CountIF function in 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