Time Series and Date Axes in ggplot2

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

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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)

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)

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)

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)

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)

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)

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)