---
title: "In-class: The Duke GPA Data Analysis"
author: "Milica Cudina"
date: "`r Sys.Date()`"
output: pdf_document
---
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```{r echo=FALSE, warning=FALSE, include=FALSE}
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---

## Task 1. (1 point)
First, we will read the data from our csv file "gpa.csv" into a data.frame called `gpa.data`:

```{r}
gpa.data=read.csv("gpa.csv")
```


If you want to see what your data.frame looks like, you can click on it in the **Global environment** in the upper right pane. The data.frame will get displayed in the upper left pane. 

## Task 2. (1 point)
You interested in the types and names of the variables in your data.frame. What do you run?

You see that the students/**cases** all have corresponding **rows**. They are labeled by the row indices. The **column** names stand for the variable names.

```{r}
ls.str(gpa.data)
```

Then, you can do a bit of exploratory analysis. 

## Task 3. (3 points)
What are the minimum and maximum GPAs? What is the mean GPA? What is the median GPA?

```{r}
gpa=gpa.data$gpa

summary(gpa)

sd(gpa)
```


## Task 4. (2 points)
Plot the histogram of the GPAs. Make sure that your plot has the main title and that the axes are also labeled. 

```{r}
hist(gpa, breaks=15,
     col="purple",
     main="Duke GPA",
     xlab="Grades")
```

## Task 5. 
Plot the histogram of the number of hours spent studying per week. Make sure that your plot has the main title and that the axes are also labeled. 

```{r}
hrs=gpa.data$studyweek
hist(hrs, breaks=15,
     col="yellow",
     main="Duke Hours Studying",
     xlab="Hours")
```

## Task 6. 
Is the mean number of hours spent studying different for females than for males? 

```{r}
hrs.f=hrs[gpa.data$gender=="female"]
hrs.m=hrs[gpa.data$gender=="male"]
mean(hrs.f)
mean(hrs.m)

#just for laughs, here is a transparent histogram #superimposition

hist(hrs.f, breaks=15, col=rgb("red"=1, "green"=0,
                                    "blue"=0,  alpha=0.25))
hist(hrs.m, breaks=10, col=rgb("red"=0, "green"=0,
                                    "blue"=1,  alpha=0.25), add=TRUE)
```

## Task 7. 
Any difference in the GPA?

```{r}
gpa.f=gpa[gpa.data$gender=="female"]
gpa.m=gpa[gpa.data$gender=="male"]
mean(gpa.f)
mean(gpa.m)
```

## Task 8. 
Is there a relationship between the hours studied and the GPA?

```{r}
plot(hrs, gpa, col="lightblue", pch=19, 
     main="Studying vs. GPA", 
     xlab="Hours per week",
     ylab="GPA")

lin.mod=lm(gpa ~ hrs)
abline(lin.mod, col="blue", lwd=2)

lin.mod
```

## Task 9. 
Is there an effect of gender? Let's create a scatterplot with the two categories indicated by different colors. 

```{r}
plot(gpa.data$studyweek, gpa.data$gpa, 
     col=as.factor(gpa.data$gender),
     pch=19,
     main="Studying vs. GPA", 
     xlab="Hours per week",
     ylab="GPA")
legend(40, 4.75, legend=c("Female", "Male"),
       col=c("Black", "Red"), pch=19,
       cex=0.8)
```
Or, choosing our colors
```{r}
plot(gpa.data$studyweek, gpa.data$gpa, col=c("orange","purple")[as.factor(gpa.data$gender)], pch=19,
     main="Studying vs. GPA", 
     xlab="Hours per week",
     ylab="GPA") 
legend(40, 4.75, legend=c("Female", "Male"),
       col=c("Orange", "Purple"), pch=19,
       cex=0.8)
```