![\"\"](\"http:\/\/www.decisionsciencenews.com\/wp-content\/uploads\/2011\/08\/nyc_rest-.png\" "\\"nyc_rest\\"")
<\/a><\/p>\nDecision Science News is no stranger to misleading infographics in free New York newspapers<\/a>. We could upgrade to real newspapers, but we find that playing “spot the infographic flaw” really makes the time fly on the subway.<\/p>\n We saw the above graphic in a paper called Metro<\/a>. In New York City, restaurants are graded by health inspectors and receive an “A”, “B”, or “C” rating (any lower than C and they are shut down). This graphic was supposed to inform us about the percentage of restaurants with As, by borough and citywide. Can you spot the goof?<\/p>\n You might be curious how the weighted average of 73.3%, 62.8%, 63.6%, 61.4%, and 62.2% could be 69% (shown in the red box) given that 73.3% gets the smallest weight in the average. <\/p>\n Ignoring the top row in the table, a simple calculation from the remaining numbers gives 63.6% as the percentage of restaurants with As. But which stat is correct? Perhaps the top row is correct and some other numbers in the table are wrong.<\/p>\n Amazingly, the same day, AM New York<\/a>, yet another free paper, ran more or less the same story, but with different numbers. Based on those, 68% of restaurants had As. Disappointingly, all their by-borough percentages failed to line up with hose from Metro (see R code at end of this article).<\/p>\n Decision Science News then tried to cut out the middleman and hit the New York City Department of Health and Mental Hygiene Website<\/a>.\u00a0Pie charts are much maligned, but when it comes to the topic of food safety, why not? If it were up to us, we would have drawn in a crust and whipped cream, but then our taste in charts is controversial<\/a>.<\/p>\n <\/p>\n So, we now have four candidate figures, 69%, 63.6%, 68% and 69%, which are in no way independent, but do suggest the answer is “just shy of 70%”.<\/p>\n Another interesting tidbit in the health department’s report is that the restaurant grades may be effective at changing restaurants’ behavior. At first inspection, 39% of restaurants got As, 34% got Bs, and 27% got Cs. From page 3:<\/p>\n Among those scoring in the B range on initial inspection, nearly 40% improved to earn an A on reinspection. Of restaurants that scored in the C range on their initial inspection, 72% improved enough to earn an A or B on re-inspection.<\/p><\/blockquote>\n There you have it!<\/p>\n R CODE FOR R NERDS<\/a>
Taken from http:\/\/www.nyc.gov\/html\/doh\/downloads\/pdf\/rii\/restaurant-grading-1-year-report.pdf<\/a><\/font><\/p>\n
\n
\n################
\n#Metro data
\ngraded=c(22454,2204,5235,9086,5030,899)
\nasMetrostated=c(.69,.662,.628,.636,.614,.733)
\nasMetrocount=round(asMetrostated*graded)
\nmetro=data.frame(graded,asMetrostated,asMetrocount)
\nrow.names(metro)=c(\"citywide\",\"bronx\",\"brooklyn\",\"manhattan\",\"queens\",\"statenIsland\")
\nmetro
\nsprintf(\"Total graded: %d\", sum(metro[2:6,1]))
\nsprintf(\"Total As: %d\", sum(metro[2:6,3]))
\nsprintf(\"Percent As: %.2f\", (sum(metro[2:6,3]) \/ sum(metro[2:6,1]) * 100))
\n################
\n#AM New York data
\nstatenIsland=c(644,73,20,82)
\nqueens=c(3009,601,152,806)
\nbrooklyn=c(3197,619,152,774)
\nbronx=c(1394,260,54,332)
\nmanhattan=c(5792,1006,256,1314)
\nam=data.frame(bronx,brooklyn,manhattan,queens,statenIsland)
\nam[5,]=apply(am[1:4,],2,sum)
\nam[6,] = am[1,]\/am[5,]
\nrow.names(am)=c(\"A\",\"B\",\"C\",\"GradePending\",\"total\",\"As\")
\nround(am,2)
\nsprintf(\"Percent As: %.2f\", sum(am[1,]) \/ sum(am[5,]))<\/code><\/p>\n