R: Need Finite 'Ylim' Values in Function

R: need finite 'ylim' values in function

In plot1, change y = range(x$VALUE) to y = range(x$VALUE, na.rm=TRUE) to remove NA missing values.

You have another problem, namely that in both functions you refer to ind[[i]], which I assume means at some point this code was part of a loop. My guess is all the references to ind[[i]] should be x.

need finite 'ylim' values-error

Try this:

plot(sapply(dataliste, function(x)gsub(",", ".", x)))

As Roman Luštrik pointed out, you most likely have characters in your data. You can generally plot them or convert them with as.numeric. However, since you have a , instead of a . in your strings, the conversion to numeric fails.
Example:

> as.numeric("9,02E+11")
[1] NA
Warning message:
NAs introduced by coercion 
> as.numeric("9.02E+11")
[1] 9.02e+11

With gsub, as above, you can substitute the , for a . for each number and plotting should work.

Error in plot.window(...) : need finite 'ylim' values - SPC charts in R

Add the ylim as vector:

qcc::qcc(my.data, type="R", nsigmas=3, ylim=c(0,300))

produces:

R: Error in plot.window(...) : need finite 'ylim' values

According to the KLx.dist documentation, this funciton requires data matrix as input. In the iris dataset, we then need to remove the Species column which is a factor variable. Removing the Species column before sampling would solve the problem :

data(iris)

library(FNN)
library(dplyr)

#create two data sets
df = iris[,1:4]
data1 = sample_n(df, 20)
data2 = sample_n(df, 20)

#plot KL divergence
plot(KLx.dist(data1,data2))

Error in plot.window(...) : need finite 'ylim' values to generate graph in R

When there are only NA elements, and if we use range/min/max with na.rm = TRUE, it returns Inf

> min(NA, na.rm = TRUE)
[1] Inf
Warning message:
In min(NA, na.rm = TRUE) : no non-missing arguments to min; returning Inf
> max(NA, na.rm = TRUE)
[1] -Inf
Warning message:
In max(NA, na.rm = TRUE) : no non-missing arguments to max; returning -Inf
> range(NA, na.rm = TRUE)
[1]  Inf -Inf
Warning messages:
1: In min(x, na.rm = na.rm) :
  no non-missing arguments to min; returning Inf
2: In max(x, na.rm = na.rm) :
  no non-missing arguments to max; returning -Inf

One option is to return 0 if it is NA

...
 maxrange <-  range(min(0, datas$Numbers, na.rm = TRUE), na.rm = TRUE)
...

-full code

f1 <- function(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))
  
  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"))
  
  SPV <- SPV %>%
    filter(date2 == dmda, Category == CategoryChosse) %>%
    select(-any_of(dropnames))
  
  if(length(grep("DR0", names(SPV))) == 0) {
    SPV[mat1] <- NA_real_
  }
  
  datas <-SPV %>%
    filter(date2 == ymd(dmda)) %>%
    group_by(Category) %>%
    summarize(across(starts_with("DR0"), sum)) %>%
    pivot_longer(cols= -Category, names_pattern = "DR0(.+)", values_to = "val") %>%
    mutate(name = readr::parse_number(name))
  colnames(datas)[-1]<-c("Days","Numbers")
  
  
  datas <- datas %>% 
    group_by(Category) %>% 
    slice((as.Date(dmda) - min(as.Date(df1$date1) [
      df1$Category == first(Category)])):max(Days)+1) %>%
    ungroup
  
  m<-df1 %>%
    group_by(Category,Week) %>%
    summarize(across(starts_with("DR1"), mean))
  
  m<-subset(m, Week == df1$Week[match(ymd(dmda), ymd(df1$date2))] & Category == CategoryChosse)$DR1
  
  maxrange <-  range(min(0, datas$Numbers, na.rm = TRUE), na.rm = TRUE)
  maxrange[2] <- maxrange[2] - (maxrange[2] %%10) + 35
  
  max<-max(datas$Days, na.rm = TRUE)+1

    plot(Numbers ~ Days,  xlim= c(0,max),  ylim= c(0,maxrange[2]),
       xaxs='i',data = datas,main = paste0(dmda, "-", CategoryChosse))

  if (nrow(datas)<=2){
    abline(h=m,lwd=2) 
    points(0, m, col = "red", pch = 19, cex = 2, xpd = TRUE)
    text(.1,m+ .5, round(m,1), cex=1.1,pos=4,offset =1,col="black")}
  
  else if(any(table(datas$Numbers) >= 3) & length(unique(datas$Numbers)) == 1){
    yz <- unique(datas$Numbers)
    lines(c(0,datas$Days), c(yz, datas$Numbers), lwd = 2)
    points(0, yz, col = "red", pch = 19, cex = 2, xpd = TRUE)
    text(.1,yz+ .5,round(yz,1), cex=1.1,pos=4,offset =1,col="black")}
  
  else{
    mod <- 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(mod,newdata = new.data),lwd=2)
    coef<-coef(mod)[2]
    points(0, coef, col="red",pch=19,cex = 2,xpd=TRUE)
    text(.99,coef + 1,max(0, round(coef,1)), cex=1.1,pos=4,offset =1,col="black")
  }
  
}

-testing

f1("2021-07-02", "ABC")

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