+
+
+
+
+
+
+Assignment 2
+Fei Wang
+October 6, 2020
+ +#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)
#After adding the breaks of 100, the different of frequency looks more dramatic as the first column looks much taller than the rest since it's been divided into smaller segments of watchtime.There are more specific information on how many people there are for different watch time after the change.#
+
+
+#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
-
+
- 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. +
#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
+
+score <- rnorm(100, 75, 15)
+hist(score,breaks = 30)
S1 <- data.frame(score)
+
+
+S2 <- data.frame(rep(100,5))
+names(S2) <- c("score")
+S3 <- bind_rows(S1,S2)
+
+interest <- c("sport","music","nature","literature")
+S1$interest <- sample(interest, 100, replace =TRUE)
+S1$stid <- seq(1,100,1)-
+
- Using base R commands, draw a histogram of the scores. Change the breaks in your histogram until you think they best represent your data. +
hist(S1$score)
hist(S1$score, breaks = 10)
-
+
- Create a new variable that groups the scores according to the breaks in your histogram. +
#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]
+S1$breaks <- cut(S1$score, breaks = 10, labels = label)
+
+letters## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
+## [20] "t" "u" "v" "w" "x" "y" "z"LETTERS## [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
+## [20] "T" "U" "V" "W" "X" "Y" "Z"-
+
- 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. +
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
+S1$colors <- brewer.pal(10, "Set3")
+
+#Use named palette in histogram
+hist(S1$score, col = S1$colors)
-
+
- Create a boxplot that visualizes the scores for each interest group and color each interest group a different color. +
#Make a vector of the colors from RColorBrewer
+interest.col <- brewer.pal(4, "Dark2")
+boxplot(score ~ interest, S1, col = interest.col)
-
+
- Now simulate a new variable that describes the number of logins that students made to the educational game. They should vary from 1-25. +
S1$login <- sample(1:25, 100, replace = TRUE)-
+
- Plot the relationships between logins and scores. Give the plot a title and color the dots according to interest group. +
plot(S1$login, S1$score, col = S1$colors, main = "Student Logins vs Scores")
S1$col1 <- ifelse(S1$interest == "music", "red", "green")-
+
- 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. +
AirPassengers## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
+## 1949 112 118 132 129 121 135 148 148 136 119 104 118
+## 1950 115 126 141 135 125 149 170 170 158 133 114 140
+## 1951 145 150 178 163 172 178 199 199 184 162 146 166
+## 1952 171 180 193 181 183 218 230 242 209 191 172 194
