Jun 11

I came across Randy Olson’s (editor at @DataIsBeautiful) tweet about causes of death in the US.

I thought I would replicate the information here using R and localize it for Kuwait – nothing fancy.

I will keep the code to the end because I think the results are quite interesting. The data source is from the UN Data portal

Here’s Randy Olson’s Tweet before we get going:

```
```Less misleading remake of "How we #Die: Comparing the causes of death in 1900 vs. 2010." #health #dataviz pic.twitter.com/xw0AJ5tYRA

— Randy Olson (@randal_olson) June 9, 2014

I always had the impression that the biggest issues with mortality in Kuwait were car accidents. Perhaps this is a bias introduced by media coverage. I always thought that if someone managed to survive their entire lives in Kuwait without dying in an accident, then only one other thing could get them ~ and that was cancer. This is not far from the truth:

What did catch me off guard is the number of people who die from circulatory system disease and heart disease. The numbers are not only large, but the trump both accidents and cancer figures. Interestingly enough, respiratory system diseases start to show up in 2006 just as problems with circulatory and pulmonary problems become more prevalent.

I thought that this surely is controlled within a demographic group.

So I decided to split the data into gender and age.

Looking at the gender differences the first eye-popping fact is that less women seem to be dying … this is misleading because the population is generally bias towards men. There are about 9 men for every 5 women in Kuwait.

The other eye-popping item that appears is in accidents. Less women pass away from accidents compared to men – a lot less! Is this indicative that women are safer drivers than their counterparts? Perhaps. In some nations this figure would indeed be zero because of social and legal constraints … it’s not necessarily good news … but it does stand out!

Proportionally there is a higher rate of mortality due to cancer in the female population vs. the male population.

Lastly, men seem to be more susceptible to death from heart diseases and circulatory system diseases. This might make you think why? Heart diseases and circulatory system diseases are exacerbated by sedentry life styles, poor diets, and other factors such as the consumption of tobacco. We have already looked at obesity in Kuwait … perhaps a deeper dive might shed some light on this matter.

For now, lets move on … surely younger people do not suffer from heart diseases …

This one confirms that if an accident does not get you before you’re 25 then the rest of the diseases are coming your way.

People fall victim to circulatory, respiratory, and heart diseases at extremely young ages. In fact what we see here is that irrespective of age group, after the age of 40 the mortality rates are the same for these three diseases.

On the other hand, accident mortalities go down as people shift to older age groups but are displaced by cancer. What is terribly depressing about this graph are the number of people below the ages of 19 that die in accidents. These might be just numbers, but in reality these are very real names to families.

The graphs were just a fun way to play with R. What we can take away is that of the 5 main causes for mortality in Kuwait – Cancer, Heart Diseases, Circulatory Diseases, Respiratory Diseases, and Accidents – 4 of them are addressible through policy, regulation, and raising public awareness for social/behavioural impact.

You can download the Kuwait dataset here or from the UN’s Data Portal.

Load up R and run the code below – fully commented for your geeky enjoyment:

