Lab3.Rmd

---
title: "DATA1001/1901 Lab3"
subtitle: "Graphical and Numerical Summaries"
author: "© University of Sydney 2021"
output:
  html_document:
    theme: flatly
    number_sections: yes
    css: 
      - https://use.fontawesome.com/releases/v5.0.6/css/all.css
    toc: true
    toc_float: true
    toc_depth: 4
    code_folding: show
---

# Live Demo

1. We will demonstrate how to produce numerical summaries for `iris`.

```{r, eval = F}
# quantitative variable
mean(iris$Sepal.Length)
median(iris$Sepal.Length)
summary(iris$Sepal.Length)  

# qualitative variable
table(iris$Species) 

# qualitative variable + quantitative variable
table(iris$Species, iris$Sepal.Length) 
```

2. We will demonstrate how to import data into RStudio, using the Australian road fatalities data.

```{r, eval = F}
# import from a url.
road = read.csv("http://www.maths.usyd.edu.au/u/UG/JM/DATA1001/r/current/data/2016Fatalities.csv")

# import data from a folder
# getwd()  # this checks the working directory
road1 = read.csv("/data/2016Fatalities.csv")
```

<br>

# <i class="fa fa-th-list"></i> Summary

You are beginning to learn how to:

- pose research questions
- choose what graphical and numerical summaries are appropriate for given variables
- interpret the output.

<br>

Type of Variable | Referred to in R |
-------- | ------- | 
Qualitative / Categorical | `factor`
Quantitative / Numerical | `num`
| |

Type of Data | Type of Graphical Summary | In base R |
------------------------ | -------------------------- | -------------- | 
1 Qualitative Variable | Barplot | `barplot()` |
2 Qualitative Variables |  Double (clustered) Barplot |  `barplot()` |
1 Quantitative Variable | Histogram or Boxplot  | `hist()`, `boxplot()`|
2 Quantitative Variables | Scatterplot | `plot()`|
1 Quantitative & 1 Qualitative Variable | Double (comparative) boxplot | `boxplot()`|
| | |

<br>

# Have a go in base R <i class="fas fa-car"></i>

- Now you're ready to try some interesting data! Don't get bamboozled by all the code, rather see what everything does!


- Consider the Australian road fatalities from 1989 (a bigger version of the data used in Week 2 lectures). The data is sourced from [BITRE](https://bitre.gov.au/statistics/safety/fatal_road_crash_database.aspx).


## Initial Data Analysis

- Upload the data.

```{r}
# Read data from url into R
road = read.csv("http://www.maths.usyd.edu.au/u/UG/JM/DATA1001/r/current/data/AllFatalities.csv")
```

Note: An alternative way is to download the data from Canvas, store the data in `DATA1001files/data` and upload from there. You will need to use this method in future projects, when you upload your own data.

```{r,eval=F}
# Read data from url into R
road = read.csv("data/AllFatalities.csv",header=T)
```

- Produce a snapshot of the data. 
```{r,eval=F}
str(road)
```


<br>

## Research Questions

### Were there more fatalities on a certain day of the week? 

- Here we consider **1 qualitative variable**: the road fatalities across the *days of the week*.

- 1st isolate the variable Dayweek. Check how R classifies it. Produce a barplot. What is annoying about it?

```{r}
class(road$Dayweek)
barplot(table(road$Dayweek))
```

- We can re-order the categories for `dayweek` and the produce a barplot. What pattern emerges? Suggest possible reasons for it? 

```{r}
orderdayweek = ordered(road$Dayweek, levels=c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
barplot(table(orderdayweek))
barplot(table(orderdayweek),las=2)
```

<br>

### Was there any pattern in how buses were involved in fatalities, on different days of the week?

- Here we consider **2 qualitative variables**: the road fatalities across the *days of the week*, cross-classified by *bus involvement*.

- Is there any pattern?

```{r}
road1 = table(road$Bus_Involvement, road$Dayweek)
road1
barplot(road1, main = "Fatalities by Day of the Week and Bus Involvement", xlab = "Day of the week", 
    col = c("lightblue", "lightgreen"), legend = rownames(road1))
```


<br>

### Was there any pattern in how heavy rigid trucks were involved in fatalities, on different days of the week?

- Here we consider **2 qualitative variables**: the road fatalities across the *days of the week*, cross-classified by *heavy rigid truck involvement*.

