updates the data.table-like object to based on the data.frame-like from object,
using date_column to determine which rows have been updated and updating only those with matching primary_keys.
Usage
update_table(
from,
to,
date_column = NULL,
primary_keys = NULL,
delete = FALSE,
incremental = TRUE,
...
)
# Default S3 method
update_table(
from,
to,
date_column = NULL,
primary_keys = NULL,
delete = FALSE,
incremental = TRUE,
...
)
# S3 method for class 'data.table'
update_table(
from,
to,
date_column = NULL,
primary_keys = NULL,
delete = FALSE,
incremental = TRUE,
...
)
update_table_date_only(from, to, date_column = NULL, prefer = "to")
# S3 method for class 'data.table'
update_table_date_only(from, to, date_column = NULL, prefer = "to")
# Default S3 method
update_table_date_only(from, to, date_column = NULL, prefer = "to")Arguments
- from
the source object
- to
the object to be updated
- date_column
string or tidy-selected column identifying the date column to use to determine which rows to update
- primary_keys
vector of strings or tidy-selected columns identifying the primary keys to determine which rows to update
- delete
whether to delete rows in
tomissing fromfrom, default is not to delete the rows- incremental
whether or not to update the table incrementally or to simply overwrite the existing table with a new one, the default is
TRUE- ...
Arguments passed on to
update_table_date_only- prefer
"from" or "to": which table to prefer on conflict during a date-only update. The default is to prefer the destination table, so that existing data in
tois not overwritten.
Functions
update_table_date_only(): update incrementally when primary_keys not given but date_column givenupdate_table_date_only(data.table): update incrementally when primary_keys not given but date_column givenupdate_table_date_only(default): update incrementally when primary_keys not given but date_column given
Examples
library(data.table)
expect <- data.table(expand.grid(x = 1:100, y = 1:100))[, data := runif(.N)]
# divide the data
from <- copy(expect)[1:9000]
to <- copy(expect)[1000:10000]
# and mung it up
to[1:5000, data := runif(.N)]
#> x y data
#> <int> <int> <num>
#> 1: 100 10 0.97130252
#> 2: 1 11 0.05546916
#> 3: 2 11 0.20986848
#> 4: 3 11 0.72650247
#> 5: 4 11 0.08130657
#> ---
#> 8997: 96 100 0.21371826
#> 8998: 97 100 0.96957901
#> 8999: 98 100 0.50544923
#> 9000: 99 100 0.93567441
#> 9001: 100 100 0.70146759
update_table(from, to, primary_keys = c(x, y)) == expect
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#> [3411,] FALSE FALSE FALSE
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#> [9697,] TRUE TRUE TRUE
#> [9698,] FALSE FALSE FALSE
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#> [9798,] TRUE TRUE TRUE
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#> [9899,] TRUE TRUE TRUE
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#> [9994,] FALSE FALSE FALSE
#> [9995,] FALSE FALSE FALSE
#> [9996,] FALSE FALSE FALSE
#> [9997,] FALSE FALSE FALSE
#> [9998,] FALSE FALSE FALSE
#> [9999,] FALSE FALSE FALSE
#> [10000,] TRUE TRUE TRUE
# TRUE