Kolkata Map

Geo Mapping

Basic Charts

1. Pie Chart

Topping	Slices
Mushrooms	3
Onions	1
Olives	1
Zucchini	1
Pepperoni	2

2. Donut Chart

Task	Hours per Day
Work	11
Eat	2
Commute	2
Watch TV	2
Sleep	7

3.  Histogram

Dinosaur	Length
Acrocanthosaurus (top-spined lizard)	12.2
Albertosaurus (Alberta lizard)	9.1
Allosaurus (other lizard)	12.2
Apatosaurus (deceptive lizard)	22.9
Archaeopteryx (ancient wing)	0.9
Argentinosaurus (Argentina lizard)	36.6
Baryonyx (heavy claws)	9.1
Brachiosaurus (arm lizard)	30.5
Ceratosaurus (horned lizard)	6.1
Coelophysis (hollow form)	2.7
Compsognathus (elegant jaw)	0.9
Deinonychus (terrible claw)	2.7
Diplodocus (double beam)	27.1
Dromicelomimus (emu mimic)	3.4
Gallimimus (fowl mimic)	5.5
Mamenchisaurus (Mamenchi lizard)	21
Megalosaurus (big lizard)	7.9
Microvenator (small hunter)	1.2
Ornithomimus (bird mimic)	4.6
Oviraptor (egg robber)	1.5
Plateosaurus (flat lizard)	7.9
Sauronithoides (narrow-clawed lizard)	2
Seismosaurus (tremor lizard)	45.7
Spinosaurus (spiny lizard)	12.2
Supersaurus (super lizard)	30.5
Tyrannosaurus (tyrant lizard)	15.2
Ultrasaurus (ultra lizard)	30.5
Velociraptor (swift robber)	1.8

4.  Line Graph

Year	Sales	Expenses
2004	1,000	400
2005	1,170	460
2006	660	1,120
2007	1,030	540

Geo Visualization

South Asia

Visualization Class 2

 Introduction

ggplot is the grammar of graphics created by Hadley Wickham. It has significantly reduced the difficulty of creating plots. We create plots or graphics to understand the characteristics of the features that we’re going to use in modelling or simple understanding for business scenario. This post is created for people who are new to ggplot but have knowledge of R. To create the plots, we are going to use the data of iris setosa classification scenario.

Understanding qplot

qplot is used when we want to create a plot without much tweaking of graphics. We leave the graphics to the ggplot library’s default settings. And it gives more colourful plots when we compare with the default plot that we get in R.

Scatter Plot

# Plotting the Sepal length vs Petal Length of the iris dataset.
qplot(Sepal.Length, Petal.Length, data = iris)

# Plotting the same as above but also adding a new dimension.
qplot(Sepal.Length, Petal.Length, data = iris, color = Species)

# Adding one more dimension of size of the bubble or the point.
qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width)

# Adding a little bit more transparency effect to avoid showing the overlapping. Alpha has to be added to include the transparency parameter.
qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width, alpha = I(0.7))

Now we will Create Our own Dataset to visualise and learn different Features of ggplot.

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

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

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

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)