Book Review: Coalition Years

THE COALITION YEARS by Pranab Mukherjee


The book by Pranab Mukherjee is an ode to his political career. Through the course of the book,
Pranab Mukherjee comes out as an honest man assuming role the party expects from him. A true Congressmen, Pranab Da carries out the chores with arduous sincerity in the face of Coalition politics. The book can be seen in the light of the following points:

• The book starts with the fag end of Atal Ji's government and marks how Pranab Da reinvigorated the Congress party amid the possibility of an NDA-2. Having worked right from grass root levels to Party ranks, Pranab Da garnered respect of the partymen and knew how to connect with people at a personal level. His proximity to 10 Janpath gave gravity to his stature within party lines.An incident during the pre poll campaign in 2004 is of special mention where he visits a village in Left dominated West Bengal and interacts with a boy. Pranab Da assures him of a bund's construction as the first thing once congress comes into power
• His stint as a Defence minister saw an appreciation in defence allocation.During his tenure, he signed pacts with US as well as Russia on major defence cooperation. For most part, he focussed on modernisation of the warfare and transfer of defence technologies with a 'Make in India' thrust in mind .
• The controversial Nuclear deal materialised while he was the Minister of External Affairs . To bring US on the same page, he connected with condoleezza rice at a personal level and made sure that the sanctions put on India by the NSG were softened. This marked the beginning of a landmark era in India-US relationships
• Of all the hats he had donned, the one he had most experience was the Finance Ministry. Introduction of the retrospective tax act, his expansionary policies during 2010,2011 regime and his aggressive stance on implementation of GST, UID made him the man with an independent stance in an otherwise subservient congress
• One aspect according to me that made him class apart was his ability to maintain cordial relationship with all and sundry of the political class. I would like to mention two instances that describe his ability to connect with fellow politicians
○ During the run for president, Bal Thackeray rebuffed his party's stance and supported Pranab Da. As a gesture of gratitude, he went to meet Thackeray at his residence where the later quipped-" It is but obvious that a Maratha lion supports Bengal Tiger"
○ The much debated GST bill was eventually passed by both the houses in 2017 . Since Pranab Da shared cordial relationship with the PM, Narendra Modi gave a congratulatory call to apprise him of the development since he was a staunch supporter of the bill during his tenure as the Finance Minister.

In the end I would like to say that Pranab Mukherjee, in true regards, is the son of the soil. He is definitely the PM India never had


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Bog 12: Cumulative Sum in R

Cumulative Sum using R

Parag Verma

Introduction

In almost all business problems, the focus is always to look at key takeaways in any analyses.Most Academic disciplines teach a concept known as Pareto Principle which states that 80% of the effect comes from 20% of the causes(also read as cases). If we look at an analyses standpoint, it is always advisable to look at the LOW HANGIN FRUITS first. According to me, these low hanging fruits are established keeping Pareto Principle in mind.

The purpose of writing this blog is to introduce the concept of Cumulative sum in R that helps you deal with practical scenarios while working with datasets. Some of problems we face while handling data set are listed below:

• Create cumulative sum of Sales/Gross Margin/Net Cash flow/Cases around key Attributes such as COuntry, Product Category etc
• Identify groups/Combination that contribute to 80% of the Impact(Sales, Gross Margin etc)
• Identify Products that can help Sales Rep achieve Sales Target
• Quota Allocation exerise where quota is given based on relative contribution of each Product or Region

In this blog, we will look at how to proceed for each of the cases mentioned above

Installing the library: dplyr and tidyr

if(!require("dplyr")){
  
  install.packages("dplyr")
}else{
  
  library(dplyr)
}

if(!require("tidyr")){
  
  install.packages("tidyr")
}else{
  
  library(tidyr)
}

Importing the dataset

For this exercise we will look at the Cybersecurity breaches reported to the US Department of Health and Human Services.There are 1151 records in the data set with 9 variables. Our aim is to calculate the Percentage of Cases reported at each Sate at an Entity Level.Here we will look at the concept of cumulative sum in R

# Ecdat library for importing the dataset

if(!require("Ecfun")){
  
  install.packages("Ecfun",dependencies = T)
}else{
  
  library(Ecfun)
}


if(!require("Ecdat")){
  
  install.packages("Ecdat",dependencies = T)
}else{
  
  library(Ecdat)
}

data(HHSCyberSecurityBreaches)
df<-HHSCyberSecurityBreaches
head(df[,1:3])%>%
knitr::kable()

Name.of.Covered.Entity	State	Covered.Entity.Type
Brooke Army Medical Center	TX	Healthcare Provider
Mid America Kidney Stone Association, LLC	MO	Healthcare Provider
Alaska Department of Health and Social Services	AK	Healthcare Provider
Health Services for Children with Special Needs, Inc.	DC	Health Plan
L. Douglas Carlson, M.D.	CA	Healthcare Provider
David I. Cohen, MD	CA	Healthcare Provider

