NYCASAMetro.Rmd

---
title: "Panel plots in R"
author: "Joyce Robbins"
output:
  slidy_presentation:
    fig_height: 4
    fig_width: 6
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE, cache = TRUE,
                      fig.align = 'center')
```

## Agenda

* Basic faceting in `ggplot2`
  
* When to free scales

* Getting data in the right form

* Plotting one variable per panel

* Design decisions with multiple variables

* Scatterplot matrices

Slides and code: https://www.github.com/jtr13/panelplots

## Without faceting

```{r}
library(tidyverse)
g <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point()
g
```

## Facet on one variable with `facet_wrap()`

facet "on" Species

```{r}
g + facet_wrap(~Species)
```

## Faceting in ggplot2

Each panel represents one categorical group / levels of a factor (type can be factor or character or integer)

```{r}
glimpse(iris)
```


## Change the layout with `nrow, ncol`

```{r}
g + facet_wrap(~Species, ncol = 1)
```

## Add regression lines


```{r}
g + geom_smooth(method = "lm", se = FALSE) + facet_wrap(~Species)
```

## Facet on two (categorical) variables with `facet_grid()`

```{r}
ggplot(mtcars, aes(hp,mpg)) + geom_point() + facet_grid(cyl~gear)
```

## Label variables (in addition to factor levels)

```{r}
ggplot(mtcars, aes(hp, mpg)) + geom_point() + facet_grid(cyl~gear, labeller = label_both)
```


## Cleveland dot plots

Note that y-axis is discrete

```{r}
g <- mtcars %>% rownames_to_column("car") %>% 
  ggplot(aes(mpg, reorder(car, mpg))) + geom_point(color = "blue") + theme_bw() +
  theme(panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank()) + ylab("")
g
```

## Facet by gear

```{r}
g + facet_grid(gear ~ ., 
               labeller = label_both)
```

## "Free" the y scale with `scales = "free_y"`

```{r}
g + facet_grid(gear ~ ., labeller = label_both, scales = "free_y")
```

## Change panel heights with `space = "free_y"`

```{r}
g + facet_grid(gear ~ ., labeller = label_both, scales = "free_y", 
               space = "free_y")
```

## In general, do not "free" numerical scales

```{r}
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() + facet_wrap(~Species, scales = "free")
```

incorrect

## In general, do not "free" numerical scales

```{r}
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() + facet_wrap(~Species)
```

correct



## Data must be in the right form to facet

One column of categorical data = one faceting direction

```{r}
library(ggplot2movies)
str(movies)
```

## Facet on `Action` column

```{r}
movies %>% filter(!is.na(budget)) %>% sample_n(1000) %>% 
ggplot(aes(budget, length)) + geom_point() +
  facet_wrap(~Action)
```

## Transform data

```{r}
mymovies <- movies %>% 
  select(title, length, budget, year, mpaa, Action:Short) %>% 
  gather(key = "genre", value, Action:Short) %>% 
  filter(value == 1) %>% 
  select(-value)

head(mymovies)
```

## Facet on `genre`

```{r}
lengthorder <- mymovies %>% group_by(genre) %>% summarize(meanlength = mean(length)) %>% arrange(desc(meanlength))
mymovies %>% filter(!is.na(budget)) %>% 
  sample_n(1000) %>% 
ggplot(aes(budget/1000000, length)) + geom_point() +
  facet_wrap(~factor(genre, levels = lengthorder$genre)) + xlab("budget (in millions)") +
  geom_hline(data = lengthorder, aes(yintercept = meanlength), color = "red") + 
  ggtitle ("Length vs. Budget", subtitle = "Red line indicates mean length")
```


## Use facets to look at distributions of numeric variables

```{r}
newmovies <- movies %>% select(mpaa, year:votes) %>% gather(key = "variable", value, -mpaa) %>% 
  filter(!is.na(value))
newmovies %>% sample_n(6)
```

##

```{r}
ggplot(newmovies, aes(value)) + geom_histogram(fill = "cornflowerblue") + facet_wrap(~variable, scales = "free")
```

variables become levels of the new "variable" column

## Facet on `mpaa` and `variable`

```{r}
ggplot(newmovies, aes(value)) + geom_histogram(fill = "cornflowerblue") + facet_grid(mpaa~variable, scales = "free") + theme_bw()
```

## One common scale 

Same technique: variables become levels of a new "variable" column

```{r, echo = FALSE}
byyear <- movies %>% select(title:votes) %>% 
  mutate(budget_millions = budget/1000000) %>% 
  select(-budget) %>% 
  gather(key = "variable", value, -title, -year) %>% 
  group_by(year, variable) %>% 
  summarize(mean = mean(value, na.rm = T))

ggplot(byyear, aes(year, mean)) + geom_line() + facet_wrap(~variable, ncol = 1,
                                                            scales = "free_y") +
  ggthemes::theme_economist()
```


## Data

