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remove dplyr from suggests

This commit is contained in:
pvictor 2021-01-15 10:53:51 +01:00
parent 63db3566fb
commit 3e85bd477a
26 changed files with 152 additions and 199 deletions
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1
DESCRIPTION
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@ -24,7 +24,6 @@ Imports:
shiny (>= 1.1.0)
Suggests:
testthat,
dplyr,
knitr,
scales,
rmarkdown,

7
R/apex-options.R
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@ -134,10 +134,9 @@ events_opts <- function(click = NULL,
#'
#' @examples
#'
#' library(dplyr)
#' data("mpg", package = "ggplot2")
#'
#' apex(count(mpg, manufacturer), aes(manufacturer, n)) %>%
#' apex(mpg, aes(manufacturer)) %>%
#' ax_plotOptions(
#' bar = bar_opts(
#' endingShape = "rounded",
@ -320,11 +319,9 @@ radialBar_opts <- function(size = NULL,
#' @export
#'
#' @examples
#'
#' library(dplyr)
#' data("mpg", package = "ggplot2")
#'
#' apex(count(mpg, cyl), aes(cyl, n), type = "donut") %>%
#' apex(mpg, aes(cyl), type = "donut") %>%
#' ax_plotOptions(
#' pie = pie_opts(
#' donut = list(size = "90%", background = "#BABABA")

