» 3: ساختار دادها در R

1-3R-programming-language-training-course-By-Babak-Babaabasi

(2)R Data Structures:

R’s basic data structures include the vector, list, matrix, data frame, and factors.

Vectors

A vector is simply a list of items that are of the same type.

To combine the list of items to a vector, use the c() function and separate the items by a comma.

In the example below, we create a vector variable called fruits, that combine strings:

# Vector of strings

fruits = c(“banana”, “apple”, “orange”)

# Print fruits

fruits

In this example, we create a vector that combines numerical values:

# Vector of numerical values

numbers = c(1, 2, 3)

# Print numbers

numbers

To create a vector with numerical values in a sequence, use the : operator:

# Vector with numerical values in a sequence

numbers = 1:10

numbers

You can also create numerical values with decimals in a sequence, but note that if the last element does not belong to the sequence, it is not used:

# Vector with numerical decimals in a sequence

numbers1 <- 1.5:6.5

numbers1

# Vector with numerical decimals in a sequence where the last element is not used

numbers2 <- 1.5:6.3

numbers2

In the example below, we create a vector of logical values:

log_values <- c(TRUE, FALSE, TRUE, FALSE)

log_values

Vector Length

To find out how many items a vector has, use the length() function

fruits <- c(“banana”, “apple”, “orange”)

length(fruits)

Sort a Vector

To sort items in a vector alphabetically or numerically, use the sort() function:

fruits <- c(“banana”, “apple”, “orange”, “mango”, “lemon”)

numbers <- c(13, 3, 5, 7, 20, 2)

sort(fruits)  # Sort a string

sort(numbers) # Sort numbers

*****Access Vectors

You can access the vector items by referring to its index number inside brackets []. The first item has index 1, the second item has index 2, and so on:

fruits <- c(“banana”, “apple”, “orange”)

# Access the first item (banana)

fruits[1]

You can also access multiple elements by referring to different index positions with the c() function:

fruits <- c(“banana”, “apple”, “orange”, “mango”, “lemon”)

# Access the first and third item (banana and orange)

fruits[c(1:3)]

fruits[c(1,3)]

You can also use negative index numbers to access all items except the ones specified:

fruits <- c(“banana”, “apple”, “orange”, “mango”, “lemon”)

# Access all items except for the first item

fruits[c(-1)]

fruits[c(-2)]

Change an Item

To change the value of a specific item, refer to the index number:

fruits <- c(“banana”, “apple”, “orange”, “mango”, “lemon”)

# Change “banana” to “pear”

fruits[1] = “pear”

# Print fruits

fruits

Repeat Vectors

To repeat vectors, use the rep() function:

Repeat each value:

repeat_each = rep(c(1,2,3), each = 3)

repeat_each

Repeat the sequence of the vector:

repeat_times <- rep(c(1,2,3), times = 3)

bb=rep(c(1,2),repeat_times=6)

repeat_times

Repeat each value independently:

repeat_indepent <- rep(c(1,2,3), times = c(5,2,1))

repeat_indepent

Generating Sequenced Vectors

One of the examples on top, showed you how to create a vector with numerical values in a sequence with the : operator

numbers <- 1:10

numbers

To make bigger or smaller steps in a sequence, use the seq() function:

numbers <- seq(from = 0, to = 100, by = 20)

numbers

help(“rep”)

Note: The seq() function has three parameters: from is where the sequence starts, to is where the sequence stops, and by is the interval of the sequence.

R Lists

A list in R can contain many different data types inside it. A list is a collection of data which is ordered and changeable.

To create a list, use the list() function:

thislist <- list(“apple”, “banana”, “cherry”)

# Print the list

thislist

Change Item Value

To change the value of a specific item, refer to the index number:

thislist <- list(“apple”, “banana”, “cherry”)

thislist[1] <- “blackcurrant”

# Print the updated list

thislist

List Length

To find out how many items a list has, use the length() function:

thislist <- list(“apple”, “banana”, “cherry”)

length(thislist)

Check if Item Exists

To find out if a specified item is present in a list, use the %in% operator:

thislist <- list(“apple”, “banana”, “cherry”,”pendar”,”maryam”)

“maryam” %in% thislist

Add List Items

To add an item to the end of the list, use the append() function:

thislist <- list(“apple”, “banana”, “cherry”)

append(thislist, “orange”)

To add an item to the right of a specified index, add “after=index number” in the append() function:

thislist = list(“apple”, “banana”, “cherry”)

append(thislist, “orange”, after = 2)

Remove List Items

You can also remove list items. The following example creates a new, updated list without an “apple” item:

thislist = list(“apple”, “banana”, “cherry”)

newlist = thislist[-1]

# Print the new list

newlist

Range of Indexes

You can specify a range of indexes by specifying where to start and where to end the range, by using the : operator:

thislist <- list(“apple”, “banana”, “cherry”, “orange”, “kiwi”, “melon”, “mango”)

(thislist)[2:5]

thislist[2:5]

Loop Through a List

You can loop through the list items by using a for loop:

thislist <- list(“apple”, “banana”, “cherry”)

for (x in thislist) {

  print(x)

}

Join Two Lists

There are several ways to join, or concatenate, two or more lists in R.

