rm(list=ls(all=TRUE))
## you have to write code below every line that starts with #. Lines that start with ##provide explanations
# clear the workspace
# load the resume.csv data
# create a cross tab of the sex and call variables in the resume data.
# Calculate the mean of the call variable for the subset of female names that sound white
# Calculate the difference in means between the call variable for the subset of female names that sound white and the female names that sounds black
# Use the ifelse function to create a new variable called carrie that is 1 if the resume name (firstname) is "Carrie" and 0 otherwise.
# calculate the mean of the call variable for all observations where the carrie variable is 1
# clear the workspace
# load the star.csv data
# Try to calculate the mean of the math variable in the star data frame without setting na.rm.
# Try to calculate the mean of the math variable with setting na.rm.
# Create an object call classcounts that consists of a table of counts for each category of classtype.
# create a basic barplot using the classcounts object
# make the barplot nice in the ways discussed in class
# Use the hist function to create a histogram for the g4math variable in the star data frame.
# make the histogram nice in the ways discussed in class