LA_Weather

Los Angeles Weather Trends over Time

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### Process

First, a Web Scrape was done to gather the data from The L.A. Almanac

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library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.1.1     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

# install.packages("rvest")
library(rvest)

## 
## Attaching package: 'rvest'

## The following object is masked from 'package:readr':
## 
##     guess_encoding

weather <- read_html("http://www.laalmanac.com/weather/we04a.php")
table <- html_nodes(weather, "table")
# look at each one
weather_table <- weather %>%
  html_nodes("table") %>%
  .[1] %>%
  html_table(fill = TRUE)

as.data.frame(weather_table) -> weather_df

View(weather_df)

#weather_df$Year
#as.Date(weather_df$Year)
weather_df %>% 
  filter(Temp %in% "High") %>% 
ggplot(aes(as.numeric(Year), as.numeric(Annual))) + geom_line() + geom_smooth()

## Warning in FUN(X[[i]], ...): NAs introduced by coercion

## Warning in FUN(X[[i]], ...): NAs introduced by coercion

## Warning in FUN(X[[i]], ...): NAs introduced by coercion

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 2 rows containing non-finite values (stat_smooth).

## Warning: Removed 2 row(s) containing missing values (geom_path).

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