+## 1953 196 196 236 235 229 243 264 272 237 211 180 201
+## 1954 204 188 235 227 234 264 302 293 259 229 203 229
+## 1955 242 233 267 269 270 315 364 347 312 274 237 278
+## 1956 284 277 317 313 318 374 413 405 355 306 271 306
+## 1957 315 301 356 348 355 422 465 467 404 347 305 336
+## 1958 340 318 362 348 363 435 491 505 404 359 310 337
+## 1959 360 342 406 396 420 472 548 559 463 407 362 405
+## 1960 417 391 419 461 472 535 622 606 508 461 390 432plot(AirPassengers)
-
+
- 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? +
iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+## 1 5.1 3.5 1.4 0.2 setosa
+## 2 4.9 3.0 1.4 0.2 setosa
+## 3 4.7 3.2 1.3 0.2 setosa
+## 4 4.6 3.1 1.5 0.2 setosa
+## 5 5.0 3.6 1.4 0.2 setosa
+## 6 5.4 3.9 1.7 0.4 setosa
+## 7 4.6 3.4 1.4 0.3 setosa
+## 8 5.0 3.4 1.5 0.2 setosa
+## 9 4.4 2.9 1.4 0.2 setosa
+## 10 4.9 3.1 1.5 0.1 setosa
+## 11 5.4 3.7 1.5 0.2 setosa
+## 12 4.8 3.4 1.6 0.2 setosa
+## 13 4.8 3.0 1.4 0.1 setosa
+## 14 4.3 3.0 1.1 0.1 setosa
+## 15 5.8 4.0 1.2 0.2 setosa
+## 16 5.7 4.4 1.5 0.4 setosa
+## 17 5.4 3.9 1.3 0.4 setosa
+## 18 5.1 3.5 1.4 0.3 setosa
+## 19 5.7 3.8 1.7 0.3 setosa
+## 20 5.1 3.8 1.5 0.3 setosa
+## 21 5.4 3.4 1.7 0.2 setosa
+## 22 5.1 3.7 1.5 0.4 setosa
+## 23 4.6 3.6 1.0 0.2 setosa
+## 24 5.1 3.3 1.7 0.5 setosa
+## 25 4.8 3.4 1.9 0.2 setosa
+## 26 5.0 3.0 1.6 0.2 setosa
+## 27 5.0 3.4 1.6 0.4 setosa
+## 28 5.2 3.5 1.5 0.2 setosa
+## 29 5.2 3.4 1.4 0.2 setosa
+## 30 4.7 3.2 1.6 0.2 setosa
+## 31 4.8 3.1 1.6 0.2 setosa
+## 32 5.4 3.4 1.5 0.4 setosa
+## 33 5.2 4.1 1.5 0.1 setosa
+## 34 5.5 4.2 1.4 0.2 setosa
+## 35 4.9 3.1 1.5 0.2 setosa
+## 36 5.0 3.2 1.2 0.2 setosa
+## 37 5.5 3.5 1.3 0.2 setosa
+## 38 4.9 3.6 1.4 0.1 setosa
+## 39 4.4 3.0 1.3 0.2 setosa
+## 40 5.1 3.4 1.5 0.2 setosa
+## 41 5.0 3.5 1.3 0.3 setosa
+## 42 4.5 2.3 1.3 0.3 setosa
+## 43 4.4 3.2 1.3 0.2 setosa
+## 44 5.0 3.5 1.6 0.6 setosa
+## 45 5.1 3.8 1.9 0.4 setosa
+## 46 4.8 3.0 1.4 0.3 setosa
+## 47 5.1 3.8 1.6 0.2 setosa
+## 48 4.6 3.2 1.4 0.2 setosa
+## 49 5.3 3.7 1.5 0.2 setosa
+## 50 5.0 3.3 1.4 0.2 setosa
+## 51 7.0 3.2 4.7 1.4 versicolor
+## 52 6.4 3.2 4.5 1.5 versicolor
+## 53 6.9 3.1 4.9 1.5 versicolor
+## 54 5.5 2.3 4.0 1.3 versicolor
+## 55 6.5 2.8 4.6 1.5 versicolor
+## 56 5.7 2.8 4.5 1.3 versicolor
+## 57 6.3 3.3 4.7 1.6 versicolor
+## 58 4.9 2.4 3.3 1.0 versicolor
+## 59 6.6 2.9 4.6 1.3 versicolor
+## 60 5.2 2.7 3.9 1.4 versicolor
+## 61 5.0 2.0 3.5 1.0 versicolor
+## 62 5.9 3.0 4.2 1.5 versicolor
+## 63 6.0 2.2 4.0 1.0 versicolor
+## 64 6.1 2.9 4.7 1.4 versicolor
+## 65 5.6 2.9 3.6 1.3 versicolor
+## 66 6.7 3.1 4.4 1.4 versicolor
+## 67 5.6 3.0 4.5 1.5 versicolor
+## 68 5.8 2.7 4.1 1.0 versicolor
+## 69 6.2 2.2 4.5 1.5 versicolor
+## 70 5.6 2.5 3.9 1.1 versicolor
+## 71 5.9 3.2 4.8 1.8 versicolor
+## 72 6.1 2.8 4.0 1.3 versicolor
