This notebook introductes the ideas of statebins.
This is for working on the Project. Find some economic data from the GeoFRED websitge. For example take the data from a map and make a statebin from it.
Use the socviz Chapter 7 Section 3 as a reference.
From the Preface of the book load the following packages, install and load the socviz and statebin package from the authors github.
library(pacman)
p_load("tidyverse", "broom", "coefplot", "cowplot",
"gapminder", "GGally", "ggrepel", "ggridges", "gridExtra",
"here", "interplot", "margins", "maps", "mapproj",
"mapdata", "MASS", "quantreg", "rlang", "scales",
"survey", "srvyr", "viridis", "viridisLite", "devtools", "socviz", "statebins")
# Enter one or more numbers separated by spaces, or an empty line to cancel
# 1:
# devtools::install_github("kjhealy/socviz")
data("election")
election %>% select(state, total_vote,
r_points, pct_trump, party, census) %>%
sample_n(5)
# Hex color codes for Dem Blue and Rep Red
party_colors <- c("#2E74C0", "#CB454A")
p0 <- ggplot(data = subset(election, st %nin% "DC"),
mapping = aes(x = r_points,
y = reorder(state, r_points),
color = party))
p1 <- p0 + geom_vline(xintercept = 0, color = "gray30") +
geom_point(size = 2)
p2 <- p1 + scale_color_manual(values = party_colors)
p3 <- p2 + scale_x_continuous(breaks = c(-30, -20, -10, 0, 10, 20, 30, 40),
labels = c("30\n (Clinton)", "20", "10", "0",
"10", "20", "30", "40\n(Trump)"))
p3 + facet_wrap(~ census, ncol=1, scales="free_y") +
guides(color=FALSE) + labs(x = "Point Margin", y = "") +
theme(axis.text=element_text(size=8))

library(maps)
us_states <- map_data("state")
head(us_states)
dim(us_states)
[1] 15537 6
p <- ggplot(data = us_states,
mapping = aes(x = long, y = lat,
group = group))
p + geom_polygon(fill = "white", color = "black")

p <- ggplot(data = us_states,
aes(x = long, y = lat,
group = group, fill = region))
p + geom_polygon(color = "gray90", size = 0.1) + guides(fill = FALSE)

p <- ggplot(data = us_states,
mapping = aes(x = long, y = lat,
group = group, fill = region))
p + geom_polygon(color = "gray90", size = 0.1) +
coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
guides(fill = FALSE)

election$region <- tolower(election$state)
us_states_elec <- left_join(us_states, election)
Joining, by = "region"
p <- ggplot(data = us_states_elec,
aes(x = long, y = lat,
group = group, fill = party))
p + geom_polygon(color = "gray90", size = 0.1) +
coord_map(projection = "albers", lat0 = 39, lat1 = 45)

p0 <- ggplot(data = us_states_elec,
mapping = aes(x = long, y = lat,
group = group, fill = party))
p1 <- p0 + geom_polygon(color = "gray90", size = 0.1) +
coord_map(projection = "albers", lat0 = 39, lat1 = 45)
p2 <- p1 + scale_fill_manual(values = party_colors) +
labs(title = "Election Results 2016", fill = NULL)
p2 + theme_map()

Trump vote default colors not so good.
p0 <- ggplot(data = us_states_elec,
mapping = aes(x = long, y = lat, group = group, fill = pct_trump))
p1 <- p0 + geom_polygon(color = "gray90", size = 0.1) +
coord_map(projection = "albers", lat0 = 39, lat1 = 45)
p1 + labs(title = "Trump vote") + theme_map() + labs(fill = "Percent")

p2 <- p1 + scale_fill_gradient(low = "white", high = "#CB454A") +
labs(title = "Trump vote")
p2 + theme_map() + labs(fill = "Percent")

Winning percentage, again default colors are not so good.
p0 <- ggplot(data = us_states_elec,
mapping = aes(x = long, y = lat, group = group, fill = d_points))
p1 <- p0 + geom_polygon(color = "gray90", size = 0.1) +
coord_map(projection = "albers", lat0 = 39, lat1 = 45)
p2 <- p1 + scale_fill_gradient2() + labs(title = "Winning margins")
p2 + theme_map() + labs(fill = "Percent")

p3 <- p1 + scale_fill_gradient2(low = "red", mid = scales::muted("purple"),
high = "blue", breaks = c(-25, 0, 25, 50, 75)) +
labs(title = "Winning margins")
p3 + theme_map() + labs(fill = "Percent")

