Adding dummy variables in panel linear model (regression) in R

Question

I have a categorical independent variable (with options of "yes" or "no") that I want to add to my panel linear model. According to the answer here: After generating dummy variables? , the lm function automatically creates dummy variables for you for categorical variables.

Does this mean that creating dummy variables through ie dummy.data.frame is unnecessary, and I can just add in my variable in the plm function and it will automatically be treated like a dummy variable (even if the data is not numerical)? And is this the same for the plm function?

Also, I don't have much data to begin with. Would it hurt if I manually turned the data into numbers (ie "yes"=1, "no"=0) without creating a dummy variable?

Answer 1

It is unnecessary to create dummy variables for use with the lm() function. To illustrate, we'll run a regression model on the mtcars data set, using am (0 = automatic, 1 = manual transmission) as a factor variable.

summary(lm(mpg ~ wt + factor(am),data=mtcars))

...and the output:

> summary(lm(mpg ~ wt + factor(am),data=mtcars))

Call:
lm(formula = mpg ~ wt + factor(am), data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5295 -2.3619 -0.1317  1.4025  6.8782 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 37.32155    3.05464  12.218 5.84e-13 ***
wt          -5.35281    0.78824  -6.791 1.87e-07 ***
factor(am)1 -0.02362    1.54565  -0.015    0.988    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.098 on 29 degrees of freedom
Multiple R-squared:  0.7528,    Adjusted R-squared:  0.7358 
F-statistic: 44.17 on 2 and 29 DF,  p-value: 1.579e-09