# In Libraries we Trust library(ggplot2) library(dplyr) # Read Data data.mortality<-read.csv('kuwait.csv') ################## # Data Munging # ################## # Change Year into a categorical variable data.mortality$Year<-as.factor(data.mortality$Year) # Rename our columns names(data.mortality)<-c("Country","Year","Area","Sex","Age","Cause","Record","Reliability","Source.Year","Value","Value.Notes") # Data cleaning, here we groups some items together for simplification. # Eg. All forms of neoplasms are grouped under "Cancer". data.mortality$Cause<-gsub(pattern=", ICD10","",x=data.mortality$Cause) data.mortality[grep(pattern="eoplasm",x=data.mortality$Cause),]$Cause<-"Cancer" data.mortality[grep(pattern="ccidents",x=data.mortality$Cause),]$Cause<-"Accidents" data.mortality[grep(pattern="not elsewhere classified",x=data.mortality$Cause),]$Cause<-"Unknown" data.mortality[grep(pattern="External causes",x=data.mortality$Cause),]$Cause<-"Unknown" data.mortality[grep(pattern="Congenital malformations",x=data.mortality$Cause),]$Cause<-"Congenital malformations" data.mortality[grep(pattern="Certain conditions originating in the perinatal period",x=data.mortality$Cause),]$Cause<-"Perinatal period conditions" data.mortality[grep(pattern="Endocrine, nutritional and metabolic",x=data.mortality$Cause),]$Cause<-"Endocrine, Nutritional & Metabolic" data.mortality[grep(pattern="Diseases of the respiratory",x=data.mortality$Cause),]$Cause<-"Respiratory Disease" data.mortality[grep(pattern="Diseases of the circulatory system",x=data.mortality$Cause),]$Cause<-"Circulatory System" data.mortality[grep(pattern="Hypertensive diseases",x=data.mortality$Cause),]$Cause<-"Cerebral" data.mortality[grep(pattern="Ischaemic heart diseases",x=data.mortality$Cause),]$Cause<-"Heart Diseases" data.mortality[grep(pattern="Cerebrovascular diseases",x=data.mortality$Cause),]$Cause<-"Cerebral" ######################### # Data Transformations # ######################### # Use the dplyr library to group items from the original data set # We want a general understanding of causes of death # We subset out All causes to avoid duplication # We subset out age groups that cause duplication # We group by Country, Year and Cause # We create a summary variable "Persons" that is the sum of the incidents # We sort by Cause for pretty graphs kw.general <- data.mortality %.% subset(!(Cause %in% "All causes")) %.% subset(Country %in% "Kuwait") %.% subset(!(Age %in% c("Total","Unknown","85 +","95 +","1","2","3","4","0"))) %.% group_by(Country) %.% group_by(Year) %.% group_by(Cause) %.% summarise(Persons=sum(Value)) %.% arrange(Cause) # We want an understanding of causes of death by age group # We subset out All causes to avoid duplication # We subset out age groups that cause duplication # We group by Country, Year, Age and Cause # We create a summary variable "Persons" that is the sum of the incidents # We sort by Cause for pretty graphs kw.age<-data.mortality %.% subset(!(Cause %in% "All causes")) %.% subset(Country %in% "Kuwait") %.% subset(!(Age %in% c("Total","Unknown","85 +","95 +","1","2","3","4","0"))) %.% group_by(Country) %.% group_by(Year) %.% group_by(Age) %.% group_by(Cause) %.% summarise(Persons=sum(Value)) %.% arrange(Cause) # We reorder our age groups manually for pretty graphs kw.age$Age<-(factor(kw.age$Age,levels(kw.age$Age)[c(1,2,6,9,12,3,15,4,5,7,8,10,11,13,14,16:28)])) # We want an understanding of causes of death by gender # We subset out All causes to avoid duplication # We subset out age groups that cause duplication # We group by Country, Year, Gender and Cause # We create a summary variable "Persons" that is the sum of the incidents # We sort by Cause for pretty graphs kw.sex<-data.mortality %.% subset(!(Cause %in% "All causes")) %.% subset(Country %in% "Kuwait") %.% subset(!(Age %in% c("Total","Unknown","85 +","95 +","1","2","3","4","0"))) %.% group_by(Country) %.% group_by(Year) %.% group_by(Sex) %.% group_by(Cause) %.% summarise(Persons=sum(Value)) %.% arrange(Cause) ######################################## # Graphing, Plotting, Dunking Cookies # ######################################## # We will limit our graphs by number of persons each incidents cause # The main reason is because we do not want a graph that looks like a chocolate chip cookie PersonLimit <- 200 # Plot the General data General<-ggplot(subset(kw.general,Persons>=PersonLimit), aes(x = Year, y = Persons)) + geom_bar(aes(fill=Cause),stat='identity', position="stack")+ ggtitle(paste("Causes of Death in Kuwait\n\n(Showing Causes Responsible for the Death of ",PersonLimit," Persons or More)\n\n"))+ theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.text=element_text(size=14), axis.text=element_text(size=12), axis.title=element_text(size=12))+ scale_fill_brewer( palette = "RdBu")+ geom_text(aes(label = Persons,y=Persons,ymax=Persons), size = 3.5, vjust = 1.5, position="stack",color=I("#000000")) # Reset the Person Limit PersonLimit <- 150 # Plot the Gender data faceted by Gender Gender<-ggplot(subset(kw.sex,Persons>=PersonLimit), aes(x = Year, y = Persons)) + geom_bar(aes(fill=Cause),stat='identity', position="stack")+ facet_wrap(~Sex)+ ggtitle(paste("Causes of Death in Kuwait by Gender\n\n(Showing Causes Responsible for the Death of ",PersonLimit," Persons or More)\n\n"))+ theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.text=element_text(size=14), axis.text=element_text(size=12), axis.title=element_text(size=12) )+ scale_fill_brewer( palette = "RdBu" )+ geom_text(aes(label = Persons,y=Persons,ymax=Persons), size = 3.5, vjust = 1.5, position="stack",color=I("#000000")) # Reset the Person Limit PersonLimit <- 30 # Plot the Age group data facted by Age Age<-ggplot(subset(kw.age,Persons>=PersonLimit), aes(x = Year, y = Persons)) + geom_bar(aes(fill=Cause),stat='identity', position="stack")+ facet_wrap(~Age)+ ggtitle(paste("Causes of Death in Kuwait by Age Group\n\n(Showing Causes Responsible for the Death of ",PersonLimit," Persons or More)\n\n"))+ theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.text=element_text(size=14), axis.text=element_text(size=12), axis.title=element_text(size=12) )+ scale_fill_brewer( palette = "RdBu" ) # Save all three plots ggsave(filename="General.png",plot=General,width=12,height=10) ggsave(filename="Age.png",plot=Age,width=12,height=10) ggsave(filename="Gender.png",plot=Gender,width=12,height=10) |

Jun 10

We covered Price Elasticity in an accompanying post. In this post we will look at how we can use this information to analyse our own product and cross product elasticity.

The scenario is as follows:

You are the owner of a corner mom and pop shop that sells eggs and cookies. You sometimes put a poster on your storefront advertising either your fresh farm eggs, or your delicious chocolate chip cookies. You are particularly concerned with the sales off eggs – your beautiful farm chicken would be terribly sad if they knew that their eggs were not doing so well.

Over a one month period, you collect information on sales of your eggs and the different prices you set for your product.

We are using a modified version of Data Apple‘s data set.

You can download the supermarket data set here. In it you will find:

**Sales**: the total eggs sold on the given day**Price Eggs**: the price of the eggs on the day**Ad Type**: the type of poster – 0 is the egg poster, 1 is the cookie poster.**Price Cookies**: the price of cookies on the day

Lets fire up R and load up the data

# Load data and output summary stats sales.data<-read.csv('supermarket.csv') #Look at column names and their classes sapply(sales.data,class) |

The output shows each column and its type:

Sales Price.Eggs Ad.Type Price.Cookies "integer" "numeric" "integer" "numeric" |

Since Ad.Type is a categorical variable, lets go ahead and change that and output the summary statistics of our dataset.

# Change Ad Type to factor sales.data$Ad.Type<-as.factor(sales.data$Ad.Type) summary(sales.data) |

From the results we find that:

- Sales of eggs ranged between 18 and 46
- Price of eggs ranged between $3.73 and $4.77
- We showed the egg poster 15 times and the cookies poster 15 times
- Price of cookies ranged between $4 and $4.81

Sales Price.Eggs Ad.Type Price.Cookies Min. :18.0 Min. :3.73 0:15 Min. :4.00 1st Qu.:25.2 1st Qu.:4.35 1:15 1st Qu.:4.17 Median :28.5 Median :4.48 Median :4.33 Mean :30.0 Mean :4.43 Mean :4.37 3rd Qu.:33.8 3rd Qu.:4.67 3rd Qu.:4.61 Max. :46.0 Max. :4.77 Max. :4.81 |

Right now we want to see if we can predict the relationship between Sales of Eggs, and everything else.

