Assignment 2-2020.Rmd

---
title: "Assignment 2"
author: "Yujie Yang"
date: "September 24, 2020"
output: html_document
---
#Part I

## Data Wrangling
In the hackathon a project was proposed to collect data from student video watching, a sample of this data is available in the file video-data.csv.

stid = student id
year = year student watched video
participation = whether or not the student opened the video
watch.time = how long the student watched the video for
confusion.points = how many times a student rewatched a section of a video
key,points = how many times a student skipped or increased the speed of a video

```{r}
#Install the 'tidyverse' package or if that does not work, install the 'dplyr' and 'tidyr' packages.

#Load the package(s) you just installed

library(tidyverse)
library(tidyr)
library(dplyr)

D1 <- read.csv("video-data.csv", header = TRUE)

#Create a data frame that only contains the years 2018
D2 <- filter(D1, year == 2018)
```

## Histograms
```{r}
#Generate a histogram of the watch time for the year 2018

hist(D2$watch.time)

#Change the number of breaks to 100, do you get the same impression?

hist(D2$watch.time, breaks = 100)

#Cut the y-axis off at 10

hist(D2$watch.time, breaks = 100, ylim = c(0,10))

#Restore the y-axis and change the breaks so that they are 0-5, 5-20, 20-25, 25-35

hist(D2$watch.time, breaks = c(0,5,20,25,35))

```

## Plots
```{r}
#Plot the number of confusion points against the watch time

plot(D1$confusion.points, D1$watch.time)

#Create two variables x & y
x <- c(1,3,2,7,6,4,4)
y <- c(2,4,2,3,2,4,3)

#Create a table from x & y
table1 <- table(x,y)

#Display the table as a Barplot
barplot(table1)

#Create a data frame of the average total key points for each year and plot the two against each other as a lines

D3 <- D1 %>% group_by(year) %>% summarise(mean_key = mean(key.points))

plot(D3$year, D3$mean_key, type = "l", lty = "dashed")

#Create a boxplot of total enrollment for three students
D4 <- filter(D1, stid == 4|stid == 20| stid == 22)
#The drop levels command will remove all the schools from the variable with no data  
D4 <- droplevels(D4)
boxplot(D4$watch.time~D4$stid, xlab = "Student", ylab = "Watch Time")
```
## Pairs
```{r}
#Use matrix notation to select columns 2, 5, 6, and 7
D5 <- D1[,c(2,5,6,7)]
#Draw a matrix of plots for every combination of variables
pairs(D5)
```
## Part II

1. Create a simulated data set containing 100 students, each with a score from 1-100 representing performance in an educational game. The scores should tend to cluster around 75. Also, each student should be given a classification that reflects one of four interest groups: sport, music, nature, literature.

```{r}
#rnorm(100, 75, 15) creates a random sample with a mean of 75 and standard deviation of 20
#filter() can be used to set a maximum and minimum value
#round() rounds numbers to whole number values
#sample() draws a random samples from the groups vector according to a uniform distribution
set.seed(123)
score<-rnorm(100,75,15)
hist(score,breaks=30)
S1<-data.frame(score)

#alternative 1
library(dplyr)
S1 <- filter(S1, score <= 100)
hist(S1$score)

S2<-data.frame(rep(100,100-NROW(S1)))#in S1, the total number is 95 instead of 100, so we add five 100 manually. However, it means it is not randomly.
names(S2) <- "score"
S3<-bind_rows(S1,S2)#stick two command together


#alternative 2
#S1$score=ifelse(S1$score>=100,100,S1$score)#if else S3$score>=100,then make it 100, otherwise make it S3$score
#S1$score=round(S1$score,0)
interest = c("sport","music","nature","literature")
S3$interest=sample(interest, 100,replace=TRUE)
S3$stid=seq(1,100,1)

#alternative 3 - take the whole thing over--it will change the mean and sd
#S1$score=S1$score-max(S1$score)
```

2. Using base R commands, draw a histogram of the scores. Change the breaks in your histogram until you think they best represent your data.

```{r}
hist(S3$score, breaks=10)
```


3. Create a new variable that groups the scores according to the breaks in your histogram.

```{r}
#cut() divides the range of scores into intervals and codes the values in scores according to which interval they fall. We use a vector called `letters` as the labels, `letters` is a vector made up of the letters of the alphabet.
label=letters[1:10]#if want the letters capitalized, use LETTERS
S3$breaks=cut(S3$score, breaks=10, labels=label)
```

