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.
The chart
#316 and
#317 gives
an introduction to
time series representation with the
dygraphs
library.
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"))