Multiple Linear Regression with character as dependent variable - r

Question

I'm currently trying do perform a multiple linear regression on the voter turnout per state within the 2020 Presidential Election.
To create this regression model I would like to use the following variables: State, Total_Voters and Population.
When I try to run my linear regression I get the following error:
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : NA/NaN/Inf in 'y'
The dataset I've gathered is quite large. I have created a new dataframe with the variables which I need as follows:
Turnout_Rate_2020 <- sqldf("SELECT State_Full, F1a AS Total_Voters, population.Pop AS Population FROM e_2020 INNER JOIN population ON population.State = e_2020.State_Full")
After that I remove all NA values:
Turnout_Rate_2020[is.na(Turnout_Rate_2020)] <- 0
After that I filter through the dataframe once more and filter out all the states which did not report:
Turnout_Rate_2020 <- sqldf("SELECT State_Full, Total_Voters, Population FROM Turnout_Rate_2020 WHERE Total_Voters <> 0 AND Total_Voters >= 0 GROUP BY State_Full")
In the end the dataframe looks like this:
With the following summary:
However when I now try to run my multiple linear regression I get the error I have showcased above. The command looks like this:
lmTurnoutRate_2020 <- lm(State_Full ~ Population + Total_Voters, data = Turnout_Rate_2020)
I'm quite new to linear regressions but I'm eager to learn. I have looked through StackOverflow for quite a bit now, and couldn't figure it out.
It would be greatly appreciated if someone here would be able to assist me.
The full script at once:
Turnout_Rate_2020 <- sqldf("SELECT State_Full, F1a AS Total_Voters, population.Pop AS Population FROM e_2020 INNER JOIN population ON population.State = e_2020.State_Full")
# Change all NA to 0
Turnout_Rate_2020[is.na(Turnout_Rate_2020)] <- 0
summary(Turnout_Rate_2020)
# Select all again and filter out states which did not report. (values that were NA)
Turnout_Rate_2020 <- sqldf("SELECT State_Full, Total_Voters, Population FROM Turnout_Rate_2020 WHERE Total_Voters <> 0 AND Total_Voters >= 0 GROUP BY State_Full")
# Does not work and if I turn variables around I get NaN values.
lmTurnoutRate_2020 <- lm(State_Full ~ Population + Total_Voters, data = Turnout_Rate_2020)
summary(lmTurnoutRate_2020)
# Does not work
ggplot(lmTurnoutRate_2020, aes(x=State_Full,y=Population)) + geom_point() + geom_smooth(method=lm, level=0.95) + labs(x = "State", y = "Voters")

Answer 1

1) The input is missing from the question so we will use mtcars and make cyl a character column. lm cannot handle that but we could create a 0/1 model matrix from cyl and run that. This performs a separate lm for each level of cyl. This would only be applicable if the dependent variable had a small number of levels as we have here. If your dependent variable is naturally or has been cut into a small number of levels that would be the situation.
(Probably in this case we want to use logistic regression as with glm and family=binomial() or ordinal logistic regression as with polr in MASS or the ordinal package or multinom in nnet package but we will show it with lm just to show it can be done although it probably shouldn't be because with only two values the dependent variable is not sufficiently gaussian.)
mtcars2 <- transform(mtcars, cyl = as.character(cyl))
lm(model.matrix(~ cyl + 0) ~ hp, mtcars2)
giving:
Call:
lm(formula = model.matrix(~cyl + 0) ~ hp, data = mtcars2)
Coefficients:
cyl4 cyl6 cyl8
(Intercept) 1.052957 0.390688 -0.443645
hp -0.004835 -0.001172 0.006007
With polr (which assumes the levels are ordered as they are with cyl):
library(MASS)
polr(cyl ~ hp, transform(mtcars2, cyl = factor(cyl)))
giving:
Call:
polr(formula = cyl ~ hp, data = transform(mtcars2, cyl = factor(cyl)))
Coefficients:
hp
0.1156849
Intercepts:
4|6 6|8
12.32592 17.25331
Residual Deviance: 20.35585
AIC: 26.35585
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
The other possibility is that your dependent variable just happens to be represented as character because of how it was created but could be numeric if one used as.numeric(...) on it. We can't tell without the input but using our example we can do this although again it is likely inappropriate because cyl has only 3 values and so does not approximate a gaussian closely enough. Your data may be different though.
lm(cyl ~ hp, transform(mtcars2, cyl = as.numeric(cyl)))
giving:
Call:
lm(formula = cyl ~ hp, data = transform(mtcars2, cyl = as.numeric(cyl)))
Coefficients:
(Intercept) hp
3.00680 0.02168