diff --git a/Class 7 Instructions.Rmd b/Class 7 Instructions.Rmd
index 5ae641a..fbe0051 100644
--- a/Class 7 Instructions.Rmd	
+++ b/Class 7 Instructions.Rmd	
@@ -1,137 +1,148 @@
----
-title: "Assignment 3"
-author: "Charles Lang"
-date: "February 13, 2016"
----
-##In this assignment you will be practising data tidying. You will be using the data we have collected from class and data generated from the instructor wearing a wristband activity tracker.
-
-##First, you need to import into R a data set containing information about Charles' activity for the last three weeks. You can find this data set within the Assignment 3 repository you cloned to create this project.
-
-##Install packages for manipulating data
-We will use two packages: tidyr and dplyr
-```{r}
-#Insall packages
-install.packages("tidyr", "dplyr")
-#Load packages
-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)
-
-#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)
-
-#R doesn't like having variable names that consist only of numbers so, as you can see, every variable starts with the letter "X". The numbers represent dates in the format year-month-day.
-
-
-```
-
-##This is not a convenient format for us to analyze. What we need is for each type of measure to be a column. Your fisrt task is to convert wide format to long format data. To do this we will use the "gather" function: gather(data, time, variables)
-
-The gather command requires the following input arguments:
-
-- data: Data object
-- key: Name of new key column (made from names of data columns)
-- value: Name of new value column
-- ...: Names of source columns that contain values
-
-```{r}
-data_long <- gather(data_wide, date, variables)
-#Rename the variables so we don't get confused about what is what!
-names(data_long) <- c("variables", "date", "measure")
-#Take a look at your new data, looks weird huh?
-View(data_long)
-```
-##Now convert this long format into separate columns using the "spread" function to separate by the type of measure
-
-The spread function requires the following input:
-
-- data: Data object
-- key: Name of column containing the new column names
-- value: Name of column containing values
-
-```{r}
-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)
-
-```{r}
-
-```
-
-##Now that you have workable student data set, subset it to create a data set that only includes data from the second class. 
-
-To do this we will use the dplyr package (We will need to call dplyr in the command by writing dplyr:: because dplyr uses commands that exist in other packages but to do different operations.) 
-
-Notice that the way we subset is with a logical rule, in this case date == 20160204. In R, when we want to say that something "equals" something else we need to use a double equals sign "==". (A single equals sign means the same as <-).
-
-```{r}
-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}
-
-```
-
-##Make a new variable
-
-It is useful to be able to make new variables for analysis. We can either apend a new variable to our dataframe or we can replace some variables with a new variable. Below we will use the "mutate" function to create a new variable "total_sleep" from the light and deep sleep variables in the instructor data.
-
-```{r}
-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}
-
-```
-
-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:
-
-```{r}
-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}
-
-```
-
-##Sumaraizing
-Next we will summarize the student data. First we can simply take an average of one of our student variables such as motivation:
-
-```{r}
-student_data %>% dplyr::summarise(mean(motivation))
-
-#That isn't super interesting, so let's break it down by week:
-
-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}
-
-```
-
-##Merging
-Now we will merge these two data frames using dplyr. 
-
-```{r}
-merge <- dplyr::full_join(instructor_week, student_week, "week")
-```
-
-##Visualize
-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}
-
-```
-
-Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit.
+---
+  title: "Assignment 3"
+author: "Charles Lang"
+date: "February 13, 2016"
+output: pdf_document
+---
+  ##In this assignment you will be practising data tidying. You will be using the data we have collected from class and data generated from the instructor wearing a wristband activity tracker.
+  
+  ##First, you need to import into R a data set containing information about Charles' activity for the last three weeks. You can find this data set within the Assignment 3 repository you cloned to create this project.
+  
+  
+  ##Install packages for manipulating data
+  We will use two packages: tidyr and dplyr
+```{r}
+#Insall packages
+install.packages("tidyr", "dplyr")
+#Load packages
+library(tidyr, dplyr)
+```
+
+##Upload wide format instructor data (instructor_activity_wide.csv)
+```{r}
+data_wide <- read.table("C:/Users/Magdalena Bennett/Dropbox/PhD Columbia/Fall 2016/Core Methods in EDM/class7-master/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)
+
+#R doesn't like having variable names that consist only of numbers so, as you can see, every variable starts with the letter "X". The numbers represent dates in the format year-month-day.
+
+
+```
+
+##This is not a convenient format for us to analyze. What we need is for each type of measure to be a column. Your fisrt task is to convert wide format to long format data. To do this we will use the "gather" function: gather(data, time, variables)
+
+The gather command requires the following input arguments:
+  
+  - data: Data object
+- key: Name of new key column (made from names of data columns)
+- value: Name of new value column
+- ...: Names of source columns that contain values
+
+```{r}
+data_long <- gather(data_wide, date, variables)
+#Rename the variables so we don't get confused about what is what!
+names(data_long) <- c("variables", "date", "measure")
+#Take a look at your new data, looks weird huh?
+View(data_long)
+```
+##Now convert this long format into separate columns using the "spread" function to separate by the type of measure
+
+The spread function requires the following input:
+  
+  - data: Data object
+- key: Name of column containing the new column names
+- value: Name of column containing values
+
+```{r}
+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)
+
+```{r}
+data_stu_raw <- read.table("C:/Users/Magdalena Bennett/Dropbox/PhD Columbia/Fall 2016/Core Methods in EDM/class7-master/student_activity.csv", sep = ",", header = TRUE)
+
+View(data_stu_raw)
+
+student_data<-spread(data_stu_raw, 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. 
+
+To do this we will use the dplyr package (We will need to call dplyr in the command by writing dplyr:: because dplyr uses commands that exist in other packages but to do different operations.) 
+
+Notice that the way we subset is with a logical rule, in this case date == 20160204. In R, when we want to say that something "equals" something else we need to use a double equals sign "==". (A single equals sign means the same as <-).
+
+```{r}
+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, table == 4)
+```
+
+##Make a new variable
+
+It is useful to be able to make new variables for analysis. We can either apend a new variable to our dataframe or we can replace some variables with a new variable. Below we will use the "mutate" function to create a new variable "total_sleep" from the light and deep sleep variables in the instructor data.
+
+```{r}
+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:
+  
+```{r}
+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
+Next we will summarize the student data. First we can simply take an average of one of our student variables such as motivation:
+  
+```{r}
+student_data %>% dplyr::summarise(mean(motivation))
+
+#That isn't super interesting, so let's break it down by week:
+
+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
+Now we will merge these two data frames using dplyr. 
+
+```{r}
+merge <- dplyr::full_join(instructor_week, student_week, "week")
+names(merge)<-c("week","mean_act_time","mean_motiv")
+```
+
+##Visualize
+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$mean_act_time,merge$mean_motiv,xlim=c(5500,7000),ylim=c(1.5,1.9))
+cor.test(merge$mean_act_time,merge$mean_motiv)
+```
+
+Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit.
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