We now want to run a regression and then do some diagnostics on our model before getting to the good stuff.

We can run the entire regression or add each variable to see the impact on the regression model. Since we have few predictors lets choose the latter option for fun.

# Create models m1<-lm(formula=Sales~Price.Eggs,data=sales.data) m2<-update(m1,.~.+Ad.Type) m3<-update(m2,.~.+Price.Cookies) mtable(m1,m2,m3) |

The results are pasted below. We end up with a model “m3” that has statistically significant predictors. Our model is:

**Sales of Eggs = 137.37 – (16.12)Price.Eggs + 4.15 (Ad.Type) – (8.71)Price.Cookies **

We look at our R^{2} and see that the regression explains 88.6% of the variance in the data. We also have a low mean squared error (2.611) compared to the other models we generated.

We can actually get better results by transforming our independent and dependent variables (e.g. LN(Sales)) but this will suffice for demonstrating how we can use regressions to calculate price elasticity.

Calls: m1: lm(formula = Sales ~ Price.Eggs, data = sales.data) m2: lm(formula = Sales ~ Price.Eggs + Ad.Type, data = sales.data) m3: lm(formula = Sales ~ Price.Eggs + Ad.Type + Price.Cookies, data = sales.data) ------------------------------------------------ m1 m2 m3 ------------------------------------------------ (Intercept) 115.366*** 101.571*** 137.370*** Price.Eggs -19.286*** -16.643*** -16.118*** Ad.Type: 1/0 4.195** 4.147*** Price.Cookies -8.711*** ------------------------------------------------ R-squared 0.722 0.793 0.886 adj. R-squared 0.712 0.778 0.872 sigma 3.924 3.444 2.611 ... ------------------------------------------------ |

We need to test for the following assumptions whenever we do regression analysis:

1. The relationship is linear

2. The errors have the same variance

3. The errors are independent of each other

4. The errors are normally distributed

First, we can address some of these points by creating plots of the model in R.

The plot on the left shows that the residuals (errors) have no pattern in variance (Good!).

The red line is concerning because it shows some curvature indicating that perhaps the relationshp is not entirely linear (hmmm…).

On the right we see that the errors are acceptably normally distributed (they are around the straight line … Good!).

To generate this plot you can use the R code below:

# Linearity Plots par(mfrow=c(1,2)) plot(m3) # Reset grid par(mfrow=c(1,1)) |

So we want to look deeper into linearity issues. We will look at multi-colinearity first:

# Multi-collinearity library(car) vif(m3) # variance inflation factors sqrt(vif(m3)) > 2 # problem? |

The code above will show if any of the variables have multicolinearity issues that could cause issues with the model’s integrity. Generally we want values less than 2, and we have values of around 1 so we are good on this front.

Price.Eggs Ad.Type Price.Cookies 1.195107 1.189436 1.006566 |

We can generate a CERES plot to assess non-linearity:

# Diagnosis: Nonlinearity crPlots(m3) |

We see that there is definitely some issues with linearity but not to an extent that it is a cause for concern for the purpose of demonstration. So we keep calm, and move on.

Lastly we want to test independence of the residuals using the Durban Watson Test:

# Diagnosis: Non-independence of Errors durbinWatsonTest(m3) |

The output shows that there is no autocorrelation issues in the model:

lag Autocorrelation D-W Statistic p-value 1 0.06348248 1.792429 0.458 Alternative hypothesis: rho != 0 |

So we are clear to move forward with Price Elasticity and Cross Product Price Elasticity!

We now have our model:

**Sales of Eggs = 137.37 – (16.12)Price.Eggs + 4.15 (Ad.Type) – (8.71)Price.Cookies **

To calculate Price Elasticity of Demand we use the formula:

PE = (ΔQ/ΔP) * (P/Q)

(ΔQ/ΔP) is determined by the coefficient -16.12 in our regression formula.

To determine (P/Q) we will use the mean Price (4.43) and mean Sales (30).

