Assignment 2 Upload

Assignment 2-2020.Rmd → Data-Wrangling-Visualization.Rmd

@@ -1,27 +1,18 @@
 ---
-title: "Assignment 2"
-author: "Charles Lang"
-date: "September 24, 2020"
+title: "Data-Wrangling-Visualization"
+author: "Nicole Schlosberg"
+date: "September 29, 2020"
 output: html_document
 ---
-#Part I
+
+
+## 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(tidyverse)
 library(tidyr)
 library(dplyr)
 
@@ -34,27 +25,21 @@ 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
@@ -68,9 +53,7 @@ table1 <- table(x,y)
 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
@@ -79,87 +62,125 @@ D4 <- filter(D1, stid == 4|stid == 20| stid == 22)
 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
-#pmax sets a maximum value, pmin sets a 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) creates a random sample with a mean of 75 and standard deviation of 15
+#filter() can be used to set max min value and can only work with a data frame, for rows
+#select() for columns
+#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)
 
-```
+#Top and tail the scores
+S1 <- filter(S1, score <= 100)
+hist(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.
+S2 <- data.frame(rep(100,5)) #repeat 100 5 times and names the column a random name that is not helpful so use the names() command to rename
+names(S2) <- "score"
+S3 <- bind_rows(S1,S2) #must make sure that the names of the columns and the type match
 
-```{r}
+#S3$score <- ifelse(S3$score >= 100, 100, S3$score)
 
+S3$score <-round(S3$score,0)
+
+interest <- c("sport", "music", "nature", "liturature")
+S3$interest <- sample(interest, 100, replace = TRUE)
+S3$stid <- seq(1,100,1)
 ```
 
+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]
+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$colors <- brewer.pal(10, "BrBG")
 #Use named palette in histogram
-
+hist(S3$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, "BuPu")
+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$score, S3$login, main = "Login vs. Score", xlab = "Score", ylab = "Login", col = interest.col) 
 ```
 
-
 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}
-
+plot(AirPassengers, type = "l", xlab = "Date", ylab = "Passenger numbers") 
 ```
 
-
-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? 
+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 appropriate to run a correlation on? 
 
 ```{r}
-
+plot(iris)
+plot(iris$Sepal.Length,iris$Sepal.Width)
+plot(iris$Petal.Length,iris$Petal.Width)
+plot(iris$Petal.Length,iris$Sepal.Length)
+plot(iris$Petal.Width,iris$Sepal.Width)
+plot(iris$Petal.Width,iris$Sepal.Length)
+plot(iris$Petal.Length,iris$Sepal.Width)
+plot(iris$Species,iris$Sepal.Width, xlab = "Species", ylab = "Sepal Width")
+plot(iris$Species,iris$Sepal.Length, xlab = "Species", ylab = "Sepal Length")
+plot(iris$Species,iris$Petal.Width, xlab = "Species", ylab = "Petal Width")
+plot(iris$Species,iris$Petal.Length, xlab = "Species", ylab = "Petal Length")
+
+#Which of these relationships is it appropriate to run a correlation on?
+#Correlation between Sepal Length and Width
+corOfSepalLW <- cor(iris$Sepal.Length, iris$Sepal.Width)
+#Correlation between Petal Length and Width
+corOfPetalLW <- cor(iris$Petal.Length, iris$Petal.Width)
+#Correlation between Petal Length and Sepal Length
+corOfLengthPS <- cor(iris$Petal.Length, iris$Sepal.Length)
+#Correlation between Petal Width and Sepal Width
+corOfWidthPS <- cor(iris$Petal.Width, iris$Sepal.Width)
 ```
 
 # Part III - Analyzing Swirl
@@ -171,6 +192,7 @@ In this repository you will find data describing Swirl activity from the class s
 ### Instructions
 
 1. Insert a new code block
+
 2. Create a data frame from the `swirl-data.csv` file called `DF1`
 
 The variables are:
@@ -188,16 +210,76 @@ The variables are:
 
 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}
+#2
+DF1 <- read.csv("swirl-data.csv", header = TRUE)
+
+#3
+DF2<- select(DF1, hash, lesson_name, attempt)
+
+#4
+DF3 <-  DF2 %>% group_by(hash, lesson_name) %>% summarise(sum_key = sum(attempt), .groups = "keep")
+```
+
 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  
+6. Convert `DF3` to this format 
+
+```{r}
+#6
+#Get rid of the NAs so the next step does not throw error and add extra column of NAs
+DF3 <- na.omit(DF3)
+DF3 <- spread(DF3, lesson_name, sum_key) 
+```
 
 7. Create a new data frame from `DF1` called `DF4` that only includes the variables `hash`, `lesson_name` and `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}
+#7
+DF4 <- select(DF1, hash, lesson_name, correct)
+
+#8
+#Correct misspelled FALS at line 809 in swirl_data.csv and subsequent DF4 dataframe from DF1
+DF4$correct <- ifelse(DF4$correct == "FALS", "FALSE", DF4$correct)
+
+#Convert the chr that was created with last back to logi
+DF4$correct <- type.convert(DF4$correct) 
+
+#Get rid of the NAs so the next steps do not throw "NAs introduced by coercion"
+DF4 <- DF4[complete.cases(DF4$correct),]
+
+#Converts logi to num so 0s and 1s instead of FALSE and TRUE
+DF4$correct <- as.numeric(DF4$correct) 
+```
 
 9. Create a new data frame called `DF5` that provides a mean score for each student on each course
 
-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}
+#9
+DF5 <- DF4 %>% group_by(hash, lesson_name) %>% summarise(mean_key = mean(correct), .groups = "keep")
+```
+
+10. 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}
+#10
+DF6 <- select(DF1, hash, lesson_name, datetime, correct)
+
+#steps to get TRUE/FALSE to 1/0
+DF6$correct <- ifelse(DF6$correct == "FALS", "FALSE", DF6$correct)
+DF6$correct <- type.convert(DF6$correct) 
+DF6 <- DF6[complete.cases(DF6$correct),]
+DF6$correct <- as.numeric(DF6$correct) 
+
+#Creating average correct for each day
+DF6 <- DF6 %>% group_by(hash, datetime, correct) %>% summarise(meanByDay = mean(correct), .groups = "keep")
+
+#Convert 'datetime' to month-day-year by converting the parsed num***
+#library(lubridate)
+#dateConverted <- mdy_hms(DF6$datetime)
+#DF6 <- separate_rows(DF6, DF6$datetime, sep = "0")
+```
+
 
-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. 

Data-Wrangling-Visualization.Rproj

@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

README.md

@@ -1,12 +1,11 @@
 # Assignment 2
 ### Data Wrangling and Visualization
 
-In Assignment 2 we will be practicing data manipulation including use of the tidyverse.
-
-The instructions to Assignment 2 are in the Assignment 2-2020.rmd file. Assignments are structured in three parts, in the first part you can just follow along with the code, in the second part you will need to apply the code, and in the third part is completely freestyle and you are expected to apply your new knowledge in a new way. 
-
-**Please complete as much as you can by midnight EDT, 10/05/20**
-
-Once you have finished, commit, push and pull your assignment back to the main branch. Include both the .Rmd file and the .html file.
-
-Good luck!
+Practicing data manipulation including use of the tidyverse.
+
+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