dplyr-ggvis: Dplyr verbs for ggvis.

Description

Applying a dplyr verb to a ggvis object creates a reactive transformation: whenever the underlying data changes the transformation will be recomputed.

Usage

# S3 method for ggvis
groups(x)
# S3 method for ggvis
group_by(.data, ..., .add = FALSE)
# S3 method for ggvis
ungroup(x, ...)
# S3 method for ggvis
summarise(.data, ...)
# S3 method for ggvis
mutate(.data, ...)
# S3 method for ggvis
arrange(.data, ...)
# S3 method for ggvis
select(.data, ...)
filter.ggvis(.data, ...)
# S3 method for ggvis
distinct(.data, ...)
# S3 method for ggvis
slice(.data, ...)
# S3 method for ggvis
rename(.data, ...)
# S3 method for ggvis
transmute(.data, ...)
# S3 method for reactive
groups(x)
# S3 method for reactive
ungroup(x, ...)
# S3 method for reactive
group_by(.data, ..., add = FALSE)
# S3 method for reactive
summarise(.data, ...)
# S3 method for reactive
mutate(.data, ...)
# S3 method for reactive
arrange(.data, ...)
# S3 method for reactive
select(.data, ...)
filter.reactive(.data, ...)
# S3 method for reactive
distinct(.data, ...)
# S3 method for reactive
slice(.data, ...)
# S3 method for reactive
rename(.data, ...)
# S3 method for reactive
transmute(.data, ...)

Arguments

Non-standard evaluation

Both dplyr and shiny do non-standard evaluation, so to help each package figure out when it should evaluate its code, reactive components in these functions must be wrapped in eval().

Examples

Run this code

library(dplyr)
base <- mtcars %>% ggvis(~mpg, ~cyl) %>% layer_points()
base %>% group_by(cyl) %>% summarise(mpg = mean(mpg)) %>%
  layer_points(fill := "red", size := 100)

base %>% filter(mpg > 25) %>% layer_points(fill := "red")

base %>% mutate(cyl = jitter(cyl)) %>% layer_points(fill := "red")

if (FALSE) {
# Dynamically restrict range using filter
mtcars %>% ggvis(~disp, ~mpg) %>%
   filter(cyl > eval(input_slider(0, 10))) %>%
   layer_points()

# Dynamically compute box-cox transformation with mutate
bc <- function(x, lambda) {
  if (abs(lambda) < 1e-6) log(x) else (x ^ lambda - 1) / lambda
}
bc_slider <- input_slider(-2, 2, 1, step = 0.1)
mtcars %>%
 ggvis(~disp, ~mpg) %>%
 mutate(disp = bc(disp, eval(bc_slider))) %>%
 layer_points()
}

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