In today’s blog, we’ll delve into two fundamental R concepts: vectors and sorting. Both are crucial when dealing with data, and a good understanding of these will lay a solid foundation for more complex data operations.

R Vectors

A vector in R is a basic data structure that stores an ordered set of similar data types. They can hold numeric value, character values, or logical values (TRUE or FALSE). Let’s learn how to create, access and manipulate them.

Creating Numeric and Character Vectors

Creating vectors is straightforward. Use the c() function, which stand for ” combine”.

# Number Vector
codes <- c(380, 124, 818)
codes

# Character Vector
country <- c("italy", "canada", "egypt")
country

Naming the Elements of a Vector

Using names() function

codes <- c(380, 124, 818)
country <- c("italy", "canada", "egypt")
names(codes) <- country

Using c() function

# Note that two lines of code below have the same result
codes <- c(italy = 380, canada = 124, egypt = 818)
codes <- c("italy" = 380, "canada" = 124, "egypt" = 818)

Generating Numeric Sequences

For generating a sequence of numbers, use seq() or : operator.

# Using seq()
sequence <- seq(from = 1, to = 10, by =2)
sequence 

# Using :
sequemce <- 1:10
sequemce

Accessing Specific Elements or Parts of a Vector

To access specific elements in a vector, use brackets [] and specify the index.

# Using square bracket to access specific elements of a vector
codes[2]
codes[c(1, 3)]
codes[1:2]

# If the entries of a vector are named, they may be accessed by referring to their name
codes["canada"]
codes[c("egypt", "italy")]

Coercing Data into Different Types

Data coercion is the process of converting data from one type to another. Below are some common R functions used for coercion:

as.numeric(): Converts to numeric type.

char_vector <- c("1", "2", "3")
num_vector <- as.numeric(char_vector)
print(num_vector)

# verify a data type
class(num_vector)

as.character(): Converts to character type.

num_vector <- c(1, 2, 3)
char_vector <- as.character(num_vector)
print(char_vector)

as.factor(): Converts to factor type.

char_vector <- c("male", "female", "male")
factor_vector <- as.factor(char_vector)
print(factor_vector)

as.integer(): Converts to integer type.

num_vector <- c(1.2, 2.5, 3.7)
int_vector <- as.integer(num_vector)
print(int_vector)

as.logical(): Converts to logical type.

Any non-zero numeric value will be converted to TRUE and zero will be converted to FALSE. For character vectors, “TRUE” will be converted to TRUE and anything else will be converted to FALSE.
```
num_vector <- c(1, 0, 2)
logical_vector <- as.logical(num_vector)
print(logical_vector)
```

Important

You can also coerce matrices and data frames to other types using these same functions. But remember that when coercing complex types like lists or data frames, every element must be convertible to the final type, or the operation will result in an error or NA values.

Question

why class(3L) is integer ?
why 3L-3 equals 0 ?

R Sorting

Sorting is an important operation when working with data in R. It helps organize data in a way that’s easier to understand and analyze.

Sorting Vectors

To sort a vector in ascending or descending order, you can use the sort() function.

# load package and dataset
library(dslabs)
data(murders)

# Sorting
x <- sort(murders$total) # puts elements in order

# Descending order
y <- sort(murders$total, decreasing = TRUE) # put elements from big to small

Finding Indices of Sorted Elements

If you want to get the indices of the sorted elements (rather than the sorted elements themselves), you can use the order() function.

index <- order(x) # returns index that will put x in order
x[index] # rearrange by this index puts elements in order
order(x)

index <- order(murders$total) # order murder numbers by small to big
murders$abb[index] # order abbreviations by total murders

Finding Maxima and Minima

R provides function max(), min(), which.max(), and which.min() to find the maximum and minimum elements and their indices:

# Max and Min
max_value <- max(murders$total) # return the largest elements
max_value

min_value <- min(murders$total) # return the smallest elements
min_value

# Indices of Max and Min
max_index <- which.max(murders$total) # index with highest number of murders(第几个数字最大)
max_index # 第5个数字最大
murders$state[max_index] # state name with highest number of total murders

min_index <- which.min(murders$total) 
min_index # 第46个数字最小
murders$state[min_index] # state name with lowest number of total murders

Ranking Elements

The rank function provides the ranks of the elements in a vector:

rank(murders$population)

Table 1: The Differences of Sort, Order and Rank
Original	Sort	Order	Rank
31	4	2	3
4	15	3	1
15	31	1	2
92	65	5	5
65	92	4	4

Tip

Explanation of Table 1

Sort: 按从小到大排列
Order: Sort对应数字在原来数字排列中的顺序
Rank: Original原来数字在Sort顺序中的排名

Vector Arithmetic

You can perform arithmetic operations between vectors and number, as well as between vectors themselves.

Arithmetic with a Single Number

You can perform an operation between a vector and a single number, which applies the operation to each element of the vector:

# Adding 2 to all elements of a vector
new_vec <- codes +2
new_vec

Arithmetic with Two Vectors

You can also perform arithmetic operations between two vectors of the same length:

# Addition of two vectors
vec1 <- c(1,2,3)
vec2 <- c(4,5,6)
result <- vec1 + vec2 # we add two vectors element-wise 
print(result)

Example for Vector Arithmetic by Murders dataset

# The name of the state with the maximum population is found by doing the following
murders$state[which.max(murders$population)]

# how to obtain the murder rate
murder_rate <- murders$total / murders$population * 100000

# ordering the states by murder rate, in decreasing order
murders$state[order(murder_rate, decreasing=TRUE)]

Conclusion

To sum up, the deeper understanding of these concepts of vectors and sorting in R will be significantly beneficial for your data analysis journey. You will encounter these operations frequently, and mastering them will make your data manipulation tasks easier and more efficient.

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