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), .Dimname
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")