utils table manipulation methods for dm_zoomed objects

Extract the first or last rows from a table. Use these methods without the '.dm_zoomed' suffix (see examples). The methods for regular dm objects extract the first or last tables.

Usage

# S3 method for dm_zoomed
head(x, n = 6L, ...)

# S3 method for dm_zoomed
tail(x, n = 6L, ...)

Arguments

x

object of class dm_zoomed

n

an integer vector of length up to dim(x) (or 1, for non-dimensioned objects). Values specify the indices to be selected in the corresponding dimension (or along the length) of the object. A positive value of n[i] includes the first/last n[i] indices in that dimension, while a negative value excludes the last/first abs(n[i]), including all remaining indices. NA or non-specified values (when length(n) < length(dim(x))) select all indices in that dimension. Must contain at least one non-missing value.

...

arguments to be passed to or from other methods.

Value

A dm_zoomed object.

Details

see manual for the corresponding functions in utils.

Examples

zoomed <- dm_nycflights13() %>%
  dm_zoom_to(flights) %>%
  head(4)
zoomed
#> # Zoomed table: flights
#> # A tibble:     4 × 19
#>    year month   day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
#>   <int> <int> <int>    <int>       <int>   <dbl>   <int>   <int>   <dbl> <chr>  
#> 1  2013     1    10        3        2359       4     426     437     -11 B6     
#> 2  2013     1    10       16        2359      17     447     444       3 B6     
#> 3  2013     1    10      450         500     -10     634     648     -14 US     
#> 4  2013     1    10      520         525      -5     813     820      -7 UA     
#> # … with 9 more variables: flight <int>, tailnum <chr>, origin <chr>,
#> #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
#> #   time_hour <dttm>, and abbreviated variable names ¹​sched_dep_time,
#> #   ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay
dm_insert_zoomed(zoomed, new_tbl_name = "head_flights")
#> ── Metadata ────────────────────────────────────────────────────────────────────
#> Tables: `airlines`, `airports`, `flights`, `planes`, `weather`, `head_flights`
#> Columns: 72
#> Primary keys: 4
#> Foreign keys: 8