# Analysis tools for Manager Joe

I’m using someone else’s data today. It’s absolutely hideously laid out. I could munge it into R but it would take absolutely ages and I’m just not doing enough with it for that to be worth doing.

So I need to have a look at about 30 survey questions using the tools available to the Average Manager Joe- a spreadsheet and the “graph” button.

It’s a real eye opener. Everything takes ages, for one thing, and everything is so janky that I’m not even really sure if I’m drawing the right conclusion. I think the most worrying thing is that the effort involved is so high that I’m losing my curiosity- I’m just trying to get it done. I’m just churning out all this rubbish, giving it a quick eyeball and crashing on.

Why does that seem so familiar? Oh yes, that’s what I’ve always assumed people have done when I read their reports. It’s a big problem, we all know it is, data is too difficult to make sense of, so people do it quickly, and wrongly. We all know this. But I’m living it right now. And I have renewed purpose to make all MY data applications beautifully easy to use. Stay tuned…

[… time passes]

I’ve come back to this post. It’s no good. I can’t do it. I’m munging the data into R, even if it will take a little while. It just goes to show, it’s really hard to get away with not doing it properly.

# Failure to produce pdf with RMarkdown tidyverse

I’m using tidyverse for everything now, as I’ve mentioned in previous posts, when I want a cup of tea I just run:


house %>%
filter(kitchen == 1) %>%
select(tea, kettle) %>%
infuse()



I just ran the following code in a vanilla RStudio setup with pdflatex installed:


---
title: "Test document"
author: "Chris Beeley"
date: "20 December 2017"
output: pdf_document
---

{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)  {r} library(tidyverse) # let's do some data stuff here...   This is the code that you get if you set up an RMarkdown project in RStudio and select “compile to LaTeX”, and you want to do some data stuff with the tidyverse package. And it produced the following error message: ! Package inputenc Error: Unicode char √ (U+221A) (inputenc) not set up for use with LaTeX. See the inputenc package documentation for explanation. Type H for immediate help. l.145 \end{verbatim} Try running pandoc with –latex-engine=xelatex. pandoc: Error producing PDF Error: pandoc document conversion failed with error 43 Execution halted I was a bit confused by this for quite a while, the answer of course turns out to be the lovely messages which the tidyverse produces on loading: With the default message = TRUE behaviour in the code chunk pandoc ends up trying to render those little ticks in LaTeX. Evidently it doesn’t support unicode. So the document fails, and it’s hard to understand why until you knit to HTML and see the little ticks. Changing the knitr::opts_chunk$set(echo = TRUE) line to knitr::opts_chunk\$set(echo = TRUE, message = FALSE) fixes the problem.

I can’t help but think that this is a rare example of R getting harder to use. When I started with R 10 years ago it was much more difficult to do even simple things like load a csv file or work with dates. These days there are lots of lovely packages to help, and of course RStudio itself makes using R much more intuitive. But this is going to confuse newbies, I think, which is a bit of a shame.

There are several obvious fixes, I won’t bother to list them all, maybe make message = FALSE the default in RMarkdown documents in RStudio seems like the best one, but maybe there’s some reason they don’t want to do that.

# Font size of code in .Rpres presentations

I don’t know if I even knew about the .Rpres presentation feature in RStudio v. 0.98 and above. As I think I mentioned I’ve been rather ill for the last couple of years and I’m afraid I kind of fell out of touch with things a bit. Anyway, I’m all better now and I’m going to be giving a talk at the R User Group in Nottingham (which I love profoundly) so I thought I’d do it this new sexy way.

It seems pretty handy, haven’t made the whole presentation yet so I’m sure there’s more to come but the first thing is, dang! The code in an echo = TRUE chunk is really large! I can’t fit any output on the page!

So I found this guide to making it smaller, and lots of other nice tweaks, too.

# Better Git commit messages

Something else I’m trying to be better at is using Git. I did use it, briefly, a few years back but I never quite got the hang of it and I’ve reverted to the bad habit of having MainCode/ and TestingCode/ and TryNewFunction/ folders filled with near identical code.

So I’m back on the Git wagon again. Atom (see my previous blog post) has beautiful Git integration, as you’d expect since it was built by the GitHub people. It also enforces a couple of conventions with writing Git commit messages, which inspired me on a Google search which led me to this, a guide to writing better commit messages.

I never even thought about the art of it, but, of course, like code comments, good commit messages are essential for collaborating with anyone, even your future self.

