot(clarity, data=diamonds, geom="bar", fill=cut, position="stack")

qplot(clarity, data=diamonds, geom="bar", fill=cut, position="dodge")

qplot(clarity, data=diamonds, geom="bar", fill=cut, position="fill")

qplot(clarity, data=diamonds, geom="bar", fill=cut, position="identity")

qplot(clarity, data=diamonds, geom="freqpoly", group=cut, colour=cut, position="identity")

qplot(clarity, data=diamonds, geom="freqpoly", group=cut, colour=cut, position="stack")

Quick Introduction to ggplot2

here

This is a bare-bones introduction to ggplot2, a visualization package in R. It assumes no knowledge of R.

Preview

Let’s start with a preview of what ggplot2 can do.

Given Fisher’s iris data set and one simple command…

## R Codes

head(iris)

library(ggplot2)

qplot(Sepal.Length, Petal.Length, data = iris, color = Species)

…we can produce this plot of sepal length vs. petal length, colored by species.

R Basics

Vectors

Vectors are a core data structure in R, and are created with c(). Elements in a vector must be of the same type.

numbers = c(23, 13, 5, 7, 31)

names = c("edwin", "alice", "bob")

Elements are indexed starting at 1, and are accessed with [] notation.

#indexing

numbers[1]

names[1]

Data frames

Data frames are like matrices, but with named columns of different types (similar to database tables).

books = data.frame(

  title = c("harry potter", "war and peace", "lord of the rings"), # column named "title"

  author = c("rowling", "tolstoy", "tolkien"),

  num_pages = c("350", "875", "500")

)

You can access columns of a data frame with $.

##column access

books$title

You can also create new columns with $.

ggplot2

install.packages("ggplot2")

library(ggplot2)

Scatterplots with qplot()

## scatterplot with qplot()

head(iris)   # by default head displys first 6 rows

head(iris, n=10)   # now first 10 rows

qplot(Sepal.Length, Petal.Length, data = iris)

# Plot Sepal.Length vs. Petal.Length, using data from the `iris` data frame.

# * First argument `Sepal.Length` goes on the x-axis.

# * Second argument `Petal.Length` goes on the y-axis.

# * `data = iris` means to look for this data in the `iris` data frame.    

##R Code:

plot(Sepal.Length, Petal.Length, data = iris, color = Species) #dude!

#Similarly, we can let the size of each point denote sepal width, by adding a size = Sepal.Width argument.

#R Code:

qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width)

# We see that Iris setosa flowers have the narrowest petals.

#R Code:

qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width, alpha = I(0.7))

# By setting the alpha of each point to 0.7, we reduce the effects of overplotting

##Finally, let's fix the axis labels and add a title to the plot.

#R Code:

qplot(Sepal.Length, Petal.Length, data = iris, color = Species,

      xlab = "Sepal Length", ylab = "Petal Length",

      main = "Sepal vs. Petal Length in Fisher's Iris data")

##Other common geoms

##In the scatterplot examples above, we implicitly used a point geom, the default when you supply two arguments to qplot().

# These two invocations are equivalent.

qplot(Sepal.Length, Petal.Length, data = iris, geom = "point")

qplot(Sepal.Length, Petal.Length, data = iris)

##Barcharts: geom = "bar"

movies = data.frame(

  director = c("spielberg", "spielberg", "spielberg", "jackson", "jackson"),

  movie = c("jaws", "avatar", "schindler's list", "lotr", "king kong"),

  minutes = c(124, 163, 195, 600, 187)

)

# Plot the number of movies each director has.

qplot(director, data = movies, geom = "bar", ylab = "# movies")

# By default, the height of each bar is simply a count.

# But we can also supply a different weight.

# Here the height of each bar is the total running time of the director's movies.

qplot(director, weight = minutes, data = movies, geom = "bar", ylab = "total length (min.)")

########### Line charts: geom = "line" ##################

qplot(Sepal.Length, Petal.Length, data = iris, geom = "line", color = Species)

# Using a line geom doesn't really make sense here, but hey.

# `Orange` is another built-in data frame that describes the growth of orange trees.

qplot(age, circumference, data = Orange, geom = "line",

      colour = Tree,

      main = "How does orange tree circumference vary with age?")

# We can also plot both points and lines.

qplot(age, circumference, data = Orange, geom = c("point", "line"), colour = Tree)

Please go through my next blog here to see ggplot2 in detail.

                                                                        ***