Without Washington Dc
p0 <- ggplot(data = subset(us_states_elec,
region %nin% "district of columbia"),
aes(x = long, y = lat, group = group, fill = d_points))
p1 <- p0 + geom_polygon(color = "gray90", size = 0.1) +
coord_map(projection = "albers", lat0 = 39, lat1 = 45)
p2 <- p1 + scale_fill_gradient2(low = "red",
mid = scales::muted("purple"),
high = "blue") +
labs(title = "Winning margins")
p2 + theme_map() + labs(fill = "Percent")

Statebins
library(statebins)
statebins_continuous(state_data = election, state_col = "state",
text_color = "white", value_col = "pct_trump",
brewer_pal="Reds", font_size = 3,
legend_title="Percent Trump")
`show_guide` has been deprecated. Please use `show.legend` instead.

statebins_continuous(state_data = subset(election, st %nin% "DC"),
state_col = "state",
text_color = "black", value_col = "pct_clinton",
brewer_pal="Blues", font_size = 3,
legend_title="Percent Clinton")
`show_guide` has been deprecated. Please use `show.legend` instead.

election <- election %>% mutate(color = recode(party, Republican = "darkred",
Democrat = "royalblue"))
statebins_manual(state_data = election, state_col = "st",
color_col = "color", text_color = "white",
font_size = 3, legend_title="Winner",
labels=c("Trump", "Clinton"), legend_position = "right")
`show_guide` has been deprecated. Please use `show.legend` instead.

statebins(state_data = election,
state_col = "state", value_col = "pct_trump",
text_color = "white", breaks = 4,
labels = c("4-21", "21-37", "37-53", "53-70"),
brewer_pal="Reds", font_size = 3, legend_title="Percent Trump")
`show_guide` has been deprecated. Please use `show.legend` instead.

---
title: "statebins"
author: "Prof. Eric A. Suess"
date: "November 18, 2020"
output:
  word_document: default
  pdf_document: default
  html_notebook: default
---

This notebook introductes the ideas of statebins.

This is for working on the Project.  Find some economic data from the [GeoFRED](https://geofred.stlouisfed.org/) websitge.  For example take the data from a [map](https://geofred.stlouisfed.org/map/?th=pubugn&cc=5&rc=false&im=fractile&sb&lng=-113.91&lat=56.80&zm=3&sl&sv&sti=319&rt=state&at=Not%20Seasonally%20Adjusted,%20Annual,%20Dollars%20per%20Hour&fq=Annual&am=Average&un=lin&dt=2017-01-01) and make a statebin from it.

Use the [socviz](https://socviz.co) [Chapter 7](https://socviz.co/maps.html#maps) [Section 3](https://socviz.co/maps.html#statebins) as a reference.

From the Preface of the book load the following packages, install and load the *socviz* and *statebin* package from the authors github.

```{r}
library(pacman)
p_load("tidyverse", "broom", "coefplot", "cowplot",
                 "gapminder", "GGally", "ggrepel", "ggridges", "gridExtra",
                 "here", "interplot", "margins", "maps", "mapproj",
                 "mapdata", "MASS", "quantreg", "rlang", "scales",
                 "survey", "srvyr", "viridis", "viridisLite", "devtools", "socviz", "statebins")


# Enter one or more numbers separated by spaces, or an empty line to cancel
# 1: 

# devtools::install_github("kjhealy/socviz")
```



```{r}
data("election")

election %>% select(state, total_vote,
                    r_points, pct_trump, party, census) %>%
    sample_n(5)
```

```{r}

# Hex color codes for Dem Blue and Rep Red
party_colors <- c("#2E74C0", "#CB454A") 

p0 <- ggplot(data = subset(election, st %nin% "DC"),
             mapping = aes(x = r_points,
                           y = reorder(state, r_points),
                           color = party))

p1 <- p0 + geom_vline(xintercept = 0, color = "gray30") +
    geom_point(size = 2)

p2 <- p1 + scale_color_manual(values = party_colors)

p3 <- p2 + scale_x_continuous(breaks = c(-30, -20, -10, 0, 10, 20, 30, 40),
                              labels = c("30\n (Clinton)", "20", "10", "0",
                                         "10", "20", "30", "40\n(Trump)"))

p3 + facet_wrap(~ census, ncol=1, scales="free_y") +
    guides(color=FALSE) + labs(x = "Point Margin", y = "") +
    theme(axis.text=element_text(size=8))


