Time Series and Date Axes in R
How to plot date and time in R.
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Time Series using Axes of type date
Time series can be represented using plotly functions (line, scatter, bar etc). For more examples of such charts, see the documentation of line and scatter plots or bar charts.
For financial applications, Plotly can also be used to create Candlestick charts and OHLC charts, which default to date axes.
Plotly doesn't auto set the data type of axis to date. We have to give the values using as.Data() for an axis to mention it's data type as date.
library(tidyquant)
library(plotly)
getSymbols("GOOG",
from = "2018-01-01",
to = "2019-12-31")
## [1] "GOOG"
stock <- data.frame(GOOG$GOOG.Adjusted)
stock$GOOG.Adjusted <- stock$GOOG.Adjusted/stock$GOOG.Adjusted[1]
stock <- data.frame(stock,rownames(stock))
colnames(stock) <- append('GOOG','date')
fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~date, y = ~GOOG, name = 'GOOG')%>%
layout(showlegend = F)
options(warn = -1)
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
library(plotly)
stock <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~Date, y = ~AAPL.High)%>%
layout(showlegend = F)
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
Different Chart Types on Date Axes
Any kind of cartesian chart can be placed on date axes, for example this filled area chart of relative stock ticker values.
library(tidyquant)
library(plotly)
getSymbols("GOOG",
from = "2018-01-01",
to = "2020-01-01")
## [1] "GOOG"
stock <- data.frame(GOOG$GOOG.Adjusted)
stock$GOOG.Adjusted <- stock$GOOG.Adjusted/stock$GOOG.Adjusted[1]
stock <- data.frame(stock,rownames(stock))
colnames(stock) <- append('GOOG','date')
stock$date <- as.Date(stock$date)
fig <- plot_ly()%>%
add_trace(data = stock, type = 'scatter', mode = 'lines', fill = 'tozeroy', x = ~date, y = ~GOOG, name = 'GOOG')%>%
layout(showlegend = F, yaxis = list(range = c(0.8,1.25),
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6')
options(warn = -1)
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
Or this facetted area plot:
library(tidyquant)
library(plotly)
tickers = c("GOOG", "AAPL", "AMZN", "META", "NFLX", "MSFT")
for (i in tickers){
getSymbols(i,
from = "2018-01-01",
to = "2019-12-31")}
x <- list(
title = "date"
)
y <- list(
title = "value"
)
stock <- data.frame(GOOG$GOOG.Adjusted,
AAPL$AAPL.Adjusted,
AMZN$AMZN.Adjusted,
META$META.Adjusted,
NFLX$NFLX.Adjusted,
MSFT$MSFT.Adjusted)
stock$GOOG.Adjusted <- stock$GOOG.Adjusted/stock$GOOG.Adjusted[1]
stock$AAPL.Adjusted <- stock$AAPL.Adjusted/stock$AAPL.Adjusted[1]
stock$AMZN.Adjusted <- stock$AMZN.Adjusted/stock$AMZN.Adjusted[1]
stock$META.Adjusted <- stock$META.Adjusted/stock$META.Adjusted[1]
stock$NFLX.Adjusted <- stock$NFLX.Adjusted/stock$NFLX.Adjusted[1]
stock$MSFT.Adjusted <- stock$MSFT.Adjusted/stock$MSFT.Adjusted[1]
stock <- data.frame(stock,rownames(stock))
colnames(stock) <- append(tickers,'Dates')
ax <- list(
title = "",
zeroline = FALSE,
showline = FALSE,
showticklabels = FALSE
)
fig1 <- plot_ly(stock, type = 'scatter', mode = 'lines', fill = 'tonexty')%>%
add_trace(x = ~Dates, y = ~GOOG, name = 'GOOG')%>%
layout(legend=list(title=list(text='company')), xaxis = ax, yaxis = list(range = c(0.5,2), title = 'value'))
fig2 <- plot_ly(stock, type = 'scatter', mode = 'lines', fill = 'tonexty')%>%
add_trace(x = ~Dates, y = ~AAPL, name = 'AAPL')%>%
