Introduction

• This presentation is created as the reproducible pitch for Coursera’s “Developing Data Products” Week 4 course project.
• A Shiny app is created where the tooth length of guinea pigs is analyzed based on supplement type and dosage amount.
• The app can be found here, and the relevant codes and files can be found here.

Data

• The dataset ToothGrowth is loaded from R datasets package.
• The variables are (based on the documentation of the data):
  • len - The tooth length of the guinea pigs
  • supp - Supplement type, whether it is ascorbic acid (coded as VC) or orange juice (coded as OJ)
  • dose - Dosage amount in milligrams per day
• A linea regression model is fitted using this data, and any new values are predicted using this model.

Model Summary

data("ToothGrowth")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
model <- lm(len ~ ., data = ToothGrowth)
summary(model)

## 
## Call:
## lm(formula = len ~ ., data = ToothGrowth)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -7.085 -2.751 -0.800  2.446  9.650 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  12.4550     0.9883  12.603  < 2e-16 ***
## suppVC       -3.7000     0.9883  -3.744 0.000429 ***
## dose1         9.1300     1.2104   7.543 4.38e-10 ***
## dose2        15.4950     1.2104  12.802  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.828 on 56 degrees of freedom
## Multiple R-squared:  0.7623, Adjusted R-squared:  0.7496 
## F-statistic: 59.88 on 3 and 56 DF,  p-value: < 2.2e-16

Plots

Prediction for Supplement Type “OJ” and Dosage Amount of 1.0 mg/day

pred <- predict(model, newdata = data.frame(supp = "OJ", dose = as.factor(1.0)))
round(pred, 3)

##      1 
## 21.585