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I have created a lollipop chart that I love. However, when the code runs to create the plot, the colors of the lines, segments, and points all change from what they were set to. Everything else runs great, so this isn't the end of the world, but I am trying to stick with a color palette throughout a report.
The colors should be this ("#9a0138", and "#000775" specifically):
But come out like this:
Any ideas?
Here is the data:
TabPercentCompliant <- structure(list(Provider_ShortName = c("ProviderA", "ProviderA", "ProviderA", "ProviderB",
"ProviderB", "ProviderB", "ProviderC", "ProviderC", "ProviderC", "ProviderD"), SubMeasureID = c("AMM2", "FUH7", "HDO", "AMM2", "FUH7", "HDO", "AMM2", "FUH7", "HDO", "AMM2"), AdaptedCompliant = c(139, 2, 117, 85, 1, 33, 36, 2, 22, 43), TotalEligible = c(238, 27, 155, 148, 10, 34, 61, 3, 24, 76), PercentCompliant = c(0.584033613445378, 0.0740740740740741, 0.754838709677419, 0.574324324324324, 0.1, 0.970588235294118, 0.590163934426229, 0.666666666666667, 0.916666666666667, 0.565789473684211 ), PercentTotalEligible = c(0.00516358587173479, 0.00058578495183546, 0.00336283953831467, 0.00321096936561659, 0.000216957389568689, 0.000737655124533542, 0.001323440076369, 6.50872168706066e-05, 0.000520697734964853, 0.00164887616072203), ClaimsAdjudicatedThrough = structure(c(19024, 19024, 19024, 19024, 19024, 19024, 19024, 19024, 19024, 19024 ), class = "Date"), AdaptedNCQAMean = c(0.57, 0.39, 0.93, 0.57, 0.39, 0.93, 0.57, 0.39, 0.93, 0.57), PerformanceLevel = c(0.0140336134453782, -0.315925925925926, -0.175161290322581, 0.00432432432432439, -0.29, 0.0405882352941176, 0.0201639344262295, 0.276666666666667, -0.0133333333333334, -0.00421052631578944)), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"))
VBP_Report_Date = "2022-09-01"
And the code for the plot:
Tab_PercentCompliant %>%
filter(ClaimsAdjudicatedThrough == VBP_Report_Date) %>%
ggplot(aes(x = Provider_ShortName,
y = PercentCompliant)
) +
geom_line(aes(x = Provider_ShortName,
y = AdaptedNCQAMean,
group = SubMeasureID,
color = "#9a0138",
size = .001)
) +
geom_point(aes(color = "#000775",
size = (PercentTotalEligible)
)
) +
geom_segment(aes(x = Provider_ShortName,
xend = Provider_ShortName,
y = 0,
yend = PercentCompliant,
color = "#000775")
)+
facet_grid(cols = vars(SubMeasureID),
scales = "fixed",
space = "fixed")+
theme_classic()+
theme(legend.position = "none") +
theme(panel.spacing = unit(.5, "lines"),
panel.border = element_rect(
color = "black",
fill = NA,
linewidth = .5),
panel.grid.major.y = element_line(
color = "gray",
linewidth = .5),
axis.text.x = element_text(
angle = 65,
hjust=1),
axis.title.x = element_blank(),
axis.line = element_blank(),
strip.background = element_rect(
color = NULL,
fill = "#e1e7fa"))+
scale_y_continuous(labels = scales::percent)+
labs(title = "Test",
subtitle = "Test",
caption = "Test")
If you have an aesthetic constant, it is often easier / better to have it "outside" your aes call. If you want to have a legend for your color, then you need to keep it "inside", but you will need to manually set the colors with + scale_color/fill_manual.
I've had to cut down quite a lot in your code to make it work. I've also removed bits that are extraneous to the problem. I've removed line size = 0.001 or the line wasn't visible. I've removed the weird filter step or the plot wasn't possible.
