Data_Jamboree_ggplot_printout.Rmd

---
title: "Data Jamboree- ggplot2"
output: html_document
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(fig.show='hide', warning=FALSE, message=FALSE)
```

Link to paper on dataset: http://faculty.washington.edu/kenrice/heartgraphs/effectivegraphs.pdf

Dataset url: http://faculty.washington.edu/kenrice/heartgraphs/

###Setup

####Import packages (once per machine)
```{r install_packages, eval=FALSE}
install.packages("readr")
install.packages("ggplot2")
install.packages("ggbeeswarm") 
install.packages("dplyr")
install.packages("MASS")
install.packages("hexbin")
```

####Load packages (once per R session)
```{r load_packages}
library(readr)
library(ggplot2)
library(ggbeeswarm)
library(dplyr)
library(MASS)
library(hexbin)
```

####Import data (using readr)

- Use readr package to import our csv 
- assign the data file to an R object using (<-)

```{r import_data}
heart <- read_csv("http://faculty.washington.edu/kenrice/heartgraphs/nhaneslarge.csv", na=".") #na= tells R that . is an na value
```


- run the command head ( ) and check with your partner to see if you got the same output 
```{r head, eval=FALSE}
head(heart)
```

## Univariate Plots: Folate intake by gender

### Basic ggplot Anatomy

ggplot code makes graphs by layering imformation on top of an empty plot 
```{r basic_ggplot}
ggplot(heart, aes(x=DR1TFOLA)) 
#This says we want ggplot to use the data.frame heart and to plot DR1TFOLA on the x-axis.
```


### Histogram

create a histogram by adding a geom layer and using the "+" sign
```{r histogram}
ggplot(heart, aes(x=DR1TFOLA)) +
  geom_histogram()
```

#### Labels
change x-axis label from "DR1TFOLA" to "Folate Intake"
```{r change_folate_label}
ggplot(heart, aes(x=DR1TFOLA)) +
  geom_histogram() +
  labs(x = "Folate intake") #x-axis label
```

#### Changing Colors

change outline color
```{r histogram_outline, warning=TRUE}
ggplot(heart, aes(x=DR1TFOLA)) +
  geom_histogram(colour = "white") + 
  labs(x = "Folate intake")
```

change the fill of the bars
```{r histogram_fill}
ggplot(heart, aes(x=DR1TFOLA)) +
  geom_histogram(colour = "white", fill = "peachpuff") + #yes, that is a name of a color
  labs(x = "Folate intake")
```

#### Documentation and the Help Command
language will be different for each type of graph so make sure to reference your documentation by typing "?command"
```{r help}
?geom_histogram
```

#### Histogram Bins and ggplot Defaults
All of our histogram plots have given us this warning:
```{r error}
#`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
```
*This warning tells us that this is a default that ggplot chose for us given the data it has.*

change bin size
```{r histogram_bin}
ggplot(heart, aes(x=DR1TFOLA)) + 
  geom_histogram(colour = "white", fill = "peachpuff", bins = 50)
```

#### Facet Wrap

add facet_wrap for split plots based on a variable
```{r histogram_facet}
ggplot(heart, aes(x = DR1TFOLA)) +
  geom_histogram(colour = "white", fill = "peachpuff", bins = 50) +
  labs(x = "Folate intake") +
  facet_wrap(~gender)
```


### Density Plot

change the geom_ histogram layer to geom_density 
```{r density_facet}
ggplot(heart, aes(x = DR1TFOLA)) +
  geom_density(colour = "white", fill = "peachpuff") +  #Make sure to take out bin=50
  labs(x = "Folate intake") +
  facet_wrap(~gender)
```

#### Adding a third dimension onto one plot

plot all three variables from last plot (folate, density, and gender) on one by using color to differentiate gender  

```{r density}
ggplot(heart, aes(x = DR1TFOLA)) +
  geom_density(aes(colour = gender)) + #Notice moving the colour into aesthetics
  labs(x = "Folate intake") 
```

## Univariate Plots: Systolic blood pressure by gender

###Dotplot - Stripchart

create a stripchart of `BPXSAR`(or systolic blood pressure) by gender 
```{r dotplot}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_point() 
```

#### Customizing our Dotplot 

add an alpha in the geom layer to make our points more transparent 

 - *Alpha works on a scale from 0 (clear) to 1 (opaque)*

```{r alpha}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_point(alpha = .1)
```

add jitter to space out our points 
```{r jitter}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_jitter(alpha = .1)
```

Notes about Jitter:

- jitter automatically adds space (noise) to both the height and the width of your plots

- which variables won't be effected by noice - categorical

change jitter to width only
```{r jitter_width}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_jitter(alpha = .1, width = .5, height = 0)
```

change our x- and y-axis labels 
```{r y_axis_bp}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_jitter(alpha = .1, width = .5, height = 0) +
  labs(x = "", y = "Systolic BP (mmHg)")
```

### Beeswarm Plot

