This post shows what is possible to do for
time series visualization with the
dygraphs package, using a good amount of customization.
Reproducible code is provided.
an introduction to
time series representation with the
This page gives a more custom example based on real data (number of bikes located per day). Here is the graph and the code that allows to make it!
# Library library(dygraphs) library(xts) # To make the convertion data-frame / xts format library(tidyverse) library(lubridate) # Read the data (hosted on the gallery website) data <- read.table("https://python-graph-gallery.com/wp-content/uploads/bike.csv", header=T, sep=",") %>% head(300) # Check type of variable # str(data) # Since my time is currently a factor, I have to convert it to a date-time format! data$datetime <- ymd_hms(data$datetime) # Then you can create the xts necessary to use dygraph don <- xts(x = data$count, order.by = data$datetime) # Finally the plot p <- dygraph(don) %>% dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors="#D8AE5A") %>% dyRangeSelector() %>% dyCrosshair(direction = "vertical") %>% dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) %>% dyRoller(rollPeriod = 1) # save the widget # library(htmlwidgets) # saveWidget(p, file=paste0( getwd(), "/HtmlWidget/dygraphs318.html"))