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 5ae641a..c8cb962 100644 --- a/Class 7 Instructions.Rmd +++ b/Class 7 Instructions.Rmd @@ -18,7 +18,7 @@ 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("~/R/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) @@ -38,7 +38,7 @@ The gather command requires the following input arguments: - ...: Names of source columns that contain values ```{r} -data_long <- gather(data_wide, date, variables) +data_long <- tidyr::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? @@ -53,13 +53,15 @@ The spread function requires the following input: - value: Name of column containing values ```{r} -instructor_data <- spread(data_long, variables, measure) +instructor_data <- tidyr::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_Wide <- read.table("~/R/class7/student_activity.csv", sep = ",", header = TRUE) +Student_Data_Spread <- tidyr::spread(Student_Data_Wide, variable, measure) +View(Student_Data_Spread) ``` ##Now that you have workable student data set, subset it to create a data set that only includes data from the second class. @@ -69,13 +71,14 @@ 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} -student_data_2 <- dplyr::filter(student_data, date == 20160204) +student_data_Spread_filt1 <- dplyr::filter(Student_Data_Spread, date == 20160204) +View(student_data_Spread_filt1) ``` 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_wide_class2_t4 <- dplyr::filter(student_data_Spread_filt1, table == 4) ``` ##Make a new variable @@ -89,7 +92,9 @@ 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_sleep1 <- instructor+data$total_sleep +instructor_sleep <- data.frame(instructor_sleep1) +View(instructor_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: @@ -100,24 +105,26 @@ 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} - +sdata_wide <- dplyr::mutate(Student_Data_Spread, week = dplyr::ntile(date, 3)) +View(sdata_wide) ``` ##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} -student_data %>% dplyr::summarise(mean(motivation)) +Student_Data_Spread %>% 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_Data_Spread %>% 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 <- sdata_wide %>% dplyr::group_by(week) %>% dplyr::summarise(mean(motivation)) +instructor_week <- instructor_data %>% dplyr::group_by(week) %>% dplyr::summarise(mean(m_active_time)) ``` ##Merging @@ -125,13 +132,22 @@ Now we will merge these two data frames using dplyr. ```{r} merge <- dplyr::full_join(instructor_week, student_week, "week") +names(merge)<-c("week","mean_active_time","mean_motivation") ``` ##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} - +plot(merge$mean_active_time,merge$mean_motivation,xlim=c(1000,10000),ylim=c(2,2)) +cor.test(merge$mean_active_time,merge$mean_motivation) ``` Fnally save your markdown document and your plot to this folder and comit, push and pull your repo to submit. + +##Alcoholic +```{r} +counts <- table(instructor_data$alcoholic_beverages) +barplot(counts, main="Coping Distribution", + xlab="Number of 'help me forgets' to cope with students") +``` \ No newline at end of file diff --git a/class7.Rproj b/class7.Rproj new file mode 100644 index 0000000..8e3c2eb --- /dev/null +++ b/class7.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