CountIF function in R? - r

Question

I have the following formulas in excel, but calculation takes forever, so I would like to find a way to calculate these formulas in excel.
I'm counting the number of times an item shows up in a location (Location 1, Location 2, and External) with these formulas:
=SUMPRODUCT(($N:$N=$A2)*(RIGHT(!$C:$C)="1")
=SUMPRODUCT(($N:$N=$A2)*(RIGHT(!$C:$C)="2")
=SUMPRODUCT(($N:$N=$A2)*(LEFT($C:$C)="E"))
Here is the dataframe in which the columns with these values will be added:
> str(FinalPars)
'data.frame': 10038 obs. of 3 variables:
$ ID: int 11 13 18 22 39 181 182 183 191 192 ...
$ Minimum : num 15 6 1.71 1 1 4.39 2.67 5 5 2 ...
$ Maximum : num 15 6 2 1 1 5.48 3.69 6.5 5 2 ...
and here is the dataset to which the ItemID will be matched (This is a master list of all locations each item is stored in):
> str(StorageLocations)
'data.frame': 14080 obs. of 3 variables:
$ ID : int 1 2 3 4 5 6 7 8 9 10 ...
$ CLASSIFICATION : Factor w/ 3 levels "Central 1","Central 2",..: 3 3 3 1 2 3 3 1 2 3 ...
$ Cart Descr : Factor w/ 145 levels "Closet1",..: 36 41 110 1 99 58 60 14 99 60 ...
Sample of Storage Location Data Frame:
ID Classification Cart Descr
123 Central 1 Main Store Room
123 Central 2 Secondary Store Room
123 External Closet 1
123 External Closet 2
123 External Closet 3
So the output for the above would be added to the data frame total pars as the new colums Central 1, Central 2, and External and count the number of times the item was IDd as in those locations:
ID Minimum Maximum Central 1 Central 2 External
123 10 15 1 1 3
This was my output in Excel - a Count of the # of times an item was identified as Central 1, Central 2, or External
If anyone knows the comparable formula in R it would be great!

Answer 1

It's hard to know what you are really asking for without example data. I produced an example below.
Location <- c(rep(1,4), rep(2,4), rep(3,4))
Item_Id <- c(rep(1,2),rep(2,3),rep(1,2),rep(2,2),rep(1,3))
Item_Id_Want_to_Match <- 1
df <- data.frame(Location, Item_Id)
> df
Location Item_Id
1 1 1
2 1 1
3 1 2
4 1 2
5 2 2
6 2 1
7 2 1
8 2 2
9 3 2
10 3 1
11 3 1
12 3 1
sum(ifelse(df$Location == 1 & df$Item_Id == Item_Id_Want_to_Match, df$Item_Id*df$Location,0))
> sum(ifelse(df$Location == 1 & df$Item_Id == Item_Id_Want_to_Match, df$Item_Id*df$Location,0))
[1] 2
EDIT:
ID <- rep(123,5)
Classification <- c("Central 1", "Central 2", rep("External",3))
df <- data.frame(ID, Classification)
df$count <- 1
ID2 <- 123
Min <- 10
Max <- 15
df2 <- data.frame(ID2, Min, Max)
library(dplyr)
count_df <- df %>%
group_by(ID, Classification) %>%
summarise(count= sum(count))
> count_df
Source: local data frame [3 x 3]
Groups: ID
ID Classification count
1 123 Central 1 1
2 123 Central 2 1
3 123 External 3
library(reshape)
new_df <- recast(count_df, ID~Classification, id.var=c("ID", "Classification"))
> new_df
ID Central 1 Central 2 External
1 123 1 1 3
merge(new_df, df2, by.x="ID", by.y="ID2")
> merge(new_df, df2, by.x="ID", by.y="ID2")
ID Central 1 Central 2 External Min Max
1 123 1 1 3 10 15