transition_manual.Rmd

---
title: "transition_manual"
author: "Adam Gruer + Saskia Freytag"
date: "22/11/2018"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(gganimate)
library(okcupiddata)
library(emo)
library(ggtextures)
theme_set(theme_minimal())
```

## Explain transition_manual

A static plot
```{r static plot}
cars_plot <- ggplot(mtcars, aes(disp, mpg), ) + 
  geom_point(colour = "purple", size = 3) 
cars_plot

```

Facet by cylinder
```{r a facet}
cars_plot + facet_wrap(~cyl)

```

##Animate

Transition manual will show one frame per level of the supplied variable.
The `{current_frame}` can be used anywhere that accepts a string (I think!)
to display the value of the 'frame' variable at each step of the animation.

```{r animate}
cars_plot + transition_manual(cyl) +
  labs(title = "{current_frame}")
```

## More complex example

In this example we will be using the `okcupiddata` `r emo::ji("package")` `r emo::ji("heart")` to visualize how feelings about pets change with star sign, sex and relationship status. First let's begin with some data wrangling.   

Intitally, we extract the columns we are interested in in the `profiles` data.

```{r data-load}
data(profiles)
profiles_red <- profiles %>% select(pets, sex, status, sign)
```

Unfortunatley, the `sign` column does not simply contain the 12 star signs, but includes people's feelings about the importance of star signs. We will delete these for now. We will then ensure that the sign column is a factor with ordered levels for the different star signs. 

```{r sign-edit}
profiles_red <- profiles_red %>% mutate(sign = str_extract(sign, "[a-z']+[[:space:]]")) %>% mutate(sign = gsub(" ", "", sign))
profiles_red <- profiles_red %>% 
  mutate(sign = factor(sign, levels=c("aries", "taurus", "gemini", "cancer",
                                      "leo", "virgo", "libra", "scorpio",
                                      "sagittarius", "capricorn", "aquarius",
                                      "pisces"))) 
```

The next step is to entangle the `pets` column, which includes people's likes and dislikes of pets as well as their pet ownership. We will disentangle all of these into three new columns that we will call `pets_dislike`, `pets_like` and `pets_has`.

```{r pets-edit}
profiles_red <- profiles_red %>% 
  mutate(pets_dislike = case_when(
    str_detect(pets, "dislikes dogs and dislikes cats") ~ "dogs_cats",
    str_detect(pets, "dislikes dogs") ~ "dogs", 
    str_detect(pets, "dislikes cats") ~ "cats"))
profiles_red <- profiles_red %>% mutate(sign = gsub(" ", "", sign))
profiles_red <- profiles_red %>% 
  mutate(pets_like = case_when(
    str_detect(pets, "likes dogs and likes cats") ~ "dogs_cats",
    str_detect(pets, "likes dogs") ~ "dogs", 
    str_detect(pets, "likes cats") ~ "cats"))
profiles_red <- profiles_red %>% 
  mutate(pets_has = case_when(
    str_detect(pets, "has dogs and has cats") ~ "dogs_cats",
    str_detect(pets, "has dogs") ~ "dogs", 
    str_detect(pets, "has cats") ~ "cats"))
```

Also let's get rid of individuals that did not answer the star sign question.

```{r delete-na}
profiles_red <- profiles_red %>% filter(!is.na(sign))
```

We will visualize individuals that dislike pets first. In order to make it look way, way cooler, we will use the `ggtextures` `r emo::ji("package")` to add some much needed fun into this plot. This is super easy as all you have to do is to find some images you like and use the wonderful functions `geom_textured_bar` in combination with `scale_image_manual`.

```{r dislike-plot}
profiles_red2 <- profiles_red %>% filter(!is.na(pets_dislike))

images = c(
 dogs= "https://www.doggroomingtutorial.com/wp-content/uploads/2013/03/hairless-dogs.jpg",
 cats= "https://cf.ltkcdn.net/cats/images/std/143224-306x425-Full-shot-of-Sphynx-cat.jpg",
 dogs_cats = "https://us.123rf.com/450wm/fotojagodka/fotojagodka1704/fotojagodka170400004/76409120-canadian-sphynx-cat-and-thai-ridgeback-dog-in-front-of-white-background.jpg?ver=6"
)

dislike_plot <- ggplot(profiles_red2, aes(sex, image = pets_dislike)) +
  geom_textured_bar() +
  scale_image_manual(values = images)

dislike_plot + transition_manual(sign) + labs(title="{current_frame}")
```

Finally, we will visualize peoples like of pets by their relationship status and star sign. Instead of doing it with counts, we will visualize the proportions!

```{r like-plot}
profiles_red3 <- profiles_red %>% filter(!is.na(pets_like))
like_plot <- ggplot(profiles_red3, aes(x = status)) + geom_bar(aes(fill=pets_like), position="fill") 

images = c(
 dogs= "https://www.akc.org/wp-content/themes/akc/component-library/assets//img/welcome.jpg",
 cats= "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS2WhOti17crdhQ5YaH0tWnaPwy-EL7rVEWpyH_VABFAmjfA8mZKA",
 dogs_cats = "https://www.southernmallee.sa.gov.au/webdata/resources/images/Cat_and_Dog.jpg"
)

like_plot <- ggplot(profiles_red3, aes(status, image = pets_like, class=pets_like)) +
  geom_textured_bar(position="fill") +
  scale_image_manual(values = images) +
  ylab("proportion")

like_plot + transition_manual(sign) + labs(title="{current_frame}")
```