Зміст курсу

R Introduction: Part II

1. Matrices

What is a Matrix?matrix() Function Transposing Matrix Indexation Namings Operation with Matrices Sales and Revenue

2. Data Frames

What is a Data Frame?data.frame() Function Columns Accessors Logical Indexing Manipulating Columns Manipulating Rows Data Extraction

3. Lists

What is a List?Namings Extracting List Elements Modifying

Manipulating Rows

Now let's learn how to add/delete rows. Let's consider two methods. The first is more applicable for adding a single row.

The method is to assign a new row to the last plus one index of an existing data frame (to get the number of rows, use the nrow() function). Remember that you can not store data of different types using vectors. So, you need to assign either a data frame or a list with new values. Do not worry if you do not know lists. In simple words, this is like a vector that allows us to store data of different types. Let's represent that. The initial data frame is shown below.


              123456789
            
# Creating a data frame
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
job <- c('Teacher', 'Doctor', 'Manager')
test <- data.frame(name, age, gender, job, stringsAsFactors = FALSE)
# Adding new row
test[nrow(test) + 1, ] <- list('Angela', 35, 'F', 'Accountant')
test # Output data frame

Note
By default, when changing a data frame, any new string values in a list are automatically converted to factors. To prevent this automatic conversion, the parameter stringsAsFactors = FALSE should be specified during the creation of the data frame. This approach should be applied whenever you are modifying rows in the data frame.

You could do this by using a new data frame and the merge function. This method requires the same column names and setting of necessary parameters (all = T).


              1234567891011
            
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
job <- c('Teacher', 'Doctor', 'Manager')
test <- data.frame(name, age, gender, job, stringsAsFactors = FALSE)
test[nrow(test) + 1, ] <- list('Angela', 35, 'F', 'Accountant')

# Data of new person as data frame
new_person <- data.frame('Angela', 35, 'F', 'Accountant')
colnames(new_person) <- colnames(test) # Set column names
merge(test, new_person, all = T) # Merge data frames

As you can see, the outputs are identical. To remove rows out of the data frame, use square quotes and put a minus sign to the left of the row index. For example, test[-1,] will remove the first row (the same as for matrices)

Завдання

Let's continue working with the store data frame.

Remove the 'Dining chair' row (index 4) out of the store data frame. Reassign the result to the store variable.
Add a new row to the data frame store using the list approach with the data below.

Item	Price	Sold
Kitchen Cabinet	70	67

Output modified data frame.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 2. Розділ 6

Manipulating Rows

Now let's learn how to add/delete rows. Let's consider two methods. The first is more applicable for adding a single row.


              123456789
            
# Creating a data frame
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
job <- c('Teacher', 'Doctor', 'Manager')
test <- data.frame(name, age, gender, job, stringsAsFactors = FALSE)
# Adding new row
test[nrow(test) + 1, ] <- list('Angela', 35, 'F', 'Accountant')
test # Output data frame

Note
By default, when changing a data frame, any new string values in a list are automatically converted to factors. To prevent this automatic conversion, the parameter stringsAsFactors = FALSE should be specified during the creation of the data frame. This approach should be applied whenever you are modifying rows in the data frame.

You could do this by using a new data frame and the merge function. This method requires the same column names and setting of necessary parameters (all = T).


              1234567891011
            
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
job <- c('Teacher', 'Doctor', 'Manager')
test <- data.frame(name, age, gender, job, stringsAsFactors = FALSE)
test[nrow(test) + 1, ] <- list('Angela', 35, 'F', 'Accountant')

# Data of new person as data frame
new_person <- data.frame('Angela', 35, 'F', 'Accountant')
colnames(new_person) <- colnames(test) # Set column names
merge(test, new_person, all = T) # Merge data frames

Завдання

Let's continue working with the store data frame.

Remove the 'Dining chair' row (index 4) out of the store data frame. Reassign the result to the store variable.
Add a new row to the data frame store using the list approach with the data below.

Item	Price	Sold
Kitchen Cabinet	70	67

Output modified data frame.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 2. Розділ 6

Manipulating Rows

Now let's learn how to add/delete rows. Let's consider two methods. The first is more applicable for adding a single row.


              123456789
            
# Creating a data frame
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
job <- c('Teacher', 'Doctor', 'Manager')
test <- data.frame(name, age, gender, job, stringsAsFactors = FALSE)
# Adding new row
test[nrow(test) + 1, ] <- list('Angela', 35, 'F', 'Accountant')
test # Output data frame

Note
By default, when changing a data frame, any new string values in a list are automatically converted to factors. To prevent this automatic conversion, the parameter stringsAsFactors = FALSE should be specified during the creation of the data frame. This approach should be applied whenever you are modifying rows in the data frame.

You could do this by using a new data frame and the merge function. This method requires the same column names and setting of necessary parameters (all = T).


              1234567891011
            
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
job <- c('Teacher', 'Doctor', 'Manager')
test <- data.frame(name, age, gender, job, stringsAsFactors = FALSE)
test[nrow(test) + 1, ] <- list('Angela', 35, 'F', 'Accountant')

# Data of new person as data frame
new_person <- data.frame('Angela', 35, 'F', 'Accountant')
colnames(new_person) <- colnames(test) # Set column names
merge(test, new_person, all = T) # Merge data frames

Завдання

Let's continue working with the store data frame.

Remove the 'Dining chair' row (index 4) out of the store data frame. Reassign the result to the store variable.
Add a new row to the data frame store using the list approach with the data below.

Item	Price	Sold
Kitchen Cabinet	70	67

Output modified data frame.

Все було зрозуміло?

Дякуємо за ваш відгук!

Now let's learn how to add/delete rows. Let's consider two methods. The first is more applicable for adding a single row.


              123456789
            
# Creating a data frame
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
job <- c('Teacher', 'Doctor', 'Manager')
test <- data.frame(name, age, gender, job, stringsAsFactors = FALSE)
# Adding new row
test[nrow(test) + 1, ] <- list('Angela', 35, 'F', 'Accountant')
test # Output data frame

Note
By default, when changing a data frame, any new string values in a list are automatically converted to factors. To prevent this automatic conversion, the parameter stringsAsFactors = FALSE should be specified during the creation of the data frame. This approach should be applied whenever you are modifying rows in the data frame.

You could do this by using a new data frame and the merge function. This method requires the same column names and setting of necessary parameters (all = T).


              1234567891011
            
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
job <- c('Teacher', 'Doctor', 'Manager')
test <- data.frame(name, age, gender, job, stringsAsFactors = FALSE)
test[nrow(test) + 1, ] <- list('Angela', 35, 'F', 'Accountant')

# Data of new person as data frame
new_person <- data.frame('Angela', 35, 'F', 'Accountant')
colnames(new_person) <- colnames(test) # Set column names
merge(test, new_person, all = T) # Merge data frames

Завдання

Let's continue working with the store data frame.

Remove the 'Dining chair' row (index 4) out of the store data frame. Reassign the result to the store variable.
Add a new row to the data frame store using the list approach with the data below.

Item	Price	Sold
Kitchen Cabinet	70	67

Output modified data frame.

Секція 2. Розділ 6