The most common way is to use the c() function, which combines two elements together:

list1 = list(“a”, “b”, “c”)

list2 = list(1,2,3)

list3 = c(list1,list2)

list3

# list1 and list2 are uni-dimensional lists

list1 = list (c(1:5), “hi”, 0 + 5i)

list1

list2 = list(c(6:8))

list2

# create a list_data with two lists as its elements

list_data = list(list1, list2)

print (“The two-dimensional list is : “)

print (list_data)

cat(“Length of nested list is : “,

     length (list_data))

cat(“Length of first inner list is : “,

     length (list_data[[1]]))

R Matrices

A matrix is a two dimensional data set with columns and rows.

A column is a vertical representation of data, while a row is a horizontal representation of data.

A matrix can be created with the matrix() function. Specify the nrow and ncol parameters to get the amount of rows and columns:

# Create a matrix

thismatrix = matrix(c(1,2,3,4,5,6), nrow = 3, ncol = 2)

# Print the matrix

thismatrix

You can also create a matrix with strings:

thismatrix <- matrix(c(“apple”, “banana”, “cherry”, “orange”), nrow = 2, ncol = 2)

thismatrix

Access Matrix Items

You can access the items by using [ ] brackets. The first number “1” in the bracket specifies the row-position, while the second number “2” specifies the column-position:

thismatrix <- matrix(c(“apple”, “banana”, “cherry”, “orange”), nrow = 2, ncol = 2)

thismatrix[1, 2]

The whole row can be accessed if you specify a comma after the number in the bracket:

thismatrix <- matrix(c(“apple”, “banana”, “cherry”, “orange”), nrow = 2, ncol = 2)

thismatrix[2,]

The whole column can be accessed if you specify a comma before the number in the bracket:

thismatrix <- matrix(c(“apple”, “banana”, “cherry”, “orange”), nrow = 2, ncol = 2)

thismatrix[,2]

R Arrays

Compared to matrices, arrays can have more than two dimensions.

We can use the array() function to create an array, and the dim parameter to specify the dimensions:

# An array with one dimension with values ranging from 1 to 24

thisarray <- c(1:24)

thisarray

# An array with more than one dimension

multiarray <- array(thisarray, dim = c(4, 3, 2))

multiarray

How does dim=c(4,3,2) work? The first and second number in the bracket specifies the amount of rows and columns. The last number in the bracket specifies how many dimensions we want.

Note: Arrays can only have one data type.

Access Array Items

You can access the array elements by referring to the index position. You can use the [] brackets to access the desired elements from an array:

thisarray <- c(1:24)

multiarray <- array(thisarray, dim = c(4, 3, 2))

multiarray[2, 3, 2]

You can also access the whole row or column from a matrix in an array, by using the c() function:

thisarray <- c(1:24)

# Access all the items from the first row from matrix one

multiarray <- array(thisarray, dim = c(4, 3, 2))

multiarray[c(1),,1]

# Access all the items from the first column from matrix one

multiarray <- array(thisarray, dim = c(4, 3, 2))

multiarray[,c(1),1]

Check if an Item Exists

To find out if a specified item is present in an array, use the %in% operator:

thisarray <- c(1:24)

multiarray <- array(thisarray, dim = c(4, 3, 2))

2 %in% multiarray

Amount of Rows and Columns

Use the dim() function to find the amount of rows and columns in an array:

thisarray <- c(1:24)

multiarray <- array(thisarray, dim = c(4, 3, 2))

dim(multiarray)

Array Length

Use the length() function to find the dimension of an array:

thisarray <- c(1:24)

multiarray <- array(thisarray, dim = c(4, 3, 2))

length(multiarray)

Loop Through an Array

You can loop through the array items by using a for loop:

thisarray <- c(1:24)

multiarray <- array(thisarray, dim = c(4, 3, 2))

for(x in multiarray){

  print(x)

}

R Data Frames

Data Frames are data displayed in a format as a table.

Data Frames can have different types of data inside it. While the first column can be character, the second and third can be numeric or logical. However, each column should have the same type of data.

