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