Get sum of unique rows in table function in R - r

Question

Suppose I have data which looks like this
Id Name Price sales Profit Month Category Mode Supplier
1 A 2 5 8 1 X K John
1 A 2 6 9 2 X K John
1 A 2 5 8 3 X K John
2 B 2 4 6 1 X L Sam
2 B 2 3 4 2 X L Sam
2 B 2 5 7 3 X L Sam
3 C 2 5 11 1 X M John
3 C 2 5 11 2 X L John
3 C 2 5 11 3 X K John
4 D 2 8 10 1 Y M John
4 D 2 8 10 2 Y K John
4 D 2 5 7 3 Y K John
5 E 2 5 9 1 Y M Sam
5 E 2 5 9 2 Y L Sam
5 E 2 5 9 3 Y M Sam
6 F 2 4 7 1 Z M Kyle
6 F 2 5 8 2 Z L Kyle
6 F 2 5 8 3 Z M Kyle
if I apply table function, it will just combines are the rows and result will be
K L M
X 4 4 1
Y 2 1 3
Z 0 1 2
Now what if I want not the sum of all rows but only sum of those rows with Unique Id
so it looks like
K L M
X 2 2 1
Y 1 1 2
Z 0 1 1
Thanks

Answer 1

If df is your data.frame:
# Subset original data.frame to keep columns of interest
df1 <- df[,c("Id", "Category", "Mode")]
# Remove duplicated rows
df1 <- df1[!duplicated(df1),]
# Create table
with(df1, table(Category, Mode))
# Mode
# Category K L M
# X 2 2 1
# Y 1 1 2
# Z 0 1 1
Or in one line using unique
table(unique(df[c("Id", "Category", "Mode")])[-1])
df <- structure(list(Id = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L,
4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L), Name = structure(c(1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L), .Label = c("A",
"B", "C", "D", "E", "F"), class = "factor"), Price = c(2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), sales = c(5L, 6L, 5L, 4L, 3L, 5L, 5L, 5L, 5L, 8L, 8L, 5L,
5L, 5L, 5L, 4L, 5L, 5L), Profit = c(8L, 9L, 8L, 6L, 4L, 7L, 11L,
11L, 11L, 10L, 10L, 7L, 9L, 9L, 9L, 7L, 8L, 8L), Month = c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L), Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("X", "Y", "Z"
), class = "factor"), Mode = structure(c(1L, 1L, 1L, 2L, 2L,
2L, 3L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 3L, 3L, 2L, 3L), .Label = c("K",
"L", "M"), class = "factor"), Supplier = structure(c(1L, 1L,
1L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L
), .Label = c("John", "Kyle", "Sam"), class = "factor")), .Names = c("Id",
"Name", "Price", "sales", "Profit", "Month", "Category", "Mode",
"Supplier"), class = "data.frame", row.names = c(NA, -18L))

Answer 2

We can try
library(data.table)
dcast(unique(setDT(df1[c('Category', 'Mode', 'Id')])),
Category~Mode, value.var='Id', length)
# Category K L M
#1: X 2 2 1
#2: Y 1 1 2
#3: Z 0 1 1
Or with dplyr
library(dplyr)
df1 %>%
distinct(Id, Category, Mode) %>%
group_by(Category, Mode) %>%
tally() %>%
spread(Mode, n, fill=0)
# Category K L M
# (chr) (dbl) (dbl) (dbl)
#1 X 2 2 1
#2 Y 1 1 2
#3 Z 0 1 1
Or as #David Arenburg suggested, a variant of the above is
df1 %>%
distinct(Id, Category, Mode) %>%
select(Category, Mode) %>%
table()