IM371- Data Mining

Dataset contains information of 200 students. Their scores in different subjects and their educational choices (general, academic or vocational). There are other variables indicating their socio economic status and their gender.

• female –> indicates gender
• ses –> indicates socio economic status
• schtyp –> indicates nominal type of school (1=public 2=private)
• prog –> indicates educational choices (general, academic or vocational)
• read write math science socst –> indicates scores in each course subject
• honors –> indicates binary data, is student honors English (0/1)?
• awards –> indicates the number of awards each students have earned
• cid –> indicates a categorical data

1 - Load data

library(haven)
hsbdemo <- read_dta("hsbdemo.dta")
print(hsbdemo)

2 - Plot using Lattice

library("sjlabelled")
library("lattice")
xyplot(as_label(female) ~ awards | factor(as_label(ses)), data=hsbdemo,
   type=c("p"),
   main="Gender vs. Awards (factored by Socio Economic Status)",
   xlab="The number of awards each students have earned",
   ylab="Gender",
)

3 - Plot using ggplot2

3.1 - Plot using ggplot2 (bar)

library("ggplot2")
p <- ggplot(hsbdemo, aes(as_label(prog))) + geom_bar()
p + ggtitle("Plot of Educational Choices counts") + xlab("Educational Choices") + ylab("Count")

3.2 - Plot using ggplot2 (bar with Facet)

p <- ggplot(hsbdemo, aes(as_label(prog))) + geom_bar(aes(fill=as_label(honors))) + facet_grid(. ~ as_label(female))
p + ggtitle("Plot of Educational Choices Counts. Facet by Gender, and filled by English Honors") + xlab("Educational Choices") + ylab("Count") + labs(fill = "English Honors")

3.3 - Plot using ggplot2 (Scatterplot with smooth)

g <- ggplot(hsbdemo, aes(read, write))
g + geom_point() + 
  geom_smooth(method="lm") +
  labs(title="Scatterplot with overlapping points", 
      subtitle="Scores in Reading vs Writing", 
       x="Scores of Reading subject", 
       y="Scores of Writing subject", 
       caption="Smooth medthod: lm")

4 - Plot using plotly

4.1 - Plot using plotly (pie)

library(plotly)
fig <- plot_ly(hsbdemo, labels = ~as_label(ses), values = ~awards, type = 'pie')
fig <- fig %>% layout(title = 'Number of Awards for each Socio Economic Status')
fig

4.2 - Plot using plotly (scatter)

library(plotly)
fig <- plot_ly(data = hsbdemo, x = ~math, y = ~science, color = ~awards)
fig <- fig %>% layout(title = 'Scores in Math vs Science subjects, colored by Awards Earned')
fig