diff --git a/Assignment 2-2020.Rmd b/Assignment 2-2020.Rmd index 081fcec..48db062 100644 --- a/Assignment 2-2020.Rmd +++ b/Assignment 2-2020.Rmd @@ -95,13 +95,30 @@ pairs(D5) #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 +``` +```{r} +rnorm(100,75,15) +``` +```{r} + +score <- rnorm(100,75,15) +s1 <- data.frame(score) +s1 <- filter(s1, score <= 100) +s2 <- data.frame(rep(100, 100 - nrow(s1))) +names(s2) <- "score" +interest <- c("sport", "music", "nature", "literature") + ``` 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(score, breaks = 10) +``` +```{r} + ``` @@ -113,6 +130,16 @@ pairs(D5) ``` +```{r} +s3 <- bind_rows(s1,s2) +s3$score <- ifelse(s3$score > 100,100, s3$score ) +s3$score <- round(s3$score,0) +s3$interest <- sample(interest, 100, replace = TRUE) +s3$stid <- seq(1,100,1) +label <- letters[1:10] +s3$breaks <- cut(s3$score, breaks = 10, labels = label) +``` + 4. Now using the colorbrewer package (RColorBrewer; http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) design a pallette and assign it to the groups in your data on the histogram. ```{r} @@ -124,6 +151,20 @@ library(RColorBrewer) #Use named palette in histogram +``` +```{r} +display.brewer.all() +``` + + +```{r} +s3$colors <- brewer.pal(10, "Set3") +``` + + +```{r} +hist(s3$score, col = s3$colors) + ``` @@ -132,34 +173,46 @@ library(RColorBrewer) ```{r} #Make a vector of the colors from RColorBrewer +interest.col <- brewer.pal(4, "Dark2") +``` + + +```{r} +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") ``` 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(AP) ``` 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} +i <- data.frame(iris) +plot(i) +``` +```{r} +pairs(iris) ``` # Part III - Analyzing Swirl @@ -172,6 +225,9 @@ In this repository you will find data describing Swirl activity from the class s 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) +``` The variables are: @@ -184,20 +240,43 @@ The variables are: `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` +3. Create a new data frame that only includes the variables `hash`, `lesson_name` and `attempt` called `DF2`DF3 +```{r} +DF2 <- data.frame(DF1$hash, DF1$lesson_name, DF1$attempt) +names(DF2) <- c("hash", "lesson_name", "attempt") +``` 4. Use the `group_by` function to create a data frame that sums all the attempts for each `hash` by each `lesson_name` called `DF3` +```{r} +DF3 <- DF1 %>% group_by(hash, lesson_name) %>% summarise(sum_attempt = sum(attempt)) +``` + 5. On a scrap piece of paper draw what you think `DF3` would look like if all the lesson names were column names 6. Convert `DF3` to this format 7. Create a new data frame from `DF1` called `DF4` that only includes the variables `hash`, `lesson_name` and `correct` +```{r} +DF4 <- data.frame(DF1$hash, DF1$lesson_name , DF1$correct) +names(DF4) <- c("hash", "lesson_name", "correct") +``` + + +8. Convert the `correct` variable so that `TRUE` is coded as the **number** `1` and `FALSE` is coded as `0` -8. Convert the `correct` variable so that `TRUE` is coded as the **number** `1` and `FALSE` is coded as `0` +```{r} +DF4[,"correct"] <- lapply(is.logical(DF4[,"correct"]), as.numeric) +``` 9. Create a new data frame called `DF5` that provides a mean score for each student on each course +```{r} + DF5 <- DF1 %>% group_by(hash, course_name) %>% summarise(mean_score = mean(score)) +``` 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 <- as.Date(as.Date(DF1$datetime, origin = "0000-01-01"), "%m%d%y") +``` 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. diff --git a/Assignment-2-2020.html b/Assignment-2-2020.html new file mode 100644 index 0000000..05a53d6 --- /dev/null +++ b/Assignment-2-2020.html @@ -0,0 +1,644 @@ + + + + + + + + + + + + + + + +Assignment 2 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +

#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

+
#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)
+
## ── Attaching packages ───────────────── tidyverse 1.3.0 ──
+
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
+## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
+## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
+## ✓ readr   1.3.1     ✓ forcats 0.5.0
+
## ── Conflicts ──────────────────── tidyverse_conflicts() ──
+## x dplyr::filter() masks stats::filter()
+## x dplyr::lag()    masks stats::lag()
+
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

+
#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

+
#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))
+
## `summarise()` ungrouping output (override with `.groups` argument)
+
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