layout(legend=list(title=list(text='company')), xaxis = ax, yaxis = list(range = c(0.5,2),title = '', showticklabels = FALSE))
fig3 <- plot_ly(stock, type = 'scatter', mode = 'lines', fill = 'tonexty')%>%
add_trace(x = ~Dates, y = ~AMZN, name = 'AMZN')%>%
layout(legend=list(title=list(text='company')), xaxis = ax, yaxis = list(range = c(0.5,2), title = 'value'))
fig4 <- plot_ly(stock, type = 'scatter', mode = 'lines', fill = 'tonexty')%>%
add_trace(x = ~Dates, y = ~META, name = 'META')%>%
layout(legend=list(title=list(text='company')), xaxis = ax, yaxis = list(range = c(0.5,2),title = '', showticklabels = FALSE))
fig5 <- plot_ly(stock, type = 'scatter', mode = 'lines', fill = 'tonexty')%>%
add_trace(x = ~Dates, y = ~NFLX, name = 'NFLX')%>%
layout(legend=list(title=list(text='company')), xaxis = list(title = 'Date'), yaxis = list(range = c(0.5,2), title = 'value'))
fig6 <- plot_ly(stock, type = 'scatter', mode = 'lines', fill = 'tonexty')%>%
add_trace(x = ~Dates, y = ~MSFT, name = 'MSFT')%>%
layout( legend=list(title=list(text='company')), yaxis = list(range = c(0.5,2) ,showticklabels = FALSE, title =''), xaxis = list(title = 'Date'))
fig <- subplot(fig1, fig2, fig3, fig4, fig5, fig6,
nrows = 3, titleY = TRUE, titleX = TRUE) %>% layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6')
annotations = list(
list(
x = 0.225,
y = 1.0,
font = list(size = 10),
text = "company=GOOG",
xref = "paper",
yref = "paper",
xanchor = "center",
yanchor = "bottom",
showarrow = FALSE
),
list(
x = 0.775,
y = 1,
font = list(size = 10),
text = "company=AAPL",
xref = "paper",
yref = "paper",
xanchor = "center",
yanchor = "bottom",
showarrow = FALSE
),
list(
x = 0.225,
y = 0.64,
font = list(size = 10),
text = "company=AMZN",
xref = "paper",
yref = "paper",
xanchor = "center",
yanchor = "bottom",
showarrow = FALSE
),
list(
x = 0.775,
y = 0.64,
font = list(size = 10),
text = "company=META",
xref = "paper",
yref = "paper",
xanchor = "center",
yanchor = "bottom",
showarrow = FALSE
),
list(
x = 0.225,
y = 0.315,
font = list(size = 10),
text = "company=NFLX",
xref = "paper",
yref = "paper",
xanchor = "center",
yanchor = "bottom",
showarrow = FALSE
),
list(
x = 0.775,
y = 0.315,
font = list(size = 10),
text = "company=MSFT",
xref = "paper",
yref = "paper",
xanchor = "center",
yanchor = "bottom",
showarrow = FALSE
)
)
fig <- fig %>%layout(annotations = annotations, width = 900)
options(warn = -1)
fig
Configuring Tick Labels
By default, the tick labels (and optional ticks) are associated with a specific grid-line, and represent an instant in time, for example, "00:00 on February 1, 2018". Tick labels can be formatted using the tickformat attribute (which accepts the d3 time-format formatting strings) to display only the month and year, but they still represent an instant by default, so in the figure below, the text of the label "Feb 2018" spans part of the month of January and part of the month of February. The dtick attribute controls the spacing between gridlines, and the "M1" setting means "1 month". This attribute also accepts a number of milliseconds, which can be scaled up to days by multiplying by 24*60*60*1000.
Note that by default, the formatting of values of X and Y values in the hover label matches that of the tick labels of the corresponding axes, so when customizing the tick labels to something broad like "month", it's usually necessary to customize the hover label to something narrower like the actual date, as below.
