Last updated: 2018-03-29
Code version: f93372a
I came across one website introducing plotly R package, and was addicted for a few days, just share some of these amazing plots with you. The main scheme is introduce three basic examples, followed by scatter plot, line plot and ribbons.I will conclude this section with two great animation examples.
##install.packages("plotly")
##packageVersion("plotly")
library(plotly)
library(ggplot2)Interface modified by layout() function:“click + drag”event, rangeslider() will add a slider under this figure.
library(forecast)
p <- ggplot(fortify(gold), aes(x, y)) + geom_line()
gg <- ggplotly(p)
layout(gg, dragmode = "pan")##rangeslider(gg)Look at and learn the basic structre , firstly use ggplotly(), then %>% link several matrics.
p <- ggplot(MASS::geyser, aes(x = waiting, y = duration)) +
geom_density2d()
ggplotly(p, originalData = FALSE) %>%
group_by(piece) %>%
slice(which.min(y)) %>%
add_text(
text = ~level, size = I(10), color = I("black"), hoverinfo = "none"
)p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() + geom_smooth()
style(p, hoverinfo = "none", traces = 2:3)Of course, we even could add annotations in detailed positions by function add_annotations() and add_segments().
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() + geom_smooth()
p %>%
ggplotly(layerData = 2, originalData = FALSE) %>%
add_fun(function(p) {
p %>% slice(which.max(se)) %>%
add_segments(x = ~x, xend = ~x, y = ~ymin, yend = ~ymax) %>%
add_annotations("Maximum uncertainty", ax = 60)
}) %>%
add_fun(function(p) {
p %>% slice(which.min(se)) %>%
add_segments(x = ~x, xend = ~x, y = ~ymin, yend = ~ymax) %>%
add_annotations("Minimum uncertainty")
})##I(2)is triangle, I(3)is +
subplot(
plot_ly(x = 1:25, y = 1:25, symbol = I(1:25), name = "pch"),
plot_ly(mpg, x = ~cty, y = ~hwy) %>%
add_markers(symbol = I(1), color = I("red"),name = "red")
)p <- plot_ly(mpg, x = ~cty, y = ~hwy, alpha = 0.5)
subplot(
add_markers(p, color = ~cyl, showlegend = F,name = "mpg") %>%
colorbar(title = "Viridis")
)Last ones are two 3D examples, you are free to rotate the figures.
plot_ly(mpg, x = ~cty, y = ~hwy, z = ~cyl) %>%
add_markers(color = ~cyl)x <- seq_len(nrow(volcano)) + 100
y <- seq_len(ncol(volcano)) + 500
plot_ly() %>% add_surface(x = ~x, y = ~y, z = ~volcano)A powerful functiom to visualize data iris:
pm <- GGally::ggpairs(iris)
ggplotly(pm)Lower image are the same data with upper image, just to compare the effects of add_lines(p, color = ~city) and add_lines(p, linetype = ~city).
library(dplyr)
top5 <- txhousing %>%
group_by(city) %>%
summarise(m = mean(sales, na.rm = TRUE)) %>%
arrange(desc(m)) %>%
top_n(5)
p <- semi_join(txhousing, top5, by = "city") %>%
plot_ly(x = ~date, y = ~median)
subplot(
add_lines(p, color = ~city),
add_lines(p, linetype = ~city),
shareX = TRUE, nrows = 2
)library(broom)
m <- lm(mpg ~ wt, data = mtcars)
broom::augment(m) %>%
plot_ly(x = ~wt, showlegend = FALSE) %>%
add_markers(y = ~mpg, color = I("red")) %>%
add_ribbons(ymin = ~.fitted - 1.96 * .se.fit,
ymax = ~.fitted + 1.96 * .se.fit, color = I("gray80")) %>%
add_lines(y = ~.fitted, color = I("steelblue"))Return to use function ggplotly()
data(gapminder, package = "gapminder")
head(gapminder)# A tibble: 6 x 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.gg <- ggplot(gapminder, aes(gdpPercap, lifeExp, color = continent)) +
geom_point(aes(size = pop, frame = year, ids = country)) +
scale_x_log10()
ggplotly(gg)meanLife <- with(gapminder, tapply(lifeExp, INDEX = continent, mean))
gapminder$continent <- factor(
gapminder$continent, levels = names(sort(meanLife))
)
base <- gapminder %>%
plot_ly(x = ~gdpPercap, y = ~lifeExp, size = ~pop,
text = ~country, hoverinfo = "text") %>%
layout(xaxis = list(type = "log"))
base %>%
add_markers(data = gapminder, frame = ~continent) %>%
hide_legend() %>%
animation_opts(frame = 1000, transition = 0, redraw = FALSE)sessionInfo()R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] broom_0.4.3 dplyr_0.7.4 bindrcpp_0.2 forecast_8.2 plotly_4.7.1
[6] ggplot2_2.2.1
loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 lattice_0.20-35 tidyr_0.7.2
[4] zoo_1.8-1 utf8_1.1.3 assertthat_0.2.0
[7] rprojroot_1.3-2 digest_0.6.15 lmtest_0.9-35
[10] psych_1.7.8 mime_0.5 R6_2.2.2
[13] plyr_1.8.4 backports_1.1.2 evaluate_0.10.1
[16] httr_1.3.1 pillar_1.2.1 rlang_0.2.0
[19] lazyeval_0.2.1 curl_3.1 rstudioapi_0.7
[22] data.table_1.10.4-3 fracdiff_1.4-2 TTR_0.23-3
[25] rmarkdown_1.8 labeling_0.3 stringr_1.2.0
[28] foreign_0.8-69 htmlwidgets_0.9 munsell_0.4.3
[31] shiny_1.0.5 compiler_3.4.2 httpuv_1.3.5
[34] pkgconfig_2.0.1 mnormt_1.5-5 htmltools_0.3.6
[37] nnet_7.3-12 tibble_1.4.2 quadprog_1.5-5
[40] reshape_0.8.7 viridisLite_0.3.0 crayon_1.3.4
[43] MASS_7.3-48 grid_3.4.2 nlme_3.1-131
[46] jsonlite_1.5 xtable_1.8-2 GGally_1.3.2
[49] gtable_0.2.0 git2r_0.21.0 magrittr_1.5
[52] scales_0.5.0.9000 quantmod_0.4-12 cli_1.0.0
[55] stringi_1.1.6 reshape2_1.4.3 tseries_0.10-43
[58] timeDate_3043.102 xts_0.10-2 RColorBrewer_1.1-2
[61] tools_3.4.2 glue_1.2.0 purrr_0.2.4
[64] crosstalk_1.0.0 parallel_3.4.2 yaml_2.1.16
[67] colorspace_1.3-2 knitr_1.18 bindr_0.1 This R Markdown site was created with workflowr