+## 73 6.3 2.5 4.9 1.5 versicolor
+## 74 6.1 2.8 4.7 1.2 versicolor
+## 75 6.4 2.9 4.3 1.3 versicolor
+## 76 6.6 3.0 4.4 1.4 versicolor
+## 77 6.8 2.8 4.8 1.4 versicolor
+## 78 6.7 3.0 5.0 1.7 versicolor
+## 79 6.0 2.9 4.5 1.5 versicolor
+## 80 5.7 2.6 3.5 1.0 versicolor
+## 81 5.5 2.4 3.8 1.1 versicolor
+## 82 5.5 2.4 3.7 1.0 versicolor
+## 83 5.8 2.7 3.9 1.2 versicolor
+## 84 6.0 2.7 5.1 1.6 versicolor
+## 85 5.4 3.0 4.5 1.5 versicolor
+## 86 6.0 3.4 4.5 1.6 versicolor
+## 87 6.7 3.1 4.7 1.5 versicolor
+## 88 6.3 2.3 4.4 1.3 versicolor
+## 89 5.6 3.0 4.1 1.3 versicolor
+## 90 5.5 2.5 4.0 1.3 versicolor
+## 91 5.5 2.6 4.4 1.2 versicolor
+## 92 6.1 3.0 4.6 1.4 versicolor
+## 93 5.8 2.6 4.0 1.2 versicolor
+## 94 5.0 2.3 3.3 1.0 versicolor
+## 95 5.6 2.7 4.2 1.3 versicolor
+## 96 5.7 3.0 4.2 1.2 versicolor
+## 97 5.7 2.9 4.2 1.3 versicolor
+## 98 6.2 2.9 4.3 1.3 versicolor
+## 99 5.1 2.5 3.0 1.1 versicolor
+## 100 5.7 2.8 4.1 1.3 versicolor
+## 101 6.3 3.3 6.0 2.5 virginica
+## 102 5.8 2.7 5.1 1.9 virginica
+## 103 7.1 3.0 5.9 2.1 virginica
+## 104 6.3 2.9 5.6 1.8 virginica
+## 105 6.5 3.0 5.8 2.2 virginica
+## 106 7.6 3.0 6.6 2.1 virginica
+## 107 4.9 2.5 4.5 1.7 virginica
+## 108 7.3 2.9 6.3 1.8 virginica
+## 109 6.7 2.5 5.8 1.8 virginica
+## 110 7.2 3.6 6.1 2.5 virginica
+## 111 6.5 3.2 5.1 2.0 virginica
+## 112 6.4 2.7 5.3 1.9 virginica
+## 113 6.8 3.0 5.5 2.1 virginica
+## 114 5.7 2.5 5.0 2.0 virginica
+## 115 5.8 2.8 5.1 2.4 virginica
+## 116 6.4 3.2 5.3 2.3 virginica
+## 117 6.5 3.0 5.5 1.8 virginica
+## 118 7.7 3.8 6.7 2.2 virginica
+## 119 7.7 2.6 6.9 2.3 virginica
+## 120 6.0 2.2 5.0 1.5 virginica
+## 121 6.9 3.2 5.7 2.3 virginica
+## 122 5.6 2.8 4.9 2.0 virginica
+## 123 7.7 2.8 6.7 2.0 virginica
+## 124 6.3 2.7 4.9 1.8 virginica
+## 125 6.7 3.3 5.7 2.1 virginica
+## 126 7.2 3.2 6.0 1.8 virginica
+## 127 6.2 2.8 4.8 1.8 virginica
+## 128 6.1 3.0 4.9 1.8 virginica
+## 129 6.4 2.8 5.6 2.1 virginica
+## 130 7.2 3.0 5.8 1.6 virginica
+## 131 7.4 2.8 6.1 1.9 virginica
+## 132 7.9 3.8 6.4 2.0 virginica
+## 133 6.4 2.8 5.6 2.2 virginica
+## 134 6.3 2.8 5.1 1.5 virginica
+## 135 6.1 2.6 5.6 1.4 virginica
+## 136 7.7 3.0 6.1 2.3 virginica
+## 137 6.3 3.4 5.6 2.4 virginica
+## 138 6.4 3.1 5.5 1.8 virginica
+## 139 6.0 3.0 4.8 1.8 virginica
+## 140 6.9 3.1 5.4 2.1 virginica
+## 141 6.7 3.1 5.6 2.4 virginica
+## 142 6.9 3.1 5.1 2.3 virginica
+## 143 5.8 2.7 5.1 1.9 virginica
+## 144 6.8 3.2 5.9 2.3 virginica
+## 145 6.7 3.3 5.7 2.5 virginica
+## 146 6.7 3.0 5.2 2.3 virginica
+## 147 6.3 2.5 5.0 1.9 virginica
+## 148 6.5 3.0 5.2 2.0 virginica
+## 149 6.2 3.4 5.4 2.3 virginica
+## 150 5.9 3.0 5.1 1.8 virginicaplot(iris)
#The one on the third row and fourth column is appropriate to run a correlation on, which is the relationship between Petal.Width and Petal.Length.
