CODEWITHSUNDEEP
rdata.tablelookupvlookup
Alexis
Alexis

Reputation: 271

Vlookup with data.table in R

First, let's start with DataTable 1 (DF1) :

          date id sales cost city  name
 1: 06/19/2016  1   149  101  MTL Bank1
 2: 06/20/2016  1   150  102  MTL Bank1
 3: 06/21/2016  1   151  104  MTL Bank1
 4: 06/22/2016  1   152  107  MTL Bank1
 5: 06/23/2016  1   155   99  MTL Bank1
 6: 06/19/2016  2    84   55   NY Bank2
 7: 06/20/2016  2    83   55   NY Bank2
 8: 06/21/2016  2    80   56   NY Bank2
 9: 06/22/2016  2    81   57   NY Bank2
10: 06/23/2016  2    97   58   NY Bank2

library(data.table)
DF1 <- data.table(c("06/19/2016", "06/20/2016", "06/21/2016", "06/22/2016", 
                    "06/23/2016", "06/19/2016", "06/20/2016", "06/21/2016",
                    "06/22/2016", "06/23/2016"),
                  c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2),
                  c(149, 150, 151, 152, 155, 84, 83, 80, 81, 97),
                  c(101, 102, 104, 107, 99, 55, 55, 56, 57, 58),
                  c("MTL", "MTL", "MTL", "MTL", "MTL", "NY", "NY", 
                    "NY", "NY", "NY"))
colnames(DF1) <- c("date", "id", "sales", "cost", "city")

I want to add the column name shown above using the lookup table:

   id  name start_date   end_date status
1:  1 Bank1 06/19/2016 06/25/2016      0
2:  2 Bank2 06/27/2016 06/27/2017      0
3:  3 Bank3 06/22/2016 06/24/2017      1
4:  4 Bank3 06/23/2016 12/23/2016      1

lookup <- data.table(c(1, 2, 3, 4),
                     c("Bank1", "Bank2", "Bank3", "Bank3"),
                     c("06/19/2016", "06/27/2016", "06/22/2016", "06/23/2016"),
                     c("06/25/2016", "06/27/2017", "06/24/2017", "12/23/2016"),
                     c("0", "0", "1", "1"))
colnames(lookup) <- c("id", "name", "start_date", "end_date", "status")

In that case, I would use the id to find the name. When I try merge, I always have new rows in DF1 that contains NA.

Upvotes: 3

Views: 2928

Answers (2)

lmo
lmo

Reputation: 38520

Here is a slightly different setup: I'm assuming that id is unique for both data sets and that the lookup data contains all of ids present in your main dataset.

I subset lookup to just the id and name. To assure that there are no repetitions (multiple dates) in the lookup, I use unique.

DF1[unique(lookup[, .(id, name)]), on="id"][!is.na(sales)]

    id       date sales cost city  name
 1:  1 06/19/2016   149  101  MTL Bank1
 2:  1 06/20/2016   150  102  MTL Bank1
 3:  1 06/21/2016   151  104  MTL Bank1
 4:  1 06/22/2016   152  107  MTL Bank1
 5:  1 06/23/2016   155   99  MTL Bank1
 6:  2 06/19/2016    84   55   NY Bank2
 7:  2 06/20/2016    83   55   NY Bank2
 8:  2 06/21/2016    80   56   NY Bank2
 9:  2 06/22/2016    81   57   NY Bank2
10:  2 06/23/2016    97   58   NY Bank2

This is called a left join. I use [!is.na()] at the end to drop banks 3 and 4 which don't have an observation in the main data set.

Upvotes: 2

Austin
Austin

Reputation: 326

DF1<-merge(DF1, lookup[,.(id, name)], by='id', all.x=TRUE, all.y=FALSE)

I think the merge command is what you are looking for here, but you were missing the all.y = FALSE bit. What we are doing here is merging DF1 with the lookup data table and we are telling R to include all of the rows in x, but only the rows in y that match up with the rows in x where x is DF1 and y is lookup. The lookup[,.(id, name)] means we only want the column id (to match with DF1) and the column 'name'. If there were rows in DF1 that did not have a matching row in lookup, they would show up as NA's because of the all.x=TRUE.

Upvotes: 4

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