pm-lecture-note.Rmd

---
layout: page
title: RStudio Trainer Program
subtitle: "Tidyverse: Predictive Model - Lecture Note"
author:
    name: xwMOOC
    url: https://www.facebook.com/groups/tidyverse/
    affiliation: Tidyverse Korea
date: "`r Sys.Date()`"
output:
  html_document: 
    toc: yes
    toc_float: true
    highlight: tango
    code_folding: show
    number_section: true
    self_contained: true
editor_options: 
  chunk_output_type: console
---
 
``` {r, include=FALSE}
# source("tools/chunk-options.R")

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE,
                    comment="", digits = 3, tidy = FALSE, prompt = FALSE, fig.align = 'center')

```

# Lecture Note {#RStudio-lecture-note}

- Identify the target variable
- Perform EDA
- Visualize relationships

# One Country - Data {#RStudio-lecture-note-one}

## Dataframe {#RStudio-lecture-note-df}

```{r gapminder}
library(tidyverse)
library(gapminder)

korea_tbl <- gapminder %>% 
  filter(str_detect(country, "Korea, Rep.")) %>% 
  dplyr::select(year, pop, gdpPercap, lifeExp) %>% 
  mutate(pop = pop / 10^6)
  
DT::datatable(korea_tbl)
```

## Visualize a relation {#gapminder-visualize-relation}

```{r gapminder-one-visualize}
korea_tbl %>% 
  ggplot(aes(x=gdpPercap, y=lifeExp)) +
    geom_point() +
    geom_line() +
    labs(x="GDP per capita", 
         y="Life Expectancy", 
         title="Relationship between GDP per Capita and Life Expectancy")
```

## Visualize many relations {#gapminder-visualize-relations}

```{r gapminder-one-visualize-many}
korea_tbl %>% 
  gather(variable, value, -lifeExp) %>% 
  ggplot(aes(x=value, y=lifeExp)) +
    geom_point() +
    geom_line() +
    facet_wrap(~variable) 
```

```{r gapminder-one-visualize-many-decoration}
korea_tbl %>% 
  gather(variable, value, -lifeExp) %>% 
  ggplot(aes(x=value, y=lifeExp)) +
    geom_point() +
    geom_line() +
    facet_wrap(~variable, scale = "free") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
        labs(x="", 
             y="Life Expectancy",
             title="Relationship between Life Expectancy and main factors")
```

hint: [stackoverflow, "Rotating and spacing axis labels in ggplot2"](https://stackoverflow.com/questions/1330989/rotating-and-spacing-axis-labels-in-ggplot2)


# One Country - Model {#RStudio-lecture-note-one-lm}

```{r gapminder-lm}
korea_lm <- lm(lifeExp ~ year, data=korea_tbl)
summary(korea_lm)
```

## Introduce `broom` {#RStudio-lecture-note-one-lm-broom}

```{r gapminder-lm-summary}
library(broom)
glance(korea_lm)
tidy(korea_lm)
augment(korea_lm) %>% 
  select(.resid) %>% 
  summary(.)
```

## One Country - Model with multiple variables {#RStudio-lecture-note-one-lm-variables}

```{r gapminder-lm-variable}
korea_multiple_lm <- lm(lifeExp ~ ., data=korea_tbl)

glance(korea_multiple_lm)
tidy(korea_multiple_lm)
```

## One Country - Best Model Selection {#RStudio-lecture-note-one-lm-best}

```{r gapminder-lm2-best}
korea_full_lm <- lm(lifeExp ~ ., data = korea_tbl)
korea_step <- MASS::stepAIC(korea_full_lm, trace = FALSE)
korea_step
```

# Many Simple Models {#RStudio-lecture-note-many-simple}

```{r gapminder-lm-many-models}
gap_nest <- gapminder %>% 
  select(-continent) %>% 
  group_by(country) %>% 
  nest()

## Hello model
(gap_many_tbl <- gap_nest %>% 
  mutate(model = map(data, ~lm(lifeExp ~ year, data=.))) %>% 
  mutate(model_glance = map(model, broom::glance),
         rsquare = map_dbl(model_glance, ~.$r.squared)) %>% 
  arrange(rsquare))
```

## Let's visualize best and worst {#RStudio-lecture-note-worst-best}

```{r gapminder-lm-many-models-best-worst}
worst_country <- gap_many_tbl %>% 
  pull(country) %>% 
  head(3) %>% 
  as.character()

best_country <- gap_many_tbl %>% 
  pull(country) %>% 
  tail(3) %>% 
  as.character()

countries <- c(worst_country, best_country)

gapminder %>% 
  filter(country %in% countries) %>% 
  mutate(country = as.character(country)) %>% 
  select(country, year, lifeExp) %>% 
  mutate(country = fct_relevel(country, countries)) %>% 
  mutate(worst_best = if_else(country %in% best_country, "Best", "Worst")) %>% 
  ggplot(aes(x=year, y=lifeExp, color=worst_best)) +
    geom_point() +
    geom_line() +
    facet_wrap(~country, scale = "free") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
        labs(x="", 
             y="Life Expectancy",
             title="Relationship between Life Expectancy and main factors")

```

## One more thing {#RStudio-lecture-note-worst-best-one-more}

```{r gapminder-lm-many-models-best-worst-one-more}
gap_many_tbl %>% 
  select(country, data, rsquare) %>% 
  filter(country %in% countries) %>% 
  unnest(data) %>% 
  ungroup() %>% 
  mutate(country = as.character(country)) %>% 
  select(country, year, lifeExp, rsquare) %>% 
  mutate(country = fct_relevel(country, countries)) %>% 
  mutate(country = glue::glue("{country}, {round(rsquare, 3)}")) %>% 
  ggplot(aes(x=year, y=lifeExp)) +
    geom_point() +
    geom_line() +
    facet_wrap(~country, scale = "free") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
        labs(x="", 
             y="Life Expectancy",
             title="Relationship between Life Expectancy and main factors",
             color="")
```