Call function to generate the value but without plotting the graph

I have a function where it is possible to generate a map and a coef_val value, as you can see, but I would like to know if it is possible to call this same function and get just the generated value, but without plotting the graph? One possibility that I know exists is to make a new function, for example, f2, without the plot part, but I wouldn't want to do it that way. Is there another way, can you help me?

library(dplyr)
library(tidyverse)
library(lubridate)


Test <- structure(
  list(date1= c("2021-06-28","2021-06-28"),
       date2 = c("2021-07-01","2021-07-01"),
       Category = c("FDE","ABC"),
       Week= c("Friday","Monday"),
       DR1 = c(14,11),
       DR01 = c(14,12), DR02= c(14,12),DR03= c(19,15),
       DR04 = c(15,14),DR05 = c(15,14),
       DR06 = c(12,14)),
  class = "data.frame", row.names = c(NA, -2L))


f1 <- function(df1, dmda, CategoryChosse) {
  
  x<-df1 %>% select(starts_with("DR0"))
  
  x<-cbind(df1, setNames(df1$DR1 - x, paste0(names(x), "_PV")))
  PV<-select(x, date2,Week, Category, DR1, ends_with("PV"))
  
  med<-PV %>%
    group_by(Category,Week) %>%
    summarize(across(ends_with("PV"), median),.groups = 'drop')
  
  SPV<-df1%>%
    inner_join(med, by = c('Category', 'Week')) %>%
    mutate(across(matches("^DR0\\d+$"), ~.x + 
                    get(paste0(cur_column(), '_PV')),
                  .names = '{col}_{col}_PV')) %>%
    select(date1:Category, DR01_DR01_PV:last_col())
  
  SPV<-data.frame(SPV)
  
  mat1 <- df1 %>%
    filter(date2 == dmda, Category == CategoryChosse) %>%
    select(starts_with("DR0")) %>%
    pivot_longer(cols = everything()) %>%
    arrange(desc(row_number())) %>%
    mutate(cs = cumsum(value)) %>%
    filter(cs == 0) %>%
    pull(name)
  
  (dropnames <- paste0(mat1,"_",mat1, "_PV"))
  
  datas<-SPV %>%
    filter(date2 == ymd(dmda)) %>%
    group_by(Category) %>%
    summarize(across(starts_with("DR0"), sum),.groups = 'drop') %>%
    pivot_longer(cols= -Category, names_pattern = "DR0(.+)", values_to = "val") %>%
    mutate(name = readr::parse_number(name))
  colnames(datas)[-1]<-c("Days","Numbers")
  
  if(as.Date(dmda) < min(as.Date(df1$date1))){
    datas <- datas %>% 
      group_by(Category) %>% 
      slice(1:max(Days)+1) %>%
      ungroup
  }else{
    datas <- datas %>% 
      group_by(Category) %>% 
      slice((as.Date(dmda) - min(as.Date(df1$date1) [
        df1$Category == first(Category)])):max(Days)+1) %>%
      ungroup
  }
  
  plot(Numbers ~ Days,  xlim= c(0,45), ylim= c(0,30),
       xaxs='i',data = datas,main = paste0(dmda, "-", CategoryChosse))
  
  model <- nls(Numbers ~ b1*Days^2+b2,start = list(b1 = 0,b2 = 0),data = datas, algorithm = "port")
  
  new.data <- data.frame(Days = with(datas, seq(min(Days),max(Days),len = 45)))
  new.data <- rbind(0, new.data)
  lines(new.data$Days,predict(model,newdata = new.data),lwd=2)
  coef_val<-coef(model)[2]
  points(0, coef_val, col="red",pch=19,cex = 2,xpd=TRUE)
  return(coef_val)
}

f1(Test, "2021-07-01", "ABC")
b2 
12.5

enter image description here

You can add an argument to the existing function to specify if a plot is required :

f1 <- function(df1, dmda, CategoryChosse,doplot=TRUE) {
... your start-code
if (doplot==TRUE) {
  plot(Numbers ~ Days,  xlim= c(0,45), ylim= c(0,30),
       xaxs='i',data = datas,main = paste0(dmda, "-", CategoryChosse))
}
... your end-code
}