lesson3.Rmd

---
title: "R Notebook"
output:
  pdf_document: default
  html_notebook: default
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 

```{r}
plot(cars)
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.

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


When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

```{r}
surveys <- read_csv('data_raw/portal_data_joined.csv')
```

```{r}
surveys_no_na <- surveys %>% 
  filter(!is.na(weight),
         !is.na(hindfoot_length),
         !is.na(sex))
head(surveys_no_na)
```

```{r}
species_counts <- surveys_no_na %>% 
  count(species_id) %>% 
  filter(n >= 50)
head(species_counts)
```

```{r}
surveys_complete <- surveys_no_na %>% 
  filter(species_id %in% species_counts$species_id)
```

Now, we're ready to make some graphs!
(Taken from https://datacarpentry.org/R-ecology-lesson/04-visualization-ggplot2.html)

GGPLOT uses the basic template:
`ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()`

```{r}
ggplot(data = surveys_complete,
       mapping = aes(x = weight,
                     y = hindfoot_length))
```

```{r}
ggplot(data = surveys_complete,
       mapping = aes(x = weight,
                     y = hindfoot_length)) +
  geom_point()
```

```{r}
ggplot(data = surveys_complete,
       mapping = aes(x = weight,
                     y = hindfoot_length)) +
  geom_point(alpha = 0.1)
```

```{r}
ggplot(data = surveys_complete,
       mapping = aes(x = weight,
                     y = hindfoot_length)) +
  geom_point(alpha = 0.1, colour = "blue")
```

```{r}
ggplot(data = surveys_complete,
       mapping = aes(x = weight,
                     y = hindfoot_length,
                     color = species_id)) +
  geom_point(alpha = 0.1)
```

Use what you just learned to create a scatter plot of weight over species_id with the plot types showing in different colors.

```{r}
ggplot(data = surveys_complete,
       mapping = aes(x = species_id,
                     y = weight,
                     color = plot_type)) +
  geom_point(alpha = 0.1)
```

```{r}
ggplot(data = surveys_complete,
       mapping = aes(x = species_id,
                     y = weight)) +
  geom_boxplot()
```

```{r}
ggplot(data = surveys_complete,
       mapping = aes(x = species_id,
                     y = weight)) +
  geom_boxplot() +
  geom_jitter(alpha=0.3, color = "tomato")
```

```{r}
ggplot(data = surveys_complete,
       mapping = aes(x = species_id,
                     y = weight,
                     color = sex)) +
  geom_jitter(alpha=0.3) + geom_boxplot(alpha=0.1)
  
```

```{r}
yearly_counts <- surveys_complete %>% 
  count(year, genus)
head(yearly_counts)
```

```{r}
ggplot(data = yearly_counts,
       mapping = aes(x = year,
                     y = n)) +
  geom_line()
```

```{r}
ggplot(data = yearly_counts,
       mapping = aes(x = year,
                     y = n,
                     group = genus)) +
  geom_line()
```

```{r}
ggplot(data = yearly_counts,
       mapping = aes(x = year,
                     y = n,
                     color = genus)) +
  geom_line()
```

```{r}
ggplot(data = yearly_counts,
       mapping = aes(x = year,
                     y = n)) +
  geom_line() +
  facet_wrap(facets = vars(genus))
```

```{r}
yearly_sex_counts <- surveys_complete %>% 
  count(year, genus, sex)
head(yearly_sex_counts)
```

```{r}
ggplot(data = yearly_sex_counts,
       mapping = aes(x = year,
                     y = n,
                     color = sex)) +
  geom_line() +
  facet_wrap(facets = vars(genus))
```

```{r}
ggplot(data = yearly_sex_counts,
       mapping = aes(x = year,
                     y = n,
                     color = sex)) +
  geom_line() +
  facet_grid(rows = vars(sex),
             cols = vars(genus))
```

```{r}
ggplot(data = yearly_sex_counts,
       mapping = aes(x = year,
                     y = n,
                     color = sex)) +
  geom_line() +
  facet_grid(rows = vars(genus))
```

```{r}
ggplot(data = yearly_sex_counts,
       mapping = aes(x = year,
                     y = n,
                     color = sex)) +
  geom_line() +
  facet_wrap(facets = vars(genus)) +
  theme_bw()
```

```{r}
yearly_weights <- surveys_complete %>% 
  group_by(year, species_id) %>% 
  summarize(mean_weight = mean(weight))
head(yearly_weights)
```

```{r}
ggplot(data = yearly_weights,
       mapping = aes(x = year,
                     y = mean_weight)) +
  geom_line() +
  facet_wrap(facets = vars(species_id)) +
  theme_minimal()
```

```{r}
weight_graph <- ggplot(data = yearly_weights,
       mapping = aes(x = year,
                     y = mean_weight)) +
  geom_line() +
  facet_wrap(facets = vars(species_id)) +
  theme_minimal()
ggsave('weight_graph.png', plot = weight_graph, width = 10, dpi=500)
```