+
+
+
+
+
+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
+-
+
- Insert a new code block +
- Create a data frame from the
swirl-data.csvfile calledDF1
+
DF1 <- read_csv("swirl-data.csv")## Parsed with column specification:
+## cols(
+## course_name = col_character(),
+## lesson_name = col_character(),
+## question_number = col_double(),
+## correct = col_character(),
+## attempt = col_double(),
+## skipped = col_logical(),
+## datetime = col_double(),
+## hash = col_double()
+## )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
-
+
- Create a new data frame that only includes the variables
hash,lesson_nameandattemptcalledDF2
+
DF2 <- DF1[c("hash", "lesson_name", "attempt")]-
+
- Use the
group_byfunction to create a data frame that sums all the attempts for eachhashby eachlesson_namecalledDF3
+
DF3 <- DF2 %>% group_by(hash, lesson_name) %>% summarise(attempt = sum(attempt), .groups = "keep")-
+
On a scrap piece of paper draw what you think
DF3would look like if all the lesson names were column names
+Convert
DF3to this format
+
library(tidyverse)
+library(tidyr)
+DF7 <- DF3 %>% spread(key = lesson_name, value = attempt)
+head(DF7)## # A tibble: 6 x 33
+## # Groups: hash [6]
+## hash Base_Plotting_S… `Basic Building… Clustering_Exam… `Dates and Time…
+## <dbl> <dbl> <dbl> <dbl> <dbl>
+## 1 2864 NA 29 NA NA
+## 2 4807 NA 49 NA 51
+## 3 6487 NA 25 NA NA
+## 4 8766 NA NA NA NA
+## 5 11801 NA 16 NA 18
+## 6 12264 NA NA NA NA
+## # … with 28 more variables: Exploratory_Graphs <dbl>, Fu <dbl>,
+## # Functions <dbl>, Graphics_Devices_in_R <dbl>, `Grouping and C` <dbl>,
+## # `Grouping and Chaining w` <dbl>, `Grouping and Chaining with dplyr` <dbl>,
+## # Hierarchica <dbl>, Hierarchical_Clustering <dbl>, K_Means_Clustering <dbl>,
+## # Lo <dbl>, Logic <dbl>, Looking <dbl>, `Looking at Data` <dbl>,
+## # Manipulatin <dbl>, `Manipulating Data with dplyr` <dbl>, `Matrices and Data
+## # Frames` <dbl>, `Missing Values` <dbl>, Plotting_Systems <dbl>,
+## # Principles_of_Analytic_Graphs <dbl>, Subsetti <dbl>, `Subsetting
+## # Vectors` <dbl>, `Tidying Data` <dbl>, `Tidying Data with tid` <dbl>,
+## # `Tidying Data with tidyr` <dbl>, Vectors <dbl>, `Workspace and
+## # Files` <dbl>, `<NA>` <dbl>-
+
- Create a new data frame from
DF1calledDF4that only includes the variableshash,lesson_nameandcorrect
+
DF4 <- DF1[c("hash","lesson_name","correct")]-
+
- Convert the
correctvariable so thatTRUEis coded as the number1andFALSEis coded as0
+
DF4$correct <- ifelse(DF4$correct == TRUE, 1, 0)-
+
- Create a new data frame called
DF5that provides a mean score for each student on each course
+
DF5 <- DF4 %>% group_by(hash, lesson_name) %>% summarise(score = mean(correct), .groups = "keep")-
+
- Extra credit Convert the
datetimevariable into month-day-year format and create a new data frame (DF6) that shows the average correct for each day
+
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.
+