Apply a rule to calculate sum of specific - r

Question

Hi I have a data set like this.
Num C Pr Value Volume
111 aa Alen 111 222
111 aa Paul 100 200
222 vv Iva 444 555
222 vv John 333 444
I would like to filter the data according to Num and to add a new row where take the sum of column Value and Volume but to keep the information of column Num and C, but in column Pr to put Total. It should look like this way.
Num C Pr Value Volume
222 vv Total 777 999
Could you suggest me how to do it? I would like only for Num 222.
When I try to use res command I end up with this result.
# Num C Pr Value Volume
1: 111 aa Alen 111 222
2: 111 aa Paul 100 200
3: 111 aa Total NA NA
4: 222 vv Iva 444 555
5: 222 vv John 333 444
6: 222 vv Total NA NA
What cause this?
The structure of my data is the following one.
'data.frame': 4 obs. of 5 variables:
$ Num : Factor w/ 2 levels "111","222": 1 1 2 2
$ C : Factor w/ 2 levels "aa","vv": 1 1 2 2
$ Pr : Factor w/ 4 levels "Alen","Iva","John",..: 1 4 2 3
$ Value : Factor w/ 4 levels "100","111","333",..: 2 1 4 3
$ Volume: Factor w/ 4 levels "200","222","444",..: 2 1 4 3

Answer 1

We could use data.table. We convert the 'data.frame' to 'data.table' (setDT(df1)), grouped by 'Num', 'C' columns and specifying the columns to do the sum in .SDcols, we loop those columns using lapply, get the sum, and create the 'Pr' column. We can rbind the original dataset with the new summarised output ('DT1') and order the result based on 'Num'.
library(data.table)#v1.9.5+
DT1 <- setDT(df1)[,lapply(.SD, sum) , by = .(Num,C),
.SDcols=Value:Volume][,Pr:='Total'][]
rbind(df1, DT1)[order(Num)]
# Num C Pr Value Volume
#1: 111 aa Alen 111 222
#2: 111 aa Paul 100 200
#3: 111 aa Total 211 422
#4: 222 vv Iva 444 555
#5: 222 vv John 333 444
#6: 222 vv Total 777 999
This can be done using base R methods as well. We get the sum of 'Value', 'Volume' columns grouped by 'Num', 'C', using the formula method of aggregate, transform the output by creating the 'Pr' column, rbind with original dataset and order the output ('res') based on 'Num'.
res <- rbind(df1,transform(aggregate(.~Num+C, df1[-3], FUN=sum), Pr='Total'))
res[order(res$Num),]
# Num C Pr Value Volume
#1 111 aa Alen 111 222
#2 111 aa Paul 100 200
#5 111 aa Total 211 422
#3 222 vv Iva 444 555
#4 222 vv John 333 444
#6 222 vv Total 777 999
EDIT: Noticed that the OP mentioned filter. If this is for a single 'Num', we subset the data, and then do the aggregate, transform steps.
transform(aggregate(.~Num+C, subset(df1, Num==222)[-3], FUN=sum), Pr='Total')
# Num C Value Volume Pr
#1 222 vv 777 999 Total
Or we may not need aggregate. After subsetting the data, we convert the 'Num' to 'factor', loop through the output dataset ('df2') get the sum if it the column is numeric class or else we get the first element and wrap with data.frame.
df2 <- transform(subset(df1, Num==222), Num=factor(Num))
data.frame(c(lapply(df2[-3], function(x) if(is.numeric(x))
sum(x) else x[1]), Pr='Total'))
# Num C Value Volume Pr
#1 222 vv 777 999 Total
data
df1 <- structure(list(Num = c(111L, 111L, 222L, 222L), C = c("aa", "aa",
"vv", "vv"), Pr = c("Alen", "Paul", "Iva", "John"), Value = c(111L,
100L, 444L, 333L), Volume = c(222L, 200L, 555L, 444L)), .Names = c("Num",
"C", "Pr", "Value", "Volume"), class = "data.frame",
row.names = c(NA, -4L))

Answer 2

Or using dplyr:
library(dplyr)
df1 %>%
filter(Num == 222) %>%
summarise(Value = sum(Value),
Volume = sum(Volume),
Pr = 'Total',
Num = Num[1],
C = C[1])
# Value Volume Pr Num C
# 1 777 999 Total 222 vv
where we first filter to keep only Num == 222, and then use summarise to obtain the sums and the values for Num and C. This assumes that:
You do not want to get the result for each unique Num (I select one here, you could select multiple). If you need this, use group_by.
There is only ever one C for every unique Num.

Answer 3

You can also use a dplyr package:
df %>%
filter(Num == 222) %>%
group_by(Num, C) %>%
summarise(
Pr = "Total"
, Value = sum(Value)
, Volume = sum(Volume)
) %>%
rbind(df, .)
# Num C Pr Value Volume
# 1 111 aa Alen 111 222
# 2 111 aa Paul 100 200
# 3 222 vv Iva 444 555
# 4 222 vv John 333 444
# 5 222 vv Total 777 999
If you want the total for each Num value you just comment a filter line