Ensure every chapter has a heading
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title: Communicate your work
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title: Communicate your work
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---
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---
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# Communicate your work
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Reproducible, literate code is the data science equivalent of the Scientific Report (i.e, Intro, Methods and materials, Results, Discussion).
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Reproducible, literate code is the data science equivalent of the Scientific Report (i.e, Intro, Methods and materials, Results, Discussion).
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title: Databases
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title: Databases
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---
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---
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# Databases
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### Two-table verbs
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### Two-table verbs
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Each two-table verb has a straightforward SQL equivalent:
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Each two-table verb has a straightforward SQL equivalent:
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title: Model
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title: Model
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---
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---
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# Model
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A model is a function that summarizes how the values of one variable vary in response to the values of other variables. Models play a large role in hypothesis testing and prediction, but for the moment you should think of models just like you think of statistics. A statistic summarizes a *distribution* in a way that is easy to understand; and a model summarizes *covariation* in a way that is easy to understand. In other words, a model is just another way to describe data.
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A model is a function that summarizes how the values of one variable vary in response to the values of other variables. Models play a large role in hypothesis testing and prediction, but for the moment you should think of models just like you think of statistics. A statistic summarizes a *distribution* in a way that is easy to understand; and a model summarizes *covariation* in a way that is easy to understand. In other words, a model is just another way to describe data.
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This chapter will explain how to build useful models with R.
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This chapter will explain how to build useful models with R.
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```
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```
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```{r}
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```{r}
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library(splines)
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tidy(lm(income ~ ns(education, knots = c(10, 17)), data = heights))
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tidy(lm(income ~ ns(education, knots = c(10, 17)), data = heights))
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tidy(lm(income ~ ns(education, df = 4), data = heights))
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tidy(lm(income ~ ns(education, df = 4), data = heights))
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```
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```
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```{r}
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```{r}
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gam(income ~ s(education), data = heights)
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gam(income ~ s(education), data = heights)
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ggplot(data = heights, mapping = aes(x= education, y = income)) +
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ggplot(data = heights, mapping = aes(x = education, y = income)) +
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geom_point() +
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geom_point() +
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geom_smooth(method = gam, formula = y ~ s(x))
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geom_smooth(method = gam, formula = y ~ s(x))
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```
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```
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title: Save time by programming
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title: Save time by programming
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---
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---
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# Programming
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Computer-human communication matters.
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Computer-human communication matters.
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library(magrittr)
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library(magrittr)
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```
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```
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## Robust code
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# Robust code
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(This is an advanced topic. You shouldn't worry too much about it when you first start writing functions. Instead you should focus on getting a function that works right for the easiest 80% of the problem. Then in time, you'll learn how to get to 99% with minimal extra effort. The defaults in this book should steer you in the right direction: we avoid teaching you functions with major suprises.)
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(This is an advanced topic. You shouldn't worry too much about it when you first start writing functions. Instead you should focus on getting a function that works right for the easiest 80% of the problem. Then in time, you'll learn how to get to 99% with minimal extra effort. The defaults in this book should steer you in the right direction: we avoid teaching you functions with major suprises.)
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title: Do science with data
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title: Do science with data
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---
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---
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# Do science with data
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The scientific method guides data science. Data science solves known problems with the scientific method.
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The scientific method guides data science. Data science solves known problems with the scientific method.
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title: Understand your data
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title: Understand your data
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---
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---
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# Understand your data
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Data poses a cognitive problem; Data comprehension is a skill.
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Data poses a cognitive problem; Data comprehension is a skill.
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2
work.Rmd
2
work.Rmd
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title: Work with your data
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title: Work with your data
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---
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---
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# Work with your data
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With data, the relationships between values matter as much as the values themselves. Tidy data encodes those relationships.
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With data, the relationships between values matter as much as the values themselves. Tidy data encodes those relationships.
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