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Let's say I have a matrix called x.
x <- structure(c(1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1),
.Dim = c(5L, 4L), .Dimnames = list(c("Cake", "Pie", "Cake", "Pie", "Pie"),
c("Mon", "Tue", "Wed", "Thurs")))
x
Mon Tue Wed Thurs
Cake 1 0 1 1
Pie 0 0 1 1
Cake 1 1 0 1
Pie 0 0 1 1
Pie 0 0 1 1
I want to sum each column grouped by row names:
Mon Tue Wed Thurs
Cake 2 1 1 2
Pie 0 0 3 3
I've tried using addmargins(x), but that just gives me the sum of each column and row. Any suggestions? I searched other questions, but couldn't figure this out.
802
If so, one way to achieve this is in your design tool, go to column (row) group, then add the total for the correspond group like below: The sum will automatically pop out to the right down corner of the matrix. If this is not what you need, please kindly clarify your request. Hope this could be helpful.
Given a matrix of order m×n, the task is to find out the sum of each row and each column of a matrix. The sum of each row and each column can be calculated by traversing through the matrix and adding up the elements. Below is the implementation of the above approach: echo "Sum of the row " . $i .
How to find the row sum for each column by row name in an R matrix? To find the row sum for each column by row name, we can use rowsum function. For example, if we have a matrix called M then the row sums for each column with row names can be calculated by using the command rowsum (M,row.names (M)).
1 Select Group by on the Home tab. 2 Select the Advanced option, so you can select multiple columns to group by. 3 Select the Country and Sales Channel columns. 4 In the New columns section, create a new column where the name is Total units, the aggregate operation is Sum, and the column used is Units. 5 Hit OK
Here's a vectorized base solution
rowsum(df, row.names(x))
# Mon Tue Wed Thurs
# Cake 2 1 1 2
# Pie 0 0 3 3
Or data.table version using keep.rownames = TRUE in order to convert your row names to a column
library(data.table)
as.data.table(x, keep.rownames = TRUE)[, lapply(.SD, sum), by = rn]
# rn Mon Tue Wed Thurs
# 1: Cake 2 1 1 2
# 2: Pie 0 0 3 3
You can try this
df <- read.table(head=TRUE, text="
Name Mon Tue Wed Thurs
Cake 1 0 1 1
Pie 0 0 1 1
Cake 1 1 0 1
Pie 0 0 1 1
Pie 0 0 1 1")
aggregate(. ~ Name, data=df, FUN=sum)
## Name Mon Tue Wed Thurs
## 1 Cake 2 1 1 2
## 2 Pie 0 0 3 3
also with dplyr
library(dplyr)
group_by(df, Name) %>%
summarise(Mon = sum(Mon), Tue = sum(Tue), Wed = sum(Wed), Thurs = sum(Thurs))
or better
group_by(df, Name) %>%
summarise_each(funs(sum))
An approach using plyr:
ldply(split(df, df$Name), function(u) colSums(u[-1]))
# .id Mon Tue Wed Thurs
#1 Cake 2 1 1 2
#2 Pie 0 0 3 3
Data:
df = structure(list(Name = structure(c(1L, 2L, 1L, 2L, 2L), .Label = c("Cake",
"Pie"), class = "factor"), Mon = c(1L, 0L, 1L, 0L, 0L), Tue = c(0L,
0L, 1L, 0L, 0L), Wed = c(1L, 1L, 0L, 1L, 1L), Thurs = c(1L, 1L,
1L, 1L, 1L)), .Names = c("Name", "Mon", "Tue", "Wed", "Thurs"
), row.names = c(NA, -5L), class = "data.frame")
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