library(tidyquant)
library(plotly)
tickers = c("GOOG", "AAPL", "AMZN", "META", "NFLX", "MSFT")
for (i in tickers){
getSymbols(i,
from = "2018-01-01",
to = "2019-12-31")}
stock <- data.frame(GOOG$GOOG.Adjusted,
AAPL$AAPL.Adjusted,
AMZN$AMZN.Adjusted,
META$META.Adjusted,
NFLX$NFLX.Adjusted,
MSFT$MSFT.Adjusted)
stock$GOOG.Adjusted <- stock$GOOG.Adjusted/stock$GOOG.Adjusted[1]
stock$AAPL.Adjusted <- stock$AAPL.Adjusted/stock$AAPL.Adjusted[1]
stock$AMZN.Adjusted <- stock$AMZN.Adjusted/stock$AMZN.Adjusted[1]
stock$META.Adjusted <- stock$META.Adjusted/stock$META.Adjusted[1]
stock$NFLX.Adjusted <- stock$NFLX.Adjusted/stock$NFLX.Adjusted[1]
stock$MSFT.Adjusted <- stock$MSFT.Adjusted/stock$MSFT.Adjusted[1]
stock <- data.frame(stock,rownames(stock))
colnames(stock) <- append(tickers,'Dates')
fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~Dates, y = ~GOOG, name = 'GOOG')%>%
add_trace(x = ~Dates, y = ~AAPL, name = 'AAPL')%>%
add_trace(x = ~Dates, y = ~AMZN, name = 'AMZN')%>%
add_trace(x = ~Dates, y = ~META, name = 'META')%>%
add_trace(x = ~Dates, y = ~NFLX, name = 'NFLX')%>%
add_trace(x = ~Dates, y = ~MSFT, name = 'MSFT')%>%
layout(title = 'custom tick labels',legend=list(title=list(text='variable')),
xaxis = list(dtick = "M1", tickformat="%b<br>%Y"), width = 1000)
options(warn = -1)
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6')
fig
Moving Tick Labels to the Middle of the Period
new in 4.10
By setting the ticklabelmode attribute to "period" (the default is "instant") we can move the tick labels to the middle of the period they represent. The gridlines remain at the beginning of each month (thanks to dtick="M1") but the labels now span the month they refer to.
library(tidyquant)
library(plotly)
tickers = c("GOOG", "AAPL", "AMZN", "META", "NFLX", "MSFT")
for (i in tickers){
getSymbols(i,
from = "2018-01-01",
to = "2019-12-31")}
stock <- data.frame(GOOG$GOOG.Adjusted,
AAPL$AAPL.Adjusted,
AMZN$AMZN.Adjusted,
META$META.Adjusted,
NFLX$NFLX.Adjusted,
MSFT$MSFT.Adjusted)
stock$GOOG.Adjusted <- stock$GOOG.Adjusted/stock$GOOG.Adjusted[1]
stock$AAPL.Adjusted <- stock$AAPL.Adjusted/stock$AAPL.Adjusted[1]
stock$AMZN.Adjusted <- stock$AMZN.Adjusted/stock$AMZN.Adjusted[1]
stock$META.Adjusted <- stock$META.Adjusted/stock$META.Adjusted[1]
stock$NFLX.Adjusted <- stock$NFLX.Adjusted/stock$NFLX.Adjusted[1]
stock$MSFT.Adjusted <- stock$MSFT.Adjusted/stock$MSFT.Adjusted[1]
stock <- data.frame(stock,rownames(stock))
colnames(stock) <- append(tickers,'Dates')
fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~Dates, y = ~GOOG, name = 'GOOG')%>%
add_trace(x = ~Dates, y = ~AAPL, name = 'AAPL')%>%
add_trace(x = ~Dates, y = ~AMZN, name = 'AMZN')%>%
add_trace(x = ~Dates, y = ~META, name = 'META')%>%
add_trace(x = ~Dates, y = ~NFLX, name = 'NFLX')%>%
add_trace(x = ~Dates, y = ~MSFT, name = 'MSFT')%>%
layout(title = 'custom tick labels with ticklabelmode="period"',legend=list(title=list(text='variable')),
xaxis = list(dtick = "M1", tickformat="%b\n%Y",
ticklabelmode="period"), width = 1000)
options(warn = -1)
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6')
fig
Summarizing Time-series Data with Histograms
Plotly histograms are powerful data-aggregation tools which even work on date axes. In the figure below, we pass in daily data and display it as monthly averages by setting histfunc="avg and xbins_size="M1".
library(plotly)
stock <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
stock$Date = as.Date(stock$Date)
fig <- plot_ly(stock, x = ~AAPL.Close,y = ~Date, type = 'scatter', mode = 'markers', name = 'daily') %>%
add_trace(data = stock, x = ~AAPL.Close, type = 'histogram', histfunc = 'avg', xbins = list(size = "M1"),
name = 'monthly average')
fig <- fig %>%
layout(xaxis = list(title = 'avg of AAPL.Close',
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6',
title = 'Histogram on Date Axes',
bargap = 0.1)
fig <- fig %>%
layout(hovermode="x unified",
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
Displaying Period Data
new in 4.11
If your data coded "January 1" or "January 31" in fact refers to data collected throughout the month of January, for example, you can configure your traces to display their marks at the start end, or middle of the month with the xperiod and xperiodalignment attributes. In the example below, the raw data is all coded with an X value of the 10th of the month, but is binned into monthly periods with xperiod="M1" and then displayed at the start, middle and end of the period.
