Chapter 45 Ages Commonly Affected

We examine the Top Most Common Ages associated with the Adverse Events and plot them in the bar plot.

TransformIntoYears = function(ds)
{
  if(!is.na(ds["AgeAtAdverseEvent"]))
  { 
    x = as.numeric(ds["AgeAtAdverseEvent"])
    
    ds["AgeAtAdverseEvent"] = as.numeric(ds["AgeAtAdverseEvent"])

     if(ds["AgeUnit"] == "Month(s)")
    {
      ds["AgeAtAdverseEventInYears"] = x/12
      
    }
    else if (ds["AgeUnit"] == "Weeks(s)")
    {
      ds["AgeAtAdverseEventInYears"] = x*7/365
    }
    else if (ds["AgeUnit"] == "Day(s)")
    {
      ds["AgeAtAdverseEventInYears"] = x*1/365
    }
    else if (ds["AgeUnit"] == "Decade(s)")
    {
      ds["AgeAtAdverseEventInYears"] = x*10
    }  
  }
  
  return(ds)
}

AdverseFoodEvents = AdverseFoodEvents %>%
  mutate(AgeAtAdverseEventInYears = AgeAtAdverseEvent) 

AdverseFoodEvents$AgeAtAdverseEventInYears = 
  as.numeric(AdverseFoodEvents$AgeAtAdverseEventInYears)

AdverseFoodEvents = sapply(AdverseFoodEvents,TransformIntoYears)

AdverseFoodEvents = as.data.frame(AdverseFoodEvents)

AdverseFoodEvents %>%
  filter(!is.na(AgeAtAdverseEventInYears)) %>%
  group_by(AgeAtAdverseEventInYears) %>%
  summarise(Count = n()) %>%
  arrange(desc(Count)) %>%
  ungroup() %>%
  mutate(AgeAtAdverseEventInYears = reorder(AgeAtAdverseEventInYears,Count)) %>%
  head(20) %>%
  
  ggplot(aes(x = AgeAtAdverseEventInYears,y = Count)) +
  geom_bar(stat='identity',colour="white", fill = fillColor2) +
  geom_text(aes(x = AgeAtAdverseEventInYears, y = 1, label = paste0("(",Count,")",sep="")),
            hjust=0, vjust=.5, size = 4, colour = 'black',
            fontface = 'bold') +
  labs(x = 'Age in Years', 
       y = 'Count', 
       title = 'Age and Count') +
  coord_flip() + 
  theme_bw()