Therefore we have PE = -16.12 * 4.43/30 = -2.38

This means that an increase in the price of eggs by 1 unit will decrease the sales by 2.38 units.

To calculate Cross Price Elasticity of Demand we are essentially looking for how the price of cookies impacts the sales of eggs. So we use the formula:

CPE_{cookies} = (ΔQ/ΔP_{cookies}) * (P_{cookies}/Q)

We know from our regression that (ΔQ/ΔP_{cookies}) is the coefficient of Price of Cookies (-8.71).

We use the mean price of cookies and mean sales for the rest of the formula giving (4.37/30)

CPE_{cookies} = -8.71 * (4.37/30) = -1.27

This means that an increase in the price of cookies by 1 unit will decrease the sales of eggs by 1.27 units.

We now know that the price of eggs and price of cookies are complementary to one another in this scenario. Since you only sell too products, one explanation could be that people who come in for cookies and eggs would rather get them elsewhere if the price is too high.

Also, it means that if you had to choose between a price cut on cookies or eggs, go with cookies!

You are now in an ideal situation where you can run an optimization function to set the right price for both cookies and eggs.

This is out of the scope of this post, but if you’re interested in doing that check out R’s optim() function ~ or leave a comment below 😛

Can you figure out what to do with the Ads?

I’ve included some ideas in the complete code below:

################################### # Warm-up ... data and stuff # ################################### # Load data and output summary stats sales.data<-read.csv('supermarket.csv') #Look at column names and their classes sapply(sales.data,class) # Change Ad.Type to factor and print Summary Stats sales.data$Ad.Type<-as.factor(sales.data$Ad.Type) summary(sales.data) #################### # Create Models # #################### # Load required library library(memisc) # Create models m1<-lm(formula=Sales~Price.Eggs,data=sales.data) m2<-update(m1,.~.+Ad.Type) m3<-update(m2,.~.+Price.Cookies) mtable(m1,m2,m3) #################### # DIAGNOSTICS # #################### # Linearity Plots par(mfrow=c(1,2)) plot(m3) par(mfrow=c(1,1)) # Multi-collinearity library(car) vif(m3) # variance inflation factors sqrt(vif(m3)) > 2 # problem? # Diagnosis: Nonlinearity crPlots(m3) # Diagnosis: Non-independence of Errors # We want a D-W Statistic close to 2 durbinWatsonTest(m3) ######################### # Price Elasticity # ######################### # Calculate Price Elasticity PE<-as.numeric(m3$coefficients["Price.Eggs"] * mean(sales.data$Price.Eggs)/mean(sales.data$Sales)) CPEcookies<-as.numeric(m3$coefficients["Price.Cookies"] * mean(sales.data$Price.Cookies)/mean(sales.data$Sales)) # Print Results PE CPEcookies #################################### # BONUS - What about the ads? # #################################### # Subset the data sales.adEggs <- subset(sales.data,Ad.Type==0) sales.adCookies <- subset(sales.data,Ad.Type==1) # Diagnostic on subsets' means and if they are different ... they are. wilcox.test(x=sales.adCookies$Sales,y=sales.adEggs$Sales,exact=F,paired=T) # On average, does the advert for eggs generate higher sales for eggs? mean(sales.adEggs$Sales) >= mean(sales.adCookies$Sales) |

Jun 10

The concept of price elasticity is pretty easy to grasp.

In economics, it is a measure of how sensitive demand or supply is to price.

In marketing, it is how sensitive consumers are to a change in price of a product.

There are many determinant of elasticity that are both supply-side and demand-side. Nevertheless we can distinguish two types of price elasticity:

__Own price elasticity__: changes in demand of a single product due to its price

__Cross price elasticity__: with changes in demand of one product due to changes in price of another.

Understanding elasticity allows us to identify the optimal point for revenue – since we have reducing marginal returns on price increments. Graphically the optimal point lies at the frontier of the Price vs. Quantity graph depicted below:

Imagine you were going to buy an apple from the local market where there are only 100 apples and 100 customers including yourself. The price of apples on the day is $2 and every person can only buy 1 apple.