Tips: when defining a global aesthetic with ggplot(aes(x = ... etc), you don't need to specify this aesthetic in each geom layer (those aesthetics will be inherited)- makes a more concise / readable code.
library(ggplot2)
ggplot(TabPercentCompliant, aes(x = Provider_ShortName, y = PercentCompliant)) +
geom_line(aes(y = AdaptedNCQAMean, group = SubMeasureID),
color = "#9a0138") +
geom_point(aes(size = PercentTotalEligible), color = "#000775") +
geom_segment(aes(xend = Provider_ShortName, y = 0, yend = PercentCompliant),
color = "#000775") +
facet_grid(~SubMeasureID) +
theme(strip.background = element_rect(color = NULL, fill = "#e1e7fa"))
Here is the final code. Thanks again tjebo!
# Lollipop Chart ----------------------------------------------------------
Tab_PercentCompliant %>%
filter(ClaimsAdjudicatedThrough == VBP_Report_Date) %>%
ggplot(aes(x = Provider_ShortName,
y = PercentCompliant)
) +
geom_line(aes(y = AdaptedNCQAMean,
group = SubMeasureID),
color = "#9a0138"
) +
geom_point(aes(size = PercentTotalEligible),
color = "#000775",
) +
geom_segment(aes(xend = Provider_ShortName,
y = 0,
yend = PercentCompliant),
color = "#000775"
)+
facet_grid(cols = vars(SubMeasureID)
)+
theme_bw()+
theme(legend.position = "none",
axis.text.x = element_text(
angle = 65,
hjust=1),
axis.title.x = element_blank(),
axis.line = element_blank(),
strip.background = element_rect(
fill = "#e1e7fa"))+
scale_y_continuous(labels = scales::percent)+
labs(title = "Test",
subtitle = "Test",
caption = "Test")
I am new to R and trying to add count labels to my boxplots, so the sample size per boxplot shows in the graph.
This is my code:
bp_east_EC <-total %>% filter(year %in% c(1977, 2020, 2021, 1992),
sampletype == "groundwater",
East == 1,
#EB == 1,
#N59 == 1,
variable %in% c("EC_uS")) %>%
ggplot(.,aes(x = as.character(year), y = value, colour = as.factor(year))) +
theme_ipsum() +
ggtitle("Groundwater EC, eastern Curacao") +
theme(plot.title = element_text(hjust = 0.5, size=14)) +
theme(legend.position = "none") +
labs(x="", y="uS/cm") +
geom_jitter(color="grey", size=0.4, alpha=0.9) +
geom_boxplot() +
stat_summary(fun.y=mean, geom="point", shape=23, size=2) #shows mean
I have googled a lot and tried different things (with annotate, with return functions, mtext, etc), but it keeps giving different errors. I think I am such a beginner I cannot figure out how to integrate such suggestions into my own code.
Does anybody have an idea what the best way would be for me to approach this?
I would create a new variable that contained your sample sizes per group and plot that number with geom_label. I've generated an example of how to add count/sample sizes to a boxplot using the iris dataset since your example isn't fully reproducible.
library(tidyverse)
data(iris)
# boxplot with no label
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot()
# boxplot with label
iris %>%
group_by(Species) %>%
mutate(count = n()) %>%
mutate(mean = mean(Sepal.Length)) %>%
ggplot(aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
geom_label(aes(label= count , y = mean + 0.75), # <- change this to move label up and down
size = 4, position = position_dodge(width = 0.75)) +
geom_jitter(alpha = 0.35, aes(color = Species)) +
stat_summary(fun = mean, geom = "point", shape = 23, size = 6)
I have been trying to fit a non-linear regression line into my standard curve. However, I am getting the following error:
The main problem is that with the linear regression line I could use a simple command like:
stat_cor(label.y = c(825),
label.x = c(0.88),
aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")))+
stat_regline_equation(label.x=0.88, label.y=750)+
And the equation for the linear regression line with an a, and b values appear. In this case after using the following:
stat_smooth(method= "nlm",
formula = y~a*x/(b+x),
method.args = list( start = c(a = 3.8, b = 1457.2)),
se=FALSE)+
I am getting the above error.
