From fa2c4e575b4f44a00092646eafaa7d3067c414d3 Mon Sep 17 00:00:00 2001 From: KarenZhuqianZhou Date: Tue, 27 Sep 2016 17:55:50 -0400 Subject: [PATCH] Zhuqian's assignment 3 --- .gitignore | 4 + Class_7_Instructions.html | 250 ++++++++++++++++++++++++++++++++++++++ Zhuqian Zhou-Plot.png | Bin 0 -> 3444 bytes class7.Rproj | 13 ++ 4 files changed, 267 insertions(+) create mode 100644 .gitignore create mode 100644 Class_7_Instructions.html create mode 100644 Zhuqian Zhou-Plot.png create mode 100644 class7.Rproj 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.html b/Class_7_Instructions.html new file mode 100644 index 0000000..9d5a255 --- /dev/null +++ b/Class_7_Instructions.html @@ -0,0 +1,250 @@ + + + + + + + + + + + + + + + +Assignment 3 + + + + + + + + + + + + + + + + + + + + + + + + + + +
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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.

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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.

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Install packages for manipulating data

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We will use two packages: tidyr and dplyr

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#Insall packages
+install.packages("tidyr")
+install.packages("dplyr")
+#Load packages
+library(tidyr, dplyr)
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Upload wide format instructor data (instructor_activity_wide.csv)

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data_wide <- read.table("~/Career/TC/Courses/Core Methods in EDM/class7/instructor_activity_wide.csv", sep = ",", header = TRUE)
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+#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)
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+#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.
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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)

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The gather command requires the following input arguments:

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  • data: Data object
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  • key: Name of new key column (made from names of data columns)
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  • value: Name of new value column
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  • …: Names of source columns that contain values
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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)
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Now convert this long format into separate columns using the “spread” function to separate by the type of measure

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The spread function requires the following input:

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  • data: Data object
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  • key: Name of column containing the new column names
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  • value: Name of column containing values
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instructor_data <- spread(data_long, variables, measure)
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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)

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student_activity <- read.table("~/Career/TC/Courses/Core Methods in EDM/class7/student_activity.csv", sep = ",", header = TRUE)
+student_data <- spread(student_activity, variable, measure)
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Now that you have workable student data set, subset it to create a data set that only includes data from the second class.

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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.)

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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 <-).

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student_data_2 <- dplyr::filter(student_data, date == 20160204)
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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:

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student_data_3 <- dplyr::filter(student_data, table == 4)
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Make a new variable

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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.

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instructor_data <- dplyr::mutate(instructor_data, total_sleep = s_deep + s_light)
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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:

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instructor_sleep <- dplyr::select(instructor_data, total_sleep)
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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:

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instructor_data <- dplyr::mutate(instructor_data, week = dplyr::ntile(date, 3))
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Create the same variables for the student data frame, write your code in the code chunk below:

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student_data <- dplyr::mutate(student_data, week=dplyr::ntile(date, 3))
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Sumaraizing

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Next we will summarize the student data. First we can simply take an average of one of our student variables such as motivation:

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student_data %>% dplyr::summarise(mean(motivation))
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+#That isn't super interesting, so let's break it down by week:
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+student_data %>% dplyr::group_by(date) %>% dplyr::summarise(mean(motivation))
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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:

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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))
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Merging

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Now we will merge these two data frames using dplyr.

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merge <- dplyr::full_join(instructor_week, student_week, "week")
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Visualize

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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:

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names(merge)<-c("week", "avg_student", "avg_instructor")
+plot(merge$avg_student, merge$avg_instructor)
+cor.test(merge$avg_student, merge$avg_instructor)
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Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit.

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