library(plotly)
date = c(as.Date("2020-01-10"), as.Date("2020-02-10"), as.Date("2020-03-10"),
as.Date("2020-04-10"), as.Date("2020-05-10"), as.Date("2020-06-10"))
value = c(1,2,3,1,2,3)
df = data.frame(date, value)
fig <- plot_ly() %>%
add_trace(df, x = ~date, y = ~value, type = 'scatter', mode = 'lines+markers', name="Raw Data",
marker = list(symbol="star")) %>%
add_trace(df, x = ~date, y = ~value, type = 'scatter', mode = 'lines+markers', name="Start-aligned",
xperiod="M1", xperiodalignment="start") %>%
add_trace(df, x = ~date, y = ~value, type = 'scatter', mode = 'lines+markers', name="Middle-aligned",
xperiod="M1", xperiodalignment="middle") %>%
add_trace(df, x = ~date, y = ~value, type = 'scatter', mode = 'lines+markers', name="End-aligned",
xperiod="M1", xperiodalignment="end") %>%
add_trace(df, x = ~date, y = ~value, type = 'bar', name="Middle-aligned")
fig <- fig %>%
layout(xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
Hover Templates with Mixtures of Period data
New in v5.0
When displaying periodic data with mixed-sized periods (i.e. quarterly and monthly) in conjunction with x or x unified hovermodes and using hovertemplate, the xhoverformat attribute can be used to control how each period's X value is displayed, and the special %{xother} hover-template directive can be used to control how the X value is displayed for points that do not share the exact X coordinate with the point that is being hovered on. %{xother} will return an empty string when the X value is the one being hovered on, otherwise it will return (%{x}). The special %{_xother}, %{xother_} and %{_xother_} variations will display with spaces before, after or around the parentheses, respectively.
library(plotly)
fig <- plot_ly() %>%
add_trace(x = c(as.Date("2020-01-01"), as.Date("2020-04-01"), as.Date("2020-07-01")),
y = c(1000, 1500, 1700),
type = 'bar',
xperiod="M3",
xperiodalignment="middle",
hovertemplate="%{y}%{_xother}") %>%
add_trace(x = c(as.Date("2020-01-01"), as.Date("2020-02-01"), as.Date("2020-03-01"),
as.Date("2020-04-01"), as.Date("2020-05-01"), as.Date("2020-06-01"),
as.Date("2020-07-01"), as.Date("2020-08-01"), as.Date("2020-09-01")),
y = c(1100,1050,1200,1300,1400,1700,1500,1400,1600),
type = 'scatter', mode = 'lines+markers',
xperiod="M1",
xperiodalignment="middle",
hovertemplate="%{y}%{_xother}")
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
Time Series Plot with Custom Date Range
The data range can be set manually using layout.xaxis.range objects.
library(plotly)
stock <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~Date, y = ~AAPL.High)%>%
layout(showlegend = F, xaxis = list(range = c('2016-07-01','2016-12-31')))
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
Time Series With Range Slider
A range slider is a small subplot-like area below a plot which allows users to pan and zoom the X-axis while maintaining an overview of the chart. Check out the reference for more options: https://plotly.com/r/range-slider/
library(plotly)
stock <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~Date, y = ~AAPL.High)%>%
layout(showlegend = F, title='Time Series with Rangeslider',
xaxis = list(rangeslider = list(visible = T)))
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
Time Series with Range Selector Buttons
Range selector buttons are special controls that work well with time series and range sliders, and allow users to easily set the range of the x-axis. Check out the reference for more options: https://plotly.com/r/range-slider/
library(plotly)
stock <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
add_trace(x = ~Date, y = ~AAPL.High)%>%
layout(showlegend = F, title='Time Series with Range Slider and Selectors',
xaxis = list(rangeslider = list(visible = T),
rangeselector=list(
buttons=list(
list(count=1, label="1m", step="month", stepmode="backward"),
list(count=6, label="6m", step="month", stepmode="backward"),
list(count=1, label="YTD", step="year", stepmode="todate"),
list(count=1, label="1y", step="year", stepmode="backward"),
list(step="all")
))))
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', margin = 0.1, width = 900)
fig
Customizing Tick Label Formatting by Zoom Level
The tickformatstops attribute can be used to customize the formatting of tick labels depending on the zoom level. Try zooming in to the chart below and see how the tick label formatting changes. Check out the reference for more options: https://plotly.com/r/tick-formatting/