You may ask where I got the a, and b values? I got them from:
nls(y~a*x/(b+x))
That has shown:
I do not know where I am making mistakes.
This is the entire code for my graph
library(tidyverse)
library(tidyr)
library(dplyr)
library(readr)
library(ggplot2)
library(ggpubr)
ggplot(data = STD, aes(x = Absorbance, y = STD)) +
labs(title = "Quantifying PGD2 in cell culture lysates and its enzymatic reactions ",
caption = "PGD2 ELISA")+
geom_point(colour = "#69b3a2")+
stat_smooth(method= "nlm",
formula = y~a*x/(b+x),
method.args = list( start = c(a = 3.8, b = 1457.2)),
se=FALSE)+
xlab(expression(paste("%B/"~B[0])))+
ylab(expression(paste("Prostaglandin"~ D[2], ~~ " MOX Concentration (pg/ml) ")))+
theme(plot.background = element_rect(fill = "transparent"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))+
theme(legend.spacing.y = unit(0.01, "cm"))+
theme(legend.position = c(0.77, .91),
legend.background = element_rect(colour = NA, fill = NA))+
theme(plot.title = element_text(size = 12, face = "bold.italic"),
plot.caption = element_text(hjust = 0))
That gives the following outcome
And this is DataUsed
So, I think I have found a solution to my problem. I installed the install.packages(drc) in which the four parametric function is included. I set up my data model <- drm(STD ~ Absorbance, fct = LL.4(), data = STD), then plot(model) , and I got
I know it requires some alternations to make it look more professional, but it is just a cosmetic thing that I should be fine to do. Thank you #stefan for your time.
I would like to plot a sophisticated graph in Julia. The code below is in Julia's version using ggplot.
using CairoMakie, DataFrames, Effects, GLM, StatsModels, StableRNGs, RCall
#rlibrary ggplot2
rng = StableRNG(42)
growthdata = DataFrame(; age=[13:20; 13:20],
sex=repeat(["male", "female"], inner=8),
weight=[range(100, 155; length=8); range(100, 125; length=8)] .+ randn(rng, 16))
mod_uncentered = lm(#formula(weight ~ 1 + sex * age), growthdata)
refgrid = copy(growthdata)
filter!(refgrid) do row
return mod(row.age, 2) == (row.sex == "male")
end
effects!(refgrid, mod_uncentered)
refgrid[!, :lower] = #. refgrid.weight - 1.96 * refgrid.err
refgrid[!, :upper] = #. refgrid.weight + 1.96 * refgrid.err
df= refgrid
ggplot(df, aes(x=:age, y=:weight, group = :sex, shape= :sex, linetype=:sex)) +
geom_point(position=position_dodge(width=0.15)) +
geom_ribbon(aes(ymin=:lower, ymax=:upper), fill="gray", alpha=0.5)+
geom_line(position=position_dodge(width=0.15)) +
ylab("Weight")+ xlab("Age")+
theme_classic()
However, I would like to modify this graph a bit more. For example, I would like to change the scale of the y axis, the colors of the ribbon, add some error bars, and also change the text size of the legend and so on. Since I am new to Julia, I am not succeding in finding the equivalent language code for these modifications. Could someone help me translate this R code below of ggplot into Julia's language?
