upgrade to ApexCharts 3.6.5
This commit is contained in:
parent
ce529381f0
commit
89392363bd
|
@ -1,5 +1,5 @@
|
|||
dependencies:
|
||||
- name: apexcharts
|
||||
version: 3.6.3
|
||||
version: 3.6.5
|
||||
src: htmlwidgets/lib/apexcharts-3.6
|
||||
script: apexcharts.min.js
|
||||
|
|
File diff suppressed because one or more lines are too long
|
@ -32,7 +32,7 @@ library(apexcharter)
|
|||
|
||||
Simple bar charts can be created with:
|
||||
|
||||
```{r}
|
||||
```{r column}
|
||||
data("mpg")
|
||||
n_manufac <- count(mpg, manufacturer)
|
||||
|
||||
|
@ -41,14 +41,14 @@ apex(data = n_manufac, type = "column", mapping = aes(x = manufacturer, y = n))
|
|||
|
||||
Flipping coordinates can be done by using `type = "bar"`:
|
||||
|
||||
```{r}
|
||||
```{r bar}
|
||||
apex(data = n_manufac, type = "bar", mapping = aes(x = manufacturer, y = n))
|
||||
```
|
||||
|
||||
|
||||
To create a dodge bar charts, use aesthetic `fill` :
|
||||
|
||||
```{r}
|
||||
```{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))
|
||||
|
@ -56,7 +56,7 @@ apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y =
|
|||
|
||||
For stacked bar charts, specify option `stacked` in `ax_chart` :
|
||||
|
||||
```{r}
|
||||
```{r stacked-bar}
|
||||
apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y = n, fill = year)) %>%
|
||||
ax_chart(stacked = TRUE)
|
||||
```
|
||||
|
@ -67,7 +67,7 @@ apex(data = n_manufac_year, type = "column", mapping = aes(x = manufacturer, y =
|
|||
|
||||
Simple line charts can be created with (works with `character`, `Date` or `POSIXct`):
|
||||
|
||||
```{r}
|
||||
```{r line}
|
||||
data("economics")
|
||||
economics <- tail(economics, 100)
|
||||
|
||||
|
@ -77,7 +77,7 @@ apex(data = economics, type = "line", mapping = aes(x = date, y = uempmed))
|
|||
|
||||
To represent several lines, use a `data.frame` in long format and the `group` aesthetic:
|
||||
|
||||
```{r}
|
||||
```{r lines}
|
||||
data("economics_long")
|
||||
economics_long <- economics_long %>%
|
||||
group_by(variable) %>%
|
||||
|
@ -89,7 +89,7 @@ apex(data = economics_long, type = "line", mapping = aes(x = date, y = value01,
|
|||
|
||||
Create area charts with `type = "area"`:
|
||||
|
||||
```{r}
|
||||
```{r area}
|
||||
apex(data = economics_long, type = "area", mapping = aes(x = date, y = value01, fill = variable)) %>%
|
||||
ax_chart(stacked = TRUE)
|
||||
```
|
||||
|
@ -102,20 +102,20 @@ apex(data = economics_long, type = "area", mapping = aes(x = date, y = value01,
|
|||
|
||||
Simple bar charts can be created with:
|
||||
|
||||
```{r}
|
||||
```{r scatter}
|
||||
apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg))
|
||||
```
|
||||
|
||||
Color points according to a third variable:
|
||||
|
||||
```{r}
|
||||
```{r scatter-fill}
|
||||
apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg, fill = cyl)) %>%
|
||||
ax_xaxis(tickAmount = 5)
|
||||
```
|
||||
|
||||
And change point size using `z` aesthetics:
|
||||
|
||||
```{r}
|
||||
```{r bubbles}
|
||||
apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg, z = scales::rescale(qsec)))
|
||||
```
|
||||
|
||||
|
@ -126,7 +126,7 @@ apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg, z = scales:
|
|||
|
||||
Simple pie charts can be created with:
|
||||
|
||||
```{r}
|
||||
```{r pie}
|
||||
poll <- data.frame(
|
||||
answer = c("Yes", "No"),
|
||||
n = c(254, 238)
|
||||
|
@ -141,14 +141,14 @@ apex(data = poll, type = "pie", mapping = aes(x = answer, y = n))
|
|||
|
||||
Simple radial charts can be created with (here we pass values directly in `aes`, but you can use a `data.frame`) :
|
||||
|
||||
```{r}
|
||||
```{r radial}
|
||||
apex(data = NULL, type = "radialBar", mapping = aes(x = "My value", y = 65))
|
||||
```
|
||||
|
||||
|
||||
Multi radial chart (more than one value):
|
||||
|
||||
```{r}
|
||||
```{r radial-mult}
|
||||
fruits <- data.frame(
|
||||
name = c('Apples', 'Oranges', 'Bananas', 'Berries'),
|
||||
value = c(44, 55, 67, 83)
|
||||
|
@ -164,7 +164,7 @@ apex(data = fruits, type = "radialBar", mapping = aes(x = name, y = value))
|
|||
|
||||
Simple radar charts can be created with:
|
||||
|
||||
```{r}
|
||||
```{r radar}
|
||||
mtcars$model <- rownames(mtcars)
|
||||
|
||||
apex(data = head(mtcars), type = "radar", mapping = aes(x = model, y = qsec))
|
||||
|
@ -172,7 +172,7 @@ apex(data = head(mtcars), type = "radar", mapping = aes(x = model, y = qsec))
|
|||
|
||||
With a grouping variable:
|
||||
|
||||
```{r}
|
||||
```{r radar-mult}
|
||||
# extremely complicated reshaping
|
||||
new_mtcars <- reshape(
|
||||
data = head(mtcars),
|
||||
|
@ -192,7 +192,7 @@ apex(data = new_mtcars, type = "radar", mapping = aes(x = model, y = value, grou
|
|||
|
||||
Create heatmap with :
|
||||
|
||||
```{r}
|
||||
```{r heatmap}
|
||||
txhousing2 <- txhousing %>%
|
||||
filter(city %in% head(unique(city)), year %in% c(2000, 2001)) %>%
|
||||
rename(val_med = median)
|
||||
|
|
Loading…
Reference in New Issue