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)
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)