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UK -> US spelling, addresses #1160

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intro.qmd
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@@ -46,16 +46,16 @@ Once you have tidy data, a common next step is to **transform** it.
Transformation includes narrowing in on observations of interest (like all people in one city, or all data from the last year), creating new variables that are functions of existing variables (like computing speed from distance and time), and calculating a set of summary statistics (like counts or means).
Together, tidying and transforming are called **wrangling**, because getting your data in a form that's natural to work with often feels like a fight!
Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualisation and modelling.
Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualization and modelling.
These have complementary strengths and weaknesses so any real analysis will iterate between them many times.
**Visualisation** is a fundamentally human activity.
A good visualisation will show you things that you did not expect, or raise new questions about the data.
A good visualisation might also hint that you're asking the wrong question, or that you need to collect different data.
Visualisations can surprise you and they don't scale particularly well because they require a human to interpret them.
**Visualization** is a fundamentally human activity.
A good visualization will show you things that you did not expect, or raise new questions about the data.
A good visualization might also hint that you're asking the wrong question, or that you need to collect different data.
Visualizations can surprise you and they don't scale particularly well because they require a human to interpret them.
The last step of data science is **communication**, an absolutely critical part of any data analysis project.
It doesn't matter how well your models and visualisation have led you to understand the data unless you can also communicate your results to others.
It doesn't matter how well your models and visualization have led you to understand the data unless you can also communicate your results to others.
Surrounding all these tools is **programming**.
Programming is a cross-cutting tool that you use in nearly every part of a data science project.
@@ -70,7 +70,7 @@ Throughout this book, we'll point you to resources where you can learn more.
The previous description of the tools of data science is organised roughly according to the order in which you use them in an analysis (although of course you'll iterate through them multiple times).
In our experience, however, learning data ingest and tidying first is sub-optimal, because 80% of the time it's routine and boring, and the other 20% of the time it's weird and frustrating.
That's a bad place to start learning a new subject!
Instead, we'll start with visualisation and transformation of data that's already been imported and tidied.
Instead, we'll start with visualization and transformation of data that's already been imported and tidied.
That way, when you ingest and tidy your own data, your motivation will stay high because you know the pain is worth the effort.
Within each chapter, we try and adhere to a similar pattern: start with some motivating examples so you can see the bigger picture, and then dive into the details.
@@ -358,4 +358,3 @@ knitr::kable(df, format = "markdown")
```{r}
cli:::ruler()
```