Visualisation of Indian Cricket Team's Test Match Performance (since 2000)

Often the test matches start on Thursday and end on Monday. As Monday is a working day, a lot of office goers face distraction from work as the last day of Test match is often the most exciting day with lots of action. This scheduling pattern of test matches is also evident in the ongoing India-New Zealand Kanpur Test match as well which began on 22nd September 2016, Thursday. 

Based on these thoughts, I have created a visualisation on the performance of the Indian Cricket Team in Test matched since 2000 and have tried to see if there is an observable pattern of results by last day of a test match.

Here is the link for visualisation:
https://public.tableau.com/profile/n.singh#!/

Observation:
Wins and narrow wins have occurred in matches ending on Monday.
I categorised any test win by less than 50 runs or by less than or equal to 3 wickets as a narrow win.

Feedback and comments are highly solicited. If you believe that there is a discrepancy in the visualisation, please also point out the same as well.

Thanks.

References:

1. ESPN Cricinfo http://stats.espncricinfo.com/india/engine/records/team/match_results_year.html?class=1;id=6;type=team 
2. Tableau 10 http://tableau.com 

Sentiment analysis of e-commerce firms in India: August 2016

Hello readers,

After a break of 2 months, the sentiment analysis series is back.
I hope you all must be doing very good.
Happy Ganesh Puja to my readers!

Table of sentiment analysis scores: [Note that Snapdeal's twitter handle did not respond to our request for tweets]

Sl. No.	Names	Total tweets	Positive tweets	Negative tweet	Sum	score
4	Jabong	8749	754	64	818	92
1	Amazon	6090	944	190	1134	83
2	eBay	1368	111	56	167	66
5	Myntra	1045	64	71	135	47
3	Flipkart	6986	383	723	1106	35

Total tweets: Total number of tweets returned from the official twitter handles
Positive tweets: Number of tweets that are either marked as positive or extremely positive
Negative tweets: Number of tweets that are either marked as negative or extremely negative
Sum: Sum of positive and negative tweets
Score: Positive tweets/ Total tweets

Chart of tweets categorised as positive to negative and the trend chart are as under (trend chart falls to zero for the past two months as I was not ranking the firms and has nothing to do with anything else):

(click to enlarge)

(click to enlarge)

References:

1. twitteR package for R

2. Original twitter handles of the e-commerce firms (Snapdeal's twitter handle did not respond to our request for tweets.)

3. Twitter API

4. R Programming Language