tidystep - Tidy Modeling with R part 1

Author

Tony Duan

Published

June 1, 2023

1 Software for modeling

1.1 FUNDAMENTALS FOR MODELING SOFTWARE

1.2 TYPES OF MODELS

DESCRIPTIVE MODELS

INFERENTIAL MODELS

PREDICTIVE MODELS

1.3 CONNECTIONS BETWEEN TYPES OF MODELS

1.4 SOME TERMINOLOGY

1.5 HOW DOES MODELING FIT INTO THE DATA ANALYSIS PROCESS?

2 A Tidyverse Primer

2.1 TIDYVERSE PRINCIPLES

2.2 EXAMPLES OF TIDYVERSE SYNTAX

3 A Review of R Modeling Fundamentals

Code

library(gapminder)
library(tidyverse)
library(plotly)

Reference

https://www.tmwr.org/

---
title: "Tidy Modeling with R part 1"
author: "Tony Duan"
date: "2023-06-01"
categories: [Modeling]
execute:
  warning: false
  error: false
format:
  html:
    code-fold: show
    code-tools: true
image: "cover.png"
---

https://www.tmwr.org/



## 1 Software for modeling

### 1.1 FUNDAMENTALS FOR MODELING SOFTWARE

### 1.2 TYPES OF MODELS

DESCRIPTIVE MODELS

INFERENTIAL MODELS

PREDICTIVE MODELS

### 1.3 CONNECTIONS BETWEEN TYPES OF MODELS

### 1.4 SOME TERMINOLOGY

### 1.5 HOW DOES MODELING FIT INTO THE DATA ANALYSIS PROCESS?

![](images/Screenshot%202023-06-24%20at%2012.17.49.png)

![](images/Screenshot%202023-06-24%20at%2012.19.54.png)

## 2 A Tidyverse Primer

### 2.1 TIDYVERSE PRINCIPLES

### 2.2 EXAMPLES OF TIDYVERSE SYNTAX

## 3 A Review of R Modeling Fundamentals

### 3.1 AN EXAMPLE


```{r}
library(tidyverse)

data(crickets, package = "modeldata")
names(crickets)

# Plot the temperature on the x-axis, the chirp rate on the y-axis. The plot
# elements will be colored differently for each species:
ggplot(crickets, 
       aes(x = temp, y = rate, color = species, pch = species, lty = species)) + 
  # Plot points for each data point and color by species
  geom_point(size = 2) + 
  # Show a simple linear model fit created separately for each species:
  geom_smooth(method = lm, se = FALSE, alpha = 0.5) + 
  scale_color_brewer(palette = "Paired") +
  labs(x = "Temperature (C)", y = "Chirp Rate (per minute)")
```

## 4 The Ames Housing Data
```{r}
library(modeldata) # This is also loaded by the tidymodels package
data(ames)

# or, in one line:
data(ames, package = "modeldata")

dim(ames)
```

```{r}
library(tidymodels)
tidymodels_prefer()

ggplot(ames, aes(x = Sale_Price)) + 
  geom_histogram(bins = 50, col= "white")
```

```{r}
ggplot(ames, aes(x = Sale_Price)) + 
  geom_histogram(bins = 50, col= "white") +
  scale_x_log10()
```




```{r}
ames <- ames %>% mutate(Sale_Price = log10(Sale_Price))
```


```{r}
library(tidymodels)
data(ames)
ames <- ames %>% mutate(Sale_Price = log10(Sale_Price))
```


## Reference

https://www.tmwr.org/