Assignment Class 7

.gitignore

@@ -0,0 +1,4 @@
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

Class 7 Instructions.Rmd

@@ -56,9 +56,11 @@ The spread function requires the following input:
 instructor_data <- spread(data_long, variables, measure)
 ```
 
-##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)
+##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}
+data_wide_2 <- read.table("student_activity_wide.csv", sep = ",", header = TRUE)
+> student_data_1 <- spread(data_wide_2,variable, measure)
 
 ```
 
@@ -75,7 +77,7 @@ 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_3 <- dplyr::filter(student_data_1, table == 4)
 ```
 
 ##Make a new variable
@@ -89,7 +91,7 @@ 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_data<- dplyr::mutate(instructor_data,instructor_sleep = 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 +102,7 @@ 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_1 <- dplyr::mutate(student_data_1, week = dplyr::ntile(date, 3))
 ```
 
 ##Sumaraizing
@@ -117,7 +119,8 @@ 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_data_4<- dplyr::mutate(student_data_1 %>% dplyr::group_by(week) %>% dplyr::summarise(mean(motivation)))
+instructor_week<- dplyr::mutate(instructor_data_1 %>% dplyr::group_by(week) %>% dplyr::summarise(mean(motivation)))
 ```
 
 ##Merging
@@ -131,7 +134,11 @@ 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(instructor_week,student_data_4)
+plot(student_data_4,instructor_week)
+x_motivation<-c(1.666667,1.851852,1.851852)
+y_active_time<-c(6913.25,6240.286,5956.143)
+cor.test(x_motivation,y_active_time)
 ```
 
 Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit.