I'm struggling with getting my plot title to the center using ggsurvplot...
I've seen some posts mentioning something like xxxx$plot + theme(....)
but this solution does not seem to work for me.
Here's my code, maybe you can see what I'm missing:
surv_object_CA19.9 <- Surv(time = data_OS$OS_Days / 30, event = data_OS$Status.Death)
CA19.9_surv_fit <- survfit(surv_object_CA19.9 ~ CA19.9.initial_status, data = data_OS)
CA19.9_OS <- ggsurvplot(CA19.9_surv_fit, data = data_OS, pval = TRUE, xlab = "Time [Months]",
ylab = "Overall survival", risk.table = TRUE, legend.title = "",
risk.table.col. = "strata", risk.table.y.text = FALSE, surv.scale = "percent",
break.x.by = 6, xlim = c(0, 60), legend.labs = c("Pathological", "Normal"),
title = "Overall survival for patients with initially pathological or normal CA19-9 values",
CA19.9_OS$plot + theme(plot.title = element_text(hjust = 0.5)))
Thank you for any help! I'm still new to R and not particularly a friend of it yet, so any tips are highly appreciated!
One relatively easy solution is to define your own custom theme based off of the theme that is used in ggsurvplot(). Looking at the documentation for the function shows us that it is applying via ggtheme= the theme theme_survminer(). We can create a custom function that uses %+replace% to overwrite one of the theme elements of interest from theme_survminer():
custom_theme <- function() {
theme_survminer() %+replace%
theme(
plot.title=element_text(hjust=0.5)
)
}
Then, you can use that theme by association with the ggtheme= argument of ggsurvplot():
library(ggplot2)
library(survminer)
require("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)
ggsurvplot(fit, data = lung, title='Awesome Plot Title', ggtheme=custom_theme())
#Add parameters to your theme as follows
centr = theme_grey() + theme(plot.title = element_text(hjust = 0.5, face = "bold"))
#Fit the model
fit<- survfit(Surv(time, status) ~ sex, data = lung)
#create survival plot
ggsurvplot(fit, data = lung, title="Your Title Here", ggtheme=centr)
Related
I have a dataset that I am modeling with a gam. Because there are two continuous varaibles in the gam, I have centred and scaled these variables before adding them to the model. Therefore, when I use the built-in features in gratia to show the results, the x values are not the same as the original scale. I'd like to plot the results using the scale of the original data.
An example:
library(tidyverse)
library(mgcv)
library(gratia)
set.seed(42)
df <- data.frame(
doy = sample.int(90, 300, replace = TRUE),
year = sample(c(1980:2020), size = 300, replace = TRUE),
site = c(rep("A", 150), rep("B", 80), rep("C", 70)),
sex = sample(c("F", "M"), size = 300, replace = TRUE),
mass = rnorm(300, mean = 500, sd = 50)) %>%
mutate(doy.s = scale(doy, center = TRUE, scale = TRUE),
year.s = scale(year, center = TRUE, scale = TRUE),
across(c(sex, site), as.factor))
m1 <- gam(mass ~
s(year.s, site, bs = "fs", by = sex, k = 5) +
s(doy.s, site, bs = "fs", by = sex, k = 5) +
s(sex, bs = "re"),
data = df, method = "REML", family = gaussian)
draw(m1)
How do I re-plot the last two panels in this figure to show the relationship between year and mass with ggplot?
You can't do this with gratia::draw automatically (unless I'm mistaken).* But you can use gratia::smooth_estimates to get a dataframe which you can then do whatever you like with.
To answer your specific question: to re-plot the last two panels of the plot you provided, but with year unscaled, you can do the following
# Get a tibble of smooth estimates from the model
sm <- gratia::smooth_estimates(m1)
# Add a new column for the unscaled year
sm <- sm %>% mutate(year = mean(df$year) + (year.s * sd(df$year)))
# Plot the smooth s(year.s,site) for sex=F with year unscaled
pF <- sm %>% filter(smooth == "s(year.s,site):sexF" ) %>%
ggplot(aes(x = year, y = est, color=site)) +
geom_line() +
theme(legend.position = "none") +
labs(y = "Partial effect", title = "s(year.s,site)", subtitle = "By: sex; F")
# Plot the smooth s(year.s,site) for sex=M with year unscaled
pM <- sm %>% filter(smooth == "s(year.s,site):sexM" ) %>%
ggplot(aes(x = year, y = est, color=site)) +
geom_line() +
theme(legend.position = "none") +
labs(y = "Partial effect", title = "s(year.s,site)", subtitle = "By: sex; M")
library(patchwork) # use `patchwork` just for easy side-by-side plots
pF + pM
to get:
EDIT: If you also want to shift result on the y-axis as #GavinSimpson (who is the author and maintainer of gratia) mentioned, you can do this with add_constant, adding this code before plotting above:
sm <- sm %>%
add_constant(coef(m1)["(Intercept)"]) %>%
transform_fun(inv_link(m1))
[You should also in general untransform the smooth by the inverse of the model's link function. In your case this is just the identity, so it is not necessary, but in general it would be. That's what the second step above is doing.]
