In our previous two–part series we looked at the melt function from the reshape2 package. The creator of the package, Hadley Wickham, pointed me towards tidyr and the gather function as a better alternative instead.
Before we begin: Hadley Wickham, the reshape2 package creator, pointed me in the direction of the tidyr package for melting data. I’ll take a look at it after this post. As promised from before, a look at @hadleywickham‘s reshape2 package and Home Office drugs data #ddj #rstats https://t.co/Q46eUijsaG — R For Journalists (@rforjournalists) October 18, … Read More “Melting Drugs Data: Part Two” »
We aggregated four datasets – depression, contact with mental health services, the recovery rate from anxiety and depression and antidepressant prescriptions – to produce a mental health ranking for all 209 Clinical Commissioning Groups (CCGs) in England.
In my previous post, I showed how poorer children are more likely to be fat. Think about how your graphics look on a mobile phone before you publish them. I didn’t do this properly with my last post – I published it first and then checked how it looked on mobile. That isn’t the best approach – and it … Read More “Make Your Labels Legible on Mobile” »
As we move further to the right of the chart into schools in more deprived areas, the share of children in the grey and black parts of the chart – showing overweight and very overweight kids – gets bigger.
This means that a class of year six children in inner-city Manchester will likely have bigger waistlines than one in leafy Hampshire.
A combination of bad diets and a lack of exercise means poorer children are sadly more likely to be fat at just 10 or 11 years old, with all the health problems that brings now and stores up for the future.
