Session 8 :Visualization :Tableau Getting Started

Session 6: Plotting addresses on Google Maps using R and R Google Maps

   Plotting Addresses on Google Maps using R and R GoogleMaps

In an earlier post, we had shown how locations as defined by addresses could be plotted on Google Maps but the process was rather complicated. First, the geocoding had to be done separately and secondly a lot of messy Javascript had to be coded by hand. Finally the map that was produced could only be viewed with an internet connection.

Session 6 :Visualization : Using R for Maps of India - state, district, taluka level maps

     Using R for Maps of India - state, district, taluka level maps

Displaying spatial data on maps is always interesting but most Visualisation tools do not offer facilities to create maps of India, especially at the state and lower levels. In this post, we will show how such maps can be made.The base data for such maps, the "polygons" that define the country, the states, the districts and even the talukas ( or sub-divisions) is available from an organisation called Global Administrative Areas or gadm.org. Country level files for almost all countries are available in a variety of formats including R and these are at three different levels. For India, these files can be downloaded as IND_admN.RData where N = 1,2,3. These will form the raw data from which we will create our maps.

Session 7 :Visualization : Python /Seaborne Multiple Linear Regression

Seaborn: statistical data visualization

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.

Some of the Features that seaborn offer are 

• Several built-in themes that improve on the default matplotlib aesthetics
• Tools for choosing color palettes to make beautiful plots that reveal patterns in your data
• Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data
• Tools that fit and visualize linear regression models for different kinds of independent and dependent variables
• Functions that visualize matrices of data and use clustering algorithms to discover structure in those matrices
• A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate
• High-level abstractions for structuring grids of plots that let you easily build complex visualizations

Seaborn aims to make visualization a central part of exploring and understanding data. The plotting functions operate on dataframes and arrays containing a whole dataset and internally perform the necessary aggregation and statistical model-fitting to produce informative plots. If matplotlib “tries to make easy things easy and hard things possible”, seaborn tries to make a well-defined set of hard things easy 

Grouped barplots

import seaborn as sns
sns.set(style="whitegrid")

# Load the example Titanic dataset
titanic = sns.load_dataset("titanic")

# Draw a nested barplot to show survival for class and sex
g = sns.factorplot(x="class", y="survived", hue="sex", data=titanic,
                   size=6, kind="bar", palette="muted")
g.despine(left=True)
g.set_ylabels("survival probability")

Codes for  Violinplot

import seaborn as sns

import matplotlib.pyplot as plt

sns.set(style="whitegrid")

# Load the example dataset of brain network correlations

df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)

# Pull out a specific subset of networks

used_networks = [1, 3, 4, 5, 6, 7, 8, 11, 12, 13, 16, 17]

used_columns = (df.columns.get_level_values("network")

                          .astype(int)

                          .isin(used_networks))

df = df.loc[:, used_columns]

# Compute the correlation matrix and average over networks

corr_df = df.corr().groupby(level="network").mean()

corr_df.index = corr_df.index.astype(int)

corr_df = corr_df.sort_index().T

# Set up the matplotlib figure

f, ax = plt.subplots(figsize=(11, 6))

# Draw a violinplot with a narrower bandwidth than the default

sns.violinplot(data=corr_df, palette="Set3", bw=.2, cut=1, linewidth=1)

# Finalize the figure

ax.set(ylim=(-.7, 1.05))

sns.despine(left=True, bottom=True)

Session 5 - D3J Visualization

Stacked-to-Grouped Bars

Switch between stacked and grouped layouts using sequenced transitions. Animations preserve object constancy and allow the user to follow the data across views. 

Session 4 - Creating a dashboard Part I

Creating a Dashboard - Part 1

When  you have a lot of data to be shown on a page, it makes sense to give the viewer an opportunity to filter some of the data so that he or she gets a cleaner view. In this case, we will first draw a rather clumsy Column Chart and then in the next section. The data for the chart is drawn from this spreadsheet. The chart shown below can also be seen in this regular HTML page.

Data Source 

Note how we have specified

• the  Google Docs spreadsheet : https://docs.google.com/a/yantrajaal.com/spreadsheet/ccc?key=0AhEX55Pfl1eedExUbS1xNzBuQVAyNDJTeG1weFQxbXc
• the sheet : sheet=MilkProduction
• range : range=B2:H37
• headers : headers=1
• columns : query.setQuery('select B,E,F');
• chart type : var chartMQ = new google.visualization.ColumnChart(document.getElementById('chart_divMQ'))

Basic Column Chart Showing All Data

Data Source

Session 6 -Plotting Cities in a Map

 Plotting Cities on a Map along with Data

In the code please observe the following points

1. The spreadsheet doc is identified as https://docs.google.com/a/yantrajaal.com/spreadsheet/ccc?key=0AhEX55Pfl1eedE5mUHN4QVRLNzdldEpwZnAyNy1abUE
2. The sheets as identified by sheet=CityWise2
3. The range is identified by range=B2:M23
4. The columns being selected are identified by query.setQuery('select B,D,E');
5. The region in the map is 'IN' that is India
6. The displayMode is "markers" .. which means towns and cities, had this been region we would have got full states.

Session 4 : Creating a Dashboard - Part II

Creating a Dashboard - Part II

Here we take the same data used in the previous post and convert it into a dashboard. We have replaced the chart with a chart wrapper. Added three filters. Added a dashboard component and bound the three filters to the chart wrapper. You can see the result both in this blog as well as on this regular HTML page.

