I tried to overwrite the colorbar labels, though I can not get it done, if someone could find out what is wrong in the code and let me know, it would be very appreciate. I can share the data if necessary. I also would like to know if it is possible to use widgets SELECT to select which county and hide the others, as callback.
Regards
palleteG = ['#39FF14', '#4CBB17', '#50C878', '#00A572','#2E8B57', '#0b6623'] #'#98FB98','#ffffff','#D0F0C0'
#color_mapper = LinearColorMapper(palette = palleteG, low = 25000, high = 450000)
color_mapper = LinearColorMapper(palette = palleteG, low = irl['2016'].min()*1.01, high = irl['2016'].max()*1.01)
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=6,
width=500, height=20, border_line_color=None,
location='center', orientation='horizontal',
major_label_overrides=tick_labels,
bar_line_color='#50C878',
bar_line_alpha=0.7)
ps = figure(title = 'Irish Housing Stock 2016', tools = 'pan, wheel_zoom, box_zoom, reset, hover, save',
tooltips = [('County', '#COUNTY'),('Housing Stock','#2016'), ('Population','#Population'),
('Number of Social Housing necessary','#Solution')], #,],
x_axis_location = None, y_axis_location = None, plot_width=600, plot_height=800)
ps.patches('xs', 'ys', fill_alpha = 0.7, fill_color = {'field':'2016', 'transform':color_mapper}, line_color = 'black', line_width = 0.5,
source = geo_source) # fill_color = 'green'
ps.grid.grid_line_color=None
ps.add_layout(color_bar, 'below')
show(ps)
output_file('IHS.html', mode='inline')
The challenge now it's find the right tune for the bar.
Related
I have created a small dashboard using bscols( from the crosstalkpackage. It consists of plotly graphs and their respective filter_checkboxes.
It looks pretty messy now, as the filters are not vertically aligned with their corresponding plots.
HTML_graphic
As indicated, I would like the first two checkbox sets to appear next to the second line graph (nothing to appear next to the first line graph); and the second two checkbox sets to appear next to the third line graph.
Also, I would like to create some vertical space between the three elements, as indicated by the brown and black horizontal lines.
The best solution would be to set the height of the html elements inside the bscols() command. Because in the future, I would like to programmatically save multiple of these outputs using htmltools::save_html.
The next best would be to have the output of that command somehow converted to html and add html code like line breaks or heights.
Neither I know how to do.
I came across this related question but it is unanswered: Arrange crosstalk graphs via bscols
Any suggestions on how to solve my problem?
My code
{r 002_Auto App Doc Vol_Invoice group delta plot - plot code, echo = FALSE}
# Setup of the legend for invoice plot
invoice_plot_legend <- list(
font = list(
family = "sans-serif",
size = 12,
color = "#000"),
title = list(text="<b> Delta previous month by division </b>"),
bgcolor = "#E2E2E2",
bordercolor = "#FFFFFF",
borderwidth = 2,
layout.legend = "constant",
traceorder = "grouped")
# The Shared Data format is needed for crosstalk to be able to filter the dataset upon clicking the checkboxes (division filters):
shared_invoice <- SharedData$new(Auto_App_Doc_Vol_invoiceg_plotting_tibble)
shared_invoice_KPI <- SharedData$new(Auto_App_Doc_Vol_KPI)
shared_abs <- SharedData$new(Auto_App_Doc_Vol_plotting_tibble_diff_abs)
# Setup of a bscols html widget; widths determines the widths of the input lists (here, 2: the filters, 10: the plot and legend)
# Overall KPI and invoice group plot
library(htmlwidgets)
crosstalk::bscols(
widths = c(2, 10),
list(
crosstalk::filter_checkbox("Division",
label = "Division",
sharedData = shared_invoice,
group = ~Division),
crosstalk::filter_checkbox("Rechnungsgruppe",
label = "Invoice group",
sharedData = shared_invoice,
group = ~Rechnungsgruppe),
crosstalk::filter_checkbox("Rechnungsgruppe",
label = "Invoice group",
sharedData = shared_abs,
group = ~Rechnungsgruppe),
crosstalk::filter_checkbox("Division",
label = "Division",
sharedData = shared_abs,
group = ~Division)
)
,
list(
plot_ly(data = shared_invoice_KPI, x = ~Freigabedatum_REAL_YM, y = ~KPI_current_month, meta = ~Division,
type = "scatter",
mode = "lines+text",
text = ~KPI_current_month,
textposition='top center',
hovertemplate = "%{meta}",
color = ~Diff_KPI_pp)
%>%
layout(legend = invoice_plot_legend,
title = "Automatically Approved Document Volume",
xaxis = list(title = 'Release date'),
yaxis = list(title = '%'))
,
plot_ly(data = shared_invoice, x = ~Freigabedatum_REAL_YM, y = ~n,
type = "scatter",
mode = "lines",
text = ~Rechnungsgruppe_effort,
hoverinfo = "y+text",
color = ~Difference_inline
)
%>%
layout(legend = invoice_plot_legend,
title = " ",
xaxis = list(title = 'Release date'),
yaxis = list(title = '# of Approved Documents'))
,
plot_ly(data = shared_abs, x = ~Freigabedatum_REAL_YM, y = ~n,
type = "scatter",
mode = "lines",
text = ~Lieferantenname,
hoverinfo = "y+text",
color = ~Lieferantenname_text
)
%>%
layout(legend = vendor_plot_legend,
title = "by vendor absolute delta previous month all documents",
xaxis = list(title = 'Release date'),
yaxis = list(title = '# of Approved Documents w/ & w/o effort')
)
)
)
Thank you so much!