92
R/apex-utils.R
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@ -198,13 +198,12 @@ ax_chart <- function(ax,
#' @export
#'
#' @examples
#' library(dplyr)
#' data("diamonds", package = "ggplot2")
#'
#' # Stack bar type
#' apex(
#' data = count(diamonds, cut),
#' mapping = aes(x = cut, y = n)
#' data = diamonds,
#' mapping = aes(x = cut)
#' ) %>%
#' ax_plotOptions(
#' bar = bar_opts(endingShape = "rounded", columnWidth = "10%")
@ -212,8 +211,8 @@ ax_chart <- function(ax,
#'
#' # Pie
#' apex(
#' data = count(diamonds, cut),
#' mapping = aes(x = cut, y = n),
#' data = diamonds,
#' mapping = aes(x = cut),
#' type = "pie"
#' ) %>%
#' ax_plotOptions(
@ -254,22 +253,20 @@ ax_plotOptions <- function(ax,
#' @note See \url{https://apexcharts.com/docs/options/colors/}
#'
#' @examples
#'
#' library(dplyr)
#' data("diamonds", package = "ggplot2")
#'
#' # Change default color(s)
#' apex(
#' data = count(diamonds, cut),
#' mapping = aes(x = cut, y = n)
#' data = diamonds,
#' mapping = aes(x = cut)
#' ) %>%
#' ax_colors("#F7D358")
#'
#'
#' library(scales)
#' apex(
#' data = count(diamonds, cut, color),
#' mapping = aes(x = cut, y = n, fill = color)
#' data = diamonds,
#' mapping = aes(x = cut, fill = color)
#' ) %>%
#' ax_colors(brewer_pal(palette = "Set2")(7))
ax_colors <- function(ax, ...) {
@ -300,13 +297,12 @@ ax_colors <- function(ax, ...) {
#' @note See \url{https://apexcharts.com/docs/options/datalabels/}
#'
#' @examples
#' library(dplyr)
#' data("diamonds", package = "ggplot2")
#'
#' # Add data labels
#' apex(
#' data = count(diamonds, cut),
#' mapping = aes(x = cut, y = n)
#' data = diamonds,
#' mapping = aes(x = cut)
#' ) %>%
#' ax_dataLabels(enabled = TRUE)
ax_dataLabels <- function(ax,
@ -341,13 +337,12 @@ ax_dataLabels <- function(ax,
#' @note See \url{https://apexcharts.com/docs/options/fill/}
#'
#' @examples
#' library(dplyr)
#' data("diamonds", package = "ggplot2")
#'
#' # Use a pattern to fill bars
#' apex(
#' data = count(diamonds, cut, color),
#' mapping = aes(x = color, y = n, fill = cut)
#' data = diamonds,
#' mapping = aes(x = color, fill = cut)
#' ) %>%
#' ax_fill(
#' type = "pattern",
@ -407,20 +402,19 @@ ax_fill <- function(ax,
#' @note See \url{https://apexcharts.com/docs/options/grid/}
#'
#' @examples
#' library(dplyr)
#' data("mpg", package = "ggplot2")
#'
#' # Hide Y-axis and gridelines
#' apex(
#' data = count(mpg, manufacturer),
#' mapping = aes(x = manufacturer, y = n)
#' data = mpg,
#' mapping = aes(x = manufacturer)
#' ) %>%
#' ax_grid(show = FALSE)
#'
#' # just grid lines
#' apex(
#' data = count(mpg, manufacturer),
#' mapping = aes(x = manufacturer, y = n)
#' data = mpg,
#' mapping = aes(x = manufacturer)
#' ) %>%
#' ax_grid(yaxis = list(lines = list(show = FALSE)))
#'
@ -522,20 +516,19 @@ ax_labels2 <- function(ax, labels) {
#' @note See \url{https://apexcharts.com/docs/options/legend/}
#'
#' @examples
#' library(dplyr)
#' data("mpg", package = "ggplot2")
#'
#' # Legend position
#' apex(
#' data = count(mpg, manufacturer, year),
#' mapping = aes(x = manufacturer, y = n, fill = year)
#' data = mpg,
#' mapping = aes(x = manufacturer, fill = year)
#' ) %>%
#' ax_legend(position = "right")
#'
#' # hide legend
#' apex(
#' data = count(mpg, manufacturer, year),
#' mapping = aes(x = manufacturer, y = n, fill = year)
#' data = mpg,
#' mapping = aes(x = manufacturer, fill = year)
#' ) %>%
#' ax_legend(show = FALSE)
ax_legend <- function(ax,
@ -650,13 +643,12 @@ ax_noData <- function(ax,
#' @note See \url{https://apexcharts.com/docs/options/responsive/}
#'
#' @examples
#' library(dplyr)
#' data("mpg", package = "ggplot2")
#'
#' # Open in browser and resize window
#' apex(
#' data = count(mpg, manufacturer, year),
#' mapping = aes(x = manufacturer, y = n, fill = year),
#' data = mpg,
#' mapping = aes(x = manufacturer, fill = year),
#' type = "bar"
#' ) %>%
#' ax_legend(position = "right") %>%
@ -737,13 +729,12 @@ ax_series2 <- function(ax, l) {
#' @note See \url{https://apexcharts.com/docs/options/states/}
#'
#' @examples
#' library(dplyr)
#' data("mpg", package = "ggplot2")
#'
#' # Inverse effect on hover
#' apex(
#' data = count(mpg, manufacturer),
#' mapping = aes(x = manufacturer, y = n),
#' data = mpg,
#' mapping = aes(x = manufacturer),
#' type = "bar"
#' ) %>%
#' ax_states(
@ -930,20 +921,19 @@ ax_stroke <- function(ax,
#' @note See \url{https://apexcharts.com/docs/options/tooltip/}
#'
#' @examples
#' library(dplyr)
#' data("mpg", package = "ggplot2")
#'
#' # Hide tooltip
#' apex(
#' data = count(mpg, manufacturer, year),
#' mapping = aes(x = manufacturer, y = n, fill = year)
#' data = mpg,
#' mapping = aes(x = manufacturer, fill = year)
#' ) %>%
#' ax_tooltip(enabled = FALSE)
#'
#' # Share between series
#' apex(
#' data = count(mpg, manufacturer, year),
#' mapping = aes(x = manufacturer, y = n, fill = year)
#' data = mpg,
#' mapping = aes(x = manufacturer, fill = year)
#' ) %>%
#' ax_tooltip(shared = TRUE)
#'
@ -1006,27 +996,26 @@ ax_tooltip <- function(ax,
#' @note See \url{https://apexcharts.com/docs/options/xaxis/}
#'
#' @examples
#' library(dplyr)
#' data("mpg", package = "ggplot2")
#'
#' # X axis title
#' apex(
#' data = count(mpg, manufacturer),
#' mapping = aes(x = manufacturer, y = n)
#' data = mpg,
#' mapping = aes(x = manufacturer)
#' ) %>%
#' ax_xaxis(title = list(text = "Car's manufacturer"))
#'
#' # force labels to rotate and increase height
#' apex(
#' data = count(mpg, manufacturer),
#' mapping = aes(x = manufacturer, y = n)
#' data = mpg,
#' mapping = aes(x = manufacturer)
#' ) %>%
#' ax_xaxis(labels = list(rotateAlways = TRUE, maxHeight = 180))
#'
#' # force to not rotate
#' apex(
#' data = count(mpg, manufacturer),
#' mapping = aes(x = manufacturer, y = n)
#' data = mpg,
#' mapping = aes(x = manufacturer)
#' ) %>%
#' ax_xaxis(labels = list(rotate = 0, trim = FALSE))
#'
@ -1193,28 +1182,27 @@ ax_yaxis2 <- function(ax, ...) {
#' @note See \url{https://apexcharts.com/docs/options/theme/}
#'
#' @examples
#' library(dplyr)
#' data("mpg", package = "ggplot2")
#' data("diamonds", package = "ggplot2")
#'
#' # Dark mode
#' apex(
#' data = count(mpg, manufacturer),
#' mapping = aes(x = manufacturer, y = n)
#' data = mpg,
#' mapping = aes(x = manufacturer)
#' ) %>%
#' ax_theme(mode = "dark")
#'
#' # Use predefined palette (1 to 10)
#' apex(
#' data = count(diamonds, cut, color),
#' mapping = aes(x = color, y = n, fill = cut)
#' data = diamonds,
#' mapping = aes(x = color, fill = cut)
#' ) %>%
#' ax_theme(palette = "palette2")
#'
#' # monochrome palette
#' apex(
#' data = count(diamonds, cut, color),
#' mapping = aes(x = color, y = n, fill = cut)
#' data = diamonds,
#' mapping = aes(x = color, fill = cut)
#' ) %>%
#' ax_theme(monochrome = list(enabled = TRUE, color = "#0B6121"))
ax_theme <- function(ax,

10
README.md
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@ -39,11 +39,8 @@ Use `apex` function to quickly create visualizations :
```r
library(apexcharter)
data("mpg", package = "ggplot2")
n_manufac <- dplyr::count(mpg, manufacturer)
apex(data = n_manufac, type = "bar", mapping = aes(x = manufacturer, y = n))
apex(data = mpg, type = "bar", mapping = aes(manufacturer))
```
![](man/figures/apex-bar.png)
@ -65,7 +62,6 @@ All methods from ApexCharts are available with function like `ax_*` compatible w
```r
library(apexcharter)
data(mpg, package = "ggplot2")
n_manufac <- dplyr::count(mpg, manufacturer)
apexchart() %>%
ax_chart(type = "bar") %>%
@ -84,10 +80,10 @@ apexchart() %>%
) %>%
ax_series(list(
name = "Count",
data = n_manufac$n
data = tapply(mpg$manufacturer, mpg$manufacturer, length)
)) %>%
ax_colors("#112446") %>%
ax_xaxis(categories = n_manufac$manufacturer) %>%
ax_xaxis(categories = unique(mpg$manufacturer)) %>%
ax_title(text = "Number of models") %>%
ax_subtitle(text = "Data from ggplot2")
```

8
examples/apex.R
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@ -1,14 +1,10 @@
library(ggplot2)
library(dplyr)
library(apexcharter)
# make a barchart with a frequency table
data("mpg", package = "ggplot2")
apex(
data = count(mpg, manufacturer),
mapping = aes(x = manufacturer, y = n),
type = "bar"
)
apex(mpg, aes(manufacturer), type = "bar")
# timeseries
data("economics", package = "ggplot2")

