A data frame is a two-dimensional tabular data structure similar to a spreadsheet or a SQL table.
Stores different data types.
Creates seperate coloumn for each data set in the data frame.
EDUCATION
# Creating a data frame
df <- data.frame(
Name = c("Alice", "Bob", "Charlie"),
Age = c(25, 30, 22),
City = c("New York", "San Francisco", "Los Angeles")
)
dfWe can access the data frames with index number inside a [["coloum_name"] double bracket or by mentioning row or coloum number and by $coloum_name.
# Creating a data frame
df <- data.frame(
Name = c("Alice", "Bob", "Charlie"),
Age = c(25, 30, 22),
City = c("New York", "San Francisco", "Los Angeles")
)
# Accessing columns
name_column <- df$Name
age_column <- df[["Age"]]
# Accessing rows
first_row <- df[1, ]summmary() Function is used to Generates a summary of the data frame.
# Creating a data frame
df <- data.frame(
Name = c("Alice", "Bob", "Charlie"),
Age = c(25, 30, 22),
City = c("New York", "San Francisco", "Los Angeles")
)
df$Salary <- c(50000, 60000, 45000)
# Data Frame Summary
summary(df)Functions are a built in methods that performs operations fast and easy.
Operation
Syntax
Description
Example
Number of Rows
nrow(df)
Returns the number of rows in a data frame.
num_rows <- nrow(df)
Number of Columns
ncol(df)
Returns the number of columns in a data frame.
num_cols <- ncol(df)
Data Frame Structure
str(df)
Displays the structure of a data frame.
str_df <- str(df)
Data Types of Columns
sapply(df, class)
Returns the data types of columns in a data frame.
column_types <- sapply(df, class)
Summary Statistics
summary(df)
Generates a summary of the data frame.
summary_df <- summary(df)
Descriptive Statistics
summary.stats(df)
Computes additional descriptive statistics.
stats_df <- summary.stats(df
Checking for Missing Values
any(is.na(df))
Checks if there are any missing values in the data frame.
has_missing <- any(is.na(df))
Head of Data Frame
head(df)
Displays the first few rows of a data frame.
head_df <- head(df)
Tail of Data Frame
tail(df)
Displays the last few rows of a data frame.
tail_df <- tail(df)
Sorting Data Frame
df_sorted <- df[order(df$column_name), ]
Sorts a data frame based on a specific column.
df_sorted <- df[order(df$Age), ]
Filtering Rows
subset(df, condition)
Filters rows based on a specified condition.
young_people <- subset(df, Age < 30)
Selecting Columns
df_subset <- df[, c("col1", "col2")]
Selects specific columns from a data frame.
df_subset <- df[, c("Name", "Age")]
# Creating a data frame
employee_data <- data.frame(
EmployeeID = c(101, 102, 103, 104, 105),
Name = c("Alice", "Bob", "Charlie", "David", "Eva"),
Age = c(28, 35, 24, 40, 30),
Department = c("HR", "IT", "Marketing", "Finance", "IT"),
Salary = c(60000, 75000, 50000, 90000, 80000),
StringsAsFactors = FALSE
)
# Displaying the data frame
print("Original Data Frame:")
print(employee_data)
# Number of rows and columns
num_rows <- nrow(employee_data)
num_cols <- ncol(employee_data)
print(paste("Number of Rows:", num_rows))
print(paste("Number of Columns:", num_cols))
# Structure of the data frame
print("Data Frame Structure:")
str_df <- str(employee_data)
# Data types of columns
print("Data Types of Columns:")
column_types <- sapply(employee_data, class)
print(column_types)
# Summary of the data frame
print("Summary of the Data Frame:")
summary_df <- summary(employee_data)
print(summary_df)
# Descriptive statistics
print("Descriptive Statistics:")
library(psych)
stats_df <- describe(employee_data)
print(stats_df)
# Checking for missing values
has_missing <- any(is.na(employee_data))
print(paste("Has Missing Values:", has_missing))
# Sorting the data frame by Age
df_sorted <- employee_data[order(employee_data$Age), ]
print("Sorted Data Frame by Age:")
print(df_sorted)
# Filtering rows for employees younger than 30
young_employees <- subset(employee_data, Age < 30)
print("Young Employees:")
print(young_employees)
# Selecting specific columns
selected_cols <- employee_data[, c("Name", "Salary")]
print("Selected Columns:")
print(selected_cols)
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