Storms.Rmd

---
title: "Do Storms change over time?"
author: "Ashlee Patacsil-Hardin"
date: "2023-10-27"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(readr)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(sf)
library(ggmap)
library(httr)
library(mapview)
```

1. As the first step of the in-class assignment, I loaded in the data.
```{r}
storms <- read_csv("~/RStudio/ESS523A/storms.csv")
View(storms)
```

2. Next, I used the functions str() and head() to explore your dataset to understand its structure and contents.
```{r}
str(storms)
```
```{r}
head(storms)
```

3. Next, I created a new data frame called "s_cleaned" and removed all the rowns from the data frame (storms) that contain at least one "NA" value. I was then able to focus on the storms that had a value in the category section of the data. 
```{r}
s_cleaned <- na.omit(storms)
```

4.Next, I compared years to the Hurrican Force Diameter. When looking at the data, it seems that there was a decrease in hurican Force Diamter over time.
```{r}
s_cleaned %>%
ggplot(aes(x=year,y=hurricane_force_diameter)) +
  geom_smooth()+
  labs(title = "Years vs. Hurricane Force Diameter",x= "year",y="Hurricane Force Diameter")
```

4.5. I then compared years to the Tropical Storm Force Diameter. When looking at the data, it looks like the tropical storm force diameter is usually around 250.
```{r}
s_cleaned %>%
ggplot(aes(x=year,y=tropicalstorm_force_diameter,col=name)) +
  geom_smooth()+
  labs(title = "Years vs. Tropical Storm Force Diameter",x= "year",y="Tropical Storm Force Diameter")
```

5. Next, I compared the years to the category number of the storms from the cleaned data.
```{r}
s_cleaned %>%
ggplot(aes(x=year,y=category)) +
  geom_smooth()+
  labs(title = "Years vs. Cateogory",x= "year",y="Cateogory")
```

```{r}
census_api_key("AIzaSyBPb3XOSJCHTmQCs7aIKMLqA5f6GDgiOg8", install = T)
```

```{r}
readRenviron("~/.Renviron")
```


```{r}
readRenviron("~/.Renviron")
  location <- c(lon= 33.0,lat=-77.4)
my_map <- get_map(location=location, zoom=12)
```



```{r}
map <-ggplot() +
  geom_sf(data=st_as_sf(storms,coords = c("long", "lat"))) +
  labs(title="Storm Locations Over Time")

mapview(map)
```