Assgn#2

Assignment 2-2020.Rmd

@@ -96,13 +96,30 @@ pairs(D5)
 #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)
+
+library(dplyr)
+S1 <- filter(S1, score <= 100)
+hist(S1$score)
+
+S2 <- data.frame(rep(100,100-NROW(S1)))
+names(S2) <- "score"
+S3 <- bind_rows(S1,S2)
+
+interest <- c("sport", "music", "nature", "literature")
+
+S3$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 = 9)
 ```
 
 
@@ -111,19 +128,22 @@ pairs(D5)
 ```{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[1:9]
+S3$breaks <- cut(S3$score, breaks = 9, 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$colors <- brewer.pal(10, "Set3")
 #Use named palette in histogram
-
+hist(S3$score, col = S3$colors)
 ```
 
 
@@ -132,34 +152,39 @@ library(RColorBrewer)
 ```{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 = "Students Logins vs. Scores")
 
-
+S3$col1 <- ifelse(S3$interest == "sport", "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}
-
+plot(iris)
 ```
 
 # Part III - Analyzing Swirl
@@ -172,6 +197,10 @@ 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", TRUE)
+
+```
 
 The variables are:
 
@@ -185,18 +214,47 @@ The variables are:
 `hash` - anonymyzed student ID  
 
 3. Create a new data frame that only includes the variables `hash`, `lesson_name` and `attempt` called `DF2`
+```{r}
+
+DF2 <- data.frame(DF1[,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 <- DF2 %>% group_by(hash,lesson_name) %>% summarise(attempt_sum = 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  
+```{r}
+
+spread(DF3, lesson_name, attempt_sum)
+
+```
 
 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 [,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`  
+```{r}
+
+DF4$correct <- ifelse(DF4$correct == TRUE, 1, 0)
+
+```
 
 9. Create a new data frame called `DF5` that provides a mean score for each student on each course
+```{r}
+
+DF5 <- DF4 %>% group_by(hash, lesson_name) %>% summarise(mean_correct = mean(correct))
+```
 
 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