Sum Columns by Group (Row Names) in a Matrix

Sum columns by group (row names) in a matrix

Here's a vectorized base solution

rowsum(df, row.names(x))
#      Mon Tue Wed Thurs
# Cake   2   1   1     2
# Pie    0   0   3     3

Or data.table version using keep.rownames = TRUE in order to convert your row names to a column

library(data.table)
as.data.table(x, keep.rownames = TRUE)[, lapply(.SD, sum), by = rn]
#      rn Mon Tue Wed Thurs
# 1: Cake   2   1   1     2
# 2:  Pie   0   0   3     3

row and column matrix sum in R by group

We can do the sum with xtabs after changing the dimnames with the substr of 1st 4 characters

dimnames(m1) <- lapply(dimnames(m1), substr, 1, 4)
xtabs(Freq~ Var1 + Var2, as.data.frame.table(m1))
#      Var2
#Var1   UKC1 UKC2
#  UKC1   14   22
#  UKC2   46   54

data

m1 <- structure(c(1L, 5L, 9L, 13L, 2L, 6L, 10L, 14L, 3L, 7L, 11L, 15L, 
4L, 8L, 12L, 16L), .Dim = c(4L, 4L), .Dimnames = list(c("UKC1_SS1", 
"UKC1_SS2", "UKC2_SS1", "UKC2_SS2"), c("UKC1_SS1", "UKC1_SS2", 
 "UKC2_SS1", "UKC2_SS1.1")))

Sum row-wise values that are grouped by column name but keep all columns in R?

You can try ave like below (with aids of col + row)

> ave(myMat,colnames(myMat)[col(myMat)], row(myMat), FUN = sum)
     x  y x  y
[1,] 1  3 1  3
[2,] 5  9 5  9
[3,] 4 13 4 13

Sum values in rows with same names in R

We can use rowsum. Assume that the dataset showed is matrix and not data.frame as data.frame cannot have duplicated row names

rowsum(df1, row.names(df1))

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Or using aggregate

aggregate(df1, list(row.names(df1)), sum)

data

df1 <- structure(c(5L, 3L, 7L, 1L, 3L, 6L, 6L, 4L, 2L, 7L), .Dim = c(5L, 
2L), .Dimnames = list(c("bacteria", "bacteria", "bacteria", "archaea", 
"archaea"), c("category1", "category2")))

How to calculate sum of values in each column based on row names in R?

We can use colSums with startsWith

colSums(mat[startsWith(row.names(mat), "A"),])

Calculate summary statistics for each row of a matrix based on columns grouped by column names

This is the best scenario to use tapply:

tapply(t(data), list(col(data), array(colnames(data), dim(t(data)))), mean)
    A  B
1   3  8
2  13 18
3  23 28
4  33 38
5  43 48
6  53 58
7  63 68
8  73 78
9  83 88
10 93 98

tapply(data, list(t(colnames(data))[rep(1,nrow(data)), ], row(data)), mean)
  1  2  3  4  5  6  7  8  9 10
A 3 13 23 33 43 53 63 73 83 93
B 8 18 28 38 48 58 68 78 88 98

 tapply(t(data), interaction(colnames(data), col(data)), mean)
 A.1  B.1  A.2  B.2  A.3  B.3  A.4  B.4  A.5  B.5  A.6  B.6  A.7  B.7  A.8  B.8  A.9  B.9 A.10 B.10 
   3    8   13   18   23   28   33   38   43   48   53   58   63   68   73   78   83   88   93   98 

More base R solutions:

sapply(split.default(data.frame(data), colnames(data)), rowMeans)
       A  B
 [1,]  3  8
 [2,] 13 18
 [3,] 23 28
 [4,] 33 38
 [5,] 43 48
 [6,] 53 58
 [7,] 63 68
 [8,] 73 78
 [9,] 83 88
[10,] 93 98

data.frame(data) |>
  reshape(split(1:ncol(data), colnames(data)),  dir = 'long') |>
  (\(x)aggregate(.~id, x, mean))()

   id time  A  B
1   1    3  3  8
2   2    3 13 18
3   3    3 23 28
4   4    3 33 38
5   5    3 43 48
6   6    3 53 58
7   7    3 63 68
8   8    3 73 78
9   9    3 83 88
10 10    3 93 98

R sum rows of matrix by column name

You can use rowsum with the column names as group variable:

t(rowsum(t(z), colnames(z)))

#      a  b  c
#[1,]  8 20  9
#[2,] 11  7  3
#[3,]  8 18  8
#[4,]  8 11 10

Row-wise sum of values grouped by columns with same name

We can transpose dat , calculate rowsum per group (colnames of the original dat), then transpose the result back to original structure.

t(rowsum(t(dat), group = colnames(dat), na.rm = T))
#  A C G T
#1 1 0 1 0
#2 4 0 6 0
#3 0 1 0 1
#4 2 0 1 0
#5 1 0 1 0
#6 0 1 0 1
#7 0 1 0 1

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