I would like to combine text and a figure in a Leaflet Popup. I saw this on a website of Deutsche Bahn: Multi-Object-Popup
Website:
strecken.info
For me it would be sufficient to combine two of these 4 shown "windows" -> One text (paste0()) and one ggplot-figure). Is this possible in R?
Best regards and thank you very much :)
My Code so far:
ll_maps %>%
addCircles(
data = df_temp,
lng = ~x_coord,
lat = ~y_coord,
weight = 1,
radius = 1000,
popup = ~lapply(leafpop::popupGraph(pic_list_temp, width = 500, height = 500), HTML),
label = ~lapply(paste0("<br><b>Textline1</b> = ", tl1_object,
"<br><b>Textline2</b> = ", tl2_object), HTML),
popupOptions = popupOptions(maxWidth = 500),
labelOptions = labelOptions(textsize = "12px"),
opacity = 1,
fillOpacity = 0.5,
color = "red")
Now I would like to combine the label and the popup into one popup so to speak :)
I have plotted two graphs using plotly dash. But when the y-axis / x-axis tick size is more it gets cut off.
Y-axis :
Code :
data = [go.Scatter(x = df[df['S2PName-Category']==category]['S2BillDate'],
y = df[df['S2PName-Category']==category]['totSale'],
mode = 'markers+lines',
name = category) for category in df['S2PName-Category'].unique()]
layout = go.Layout(title='Category Trend',
xaxis = dict(title = 'Time Frame', tickformat = '%d-%b-%y'),
yaxis = dict(tickprefix= '₹', tickformat=',.2f',type='log'),
hovermode = 'closest',
plot_bgcolor = colors['background'],
paper_bgcolor = colors['background'],
font = dict(color = colors['text'])
)
X-Axis :
Code :
data = [go.Scatter(x = df[df['S2PName']==item]['S2BillDate'],
y = df[df['S2PName']==item]['totSale'],
mode = 'markers+lines',
name = item) for item in items]
layout = go.Layout(title='Category Trend',
xaxis = dict(title = 'Time Frame' , tickformat = '%d-%b'),
yaxis = dict(tickprefix= '₹', tickformat=',.2f',type='log',autorange = True),
hovermode = 'closest',
plot_bgcolor = colors['background'],
paper_bgcolor = colors['background'],
font = dict(color = colors['text'])
)
In the above 2 graphs , as the length of the tick value increases, it gets cut off . Is there a better way to handle this ?
Credit for #Flavia Giammarino in comments for the reference to the docs. I'm posting the answer for completeness.
https://plotly.com/python/setting-graph-size/
From that link the example below shows how to set margin:
fig.update_layout(
margin=dict(l=20, r=20, t=20, b=20),
)
Where l r t b correspond to left, right, top, bottom.
I had a similar problem with some Dash/Plotly charts and long y axis labels being truncated or hidden. There didn't seem to be much information or documentation on this issue, so it took a while to solve.
Solution: add this code to the layout settings to prevent truncation of the y axes labels:
fig.update_layout(
yaxis=dict(
automargin=True
)
)
or you can update the yaxes setting specifically:
fig.update_yaxes(automargin=True)
Update: I tried another version of Plotly (5.10 or above) which mentions setting the automargin setting to any combination of automargin=['left+top+right+bottom'] with similar results. This still seems a bit unstable and doesn't solve all possible scenarios or corner cases, but works fine in most cases, especially when the browser window is maximized.
