Question

I have a data set called ACS that I set my_data <- read.csv('acs_ny_CSV.csv'). One of the values in the data set here is FamilyIncome having a value from 50 to 1 mill plus FamilyIncome Min. : 50 1st Qu.: 52540 Median : 87000 Mean : 110281 3rd Qu.: 133800 Max. :1605000 I need to convert this value to a 0 and 1 as I need to "Make a binary variable with value TRUE for income above $150,000 and FALSE for income below." Can yo tell me what I need to do for coding? For additional info the overall problem I am solving is that I need this info for is -...... Use the subset (acs_ny.csv) of the 2010 American Community Survey (ACS) for New York state found here http://www.jaredlander.com/data/acs_ny.csv , make a logistic regression model in R. predict whether a household has an income > than $150,000. Explain your results including deviance residuals, coefficients, and AIC. Make a coefficient plot for logistic regression on family income greater than $150,000. Make a new binary variable with value TRUE for income above $150,000 and FALSE for income below. Make a density plot of family income to see distribution. Use glm() function to perform logistic regression in R.

I am going to use the code

model <- glm(formula=FamilyIncome~.,data=my_data,family='binomial')

which I am hoping once the Family income has been changed to True for > = 150,000 and False for < 150,000 false will run and work

Answer 1

m = 150000
data[data$FamilyIncome > m,'y'] = TRUE
data[data$FamilyIncome <= m,'y'] = FALSE

Above will create a new column y with your 1 or 0 category

You can remove FamilyIncome now

data$FamilyIncome <- NULL

Then run the code

model <- glm(formula=y~.,data=my_data,family='binomial')