18
examples/ax_chart.R
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@ -1,24 +1,24 @@
library(dplyr)
library(apexcharter)
data("diamonds", package = "ggplot2")
## Stack bar type
# default is dodge
apex(
data = count(diamonds, cut, color),
mapping = aes(x = cut, y = n, fill = color)
data = diamonds,
mapping = aes(x = cut, fill = color)
)
# stack
apex(
data = count(diamonds, cut, color),
mapping = aes(x = cut, y = n, fill = color)
data = diamonds,
mapping = aes(x = cut, fill = color)
) %>%
ax_chart(stacked = TRUE)
# stack filled
apex(
data = count(diamonds, cut, color),
mapping = aes(x = cut, y = n, fill = color)
data = diamonds,
mapping = aes(x = cut, fill = color)
) %>%
ax_chart(stacked = TRUE, stackType = "100%")
@ -29,8 +29,8 @@ apex(
# Hide the toolbar
apex(
data = count(diamonds, cut, color),
mapping = aes(x = cut, y = n, fill = color)
data = diamonds,
mapping = aes(x = cut, fill = color)
) %>%
ax_chart(toolbar = list(show = FALSE))

5
examples/ax_yaxis2.R
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@ -1,10 +1,9 @@
library(apexcharter)
library(dplyr)
data("economics_long", package = "ggplot2")
eco <- economics_long %>%
filter(variable %in% c("pce", "pop")) %>%
mutate(value = round(value))
subset(variable %in% c("pce", "pop")) %>%
transform(value = round(value))
# add second y-axis
apex(eco, aes(x = date, y = value, color = variable), type = "line") %>%

23
examples/compute-count.R
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@ -5,8 +5,10 @@ data("mpg", package = "ggplot2")
### No grouping
# Both charts should be equivalent
n_manufac <- dplyr::count(mpg, manufacturer)
apex(data = n_manufac, type = "column", mapping = aes(x = manufacturer, y = n))
n_manufac <- as.data.frame(table(
manufacturer = mpg$manufacturer
))
apex(data = n_manufac, type = "column", mapping = aes(x = manufacturer, y = Freq))
apex(data = mpg, type = "column", mapping = aes(x = manufacturer))
@ -14,8 +16,19 @@ apex(data = mpg, type = "column", mapping = aes(x = manufacturer))
### With groups
# Both charts should be equivalent
n_manufac_year <- dplyr::count(mpg, manufacturer, year)
apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y = n, fill = year))
n_manufac_year <- as.data.frame(table(
manufacturer = mpg$manufacturer,
year = mpg$year
))
apex(
data = n_manufac_year,
type = "column",
mapping = aes(x = manufacturer, y = n, fill = year)
)
apex(data = mpg, type = "column", mapping = aes(x = manufacturer, fill = year))
apex(
data = mpg,
type = "column",
mapping = aes(x = manufacturer, fill = year)
)

8
man/apex.Rd
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@ -58,16 +58,12 @@ data, mapping and type of chart.
}
\examples{
library(ggplot2)
library(dplyr)
library(apexcharter)
# make a barchart with a frequency table
data("mpg", package = "ggplot2")
apex(
data = count(mpg, manufacturer),
mapping = aes(x = manufacturer, y = n),
type = "bar"
)
apex(mpg, aes(manufacturer), type = "bar")
# timeseries
data("economics", package = "ggplot2")

18
man/ax_chart.Rd
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@ -82,27 +82,27 @@ A \code{apexcharts} \code{htmlwidget} object.
Chart parameters
}
\examples{
library(dplyr)
library(apexcharter)
data("diamonds", package = "ggplot2")
## Stack bar type
# default is dodge
apex(
data = count(diamonds, cut, color),
mapping = aes(x = cut, y = n, fill = color)
data = diamonds,
mapping = aes(x = cut, fill = color)
)
# stack
apex(
data = count(diamonds, cut, color),
mapping = aes(x = cut, y = n, fill = color)
data = diamonds,
mapping = aes(x = cut, fill = color)
) \%>\%
ax_chart(stacked = TRUE)
# stack filled
apex(
data = count(diamonds, cut, color),
mapping = aes(x = cut, y = n, fill = color)
data = diamonds,
mapping = aes(x = cut, fill = color)
) \%>\%
ax_chart(stacked = TRUE, stackType = "100\%")
@ -113,8 +113,8 @@ apex(
# Hide the toolbar
apex(
data = count(diamonds, cut, color),
mapping = aes(x = cut, y = n, fill = color)
data = diamonds,
mapping = aes(x = cut, fill = color)
) \%>\%
ax_chart(toolbar = list(show = FALSE))

