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Use tibble and tribble everywhere 

Fixes #411
This commit is contained in:
hadley 2016-10-03 07:29:36 -05:00
parent 98dc37c2f4
commit ac4a138ed6
3 changed files with 8 additions and 8 deletions
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4
model-basics.Rmd
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@ -327,7 +327,7 @@ You've seen formulas before when using `facet_wrap()` and `facet_grid()`. In R,
The majority of modelling functions in R use a standard conversion from formulas to functions. You've seen one simple conversion already: `y ~ x` is translated to `y = a_1 + a_2 * x`. If you want to see what R actually does, you can use the `model_matrix()` function. It takes a data frame and a formula and returns a tibble that defines the model equation: each column in the output is associated with one coefficient in the model, the function is always `y = a_1 * out1 + a_2 * out_2`. For the simplest case of `y ~ x1` this shows us something interesting: The majority of modelling functions in R use a standard conversion from formulas to functions. You've seen one simple conversion already: `y ~ x` is translated to `y = a_1 + a_2 * x`. If you want to see what R actually does, you can use the `model_matrix()` function. It takes a data frame and a formula and returns a tibble that defines the model equation: each column in the output is associated with one coefficient in the model, the function is always `y = a_1 * out1 + a_2 * out_2`. For the simplest case of `y ~ x1` this shows us something interesting:
```{r} ```{r}
df <- frame_data( df <- tibble::tribble(
~y, ~x1, ~x2, ~y, ~x1, ~x2,
4, 2, 5, 4, 2, 5,
5, 1, 6 5, 1, 6
@ -356,7 +356,7 @@ The following sections expand on how this formula notation works for categorcal
Generating a function from a formula is straight forward when the predictor is continuous, but things get a bit more complicated when the predictor is categorical. Imagine you have a formula like `y ~ sex`, where sex could either be male or female. It doesn't make sense to convert that to a formula like `y = x_0 + x_1 * sex` because `sex` isn't a number - you can't multiply it! Instead what R does is convert it to `y = x_0 + x_1 * sex_male` where `sex_male` is one if `sex` is male and zero otherwise: Generating a function from a formula is straight forward when the predictor is continuous, but things get a bit more complicated when the predictor is categorical. Imagine you have a formula like `y ~ sex`, where sex could either be male or female. It doesn't make sense to convert that to a formula like `y = x_0 + x_1 * sex` because `sex` isn't a number - you can't multiply it! Instead what R does is convert it to `y = x_0 + x_1 * sex_male` where `sex_male` is one if `sex` is male and zero otherwise:
```{r, echo = FALSE} ```{r, echo = FALSE}
df <- frame_data( df <- tibble::tribble(
~ sex, ~ response, ~ sex, ~ response,
"male", 1, "male", 1,
"female", 2, "female", 2,

2
model-many.Rmd
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@ -376,7 +376,7 @@ df %>%
Another example of this pattern is using the `map()`, `map2()`, `pmap()` from purrr. For example, we could take the final example from [Invoking different functions] and rewrite it to use `mutate()`: Another example of this pattern is using the `map()`, `map2()`, `pmap()` from purrr. For example, we could take the final example from [Invoking different functions] and rewrite it to use `mutate()`:
```{r} ```{r}
sim <- tibble::frame_data( sim <- tibble::tribble(
~f, ~params, ~f, ~params,
"runif", list(min = -1, max = -1), "runif", list(min = -1, max = -1),
"rnorm", list(sd = 5), "rnorm", list(sd = 5),

10
tidy.Rmd
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@ -206,7 +206,7 @@ As you might have guessed from the common `key` and `value` arguments, `spread()
Carefully consider the following example: Carefully consider the following example:
```{r, eval = FALSE} ```{r, eval = FALSE}
stocks <- data_frame( stocks <- tibble::tibble(
year = c(2015, 2015, 2016, 2016), year = c(2015, 2015, 2016, 2016),
half = c( 1, 2, 1, 2), half = c( 1, 2, 1, 2),
return = c(1.88, 0.59, 0.92, 0.17) return = c(1.88, 0.59, 0.92, 0.17)
@ -232,7 +232,7 @@ As you might have guessed from the common `key` and `value` arguments, `spread()
the problem? the problem?
```{r} ```{r}
people <- frame_data( people <- tibble::tribble(
~name, ~key, ~value, ~name, ~key, ~value,
#-----------------|--------|------ #-----------------|--------|------
"Phillip Woods", "age", 45, "Phillip Woods", "age", 45,
@ -247,7 +247,7 @@ As you might have guessed from the common `key` and `value` arguments, `spread()
What are the variables? What are the variables?
```{r} ```{r}
preg <- frame_data( preg <- tibble::tribble(
~pregnant, ~male, ~female, ~pregnant, ~male, ~female,
"yes", NA, 10, "yes", NA, 10,
"no", 20, 12 "no", 20, 12
@ -353,7 +353,7 @@ Changing the representation of a dataset brings up an important subtlety of miss
Let's illustrate this idea with a very simple data set: Let's illustrate this idea with a very simple data set:
```{r} ```{r}
stocks <- data_frame( stocks <- tibble::tibble(
year = c(2015, 2015, 2015, 2015, 2016, 2016, 2016), year = c(2015, 2015, 2015, 2015, 2016, 2016, 2016),
qtr = c( 1, 2, 3, 4, 2, 3, 4), qtr = c( 1, 2, 3, 4, 2, 3, 4),
return = c(1.88, 0.59, 0.35, NA, 0.92, 0.17, 2.66) return = c(1.88, 0.59, 0.35, NA, 0.92, 0.17, 2.66)
@ -397,7 +397,7 @@ stocks %>%
There's one other important tool that you should know for working with missing values. Sometimes when a data source has primarily been used for data entry, missing values indicate that the previous value should be carried forward: There's one other important tool that you should know for working with missing values. Sometimes when a data source has primarily been used for data entry, missing values indicate that the previous value should be carried forward:
```{r} ```{r}
treatment <- frame_data( treatment <- tibble::tribble(
~ person, ~ treatment, ~response, ~ person, ~ treatment, ~response,
"Derrick Whitmore", 1, 7, "Derrick Whitmore", 1, 7,
NA, 2, 10, NA, 2, 10,