Sometimes in data analysis is necessary to rearrange data. Called transpose data, these rearrangements help you to get a different perspective on the data so you can see things you may not notice in other formats. This can be particularly helpful when your data is in a matrix format that may not be arranged in the best possible way.
Description of the transpose function.
The transpose function with the form of transpose (l, fill, ignore.empty, keep.name) that produces a transpose in r.
- l – The list to transpose
- fill – Allows you to fill in missing values with the value of your choice. It is “NA” by fault.
- ignore.empty – Tells the function whether or not to ignore empty values.
- keep.name – allow you to keep the column names as a separate column.
All of the arguments except for the first one have default values and do not have to be entered.
Explanation of the transpose function.
In a data frame or table the transposed result swaps column and row. In a vector list the transposed result lines up the corresponding vector elements. It works great on matrices, data frames, and vector lists. However, it does not work for any variable that does not contain a list of values. The swapping of columns and rows can help provide a different perspective on the data. In data analysis getting different perspectives on the data by placing it in different formats can often reveal patterns that would not be seen otherwise.
Examples of the transpose function in action.
Each of the three following examples shows different ways of using the transpose function.
> group = data.frame(
+ name = c(“Bob”, “Tim”,”Sue”,”Tim”,”Betty”,”Charles”),
+ age = c( 25, 30, 33, 35, 86, 56),
+ pay = c(200, 300, 333, 250, 400, 350)
+ )
> group
name age pay
1 Bob 25 200
2 Tim 30 300
3 Sue 33 333
4 Tim 35 250
5 Betty 86 400
6 Charles 56 350
> transpose(group)
V1 V2 V3 V4 V5 V6
1 Bob Tim Sue Tim Betty Charles
2 25 30 33 35 86 56
3 200 300 333 250 400 350
This example shows how the columns and rows are swapped by the transpose function. If you look closely at the printout of the data frame “group” and the transpose of the data frame “group” this is clearly seen.
> group = data.frame(
+ name = c(“Bob”, “Tim”,”Sue”,”Tim”,”Betty”,”Charles”),
+ age = c( 25, 30, 33, 35, 86, 56),
+ pay = c(200, 300, 333, 250, 400, 350)
+ )
> transpose(group, keep.names=”label”)
label V1 V2 V3 V4 V5 V6
1 name Bob Tim Sue Tim Betty Charles
2 age 25 30 33 35 86 56
3 pay 200 300 333 250 400 350
This example is the same as the first but it includes the argument ‘keep.names= “label”‘ and as a result, it added a new column. The first column now consists of the column names in the original data frame.
> td = list(1:5, c(“a”,”b”,”c”))
> transpose(td, fill=0)
“1” “a”
“2” “b”
“3” “c”
“4” “0”
“5” “0”
This example illustrates the fill argument being used to replace the two missing values in a vector list.
The transpose function is a handy formatting function that can provide a different perspective on a data set by rearranging it. It provides a way of swapping the columns and rows in a data frame.