I have tried using split trace with scatterpolar and it seems to partly work but can't get it to plot the values for all 10 variables. So I want each row (identified by "ean") be plotted as its own line using the values from X1 to X10.
library(tidyverse)
library(vroom)
library(plotly)
types <- rep(times = 10, list(
col_integer(f = stats::runif,
min = 1,
max = 5)))
products = bind_cols(
tibble(ean = sample.int(1e9, 25)),
tibble(kategori = sample(c("kat1", "kat2", "kat3"), 25, replace = TRUE)),
gen_tbl(25, 10, col_types = types)
)
plot_ly(
products,
type = 'scatterpolar',
mode = "lines+markers",
r = ~X1,
theta = ~"X1",
split = ~ean
)
How can I get plotly to plot all variables in the radarchart (X1-X10)? Usually I would select the columns with X1:X10 but I can't do that here (I think it has to do with that ~ is used to select variable here).
So I want the result to look something like this (but I only show lines and not filled polygons and I would have more products). So in the end 25 products is a lot but I am connecting it so that the user can select the diagrams it wants to show.
In plotly it's convenient to use data in long format - see ?gather.
Please check the following:
library(dplyr)
library(tidyr)
library(vroom)
library(plotly)
types <- rep(times = 10, list(
col_integer(f = stats::runif,
min = 1,
max = 5)))
products = bind_cols(
tibble(ean = sample.int(1e9, 25)),
tibble(kategori = sample(c("kat1", "kat2", "kat3"), 25, replace = TRUE)),
gen_tbl(25, 10, col_types = types)
)
products_long <- gather(products, "key", "value", -ean, -kategori)
plot_ly(
products_long,
type = 'scatterpolar',
mode = "lines+markers",
r = ~value,
theta = ~key,
split = ~ean
)
I'm attempting to get a r visualization running in PowerBI. It runs fine in R, but for some reason it never finishes loading in PowerBI (no error message, just the timeout screen after 5 minutes). After some experimenting, I've noticed that if I remove one plotly overlay from the create and save widget section, it will load fine. It doesn't matter which one.
I am new to R and powerBi, so any advice on a workaround would be really appreciated.
source('./r_files/flatten_HTML.r')
############### Library Declarations ###############
libraryRequireInstall("ggplot2");
libraryRequireInstall("plotly");
####################################################
################### Actual code ####################
# plot histogram of risk density using monte carlo output
x = Values[,1]; #grab first column of dataframe as dataframe
# create CDF function and overlay onto histogram
cdf = ecdf(x);
# calculate mean cordinates to draw a mean line for selected data
meancordinates = function(xdata) {
v = sum(xdata)
meanxcord = v/length(xdata)
meancord = list(meanxcord = meanxcord, meanycord = cdf(meanxcord))
return(meancord)
};
mean = meancordinates(x);
# calculate median cordinates to draw a median line for selected data
mediancordinates = function(xdata) {
medianxcord = median(xdata)
mediancord = list(medianxcord = medianxcord, medianycord = cdf(medianxcord))
return(mediancord)
};
median = mediancordinates(x)
# calculate the 80% cordinates to draw a 80% line for selected data
eightycordinates = function(xdata) {
eightyxcord = x[which(abs(cdf(xdata)-0.80) == min(abs(cdf(xdata)-0.80)))]
eightycord = list(eightyxcord = eightyxcord, eightyycord = cdf(eightyxcord))
return(eightycord);
}
eighty = eightycordinates(x);
####################################################
############# Create and save widget ###############
p = plot_ly(x = x, type = "histogram", histnorm = "probability density", name = "Histogram")
p = p %>% add_segments(
x = median$medianxcord, xend = median$medianxcord,
y = 0, yend = median$medianycord,
name = "Median")
p = p %>% add_segments(
x = eighty$eightyxcord, xend = eighty$eightyxcord,
y = 0, yend = eighty$eightyycord,
name = "80%")
p = p %>% add_segments(
x = mean$meanxcord, xend = mean$meanxcord,
y = 0, yend = mean$meanycord,
name = "Mean")
p = p %>% add_lines(x = x, y = cdf(x), name = "CDF");
internalSaveWidget(p, 'out.html');
####################################################
I have boxplots on highcharter and I would like to customize both the
Fill color
Border color
Here is my code
df = data.frame(cbind(categ = rep(c('a','b','c','d')),value = rnorm(1000)))
hcboxplot(var = df$categ, x = as.numeric(df$value)) %>%
hc_chart(type = "column") %>%
hc_colors(c("#203d7d","#a0a0ed","#203d7e","#a0a0ad"))
The hc_colors works only if I put var2 instead of var but then the box plot are shrunken...
