RPubs link information

• It is not necessary (That is, it is optional and not compulsory) but if you like you can publish your presentation to RPubs (see here) and add this link to your presentation here.

• Rpubs link comes here: www………

Introduction

• The following link will help you with creating R Markdown Slidy Presentations
• http://rmarkdown.rstudio.com/slidy_presentation_format.html
• Don’t forget about Bootcamp 4
• A good introduction provides a brief background to the problem, defines important terms, and leads to a strong rationale.

Introduction Cont.

• Keep everything short and straight to the point.
• Use bullet points to help minimise text.
• Add relevant images to make your presentation more appealing
• Remember, you have a maximum of 20 slides to fit everything in.
• Ensure each slide fits on one screen. The reader should not have to scroll down.

Problem Statement

• Do smokers have a different mean medical charge than non-smokers?
• How well do smoking status, age and BMI together explain charges?
• Tasks 5 and 7 were chosen

Data

• Dataset link - https://www.kaggle.com/datasets/mirichoi0218/insurance/data
• You can use multiple slides to break up your sections.
• Explain how you collected your data.
• There should be enough detail here so that someone else could replicate your data collection.
• Explain the sampling method if known
• Ensure you reference the data source if you have used Open Data

# insurance dataset
insurance_csv_dt <- read.csv("insurance.csv", stringsAsFactors = TRUE)
dim(insurance_csv_dt)

## [1] 1338    7

Data Cont.

• List and explain the important variables.
• If you have factors, explain each level.
• Explain the scale of numeric variables.
• Justify everything that you do to preprocess the data.

Descriptive Statistics and Visualisation

• Summarise the important variables in your investigation.
• Use visualisation to highlight interesting features of the data and tell the overall story.
• Explain how you dealt with data issues (if any), e.g. missing data and outliers.
• Here are the examples of R chunks and outputs

# missing values
colSums(is.na(insurance_csv_dt))

##      age      sex      bmi children   smoker   region  charges 
##        0        0        0        0        0        0        0

# duplicate rows
duplicate_rows <- sum(duplicated(insurance_csv_dt))

# remove duplicates
insurance_csv_dt <- distinct(insurance_csv_dt)

# non-smoker
insurance_csv_dt$smoker <- relevel(insurance_csv_dt$smoker, ref = "no")  

# dataset summary
summary(insurance_csv_dt[, c("age", "bmi", "children", "charges")])

##       age             bmi           children        charges     
##  Min.   :18.00   Min.   :15.96   Min.   :0.000   Min.   : 1122  
##  1st Qu.:27.00   1st Qu.:26.29   1st Qu.:0.000   1st Qu.: 4746  
##  Median :39.00   Median :30.40   Median :1.000   Median : 9386  
##  Mean   :39.22   Mean   :30.66   Mean   :1.096   Mean   :13279  
##  3rd Qu.:51.00   3rd Qu.:34.70   3rd Qu.:2.000   3rd Qu.:16658  
##  Max.   :64.00   Max.   :53.13   Max.   :5.000   Max.   :63770

# Medical charges basedon smoker
ggplot(insurance_csv_dt, aes(x = charges)) +
  geom_histogram(bins = 30, fill = "steelblue", colour = "white") +
  facet_wrap(~ smoker, labeller = label_both, scales = "free_y") +
  labs(x = "Medical charges", y = "Count") +
  theme_minimal()

ggplot(insurance_csv_dt, aes(x = smoker, y = charges, fill = smoker)) +
  geom_boxplot(show.legend = FALSE) +
  labs(x = "Smoker", y = "Medical charges") +
  theme_minimal()

# charges based on ages
ggplot(insurance_csv_dt, aes(age, charges, colour = smoker)) +
  geom_point(alpha = .5) + theme_minimal() + labs(x = "Age", y = "Charges")

# charges based on BMI
ggplot(insurance_csv_dt, aes(bmi, charges, colour = smoker)) +
  geom_point(alpha = .5) + theme_minimal() + labs(x = "BMI", y = "Charges")

Decsriptive Statistics Cont.

