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.