Description

Directions: Create an R Markdown document to complete the tasks below. Include all necessary lines of code and explain your work using complete sentences. Both the code you write and the outputs from R should be included in the compiled/knitted HTML document. Submit both the .Rmd and .html files to CCLE. Name the files #########-lab01.Rmd and #########-lab01.html where the ######### are replaced by your Bruin ID.
1 Perform basic calculations in R
1.1 Use R as a calculator and show how to get answers for:
Note: You don’t need to explain your work for this problem.
1.
2.
1.2 Vectorized operations
Note: You don’t need to explain your work for this problem.
1. Use a single line of code to create a vector of the squared values of all the even numbers between -50 and 50, i.e. (−502,−482,…,502).
1.3 Sample vs. population standard deviation
The equation for calculating the standard deviation of a population is
v
uut 1 XN (xi − µ)2
N
i=1
where N is the size of the population, xi are the individual values of the population, and µ = N1 PNi=1 xi is the population mean.
The equation for calculating the standard deviation of a sample is
v
uutn −1 1 Xn (xi − x¯)2
i=1
where n is the size of the sample, xi are the individual values from the sample, and x¯ = n1 Pni=1 xi is the sample mean.
1. Create a numeric vector called values containing the values: 27,36,50,−24,9,−38.
2. Use R’s sd() function to calculate the standard deviation of values
3. Using R’s sum(), mean(), sqrt() and length() functions (Use each of them once), calculate the population and sample standard deviation of the values.
4. Does R’s sd() function calculate the sample or population standard deviation? Write down another source where we could have looked to answer this question without carrying out the calculations?
1.4 Vector classes
As we mentioned during the lecture, vectors in R are always a single class (Meaning if we mix numbers and strings/characters together in a vector, the numbers are coerced to strings).
1. Create a vector called numbers containing 4 unique numbers of your choosing. Create a vector called strings containing 3 unique strings of your choosing. Create a vector called booleans containing two
TRUEs and two FALSE values (i.e. TRUE, TRUE, FALSE, FALSE). Protip: You can actually just use T and F for boolean (logical) values in R.
2. Write down the classes of:
1. A vector containing numbers and strings.
2. A vector containing numbers and booleans.
3. A vector containing strings and booleans.
4. A vector containing numbers, strings and booleans.
3. Why do you think R coerces the different combination of vectors (numbers, strings and booleans)
into these classes?
2 Reading in different data file types
2.1 Childhood Respiratory Disease
The following link contains information about an observational study conducted to measure the effect of smoking on young people’s lung capacities: http://www.statsci.org/data/general/fev.html
The link to the data for this study can be found here: http://www.statsci.org/data/general/fev.txt
1. Without downloading the data onto your laptop, use the read.table() function to read the data straight from the URL and name it crd (for Childhood Respiratory Disease). Hint: For this step, just use the file argument to tell R the URL where the data is located.
• The first row of data in the file is actually the names of the variables. Be sure to find the specify the appropriate argument for read.table() so that the variable names are read in as variable names and NOT the values of the first observation. Hint: Check the help documentation for read.table() for the name of the appropriate argument.
2. Print the names of the variables using an R function.
3. Based on the following line of code, change the variable names so that (1) they’re all lower-case letters and (2) rename the FEV variable as lung_cap.
names(data) <- c(“new_name_1”, “new_name_2”, …, “new_name_n”)
4. Print the names of the variables again using an R function.
5. Run the line of code below to answer and then justify the question: Which is larger, the proportion of female smokers or the proportion of male smokers?
table(crd$sex, crd$smoker)
2.2 Reading Stata, SAS and SPSS files
Stata, SAS and SPSS are other statistical analysis softwares used in academia and industry (Stata is popular in economics, SPSS is popular in other humanities & psychology and SAS is a popular alternative to R).
These softwares export data in their own specialized formats, much like R exports data as .Rda files. Specifically, Stata exports data as .DTA files, SAS exports data as .sas7bdat files (among others) and SPSS exports data as .sav files.
Below are some links to various Stata, SAS and SPSS data files:
• http://www.sjsu.edu/people/carlos.e.garcia/courses/soci104/Course-Assignments/104data_2014.sav
• http://qcpages.qc.cuny.edu/~rvesselinov/statadata/WAGEPAN.DTA
• http://biostat3.net/download/sas/colon.sas7bdat Perform the following tasks using the links provided above.
1. Install and load the haven package into R:
• Use install.packages(“package_name”) to install packages. Remember, installing packages should happen in the console and not in R scripts nor R Markdown documents.
• Use library(package_name) to load the functions in the package into R. Remember, to load packages in R Markdown files before using functions from the package to avoid errors.
2. In the Packages pane in RStudio, click the name of the package you installed to open a list of functions. Find and use the appropriate functions to load the following data sets from the links listed (much like you did for the Childhood Respiratory Disease, that is, read the data straight from the URLs.)
3. Use R functions to:
1. Print the names of the variables in the SPSS data.
2. Print the number of observations and variables in the Stata data.
4. Install and load the knitr package into R. Use the kable and head functions to print the first six rows in the SAS data as a table.
3 Create and interpret plots
1. Install and load the ggplot2 package.
2. Use ggplot2 functions to create a histogram for the ages of people in the Childhood Respiratory Disease data. Specify the argument binwidth = 1.
3. Use ggplot2 functions to create a bargraph of whether people are current or non-smokers in the Childhood Respiratory Disease data. Let the fill of the bars be based on the sex of the people and use the argument position = “dodge” to make the bars side-by-side.
4. Use ggplot2 functions to create a scatterplot where the height of people in the Childhood Respiratory Disease data is on the x-axis, their lung capacities is on the y-axis and the color of the points are colored based on whether they are a current or non-smoker. Include the code + theme_minimal() to your plot to change the appearence of the plot.