从较小的 data.tables 填充 data.table

Question

I am looking for a way to fill a Result data.table from smaller data.tables that come from calculations. My approach was the following:

#CREATE EXAMPLE

library(data.table)

# The empty table to be filled

DT <- data.table(
   "ID" = c("a", "b", "c", "d"),
   "A" = numeric(4),
   "B" = numeric(4))

   ID A B
1:  a 0 0
2:  b 0 0
3:  c 0 0
4:  d 0 0

# Table with part of the results
DT_short <- data.table(
         "ID" = c("a", "b", "d"),
         "A" = 1:3,
         "B" = 1:3)

   ID A B
1:  a 1 1
2:  b 2 2
3:  d 3 3

What I would like to do is to fill rows and columns according to their name. I managed to access the part of the big data.table I want to change by

nm1 <- names(DT_short)
DT[ID %in% DT_short[, ID], ..nm1]
#Bonus question: Why do I have to assign nm1 before, how do I make it work directly in []?

Now I would like to replace this part of DT by the small table DT_short , but everything I tried (like <- or := , or some kind of merge ) didn't work. Eg error object '..nm1' not found for DT[ID %in% DT_short[, ID], ..nm1] <- DT_short

Please help me by providing a solution or pointing me in the right direction. (Since the data I am working with is rather small - 10^2 columns, 10^2 rows, ~40 small files to be combined, number<10^9 per field - and other people will use my code readability is more important than performance.)

EDIT

In response to Ronak Shah. When I test your solution with the code below it works perfectly well without any errors/warnings. Before accepting the solution I would like to make sure it works for others as well / know why it causes warnings for you and not me.

library(data.table)
packageVersion('data.table')
#[1] ‘1.12.8’

#the empty table to be filled
DT <- data.table(
  "ID" = c("a", "b", "c", "d"),
  "A" = numeric(4),
  "B" = numeric(4),
  "C" = numeric(4)
)
#   ID A B C
#1:  a 0 0 0
#2:  b 0 0 0
#3:  c 0 0 0
#4:  d 0 0 0

#table with part of the results
DT_short <- data.table(
  "ID" = c("a", "b", "d"),
  "A" = 1:3,
  "B" = 1:3
)
#   ID A B
#1:  a 1 1
#2:  b 2 2
#3:  d 3 3

#table with part of the results 2
DT_shorter <- data.table(
  "ID" = c("c"),
  "A" = 7,
  "B" = 70,
  "C" = 3.14
)
#   ID A  B    C
#1:  c 7 70 3.14


DT[match(DT_short$ID, DT$ID), match(names(DT_short), names(DT))] <- DT_short
DT[match(DT_shorter$ID, DT$ID), match(names(DT_shorter), names(DT))] <- DT_shorter
DT
#   ID A  B    C
#1:  a 1  1 0.00
#2:  b 2  2 0.00
#3:  c 7 70 3.14
#4:  d 3  3 0.00

Answer 1

Here is one possible approach. For each column in mycols , you want to assign values from DT_short . When you do that, you want to use match() and get indices, and use it to create a new vector. Once you create a new data.table, you want to replace NAs with 0.

library(data.table)

mycols <- names(DT)[2:3]

as.data.table(lapply(mycols, function(x){
    DT_short[match(x = DT$ID, table = DT_short$ID), ..x]}))[,
      (mycols) := replace(x = .SD, list = is.na(.SD), values = 0),
      .SDcols = mycols][]

#   A B
#1: 1 1
#2: 2 2
#3: 0 0
#4: 3 3

Answer 2

Another option is to use an update join:

cols <- setdiff(names(DT_short), "ID")
DT[DT_short, on=.(ID), (cols) := mget(paste0("i.", cols))]

Answer 3

Since you mentioned that you are ok with other solutions, this part is easy to do with base R data.frames by subsetting row and columns of smaller dataframes from the bigger ones and assigning the shorter dataframe.

df1 <- data.frame(DT)
df2 <- data.frame(DT_short)
df1[match(df2$ID, df1$ID), match(names(df2), names(df1))] <- df2

df1
#  ID A B
#1  a 1 1
#2  b 2 2
#3  c 0 0
#4  d 3 3

---

I don't think it is right to do the same with data.table but if we run the above code it works (atleast for the example shared)

DT[match(DT_short$ID, DT$ID), match(names(DT_short), names(DT))] <- DT_short

but it returns a big warning message which kind of confirms this isn't the right approach for data.tables.