Description Usage Arguments Value Examples
plotly_data()
returns data associated with
a plotly visualization (if there are multiple data frames, by default,
it returns the most recent one).
1 | plotly_data(p, id = p$x$cur_data)
|
p |
a plotly visualization |
id |
a character string or number referencing an "attribute layer". |
returns a data frame
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # use group_by() to define groups of visual markings
p <- txhousing %>%
group_by(city) %>%
plot_ly(x = ~date, y = ~sales)
p
# plotly objects preserve data groupings
groups(p)
plotly_data(p)
# dplyr verbs operate on plotly objects as if they were data frames
p <- economics %>%
plot_ly(x = ~date, y = ~unemploy / pop) %>%
add_lines() %>%
mutate(rate = unemploy / pop) %>%
filter(rate == max(rate))
plotly_data(p)
add_markers(p)
layout(p, annotations = list(x = ~date, y = ~rate, text = "peak"))
# use group_by() + do() + subplot() for trellis displays
d <- group_by(mpg, drv)
plots <- do(d, p = plot_ly(., x = ~cty, name = ~drv))
subplot(plots[["p"]], nrows = 3, shareX = TRUE)
# arrange displays by their mean
means <- summarise(d, mn = mean(cty, na.rm = TRUE))
means %>%
left_join(plots) %>%
arrange(mn) %>%
.[["p"]] %>%
subplot(nrows = NROW(.), shareX = TRUE)
# more dplyr verbs applied to plotly objects
p <- mtcars %>%
plot_ly(x = ~wt, y = ~mpg, name = "scatter trace") %>%
add_markers()
p %>% slice(1) %>% plotly_data()
p %>% slice(1) %>% add_markers(name = "first observation")
p %>% filter(cyl == 4) %>% plotly_data()
p %>% filter(cyl == 4) %>% add_markers(name = "four cylinders")
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