I am trying to create a HTML report from the DataExplorer::create_report(). The code is as follows
DataExplorer::create_report(iris, config = list(add_plot_qq = FALSE, global_ggtheme = quote(theme_minimal(base_size = 14))))
The code creates "report.html", which is blank when I open it in any browser. I am using the DataExplorer version 0.8.0
Nick's answer is correct. I made some updates in v0.8 to simplify report customization, i.e., #87. However, I would like to use this section to provide a little more information on that. Please do not accept this as an answer.
configure_report helps you write less code in terms adding/removing sections, as well as editing themes. However, the output is no different from the list output from previous versions. If you want, you can still make your own list files and pass it to create_report. The template is here:
config <- list(
"introduce" = list(),
"plot_intro" = list(),
"plot_str" = list(
"type" = "diagonal",
"fontSize" = 35,
"width" = 1000,
"margin" = list("left" = 350, "right" = 250)
),
"plot_missing" = list(),
"plot_histogram" = list(),
"plot_qq" = list(sampled_rows = 1000L),
"plot_bar" = list(),
"plot_correlation" = list("cor_args" = list("use" = "pairwise.complete.obs")),
"plot_prcomp" = list(),
"plot_boxplot" = list(),
"plot_scatterplot" = list(sampled_rows = 1000L)
)
After that, you can just call create_report as usual:
create_report(iris, config = config)
Hope this helps!
For those searching for the answer use: config = configure_report() instead of config = list()
DataExplorer::create_report(iris,
config = configure_report(add_plot_qq = FALSE,
global_ggtheme = quote(theme_minimal(base_size = 14))))
I have the following mixed effects model:
p1 <- lmer(log(price) ~ year*loca + (1|author), data = df)
'year' is continuous
'loca' is categorical variable with 2 levels
I am trying to plot the significant interaction from this model.
The following code (using the visreg package) plots the lines from each of the two 'loca' but it does not produce a 95% confidence band:
visreg(p1, "year", by = "loca", overlay = T,
line=list(lty = 1, col = c("grey", "black")), points=list(cex=1, pch=19,
col = c("grey", "black")), type="conditional", axes = T)
Then, I tried the following code which allows me to plot the lines, but with no data points on top and no CIs:
visreg(p1, "year", by = "loca", overlay = T,
line=list(lty = 1, col = c("grey60", "black")), points=list(cex=1,
pch=19, col = c("grey", "black")),
type="conditional", trans = exp, fill.par = list(col = c("grey80",
"grey70")))
I get CI bands when I use type = 'contrast' rather than 'conditional'. However, this doesn't work when I try to backtransform the price as above using trans = exp.
Overall I need to be able to plot the interaction with the following attributes:
Confidence bands
backtransformed points
predicted line (one for each level of 'loca')
More than happy to try other methods....but I can't seem to find any that work so far.
Help much appreciated!
one possibility is with the use of the effects package:
library(effects)
eff.p1 <- effect("year*loca", p1, KR=T)
then you could either directly plot it with what the package provides and customize it from there:
plot(eff.p1)
or take what effect produces and plot it with ggplot in a nicer plot:
eff.p1 <- as.data.frame(eff.p1)
ggplot(eff.p1, aes(year, linetype=factor(loca),
color = factor(loca))) +
geom_line(aes(y = fit, group=factor(loca)), size=1.2) +
geom_line(aes(y = lower,
group=factor(loca)), linetype =3) +
geom_line(aes(y = upper,
group=factor(loca)), linetype =3) +
xlab("year") +
ylab("Marginal Effects on Log Price") +
scale_colour_discrete("") +
scale_linetype_discrete("") +
labs(color='loca') + theme_minimal()
I can't really try the code without the data, but I think it should work.
This should do the trick:
install.packages(sjPlot)
library(sjPlot)
plot_model(p1, type = "int", terms = c(year,loca), ci.lvl = 0.95)
Although it comes out with some warnings about labels, testing on my data, it does the back transformation automatically and seems to work fine. Customising should be easy, because I believe sjPlot uses ggplot.
EDIT: #Daniel points out that alternative options which allow more customization would be plot_model(type = "pred", ...) or plot_model(type = "eff", ...)