Histograms

This post is part of a series on plotting in R for beginner’s. Take what you want, leave what you don’t – it’s a sushi train of information.

Use the function hist( ) to plot historgrams. These fancy-pants plots are perfect for checking how data are distributed. Is it normal? Uniform? Bimodal?

hist(data)

Rplot
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Have you heard of the shit sandwich? Tell ’em something good, tell ’em something shit, then finish with another good thing. It’s one way to ensure we give positive feedback. It’s a nice thing to do, but when it comes to post-grad students, it’s vital.

There’s two reasons positive feedback is important:

Source: newfrontierconsulting.com.au

Our sanity is on the line

We like pats on the back because Science is a cruel, cruel world, and we’re still adjusting to that. We are determined to grow skin as thick as rhino hide, but most of us are not there yet. Do not underestimate the power of a simple ‘well done’ – it can make a student’s week! This type of thumbs-up, good job feedback is the life source of students who are pouring blood, sweat and tears into a thesis.

We need to know what we’re doing right, so we can keep doing it

This is pure information phishing. We need to know when we’re on to a good thing, not only when we haven’t got it quite right. Getting a draft back with comments only on what can be improved is like having a presence-absence dataset with only absences. We’re left assuming the stuff we’ve written in the chunks without any comments is good. Or is it great? Just passable? Or simply not wrong enough to warrant a comment?

A shit sandwich for supervisors

Supervisors, we love your work. Good job and well done – we know we’re a handful sometimes. It would be helpful to get more positive feedback so we know what we’re doing right and to build our confidence. But your constructive criticism is invaluable and makes us better scientists. Thanks!

A poorly kept Dropbox is like an abandoned Soviet Union library.

Dropbox and the cloud have E-volutionized collaboration. But Dropboxing has rules. Just like you can’t talk about ex’s on a first date or eat dim sims in a shared office. Stay in good grace with your collaborators with good Dropbox etiquette. Read More

Riparian vegetation of the Goulburn River, northern Victoria, Australia.

Riparian vegetation of the Goulburn River, northern Victoria, Australia.

Hi there! Welcome back to a Beginner’s Introduction to R, Written by a Beginner for a Slightly More Beginning Beginner, or BIR-WBSMBB. Just joking! Today’s topic is far less sexy than my previous post on generalized linear models in R; today it’s all about subsetting data with a tangent on data formatting – or is it the other way around?
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Our cute and furry critter for today - the infamous and rather vocal Sugar Glider. #macrofaunabias

Our cute and furry critter for today – the infamous and rather vocal Sugar Glider. #macrofaunabias

The sun is shining, the birds are singing and here we are again: A Beginner’s Introduction to R: Written By a Beginner for a Slightly More Beginning Beginner. Today, in an effort to relieve a fellow Master of Science student’s fear of the unknown, we’re learning how to run a Generalized Linear Model in R. Read More

Box-ironbark woodland, Chiltern National Park.

Box-ironbark woodland, Chiltern National Park.

Hi guys! Welcome back to A Beginner’s Introduction to R: Written by a Beginner for a Slightly More Beginning Beginner. Today we’re going to find out how to conduct a simple correlation.

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The Regent Honeyeater is critically endangered. Check out the Regent Honeyeater Project. http://regenthoneyeater.org.au/index.php

The Regent Honeyeater is critically endangered. Check out the Regent Honeyeater Project.

Friends! Welcome back to A Beginner’s Introduction to R: Written by a Beginner for a Slightly More Beginning Beginner. In Lesson 1 we learned how to load our data into R and in Lesson 2 we learned how to navigate our data. It’s time to learn how to plot our data.

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Welcome to Lesson 2 in A Beginner’s Introduction to R: Written by a Beginner For a Slightly More Beginning Beginner.

This post has been a long time coming. My excuse is my readership reduced to zero after my mum, when faced with insoluble Excel-madness, got half way through Lesson 1, gave up and paid me to do her stats for her. And like every honest ecology student, I took that money, bought a case of beer shiny, new sleeping bag and went hiking. With positive reinforcement like that, I don’t think I’ll tell her about this post. Mum’s the word, readers – I’ve got my eye on a new thermarest!

Excursion with mum-funded sleeping bag. Mt Sugarloaf summit, Cathedral Ranges, looking over the Acheron Valley.

In Lesson 1, we became acquainted with R. We discovered that R uses the R console to communicate with us and we use R scripts to talk to R. We learned how to execute commands (tell R to do something), set our working directory (tell R where to look for files), how to load and look at our data.

The plural of platypus is platypus or platypuses, not platypi. Actually, technically, it would be platypodes, since ‘…pus’ (meaning foot) is greek derived and the plural of ‘pus’ in Greek is ‘…podes’. But that’s enough semantics.

In this post we’re going to figure out how to navigate our data by executing commands instead of clicking our mouse  like we would in Excel. And if all goes well, we’ll overcome the anxiety of not being able to constantly see our data. Or instead, like me, you’ll just develop an incessant compulsion to use the function head( ) to look at your data.

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There’s an imposter among us.

Imposter Syndrome is the feeling that one is not qualified for one’s position and the consequent fear of exposure and failure it produces. There’s been a lot of discussion about Imposter Syndrome in the QAECO lab this week, particularly the links with equity between the sexes as well as potential management techniques. The very fact we’re having this discussion is fantastic and has inspired me to share my experience as both an imposter and a member of a disadvantaged group, that of the lower socio-economic demographics.

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I’m a relative newbie to the wondrous world of R. I’ve spent many arduous years ‘filling down’ columns in excel, getting annoyed that excel doesn’t have a standard error function and being dismayed at what excel produces as default graphs. Sure, Excel’s colourful and exciting default graphs have a place…that place is Grade 5 ‘Show and Tell’. I, for one, love colour and excitement but as scientists if our graphs were colourful and exciting, well, then there would be no room left for our colourful and exciting personalities! At least that’s what I tell myself when I’m making the conventional (and utterly lacking in personality) black and white graphs, with a touch of grey if I’m lucky.

Anyway, I digress. This post (hopefully the first in a series) is intended to gently coax those who R still using Excel to the dark side. I’ve been inspired to embark on such an endeavour by 2 things. Firstly, a couple of fellow R nerds from QAECO post updates on their blogs of their Rchievments and other handy R hints, such as tweeting from R. These posts are great, but since I’m only an R white belt, I couldn’t imagine being able to implement such seemingly complicated programming. Not to worry though, I didn’t let it get me down – I have it on good standing that both Will and John are programming Miyagis.

The second motivating factor is my Mum. How sweet, right? I know, I try. She was trying to do quite simple calculations (count, sum, average) on a massive dataset (250,000 individuals) and an older version of Excel wouldn’t even let her load the entire file – it stopped at 160,000! Then she was trying to subset her dataset and subset her subsets, and it was a nightmare. I told her she needed a data monkey, a data monkey would solve this problem. But short of capturing an elusive data monkey and imprisoning it to a life of Excel-induced suffering, she should learn how to use R. “I wouldn’t even know where to start”, she said. And this was the light-bulb moment. I realised I can fill a gap here! People are being cast down and disillusioned by Excel, and have that, I will not. But people are also being intimidated by R and its power, and have that, also I will not. So, from here on in, consider this Lesson 1 of ‘A Beginners Introduction to R written by a beginner for a slightly more beginning beginner’. And if no-one else reads this, at least I know I’ll have one reader: my Mum. She counts, right?…right?

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