An Annotated R History Log - Part 3

data(package="ggplot2") - show the datasets in the package ggplot2.

require(ggplot2) - import the package into the workspace. Then you can get at the datasets like this:

diamonds - print the diamonds dataset out.

list.files() - list files in the working directory.

getwd() - what is the working directory.

setwd(val) set the working directory.

read.csv("unemp.csv") - read the comma separated file unemp.csv from the workspace (would be better off assigning this to a variable with <-

unemp[x] - show the x entry of dataset unemp. 1 indexed.

t(unemp) - transpose the unemp dataset.

sub() - perform text substitution using regular expressions. First match only. gsub for global.

plot(ts(unemp), ylab="Live Register", xlab="Date", main="Irish Unemployment Numbers", type="o")
plot a time series plot of the unemp dataset. x and y axis labels are given along with a title. The chart type is o for overwrite (symbols with lines overwritten)

summary(unemp) - provide summary information on the unemp dataset. min, max median etc.

An Annotated R History Log - Part 2

quit() - end the current R session. q() works too.

x<-c(1,2,4,5) - Create a vector containing the numbers given.

mean(x) - calculate the mean of those numbers

barplot(x) - chart the values of x. Starting to get interesting.

abline(h=mean(x)) - add an a-b line to the previous plot. The line is horizontal with a height of mean(x). Useful for adding 'targets' to a chart.

help(package=ggplot2) - an extended use of the help function. Profice a value to the package parameter.

data() - list the data sources available in the current session.

AirPassengers - One of the included datasets. Entering it like this just prints it.

plot(AirPassengers) - produce a plot of the number of air passengers over time. This is what it looks like. I am a long way from being able to quantify that seasonal variation, but it is an interesting thing to look at when I know how.

AirPassengers plot

women - dataset of womens height vs weight.

plot(women$height, women$weight) - plots a scatter of womens weight vs weight, but unnecessary as the data is in columns. plot(women) would do the same job.

mdeaths - this one is the monthly deaths from lung disease in the UK.

Note that you can use tab to autocomplete a lot of stuff in R. For example datasets. There are a large number of them and tab allows you to pick out the ones you need nice and quick.

Note: Typing help(dataset) gives you information on the dataset, including sources.

uspop - population of the United States.

There are a lot of datasets that some with R. They are in nice formats unlike most of the stuff I have tried to play with so far. Hopefully with increased skill I will be able to press the data into the format I need to plot and manipulate it.

An Annotated R History Log

As part of my learning of the R statistical programming language I am reviewing the contents of my history() log at the end of each days session. Here is a dump of that with an explanation of what I was trying to do:


help(solve) - get help on the solve function. Something to do with solving systems of equations. Not what I was looking for.

help() - help on 'help'. Not that useful.

help.start() - starts the help server and shows you the home page. Has some handy reference links, but just using Google whenever I get stuck seems to be doing the trick.

help(mean) - what is a mean.

example(mean) - this is handy. Gives you just the example section of the help. Without starting the http help server which can be a bit annoying.

 x <- c(0:10, 50) - create a vector containing the numbers 0 to 10 and 50. c stands for combine.

x - this just prints the values of x. You can use print(x), but I am not sure why you would.

mean(x) <- calc the mean of a vector x.

help(sink) - this function sends r data to a destination. A file for example.

ls()  - show all the objects in the current session.

rm(x) - remove a named object from the current session.

history() - show this history file. Be careful to use this version: history(max.show=Inf) if you want to see more than 25 lines.


This is all a bit basic at the moment, but hopefully with time it will get a lot more useful to people who want to try to learn R too.

Goal 3 - Get to 11 Stone

This is the third of 4 goals that I am aiming for over the next 3 months. In the first quarter of this year I set myself a goal of getting to 11.5 stone from about 12. I made that, but only just. I researched the best value to aim for. I reckoned that 'I look thin enough', or 'I look fat' are not precise measurements. I want to find out what I should weigh and then aim for that in the long term. 
I tried this http://www.bmicalculator.ie/ first. It said I was fine at 11.5 stone for my height, but above the mid point on the ideal BMI band. This seemed to suggest that I could be lighter and better off. Then I tried this one: http://nhlbisupport.com/bmi/bmi-m.htm

I got a number out of this 23.6 as my current BMI. The range for my height is 18.5–24.9. The mid point of this range is 21.7. I then entered values into the weight box progressively until I got to 21.7. This gives me an ideal BMI (assuming the midpoint is ideal) of 10.5 stone. Quite a bit of weight to lose. 

All of this ignores things like body composition - 11.5 stone of fat is worse than... ,activity levels and other important stuff. That said, being lighter is generally considered healthier. Rats live longer(!) This is some information about visceral fat. Dangerous stuff. Better to be safe than sorry I reckon. 
Hence Goal 3. First week of the quarter is over now and I think I have done quite well. I did not miss any workout days (there are 5 per week). Will see if I can get to 11 stone which is stage 2 of my weight loss plan. I am doing quite a few weight training sessions at the moment - will be interested to see if this makes is easier or harder to get 'lighter'.

Another useful resource for learning R

This is a pretty good presentation from UCLA for people who need to use R. Have only started it, but it is not moving too slow, or too fast. I reckon a few more tutorials like this and I will be able to manipulate real basic real world data. There seems to be a lot of data cropping up in public these days, so hopefully I can take an objective look at some of it.