Sum of columns into a new column

[Hersh]

Good morning all,
I am new to R and have searched long enough for an answer to a fairly basic problem ...
I have a dataset with various variables. My columns 6 to 50 are particle volumes and I would just like to have a new column (hence a new variable) that is the sum of all the other columns:

For example: imagine that I have 4 columns A B C and D:

A B C D
0 1 1 2

I would then like to have a column E which for this row indicates 4

I hope I was pretty much clear ^^

thank you

[jrkrideau]

?rowSums

mat1  <-  matrix(1:20, ncol = 4)
rs  <-  rowSums(mat1)
mat2  <-  cbind(mat1, rs)

[Hersh]

Thanks a lot for your answer !
I don't understand what your 1:20 is and the ncol = 4?
I was thinking of using the mutate function to create a new variable which would be the sum of my columns.
Basically I have a basic data set called dataset1 with my variables 6 to 50 were volumes according to particle sizes, each variable has the mention LPM in its name. I just want to add all the volumes of each of the sizes to have only one variable and not 44

Sites LPM6 LPM7 ................ LPM49 LPM 50
1 1 0 ...................... 2 0
2 0 2 .......................1 0
3 1 2 .......................1 1
4 2 1 .......................0 1
.... .... .. ...................... .. ...

sites LPMtotal
1 3
2 3
3 4
4 4

I would like to have a column named LPMtotal which for each of my lines it makes me the sum of all the LPMs so there in my example 3

basically have a variable LPMtotal which is the sum of the LPM variables from column 6 to column 50

[jrkrideau]

mat1 <- matrix(1:20, ncol = 4)
This just creates a matrix as sample data. I created a matrix of the numbers from 1 to 20 arranged in 4 rows. The ncol specifies the number of rows.
.

mat1
     [,1] [,2] [,3] [,4]
[1,]    1    6   11   16
[2,]    2    7   12   17
[3,]    3    8   13   18
[4,]    4    9   14   19
[5,]    5   10   15   20

It would help to have some data---see the FAQ posted below but if I understand you properly you just need to do:

rs  <-  rowSums(mydata[ , 6:44])  

and then do a cbind().

Here is a toy example with "dat1" as a dataframe.

dat1  <-  structure(list(rr = c("a", "b", "c", "d", "e"), X1 = 1:5, X2 = 6:10, 
    X3 = 11:15, X4 = 16:20), class = "data.frame", row.names = c(NA, 
-5L))


rs  <-  rowSums(dat1[ , 2 : 5])

dat2  <-  cbind(dat1, rs)

I do not usually use tidyverse so I don"t know if mutate() is the correct approach. My approach is simple basic R which will do the job. Note, if you have NAs in the data you will need to use

rowSums(mydata[ , 6:44], na.rm = TRUE)

Some suggestions on asking questions.

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

Good luck

[Hersh]

Hello !
I was finally able to find out how to do it! I used mutate, thank you very much for your help, I was able to learn a lot about the different ways of using R:
if you are interested, here is how I proceeded:

x <- dataset2%>%
select (starts_with ("LPM bio"))

dataset3 <- mutate (dataset2, LPMtotal = rowSums (x))