At a price of $2 perhaps 80 people would be willing and able to buy an apple at the $2. If the price of an apple suddenly became $100 per apple, chances are no one would be willing and/or able to buy an apple. At a price lower than $2 we might see a few more people buying apples, or none at all.

The same dynamic applies to quantities where a drop or rise in the availability of apples would impact how much people are able and willing to pay for a product.

We therefore arrive at the formula for price elasticity of demand is given by either of the following:

- PE = (ΔQ/Q) / (ΔP/P)
- PE = (ΔQ/ΔP) * (P/Q)

- P is price
- Q is quantity
- ΔQ is the change in quantity
- ΔP is the change in price

Let us look at an example.

Imagine you are a sales person in the market and you sell apples .. green shiny ones that makes people’s mouths water with delight. You currently sell like everyone else in the market an apple for $2 and sell 100 apples each day.

You want to sell more and therefore you decide to play the price game (…bad idea, but that’s for another post…) and cut the price to $1.99 (…nice move, sales person, nice move…). The next day rolls over and you end up selling 110 apples.

How elastic is demand?

It’s 20 … but what does that mean?

It means for every percentage drop in price, you get 20 percent increase in quantity.

Generally, we can interpret price elasticity values with the following ranges:

- PE equal to 0: Perfectly inelastic demand – meaning that price has no impact on quantity.
- PE between -1 and 1: Relatively inelastic demand
- PE larger than 1: Changes in price impact demand in opposite direction.
- PE less than -1: Changes in price impact demand in the same direction.

Imagine you grew your business and now own an entire supermarket.

You now sell apples, juice, eggs, flour, cookies and all sorts of great products!

You start thinking about the pricing strategy of your products and you set prices for each product. You notice that changing the price of eggs impacts sales (as it should do!). You start to do the elasticity calculations and you realize that increasing the price of flour reduces sales not only in flour, but also in eggs. Another oddity is that eggs sales pick up slightly when you increase the price of cookies.

Soon you realise that the changes you make in the price of flour impact that changes in quantities sold of eggs. Likewise, the increase in price of cookies impacts the sales of eggs. People start making their own cookies rather than buying them ready made if they are too expensive!

This is called cross price elasticity and is calculated as follows:

- CPE
_{eggs,cookies}= (%ΔQ_{eggs}/ %ΔP_{cookies})

We calculate %ΔQ and %ΔP as follows:

- %ΔQ = Q
_{start}– Q_{end}/ (Q_{start}+ Q_{end}) ÷ 2 - %ΔP = P
_{start}– P_{end}/ (P_{start}+ P_{end}) ÷ 2

Lets take a look at an example. Suppose that you had cookies priced at $5 and eggs at $2 selling 100 units and 50 units respectively. You decided to increase the price of cookies because they are awesome and stuff to $8! Your customers are angry cookie monsters and you only manage to sell 10 cookies after the price hike. On the other hand, your egg sales fly through the roof at 100 units. It seems like many cookie monsters decided to bake at home.

We would then get CPE_{eggs,cookies} = %ΔQ_{eggs} / %ΔP_{cookies} = (~33%) / (~23%)

This gives a CPE of 1.44 … but what does this mean?

The positive relationship between eggs and cookies indicate that they are good substitutes for one another. That a change in price of eggs by one percent increases the sales of cookies by 1.44. If the relationship was negative, they would be complements to one another.

We now know that elasticity tells us about how sensitive our customers are to the price of one product or the relative price of another. We can use this information provided historical data, but as the adage goes: hindsight is 20-20.

The next step would be to be able to predict or model the relationship between quantity and price and determine elasticity. This should start to sound familiar if you have had any experience with regression modelling. A regression model would take the form of:

- Quantity
_{eggs}= A x Price_{eggs}+ B x Price_{cookies}+ e

Where A and B are some coefficient and e is a constant.

We would then have the predicted relationship between quantity and the price of each product in our supermarket. Calculating price elasticity and cross price elasticity then becomes relatively easy.

This is precisely what we will do with R in the next post on Price Elasticity with R!

## Chatter