Create Dummy Variables

Load Packages

library(fastDummies)
library(tidyverse)
library(psych)

Create a DataSet

# Create a vector of race scores
race <- c("White", "Black", "Asian", "Hispanic", "Other")

# Generate random income values for each race (100 cases)
set.seed(123) # for reproducibility
income <- round(runif(100, min = 20000, max = 100000), digits = 2)

# Repeat each race 20 times to get 100 cases
race <- rep(race, each = 20)

# Combine race and income into a data frame
data <- data.frame(race, income)

# Print the first few rows of the dataset
print(head(data))

##    race   income
## 1 White 43006.20
## 2 White 83064.41
## 3 White 52718.15
## 4 White 90641.39
## 5 White 95237.38
## 6 White 23644.52

data<-data %>% dummy_cols(select_columns = "race")

Regress Income on Race (African Americans as the Reference Category)

fit<-lm(income ~ race_Asian + race_Hispanic + race_Other + race_White, data=data)
summary(fit)

## 
## Call:
## lm(formula = income ~ race_Asian + race_Hispanic + race_Other + 
##     race_White, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -44169 -19531  -1137  18010  40481 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      66138       5066  13.055   <2e-16 ***
## race_Asian      -15015       7165  -2.096   0.0388 *  
## race_Hispanic    -7004       7165  -0.977   0.3308    
## race_Other       -7173       7165  -1.001   0.3193    
## race_White       -2073       7165  -0.289   0.7730    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22660 on 95 degrees of freedom
## Multiple R-squared:  0.05237,    Adjusted R-squared:  0.01247 
## F-statistic: 1.313 on 4 and 95 DF,  p-value: 0.2709