The Dashboard on this blog

Data Source

Session 4 : The best of R and Google Charts

Data: StateMilk • Chart ID: GeoChartID9147ed26b9d • googleVis-0.6.2
R version 3.2.5 (2016-04-14) • Google Terms of Use • Documentation and Data Policy

Data: data • Chart ID: BarChartID914b485d8e • googleVis-0.6.2

Session 4:Visualisation:Specifying Range of Data and Selecting Columns:Google charts with Google Docs data

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

In the previous post, we had described how to create a basic Google Chart from data stored in a Google Doc spreadsheet. In that example, the data was picked up from the default first sheet of the spreadsheet and the data was located in the top left corner. In reality, the data could be stored in any of the other sheets and could be located in any portion of the sheet.

In this example, the Google Docs spreadsheet, has four sheets. For the purpose of drawing our chart we would like to specify that

1. Data to be picked up from sheet named "Demo3"
2. Within this sheet,  from the range C3:I23
3. Within this range from the columns C, D, G, H
4. Given the nature of the data we would like to multiply column G by 1000 before it is plotted

Data Visualization in R

                BASIC VISUALIZATIONS

1. Basic graphs in R can be created quite easily. The plot command is the command to note.
2. It takes in many parameters from x axis data , y axis data, x axis labels, y axis labels, color and title. To create line graphs, simply use the parameter, type=l.
3. If you want a boxplot, you can use the word boxplot, and for barplot use the barplot function.

      Basic Visualization which we are going to perform here

1. Histogram
2. Bar / Line Chart
3. Box plot
4. Scatter plo

1. Histogram

Histogram is basically a plot that breaks the data into bins (or breaks) and shows frequency distribution of these bins.  You can change the breaks also and see the effect it has data visualization in terms of understandability.

Let me give you an example.

Note: We have used par(mfrow=c(2,5)) command to fit multiple graphs in same page for sake of clarity( see the code below).

The following commands show this in a better way. In the code below, the main option sets the Title of Graph and the col option calls in the color pallete from RColorBrewer to set the colors.

library(RColorBrewer)

data(VADeaths)
par(mfrow=c(2,3))
hist(VADeaths,breaks=10, col=brewer.pal(3,"Set3"),main="Set3 3 colors")
hist(VADeaths,breaks=3 ,col=brewer.pal(3,"Set2"),main="Set2 3 colors")
hist(VADeaths,breaks=7, col=brewer.pal(3,"Set1"),main="Set1 3 colors")
hist(VADeaths,,breaks= 2, col=brewer.pal(8,"Set3"),main="Set3 8 colors")
hist(VADeaths,col=brewer.pal(8,"Greys"),main="Greys 8 colors")
hist(VADeaths,col=brewer.pal(8,"Greens"),main="Greens 8 colors")

2. Bar/ Line Chart

Line Chart

Below is the line chart showing the increase in air passengers over given time period. Line Charts are commonly preferred when we are to analyse a trend spread over a time period. Furthermore, line plot is also suitable to plots where we need to compare relative changes in quantities across some variable (like time). Below is the code:

plot(AirPassengers,type="l")  #Simple Line Plot

Bar Chart

Bar Plots are suitable for showing comparison between cumulative totals across several groups.  Stacked Plots are used for bar plots for various categories. Here’s the code:

barplot(iris$Petal.Length) #Creating simple Bar Graph
barplot(iris$Sepal.Length,col  = brewer.pal(3,"Set1"))
barplot(table(iris$Species,iris$Sepal.Length),col  = brewer.pal(3,"Set1")) #Stacked Plot

3. Box Plot ( including group-by option )


Box Plot  shows 5 statistically significant numbers- the minimum, the 25th percentile, the median, the 75th percentile and the maximum. It is thus useful for visualizing the spread of the data is and deriving inferences accordingly. Here’s the basic code:
boxplot(iris$Petal.Length~iris$Species) #Creating Box Plot between two variable

Let’s understand the code below:


In the example below,  I have made 4 graphs in one screen. By using the ~ sign, I can visualize how the spread (of Sepal Length)  is across various categories ( of Species). In the last two graphs I have shown the example of color palettes. A color palette is a group of colors that is used to make the graph more appealing  and helping create visual distinctions in the data.


  data(iris)

  par(mfrow=c(2,2))

  boxplot(iris$Sepal.Length,col="red")

  boxplot(iris$Sepal.Length~iris$Species,col="red")

  boxplot(iris$Sepal.Length~iris$Species,col=heat.colors(3))

  boxplot(iris$Sepal.Length~iris$Species,col=topo.colors(3))















4. Scatter Plot (including 3D and other features)


Scatter plots help in visualizing data easily and for simple data inspection.  Here’s the code for simple scatter and multivariate scatter plot:



plot(x=iris$Petal.Length) #Simple Scatter Plot



plot(x=iris$Petal.Length,y=iris$Species) #Multivariate Scatter Plot



Scatter Plot Matrix can help visualize multiple variables across each other.

plot(iris,col=brewer.pal(3,"Set1"))

DETAILED INTRODUCTION -GGPLOT2

Detailed Introduction

                                                       DETAILED INTRODUCTION

About the ggplot2 Package

Introduction "ggplot2 is an R package for producing statistical, or data, graphics, but it is unlike most other graphics packages because it has a deep underlying grammar. This grammar, based on the Grammar of Graphics (Wilkinson, 2005), is composed of a set of independent components that can be composed in many different ways. [..] Plots can be built up iteratively and edited later. A carefuly chosen set of defaults means that most of the time you can produce a publication-quality graphic in seconds, but if you do have speical formatting requirements, a comprehensive theming system makes it easy to do what you want. [..] ggplot2 is designed to work in a layered fashion, starting with a layer showing the raw data then adding layers of annotation and statistical summaries.
 ggplot2,

First We look into the header of Diamonds and Mtcars 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()

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))

                                                          Use alpha blending

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

                          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))

                   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")