A Walkthrough of ioslides

In order to learn ioslides, I want to create a very simple slideshow using a simple simulated dataframe about study time and exam scores.

First, We Need to Create the Data

students <- data.frame(
  hours = runif(100, 1, 10)
)

students$score <- 50 + 5 * students$hours + rnorm(100, 0, 8)
head(students)
     hours     score
1 5.567304  74.63142
2 3.760917  66.12813
3 4.842169  85.15448
4 7.237919 103.29173
5 1.766224  62.87767
6 3.028930  71.43539

We Can Look at a Summary of the Data

summary(students)
     hours           score       
 Min.   :1.130   Min.   : 42.91  
 1st Qu.:3.132   1st Qu.: 63.74  
 Median :4.947   Median : 74.89  
 Mean   :5.008   Mean   : 75.41  
 3rd Qu.:6.652   3rd Qu.: 83.89  
 Max.   :9.652   Max.   :115.72

We Can Make a Scatter Plot Using ggplot (Plot will be on the next slide)

plot1 <- ggplot(students, aes(hours, score)) +
    geom_point(size=0.5, color="tomato3") +
    labs(title = "Study Hours vs. Exam Score", 
         x = "Hours Studied", 
         y = "Exam Score")

Scatter Plot Mentioned in Previous Slide

We Can Also Build a Regression Model

Here’s the equation that we could use: \[ Exam Score = \beta_0 + \beta_1 \cdot Study Hours \] We’re going to create a plot of the regression line, so let’s build the model using R.

Here is our Model Using R

model <- lm(score ~ hours, data=students)

summary(model)
Call:
lm(formula = score ~ hours, data = students)

Residuals:
    Min      1Q  Median      3Q     Max 
-16.588  -6.136   1.481   6.038  15.866 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  48.4207     1.8405   26.31   <2e-16 ***
hours         5.3890     0.3334   16.16   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.743 on 98 degrees of freedom
Multiple R-squared:  0.7272,    Adjusted R-squared:  0.7244 
F-statistic: 261.2 on 1 and 98 DF,  p-value: < 2.2e-16

Regression Line

`geom_smooth()` using formula = 'y ~ x'

We Can Analyze the Regression Line

We can analyze the regression line by using this equation: \[ \sum_{i=1}^{n}{(y_i - \bar{y})^2} \] This equation is used to find the variance.

We Can Make an Interactive Plot (Plot will be on the next slide)

plot2 <-plot_ly(data = students,
        x = ~hours,
        y = ~score,
        type = "scatter",
        mode = "markers") %>%
  add_lines(x = ~hours, y = fitted(model),
            name = "Regression Line") %>%
  layout(title = "Interactive Study Hours vs.   Exam Score",
         xaxis = list(title = "Hours Studied"),
         yaxis = list(title = "Exam Score"),
         showlegend = FALSE)

Interactive Plot Mentioned in Previous Slide