10
man/ax_colors.Rd
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@ -21,22 +21,20 @@ Colors
See \url{https://apexcharts.com/docs/options/colors/}
}
\examples{
library(dplyr)
data("diamonds", package = "ggplot2")
# Change default color(s)
apex(
data = count(diamonds, cut),
mapping = aes(x = cut, y = n)
data = diamonds,
mapping = aes(x = cut)
) \%>\%
ax_colors("#F7D358")
library(scales)
apex(
data = count(diamonds, cut, color),
mapping = aes(x = cut, y = n, fill = color)
data = diamonds,
mapping = aes(x = cut, fill = color)
) \%>\%
ax_colors(brewer_pal(palette = "Set2")(7))
}

5
man/ax_dataLabels.Rd
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@ -46,13 +46,12 @@ Labels on data
See \url{https://apexcharts.com/docs/options/datalabels/}
}
\examples{
library(dplyr)
data("diamonds", package = "ggplot2")
# Add data labels
apex(
data = count(diamonds, cut),
mapping = aes(x = cut, y = n)
data = diamonds,
mapping = aes(x = cut)
) \%>\%
ax_dataLabels(enabled = TRUE)
}

5
man/ax_fill.Rd
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@ -43,13 +43,12 @@ Fill property
See \url{https://apexcharts.com/docs/options/fill/}
}
\examples{
library(dplyr)
data("diamonds", package = "ggplot2")
# Use a pattern to fill bars
apex(
data = count(diamonds, cut, color),
mapping = aes(x = color, y = n, fill = cut)
data = diamonds,
mapping = aes(x = color, fill = cut)
) \%>\%
ax_fill(
type = "pattern",

9
man/ax_grid.Rd
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@ -51,20 +51,19 @@ Add grids on chart
See \url{https://apexcharts.com/docs/options/grid/}
}
\examples{
library(dplyr)
data("mpg", package = "ggplot2")
# Hide Y-axis and gridelines
apex(
data = count(mpg, manufacturer),
mapping = aes(x = manufacturer, y = n)
data = mpg,
mapping = aes(x = manufacturer)
) \%>\%
ax_grid(show = FALSE)
# just grid lines
apex(
data = count(mpg, manufacturer),
mapping = aes(x = manufacturer, y = n)
data = mpg,
mapping = aes(x = manufacturer)
) \%>\%
ax_grid(yaxis = list(lines = list(show = FALSE)))

9
man/ax_legend.Rd
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@ -83,20 +83,19 @@ Legend properties
See \url{https://apexcharts.com/docs/options/legend/}
}
\examples{
library(dplyr)
data("mpg", package = "ggplot2")
# Legend position
apex(
data = count(mpg, manufacturer, year),
mapping = aes(x = manufacturer, y = n, fill = year)
data = mpg,
mapping = aes(x = manufacturer, fill = year)
) \%>\%
ax_legend(position = "right")
# hide legend
apex(
data = count(mpg, manufacturer, year),
mapping = aes(x = manufacturer, y = n, fill = year)
data = mpg,
mapping = aes(x = manufacturer, fill = year)
) \%>\%
ax_legend(show = FALSE)
}

9
man/ax_plotOptions.Rd
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@ -36,13 +36,12 @@ A \code{apexcharts} \code{htmlwidget} object.
Specific options for chart
}
\examples{
library(dplyr)
data("diamonds", package = "ggplot2")
# Stack bar type
apex(
data = count(diamonds, cut),
mapping = aes(x = cut, y = n)
data = diamonds,
mapping = aes(x = cut)
) \%>\%
ax_plotOptions(
bar = bar_opts(endingShape = "rounded", columnWidth = "10\%")
@ -50,8 +49,8 @@ apex(
# Pie
apex(
data = count(diamonds, cut),
mapping = aes(x = cut, y = n),
data = diamonds,
mapping = aes(x = cut),
type = "pie"
) \%>\%
ax_plotOptions(

