This is a method for the tidyr pivot_wider()
generic. It is translated to
data.table::dcast()
# S3 method for dtplyr_step pivot_wider( data, id_cols = NULL, names_from = name, names_prefix = "", names_sep = "_", names_glue = NULL, names_sort = FALSE, names_repair = "check_unique", values_from = value, values_fill = NULL, values_fn = NULL, ... )
data | A |
---|---|
id_cols | < |
names_from | < If |
names_prefix | String added to the start of every variable name. This is
particularly useful if |
names_sep | If |
names_glue | Instead of |
names_sort | Should the column names be sorted? If |
names_repair | What happens if the output has invalid column names?
The default, |
values_from | < If |
values_fill | Optionally, a (scalar) value that specifies what each
This can be a named list if you want to apply different aggregations to different value columns. |
values_fn | A function, the default is |
... | Additional arguments passed on to methods. |
#> Source: local data table [114 x 3] #> Call: `_DT22` #> #> fish station seen #> <fct> <fct> <int> #> 1 4842 Release 1 #> 2 4842 I80_1 1 #> 3 4842 Lisbon 1 #> 4 4842 Rstr 1 #> 5 4842 Base_TD 1 #> 6 4842 BCE 1 #> # … with 108 more rows #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results#> Source: local data table [19 x 12] #> Call: dcast(`_DT22`, formula = fish ~ station, value.var = "seen") #> #> fish Release I80_1 Lisbon Rstr Base_TD BCE BCW BCE2 BCW2 MAE MAW #> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> #> 1 4842 1 1 1 1 1 1 1 1 1 1 1 #> 2 4843 1 1 1 1 1 1 1 1 1 1 1 #> 3 4844 1 1 1 1 1 1 1 1 1 1 1 #> 4 4845 1 1 1 1 1 NA NA NA NA NA NA #> 5 4847 1 1 1 NA NA NA NA NA NA NA NA #> 6 4848 1 1 1 1 NA NA NA NA NA NA NA #> # … with 13 more rows #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results# Fill in missing values fish_encounters_dt %>% pivot_wider(names_from = station, values_from = seen, values_fill = 0)#> Source: local data table [19 x 12] #> Call: dcast(`_DT22`, formula = fish ~ station, value.var = "seen", #> fill = 0) #> #> fish Release I80_1 Lisbon Rstr Base_TD BCE BCW BCE2 BCW2 MAE MAW #> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> #> 1 4842 1 1 1 1 1 1 1 1 1 1 1 #> 2 4843 1 1 1 1 1 1 1 1 1 1 1 #> 3 4844 1 1 1 1 1 1 1 1 1 1 1 #> 4 4845 1 1 1 1 1 0 0 0 0 0 0 #> 5 4847 1 1 1 0 0 0 0 0 0 0 0 #> 6 4848 1 1 1 1 0 0 0 0 0 0 0 #> # … with 13 more rows #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results# Generate column names from multiple variables us_rent_income_dt <- lazy_dt(us_rent_income) us_rent_income_dt#> Source: local data table [104 x 5] #> Call: `_DT23` #> #> GEOID NAME variable estimate moe #> <chr> <chr> <chr> <dbl> <dbl> #> 1 01 Alabama income 24476 136 #> 2 01 Alabama rent 747 3 #> 3 02 Alaska income 32940 508 #> 4 02 Alaska rent 1200 13 #> 5 04 Arizona income 27517 148 #> 6 04 Arizona rent 972 4 #> # … with 98 more rows #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results#> Source: local data table [52 x 6] #> Call: dcast(`_DT23`, formula = GEOID + NAME ~ variable, value.var = c("estimate", #> "moe")) #> #> GEOID NAME estimate_income estimate_rent moe_income moe_rent #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 01 Alabama 24476 747 136 3 #> 2 02 Alaska 32940 1200 508 13 #> 3 04 Arizona 27517 972 148 4 #> 4 05 Arkansas 23789 709 165 5 #> 5 06 California 29454 1358 109 3 #> 6 08 Colorado 32401 1125 109 5 #> # … with 46 more rows #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results# When there are multiple `names_from` or `values_from`, you can use # use `names_sep` or `names_glue` to control the output variable names us_rent_income_dt %>% pivot_wider( names_from = variable, names_sep = ".", values_from = c(estimate, moe) )#> Source: local data table [52 x 6] #> Call: dcast(`_DT23`, formula = GEOID + NAME ~ variable, value.var = c("estimate", #> "moe"), sep = ".") #> #> GEOID NAME estimate.income estimate.rent moe.income moe.rent #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 01 Alabama 24476 747 136 3 #> 2 02 Alaska 32940 1200 508 13 #> 3 04 Arizona 27517 972 148 4 #> 4 05 Arkansas 23789 709 165 5 #> 5 06 California 29454 1358 109 3 #> 6 08 Colorado 32401 1125 109 5 #> # … with 46 more rows #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results# Can perform aggregation with values_fn warpbreaks_dt <- lazy_dt(as_tibble(warpbreaks[c("wool", "tension", "breaks")])) warpbreaks_dt#> Source: local data table [54 x 3] #> Call: `_DT24` #> #> wool tension breaks #> <fct> <fct> <dbl> #> 1 A L 26 #> 2 A L 30 #> 3 A L 54 #> 4 A L 25 #> 5 A L 70 #> 6 A L 52 #> # … with 48 more rows #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results#> Source: local data table [3 x 3] #> Call: dcast(`_DT24`, formula = tension ~ wool, value.var = "breaks", #> fun.aggregate = function (x, ...) #> UseMethod("mean")) #> #> tension A B #> <fct> <dbl> <dbl> #> 1 L 44.6 28.2 #> 2 M 24 28.8 #> 3 H 24.6 18.8 #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results