#################################################################################
## EXERCISE 4
#################################################################################

## Import a csv

cars_df <- read.csv("./data/stopping_dist_cars.csv")
cars_df

# How many rows? columns?

nrow(cars_df)
ncol(cars_df)
dim(cars_df)

## What are the names of the columns?

names(cars_df)

## You can grab the values of a single column with $:

cars_df$speed

## What is the mean of the speed column?

mean(cars_df$speed)
# or:
cars_df$speed %>% mean()

## Make a scatter plot of speed vs breaking distance:

plot(cars_df$speed, cars_df$breaking_dist, pch = 16)

## CHALLENGE 1: What is the mean reaction distance?

mean(cars_df$reaction_dist)

## CHALLENGE 2: Compute the frequency table in the surface column

table(cars_df$surface)

####################################################
## IMPORT ANOTHER CSV FROM A URL
####################################################

animals_df <- read.csv("https://raw.githubusercontent.com/ucanr-igis/intror_oct22/main/data/animals.csv")

## Preview the data frame in a View window

View(animals_df)

## Look for NA values. This means 'not available' which is usually synonymous with 'missing'

## Compute some summary stats

mean(animals_df$Weight)

## Fortunately, mean() has an optional argument we can use to ignore NAs

mean(animals_df$Weight, na.rm = TRUE)

#####################################################################
## CREATING A DATA FRAME FROM SCRATCH
#####################################################################

## First create vectors for each of the columns. They should all be the same length.

countries = c("Canada", "Costa Rica", "Mexico", "United States")
populations = c(10, 25, 20, 30)
areas = c(30, 10, 20, 35)

## We can create the data frame with the data.frame() function,
## passing in the vectors we just name. Note we can change column names

stats_df = data.frame(country = countries, pop = populations, area = areas)
stats_df

################################################################
### BASIC GGPLOT
#################################################################

library(palmerpenguins)
library(ggplot2)

head(penguins)

ggplot(penguins, aes(x = flipper_length_mm, y = bill_length_mm, color = species)) +
  geom_point() +
  ggtitle("Bill Length vs Flipper Length for 3 Species of Penguins")