- Investigate whether the involvement of heavy rigid trucks differs across the days?

```{r}
road2 = table(road$Hvy_Rigid_Truck_Involvement, road$Dayweek)
road2
barplot(road2, main = "Fatalities by Day of the Week and Heavy Rigid Involvement", xlab = "Day of the week", 
    col = c("lightpink","lightblue", "lightgreen"), legend = rownames(road2))
```

<br>

### Were there more fatalities in certain age groups?

- Here we consider **1 quantitative variable**: *fatalities*.

- 1st isolate the variable Age. How does R classify it?

```{r}
class(road$Age)
```

- Change the classification to a quantitative variable.
```{r}
road$Age = as.numeric(as.character(road$Age))
class(road$Age)
```

- Produce graphical summaries. What patterns are revealed?
```{r}
hist(road$Age, prob=T)
boxplot(road$Age)
```

- We can customise the plots.
```{r}
hist(road$Age,freq=FALSE,main="Histogram",ylab="Probabilities", col="green")
boxplot(road$Age,horizontal=TRUE,col="red")
```

<br>

### Does biological sex affect the number of fatalities across age groups?

- Here we consider **1 quantitative variable divided by 1 qualitative variable**.

- Control for biological sex - ie consider fatalities by age divided by biological sex.
```{r}
ageF = road$Age[road$Gender == "Female"]
ageM = road$Age[road$Gender == "Male"]
par(mfrow = c(2, 1))
boxplot(ageF,horizontal=T, col="light blue")
boxplot(ageM,horizontal=T)
```

- You can put 2 plots next to each other.
```{r}
par(mfrow=c(1,2))
boxplot(ageF,horizontal=T, col="light blue")
boxplot(ageM,horizontal=T)
```

<br>


### Explore
Explore another variable.

<br>


# Now try in ggplot


1st, read through this [Overview](https://ggplot2.tidyverse.org/) and re-read RGuide Chapter 5.

2nd, load the package `ggplot2` or `tidyverse` (which includes `ggplot2`).

```{r}
road1 = read.csv("http://www.maths.usyd.edu.au/u/UG/JM/DATA1001/r/current/data/AllFatalities.csv")  # Start again with the raw data frame
library(tidyverse)
str(road1)
```

Redo the Road Fatalities exercises using ggplot. 

## Research Questions

### Were there more fatalities on a certain day of the week? 

- Here we consider **1 qualitative variable**: the road fatalities across the *days of the week*.

```{r}
p = ggplot(road1, aes(x = Dayweek))  # Defines the x axis (1 variable).
p + geom_bar() 
```

```{r}
road1$Dayweek = factor(road1$Dayweek, levels=c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
p = ggplot(road1, aes(x = Dayweek))  
p + geom_bar() 
```

### Was there any pattern in how buses were involved in fatalities, on different days of the week?

- Here we consider **2 qualitative variables**: the road fatalities across the *days of the week*, cross-classified by *bus involvement*.

```{r}
p + geom_bar(aes(fill=Bus_Involvement))
```

### Was there any pattern in how heavy rigid trucks were involved in fatalities, on different days of the week?

- Here we consider **2 qualitative variables**: the road fatalities across the *days of the week*, cross-classified by *heavy rigid truck involvement*.

- Investigate whether the involvement of heavy rigid trucks differs across the days?

```{r}
p + geom_bar(aes(fill=Hvy_Rigid_Truck_Involvement))
```

### Were there more fatalities in certain age groups?

- Here we consider **1 quantitative variable**: *fatalities*.

```{r}
# Change classification of Age variable (factor -> integer)
class(road1$Age)
road1$Age = as.numeric(as.character(road1$Age))
class(road1$Age)


# Histogram
p1 = ggplot(road1, aes(x = Age))  
p1 + geom_histogram()

# Boxplot
# Note for a simple boxplot, you need to make the x-axis empty.
p2 = ggplot(road1, aes(x="",y=Age))
p2 + geom_boxplot()  
```

### Does biological sex affect the number of fatalities across age groups?

- Here we consider **1 quantative variable divided by 1 qualitative variable**.

- Control for biological sex - ie consider fatalities by age divided by biological sex.
```{r}
p3 = ggplot(road1, aes(x = Gender,y = Age))  
p3 + geom_boxplot()   
```

<br>

# DATA1901 Extension (plotly)

Here we introduce the cool interactive tool called `plotly`, which is automatically part of the `ggplot2` package.

- Work through the [RGuide 5.7](http://www.maths.usyd.edu.au/u/UG/JM/DATA1001/r/current/guides/RGuide.html).

- Now try some plots with the Road Fatality data.

```{r}
library('plotly')
p4 = plot_ly(road1, x = ~Age, color = ~Gender, type = 'box') 
p4 
```