In your example, this results in:
*As mentioned in the custom-plotting vignette for gratia, the goal of draw not to be fully customizable, but just to be useful default. See there for recommendations about custom plots.
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'm working now in a statistics project and recently started with R. I have some problems with the visualization. I found a lot of different tutorials about how to add percentage labels in pie charts, but after one hour of trying I still don't get it. Maybe something is different with my data frame so that this doesn't work?
It's a data frame with collected survey answers, so I'm not allowed to publish them here. The column in question (geschäftliche_lage) is a factor with three levels ("Gut", "Befriedigend", "Schlecht"). I want to add percentage labels for each level.
I used the following code in order to create the pie chart:
dataset %>%
ggplot(aes(x= "", fill = geschäftliche_lage)) +
geom_bar(stat= "count", width = 1, color = "white") +
coord_polar("y", start = 0, direction = -1) +
scale_fill_manual(values = c("#00BA38", "#619CFF", "#F8766D")) +
theme_void()
This code gives me the desired pie chart, but without percentage labels. As soon as a I try to add percentage labels, everything is messed up. Do you know a clean code for adding percentage labels?
If you need more information or data, just let me know!
Greetings
Using mtcars as example data. Maybe this what your are looking for:
library(ggplot2)
ggplot(mtcars, aes(x = "", fill = factor(cyl))) +
geom_bar(stat= "count", width = 1, color = "white") +
geom_text(aes(label = scales::percent(..count.. / sum(..count..))), stat = "count", position = position_stack(vjust = .5)) +
coord_polar("y", start = 0, direction = -1) +
scale_fill_manual(values = c("#00BA38", "#619CFF", "#F8766D")) +
theme_void()
Created on 2020-05-25 by the reprex package (v0.3.0)
I've found that with Plotly with R, when I'm faceting plots, they often don't translate properly from R to Plotly.
For example, my graph plotted in R looks like so:
When I send it to plotly, it looks like so:
(Some data has been hidden from both plots for confidentiality reasons)
My code looks like so:
plot <- ggplot(sytoxG_data_no_NC) +
geom_ribbon(data = confidence_intervals_SG, mapping = aes(x = time_elapsed, ymin = phenotype_value.NC.lower, ymax = phenotype_value.NC.upper,
fill = "red", colour = NULL), alpha = 0.6) +
scale_fill_manual(name = "Legend",
values = c('red'),
labels = c('Negative Control')) +
xlab("Time Elapsed") +
ylab("Sytox Green") +
ggtitle("Sytox Green - Facets: Pathway") +
facet_wrap(~Pathway, ncol=6, scales = "fixed") +
theme(panel.grid = element_blank(),
axis.ticks.length = unit(0, "cm"),
panel.background = element_rect(fill = "white"),
strip.text.x = element_text(size=4),
axis.text = element_blank())
response <- py$ggplotly(plot, kwargs=list(world_readable=FALSE, filename="SG_sparklines_by_pathway", fileopt="overwrite"))
The issue might very well be with geom_ribbon rather than facets... Can you please upgrade your "plotly" package and give it another try?
I wound up using facet_grid instead of facet_wrap. Something like this:
+ facet_grid(~Pathway, scales = "free", space="free")
I have the code below that is a combination of two boxplots and dot plots in one. It is a representation of barring density in 4 different species. The grey depicts the males and the tan the females.
data<-read.csv("C:/Users/Jeremy/Documents/A_Trogon rufus/Black-and-White/BARDATA_boxplots_M.csv")
datF<-read.csv("C:/Users/Jeremy/Documents/A_Trogon rufus/FEMALES_BW&Morphom.csv")
cleandataM<-subset(data, data$Age=="Adult" & data$White!="NA", select=(OTU:Density))
cleandatF<-subset(datF, datF$Age=="Adult", select=(OTU:Density))
dataM<- as.data.frame(cleandataM)
dataF<- as.data.frame(cleandatF)
library(ggplot2)
ggplot(dataM, aes(factor(OTU), Density))+
geom_boxplot(data=dataF,aes(factor(OTU),Density), fill="AntiqueWhite")+
geom_boxplot(fill="lightgrey", alpha=0.5)+
geom_point(data=dataF,position = position_jitter(width = 0.1), colour="tan")+
geom_point(data=dataM, position = position_jitter(width = 0.1), color="DimGrey")+ scale_x_discrete(name="",limits=order)+
scale_y_continuous(name="Bar Density (bars/cm)")+
theme(panel.background = element_blank(),panel.grid.minor=element_blank(),
panel.grid.major=element_blank(),axis.line = element_line(colour = "black"),
axis.title.y = element_text(colour="black", size=14),
axis.text.y = element_text(colour="black", size=12),
axis.text.x = element_text(colour="black", size=14))
This works just fine.
However, when I try to add a legend as:
legend("topright", inset=.01, bty="n", cex=.75, title="Sex",
c("Male", "Female"), fill=c("lightgrey", "black")
It returns the following Error:
Error in strwidth(legend, units = "user", cex = cex, font = text.font) :
plot.new has not been called yet
Please, is there someone who could suggest how to correct this?