I'm trying to set extremes on a time series stock chart that corresponds to a user pushing a button. Here's the breakdown:
User clicks on a button on the top of the chart (I have edited the "All", "1M", "3M" buttons typically at the top)
When the button is clicked, a custom area on the xAxis (2 months) is zoomed in on. For example, October 1st through December first. Right now, the zoom goes to the end of the graph.
It's very similar to the below link.
X Axis Set Extremes
My R code right now for the button is the following:
hc_rangeSelector(buttons=list(list(type='month', text='New', count=2)))
This says I am looking for a month interval zoom, the text is "New", and it shows 2 months. I've seen that setExtremes is the function i'm looking for but I haven't seen it implemented using R.
You could place a JavaScript function in chart.events.load option in Highcharter. Using Renderer you could add a button that will use setExtremes function on click.
Demo in JSFiddle (without Highcharter nor `R, data is different, but functionality of the button is the same): http://jsfiddle.net/e69eLm6q/
Code to run in R:
library("quantmod")
usdjpy <- getSymbols("USD/JPY", src = "oanda", auto.assign = FALSE)
eurkpw <- getSymbols("EUR/KPW", src = "oanda", auto.assign = FALSE)
hc <- highchart(type = "stock") %>%
hc_title(text = "Charting some Symbols") %>%
hc_add_series(data = usdjpy, id = "usdjpy", pointInterval = 36000000) %>%
hc_add_series(data = eurkpw, id = "eurkpw", pointInterval = 36000000) %>%
hc_rangeSelector(buttons=list(list(type='month', text='New', count=2))) %>%
hc_chart(
events = list(
load = JS("function(){
var chart = this;
chart.renderer.button('do stuff',200, 100)
.attr({
zIndex: 3
})
.on('click', function () {
chart.xAxis[0].setExtremes(Date.UTC(1970, 4, 1), Date.UTC(1970, 6, 1));
})
.add();
}")
)
)
hc
Related
I would like to add the ability to click on imbedded hyperlinks in my floating bar chart created using hc_vistime. Using Hyperlink bar chart in Highcharter, I can activate the link, but it navigates to "file not found". Below is my code.
df1<-data.frame(event=c("store","home","school"),
group=c("john","steve","john"),
start=c(as.POSIXct("2022-9-15"),as.POSIXct("2022-9-16"),as.POSIXct("2022-9-17")),
end=c(as.POSIXct("2022-9-15"),as.POSIXct("2022-9-16"),as.POSIXct("2022-9-17")),
color=c("#D3D3D3","#81ccfe","#D3D3D3"),
url=c("https://www.google.com/","https://www.bing.com/","https://www.msn.com/"))
a<- hc_vistime(df1, optimize_y = T, col.group = "group", show_labels = TRUE)%>%
hc_plotOptions(
series = list(
cursor = "pointer",
point = list(
events = list(
click = JS( "function () { location.href = this.df1$url; }")))))
a
I am confused on how to appropriately link the location.ref command to df1, given the difference in coding structure between vistime and "normal" bar charts.
Thank you in advance.
Anyone created a leaflet map in Code Workbook using r-Leaflet? I have a functioning script that runs (also double checked in R) but how do I get it to visualise and then use in a Report etc. I have tried various tweaks on what may get it to run but no success - any ideas
leaflet_map <- function(map_data) {
library(leaflet)
data<-map_data
# first cut the continuous variable into bins
# these bins are now factors
data$Fill_rateLvl <- cut(data$Fill_rate,
c(0,.5,0.6,0.7,0.8,0.9,1), include.lowest = T,
labels = c('<50%', '50-60%', '60-70%', '70-80%', '80-90%','90-100%'))
# then assign a palette to this using colorFactor
# in this case it goes from red for the smaller values to yellow and green
# standard stoplight for bad, good, and best
FillCol <- colorFactor(palette = 'RdYlGn', data$Fill_rateLvl)
m<-leaflet() %>%
addTiles() %>%
addProviderTiles(providers$CartoDB.Positron)%>%
setView(lng = -0, lat = 50, zoom = 8) %>%
addCircleMarkers(data = data, lat = ~lat, lng = ~long,
color = ~FillCol(Fill_rateLvl), popup = data$Lead_employer,
radius = ~sqrt(Fill_rate*50), group = 'Fill rate') %>%
addLegend('bottomright', pal = FillCol, values = data$Fill_rateLvl,
title = 'Fill rate for next two weeks',
opacity = 1)
return(NULL)
}
I am not familiar with R in code workbook, but it sounds to me that you need to materialize your leaflet map as a dataset and then consume it in some sort of map compatible UI.
