Time Series and Date Axes in ggplot2

How to make Time Series and Date Axes in ggplot2 with Plotly.

New to Plotly?

Plotly is a free and open-source graphing library for R. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Continuous Scale

library(plotly)
library(tidyverse)
library(tidyquant)
library(ggplot2)

data("FANG") 
AMZN <- tq_get("AMZN", get = "stock.prices", from = "2000-01-01", to = "2016-12-31")

p <- AMZN %>%
    ggplot(aes(x = date, y = adjusted)) +
    geom_line(color = palette_light()[[1]]) + 
    scale_y_continuous() +
    labs(title = "AMZN Line Chart", 
         subtitle = "Continuous Scale", 
         y = "Closing Price", x = "") + 
    theme_tq()

ggplotly(p)
 Click to copy

Log Scale

library(plotly)
library(tidyverse)
library(tidyquant)
library(ggplot2)

data("FANG") 
AMZN <- tq_get("AMZN", get = "stock.prices", from = "2000-01-01", to = "2016-12-31")

p <- AMZN %>%
    ggplot(aes(x = date, y = adjusted)) +
    geom_line(color = palette_light()[[1]]) + 
    scale_y_log10() +
    labs(title = "AMZN Line Chart", 
         subtitle = "Log Scale", 
         y = "Closing Price", x = "") + 
    theme_tq()

ggplotly(p)
 Click to copy

Regression trendlines

library(plotly)
library(tidyverse)
library(tidyquant)
library(ggplot2)

data("FANG") 
AMZN <- tq_get("AMZN", get = "stock.prices", from = "2000-01-01", to = "2016-12-31")

p <- AMZN %>%
    ggplot(aes(x = date, y = adjusted)) +
    geom_line(color = palette_light()[[1]]) + 
    scale_y_log10() +
    geom_smooth(method = "lm") +
    labs(title = "AMZN Line Chart", 
         subtitle = "Log Scale, Applying Linear Trendline", 
         y = "Adjusted Closing Price", x = "") + 
    theme_tq()

ggplotly(p)
 Click to copy

Charting volume

We can use the geom_segment() function to chart daily volume, which uses xy points for the beginning and end of the line. Using the aesthetic color argument, we color based on the value of volume to make these data stick out.

library(plotly)
library(tidyverse)
library(tidyquant)
library(ggplot2)

data("FANG") 
AMZN <- tq_get("AMZN", get = "stock.prices", from = "2000-01-01", to = "2001-06-01")

p <- AMZN %>%
    ggplot(aes(x = date, y = volume)) +
    geom_segment(aes(xend = date, yend = 0, color = volume)) + 
    geom_smooth(method = "loess", se = FALSE) +
    labs(title = "AMZN Volume Chart", 
         subtitle = "Charting Daily Volume", 
         y = "Volume", x = "") +
    theme_tq() +
    theme(legend.position = "none") 

ggplotly(p)
 Click to copy

And, we can zoom in on a specific region. Using scale_color_gradient we can quickly visualize the high and low points, and using geom_smooth we can see the trend.

library(plotly)
library(tidyverse)
library(tidyquant)
library(ggplot2)

data("FANG") 
AMZN <- tq_get("AMZN", get = "stock.prices", from = "2000-01-01", to = "2016-12-31")

end <- as_date("2016-12-31")
start <- end - weeks(24)
p <- AMZN %>%
    filter(date >= start - days(50)) %>%
    ggplot(aes(x = date, y = volume)) +
    geom_segment(aes(xend = date, yend = 0, color = volume)) +
    geom_smooth(method = "loess", se = FALSE) +
    labs(title = "AMZN Bar Chart", 
         subtitle = "Charting Daily Volume, Zooming In", 
         y = "Volume", x = "") + 
    coord_x_date(xlim = c(start, end)) +
    scale_color_gradient(low = "red")

ggplotly(p)
 Click to copy

Themes

The tidyquant package comes with three themes to help quickly customize financial charts:

Light: theme_tq() + scale_color_tq() + scale_fill_tq()
Dark: theme_tq_dark() + scale_color_tq(theme = "dark") + scale_fill_tq(theme = "dark")
Green: theme_tq_green() + scale_color_tq(theme = "green") + scale_fill_tq(theme = "green")

library(plotly)
library(tidyverse)
library(tidyquant)
library(ggplot2)

data("FANG") 

n_mavg <- 50 # Number of periods (days) for moving average
p <- FANG %>%
    filter(date >= start - days(2 * n_mavg)) %>%
    ggplot(aes(x = date, y = close, color = symbol)) +
    geom_line(size = 1) +
    geom_ma(n = 15, color = "darkblue", size = 1) + 
    geom_ma(n = n_mavg, color = "red", size = 1) +
    labs(title = "Dark Theme",
         x = "", y = "Closing Price") +
    coord_x_date(xlim = c(start, end)) +
    facet_wrap(~ symbol, scales = "free_y") +
    theme_tq_dark() +
    scale_color_tq(theme = "dark") +
    scale_y_continuous(labels = scales::dollar)

ggplotly(p)
 Click to copy

What About Dash?

Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash for R at https://dashr.plot.ly/installation.

Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:

library(plotly)

fig <- plot_ly() 
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... ) 

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)

app <- Dash$new()
app$layout(
    htmlDiv(
        list(
            dccGraph(figure=fig) 
        )
     )
)

app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)
 Click to copy