• You can use the knitr:kable function to print nice HTML tables. Here is an example R code:

# descriptive statistics
insurance_csv_dt %>%
  group_by(smoker) %>%
  summarise(n = n(),Min = min(charges),Q1 = quantile(charges, .25),
            Median = median(charges),
            Q3 = quantile(charges, .75),
            Max = max(charges),
            Mean = mean(charges),
            SD = sd(charges)) -> table1
kable(table1, digits = 0)

# mean charges based on regions
insurance_csv_dt %>%
  group_by(region, smoker) %>%
  summarise(mean_charges = mean(charges), .groups = "drop") %>%
  ggplot(aes(x = region, y = mean_charges, fill = smoker)) +
  geom_col(position = "dodge") +
  labs(x = "Region", y = "Mean charges") +
  theme_minimal()

# charges by BMI category and smoking status
insurance_csv_dt %>%
  mutate(bmi_group = cut(bmi, breaks = c(0, 18.5, 25, 30, Inf),
                         labels = c("Underweight", "Normal", "Overweight", "Obese"),
                         right = FALSE)) %>%
  ggplot(aes(x = bmi_group, y = charges, fill = smoker)) +
  geom_boxplot() +
  labs(x = "BMI category", y = "Charges") +
  theme_minimal()

Hypothesis Testing

• Apply an appropriate hypothesis test for your investigation.
• Ensure you state and check any assumptions.
• Report the appropriate values and interpret the results.
• Here is an example:

par(mfrow = c(1, 2))
for (g in c("no", "yes")) {
  x <- insurance_csv_dt$charges[insurance_csv_dt$smoker == g]
  qqnorm(x, main = paste("Smoker:", g)); qqline(x)
}

par(mfrow = c(1, 1))

smk <- insurance_csv_dt$charges[insurance_csv_dt$smoker == "yes"]
non_smk <- insurance_csv_dt$charges[insurance_csv_dt$smoker == "no"]
tt_results <- t.test(smk, non_smk, alternative = "two.sided", conf.level = 0.95)
tt_results

## 
##  Welch Two Sample t-test
## 
## data:  smk and non_smk
## t = 32.742, df = 311.88, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  22190.79 25028.35
## sample estimates:
## mean of x mean of y 
##  32050.23   8440.66

# robustness check on log scale
t.test(log(charges) ~ smoker, data = insurance_csv_dt)$p.value

## [1] 6.784987e-234

Regression Model

lm_mdl <- lm(log(charges) ~ age + bmi + smoker, data = insurance_csv_dt)
summary(lm_mdl)

## 
## Call:
## lm(formula = log(charges) ~ age + bmi + smoker, data = insurance_csv_dt)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.22166 -0.21732 -0.03983  0.10344  2.07125 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.0761244  0.0715858   98.85  < 2e-16 ***
## age         0.0350930  0.0009117   38.49  < 2e-16 ***
## bmi         0.0107853  0.0020982    5.14 3.15e-07 ***
## smokeryes   1.5451927  0.0315190   49.02  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.465 on 1333 degrees of freedom
## Multiple R-squared:  0.7443, Adjusted R-squared:  0.7438 
## F-statistic:  1294 on 3 and 1333 DF,  p-value: < 2.2e-16

# Model Diagnostics
par(mfrow = c(2, 2)); plot(lm_mdl); par(mfrow = c(1, 1))

# percentage chnage in charges per unit increase
round((exp(coef(lm_mdl)[-1]) - 1) * 100, 1)

##       age       bmi smokeryes 
##       3.6       1.1     368.9

Discussion

• Discuss the major findings of your investigation
• Discuss any strengths and limitations.
• Propose directions for future investigations.
• This is a good place to re-state your findings as a final conclusion. What is the one take home message the reader should leave with?
• Your final conclusion needs to be very clear.

References

• Provide a list of any references you use in the presentation.