Create R Data Frame

Here’s an example of creating a data frame in R. Let’s consider a simple dataset for demonstration purposes.

# Create a data frame
data <- data.frame(
  Name = c("John", "Anna", "Peter", "Linda"),
  Age = c(28, 24, 35, 32),
  Country = c("USA", "UK", "Australia", "Germany")
)

# Print the data frame
print(data)

When you run this code, it creates a data frame named data with three columns (Name, Age, and Country) and four rows, each representing a person. The output will look like this:

   Name Age    Country
1  John  28        USA
2  Anna  24         UK
3 Peter  35 Australia
4 Linda  32    Germany

Adding More Data

You can add more data to your data frame by using the rbind function for rows or the cbind function for columns. Here’s how you can do it:

# Create an initial data frame
data <- data.frame(
  Name = c("John", "Anna", "Peter", "Linda"),
  Age = c(28, 24, 35, 32),
  Country = c("USA", "UK", "Australia", "Germany")
)

# Create a new row to add
new_row <- data.frame(
  Name = "Emily",
  Age = 27,
  Country = "Canada"
)

# Add the new row to the data frame
data <- rbind(data, new_row)

# Print the updated data frame
print(data)

The output will now include Emily:

   Name Age    Country
1  John  28        USA
2  Anna  24         UK
3 Peter  35 Australia
4 Linda  32    Germany
5 Emily  27     Canada

Adding a New Column

To add a new column, you can use the $ operator to assign values directly to a new column name in your data frame.

# Create an initial data frame
data <- data.frame(
  Name = c("John", "Anna", "Peter", "Linda"),
  Age = c(28, 24, 35, 32),
  Country = c("USA", "UK", "Australia", "Germany")
)

# Add a new column for Occupation
data$Occupation <- c("Engineer", "Doctor", "Teacher", "Lawyer")

# Print the updated data frame
print(data)

The output will now include an Occupation column:

   Name Age    Country Occupation
1  John  28        USA    Engineer
2  Anna  24         UK      Doctor
3 Peter  35 Australia     Teacher
4 Linda  32    Germany     Lawyer

Basic Data Frame Operations

You can perform various operations on data frames, such as filtering, sorting, and grouping, using functions like subset, order, and aggregate, or by using packages like dplyr which provides a grammar of data manipulation.

# Filtering
filtered_data <- subset(data, Age > 30)

# Sorting
sorted_data <- data[order(data$Age), ]

# Using dplyr for more complex operations
library(dplyr)

grouped_data <- data %>%
  group_by(Country) %>%
  summarise(AvgAge = mean(Age))

These are just basic examples of working with data frames in R. Data frames are powerful and flexible, allowing you to store and manipulate tabular data efficiently.