When doing data science, it is sometimes necessary to sort alphabetically in r. When working with r programming this is a straightforward process, though it can get a little complicated when working with data frames. This is because sometimes you may have to use more than one column. When this is the case, you still use a single function, but you need to refer to both columns.
How to sort Alphabetically in R
When you sort alphabetically in r you have two functions that you can use depending upon whether you are sorting a vector or a data frame. When sorting a vector, you use the sort function which has the format of sort(vector) where “vector” is the vector being sorted. When sorting a data frame, you use the order function which has two different formats depending on how many columns are being sorted. When sorting a single column the format is df[order(df$column) and when sorting two or more columns the format is df[with(df, order(column1, column2…)). In both cases, “df” is the data frame and the “columns” are the columns being sorted. Both functions are base r functions that put data in alphabetical order.
Explanation
The functions used to sort alphabetically in r are the sort command and order function. Both functions sort the data in ascending order and neither supplies a way to do it in reverse order. On the other hand, the arrange function has an option for descending order. The sort function simply arranges the data in the vector in alphabetical or ascending numerical order. The order function sorts the data in the entire data frame according to the order of the columns in the list given in the function. Both functions automatically arrange the data in ascending order whether the data be alphabetical or numerical.
Examples of how to sort Alphabetically in R
Here we have three examples that show how to sort alphabetically in r. Each one illustrates different circumstances under which the data is being sorted.
> x = c(“F”, “A”, “E”, “C”, “D”, “B”)
> sort(x)
[1] “A” “B” “C” “D” “E” “F”
In this example, we use the sort function to sort a character vector. This changes its sort order to alphabetical ordering.
> df = data.frame(X = c(“F”, “A”, “E”, “C”, “D”, “B”),
+ Y = c( 44, 22, 33, 29, 31, 16))
> df
X Y
1 F 44
2 A 22
3 E 33
4 C 29
5 D 31
6 B 16
> df[order(df$X),]
X Y
2 A 22
6 B 16
4 C 29
5 D 31
3 E 33
1 F 44
In this example, we use the order function to sort a data frame according to the letter order in the first column. The column name is referred to in the order function.
> df = data.frame(Z = c(“A”, “A”,”B”, “B”, “A”, “B”),
+ X = c(“F”, “A”, “E”, “C”, “D”, “B”),
+ Y = c( 44, 22, 33, 29, 31, 16))
> df
Z X Y
1 A F 44
2 A A 22
3 B E 33
4 B C 29
5 A D 31
6 B B 16
> df[with(df, order(Z, X)), ]
Z X Y
2 A A 22
5 A D 31
1 A F 44
6 B B 16
4 B C 29
3 B E 33
In this example, we are using the order function to sort two columns. They are both referred to in the function in the order that they are sorted.
Applications of this function
There are many applications for sorting vectors and data frames alphabetically. When sorting data, it is most often best to reorder either alphabetically or numerically. This type of sorting makes specific data easier to find. It is easier to find specific data points visually when they are in alphabetical or numerical ordering because we are accustomed to looking for things by these types of arrangements. It is also easier to write a search routine for a program that search is in either alphabetical or numerical ordering. This is the case because we are accustomed to each of these arrangements. As a result, they naturally make sense to our thinking after we have learned to read and count.
When you sort alphabetically in r you have three ways of doing so using two distinct functions. The sort function is used when working with a vector, and the order function is used when working with a data frame. Both functions are easy to use, but when working with a data frame you need to keep the number of columns that you are sorting in mind and change your formatting accordingly. This is a process that you will find useful because it makes individual data points easier to find.