Nice graphic? Are they taking the p …

Yes, it started with a tweet: Nice graphic on urine components via https://t.co/sfuXNB02sF pic.twitter.com/vhVLahQ8su — Metabolomics (@metabolomics) January 31, 2017 By what measure is this a “nice graphic”? First, the JPEG itself is low-quality. Second, it contains spelling and numerical errors (more on that later). And third…do I have to spell this out…those are 3D pie charts. Can it be fixed? So far as I know, there isn’t a tool to generate data by extracting labels from images, so I sat down and typed in the numbers manually. Here they are for download. The top and bottom pie charts are identified by “all” and “other”, respectively. Better make sure those percentages total 100, before we get into charts. library(ggplot2) library(dplyr) library(readr) urine1 <- read_csv("urine1.csv", col_names = FALSE) colnames(urine1) <- c("component", "all_other", "percent") # top chart - good! urine1 %>% filter(all_other == "all") %>% summarise(total = sum(percent)) %>% glimpse() # Observations: 1 # Variables: 1 # $ total <dbl> 99.9 # bottom chart - not good urine1 %>% filter(all_other == "other") %>% summarise(total = sum(percent)) %>% glimpse() # Observations: 1 # Variables: 1 # $ total <dbl> 113.61 Slices in the bottom chart sum to 113.61%. Problem. Not being an expert in urine composition I have no idea which figures m...
Source: What You're Doing Is Rather Desperate - Category: Bioinformatics Authors: Tags: R statistics charts data visualisation Source Type: blogs