datasaurus.Rmd

---
title: "datasaurus"
author: "Aurélien Ginolhac"
date: "5/7/2017"
output:
  unilur::tutorial_html_solution:
    collapse: TRUE
    solution_suffix: _exercise
  unilur::tutorial_html: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(knitr)
library(kableExtra)
options(knitr.table.format = "html")
```

### datasaurus

- check if you have the package `datasauRus` installed

```{r}
library(datasauRus)
```

- should return nothing. If `there is no package called ‘datasauRus’` appears, it means that the package needs 
to be installed. Use this:

```{r, eval=FALSE}
install.packages("datasauRus")
```

### Explore the dataset

Since we are dealing with a `tibble`, we can just type

```{r, eval = FALSE}
datasaurus_dozen
```


only the first **10** rows are displayed.

```{r, echo = FALSE}
datasaurus_dozen %>%
  head(10) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)
```

- what are the dimensions of this dataset? Rows and columns?

```{r, solution = TRUE}
# dim() returns the dimensions of the data frame, i.e number of rows and columns
dim(datasaurus_dozen)
# ncol() only number of columns
ncol(datasaurus_dozen)
# nrow() only number of rows
nrow(datasaurus_dozen)
```

- assign the `datasaurus_dozen` to the `datasaurus_dozen` object. This aims at populating the **Global Environment**

```{r, solution = TRUE}
datasaurus_dozen <- datasaurus_dozen
```


- using Rstudio, those dimensions are now also reported within the interface, where?

```{asis, solution = TRUE}
in the Environment panel -> Global Environment
```

### How many datasets are present?

```{asis,  boxtitle = "Tip", box = "lightblue"}
you want to count the number of **unique** elements in the column **dataset**.
The function **length()** returns the length of a vector, such as the unique elements
```

```{r, solution = TRUE}
unique(datasaurus_dozen$dataset) %>% length()
```


### Check summary statistics per dataset

- compute the mean of the `x` & `y` column. For this, you need to `group_by()` the appropriate column and then `summarise()`

```{asis,  boxtitle = "Tip", box = "lightblue"}
In `summarise()` you can define as many new columns as you wish. No need to call it for every single variable.
```

```{r, eval = FALSE, solution = TRUE}
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(mean_x = mean(x),
            mean_y = mean(y))
```

```{r, echo = FALSE, solution = TRUE}
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(mean_x = mean(x),
            mean_y = mean(y)) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)
```

- compute the standard deviation of the `x` & `y` column in a same way

```{r, eval = FALSE, solution = TRUE}
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(sd_x = sd(x),
            sd_y = sd(y))

```
```{r, echo = FALSE, solution = TRUE}
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(sd_x = sd(x),
            sd_y = sd(y)) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)
```

- bonus: do then all in one go using `summarise_if` so we exclude the `dataset` column and compute the others

```{r, eval = FALSE, solution = TRUE}
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise_if(is.double, funs(mean = mean, sd = sd))
```
```{r, echo = FALSE, solution = TRUE}
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise_if(is.double, funs(mean = mean, sd = sd)) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)
```


- what can you conclude?

```{asis, solution = TRUE}
all mean and sd are the same for the 13 datasets
```

### Plot the _datasauRus_ 

- plot the `datasaurus_dozen` with `ggplot` such the **aesthetics** are `aes(x = x, y = y)` 
with the **geometry** `geom_point()`

```{asis,  boxtitle = "Tip", box = "lightblue"}
the `ggplot()` and `geom_point()` functions must be linked with a **+** sign
```

```{r, fig.height = 8, fig.asp = 1, solution = TRUE}
ggplot(datasaurus_dozen, aes(x = x, y = y)) +
  geom_point()
```

- reuse the above command, and now colored by the `dataset` column

```{r, fig.height = 8, fig.asp = 1, solution = TRUE}
ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset)) +
  geom_point()
```

- expand now by getting one `dataset` per **facet**

```{r, fig.height = 8, fig.asp = 1, solution = TRUE}
ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset)) +
  geom_point() +
  facet_wrap(~ dataset, ncol = 3)
```


- tweak the theme and use the `theme_void` and remove the legend

```{r, fig.height = 8, fig.asp = 1, solution = TRUE}
ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset)) +
  geom_point() +
  theme_void() +
  theme(legend.position = "none") +
  facet_wrap(~ dataset, ncol = 3)
```

- are the datasets actually that similar?

```{asis, solution = TRUE}
no ;) We were fooled by the summary stats
```

### Animation 

- install the `gganimate` package, its dependencies will be automatically installed.
```{r, eval = FALSE, solution = TRUE}
install.packages("gganimate")
```

- add the `dataset` variable to the `frame` argument in the `aes()` function call

```{r, solution = TRUE}
library(gganimate)

p <- ggplot(datasaurus_dozen, aes(x = x, y = y, frame = dataset)) +
  geom_point() +
  theme_gray(20) +
  theme(legend.position = "none")

gganimate(p, title_frame = TRUE, "./img/dino.gif")
```

```{asis, solution = TRUE}
![](./img/dino.gif)
```



### Conclusion

> never trust summary statistics alone; always visualize your data | Alberto Cairo

**Authors**

- Alberto Cairo, (creator)
- Justin Matejka
- George Fitzmaurice
- Lucy McGowan

from this [post](https://itsalocke.com/datasaurus-now-cran/)