# Ellen Townsend: Small talk saves lives — IMH Blog (Nottingham)

It sounds much too simple doesn’t it? Making small talk could save a life. But the truth is, it really could. Today SHRG is supporting the campaign launched by the Samaritans. They are asking us all to be courageous and strike up a conversation with someone if we are worried about them at a railway […]

# Filtering data straight into a plot with tidyverse

I’m still trying to go full tidyverse, as I believe I mentioned a while back. It’s clearly a highly useful approach, but on top of this I see a load of code in blogs and tutorials that uses a tidy approach. So unless I learn it I’m not going to have a lot of luck reading it. I saw somebody do the following a little while back and I really like it so I thought I’d share it.

In days gone by I would draw lots of graphs in an RMarkdown document like this:


firstFilteredDataset = subset(wholeData,
Date > as.Date("2017-04-01"))

ggplot(firstFilteredDataset,
aes(x = X1, y = y)) + geom_... etc.

secondFilteredDataset = subset(wholeData,
Date > as.Date("2015-01-01"))

ggplot(secondFilteredDataset,
aes(x = X1, y = y)) + geom_... etc.

thirdFilteredDataset = ... etc.



It’s fine, there’s nothing wrong with doing that, really. The two drawbacks are firstly that the code looks a bit ungainly, creating lots of objects that are used once and then forgotten about, and secondly it is filling your RAM with data. Not really a problem on my main box, which has 16GB of RAM, but it’s a bad habit and you may come unstuck somewhere else where RAM is more limited- like for example when you’re running code on a server.

So I saw some code on the internet the other day and they just piped data straight from a dplyr filter statement to a ggplot instruction. No muss, no fuss, the data is defined in the same function in which it’s used, and you’re not making loads of objects and leaving them lying around. So here’s an example:


library(tidyverse)

mpg %>%
filter(cyl == 4) %>%
group_by(drv) %>%
summarise(highwayMilesPG = mean(hwy)) %>%
ggplot(aes(x = drv, y = highwayMilesPG)) +
geom_bar(stat = "identity")



There’s only one word for it- it’s tidy! I like it!

# One editor to rule them all- Atom

I’m very happy using RStudio for all my R code. It goes without saying that the support for R coding built into RStudio is phenomenal. If you don’t know loads of cool stuff RStudio does, you’re missing out, but that’s a blog post on its own.

I’ve never quite been happy with my choice for other general editing, though. Sometimes I write PHP, HTML, markdown, Python, or something else, and I’ve never really found an editor that I love. Geany is pretty good and that’s what I have been using when I write PHP or HTML. I tended to write markdown in RStudio, which is kind of stupid, since RStudio is an awfully big hammer to crack that nut, but it does support markdown and I’m familiar with RStudio, so I was happy enough doing that. I never really found a Python IDE that I loved. As far as I can tell there isn’t really an RStudio equivalent in the Python world, something so well featured and brilliant that it’s really the only choice unless you have a very particular reason to use something else.

So about a year ago I gave Atom a try. It had been out of beta for about a year by that point. I don’t really remember it clearly now but it seemed a bit clunky and I just rapidly gave up (to be fair, this may have just been me being thick, I’ve no idea how much it has really improved since). It just didn’t grab me. I keep seeing it mentioned everywhere and I thought I would give it another go.

This time I was hooked straight away. It’s described as a “hackable editor for the 21st Century” and that’s the real strength of it. The actual interface is very clean and simple, no bells and whistles, but it comes bundled with some plugins and there is a thriving ecosystem of user contributed packages that can make Atom, it seems so far, anything you want it to be.

I think I love Atom for the same reason I love R. It has a big ecosystem of packages around it, and whatever problem you want to solve, as Apple almost said of the iPhone, “there’s a package for that”.

Your needs will be different from mine, of course, but I recommend you give Atom a try if you haven’t already. It supports Markdown preview out of the box. So far I have installed two packages- platformio-ide-terminal, and script. Platformio-ide-terminal allows you to spawn a terminal underneath your code window, which I have been mainly using to run pandoc on my markdown files. Script will run your code for you (sections, the whole thing, etc.) all with a shortcut key. So far I’ve been using that for testing Python scripts. Oh yes, and the markdown editor supports word completion out of the box too, not code, just normal words, which is more useful than it sounds.

While I’ve been Googling Atom to find the links to put in this post I have found two really cool things that I didn’t know about. Firstly, there is the Hydrogen package, which allows Jupyter like functionality in Atom. If you don’t know what Jupyter is, you should find out, but essentially it allows you to weave together your code and output, just like you can with RMarkdown.

And secondly Atom themselves have just released teletype which is a tool that allows collaboration on code files right inside the Atom editor. I don’t really need to do that, not that I can think of anyway, but you have to admit it’s pretty awesome. They’ve solved a lot of the problems with code collaboration elsewhere, as well, have a look at the blog post for more details.

So go give Atom a try. I’ll try to post any more Atom-related awesomeness that I see on my travels.