ggplot_day1:2.Rmd

---
title: "ggplot2_tutorial_2017"
author: "Chad Fibke"
date: '2017-10-13'
output: github_document
---


```{r}
library(tidyverse)
library(gapminder)
library(RColorBrewer)

```

# Day.1 Oct13

```{r scatter.plot, echo=TRUE}
summary(mpg)
glimpse(mpg)
??mpg
mpg %>% 
  ggplot(aes( x = displ, y = cty))+ 
  geom_point()+
  theme_classic()+
  geom_rect(aes(xmin = 3, xmax = 4, ymin = 15, ymax = 20), alpha = 0.0001, color = "red")
  

# This can help to highlight any data!

#xmin - (required) left edge of rectangle
#xmax - (required) right edge of rectangle
#ymin - (required) bottom edge of rectangle
#ymax - (required) top edge of rectangle
#size - (default: 0.5) line width of the rectangle's outline
#linetype - (default: 1=solid) line type of the rectangle's outline
#color - (default: NA=no outline) color of the rectangle's outline
#fill - (default: "grey20") fill color of the rectangle
#alpha - (default: 1=opaque) transparency of the rectangle's fil

```

```{r}

mpg %>% 
  ggplot(aes(x = displ, y = class, color = trans))+
  geom_point()+
  facet_wrap(~class)
```


```{r}
#dimonds and cuts
glimpse(diamonds)   

# so we will use a bar chart if we are looking at cut
diamonds %>% 
  ggplot(aes(x = cut, y = price))+
  geom_boxplot(aes(fill = cut))+
  theme_classic()+
  facet_wrap(~cut, scales = "free")


diamonds %>% 
  ggplot(aes(x = cut))+
  geom_bar(aes(fill = cut))+
  theme_classic()+
  theme(legend.box.background = element_rect()) 
# remeber we have to tell r the element line, rect or text\

```


some scatter plot
```{r}
mpg %>% 
  ggplot(aes(x = displ, y = hwy))+
  geom_point()+
  geom_smooth(color = "red", span = 0.2) # the smaller the span then the sliding widow will be very small!
```


```{r}
mpg %>% 
  ggplot(aes( x = displ))+
  geom_smooth( aes(y = hwy , color = "hwy"), size = 1.3, span = 100)+
  geom_smooth( aes(y = cty , color = "cty"), size = 1.3, span = 0.2)+
   geom_point(aes( y = cty), color = "red")+
  geom_point(aes(y=hwy), color = "blue")+
  theme_classic()+
  geom_hline(yintercept = mean(mpg$hwy))+
    geom_hline(yintercept = mean(mpg$cty))
  
#If you speficy your asthetics at the begining, it is carried out for the rest of the layers. Now once we add a layer we can overright these aes **per** layer
```

# now we can extract the the graphs 

```{r}
#ggsave()
```

### Here we work with some dplyr and gapminder
```{r}
gapminder %>% 
  ggplot(aes( x = year, y = lifeExp, group = country))+
  geom_line(aes(color = continent), alpha = 0.5)+
  geom_point(alpha = 0.1)+
  theme_classic()+
  facet_wrap(~continent, ncol = 5)+
  theme(legend.box.background = element_rect(),
        strip.background = element_rect(fill = "orange"),
        axis.text.x = element_text(angle = 90))

#remeber the theme only going to tweak the alredy established axis and all of that1

# for facet grid (rows~columns)
 #(if you see row ~ . ) the . is nothing  or we can even add multiple matrix combos (.~x1 + x2) 
```

# faceting

```{r}
mpg %>% 
  ggplot(aes( x = cyl, group = class, fill = class))+
  geom_histogram(binwidth = 1, color = "black")+
  facet_wrap(~class)+
  theme_bw()
```


# ggplot day 2 
  talks about scaling:
  
  #c("fixed", "free_x", "free_y", "free")) : 'arg' should be one of “fixed”, “free_x”, “free_y”, “free”
```{r}
mpg %>% 
  ggplot(aes( x = cyl, group = class, fill = class))+
  geom_histogram(binwidth = 1, color = "black")+
  facet_wrap(~class, scales = "free_x")+
  theme_bw()
```

```{r}

msleep %>% 
  ggplot(aes(x = sleep_cycle, y = sleep_total, color = vore)) +
  geom_point()+
  ggtitle("how things sleep")+
  xlab("Sleep Cycle")+
  ylab("Total amount of sleep")+
  scale_colour_manual( name = "Diet", #this will allow us to pick what we want in the legend
                       values = c("red", "blue", "green", "black"),
                       breaks = c("carni", "herbi", "insecti","omni"))
 
  
  
```
# lets add some color:

```{r}
display.brewer.all()

```
```{r}
msleep %>% 
  ggplot(aes(x= brainwt, fill = vore))+
  geom_histogram(binwidth = 1 )+
  scale_x_log10()+
  scale_fill_brewer("RDGry")
```

# how to work with positioning:

```{r}
diamonds %>% 
  ggplot(aes(color, fill = cut))+
  geom_bar(position = "dodge")
  
```

```{r}
mpg %>% 
ggplot(aes(x= cty, y = hwy))+
  geom_point()+
  geom_text(aes(label = class ))+
  annotate("label", 15, 40, label = "this is not a drill") #the "lable" will give us a outlined box!
```
exercise:
```{r}
msleep %>% 
  ggplot(aes(x = sleep_total, y =bodywt))+
  geom_text(aes(label = genus, color = genus))+
  scale_y_log10()+
  theme(legend.position = "")
  
  
```

#exerice with overplotting:

```{r}
diamonds %>% 
  ggplot(aes(x = carat, y = price))+
  geom_point(alpha = 0.2)

ggsave("plot.pdf", device = "pdf")

```

![](plot.pdf)