The names of the columns are:

• Name.of.Covered.Entity:A character vector identifying the organization involved in the breach.
• State: State abbreviation
• Covered.Entity.Type: A factor giving the organization type of the covered entity
• Individuals.Affected: An integer giving the number of humans whose records were compromised in the breach.
• Breach.Submission.Date
• Type.of.Breach: A factor giving one of 29 different combinations of 7 different breach types,
• Location.of.Breached.Information: A factor giving one of 47 different combinations of 8 different location categories: “Desktop Computer”, “Electronic Medical Record”, “Email”, “Laptop”, “Network Server”, “Other”, “Other Portable Electronic Device”, “Paper/Films”
• Business.Associate.Present:Logical = (Covered.Entity.Type == “Business Associate”)
• Web.Description: A character vector giving a narrative description of the incident.

Percentage Cases within Each State from Entity

df.interim<-df%>%
  group_by(Covered.Entity.Type,State)%>%
  summarise(Total_Cases=sum(Individuals.Affected))%>%
  arrange(Covered.Entity.Type,desc(Total_Cases))%>%
  ungroup()%>%
  group_by(Covered.Entity.Type)%>%
  mutate(Perc_Contribution=round(100*Total_Cases/sum(Total_Cases),1))

knitr::kable(df.interim%>%
               head())

Covered.Entity.Type	State	Total_Cases	Perc_Contribution
Business Associate	VA	5093217	22.7
Business Associate	TN	4547412	20.3
Business Associate	NJ	2964033	13.2
Business Associate	TX	2582619	11.5
Business Associate	NY	2033804	9.1
Business Associate	PR	1069166	4.8

Here we can see that the records are only related to Business Associate since I took head of the resultant data frame. Lets now look at top 6 records within each group

Top 5 records from each COnvered Entity Type

Top5<-df.interim%>%
  mutate(Index=1:n())%>%
  filter(Index<6)%>%
  select(-Index)

knitr::kable(Top5)

Covered.Entity.Type	State	Total_Cases	Perc_Contribution
Business Associate	VA	5093217	22.7
Business Associate	TN	4547412	20.3
Business Associate	NJ	2964033	13.2
Business Associate	TX	2582619	11.5
Business Associate	NY	2033804	9.1
Health Plan	FL	1240326	23.0
Health Plan	TN	1110698	20.6
Health Plan	MT	1062509	19.7
Health Plan	TX	568640	10.6
Health Plan	NY	390830	7.3
Healthcare Clearing House	FL	10000	56.3
Healthcare Clearing House	SC	4204	23.7
Healthcare Clearing House	PA	2300	13.0
Healthcare Clearing House	WA	1250	7.0
Healthcare Provider	IL	4335571	32.5
Healthcare Provider	CA	2098208	15.7
Healthcare Provider	FL	1583608	11.9
Healthcare Provider	AL	977995	7.3
Healthcare Provider	GA	491932	3.7

Lets now look at States contributing to 80% of the Grievances within each Entity Type

States Contributing to 80% of the Cases

I will break this into two steps.

• First we will calcualte the Cumulative Percentage
• Second we will filter out values less than 80%

Perc_80<-df.interim%>%
  mutate(CumulativePerc_Contribution=cumsum(Perc_Contribution))%>% # First Step
  filter(CumulativePerc_Contribution < 80) # Second Step

knitr::kable(Perc_80)

Covered.Entity.Type	State	Total_Cases	Perc_Contribution	CumulativePerc_Contribution
Business Associate	VA	5093217	22.7	22.7
Business Associate	TN	4547412	20.3	43.0
Business Associate	NJ	2964033	13.2	56.2
Business Associate	TX	2582619	11.5	67.7
Business Associate	NY	2033804	9.1	76.8
Health Plan	FL	1240326	23.0	23.0
Health Plan	TN	1110698	20.6	43.6
Health Plan	MT	1062509	19.7	63.3
Health Plan	TX	568640	10.6	73.9
Healthcare Clearing House	FL	10000	56.3	56.3
Healthcare Provider	IL	4335571	32.5	32.5
Healthcare Provider	CA	2098208	15.7	48.2
Healthcare Provider	FL	1583608	11.9	60.1
Healthcare Provider	AL	977995	7.3	67.4
Healthcare Provider	GA	491932	3.7	71.1
Healthcare Provider	TN	467261	3.5	74.6
Healthcare Provider	TX	341041	2.6	77.2
Healthcare Provider	NY	334068	2.5	79.7

Final Comments

We have seen how we can use group_by and cumsum function to get an idea of important things at an overall level.This helps us gain an understanding of States driving the numbers at Entity Level

Link to Previous R Blogs

https://ml-withparag.com/

List of Datasets for Practise

https://hofmann.public.iastate.edu/data_in_r_sortable.html

https://vincentarelbundock.github.io/Rdatasets/datasets.html