t1= filter(df, sex=="male") %>% slice_max(df$weight)
ggplot(df, aes(age, weight, group = sex, shape= sex, linetype=sex,fill=sex, colour=sex)) +
geom_line(position=position_dodge(width=0.15)) +
geom_point(position=position_dodge(width=0.15)) +
geom_errorbar(aes(ymin = lower, ymax = upper),width = 0.1,
linetype = "solid",position=position_dodge(width=0.15))+
geom_ribbon(aes(ymin = lower, ymax = upper, fill = sex, colour = sex), alpha = 0.2) +
geom_text(data = t1, aes(age, weight, label = round(weight, 1)), hjust = -0.25, size=7,show_guide = FALSE) +
scale_y_continuous(limits = c(70, 150), breaks = seq(80, 140, by = 20))+
theme_classic()+
scale_colour_manual(values = c("orange", "blue")) +
guides(color = guide_legend(override.aes = list(linetype = c('dotted', 'dashed'))),
linetype = "none")+
xlab("Age")+ ylab("Average marginal effects") + ggtitle("Title") +
theme(
axis.title.y = element_text(color="Black", size=28, face="bold", hjust = 0.9),
axis.text.y = element_text(face="bold", color="black", size=16),
plot.title = element_text(hjust = 0.5, color="Black", size=28, face="bold"),
legend.title = element_text(color = "Black", size = 13),
legend.text = element_text(color = "Black", size = 16),
legend.position="bottom",
axis.text.x = element_text(face="bold", color="black", size=11),
strip.text = element_text(face= "bold", size=15)
)
As I commented before, you can use R-strings to run R code. To be clear, this isn't like your post's approach where you piece together many Julia objects that wrap many R objects, this is RCall converting a Julia Dataframe to an R dataframe then running your R code.
Running an R script may not seem very Julian, but code reuse is very Julian. Besides, you're still using an R library and active R session either way, and there might even be a slight performance benefit from reducing how often you make wrapper objects and switch between Julia and R.
## import libraries for Julia and R; still good to do at top
using CairoMakie, DataFrames, Effects, GLM, StatsModels, StableRNGs, RCall
R"""
library(ggplot2)
library(dplyr)
"""
## your Julia code without the #rlibrary or ggplot lines
rng = StableRNG(42)
growthdata = DataFrame(; age=[13:20; 13:20],
sex=repeat(["male", "female"], inner=8),
weight=[range(100, 155; length=8); range(100, 125; length=8)] .+ randn(rng, 16))
mod_uncentered = lm(#formula(weight ~ 1 + sex * age), growthdata)
refgrid = copy(growthdata)
filter!(refgrid) do row
return mod(row.age, 2) == (row.sex == "male")
end
effects!(refgrid, mod_uncentered)
refgrid[!, :lower] = #. refgrid.weight - 1.96 * refgrid.err
refgrid[!, :upper] = #. refgrid.weight + 1.96 * refgrid.err
df= refgrid
## convert Julia's df and run your R code in R-string
## - note that $df is interpolation of Julia's df into R-string,
## not R's $ operator like in rdf$weight
## - call the R dataframe rdf because df is already an R function
R"""
rdf <- $df
t1= filter(rdf, sex=="male") %>% slice_max(rdf$weight)
ggplot(rdf, aes(age, weight, group = sex, shape= sex, linetype=sex,fill=sex, colour=sex)) +
geom_line(position=position_dodge(width=0.15)) +
geom_point(position=position_dodge(width=0.15)) +
geom_errorbar(aes(ymin = lower, ymax = upper),width = 0.1,
linetype = "solid",position=position_dodge(width=0.15))+
geom_ribbon(aes(ymin = lower, ymax = upper, fill = sex, colour = sex), alpha = 0.2) +
geom_text(data = t1, aes(age, weight, label = round(weight, 1)), hjust = -0.25, size=7,show_guide = FALSE) +
scale_y_continuous(limits = c(70, 150), breaks = seq(80, 140, by = 20))+
theme_classic()+
scale_colour_manual(values = c("orange", "blue")) +
guides(color = guide_legend(override.aes = list(linetype = c('dotted', 'dashed'))),
linetype = "none")+
xlab("Age")+ ylab("Average marginal effects") + ggtitle("Title") +
theme(
axis.title.y = element_text(color="Black", size=28, face="bold", hjust = 0.9),
axis.text.y = element_text(face="bold", color="black", size=16),
plot.title = element_text(hjust = 0.5, color="Black", size=28, face="bold"),
legend.title = element_text(color = "Black", size = 13),
legend.text = element_text(color = "Black", size = 16),
legend.position="bottom",
axis.text.x = element_text(face="bold", color="black", size=11),
strip.text = element_text(face= "bold", size=15)
)
"""
The result is the same as your post's R code:
I used Vega-Lite (https://github.com/queryverse/VegaLite.jl) which is also grounded in the "Grammar of Graphics", and LinearRegression (https://github.com/ericqu/LinearRegression.jl) which provides similar features as GLM, although I think it is possible to get comparable results with the other plotting and linear regression packages. Nevertheless, I hope that this gives you a starting point.