```

```{r}
library(maps)
us_states <- map_data("state")
head(us_states)

dim(us_states)

p <- ggplot(data = us_states,
            mapping = aes(x = long, y = lat,
                          group = group))

p + geom_polygon(fill = "white", color = "black")

p <- ggplot(data = us_states,
            aes(x = long, y = lat,
                group = group, fill = region))

p + geom_polygon(color = "gray90", size = 0.1) + guides(fill = FALSE)

p <- ggplot(data = us_states,
            mapping = aes(x = long, y = lat,
                          group = group, fill = region))

p + geom_polygon(color = "gray90", size = 0.1) +
    coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
    guides(fill = FALSE)
```

```{r}
election$region <- tolower(election$state)
us_states_elec <- left_join(us_states, election)

p <- ggplot(data = us_states_elec,
            aes(x = long, y = lat,
                group = group, fill = party))

p + geom_polygon(color = "gray90", size = 0.1) +
    coord_map(projection = "albers", lat0 = 39, lat1 = 45) 


```


```{r}
p0 <- ggplot(data = us_states_elec,
             mapping = aes(x = long, y = lat,
                           group = group, fill = party))
p1 <- p0 + geom_polygon(color = "gray90", size = 0.1) +
    coord_map(projection = "albers", lat0 = 39, lat1 = 45) 
p2 <- p1 + scale_fill_manual(values = party_colors) +
    labs(title = "Election Results 2016", fill = NULL)
p2 + theme_map() 
```

Trump vote default colors not so good.

```{r}
p0 <- ggplot(data = us_states_elec,
             mapping = aes(x = long, y = lat, group = group, fill = pct_trump))

p1 <- p0 + geom_polygon(color = "gray90", size = 0.1) +
    coord_map(projection = "albers", lat0 = 39, lat1 = 45) 

p1 + labs(title = "Trump vote") + theme_map() + labs(fill = "Percent")

p2 <- p1 + scale_fill_gradient(low = "white", high = "#CB454A") +
        labs(title = "Trump vote") 
p2 + theme_map() + labs(fill = "Percent")
```

Winning percentage, again default colors are not so good.

```{r}
p0 <- ggplot(data = us_states_elec,
             mapping = aes(x = long, y = lat, group = group, fill = d_points))

p1 <- p0 + geom_polygon(color = "gray90", size = 0.1) +
    coord_map(projection = "albers", lat0 = 39, lat1 = 45) 

p2 <- p1 + scale_fill_gradient2() + labs(title = "Winning margins") 
p2 + theme_map() + labs(fill = "Percent")

p3 <- p1 + scale_fill_gradient2(low = "red", mid = scales::muted("purple"),
                                high = "blue", breaks = c(-25, 0, 25, 50, 75)) +
    labs(title = "Winning margins") 
p3 + theme_map() + labs(fill = "Percent")
```

Without Washington Dc

```{r}
p0 <- ggplot(data = subset(us_states_elec,
                           region %nin% "district of columbia"),
             aes(x = long, y = lat, group = group, fill = d_points))

p1 <- p0 + geom_polygon(color = "gray90", size = 0.1) +
    coord_map(projection = "albers", lat0 = 39, lat1 = 45) 

p2 <- p1 + scale_fill_gradient2(low = "red",
                                mid = scales::muted("purple"),
                                high = "blue") +
    labs(title = "Winning margins") 
p2 + theme_map() + labs(fill = "Percent")
```

Statebins

```{r}
library(statebins)

statebins_continuous(state_data = election, state_col = "state",
                     text_color = "white", value_col = "pct_trump",
                     brewer_pal="Reds", font_size = 3,
                     legend_title="Percent Trump")

statebins_continuous(state_data = subset(election, st %nin% "DC"),
                     state_col = "state",
                     text_color = "black", value_col = "pct_clinton",
                     brewer_pal="Blues", font_size = 3,
                     legend_title="Percent Clinton")
```

```{r}
election <- election %>% mutate(color = recode(party, Republican = "darkred",
                                               Democrat = "royalblue"))

statebins_manual(state_data = election, state_col = "st",
                 color_col = "color", text_color = "white",
                 font_size = 3, legend_title="Winner",
                 labels=c("Trump", "Clinton"), legend_position = "right")

statebins(state_data = election,          
          state_col = "state", value_col = "pct_trump",
          text_color = "white", breaks = 4,
          labels = c("4-21", "21-37", "37-53", "53-70"),
          brewer_pal="Reds", font_size = 3, legend_title="Percent Trump")
```

