itle: “Week 7: Apply it to your data 6”

uthor: “Anil Eser”

ate: “2024-06-21”

utput: html_document

ditor_options:

chunk_output_type: console

Import Data

# Excel File
data <- read_excel("../00_data/myData.xlsx")

Introduction

Questions

Variation

Visualizing distributions

data %>%
    ggplot(aes(x = stock_symbol)) +
    geom_bar()

data %>%
    ggplot(mapping = aes(x = low)) +
    geom_histogram(binwidth = 30)

data %>%
    ggplot(aes(x = adj_close, color = stock_symbol)) +
    geom_freqpoly()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

data %>%
    ggplot(aes(x = adj_close, color = "NVDA")) +
    geom_freqpoly()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

data %>%
    ggplot(aes(x = adj_close, color  = "MSFT")) +
    geom_freqpoly()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

data %>%
    ggplot(aes(x = adj_close, color  = "INTC")) +
    geom_freqpoly()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Typical values

data %>%
    
    filter(adj_close > 20) %>%
    
    ggplot(aes(x = high)) +
    geom_histogram(binwidth = 30)

Unusual values

 data %>%
    ggplot(aes(y = open)) +
    geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Two Continuous Variables

library(hexbin) 
data %>%
    ggplot(aes(x = open, y = close )) +
    geom_hex()

A Categorical and Continuous Variable

data %>%
    
    ggplot(aes(x = stock_symbol, y = adj_close)) +
    geom_boxplot()

Covariation

A categorical and continuous variable

Two categorical variables

Two continous variables

Patterns and models