5
man/ax_responsive.Rd
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@ -21,13 +21,12 @@ Responsive options
See \url{https://apexcharts.com/docs/options/responsive/}
}
\examples{
library(dplyr)
data("mpg", package = "ggplot2")
# Open in browser and resize window
apex(
data = count(mpg, manufacturer, year),
mapping = aes(x = manufacturer, y = n, fill = year),
data = mpg,
mapping = aes(x = manufacturer, fill = year),
type = "bar"
) \%>\%
ax_legend(position = "right") \%>\%

5
man/ax_states.Rd
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@ -27,13 +27,12 @@ Charts' states
See \url{https://apexcharts.com/docs/options/states/}
}
\examples{
library(dplyr)
data("mpg", package = "ggplot2")
# Inverse effect on hover
apex(
data = count(mpg, manufacturer),
mapping = aes(x = manufacturer, y = n),
data = mpg,
mapping = aes(x = manufacturer),
type = "bar"
) \%>\%
ax_states(

13
man/ax_theme.Rd
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@ -27,28 +27,27 @@ Theme for charts
See \url{https://apexcharts.com/docs/options/theme/}
}
\examples{
library(dplyr)
data("mpg", package = "ggplot2")
data("diamonds", package = "ggplot2")
# Dark mode
apex(
data = count(mpg, manufacturer),
mapping = aes(x = manufacturer, y = n)
data = mpg,
mapping = aes(x = manufacturer)
) \%>\%
ax_theme(mode = "dark")
# Use predefined palette (1 to 10)
apex(
data = count(diamonds, cut, color),
mapping = aes(x = color, y = n, fill = cut)
data = diamonds,
mapping = aes(x = color, fill = cut)
) \%>\%
ax_theme(palette = "palette2")
# monochrome palette
apex(
data = count(diamonds, cut, color),
mapping = aes(x = color, y = n, fill = cut)
data = diamonds,
mapping = aes(x = color, fill = cut)
) \%>\%
ax_theme(monochrome = list(enabled = TRUE, color = "#0B6121"))
}

9
man/ax_tooltip.Rd
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@ -69,20 +69,19 @@ Tooltip options
See \url{https://apexcharts.com/docs/options/tooltip/}
}
\examples{
library(dplyr)
data("mpg", package = "ggplot2")
# Hide tooltip
apex(
data = count(mpg, manufacturer, year),
mapping = aes(x = manufacturer, y = n, fill = year)
data = mpg,
mapping = aes(x = manufacturer, fill = year)
) \%>\%
ax_tooltip(enabled = FALSE)
# Share between series
apex(
data = count(mpg, manufacturer, year),
mapping = aes(x = manufacturer, y = n, fill = year)
data = mpg,
mapping = aes(x = manufacturer, fill = year)
) \%>\%
ax_tooltip(shared = TRUE)

13
man/ax_xaxis.Rd
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@ -68,27 +68,26 @@ X-axis options
See \url{https://apexcharts.com/docs/options/xaxis/}
}
\examples{
library(dplyr)
data("mpg", package = "ggplot2")
# X axis title
apex(
data = count(mpg, manufacturer),
mapping = aes(x = manufacturer, y = n)
data = mpg,
mapping = aes(x = manufacturer)
) \%>\%
ax_xaxis(title = list(text = "Car's manufacturer"))
# force labels to rotate and increase height
apex(
data = count(mpg, manufacturer),
mapping = aes(x = manufacturer, y = n)
data = mpg,
mapping = aes(x = manufacturer)
) \%>\%
ax_xaxis(labels = list(rotateAlways = TRUE, maxHeight = 180))
# force to not rotate
apex(
data = count(mpg, manufacturer),
mapping = aes(x = manufacturer, y = n)
data = mpg,
mapping = aes(x = manufacturer)
) \%>\%
ax_xaxis(labels = list(rotate = 0, trim = FALSE))

