How To Collapse Multiple Rows in R In 1 Row (Sum & Transform Data)

Sometimes when doing data science, it is necessary to collapse rows in R, so as to make your data easier to understand or to supply additional information about the data set. Unfortunately, R programming does not provide a single way to perform this task even when working with data frames. As a result, you will use different approaches under different circumstances.

What We’re Solving For: How To Collapse Multiple Rows

Collapsing rows of data in a data frame is not a simple single-step process in R programming. This is because there is no one formula that will do the job. Even the process of collapsing empty spaces which does have a single formula that can be used but requires transforming the data frame into a data table and back again. As a result, while the same basic concepts are used in each approach, there are different specific techniques for different situations. If it includes adding the rows together you need to include a routine for adding the rows. If you are collapsing duplicates you need to find the duplicates and remove them. There is more than one way that you can carry out the specific routines for a specific situation, but it is helpful to have a pre-planned approach for each situation that can be adjusted for specific cases. All this does make collapsing rows a challenging task but once you have the basic concepts down you can work out the specifics for each case you are working with.

General Approach: Collapsing Multiple Rows in R

The basic process for collapsing rows from a dataframe in R programming involves first determining the type of collapse that you want. If you want to sum up the columns, then it is just a matter of adding up the rows and deleting the ones that you are not using. If you are transformationally collapsing data, then you need to design a routine for that transformation and then remove any unused rows. The basic concept boils down to deciding what the reason is for collapsing the rows and then determining what the end result would be like and then finding a routine to produce that result. Ultimately collapsing rows is going to involve eliminating the rows that are no longer being used. However, that is an extremely easy process that simply requires making the row number to be removed, negative. It involves moving any data being collapsed into its new row before the original one is removed. This process can be as simple or complicated as you need or want it to be.

Examples of How To Collapse Multiple Rows in R

Here are four examples of collapsing multiple rows into a single row. They include an example of a sum collapse and three examples of transforming the data. Unfortunately, there is no single formula or technique for doing this task, but there are basic concepts for how to do it, even though the precise technique varies depending upon what you are trying to do.

> A = c(1, 2, 3, 4)
> B = c(2, 4, 6, 8)
> C = c(3, 6, 9, 0)
> df = data.frame(A,B,C)
> df
1 1 2 3
2 2 4 6
3 3 6 9
4 4 8 0
> df[1,] = as.numeric(df[1,])+as.numeric(df[2,])+as.numeric(df[3,])+as.numeric(df[4,])
> df = df[-4,]
> df = df[-3,]
> df = df[-2,]
> df
1 10 20 18

This is an example of a sum collapse we are multiple rows of a data frame are collapsed into a single row By adding them together.

> A = c(“a”, “b”, “c”, “d”, “e”)
> df = data.frame(A)
> df
1 a
2 b
3 c
4 d
5 e
> df = data.frame(A = df[1,], B = df[2,], C = df[3,], D = df[4,], E = df[5,])
> df
1 a b c d e

Here is an example of transforming a single column of a data frame into a single row consisting of multiple columns. It is a simple matter of equating each of the rows to a column in the new arrangement.

> A = c(1, 1, 3, 3, 4)
> B = c(2, 2, 6, 6, 6)
> C = c(3, 3, 9, 9, 8)
> df = data.frame(A,B,C)
> df
1 1 2 3
2 1 2 3
3 3 6 9
4 3 6 9
5 4 6 8
> n = nrow(df)
> R = abs(diff(df$A))+abs(diff(df$B))+abs(diff(df$C))
> R = c(1,R)
> n = n:1
> for (x in n) {
+ if(R[x]==0){
+ df = df[-x,]
+ }
+ }
> n = nrow(df)
> n = 1:n
> row.names(df) = n
> df
1 1 2 3
2 3 6 9
3 4 6 8

Here we have an example of collapsing selected rows. In this case, it is based on duplicate rows, but you can use other criteria as well. This case is a little more complicated because it requires using a for-loop.

> library(data.table)
> R = c(3,3,3,4,4,4,5,5,5)
> A = c(‘a’,”,”,’d’,”,”,’g’,”,”)
> B = c(”,’b’,”,”,’d’,”,”,’h’,”)
> C = c(”,”,’c’,”,”,’f’,”,”,’I’)
> df = data.frame(R,A,B,C)
> df
1 3 a
2 3 b
3 3 c
4 4 d
5 4 d
6 4 f
7 5 g
8 5 h
9 5 I
> df =
> df = df[, lapply(.SD, paste0, collapse=””), by=R]
> df =
> df
1 3 a b c
2 4 d d f
3 5 g h I

Here is an example of collapsing data based on blank spaces. As long as they are a character string there is a single formula that will do the job. Unfortunately, it will not work when the spaces are either zeros or NA values. Despite the fact that there is no single way of collapsing data into a single row but once you get some basic techniques down it is not a complicated process.

Potential Applications (Collapse Multiple Rows in R)

The main application of collapsing columns is cleaning up the dataframe that you are using. For example, if it has a lot of blank spaces and other factors that helped correlate data you might be able to collapse it making it more readable. Collapsing rows of data can also be a way of adding up the values of data frame columns. Another application is to remove all duplicate rows of data. clearing up blank spaces will make a data frame easier to read while adding up columns can reduce a long list of data into a single easier to understand row. The ultimate application of collapsing data frame rows is the fact that doing so often makes the data easier to understand by reducing the number of rows that you have to go through. If you can reduce the number of rows in a large data frame without changing the meaning of the content, then it is probably worth wild if it will make the data more readable. Raw data is often hard to read, collapsing rows is one way of cleaning it up.

Collapsing many rows of data into a single row can make that data easier to read. While you need to be careful that you do not ruin the data, when done under the right circumstances it may make a lot of data easier to understand.

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