I'm trying to produce multiple charts based on different values of a specific variable in rCharts, ideally using NVD3 (nPlot). I believe this should be implementable using facets. However, I am unable to even get the example provided by rCharts to work:
p13 <- nPlot(mpg ~ wt, data = mtcars, group = "gear", type = "scatterChart")
p13$params$facet = "cyl"
p13$templates$script = system.file(
"/libraries/nvd3/layouts/nvd3FacetPlot.html",
package = "rCharts"
)
p13
This doesn't produce a chart for me. When I remove the p13$templates$script line, I get a single chart, no different from when I had not set a facet parameter. Am I missing something? I'm confident if I can simply reproduce this code to make it work I can get my own chart to work. I've installed the latest version of rCharts as of today (2/28/16).
As a separate question (if that's allowable), how do I add a title to an nPlot?
nvd3's help shows "facet has not been supported yet"
you can try the package : plotly ,it can facets well,and also have interactive effect
Related
My dataset includes the variable "annual_TRW" measured over different "survey_year", with 5 "Sample_Core_ID" within each "block" nested within each "site".
The following code was working to plot all samples for a plot subset.
pdf("plots.pdf")
library(ggplot2)
dataset %>%
group_by(site, block, Sample_Core_ID ) %>%
ggplot(., aes(x = survey_year, y = annual_TRW, colour = Sample_Core_ID)) +
geom_line() +
facet_grid(Sample_Core_ID ~ .)
dev.off()
Then, I was trying to split a faceted plot over multiple pages, with each page including the faceted plots (1 column, 5 rows) corresponding to the samples of each block. I was testing using different different functions ggplus::facet_multiple, gridExtra::marrangeGrob, and ggforce::facet_wrap_paginate, but without success i.e. pdf were created empty.
The problem is that, after trying different options, now I get the following error every time that I try to use any ggplot on the same dataset. Now, I dont manage to plot the code that worked before without giving this error.
Error in if (node$tag == "span") { : argument is of length zero
Question 1: How can I solve this error every time I try to use this ggplot?
Question 2: Once this works again, how can I split the faceted plot over multiple pages as explained before?
Thanks a lot in advance!
I tried re-running the entire code, and re-calling the packages, but it doesnt work.
Regarding Problem 1: the error was solved after updating the R version and reinstalling only the necessary packages that worked. The issue might have come from installing the packages ggplus as described here https://github.com/guiastrennec/ggplus
I have come across a beautiful chart on this webpage: https://ourworldindata.org/coronavirus and interested to know if we can build the same chart in R with functionality of having highlighted series as well as selecting any line on hovering ?
I have build static highlighted charts using gghighlight but those are not interactive.
Plotly can help in interaction but I think they don't work with gghighlight.
So how can we have the combination of both highlight and interactivity in charts as in the link shared on top ?
Is it possible to achieve same results in R ? It would be really helpful if someone could share an example or link that can help.
(UPDATE: May be I can manually highlight lines by creating a factor column instead of using gghighlight and then pass it to ggplotly but then can ggplotly or some other library provide similar results on hover ?)
(NOTE: Not looking for animation. Just need highlighted, hover over interactive chart)
Below is the snapshot of same chart hovered over US (This chart is also similar to the one shared in World Economic Forum many times.)
Using plotly you can use highlight() to achive this.
This is a slightly modified example from here:
library(plotly)
# load the `txhousing` dataset
data(txhousing, package = "ggplot2")
# declare `city` as the SQL 'query by' column
tx <- highlight_key(txhousing, ~city)
# initiate a plotly object
base <- plot_ly(tx, color = I("black")) %>%
group_by(city)
# create a time series of median house price
time_series <- base %>%
group_by(city) %>%
add_lines(x = ~date, y = ~median)
highlight(
time_series,
on = "plotly_hover",
selectize = FALSE,
dynamic = FALSE,
color = "red",
persistent = FALSE
)
I need to repeat the thing done in:
tiny pie charts to represent each point in an scatterplot using ggplot2 but I stumbled into the problem that the package ggsubplot is not available for 3.3.1 R version.
Essentially I need a histogram or a pie chart in predefined points on the scatterplot. Here is the same code that is used in the cited post:
foo <- data.frame(X=runif(30),Y=runif(30),A=runif(30),B=runif(30),C=runif(30))
foo.m <- melt(foo, id.vars=c("X","Y"))
ggplot(foo.m, aes(X,Y))+geom_point()
ggplot(foo.m) +
geom_subplot2d(aes(x = X, y = Y, subplot = geom_bar(aes(variable,
value, fill = variable), stat = "identity")), width = rel(.5), ref = NULL)
The code used libraries reshape2, ggplot2 and ggsubplot.