+
#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.
    2. +
+
#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
+
rnorm(100,75,15)
+
##   [1]  67.84873  79.80337  42.63363  85.17714  88.59926  78.46961  46.36595
+##   [8]  76.35964  79.62964  82.98106  68.94288  50.53024  66.48003  75.96119
+##  [15]  86.22544  84.49252  72.89383  65.95827  97.22493  54.58226  82.50733
+##  [22]  94.73172  76.44061  48.69983  78.32315  83.59933  67.24537  61.78429
+##  [29]  55.76038  99.00084  77.70035  80.31684  64.35895  74.29432  68.29162
+##  [36]  83.00360  65.45195 119.01862  67.00178  74.73462 100.20655  77.12589
+##  [43]  79.70799  74.96633  65.96527  99.73169  54.87964  66.05609  69.12125
+##  [50]  63.00844  73.41591  66.77133  55.24943  42.70290  69.43047  87.32524
+##  [57]  68.76847  90.57337  68.39631  70.40294  69.44273  73.48045  91.93331
+##  [64]  84.09845  68.49322  95.99793  67.21138  91.16768  77.23583  55.79581
+##  [71]  77.66248  91.68355  91.19191  76.58508  88.77551  59.62973  71.22696
+##  [78]  72.67956  69.22533  86.21434  70.26697  87.91134  56.22038  67.29668
+##  [85]  54.67929  56.39107  87.98281  69.60382  63.19514 102.80703  63.33112
+##  [92]  67.86027  86.34639  76.84172  87.01969  83.83585  77.42888  48.61309
+##  [99] 103.28562  87.18372
+
score <- rnorm(100,75,15)
+s1 <- data.frame(score)
+s1 <- filter(s1, score <= 100)
+s2 <- data.frame(rep(100, 100 - nrow(s1)))
+names(s2) <- "score"
+interest <- c("sport", "music", "nature", "literature")
+
    +
  1. Using base R commands, draw a histogram of the scores. Change the breaks in your histogram until you think they best represent your data.
    2. +
+
hist(score, breaks = 10)
+

+
    +
  1. Create a new variable that groups the scores according to the breaks in your histogram.
    2. +
+
#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.
+
s3 <- bind_rows(s1,s2)
+s3$score <- ifelse(s3$score > 100,100, s3$score )
+s3$score <- round(s3$score,0)
+s3$interest <- sample(interest, 100, replace = TRUE)
+s3$stid <- seq(1,100,1)
+label <- letters[1:10]
+s3$breaks <- cut(s3$score, breaks = 10, labels = label)
+
    +
  1. 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.
    2. +
+
library(RColorBrewer)
+#Let's look at the available palettes in RColorBrewer
+
+#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
+
+#Use named palette in histogram
+
display.brewer.all()
+

+
s3$colors <- brewer.pal(10, "Set3")
+
hist(s3$score, col = s3$colors)
+

+
    +
  1. Create a boxplot that visualizes the scores for each interest group and color each interest group a different color.
    2. +
+
#Make a vector of the colors from RColorBrewer
+
+interest.col <- brewer.pal(4, "Dark2")
+
boxplot(score ~ interest, s3, col = interest.col)
+

+
    +
  1. Now simulate a new variable that describes the number of logins that students made to the educational game. They should vary from 1-25.
    2. +
+
s3$login <- sample(1:25, 100, replace = TRUE)
+
    +
  1. Plot the relationships between logins and scores. Give the plot a title and color the dots according to interest group.
    2. +
+
plot(s3$login, s3$score, col = s3$colors, main = "Student logins vs. Scores")
+

+
    +
  1. 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.
    2. +
+
AP <- data.frame(AirPassengers)
+plot(AP)
+

+
    +
  1. 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?
    2. +
+
i <- data.frame(iris)
+plot(i)
+

+
pairs(iris)
+

+
+
+

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. +
  2. Create a data frame from the swirl-data.csv file called DF1
    3. +
+
DF1 <- read.csv("swirl-data.csv" , header = TRUE)
+

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

+
    +
  1. Create a new data frame that only includes the variables hash, lesson_name and attempt called DF2DF3
    2. +
+
DF2 <- data.frame(DF1$hash, DF1$lesson_name, DF1$attempt)
+names(DF2) <- c("hash", "lesson_name", "attempt")
+
    +
  1. Use the group_by function to create a data frame that sums all the attempts for each hash by each lesson_name called DF3
    2. +
+
DF3 <- DF1 %>% group_by(hash, lesson_name) %>% summarise(sum_attempt = sum(attempt))
+
## `summarise()` regrouping output by 'hash' (override with `.groups` argument)
+
    +

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

    2. +

  2. Convert DF3 to this format

    3. +

  3. Create a new data frame from DF1 called DF4 that only includes the variables hash, lesson_name and correct

    4. +
+
DF4 <- data.frame(DF1$hash, DF1$lesson_name , DF1$correct)
+names(DF4) <- c("hash", "lesson_name", "correct")
+
    +
  1. Convert the correct variable so that TRUE is coded as the number 1 and FALSE is coded as 0
    2. +
+
DF4[,"correct"] <- lapply(is.logical(DF4[,"correct"]), as.numeric)
+
    +
  1. Create a new data frame called DF5 that provides a mean score for each student on each course
    2. +
+
 DF5 <- DF1 %>% group_by(hash, course_name) %>% summarise(mean_score = mean(score))
+
## `summarise()` regrouping output by 'hash' (override with `.groups` argument)
+
    +
  1. Extra credit Convert the datetime variable into month-day-year format and create a new data frame (DF6) that shows the average correct for each day
    2. +
+
DF6 <- as.Date(as.Date(DF1$datetime, origin = "0000-01-01"), "%m%d%y")
+

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.

+
+
+
+ + + + +
+ + + + + + + + + + + + + + +