For example slate has a map widget which is backed by leaflets. You can find documentation and examples for it in https://www.palantir.com/docs/foundry/slate/widgets-map/
I'm building an interactive map in Leaflet using R, and I would like to use the Leaflet.MarkerCluster.PlacementStrategies sub-plugin to control the placement of markers within a cluster (when "spiderfied" on mouse click over the cluster), and always lay them out in a circle around the cluster icon, rather than along a spiral as it happens when there are more than 8 markers in a cluster with the standard Leaflet.MarkerCluster plugin that comes embedded in the leaflet htmlWidget. The code I'm using to build the map is something like this:
library(leaflet)
data_example <- data.frame(name = rep("site A", times = 14),
lon = rep(14.25000, times = 14),
lat = rep(40.83333, times = 14),
issue = paste("Issue", LETTERS[1:14]),
severity = sample(c("Mild", "Moderate", "Severe"), size = 14, replace = TRUE))
issues_pal <- colorFactor(brewer.pal(3, "RdYlBu"), levels = c("Mild", "Moderate", "Severe"), reverse = TRUE)
leaflet(data_example, options = leafletOptions(maxZoom = 7)) %>%
setView(lng = 8, lat = 50, zoom = 4) %>%
addProviderTiles(providers$Esri.WorldStreetMap) %>%
addCircleMarkers(
lng = ~ lon,
lat = ~ lat,
radius = 10,
color = "grey",
weight = 2,
fillColor = ~ issues_pal(severity),
#stroke = FALSE,
fillOpacity = 0.8,
label = ~ paste(issue, severity),
clusterOptions = markerClusterOptions(
spiderfyDistanceMultiplier = 1.1,
spiderLegPolylineOptions = list(weight = 0)),
group = df,
clusterId = df
)
In the map, when one clicks on the cluster icon, the markers are shown placed along a spiral path originating from the cluster. This is because the Leaflet.MarkerCluster plugin that control the clustering of markers in leaflet, place them along a circle only when there are up to 8 markers. But the Leaflet.MarkerCluster.PlacementStrategies should allow to specify a different placement strategy, and to have markers laid out in a circle even if they are more than 8, as shown in this page and here.
Since the sub-plugin is not yet included in leaflet package, I'd like to use the method highlighted here to use in R any leaflet JS plugins, but I'm struggling to find a way to make it work for my case. According to the method, I should first of all include the following in my code:
placement.strategies <- htmlDependency(
"Leaflet.MarkerCluster.PlacementStrategies",
"0.0.1",
src = c(href = "https://github.com/adammertel/Leaflet.MarkerCluster.PlacementStrategies/blob/master/dist/"),
script = "leaflet-markercluster.placementstrategies.src.js"
)
registerPlugin <- function(map, plugin) {
map$dependencies <- c(map$dependencies, list(plugin))
map
}
And then I should call registerPlugin(placement.strategies) in the leaflet pipe. However, I can't figure out how to adapt the following custom JS code to my needs:
leaflet() %>% setView(lng = 8, lat = 50, zoom = 4) %>%
# Register plugin on this map instance
registerPlugin(placement.strategies) %>%
# Add your custom JS logic here. The `this` keyword
# refers to the Leaflet (JS) map object.
onRender("function(el, x) {
L.esri.basemapLayer('Topographic').addTo(this);
}")
I've tried to simply specify in the call to addCircleMarkers() the elementsPlacementStrategy = 'one-circle' as a clusterOption within the markerClusterOptions(), but it does not work. I suspect the JS code I need to call from should somehow modify the option property elementsPlacementStrategy for the cluster, but how to do that I don't know. Can anyone help with this? Thank you in advance!
post scriptum
I have found a way around to get the circle placement without using the subplugin, that involves modifying in the leaflet.markercluster.js file the _circleSpiralSwitchover property to a number greater than 14 (that is the number of markers I have in the cluster). While this workaround achieves the result I wanted, it is sub-optimal, because unless I decide to modify the default .js file that is located in:
/Library/Frameworks/R.framework/Versions/3.2/Resources/library/leaflet/htmlwidgets/plugins/Leaflet.markercluster
I would need to remake the edit on the .js file every time I export the map with saveWidget.
