How can we create dummy variable in R

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A is a type of variable that we create in regression analysis so that we can represent a categorical variable as a numerical variable that takes on one of two values: zero or one.

For example, suppose we have the following dataset and we would like to use age and marital status to predict income:

To use marital status as a predictor variable in a regression model, we must convert it into a dummy variable.

Since it is currently a categorical variable that can take on three different values (“Single”, “Married”, or “Divorced”), we need to create k-1 = 3-1 = 2 dummy variables.

To create this dummy variable, we can let “Single” be our baseline value since it occurs most often. Thus, here’s how we would convert marital status into dummy variables:

This tutorial provides a step-by-step example of how to create dummy variables for this exact dataset in R and then perform regression analysis using these dummy variables as predictors.

Step 1: Create the Data

First, let’s create the dataset in R:

#create data frame
df <- data.frame(income=c(45000, 48000, 54000, 57000, 65000, 69000,
                          78000, 83000, 98000, 104000, 107000),
                 age=c(23, 25, 24, 29, 38, 36, 40, 59, 56, 64, 53),
                 status=c('Single', 'Single', 'Single', 'Single',
                          'Married', 'Single', 'Married', 'Divorced',
                          'Divorced', 'Married', 'Married'))

#view data frame
df

   income age   status
1   45000  23   Single
2   48000  25   Single
3   54000  24   Single
4   57000  29   Single
5   65000  38  Married
6   69000  36   Single
7   78000  40  Married
8   83000  59 Divorced
9   98000  56 Divorced
10 104000  64  Married
11 107000  53  Married

Step 2: Create the Dummy Variables

Next, we can use the ifelse() function in R to define dummy variables and then define the final data frame we’d like to use to build the regression model:

#create dummy variables
married <- ifelse(df$status == 'Married', 1, 0)
divorced <- ifelse(df$status == 'Divorced', 1, 0)

#create data frame to use for regression
df_reg <- data.frame(income = df$income,
                     age = df$age,
                     married = married,
                     divorced = divorced)

#view data frame
df_reg

   income age married divorced
1   45000  23       0        0
2   48000  25       0        0
3   54000  24       0        0
4   57000  29       0        0
5   65000  38       1        0
6   69000  36       0        0
7   78000  40       1        0
8   83000  59       0        1
9   98000  56       0        1
10 104000  64       1        0
11 107000  53       1        0

Step 3: Perform Linear Regression

Lastly, we can use the lm() function to fit a multiple linear regression model:

#create regression model
model <- lm(income ~ age + married + divorced, data=df_reg)

#view regression model output
summary(model)

Call:
lm(formula = income ~ age + married + divorced, data = df_reg)

Residuals:
    Min      1Q  Median      3Q     Max 
-9707.5 -5033.8    45.3  3390.4 12245.4 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  14276.1    10411.5   1.371  0.21266   
age           1471.7      354.4   4.152  0.00428 **
married       2479.7     9431.3   0.263  0.80018   
divorced     -8397.4    12771.4  -0.658  0.53187   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 8391 on 7 degrees of freedom
Multiple R-squared:  0.9008,	Adjusted R-squared:  0.8584 
F-statistic:  21.2 on 3 and 7 DF,  p-value: 0.0006865

Income = 14,276.1 + 1,471.7*(age) + 2,479.7*(married) – 8,397.4*(divorced)

We can use this equation to find the estimated income for an individual based on their age and marital status. For example, an individual who is 35 years old and married is estimated to have an income of $68,264:

Income = 14,276.2 + 1,471.7*(35) + 2,479.7*(1) – 8,397.4*(0) = $68,264

Here is how to interpret the regression coefficients from the table:

• Intercept: The intercept represents the average income for a single individual who is zero years old. Obviously you can’t be zero years old, so it doesn’t make sense to interpret the intercept by itself in this particular regression model.
• Age: Each one year increase in age is associated with an average increase of $1,471.70 in income. Since the p-value (.004) is less than .05, age is a statistically significant predictor of income.
• Married: A married individual, on average, earns $2,479.70 more than a single individual. Since the p-value (0.800) is not less than .05, this difference is not statistically significant.
• Divorced: A divorced individual, on average, earns $8,397.40 less than a single individual. Since the p-value (0.532) is not less than .05, this difference is not statistically significant.

Since both dummy variables were not statistically significant, we could drop marital status as a predictor from the model because it doesn’t appear to add any predictive value for income.