The image that I want to see is in the post cited above
UPD: I downloaded the older versions of R (3.0.2 and 3.0.3) and checkpoint package, and used:
checkpoint("2014-09-18")
as was described in the comment bellow. But I get an error:
Using binwidth 0.0946
Using binwidth 0.0554
Error in layout_base(data, vars, drop = drop) :
At least one layer must contain all variables used for facetting
Which I can't get around, because when I try to include facet, the following error comes up:
Error: ggsubplots do not support facetting
It doesn't look like ggsubplot is going to fix itself any time soon. One option would be to use the checkpoint package, and essentially "reset" your copy of R to a time when the package was compatible. This post suggests using a time point of 2014-09-18.
I am using rCharts and shiny in order to plot a variety of datasets. These datasets are dynamic and may in some cases be very big (10 000+ points in some cases). In these cases displaying the plot takes a lot of time (40s+).
Is there a way to generate an rCharts plot without the animations. This should significantly reduce the creating and rendering time of the plot.
I have looked trough the documentation (ltle that there is), but haven't found a way to do it.
The only thing that i have found is https://gist.github.com/timelyportfolio/10184829
I am using Polycharts, NVD3 and HighCharts.
EDIT: For highCharts i can do p$tooltip(enabled = F) and p$chart(animation = F). But any additional help would be appreciated, especially for Polycharts and NVD3
I am not a heavy user of Polycharts and Highcharts, so if you could add an example it would be helpful.
For NVD3 the variable is called transitionDuration.
An example of a chart without animations would be as follows:
library(rCharts)
hair_eye = as.data.frame(HairEyeColor)
p2 <- nPlot(Freq ~ Hair, group = 'Eye',
data = subset(hair_eye, Sex == "Female"),
type = 'multiBarChart')
p2$chart(transitionDuration=0)
p2
Due to static graph prepared by ggplot, we are shifting our graphs to googleVis with interactive charts. But when it comes to categorization we are facing many problems. Let me give example which will help you understand:
#dataframe
df = data.frame( x = sample(1:100), y = sample(1:100), cat = sample(c('a','b','c'), 100, replace=TRUE) )
ggplot2 provides parameter like alpha, colour, linetype, size which we can use with categories like shown below:
ggplot(df) + geom_line(aes(x = x, y = y, colour = cat))
Not just line chart, but majority of ggplot2 graphs provide categorization based on column values. Now I would like to do the same in googleVis, based on value df$cat I would like parameters to get changed or grouping of line or charts.
Note:
I have already tried dcast to make multiple columns based on category column and use those multiple columns as Y input, but that it not what I would like to do.
Can anyone help me regarding this?
Let me know if you need more information.
vrajs5 you are not alone! We struggled with this issue. In our case we wanted to fill bar charts like in ggplot. This is the solution. You need to add specifically named columns, linked to your variables, to your data table for googleVis to pick up.
In my fill example, these are called roles, but once you see my syntax you can abstract it to annotations and other cool features. Google has them all documented here (check out superheroes example!) but it was not obvious how it applied to r.
#mages has this documented on this webpage, which shows features not in demo(googleVis):
http://cran.r-project.org/web/packages/googleVis/vignettes/Using_Roles_via_googleVis.html
EXAMPLE ADDING NEW DIMENSIONS TO GOOGLEVIS CHARTS
# in this case
# How do we fill a bar chart showing bars depend on another variable?
# We wanted to show C in a different fill to other assets
suppressPackageStartupMessages(library(googleVis))
library(data.table) # You can use data frames if you don't like DT
test.dt = data.table(px = c("A","B","C"), py = c(1,4,9),
"py.style" = c('silver', 'silver', 'gold'))
# Add your modifier to your chart as a new variable e.g. py1.style
test <-gvisBarChart(test.dt,
xvar = "px",
yvar = c("py", "py.style"),
options = list(legend = 'none'))
plot(test)
We have shown py.style deterministically here, but you could code it to be dependent on your categories.
The secret is myvar.googleVis_thing_youneed linking the variable myvar to the googleVis feature.
RESULT BEFORE FILL (yvar = "py")
RESULT AFTER FILL (yvar = c("py", "py.style"))
Take a look at mages examples (code also on Github) and you will have cracked the "categorization based on column values" issue.