class7

.gitignore

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

Class 7 Instructions.Rmd

@@ -2,6 +2,7 @@
 title: "Assignment 3"
 author: "Charles Lang"
 date: "February 13, 2016"
+output: html_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.
 
@@ -10,15 +11,15 @@ date: "February 13, 2016"
 ##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)
+data_wide <- read.table("~/Desktop/FALL_2016/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 +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}
-
+Student_data <- spread(data_wide, 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. 
@@ -72,10 +73,10 @@ Notice that the way we subset is with a logical rule, in this case date == 20160
 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:
+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 t?he following chunk:
 
 ```{r}
-
+student_data_2 <- dplyr::filter(Student_data, table == 4) 
 ```
 
 ##Make a new variable
@@ -89,7 +90,7 @@ 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 +101,7 @@ 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 +118,8 @@ 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 %>% dplyr::group_by(week) %>% dplyr::summarise(mean(motivation))
+instructor_week %>% dplyr::group_by(week) %>% dplyr::summarise(mean(m_active_time))
 ```
 
 ##Merging
@@ -131,7 +133,12 @@ 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(student_week,instructor_week)
+ x <- c(1,2,3)
+ y <- c(6913.25, 6240.28571428571,5956.14285714286)
+ cor.test(x,y)
 ```
 
+
+
 Fnally 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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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

R markdown.Rmd

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+---
+title: "Markdown"
+author: "Joonyoung Park"
+date: "9/27/2016"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+## R Markdown
+
+This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
+
+When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
+
+```{r cars}
+summary(cars)
+```
+
+## Including Plots
+
+You can also embed plots, for example:
+
+```{r pressure, echo=FALSE}
+plot(pressure)
+```
+
+Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.
+
+```{r cars}
+summary (cars)
+```
+
+```{r pressure, echo=FALSE}
+plot(pressure)
+```
+