```{r beeswarm}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_beeswarm(alpha = .2) +
  labs(x = "Systolic BP (mmHg)", y = "")
```


#### Stats layers

add statistics on top of your plots with stat_summary
```{r beeswarm_mean}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_beeswarm(alpha = .2) +
  stat_summary(fun.y = "mean", geom = "point", colour = "orange") +
  labs(x = "Systolic BP (mmHg)", y = "")
```


#### Layering geom types

add other plot types as geom layers 
```{r boxplot_beeswarm}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_boxplot(outlier.shape = NA) +   #outlier.shape=NA tells boxplot not to plot outliers
  geom_beeswarm(alpha = .2) +  
  labs(x = "Systolic BP (mmHg)", y = "")
```

##### Layer order
order is important (will change the order things are plotted)
```{r beeswarm_boxplot}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_beeswarm(alpha = .2) +  
  geom_boxplot(outlier.shape = NA) +
  labs(x = "Systolic BP (mmHg)", y = "")
```



### Extra Challenge
#### Violin plot

If that was easy for you, give this a shot. Try and recreate the plots below using what you learned above.

*Hint: We are using "geom_violin".*

```{r violin, echo=FALSE, fig.show='asis', fig.height=3, fig.width=4}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_violin(alpha = .2) +
  labs(x = "Systolic BP (mmHg)", y = "")
```

Try adding some statistics to your plot. You can start with the mean and the median.
```{r violin_stats, echo=FALSE, fig.show='asis', fig.height=3, fig.width=4}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_violin(alpha = .2) +
  stat_summary(fun.y = "mean", geom = "point", colour = "orange") +
  stat_summary(fun.y = "median", geom = "point", colour = "blue") +
  labs(x = "Systolic BP (mmHg)", y = "")
```

Try adding another geom layer.

*Hint: Do you want to include your outlier points?*

```{r violin_boxplot, echo=FALSE, fig.show='asis', fig.height=3, fig.width=4}
ggplot(heart, aes(x = gender, y = BPXSAR)) +
  geom_violin(alpha = .2) +
  geom_boxplot(width = .05) +
  labs(x = "Systolic BP (mmHg)", y = "")
```

# Bivariate Plots: Age & Systolic Blood Pressure

Simple scatterplot
```{r}
ggplot(heart, aes(x = RIDAGEYR, y = BPXSAR)) +
  geom_point() +
  labs(x = "Age (years)", y = "Systolic BP (mmHg)")
```

If you have big n, try hexbin plot
```{r}
ggplot(heart, aes(x = RIDAGEYR, y = BPXSAR)) +
  geom_hex() +
  labs(x = "Age (years)", y = "Systolic BP (mmHg)")
```

Add linear regression line with SE
```{r}
ggplot(heart, aes(x = RIDAGEYR, y = BPXSAR)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(x = "Age (years)", y = "Systolic BP (mmHg)")
```

Default is loess line
```{r}
ggplot(heart, aes(x = RIDAGEYR, y = BPXSAR)) +
  geom_point() +
  geom_smooth() +
  labs(x = "Age (years)", y = "Systolic BP (mmHg)")
```

Add splines
```{r}
library(splines)
library(MASS)
ggplot(heart, aes(x = RIDAGEYR, y = BPXSAR)) +
  geom_point() +
  stat_smooth(method = "lm", formula = y ~ ns(x, 3)) +
  labs(x = "Age (years)", y = "Systolic BP (mmHg)")
```

# Multivariable Plots: Body Mass Index & Systolic BP, by gender and age

Just copy this:
```{r}
library(dplyr)
heart2 <- heart %>% 
  mutate(age_cat = cut(RIDAGEYR,c(0,30,55,100)))
```

Recreate theirs first

```{r}
ggplot(heart2, aes(x = BMXBMI, y = BPXSAR)) +
  geom_point() +
  stat_smooth(aes(colour = gender), method = "lm") +
  facet_wrap(~age_cat) +
  labs(x = "Body Mass Index"~(kg/m^2), y = "Systolic BP (mmHg)")
```

Try with facet grid, update labels

```{r}
ggplot(heart2, aes(x = BMXBMI, y = BPXSAR)) +
  geom_point() +
  stat_smooth(aes(colour = gender), method = "lm") +
  facet_grid(gender~age_cat) +
  labs(x = "Body Mass Index"~(kg/m^2), y = "Systolic BP (mmHg)")
```

Play with colors!
```{r}
ggplot(heart2, aes(x = BMXBMI, y = BPXSAR, colour = gender)) +
  geom_point(alpha = .5) +
  stat_smooth(method = "lm") +
  facet_grid(gender~age_cat) +
  theme_minimal() +
  labs(x = "Body Mass Index"~(kg/m^2), y = "Systolic BP (mmHg)") +
  scale_color_manual(values = c("#B47CC7", "#D65F5F"), guide = FALSE)
```


```{r}
my_colors <- c("#C4AD66", "#77BEDB")
ggplot(heart2, aes(x = BMXBMI, y = BPXSAR, colour = gender)) +
  geom_point(alpha = .5) +
  stat_smooth(method = "lm") +
  facet_grid(gender~age_cat) +
  labs(x = "Body Mass Index"~(kg/m^2), y = "Systolic BP (mmHg)") +
  scale_color_manual(values = my_colors, guide = FALSE)
```