---
title: "Factors"
output:
  word_document: default
  html_notebook: default
  pdf_document: default
---

Here are some examples from Chapter 15.  The examples are related to the [General Social Survey](http://gss.norc.org/) from NORC at the Unversity of Chicago.

```{r message = FALSE}
library(tidyverse)
library(forcats)

gss_cat

gss_cat %>%
  count(race)
```

Factor variables are used to make bar charts.  The *geom_bar()* counts the observations in each level of the factor.

```{r}
ggplot(gss_cat, aes(race)) +
  geom_bar()

```

Forcing NAs.

```{r}
ggplot(gss_cat, aes(race)) +
  geom_bar() +
  scale_x_discrete(drop = FALSE)
```

Modifying the order of a factor.

Examine tv watch time by religion.

```{r}
relig_summary <- gss_cat %>%
  group_by(relig) %>%
  summarise(
    age = mean(age, na.rm = TRUE),
    tvhours = mean(tvhours, na.rm = TRUE),
    n = n()
  )

relig_summary %>% ggplot(aes(tvhours, relig)) + geom_point()
```


```{r}
relig_summary %>% ggplot(aes(tvhours, fct_reorder(relig, tvhours))) +
  geom_point()
```

The *fct_reorder()* functon should be used in a mutate statement.  

Same as the last code.


```{r}
relig_summary %>%
  mutate(relig = fct_reorder(relig, tvhours)) %>%
  ggplot(aes(tvhours, relig)) +
    geom_point()
```

Now tv watch time by average age.

```{r}
rincome_summary <- gss_cat %>%
  group_by(rincome) %>%
  summarise(
    age = mean(age, na.rm = TRUE),
    tvhours = mean(tvhours, na.rm = TRUE),
    n = n()
  )

rincome_summary %>% ggplot(aes(age, fct_reorder(rincome, age))) + 
  geom_point()
```

Does this make sense?  What is wrong with this plot?

```{r}
rincome_summary %>%ggplot(aes(age, fct_relevel(rincome, "Not applicable"))) + 
  geom_point()
```

Using *mutate()*

```{r}
gss_cat %>% ggplot(aes(marital)) +
    geom_bar()

gss_cat %>% mutate(marital = marital) %>% 
  ggplot(aes(marital)) +
    geom_bar()

gss_cat %>% mutate(marital = marital %>% fct_infreq()) %>% 
  ggplot(aes(marital)) +
    geom_bar()

gss_cat %>% mutate(marital = marital %>% fct_infreq() %>% fct_rev()) %>%
  ggplot(aes(marital)) +
    geom_bar()
```

Modifying factor levels.

```{r}
gss_cat %>% count(partyid)
```

Re-coding

```{r}
gss_cat %>%
  mutate(partyid = fct_recode(partyid,
    "Republican, strong"    = "Strong republican",
    "Republican, weak"      = "Not str republican",
    "Independent, near rep" = "Ind,near rep",
    "Independent, near dem" = "Ind,near dem",
    "Democrat, weak"        = "Not str democrat",
    "Democrat, strong"      = "Strong democrat"
  )) %>%
  count(partyid)
```

Other category

```{r}
gss_cat %>%
  mutate(partyid = fct_recode(partyid,
    "Republican, strong"    = "Strong republican",
    "Republican, weak"      = "Not str republican",
    "Independent, near rep" = "Ind,near rep",
    "Independent, near dem" = "Ind,near dem",
    "Democrat, weak"        = "Not str democrat",
    "Democrat, strong"      = "Strong democrat",
    "Other"                 = "No answer",
    "Other"                 = "Don't know",
    "Other"                 = "Other party"
  )) %>%
  count(partyid)
```

Collapse a factor

```{r}
gss_cat %>%
  mutate(partyid = fct_collapse(partyid,
    other = c("No answer", "Don't know", "Other party"),
    rep = c("Strong republican", "Not str republican"),
    ind = c("Ind,near rep", "Independent", "Ind,near dem"),
    dem = c("Not str democrat", "Strong democrat")
  )) %>%
  count(partyid)
```