Storms Exploration.Rmd

---
title: "Storms-Exploration"
author: "Madison Bai"
date: "`r Sys.Date()`"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## **How do storms change over time?**

**Hypothesis:** Storms have gotten stronger over time.

Loaded in data set

```{r}
library(tidyverse)
library(tidyr)
library(ggplot2)
storm_data <- read_csv("data/storms.csv")
```

Cleaned the data for desired variables

```{r}
storm_clean <- storm_data %>%
  select(name, year, category, tropicalstorm_force_diameter, hurricane_force_diameter) %>%
  drop_na()
```

Summarized and average desired variables by year to view changes over time

```{r}
storm_averages <- storm_clean %>%
  group_by(year) %>%
  summarise(avg_category=mean(category), avg_tropicalstorm_force_diameter = mean(tropicalstorm_force_diameter), avg_hurricane_force_diameter=mean(hurricane_force_diameter))
```

Visualizing how storms change over time by category

```{r}
storm_averages %>%
 ggplot(aes(x = year, y = avg_category)) + 
  geom_line()
```

```{r}
storm_averages %>%
 ggplot(aes(x = year, y = avg_tropicalstorm_force_diameter)) + 
  geom_line()
```

```{r}
storm_averages %>%
 ggplot(aes(x = year, y = avg_hurricane_force_diameter)) + 
  geom_line()
```


**Results:** There is no discernible trend that storms have gotten stronger over time on average.

Let's explore how the average number of category has changed over time. 

```{r}
cats <- storm_clean %>%
  distinct(name, year, category) %>%
  group_by(year, category) %>%
  count(category)
```

Didn't finish T_T

Results: I don't know how to code