From 5e3f01143d44bb8d95f3c42fbab1211c569ba132 Mon Sep 17 00:00:00 2001 From: Carson Sievert <cpsievert1@gmail.com> Date: Thu, 16 Jun 2016 16:34:01 -0500 Subject: [PATCH] more documentation --- inst/htmlwidgets/plotly.js | 1 - vignettes/intro.Rmd | 23 ++++++++++++----------- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/inst/htmlwidgets/plotly.js b/inst/htmlwidgets/plotly.js index b406d6759f..3df60ef50d 100644 --- a/inst/htmlwidgets/plotly.js +++ b/inst/htmlwidgets/plotly.js @@ -27,7 +27,6 @@ HTMLWidgets.widget({ // if no plot exists yet, create one with a particular configuration if (!instance.plotly) { - console.log(x); var plot = Plotly.plot(graphDiv, x.data, x.layout, x.config); instance.plotly = true; instance.autosize = x.layout.autosize; diff --git a/vignettes/intro.Rmd b/vignettes/intro.Rmd index 2dbee86595..efc8923a71 100644 --- a/vignettes/intro.Rmd +++ b/vignettes/intro.Rmd @@ -22,14 +22,14 @@ To create a plotly visualization, start with `plot_ly()`. ```{r} library(plotly) -plot_ly(economics, x = date, y = unemploy / pop) +plot_ly(economics, x = ~date, y = ~unemploy / pop) ``` A plotly visualization is composed of one (or more) trace(s), and every trace has a `type` (the default type is 'scatter'). The arguments/properties that a trace will respect ([documented here](https://plot.ly/r/reference)) depend on it's type. A scatter trace respects `mode`, which can be any combination of "lines", "markers", "text" joined with a "+": ```{r} library(plotly) -plot_ly(economics, x = date, y = unemploy / pop, +plot_ly(economics, x = ~date, y = ~unemploy / pop, type = "scatter", mode = "markers+lines") ``` @@ -37,29 +37,30 @@ You can manually add a trace to an existing plot with `add_trace()`. In that cas ```{r} m <- loess(unemploy / pop ~ as.numeric(date), data = economics) -p <- plot_ly(economics, x = date, y = unemploy / pop, name = "raw") -add_trace(p, x = date, y = fitted(m), name = "loess") +p <- plot_ly(economics, x = ~date, y = ~unemploy / pop, name = "raw") +add_lines(p, y = ~fitted(m), name = "loess") ``` __plotly__ was designed with a [pure, predictable, and pipeable interface](https://dl.dropboxusercontent.com/u/41902/pipe-dsls.pdf) in mind, so you can also use the `%>%` operator to create a visualization pipeline: ```{r} economics %>% - plot_ly(x = date, y = unemploy / pop) %>% - add_trace(x = date, y = fitted(m)) %>% + plot_ly(x = ~date, y = ~unemploy / pop) %>% + add_lines(y = ~fitted(m)) %>% layout(showlegend = F) ``` -Furthermore, `plot_ly()`, `add_trace()`, and `layout()`, all accept a data frame as their first argument and output a data frame. As a result, we can inter-weave data manipulations and visual mappings in a single pipeline. +TODO: talk about dplyr verbs! ```{r} +library(dplyr) economics %>% - transform(rate = unemploy / pop) %>% - plot_ly(x = date, y = rate) %>% - subset(rate == max(rate)) %>% + mutate(rate = unemploy / pop) %>% + plot_ly(x = ~date, y = ~rate) %>% + filter(rate == max(rate)) %>% layout( showlegend = F, - annotations = list(x = date, y = rate, text = "Peak", showarrow = T) + annotations = list(x = ~date, y = ~rate, text = "Peak", showarrow = T) ) ```