I'm trying to render a graph in a shiny app using highcharter that shares an x-axis (days) but has multiple y-axes (a percent and a count). After some research it seems like I should use the 'hc_yAxis_multiples' method. On the left y-axis, I have % displayed. On the right y-axis, I want the count displayed. There is a line graph that is based on the left y-axis (%), and a stacked bar graph that is displayed based on the right y-axis.
I have been able to overlay the two graphs, but the bar chart portion based on the right y-axis is not formatted to the corresponding y-axis. Based on what I have been looking at, it seems like something like this would produce a result that I want:
##This first block is to show what the data types of the variables I'm using are and what the structure of my df looks like
df$inbox_rate <- df$total_inbox / df$total_volume
df$inbox_rate <- round((df$inbox_rate*100),0)
df$received_dt <- as.character(df$received_dt)
df$received_dt <- as.Date(df$received_dt, "%Y%m%d")
df <- df[order(df$received_dt),]
## This second block here is where I'm trying to build the chart with two Y-axes
hc <- highchart()%>%
hc_title(text = paste(domain_name,sep=""),align = "center") %>%
hc_legend(align = "center") %>%
hc_xAxis(type = "datetime", labels = list(format = '{value:%m/%d}')) %>%
hc_yAxis_multiples(list(title = list(text = "IPR"),labels=list(format = '{value}%'),min=0,
max=100,showFirstLabel = TRUE,showLastLabel=TRUE,opposite = FALSE),
list(title = list(text = "Total Subscribers"),min=0,max = max(df$total_users),
labels = list(format = "{value}"),showLastLabel = FALSE, opposite = TRUE)) %>%
hc_plotOptions(column = list(stacking = "normal")) %>%
hc_add_series(df,"column",hcaes(
x=received_dt,y=total_users,group=isp,yAxis=total_users)) %>%
hc_add_series(df,type="line",hcaes(
x=received_dt,y=inbox_rate,group=isp,yAxis=inbox_rate)) %>%
hc_exporting(enabled = TRUE) %>%
hc_add_theme(thm)
hc
However this produces something that looks like this.
To give more insight about the data I'm using, the domain_name is a string variable that looks like this: example.com. The total_users variable is a number that varies from 0 to about 50000. The received_dt variable is a date, formatted using as.Date(df$received_dt, "%Y%m%d"). The inbox_rate variable is a percent, from 0 to 100.
The bar counts are all displaying to the full height of the graph, even though the values of the bars vary widely. To reiterate, I want the right y-axis that the bar chart heights are based on to be the count of the df$total_users. Within the hc_yAxis_multiples function, there are two lists given. I thought that the first list gives the left y-axis, and the second gives the right. The closest answer to my question that I could find was given by this stackoverflow response
If anyone has any insight, it would be very much appreciated!
Your use of the yAxis statement in hc_add_series seems to be off. First, it should not be inside hcaes and second, it's a number specifying which axis (in order of appearance in hy_yAxis_multiple call) the series belongs to. So hc_add_series(..., yAxis = 1) should be used to assign a series to the second (right) axis.
Below is a (fully self-explaining, independent, minimal) example that shows how it should work.
library(highcharter)
df <- data.frame(
total_inbox = c(2, 3, 4, 5, 6),
total_volume = c(30, 30, 30, 30, 30),
total_users = c(300, 400, 20, 340, 330),
received_dt = c("20180202", "20180204", "20180206", "20180210", "20180212"),
isp = "ProviderXY"
)
df$inbox_rate <- df$total_inbox / df$total_volume
df$inbox_rate <- round((df$inbox_rate*100),0)
df$received_dt <- as.character(df$received_dt)
df$received_dt <- as.Date(df$received_dt, "%Y%m%d")
df <- df[order(df$received_dt),]
hc <- highchart()%>%
hc_xAxis(type = "datetime", labels = list(format = '{value:%m/%d}')) %>%
hc_yAxis_multiples(list(title = list(text = "IPR"),labels=list(format = '{value}%'),min=0,
max=100,showFirstLabel = TRUE,showLastLabel=TRUE,opposite = FALSE),
list(title = list(text = "Total Subscribers"),min=0,max = max(df$total_users),
labels = list(format = "{value}"),showLastLabel = FALSE, opposite = TRUE)) %>%
hc_plotOptions(column = list(stacking = "normal")) %>%
hc_add_series(df,type="column",hcaes(x=received_dt,y=total_users,group=isp),yAxis=1) %>%
hc_add_series(df,type="line",hcaes(x=received_dt,y=inbox_rate,group=isp))
hc
Maybe take this as an example how code in questions should be like. Copy-Paste-Runnable, no outside variables and minus all the things that dont matter here (like the theme and legend for example).