API for styling fillColor: https://api.highcharts.com/highcharts/series.boxplot.fillColor
And for "Border color": https://api.highcharts.com/highcharts/series.boxplot.color
Pure JavaScript example of how to style and define points: https://jsfiddle.net/BlackLabel/6tud3fgx
And R code:
library(highcharter)
df = data.frame(cbind(categ = rep(c('a','b','c','d', 'e')),value = rnorm(1000)))
hcboxplot(var = df$categ, x = as.numeric(df$value)) %>%
hc_chart(type = "column", events = list(
load = JS("function() {
var chart = this;
chart.series[0].points[2].update({
color: 'red'
})
chart.series[0].points[4].update({
x: 4,
low: 600,
q1: 700,
median: 800,
q3: 900,
high: 1000,
color: 'orange'
})
}")
)) %>%
hc_plotOptions(boxplot = list(
fillColor = '#F0F0E0',
lineWidth = 2,
medianColor = '#0C5DA5',
medianWidth = 3,
stemColor = '#A63400',
stemDashStyle = 'dot',
stemWidth = 1,
whiskerColor = '#3D9200',
whiskerLength = '20%',
whiskerWidth = 3,
color = 'black'
)) %>%
hc_colors(c("#203d7d","#a0a0ed","#203d7e","#a0a0ad"))
I made a couple functions to do some stuff with highcharts and boxplots. It will let you color each boxplot and fill it accordingly, and then inject new graphical parameters according to the Highcharts API, should you desire.
Check it out:
## Boxplots Data and names, note the data index (0,1,2) is the first number in the datum
series<- list(
list(
name="a",
data=list(c(0,1,2,3,4,5))
),
list(
name="b",
data=list(c(1,2,3,4,5,6))
),
list(
name="c",
data=list(c(2,3,4,5,6,7))
)
)
# Graphical attribute to be set: fillColor.
# Make the colors for the box fill and then also the box lines (make them match so it looks pretty)
cols<- viridisLite::viridis(n= length(series2), alpha = 0.5) # Keeping alpha in here! (for box fill)
cols2<- substr(cols, 0,7) # no alpha, pure hex truth, for box lines
gen_key_vector<-function(variable, num_times){
return(rep(variable, num_times))
}
kv<- gen_key_vector(variable = "fillColor", length(series))
# Make a function to put stuff in the 'series' list, requires seq_along to be used since x is the list/vector index tracker
add_variable_to_series_list<- function(x, series_list, key_vector, value_vector){
base::stopifnot(length(key_vector) == length(value_vector))
base::stopifnot(length(series_list) == length(key_vector))
series_list[[x]][length(series_list[[x]])+1]<- value_vector[x]
names(series_list[[x]])[length(series_list[[x]])]<- key_vector[x]
return(series_list[[x]])
}
## Put the extra stuff in the 'series' list
series2<- lapply(seq_along(series), function(x){ add_variable_to_series_list(x = x, series_list = series, key_vector = kv, value_vector = cols) })
hc<- highcharter::highchart() %>%
highcharter::hc_chart(type="boxplot", inverted=FALSE) %>%
highcharter::hc_title(text="This is a title") %>%
highcharter::hc_legend(enabled=FALSE) %>%
highcharter::hc_xAxis(type="category", categories=c("a", "b", "c"), title=list(text="Some x-axis title")) %>%
highcharter::hc_add_series_list(series2) %>%
hc_plotOptions(series = list(
marker = list(
symbol = "circle"
),
grouping=FALSE
)) %>%
highcharter::hc_colors(cols2) %>%
highcharter::hc_exporting(enabled=TRUE)
hc
This probably could be adjusted to work with a simple dataframe, but I think it will get you what you want for right now without having to do too much extra work. Also, maybe look into list_parse or list_parse2' fromhighcharter...it could probably help with building out theseries` object..I still need to look into that.