5
man/ax_yaxis2.Rd
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@ -19,12 +19,11 @@ Secondary Y-axis options
}
\examples{
library(apexcharter)
library(dplyr)
data("economics_long", package = "ggplot2")
eco <- economics_long \%>\%
filter(variable \%in\% c("pce", "pop")) \%>\%
mutate(value = round(value))
subset(variable \%in\% c("pce", "pop")) \%>\%
transform(value = round(value))
# add second y-axis
apex(eco, aes(x = date, y = value, color = variable), type = "line") \%>\%

3
man/bar_opts.Rd
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@ -43,10 +43,9 @@ See \url{https://apexcharts.com/docs/options/plotoptions/bar/}.
}
\examples{
library(dplyr)
data("mpg", package = "ggplot2")
apex(count(mpg, manufacturer), aes(manufacturer, n)) \%>\%
apex(mpg, aes(manufacturer)) \%>\%
ax_plotOptions(
bar = bar_opts(
endingShape = "rounded",

4
man/pie_opts.Rd
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@ -40,11 +40,9 @@ Use these options in \code{\link{ax_plotOptions}}.
See \url{https://apexcharts.com/docs/options/plotoptions/pie/}.
}
\examples{
library(dplyr)
data("mpg", package = "ggplot2")
apex(count(mpg, cyl), aes(cyl, n), type = "donut") \%>\%
apex(mpg, aes(cyl), type = "donut") \%>\%
ax_plotOptions(
pie = pie_opts(
donut = list(size = "90\%", background = "#BABABA")

