Chapter_15.Rmd

---
title: "Chapter 15"
author: "Laura"
date: "12/12/2019"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)

library(tidyverse); library(skimr); library(nycflights13); library(GGally); library(ggstance); library(lvplot); library(hexbin); library(modelr); library(magrittr); library(maps); library(stringr); library(htmlwidgets); library(forcats)

```

## Notes for Chapter 15 Factors

To work with factors, we’ll use the forcats package, which provides tools for dealing with categorical variables (and it’s an anagram of factors!).

### 15.2 Creating factors

```{r ch1521}

levels(gss_cat$race)

gss_cat %>% 
  count(race, sort = TRUE)

ggplot(gss_cat) +
  geom_bar(aes(race)) +
  scale_x_discrete(drop = FALSE)

levels(gss_cat$rincome)

gss_cat %>% 
  count(rincome, sort = TRUE) %>% 
  ggplot(aes(rincome, n)) +
    geom_col() +
    coord_flip()

```

```{r ch1522}

gss_cat %>% 
  count(relig, sort = TRUE)

gss_cat %>% 
  count(partyid, sort = TRUE)


gss_cat %>% 
  count(relig, denom, sort = TRUE) %>% 
  filter(denom != "Not applicable") %>% 
  ggplot() +
    geom_count(aes(relig, denom, size = n, color = n))


levels(gss_cat$denom)

ggplot(gss_cat) +
  geom_count(aes(relig, denom))
```

# 15.4 Modifying factor order

```{r ch1541}
by_age <- gss_cat %>%
  filter(!is.na(age)) %>%
  count(age, marital) %>%
  group_by(age) %>%
  mutate(prop = n / sum(n))

ggplot(by_age, aes(age, prop, colour = marital)) +
  geom_line(na.rm = TRUE)

ggplot(by_age, aes(age, prop, colour = fct_reorder2(marital, age, prop))) +
  geom_smooth(se = FALSE, size = 0.1) +
  labs(colour = "marital")

# entendiendo que hizo exactamente esto
gss_cat %>%
  filter(!is.na(age)) %>%
  count(age, marital) %>%
  group_by(age) %>%
  mutate(prop = n / sum(n))

```

```{r ch1542}

gss_cat %>% 
  arrange(desc(tvhours))

ggplot(gss_cat) +
  geom_density(aes(tvhours))
```
### 15.5 Modifying factor levels

```{r ex1551}

glimpse(gss_cat)
levels(gss_cat$partyid)

gss_cat %>% 
  mutate(partyid = fct_collapse(partyid,
                        rep = c("Strong republican", "Not str republican"),
                        dem = c("Strong democrat", "Not str democrat"),
                        ind = c("Ind,near rep", "Independent", "Ind,near dem"),
                        other = c("No answer", "Don't know", "Other party"))) %>%   group_by(year) %>% 
  count(partyid) %>% 
  mutate(prop = n/sum(n)) %>% 
  ggplot(aes(year, prop, color = partyid)) +
    geom_line()

```