If I have a data frame
set.seed(12345) df=data.frame(a=rnorm(5),b=rnorm(5))
I can add a row by e.g.
df[6,] =c(5,6)
If I now do the equivalent in data.table
library(data.table) dt=data.table(df) dt[6,]=c(5,6)
It fails with an error. What is the right way to insert a row into a data.table?
To insert a row, pick a cell or row that's not the header row, and right-click. To insert a column, pick any cell in the table and right-click. Point to Insert, and pick Table Rows Above to insert a new row, or Table Columns to the Left to insert a new column.
With command rbindlist from the data. table package, we can append dt_add_row and new_row row-wise. Object dt_add_row, shown in Table 2, shows the original data. table with the added row number 6.
To expand on @Franks answer, if in your particular case you are appending a row, it's :
set.seed(12345) dt1 <- data.table(a=rnorm(5), b=rnorm(5))
The following are equivalent; I find the first easier to read but the second faster:
microbenchmark( rbind(dt1, list(5, 6)), rbindlist(list(dt1, list(5, 6))) )
As we can see:
expr min lq median uq max rbind(dt1, list(5, 6)) 160.516 166.058 175.089 185.1470 457.735 rbindlist(list(dt1, list(5, 6))) 130.137 134.037 140.605 149.6365 184.326
If you want to insert the row elsewhere, the following will work, but it's not pretty:
rbindlist(list(dt1[1:3, ], list(5, 6), dt1[4:5, ]))
or even
rbindlist(list(dt1[1:3, ], as.list(c(5, 6)), dt1[4:5, ]))
giving:
a b 1: 0.5855288 -1.8179560 2: 0.7094660 0.6300986 3: -0.1093033 -0.2761841 4: 5.0000000 6.0000000 5: -0.4534972 -0.2841597 6: 0.6058875 -0.9193220
If you are modifying a row in place (which is the preferred approach), you will need to define the size of the data.table in advance i.e.
dt1 <- data.table(a=rnorm(6), b=rnorm(6)) set(dt1, i=6L, j="a", value=5) # refer to column by name set(dt1, i=6L, j=2L, value=6) # refer to column by number
Thanks @Boxuan, I have modified this answer to take account of your suggestion, which is a little faster and easier to read.
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