Presentation 4 Jose Salazar

We want to generate a dataset within R, which includes creating categorical variables that are initially character strings.

# Set a seed for reproducibility
set.seed(123)

# Create sample data in R
data <- data.frame(
  ID = 1:30,
  Gender = sample(c("Male", "Female"), 30, replace = TRUE),
  AgeGroup = sample(c("18-25", "26-35", "36-45"), 30, replace = TRUE),
  EducationLevel = sample(c("High School", "Bachelor's", "Master's", "PhD"), 30, replace = TRUE),
  stringsAsFactors = FALSE  # Ensure that character data is not automatically converted to factors
)

# Display the first few rows of the dataset
head(data)

NA

We want to display a structure of this dataset.

# Display the structure of the dataset
str(data)

'data.frame':   30 obs. of  4 variables:
 $ ID            : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Gender        : chr  "Male" "Male" "Male" "Female" ...
 $ AgeGroup      : chr  "18-25" "26-35" "36-45" "26-35" ...
 $ EducationLevel: chr  "High School" "Master's" "High School" "Bachelor's" ...

We want convert the Gender, AgeGroup, and EducationLevel columns from character strings to factors to better manage categorical data.

# Convert character columns to factors
data$Gender <- as.factor(data$Gender)
data$AgeGroup <- as.factor(data$AgeGroup)
data$EducationLevel <- as.factor(data$EducationLevel)

We want to see the structure post conversion to verify the change.

str(data)

'data.frame':   30 obs. of  4 variables:
 $ ID            : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Gender        : Factor w/ 2 levels "Female","Male": 2 2 2 1 2 1 1 1 2 2 ...
 $ AgeGroup      : Factor w/ 3 levels "18-25","26-35",..: 1 2 3 2 1 3 3 1 3 2 ...
 $ EducationLevel: Factor w/ 4 levels "Bachelor's","High School",..: 2 3 2 1 2 3 2 3 1 4 ...

We want to see a summary of these factor columns.

# Get a summary of the factor columns
summary(data$Gender)

Female   Male 
    13     17 

summary(data$AgeGroup)

18-25 26-35 36-45 
   13     7    10 

summary(data$EducationLevel)

 Bachelor's High School    Master's         PhD 
          9           8           7           6 

We want to visualize the data through bar plots

library(ggplot2)

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# Bar plot for Gender distribution
ggplot(data, aes(x = Gender)) +
  geom_bar(fill = "skyblue") +
  labs(title = "Gender Distribution", x = "Gender", y = "Count") +
  theme_minimal()

# Bar plot for Age Group distribution
ggplot(data, aes(x = AgeGroup)) +
  geom_bar(fill = "lightgreen") +
  labs(title = "Age Group Distribution", x = "Age Group", y = "Count") +
  theme_minimal()

# Bar plot for Education Level distribution
ggplot(data, aes(x = EducationLevel)) +
  geom_bar(fill = "lightcoral") +
  labs(title = "Education Level Distribution", x = "Education Level", y = "Count") +
  theme_minimal()

The bar plots show that the age group predominantly skews to the younger 18-25 demographic. It could impact the analysis if your looking for data for older age groups. A younger demographic could affect things such as health, purchasing trends, and also educational goals. I dont really see any surprising trends from this data.