Submit Class 7 Exercise.

.gitignore

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+.Rproj.user
+.Rhistory
+.RData

Class 7 Instructions.Rmd

@@ -18,7 +18,7 @@ library(tidyr, dplyr)
 
 ##Upload wide format instructor data (instructor_activity_wide.csv)
 ```{r}
-data_wide <- read.table("~/Documents/NYU/EDCT2550/Assignments/Assignment 3/instructor_activity_wide.csv", sep = ",", header = TRUE)
+data_wide <- read.table("~/Documents/EDM2016/class7/instructor_activity_wide.csv", sep = ",", header = TRUE)
 
 #Now view the data you have uploaded and notice how its structure: each variable is a date and each row is a type of measure.
 View(data_wide)
@@ -54,12 +54,15 @@ The spread function requires the following input:
 
 ```{r}
 instructor_data <- spread(data_long, variables, measure)
+View(instructor_data)
 ```
 
 ##Now we have a workable instructor data set!The next step is to create a workable student data set. Upload the data "student_activity.csv". View your file once you have uploaded it and then draw on a piece of paper the structure that you want before you attempt to code it. Write the code you use in the chunk below. (Hint: you can do it in one step)
 
 ```{r}
-
+student <- read.table("~/Documents/EDM2016/class7/student_activity.csv", sep = ",", header = TRUE)
+View(student)
+student_data <- spread(student, variable, measure)
 ```
 
 ##Now that you have workable student data set, subset it to create a data set that only includes data from the second class. 
@@ -75,7 +78,7 @@ student_data_2 <- dplyr::filter(student_data, date == 20160204)
 Now subset the student_activity data frame to create a data frame that only includes students who have sat at table 4. Write your code in the following chunk:
 
 ```{r}
-
+student_data_2_table4 <- dplyr::filter(student_data_2, table == 4)
 ```
 
 ##Make a new variable
@@ -89,7 +92,7 @@ instructor_data <- dplyr::mutate(instructor_data, total_sleep = s_deep + s_light
 Now, refering to the cheat sheet, create a data frame called "instructor_sleep" that contains ONLY the total_sleep variable. Write your code in the following code chunk:
 
 ```{r}
-
+instructor_sleep <- dplyr::select(instructor_data, total_sleep)
 ```
 
 Now, we can combine several commands together to create a new variable that contains a grouping. The following code creates a weekly grouping variable called "week" in the instructor data set:
@@ -100,7 +103,7 @@ instructor_data <- dplyr::mutate(instructor_data, week = dplyr::ntile(date, 3))
 
 Create the same variables for the student data frame, write your code in the code chunk below:
 ```{r}
-
+student_data <- dplyr::mutate(student_data, week = dplyr::ntile(date, 3))
 ```
 
 ##Sumaraizing
@@ -117,7 +120,8 @@ student_data %>% dplyr::group_by(date) %>% dplyr::summarise(mean(motivation))
 Create two new data sets using this method. One that sumarizes average motivation for students for each week (student_week) and another than sumarizes "m_active_time" for the instructor per week (instructor_week). Write your code in the following chunk:
 
 ```{r}
-
+student_week <- student_data %>% dplyr::group_by(week) %>% dplyr::summarise(mean(motivation))
+instructor_week <- instructor_data %>% dplyr::group_by(week) %>% dplyr::summarise(mean(m_active_time))
 ```
 
 ##Merging
@@ -131,7 +135,8 @@ merge <- dplyr::full_join(instructor_week, student_week, "week")
 Visualize the relationship between these two variables (mean motivation and mean instructor activity) with the "plot" command and then run a Pearson correlation test (hint: cor.test()). Write the code for the these commands below:
 
 ```{r}
-
+plot (merge$`mean(motivation)`, merge$`mean(m_active_time)`, ylab="Average Instructor Active Time by Week", xlab = "Average Student Motivation by Week", main = "Relationship between Motivation \n and Active Time")
+cor.test(merge$`mean(motivation)`, merge$`mean(m_active_time)`)
 ```
 
-Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit.
+Finally save your markdown document and your plot to this folder and comit, push and pull your repo to submit.

class7.Rproj

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