diff --git a/Assignment7class.R b/Assignment7class.R
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+#Opening data set
+data_wide <- read.table("C:/Users/lfaf373/Documents/RStudioProj/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)
+# opening the library of these two packages to tidy data
+library(tidyr, dplyr)
+#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 converts wide to long->> by date
+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)
+# Converting long format into seperate columns, seperating by type of measure
+instructor_data <- spread(data_long, variables, measure)
+# upload student data
+data_wide2<- read.csv("C:/Users/lfaf373/Documents/RStudioProj/cLASS7/student_activity.csv")
+#data_long2 <- gather(data_wide2, measure, variable)
+student_data <- spread(data_wide2, variable, measure)
+# first we have to make sure r knows to use dplyr command
+# Here we are only including data from the second clas
+student_data_2 <- dplyr::filter(student_data, date == 20160204)
+# Here we are only including data from students who sat at table 4
+# Subsetted from the original dataset
+student_data_3 <- dplyr::filter(student_data, table == 4)
+# usinging mutate to make a new variable, total sleep... which is lite plus deep sleep
+instructor_data <- dplyr::mutate(instructor_data, total_sleep = s_deep + s_light)
+#making a new data frame that only includes total_sleep 
+instructor_sleep<- dplyr::select(instructor_data, total_sleep)
+# create a grouping variable called week
+# for some reason, there are 8 days in week 1
+instructor_data <- dplyr::mutate(instructor_data, week = dplyr::ntile(date, 3))
+# Same as above but for stduent
+student_data <- dplyr::mutate(student_data, week = dplyr::ntile(date, 3))
+#
+# motivation <- dplyr::filter(student_data, variable == "motivation")
+# Summarize data
+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).
+
+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))
+
+#merge these two data set
+merge <- dplyr::full_join(instructor_week, student_week, "week")
+
+# plot mean motivation with mean activity time
+names(merge) <- c("week", "m2", "m3")
+plotting<-cor(merge$m2, merge$m3)
+plot(merge$m2, merge$m3)
diff --git a/cLASS7.Rproj b/cLASS7.Rproj
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+++ b/cLASS7.Rproj
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