Use the data.frame() function to create a data frame:

# Create a data frame

da_fr <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

# Print the data frame

da_fr

#or

View(da_fr)

a=c(“Strength”, “Stamina”, “Other”)

b=c(100, 150, 120)

c=c(60, 30, 45)

df = data.frame (

  Training = a,

  Pulse = b,

  Duration = c

)

View(df)

Summarize the Data

Use the summary() function to summarize the data from a Data Frame:

da_fr <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

da_fr

summary(da_fr)

Access Items

We can use single brackets [ ], double brackets [[ ]] or $ to access columns from a data frame:

da_fr <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

bb=da_fr[1]

bb

da_fr[[“Training”]]

da_fr$Training

Add Rows

Use the rbind() function to add new rows in a Data Frame:

da_fr <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

# Add a new row

New_row_DF <- rbind(da_fr, c(“Strength”, 110, 110))

# Print the new row

New_row_DF

Add Columns

Use the cbind() function to add new columns in a Data Frame:

Data_Frame <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

# Add a new column

New_col_DF <- cbind(Data_Frame, Steps = c(1000, 6000, 2000))

# Print the new column

New_col_DF

Remove Rows and Columns

Use the c() function to remove rows and columns in a Data Frame:

Data_Frame <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

# Remove the first row and column

Data_Frame_New <- Data_Frame[-c(1), -c(1)]

# Print the new data frame

Data_Frame_New

Amount of Rows and Columns

Use the dim() function to find the amount of rows and columns in a Data Frame:

Data_Frame <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

dim(Data_Frame)

class(Data_Frame)

You can also use the ncol() function to find the number of columns and nrow() to find the number of rows:

Data_Frame <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

ncol(Data_Frame)

nrow(Data_Frame)

Data Frame Length

Use the length() function to find the number of columns in a Data Frame (similar to ncol()):’

Data_Frame <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

ncol(Data_Frame)

nrow(Data_Frame)

length(Data_Frame)

Combining Data Frames

Use the rbind() function to combine two or more data frames in R vertically:

Data_Frame1 <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

Data_Frame2 <- data.frame (

  Training = c(“Stamina”, “Stamina”, “Strength”),

  Pulse = c(140, 150, 160),

  Duration = c(30, 30, 20)

)

New_Data_Frame <- rbind(Data_Frame1, Data_Frame2)

New_Data_Frame

And use the cbind() function to combine two or more data frames in R horizontally:

Data_Frame3 <- data.frame (

  Training = c(“Strength”, “Stamina”, “Other”),

  Pulse = c(100, 150, 120),

  Duration = c(60, 30, 45)

)

Data_Frame4 <- data.frame (

  Steps = c(3000, 6000, 2000),

  Calories = c(300, 400, 300)

)

New_Data_Frame1 <- cbind(Data_Frame3, Data_Frame4)

New_Data_Frame1

R Factors

Factors are used to categorize data. Examples of factors are:

• Demography: Male/Female
• Music: Rock, Pop, Classic, Jazz
• Training: Strength, Stamina

To create a factor, use the factor() function and add a vector as argument:

# Create a factor

music_genre <- factor(c(“Jazz”, “Rock”, “Classic”, “Classic”, “Pop”, “Jazz”, “Rock”, “Jazz”))

# Print the factor

music_genre

You can see from the example above that that the factor has four levels (categories): Classic, Jazz, Pop and Rock.

To only print the levels, use the levels() function:

music_genre <- factor(c(“Jazz”, “Rock”, “Classic”, “Classic”, “Pop”, “Jazz”, “Rock”, “Jazz”))

levels(music_genre)

You can also set the levels, by adding the levels argument inside the factor() function:

music_genre <- factor(c(“Jazz”, “Rock”, “Classic”, “Classic”, “Pop”, “Jazz”, “Rock”, “Jazz”), levels = c(“Classic”, “Jazz”, “Pop”, “Rock”, “Other”))

levels(music_genre)

Factor Length

Use the length() function to find out how many items there are in the factor:

music_genre <- factor(c(“Jazz”, “Rock”, “Classic”, “Classic”, “Pop”, “Jazz”, “Rock”, “Jazz”))

length(music_genre)

Access Factors

To access the items in a factor, refer to the index number, using [] brackets:

music_genre <- factor(c(“Jazz”, “Rock”, “Classic”, “Classic”, “Pop”, “Jazz”, “Rock”, “Jazz”))

music_genre[3]

Change Item Value

To change the value of a specific item, refer to the index number:

music_genre <- factor(c(“Jazz”, “Rock”, “Classic”, “Classic”, “Pop”, “Jazz”, “Rock”, “Jazz”))

music_genre[3] = “Pop”

music_genre[3]

Note that you cannot change the value of a specific item if it is not already specified in the factor. The following example will produce an error:

music_genre <- factor(c(“Jazz”, “Rock”, “Classic”, “Classic”, “Pop”, “Jazz”, “Rock”, “Jazz”))

music_genre[3] <- “Opera”

music_genre[3]

However, if you have already specified it inside the levels argument, it will work:

music_genre <- factor(c(“Jazz”, “Rock”, “Classic”, “Classic”, “Pop”, “Jazz”, “Rock”, “Jazz”), levels = c(“Classic”, “Jazz”, “Pop”, “Rock”, “Opera”))

music_genre[3] <- “Opera”

music_genre[3]

END(1-3)