For this project, create 2 documents. Mark this week's entry under a subheading "Week 1" within each document. "R Script.R" - contains the R scripts associated with this work "R Outputs.docx" -...

1. For this project, create 2 documents. Mark this week's entry under a subheading "Week 1" within each document.
   
   
   1. "R Script.R" - contains the R scripts associated with this work
   
   
   2. "R Outputs.docx" - contains the outputs or the results
   
   
   
   


2. Import the file into R using read.csv( ) function


3. Prepare data.frame for analysis. Arrange variables (rename, drop, or keep variables), clean data (gsub or conditional assignments with ifelse() ), and apply appropriate data structure (i.e. integer, numerical, character, factors, etc.)


4. Prepare and export at least several frequency tables, cross-tabulations, and several histograms (preferably clean and with color, with appropriate titles and axis labels, etc.). You may want to look into table( ), ftable( ), gmodles::CrossTable, hist( ), ggplot2, and plotly, among others. Question: How do you show and compare the results across multiple classes?

Submit a report that examines parts 3 and 4 and your R script.

### Week 1 scripts for baseline survey setwd("C:/Users/James/Dropbox/Common/3 Develop/NEU/NEU 2021.2B 6010 Tues 80618 Snell 125/Week 1 The Probability and Normal Distributions/Week 1 assignment") mysurvey <- read.csv("baseline="" survey.csv",stringsasfactors="FALSE)" view(mysurvey)="" str(mysurvey)="" #="" subsetting="" partial1=""><- mysurvey[c("class","id","answer1","answer2","answer3")]="" #="" vertical="" subsetting="" head(partial1)="" str(partial1)="" #="" 1="" clean="" the="" variable="" #gsub("[^0-9\\.]",="" "",="" x)="" partial1$answer2a=""><- gsub("[^0-9\\.]",="" "",partial1$answer2)="" partial1="" #="" 2="" convert="" into="" numeric="" variable="" partial1$num2=""><- as.numeric(partial1$answer2)="" partial1="" str(partial1)="" #="" 3="" tabulate="" or="" cross-tabulate="" table(partial1$num2)="" table(partial1$num2,="" partial1$num3)="" #="" create="" num3="" tabulate(partial1$num2)="" tabulate(partial1$num2,="" partial1$num3)="" #="" create="" num3="" #="" 4.1="" make="" histogram="" hist(partial1$num2+0.25)="" #="" 4.2="" make="" nicer="" histograms="" #install.packages("ggplot2",dependencies="TRUE)" library(ggplot2)="" ggplot(partial1,="" aes(x="num2," color="Answer1))" +="" geom_histogram(fill="white" )="" #="" need="" to="" modify="" to="" make="" sense="" #="" horizontal="" subsetting="" dataname[row,column]="" table(partial1$answer1)="" engineers=""><- partial1[partial1$answer1=="engineering", ] ggplot(engineers, aes(x=num2 )) + geom_histogram(fill="blue") # need to modify to make sense hist() ggplot plotly partial1[partial1$answer1="="Engineering"," ]="" ggplot(engineers,="" aes(x="num2" ))="" +="" geom_histogram(fill="blue" )="" #="" need="" to="" modify="" to="" make="" sense="" hist()="" ggplot="">