4. Now using the colorbrewer package (RColorBrewer; http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) design a pallette and assign it to the groups in your data on the histogram.

```{r}
library(RColorBrewer)
#Let's look at the available palettes in RColorBrewer
display.brewer.all()
#The top section of palettes are sequential, the middle section are qualitative, and the lower section are diverging.
#Make RColorBrewer palette available to R and assign to your bins
S3$colors=brewer.pal(10,"Set3")#the first 10 colors in set3 
#Use named palette in histogram
hist(S3$score,col=S3$colors)
```


5. Create a boxplot that visualizes the scores for each interest group and color each interest group a different color.

```{r}
#Make a vector of the colors from RColorBrewer
interest.col=brewer.pal(4,"Dark2")
boxplot(score~interest,S3,col=interest.col)
```


6. Now simulate a new variable that describes the number of logins that students made to the educational game. They should vary from 1-25.

```{r}
S3$login=sample(1:25,100,replace=TRUE)
```

7. Plot the relationships between logins and scores. Give the plot a title and color the dots according to interest group.

```{r}
plot(S3$login,S3$score,col=S3$colors,main="Student Logins vs. Scores")
S3$col1=ifelse(S3$interest=="music","red","green")

```


8. R contains several inbuilt data sets, one of these in called AirPassengers. Plot a line graph of the the airline passengers over time using this data set.

```{r}
AP=data.frame(AirPassengers)
plot(AirPassengers)
```


9. Using another inbuilt data set, iris, plot the relationships between all of the variables in the data set. Which of these relationships is it appropraiet to run a correlation on? 

```{r}
Iris=data.frame(iris)
Iris
pairs(Iris)
#I think Petal length by petal width is appropriate to run a correlation on because you can see a clear linear relationship between them. 
```
I think Petal length by petal width is appropriate to run a correlation on because you can see a clear linear relationship between them.

# Part III - Analyzing Swirl

## Data 

In this repository you will find data describing Swirl activity from the class so far this semester. Please connect RStudio to this repository.

### Instructions
  
1. Insert a new code block
2. Create a data frame from the `swirl-data.csv` file called `DF1`
```{r}
DF1 <- read.csv("swirl-data.csv", header = TRUE)
DF1=data.frame(DF1)
```

The variables are:

`course_name` - the name of the R course the student attempted  
`lesson_name` - the lesson name  
`question_number` - the question number attempted
`correct` - whether the question was answered correctly  
`attempt` - how many times the student attempted the question  
`skipped` - whether the student skipped the question  
`datetime` - the date and time the student attempted the question  
`hash` - anonymyzed student ID  

3. Create a new data frame that only includes the variables `hash`, `lesson_name` and `attempt` called `DF2`
```{r}
DF2 <- select(DF1,"hash","lesson_name","attempt")
```

4. Use the `group_by` function to create a data frame that sums all the attempts for each `hash` by each `lesson_name` called `DF3`
```{r}
DF3 <- DF2 %>% group_by(hash,lesson_name) %>% summarise(sumattempt = sum(attempt))
```

5. On a scrap piece of paper draw what you think `DF3` would look like if all the lesson names were column names

![My answer](Q5P3.jpeg)

6. Convert `DF3` to this format  
```{r}
DF3.1=spread(DF3,key=lesson_name,value=sumattempt,drop=FALSE,convert=TRUE)
```

7. Create a new data frame from `DF1` called `DF4` that only includes the variables `hash`, `lesson_name` and `correct`
```{r}
DF4 <- select(DF1,"hash","lesson_name","correct")
```

8. Convert the `correct` variable so that `TRUE` is coded as the **number** `1` and `FALSE` is coded as `0`  
```{r}
DF4$correct=factor(DF4$correct,levels=c("FALSE","TRUE"),labels=c(0,1))
```

9. Create a new data frame called `DF5` that provides a mean score for each student on each course
```{r}
DF5=data.frame(DF1[,c("hash","course_name","correct")])
DF5$correct=ifelse(DF5$correct=="TRUE",1,0)
DF5$correct=as.numeric(DF5$correct)
DF5 <- DF5 %>% group_by(hash,course_name) %>% summarise(meanscore = mean(correct,na.rm=TRUE))
```

10. **Extra credit** Convert the `datetime` variable into month-day-year format and create a new data frame (`DF6`) that shows the average correct for each day
```{r}
DF6 <- select(DF1,"datetime","correct")
DF6$datetime=format(as.Date(DF6$datetime,origin="1970-01-01"),"%m/%d/%y")
DF6$correct=ifelse(DF6$correct=="TRUE",1,0)
DF6$correct=as.numeric(DF6$correct)
DF6 <- DF6 %>% group_by(datetime) %>% summarise(avgcorrect = mean(correct))
```

Finally use the knitr function to generate an html document from your work. Commit, Push and Pull Request your work back to the main branch of the repository. Make sure you include both the .Rmd file and the .html file.