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Remove Morley example

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hadley 2016-10-04 08:28:46 -05:00
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EDA.Rmd
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@ -63,21 +63,7 @@ So far, all the data you've seen so far has been tidy. In real-life, most data i
## Variation
**Variation** is the tendency of the values of a variable to change from measurement to measurement. You can see variation easily in real life; if you measure any continuous variable twice, you will get two different results. This is true even if you measure quantities that are constant, like the speed of light (below). Each of your measurements will include a small amount of error that varies from measurement to measurement.
```{r, variation, echo = FALSE}
old <- options(digits = 7)
mat <- as.data.frame(matrix(morley$Speed + 299000, ncol = 10))
knitr::kable(
mat,
col.names = rep("", ncol(mat)),
caption = "The speed of light is a universal constant, but variation due to measurement error obscures its value. In 1879, Albert Michelson measured the speed of light 100 times and observed 30 different values (in km/sec)."
)
options(old)
```
Categorical variables can also vary if you measure across different subjects (e.g. the eye colors of different people), or different times (e.g. the energy levels of an electron at different moments).
**Variation** is the tendency of the values of a variable to change from measurement to measurement. You can see variation easily in real life; if you measure any continuous variable twice, you will get two different results. This is true even if you measure quantities that are constant, like the speed of light. Each of your measurements will include a small amount of error that varies from measurement to measurement. Categorical variables can also vary if you measure across different subjects (e.g. the eye colors of different people), or different times (e.g. the energy levels of an electron at different moments).
Every variable has its own pattern of variation, which can reveal interesting information. The best way to understand that pattern is to visualise the distribution of variable's values.
### Visualising distributions