using LinearRegression: Distributions, DataFrames, CategoricalArrays
using DataFrames, StatsModels, LinearRegression
using VegaLite
growthdata = DataFrame(; age=[13:20; 13:20],
sex=categorical(repeat(["male", "female"], inner=8), compress=true),
weight=[range(100, 155; length=8); range(100, 125; length=8)] .+ randn(16))
lm = regress(#formula(weight ~ 1 + sex * age), growthdata)
results = predict_in_sample(lm, growthdata, req_stats="all")
fp = select(results, [:age, :weight, :sex, :uclp, :lclp, :predicted]) |> #vlplot() +
#vlplot(
mark = :errorband, color = :sex,
y = { field = :uclp, type = :quantitative, title="Average marginal effects"},
y2 = { field = :lclp, type = :quantitative },
x = {:age, type = :quantitative} ) +
#vlplot(
mark = :line, color = :sex,
x = {:age, type = :quantitative},
y = {:predicted, type = :quantitative}) +
#vlplot(
:point, color=:sex ,
x = {:age, type = :quantitative, axis = {grid = false}, scale = {zero = false}},
y = {:weight, type = :quantitative, axis = {grid = false}, scale = {zero = false}},
title = "Title", width = 400 , height = 400
)
which gives:
You can change the style of the elements by changing the "config" as indicated here (https://www.queryverse.org/VegaLite.jl/stable/gettingstarted/tutorial/#Config-1).
As the Julia Vega-Lite is a wrapper to Vega-Lite additional documentation can be found on the Vega-lite website (https://vega.github.io/vega-lite/)
I checked a few examples online and I am not sure that it can be done because every plot with 2 different variables (continuous and discrete) has one of 2 options:
legend regarding the continuous variable
legend regarding the discrete variable
Just for visualization, I put here an example. Imagine that I want to have a legend for the blue line. Is it possible to do that??
The easiest approach would be to map it to a different aesthetic than you already use:
library(ggplot2)
ggplot(mtcars, aes(x = mpg, y = hp)) +
geom_point(aes(colour = as.factor(gear), size = cyl)) +
geom_smooth(method = "loess", aes(linetype = "fit"))
There area also specialised packages for adding additional colour legends:
library(ggplot2)
library(ggnewscale)
ggplot(mtcars, aes(x = mpg, y = hp)) +
geom_point(aes(colour = as.factor(gear), size = cyl)) +
new_scale_colour() +
geom_smooth(method = "loess", aes(colour = "fit"))
Beware that if you want to tweak colours via a colourscale, you must first add these before calling the new_scale_colour(), i.e.:
ggplot(mtcars, aes(x = mpg, y = hp)) +
geom_point(aes(colour = as.factor(gear), size = cyl)) +
scale_colour_manual(values = c("red", "green", "blue")) +
new_scale_colour() +
geom_smooth(method = "loess", aes(colour = "fit")) +
scale_colour_manual(values = "purple")
EDIT: To adress comment: yes it is possible with a line that is data independent, I was just re-using the data for brevity of example. See below for arbitrary line (also should work with the ggnewscale approach):
ggplot(mtcars, aes(x = mpg, y = hp)) +
geom_point(aes(colour = as.factor(gear), size = cyl)) +
geom_line(data = data.frame(x = 1:30, y = rnorm(10, 200, 10)),
aes(x, y, linetype = "arbitrary line"))