Edit:
I have expanded the example to make it work with a regular DF. As per some follow up questions, the colors are set using the viridis palette inside the make_highchart_boxplot_with_colored_factors function. If you want to allow your own palette and colors, you could expose those arguments and just include them as parameters inside the function call. The expanded example borrows how to add outliers from the highcharter library (albeit in a hacky way) and then builds everything else up from scratch. Hopefully this helps clarify my previous answer. Please note, I could probably also clean up the if condition to make it a little more brief, but I kept it verbose for illustrative purposes.
Double Edit: You can now specify a vector of colors for each level of the factor variable
library(highcharter)
library(magrittr)
library(viridisLite)
df = data.frame(cbind(categ = rep(c('a','b','c','d')),value = rnorm(1000)))
df$value<- base::as.numeric(df$value)
add_variable_to_series_list<- function(x, series_list, key_vector, value_vector){
base::stopifnot(length(key_vector) == length(value_vector))
base::stopifnot(length(series_list) == length(key_vector))
series_list[[x]][length(series_list[[x]])+1]<- value_vector[x]
names(series_list[[x]])[length(series_list[[x]])]<- key_vector[x]
return(series_list[[x]])
}
# From highcharter github pages:
hc_add_series_bwpout = function(hc, value, by, ...) {
z = lapply(levels(by), function(x) {
bpstats = boxplot.stats(value[by == x])$stats
outliers = c()
for (y in na.exclude(value[by == x])) {
if ((y < bpstats[1]) | (y > bpstats[5]))
outliers = c(outliers, list(which(levels(by)==x)-1, y))
}
outliers
})
hc %>%
hc_add_series(data = z, type="scatter", ...)
}
gen_key_vector<-function(variable, num_times){
return(rep(variable, num_times))
}
gen_boxplot_series_from_df<- function(value, by,...){
value<- base::as.numeric(value)
by<- base::as.factor(by)
box_names<- levels(by)
z=lapply(box_names, function(x) {
boxplot.stats(value[by==x])$stats
})
tmp<- lapply(seq_along(z), function(x){
var_name_list<- list(box_names[x])
#tmp0<- list(names(df)[x])
names(var_name_list)<- "name"
index<- x-1
tmp<- list(c(index, z[[x]]))
tmp<- list(tmp)
names(tmp)<- "data"
tmp_out<- c(var_name_list, tmp)
#tmp<- list(tmp)
return(tmp_out)
})
return(tmp)
}
# Usage:
#series<- gen_boxplot_series_from_df(value = df$total_value, by=df$asset_class)
## Boxplot function:
make_highchart_boxplot_with_colored_factors<- function(value, by, chart_title="Boxplots",
chart_x_axis_label="Values", show_outliers=FALSE,
boxcolors=NULL, box_line_colors=NULL){
by<- as.factor(by)
box_names_to_use<- levels(by)
series<- gen_boxplot_series_from_df(value = value, by=by)
if(is.null(boxcolors)){
cols<- viridisLite::viridis(n= length(series), alpha = 0.5) # Keeping alpha in here! (COLORS FOR BOXES ARE SET HERE)
} else {
cols<- boxcolors
}
if(is.null(box_line_colors)){
if(base::nchar(cols[[1]])==9){
cols2<- substr(cols, 0,7) # no alpha, pure hex truth, for box lines
} else {
cols2<- cols
}
} else {
cols2<- box_line_colors
}
# Injecting value 'fillColor' into series list
kv<- gen_key_vector(variable = "fillColor", length(series))
series2<- lapply(seq_along(series), function(x){ add_variable_to_series_list(x = x, series_list = series, key_vector = kv, value_vector = cols) })
if(show_outliers == TRUE){
hc<- highcharter::highchart() %>%
highcharter::hc_chart(type="boxplot", inverted=FALSE) %>%
highcharter::hc_title(text=chart_title) %>%