library(plotly)
data <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
data$Date <- as.Date(data$Date)
fig <- plot_ly(data = data, x = ~Date, y = ~mavg, type = 'scatter', mode = 'lines')%>%
layout(xaxis = list(rangeslider = list(visible = TRUE),
tickformatstops = list(
list(dtickrange=list(NULL, 1000), value="%H:%M:%S.%L ms"),
list(dtickrange=list(1000, 60000), value="%H:%M:%S s"),
list(dtickrange=list(60000, 3600000), value="%H:%M m"),
list(dtickrange=list(3600000, 86400000), value="%H:%M h"),
list(dtickrange=list(86400000, 604800000), value="%e. %b d"),
list(dtickrange=list(604800000, "M1"), value="%e. %b w"),
list(dtickrange=list("M1", "M12"), value="%b '%y M"),
list(dtickrange=list("M12", NULL), value="%Y Y")
)))
fig <- fig %>%
layout(
xaxis = list(title = '',
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(title = '',
zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
Hiding Weekends and Holidays
The rangebreaks attribute available on x- and y-axes of type date can be used to hide certain time-periods. In the example below, we show two plots: one in default mode to show gaps in the data, and one where we hide weekends and holidays to show an uninterrupted trading history. Note the smaller gaps between the grid lines for December 21 and January 4, where holidays were removed. Check out the reference for more options: https://plotly.com/r/reference/layout/xaxis/#layout-xaxis-rangebreaks
library(plotly)
stock <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
fig <- plot_ly(stock, type = 'scatter', mode = 'markers')%>%
add_trace(x = ~Date, y = ~AAPL.High)%>%
layout(showlegend = F, xaxis = list(range = c('2015-12-01', '2016-01-15')),
title="Default Display with Gaps")
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
library(plotly)
stock <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
fig <- plot_ly(stock, type = 'scatter', mode = 'markers')%>%
add_trace(x = ~Date, y = ~AAPL.High)%>%
layout(showlegend = F, xaxis = list(range = c('2015-12-01', '2016-01-15'),
rangebreaks=list(
list(bounds=list("sat", "mon")), #hide weekends
list(values=list("2015-12-25", "2016-01-01")))),
title="Hide Weekend and Holiday Gaps with rangebreaks")
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
fig
Hiding Non-Business Hours
The rangebreaks feature described above works for hiding hourly periods as well.
library(lubridate)
library(pracma)
library(plotly)
start_date <- as.Date('2020-03-01')+hours(1)
dates <- list()
while(length(dates) < 40){
if((as.numeric(format(start_date, format = "%H")) >= 9) & (as.numeric(format(start_date, format = "%H")) <=16)){
dates <- c(dates, toString(start_date))
start_date <- start_date+hours(1)
} else {
start_date <- start_date+hours(1)
}
}
#dates
val <- cumsum(rand(n = 40, m = 1)[,1]) - 0.5
data <- data.frame(matrix(unlist(dates), nrow=length(dates), byrow=TRUE))
data <- cbind(data, val)
colnames(data) <- c('date', 'value')
fig <- plot_ly(data, type = 'scatter', mode = 'markers')%>%
add_trace(x = ~date, y = ~value)%>%
layout(showlegend = F, xaxis = list(dtick=86400000.0/2,
tickformat="%H:%M\n%b\n%Y"),
title="Default Display with Gaps"
)
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
options(warn = -1)
fig
library(lubridate)
library(pracma)
library(plotly)
start_date <- as.Date('2020-03-01')+hours(1)
dates <- list()
while(length(dates) < 40){
if((as.numeric(format(start_date, format = "%H")) >= 9) & (as.numeric(format(start_date, format = "%H")) <=16)){
dates <- c(dates, toString(start_date))
start_date <- start_date+hours(1)
} else {
start_date <- start_date+hours(1)
}
}
#dates
val <- cumsum(rand(n = 40, m = 1)[,1]) - 0.5
data <- data.frame(matrix(unlist(dates), nrow=length(dates), byrow=TRUE))
data <- cbind(data, val)
colnames(data) <- c('date', 'value')
fig <- plot_ly(data, type = 'scatter', mode = 'markers')%>%
add_trace(x = ~date, y = ~value)%>%
layout(showlegend = F, xaxis = list(rangebreaks=
list(
list(bounds=list(17, 9),
pattern="hour")),#hide hours outside of 9am-5pm
dtick=86400000.0/2,
tickformat="%H:%M\n%b\n%Y"),
title="Hide Non-Business Hour Gaps with rangebreaks"
)
fig <- fig %>%
layout(
xaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
yaxis = list(zerolinecolor = '#ffff',
zerolinewidth = 2,
gridcolor = 'ffff'),
plot_bgcolor='#e5ecf6', width = 900)
options(warn = -1)
fig
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)