From 3a3be49f6fb02dadceb861b3343e7c45572a4d57 Mon Sep 17 00:00:00 2001
From: YOLO <yolo@YOLOs-Mac-mini.local>
Date: Wed, 28 Sep 2016 12:50:49 -0400
Subject: [PATCH 1/4] class7 added

---
 Class 7 Instructions.Rmd | 26 ++++++++++++++++++++------
 1 file changed, 20 insertions(+), 6 deletions(-)

diff --git a/Class 7 Instructions.Rmd b/Class 7 Instructions.Rmd
index 5ae641a..b16eb8b 100644
--- a/Class 7 Instructions.Rmd	
+++ b/Class 7 Instructions.Rmd	
@@ -1,7 +1,9 @@
+
 ---
 title: "Assignment 3"
 author: "Charles Lang"
 date: "February 13, 2016"
+##runtime: shiny
 ---
 ##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.
 
@@ -18,7 +20,7 @@ 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/EDM/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)
@@ -59,7 +61,9 @@ 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_student <- read.table("~/Documents/EDM/class7/student_activity.csv", sep = ",", header = TRUE)
+student_data <- spread(data_student, variable, measure)
+View(student_data)
 ```
 
 ##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 +79,7 @@ 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, table == 4)
 ```
 
 ##Make a new variable
@@ -89,7 +93,7 @@ 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 +104,7 @@ 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 +121,8 @@ 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
@@ -131,6 +136,15 @@ 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}
+z <- dplyr::select(merge, -week)
+Plot(z)
+
+a <- dplyr::select(z, 1)
+aa <-t(a)
+
+b <- dplyr::select(z, 2)
+bb <-t(b)
+cor.test(aa,bb)
 
 ```
 

From 11f1dbd7f56b34580485a4d7207f40c9394e9d98 Mon Sep 17 00:00:00 2001
From: YOLO <yolo@YOLOs-Mac-mini.local>
Date: Wed, 28 Sep 2016 13:27:42 -0400
Subject: [PATCH 2/4] add echo = FALSE

---
 Class 7 Instructions.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Class 7 Instructions.Rmd b/Class 7 Instructions.Rmd
index b16eb8b..51b0417 100644
--- a/Class 7 Instructions.Rmd	
+++ b/Class 7 Instructions.Rmd	
@@ -135,7 +135,7 @@ 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}
+```{r echo = FALSE}
 z <- dplyr::select(merge, -week)
 Plot(z)
 

From 4fe9d8f39225a95deba07d1939f42336ffaa4bbc Mon Sep 17 00:00:00 2001
From: YOLO <yolo@YOLOs-Mac-mini.local>
Date: Wed, 28 Sep 2016 13:30:59 -0400
Subject: [PATCH 3/4] shiny

---
 Class 7 Instructions.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Class 7 Instructions.Rmd b/Class 7 Instructions.Rmd
index 51b0417..9e41859 100644
--- a/Class 7 Instructions.Rmd	
+++ b/Class 7 Instructions.Rmd	
@@ -3,7 +3,7 @@
 title: "Assignment 3"
 author: "Charles Lang"
 date: "February 13, 2016"
-##runtime: shiny
+runtime: shiny
 ---
 ##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.
 

From 9a13cfdc64f1f25beccbe519897fd13eec279fdd Mon Sep 17 00:00:00 2001
From: YOLO <yolo@YOLOs-Mac-mini.local>
Date: Wed, 28 Sep 2016 13:56:46 -0400
Subject: [PATCH 4/4] edit

---
 .DS_Store                | Bin 0 -> 6148 bytes
 .gitignore               |   4 ++++
 Class 7 Instructions.Rmd |  25 ++++++++++++++++----
 Rplot.png                | Bin 0 -> 24370 bytes
 class7script.R           |  49 +++++++++++++++++++++++++++++++++++++++
 5 files changed, 73 insertions(+), 5 deletions(-)
 create mode 100644 .DS_Store
 create mode 100644 .gitignore
 create mode 100644 Rplot.png
 create mode 100644 class7script.R

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
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diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..5b6a065
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
diff --git a/Class 7 Instructions.Rmd b/Class 7 Instructions.Rmd
index 9e41859..4ba7520 100644
--- a/Class 7 Instructions.Rmd	
+++ b/Class 7 Instructions.Rmd	
@@ -3,7 +3,6 @@
 title: "Assignment 3"
 author: "Charles Lang"
 date: "February 13, 2016"
-runtime: shiny
 ---
 ##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.
 
@@ -20,7 +19,7 @@ library(tidyr, dplyr)
 
 ##Upload wide format instructor data (instructor_activity_wide.csv)
 ```{r}
-data_wide <- read.table("~/Documents/EDM/class7/instructor_activity_wide.csv", sep = ",", header = TRUE)
+data_wide <- read.table("~/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)
@@ -61,7 +60,7 @@ 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_student <- read.table("~/Documents/EDM/class7/student_activity.csv", sep = ",", header = TRUE)
+data_student <- read.table("~/class7/student_activity.csv", sep = ",", header = TRUE)
 student_data <- spread(data_student, variable, measure)
 View(student_data)
 ```
@@ -135,9 +134,11 @@ 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 echo = FALSE}
+
+
+```{r}
 z <- dplyr::select(merge, -week)
-Plot(z)
+plot(z)
 
 a <- dplyr::select(z, 1)
 aa <-t(a)
@@ -148,4 +149,18 @@ cor.test(aa,bb)
 
 ```
 
+##visualization result
+
+image: ![](Rplot.png)
+
+Pearson's product-moment correlation
+
+data:  aa and bb
+## t = -2.0734, df = 1, p-value = 0.2861 (not significant correlation)
+alternative hypothesis: true correlation is not equal to 0
+sample estimates:
+       cor 
+-0.9007108 
+
+
 Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit.
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diff --git a/class7script.R b/class7script.R
new file mode 100644
index 0000000..1bebd64
--- /dev/null
+++ b/class7script.R
@@ -0,0 +1,49 @@
+data_wide <- read.table("~/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)
+
+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)
+
+instructor_data <- spread(data_long, variables, measure)
+
+data_student <- read.table("~/class7/student_activity.csv", sep = ",", header = TRUE)
+student_data <- spread(data_student, variable, measure)
+View(student_data)
+
+student_data_2 <- dplyr::filter(student_data, date == 20160204)
+
+student_data_3 <- dplyr::filter(student_data, table == 4)
+
+instructor_data <- dplyr::mutate(instructor_data, total_sleep = s_deep + s_light)
+
+instructor_sleep <- dplyr::select(instructor_data, total_sleep)
+
+instructor_data <- dplyr::mutate(instructor_data, week = dplyr::ntile(date, 3))
+
+student_data <- dplyr::mutate(student_data, week = dplyr::ntile(date, 3))
+
+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))
+
+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 <- dplyr::full_join(instructor_week, student_week, "week")
+
+z <- dplyr::select(merge, -week)
+plot(z)
+
+a <- dplyr::select(z, 1)
+aa <-t(a)
+
+b <- dplyr::select(z, 2)
+bb <-t(b)
+cor.test(aa,bb)