32
vignettes/apexcharter.Rmd
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@ -18,13 +18,12 @@ knitr::opts_chunk$set(
The objective of this vignette is to show how to quickly build data visualizations with the ApexCharts JavaScript library, as well as to give an overview of the different graphics available.
Data used are from `ggplot2` package, data manipulation will be done with the `dplyr` package.
Data used are from `ggplot2` package.
```{r message=FALSE, warning=FALSE}
library(ggplot2)
library(scales)
library(dplyr)
library(apexcharter)
```
@ -35,30 +34,27 @@ Simple bar charts can be created with:
```{r column}
data("mpg")
n_manufac <- count(mpg, manufacturer)
apex(data = n_manufac, type = "column", mapping = aes(x = manufacturer, y = n))
apex(data = mpg, type = "column", mapping = aes(x = manufacturer))
```
Flipping coordinates can be done by using `type = "bar"`:
```{r bar}
apex(data = n_manufac, type = "bar", mapping = aes(x = manufacturer, y = n))
apex(data = mpg, type = "bar", mapping = aes(x = manufacturer))
```
To create a dodge bar charts, use aesthetic `fill` :
```{r dodge-bar}
n_manufac_year <- count(mpg, manufacturer, year)
apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y = n, fill = year))
apex(data = mpg, type = "column", mapping = aes(x = manufacturer, fill = year))
```
For stacked bar charts, specify option `stacked` in `ax_chart` :
```{r stacked-bar}
apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y = n, fill = year)) %>%
apex(data = mpg, type = "column", mapping = aes(x = manufacturer, fill = year)) %>%
ax_chart(stacked = TRUE)
```
@ -70,7 +66,6 @@ Simple line charts can be created with (works with `character`, `Date` or `POSIX
```{r line}
data("economics")
economics <- tail(economics, 100)
apex(data = economics, type = "line", mapping = aes(x = date, y = uempmed))
```
@ -80,9 +75,6 @@ To represent several lines, use a `data.frame` in long format and the `group` ae
```{r lines}
data("economics_long")
economics_long <- economics_long %>%
group_by(variable) %>%
slice((n()-100):n())
apex(data = economics_long, type = "line", mapping = aes(x = date, y = value01, group = variable)) %>%
ax_yaxis(decimalsInFloat = 2) # number of decimals to keep
@ -210,14 +202,14 @@ apex(mtcars, aes(rownames(mtcars), mpg), type = "polarArea") %>%
Create a heatmap with :
```{r heatmap}
txhousing2 <- txhousing %>%
filter(city %in% head(unique(city)), year %in% c(2000, 2001)) %>%
rename(val_med = median)
# create some data
sales <- expand.grid(year = 2010:2020, month = month.name)
sales$value <- sample(-10:30, nrow(sales), TRUE)
apex(
data = txhousing2,
data = sales,
type = "heatmap",
mapping = aes(x = date, y = city, fill = scales::rescale(val_med))
mapping = aes(x = year, y = month, fill = value)
) %>%
ax_dataLabels(enabled = FALSE) %>%
ax_colors("#008FFB")
@ -230,9 +222,8 @@ Create a treemap with:
```{r treemap}
data("mpg", package = "ggplot2")
n_manufac <- dplyr::count(mpg, manufacturer)
apex(n_manufac, aes(x = manufacturer, y = n), "treemap")
apex(mpg, aes(x = manufacturer), "treemap")
```
@ -242,6 +233,7 @@ Create a candlestick chart with:
```{r}
data("candles", package = "apexcharter")
apex(
candles,
aes(x = datetime, open = open, close = close, low = low, high = high),

25
vignettes/chart-options.Rmd
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@ -21,10 +21,8 @@ Packages and data used below:
```{r message=FALSE, warning=FALSE}
library(apexcharter)
library(dplyr)
data("diamonds", package = "ggplot2")
n_cut <- count(diamonds, cut)
```
@ -33,7 +31,7 @@ n_cut <- count(diamonds, cut)
You can set title, subtitle and axis' titles at once with `ax_labs()`:
```{r}
apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
apex(data = diamonds, type = "column", mapping = aes(x = cut)) %>%
ax_labs(
title = "Cut distribution",
subtitle = "Data from ggplot2",
@ -49,14 +47,14 @@ If you more control (font size, alignment, ...), you can use `ax_title()`, `ax_s
### Title
```{r}
apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
apex(data = diamonds, type = "column", mapping = aes(x = cut)) %>%
ax_title(text = "Cut distribution")
```
You can set some options, for example:
```{r}
apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
apex(data = diamonds, type = "column", mapping = aes(x = cut)) %>%
ax_title(
text = "Cut distribution",
align = "center",
@ -71,7 +69,7 @@ Full list of parameters is available here : https://apexcharts.com/docs/options/
### Subtitle
```{r}
apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
apex(data = diamonds, type = "column", mapping = aes(x = cut)) %>%
ax_title(text = "Cut distribution") %>%
ax_subtitle(text = "Data from ggplot2")
```
@ -79,7 +77,7 @@ apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
With same options than for title:
```{r}
apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
apex(data = diamonds, type = "column", mapping = aes(x = cut)) %>%
ax_title(
text = "Cut distribution",
align = "center",
@ -98,7 +96,7 @@ Full list of parameters is available here : https://apexcharts.com/docs/options/
### Axis title
```{r}
apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
apex(data = diamonds, type = "column", mapping = aes(x = cut)) %>%
ax_yaxis(title = list(text = "Count")) %>%
ax_xaxis(title = list(text = "Cut"))
```
@ -106,7 +104,7 @@ apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
With some options:
```{r}
apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
apex(data = diamonds, type = "column", mapping = aes(x = cut)) %>%
ax_yaxis(title = list(
text = "Count",
style = list(fontSize = "14px", color = "#BDBDBD")
@ -125,17 +123,12 @@ apex(data = n_cut, type = "column", mapping = aes(x = cut, y = n)) %>%
```{r message=FALSE, warning=FALSE}
library(apexcharter)
library(dplyr)
## economics dataset from ggplot2
data("economics", package = "ggplot2")
economics <- tail(economics, 50)
data("economics_long", package = "ggplot2")
economics_long <- economics_long %>%
filter(variable %in% c("pce", "pop")) %>%
group_by(variable) %>%
slice(tail(row_number(), 20))
economics <- tail(economics, 50)
economics_long <- subset(economics_long, date >= "2010-01-01")
```