From 9f26df42e681e86a94ccd4b2a8198b87cb21c536 Mon Sep 17 00:00:00 2001 From: Jing907 Date: Tue, 27 Sep 2016 17:59:24 -0400 Subject: [PATCH] Thank you for reviewing, Dr. Lang! --- .gitignore | 4 + Assignment#3_Jing.Rproj | 13 ++ Class 7 Instructions.Rmd | 54 ++++---- Class_7_Instructions.html | 251 ++++++++++++++++++++++++++++++++++++++ Rplot.png | Bin 0 -> 21006 bytes 5 files changed, 297 insertions(+), 25 deletions(-) create mode 100644 .gitignore create mode 100644 Assignment#3_Jing.Rproj create mode 100644 Class_7_Instructions.html create mode 100644 Rplot.png 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/Assignment#3_Jing.Rproj b/Assignment#3_Jing.Rproj new file mode 100644 index 0000000..8e3c2eb --- /dev/null +++ b/Assignment#3_Jing.Rproj @@ -0,0 +1,13 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX diff --git a/Class 7 Instructions.Rmd b/Class 7 Instructions.Rmd index 5ae641a..e07b4dc 100644 --- a/Class 7 Instructions.Rmd +++ b/Class 7 Instructions.Rmd @@ -1,7 +1,7 @@ --- title: "Assignment 3" -author: "Charles Lang" -date: "February 13, 2016" +author: "Jingtong Feng" +date: "September 27, 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. @@ -9,7 +9,7 @@ date: "February 13, 2016" ##Install packages for manipulating data We will use two packages: tidyr and dplyr -```{r} +```{r, eval=FALSE} #Insall packages install.packages("tidyr", "dplyr") #Load packages @@ -17,8 +17,9 @@ 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) +```{r, eval=FALSE} +data_wide <- read.table("~/Desktop/HUDK 4050/Assignment#3_Jing/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) @@ -26,7 +27,6 @@ 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) @@ -37,7 +37,7 @@ The gather command requires the following input arguments: - value: Name of new value column - ...: Names of source columns that contain values -```{r} +```{r, eval=FALSE} 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") @@ -52,14 +52,14 @@ The spread function requires the following input: - key: Name of column containing the new column names - value: Name of column containing values -```{r} +```{r, eval=FALSE} 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} - +```{r, eval=FALSE} +student_data<-spread(student_activity,variables,measure) ``` ##Now that you have workable student data set, subset it to create a data set that only includes data from the second class. @@ -68,45 +68,45 @@ To do this we will use the dplyr package (We will need to call dplyr in the comm 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} +```{r, eval=FALSE} 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} - +```{r, eval=FALSE} +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} +```{r, eval=FALSE} 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} - +```{r, eval=FALSE} +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} +```{r, eval=FALSE} 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} - +```{r, eval=FALSE} +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} +```{r, eval=FALSE} student_data %>% dplyr::summarise(mean(motivation)) #That isn't super interesting, so let's break it down by week: @@ -116,22 +116,26 @@ 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} - +```{r, eval=FALSE} +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} +```{r, eval=FALSE} 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, eval=FALSE} +average_student_motivation<-merge$`mean(motivation)` +average_instructor_activity<-merge$`mean(m_active_time)` +plot(average_student_motivation,average_instructor_activity) +cor.test(average_student_motivation,average_instructor_activity) ``` Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit. diff --git a/Class_7_Instructions.html b/Class_7_Instructions.html new file mode 100644 index 0000000..3ec6f3e --- /dev/null +++ b/Class_7_Instructions.html @@ -0,0 +1,251 @@ + + + + + + + + + + + + + + + +Assignment 3 + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + + + +
+

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

+
#Insall packages
+install.packages("tidyr", "dplyr")
+#Load packages
+library(tidyr, dplyr)
+
+
+

Upload wide format instructor data (instructor_activity_wide.csv)

+
data_wide <- read.table("~/Desktop/HUDK 4050/Assignment#3_Jing/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
    • +
+
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
    • +
+
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)

+
student_data<-spread(student_activity,variables,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 <-).

+
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:

+
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.

+
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:

+
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:

+
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:

+
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:

+
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:

+
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.

+
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:

+
average_student_motivation<-merge$`mean(motivation)`
+average_instructor_activity<-merge$`mean(m_active_time)`
+plot(average_student_motivation,average_instructor_activity)
+cor.test(average_student_motivation,average_instructor_activity)
+

Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit.

+
+
+ + + + +
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