Quick question all.
I have some data in sql server which i have loaded into RStudio. I have made a barchart for the data and now i am using leaflet library with the use of latitude and longitude to plot a point on the map. I want to be able to use popup to show a barchart in it when the user clicks on the point.
BarChart code (maybe this is a problem because i am using googleVis library so not sure if i can use this in the popup. but again this is the most appropriate bar graph i can make and need- other suggestions could be helpful as i am not a professional in R libraries yet)
Switzerland <- sqlQuery(con, "sql query")
SwitzerlandChart <- gvisBarChart(Switzerland, options = list(height=200))
For the graph plot the code is:
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addCircles(lng=8.498868, lat=46.9221, popup=paste(plot(SwitzerlandChart)))
When i run this code it opens a webpage to view my barplot.
Then i run the following:
m #Prints the graph
This prints the graph with the point in the desired location but the popup shows me a webpage instead which also only i can open.
I want to be able to plot the bargraph inside the popup please.
Hope someone can help
Maybe a little late but here's a solution. The addPopups() function in library(leaflet) seems to be able to handle .svg files. Therefore, you could simply save your plot using svg() and then read it again using readLines(). Here's a reproducible example using library(mapview):
library(lattice)
library(mapview)
library(sp)
data(meuse)
coordinates(meuse) <- ~x+y
proj4string(meuse) <- CRS("+init=epsg:28992")
clr <- rep("grey", length(meuse))
fldr <- tempfile()
dir.create(fldr)
pop <- lapply(seq(length(meuse)), function(i) {
clr[i] <- "red"
p <- xyplot(meuse$cadmium ~ meuse$copper,
col = clr, pch = 20, alpha = 0.7)
svg(filename = paste(fldr, "test.svg", sep = "/"),
width = 250 * 0.01334, height = 250 * 0.01334)
print(p)
dev.off()
tst <- paste(readLines(paste(fldr, "test.svg", sep = "/")), collapse = "")
return(tst)
})
mapview(meuse, popup = pop, cex = "cadmium")
You will see that each popup is a scatterplot. As for a leaflet example, consider this:
content <- pop[[1]]
leaflet() %>% addTiles() %>%
addPopups(-122.327298, 47.597131, content,
options = popupOptions(closeButton = FALSE)
)
In case you need the plot to be interactive, you could have a look at library(gridSVG) which is able to produce interactive svg plots from e.g. lattice or ggplot2 plots.
UPDATE:
library(mapview) now has designated functionality for this:
popupGraph: to embed lattice, ggplot2 or interactive hatmlwidgets based plots.
popupImage: to embed local or remote (web) images
This is currently only available in the development version of mapview which can be installed with:
devtools::install_github("environmentalinformatics-marburg/mapview", ref = "develop"
This may be a little late too, but here is a full leaflet implementation. I first create the plot and then use the popupGraph function to add it in.
# make a plot of the two columns in the dataset
p <- xyplot(Home ~ Auto, data = Jun, col = "orange", pch = 20, cex = 2)
# make one for each data point
p <- mget(rep("p", length(Jun)))
# color code it so that the corresponding points are dark green
clr <- rep("orange", length(Jun))
p <- lapply(1:length(p), function(i) {
clr[i] <- "dark green"
update(p[[i]], col = clr)
})
# now make the leaflet map
m1 <- leaflet() %>%
addTiles() %>%
setView(lng = -72, lat = 41, zoom = 8) %>%
# add the markers for the Jun dataset
# use the popupGraph function
addCircleMarkers(data = Jun, lat = ~Lat, lng = ~Lon,
color = ~beatCol(BeatHomeLvl), popup = popupGraph(p),
radius = ~sqrt(BeatHome*50), group = 'Home - Jun') %>%
# layer control
addLayersControl(
overlayGroups = c('Home - Jun'
),
options = layersControlOptions(collapsed = F)
) %>%
# legend for compare to average
addLegend('bottomright', pal = beatCol, values = last$BeatTotalLvl,
title = 'Compare<br>Quote Count to<br>3Mos State Avg',
opacity = 1)
m1
Here is the output.