Can this data generate this graph? This is the desired graph

Here is the data: https://github.com/UnlimitedR/share/blob/main/mydata.csv
If so, could anyone please tell me how should I implement the R codes for it? Including drawing the CI area.
This is currently my code
df <- read.csv("mydata.csv")
print(sum(df$PRS))

library(ggplot2)
df$incidence <- 100 * df$PRS / sum(df$PRS,na.rm=T)
g <- ggplot(df, aes(x = CIT, y = incidence)) +
  geom_line()
print(g)

But the result is

btw, what does the incidence of PRS means? I think PRS is a binary variable?
I dont know if I have understood the meaning of incidence of PRS correctly?

Thanks for the help from Dave, I rewrote the code with ggplot
dat <- rio::import("https://raw.githubusercontent.com/UnlimitedR/share/main/mydata.csv")
library(ggplot2)
library(ggeffects)

mod <- glm(PRS ~ CIT, data=dat, family=binomial)
g <- ggpredict(mod, terms="CIT [all]") 

ggplot(g, aes(x, y = predicted, color="yellow")) +
  geom_line() +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.5) +
  ggtitle("") +
  labs(x = "CIT", y = "Incidence of PRS, %") +
  scale_y_continuous(labels = ~sprintf("%.0f", .x*100)) +
  #scale_y_continuous(labels = percent_format())+
  theme_bw()+
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position = c(0.2,0.9))+
  scale_color_discrete(name="",
                       labels="95% CI")

However, the color seems to have a little bit problems.


Maybe something like this:
dat <- rio::import("https://raw.githubusercontent.com/UnlimitedR/share/main/mydata.csv")
library(ggplot2)
library(ggeffects)

mod <- glm(PRS ~ CIT, data=dat, family=binomial)
g <- ggpredict(mod, terms="CIT [all]") 
plot(g) + 
  ggtitle("") + 
  labs(x="CIT", y="Incidence of PRS")


Created on 2022-12-30 by the reprex package (v2.0.1)
The code above assumes that the underlying model predicting PRS with CIT is a logistic regression.
To print without the % symbol, using the code in the comments:
plot(g) + 
  ggtitle("") + 
  labs(x="CIT", y="Incidence of PRS, %") + 
  scale_y_continuous(labels = ~sprintf("%.0f", .x*100)) + 
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), 
        panel.border = element_rect(fill = NA))
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.



Edit: Modifying code in question
ggplot(g, aes(x, y = predicted)) +
  geom_line(aes(colour="Predictions\nw/95% CI")) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high, fill="Predictions\nw/95% CI"), alpha = 0.5, colour="transparent") +
  ggtitle("") +
  labs(x = "CIT", y = "Incidence of PRS, %") +
  scale_y_continuous(labels = ~sprintf("%.0f", .x*100)) +
  #scale_y_continuous(labels = percent_format())+
  theme_bw()+
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        legend.position = c(0.15,0.9)) +
  scale_fill_manual(values="gray50") + 
  scale_colour_manual(values="black") + 
  labs(colour="", fill = "")


