R & ggplot2 go great together, but the resulting graphs often feel like they could use a makeover. Don't worry - we're here to help!

For the purposes of this tutorial, I'm going to assume you've already got R & ggplot2 installed; if that's not the case, you can get R here and you can install ggplot2 by running the following command once R is installed and open:

`install.packages('ggplot2')`

Great, once that's all sorted, let's load up ggplot2:

`library(ggplot2)`

Put this Exam Anxiety data set (Field, 2013) in your working directory, and run the following command to bring the data in as a dataframe:

`examData = read.delim('Exam Anxiety.dat', header = TRUE)`

Great! Let's put together a simple scatterplot in ggplot2, comparing self-reported anxiety scores to exam scores:

`ggplot(examData, aes(y = Anxiety, x = Exam)) + geom_point()` Let's talk about what's going on in this command before we go further.

The portion before the + tells ggplot what dataframe to use, as well as which columns to use for the axes. You could stop here and have a perfectly valid command, but nothing would get plotted - ggplot2 needs to be told exactly what to do with this data. You can add instructions to ggplot telling it what to do with this data via the + operator. Here, we're telling ggplot that we want it to use points.

`ggplot(examData, aes(y = Anxiety, x = Exam)) + geom_point() + geom_smooth()` See? We add new elements by using the + operator. This simple plotting stuff should be old hat to you by now, so we're going to use gender to group the results, and add some contrasting color & point shapes:

`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth()` We added the group instructions to the base ggplot command for a few reasons; we can add it to individual commands after that point but we'd have to retype it. Since both geom_point and geom_smooth use the data, it makes sense to centralize it!

I find those confidence regions visually distracting; let's clean up the graph by getting rid of them:

`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE)` Here, we told the geom_smooth function (which draws that regression line) to ditch the confidence region with the se=false argument.

`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE) + ylab('Self-reported anxiety rating') + xlab('Exam score')` The ylab & xlab let you control the labels for their respective axes. I'm not happy with the scale on the x axis; while I'm fine with quartiles on anxiety ratings I think that exam scores will be easier to analyze if we look at 10 point intervals. Let's make that happen!

`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE) + ylab('Self-reported anxiety rating') + scale_x_continuous('Exam score', breaks=seq(0, 100, 10))` We had to get rid of the xlab command - it's a shortcut for the labeling done in the scale_x_continuous command (and there's a corresponding discrete version). Let's change those x tick labels to something wordier:

`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE) + ylab('Self-reported anxiety rating') + scale_x_continuous('Exam score', breaks=seq(0, 100, 10), labels=c('Zero', 'Ten', 'Two tens', 'Ten and two tens', 'Two score', 'L', 'Ten sixes', 'Five fourteens', 'Twenty fours', 'Possession', 'A century'))` Huh. Those labels are really cool, but I'd like to make them slanted so they don't overlap and I can read them!

`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE) + ylab('Self-reported anxiety rating') + scale_x_continuous('Exam score', breaks=seq(0, 100, 10), labels=c('Zero', 'Ten', 'Two tens', 'Ten and two tens', 'Two score', 'L', 'Ten sixes', 'Five fourteens', 'Twenty fours', 'Possession', 'A century')) + theme(axis.text.x = element_text(angle = 50, hjust = 1))` I forgot a title for this graph! Silly me!
`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE) + ylab('Self-reported anxiety rating') + scale_x_continuous('Exam score', breaks=seq(0, 100, 10), labels=c('Zero', 'Ten', 'Two tens', 'Ten and two tens', 'Two score', 'L', 'Ten sixes', 'Five fourteens', 'Twenty fours', 'Possession', 'A century')) + theme(axis.text.x = element_text(angle = 50, hjust = 1)) + labs(title='A whimsical take on the relationship between anxiety and exam score')` My, that's ugly. Let's give that title a little breathing room.

`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE) + ylab('Self-reported anxiety rating') + scale_x_continuous('Exam score', breaks=seq(0, 100, 10), labels=c('Zero', 'Ten', 'Two tens', 'Ten and two tens', 'Two score', 'L', 'Ten sixes', 'Five fourteens', 'Twenty fours', 'Possession', 'A century')) + theme(axis.text.x = element_text(angle = 50, hjust = 1)) + labs(title='A whimsical take on the relationship between anxiety and exam score') + theme(plot.title = element_text(vjust = 1))` I want serifs!

`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE) + ylab('Self-reported anxiety rating') + scale_x_continuous('Exam score', breaks=seq(0, 100, 10), labels=c('Zero', 'Ten', 'Two tens', 'Ten and two tens', 'Two score', 'L', 'Ten sixes', 'Five fourteens', 'Twenty fours', 'Possession', 'A century')) + theme(axis.text.x = element_text(angle = 50, hjust = 1)) + labs(title='A whimsical take on the relationship between anxiety and exam score') + theme(plot.title = element_text(vjust = 1)) + theme(text = element_text(family = 'serif'))` That's much more whimsical. Let's get rid of the background grey stuff in one fell swoop:
`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE) + ylab('Self-reported anxiety rating') + scale_x_continuous('Exam score', breaks=seq(0, 100, 10), labels=c('Zero', 'Ten', 'Two tens', 'Ten and two tens', 'Two score', 'L', 'Ten sixes', 'Five fourteens', 'Twenty fours', 'Possession', 'A century')) + theme(axis.text.x = element_text(angle = 50, hjust = 1)) + labs(title='A whimsical take on the relationship between anxiety and exam score') + theme(plot.title = element_text(vjust = 1)) + theme(text = element_text(family = 'serif')) + theme_bw()` Hey! What happened to our label shenanigans? I want my serifs back! I bet that ggplot tries to do everything in order, so later stuff can override earlier stuff:
`ggplot(examData, aes(y = Anxiety, x = Exam, group = Gender, color = Gender, shape = Gender)) + geom_point() + geom_smooth(se=FALSE) + ylab('Self-reported anxiety rating') + scale_x_continuous('Exam score', breaks=seq(0, 100, 10), labels=c('Zero', 'Ten', 'Two tens', 'Ten and two tens', 'Two score', 'L', 'Ten sixes', 'Five fourteens', 'Twenty fours', 'Possession', 'A century')) + labs(title='A whimsical take on the relationship between anxiety and exam score') + theme_bw() + theme(axis.text.x = element_text(angle = 50, hjust = 1)) + theme(text = element_text(family = 'serif')) + theme(plot.title = element_text(vjust = 1))` We've messed with a lot of different elements, learned that you can apply formatting anywhere from one object to the entire plot, that there are often a bunch of ways to do the same thing, and also that theming choices are applied in order. Next time, we'll look at box plots, error bars, and pie charts!

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