highcharter::hc_legend(enabled=FALSE) %>%
highcharter::hc_xAxis(type="category", categories=box_names_to_use, title=list(text=chart_x_axis_label)) %>%
highcharter::hc_add_series_list(series2) %>%
hc_add_series_bwpout(value = value, by=by, name="Outliers") %>%
hc_plotOptions(series = list(
marker = list(
symbol = "circle"
),
grouping=FALSE
)) %>%
highcharter::hc_colors(cols2) %>%
highcharter::hc_exporting(enabled=TRUE)
} else{
hc<- highcharter::highchart() %>%
highcharter::hc_chart(type="boxplot", inverted=FALSE) %>%
highcharter::hc_title(text=chart_title) %>%
highcharter::hc_legend(enabled=FALSE) %>%
highcharter::hc_xAxis(type="category", categories=box_names_to_use, title=list(text=chart_x_axis_label)) %>%
highcharter::hc_add_series_list(series2) %>%
hc_plotOptions(series = list(
marker = list(
symbol = "circle"
),
grouping=FALSE
)) %>%
highcharter::hc_colors(cols2) %>%
highcharter::hc_exporting(enabled=TRUE)
}
hc
}
# Usage:
tst_box<- make_highchart_boxplot_with_colored_factors(value = df$value, by=df$categ, chart_title = "Some Title", chart_x_axis_label = "Some X Axis", show_outliers = TRUE)
tst_box
# Custom Colors:
custom_colors_with_alpha_in_hex<- paste0(gplots::col2hex(sample(x=colors(), size = length(unique(df$categ)), replace = FALSE)), "80")
tst_box2<- make_highchart_boxplot_with_colored_factors(value = df$value, by=df$categ, chart_title = "Some Title",
chart_x_axis_label = "Some X Axis",
show_outliers = TRUE, boxcolors = custom_colors_with_alpha_in_hex)
tst_box2
tst_box3<- make_highchart_boxplot_with_colored_factors(value = df$value, by=df$categ, chart_title = "Some Title",
chart_x_axis_label = "Some X Axis",
show_outliers = TRUE, boxcolors = custom_colors_with_alpha_in_hex, box_line_colors = "black")
tst_box3
I hope this helps, please let me know if you have any more questions. I'm happy to try to help as best I can.
-nate
Since there's no highcharter answer yet, I give you at least a base solution.
First, your definition of the data frame is somewhat flawed, rather do:
dat <- data.frame(categ=c('a','b','c','d'), value=rnorm(1000))
Now, using boxplot is quite straightforward. border option colors your borders. With option col you also could color the fills.
boxplot(value ~ categ, dat, border=c("#203d7d","#a0a0ed","#203d7e","#a0a0ad"), pars=list(outpch=16))
Gives
Note: See this nice solution for further customizations.
Using a variation of the below example dimple chart, how can I get this to auto scale with Shiny bootstrap without having to hard code height and width of the chart?
#get data used by dimple for all of its examples as a first test
data <- read.delim(
"http://pmsi-alignalytics.github.io/dimple/data/example_data.tsv"
)
#eliminate . to avoid confusion in javascript
colnames(data) <- gsub("[.]","",colnames(data))
#example 27 Bubble Matrix
d1 <- dPlot(
x = c( "Channel", "PriceTier"),
y = "Owner",
z = "Distribution",
groups = "PriceTier",
data = data,
type = "bubble",
aggregate = "dimple.aggregateMethod.max"
)
d1$xAxis( type = "addCategoryAxis" )
d1$yAxis( type = "addCategoryAxis" )
d1$zAxis( type = "addMeasureAxis", overrideMax = 200 )
d1$legend(
x = 200,
y = 10,
width = 500,
height = 20,
horizontalAlign = "right"
)
d1
Hi you have to put width="100%" in dplot(), like this :
d1 <- dPlot(
x = c( "Channel", "PriceTier"),
y = "Owner",
z = "Distribution",
groups = "PriceTier",
data = data,
type = "bubble",
aggregate = "dimple.aggregateMethod.max",
width="100%"
)