```{r, eval = FALSE}
byyear <- movies %>% select(title:votes) %>% 
  mutate(budget_millions = budget/1000000) %>% 
  select(-budget) %>% 
  gather(key = "variable", value, -title, -year) %>% 
  group_by(year, variable) %>% 
  summarize(mean = mean(value, na.rm = T))
```

```{r}
glimpse(byyear)
```

```{r, eval = FALSE}
ggplot(byyear, aes(year, mean)) + geom_line() + 
  facet_wrap(~variable, ncol = 1, scales = "free_y") + ggthemes::theme_economist()
```


## Convert numerical to categorical variables

```{r}
mymovies <- mymovies %>% filter(length <= 180) %>% 
  mutate(decade = factor(round(year/10)*10))

ggplot(mymovies, aes(length)) + geom_histogram(fill = "cornflowerblue") +
  facet_wrap(~decade)
```

## ggridges

```{r}
library(ggridges)

ggplot(mymovies, aes(x=length, y=fct_rev(decade))) +
  geom_density_ridges(scale = 1.5, color = "blue", fill = "blue", alpha = .4) +
  xlab("Length (in minutes)") + 
  ylab("") +
  theme_ridges() 
```


## Design choices with multiple variables

x-axis, y-axis, row facets, column facets

color, size, shape

## Think about continuous vs. categorical variables


```{r, eval = FALSE, echo = FALSE}
topmethods <- c("E-mail", "Telephone", "Sales visit", "Web")
topretailer <- c("Department Store", "Sports Store", "Outdoors Shop")
toplines <- c("Camping Equipment", "Mountaineering Equipment",
              "Golf Equipment", "Personal Accessories")

sales <- read_csv("sales.csv") %>% 
  select(`Order method type`:`Product line`, Revenue, Date) %>% 
  mutate(Revenue = Revenue / 1000000) %>% 
  filter(`Order method type` %in% topmethods,
         `Retailer type` %in% topretailer,
         `Product line` %in% toplines) %>% 
  mutate(`Retailer type` = str_remove_all(`Retailer type`, "( Store| Shop)")) %>% 
  mutate(`Product line` = str_remove_all(`Product line`, "( Equipment| Accessories)"))

write_csv(sales, "sales2.csv")  
```

```{r}
sales <- read_csv("sales2.csv")
glimpse(sales)

```

## Start with one dimension

```{r}
sales %>% group_by(Date) %>% summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev)) + geom_line() + ylab("millions $") + ggtitle("Revenue")
```

## Consider one variable at a time


```{r}
sales %>% group_by(Date, `Product line`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = fct_reorder2(`Product line`, Date, SumRev))) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year")  + ylab("millions $") + ggtitle("Revenue by Product line") +
  labs(color = "Product line")
```


## Consider one variable at a time


```{r}
sales %>% group_by(Date, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = fct_reorder2(`Order method type`, Date, SumRev))) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year")  + ylab("millions $") + ggtitle("Revenue by Order method type") + 
  labs(color = "Order method type")
```

## Consider one variable at a time


```{r}
sales %>% group_by(Date, `Retailer type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = fct_reorder2(`Retailer type`, Date, SumRev))) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year")  + ylab("millions $") + ggtitle("Revenue by Retailer type") + 
  labs(color = "Retailer type")
```

## Add faceting (one dimension)

Reorder factor levels

```{r}
sales <- sales %>% 
  mutate(`Product line` = reorder(`Product line`, Revenue, function(x) sum(x)*-1),
         `Order method type` = reorder(`Order method type`, Revenue, function(x) sum(x)*-1),
         `Retailer type` = reorder(`Retailer type`, Revenue, function(x) sum(x)*-1))
```

## Add faceting (one dimension)

```{r}
sales %>% group_by(Date, `Product line`, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue))  %>% 
  ggplot(aes(Date, SumRev, color = `Order method type`)) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  facet_wrap(~`Product line`) +  ylab("millions $") +
  theme(legend.position = "bottom") +
  ggtitle("Revenue, faceted on product line")
```


## Faceting (two dimensions)

```{r}
sales %>% group_by(Date, `Product line`, `Retailer type`, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = `Product line`)) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  facet_grid(`Retailer type`~`Order method type`) +
  theme_bw() + theme(legend.position = "bottom") +
  ggtitle("Revenue, faceted on Order method and Retailer type")
```

## Experiment

```{r}
sales %>% group_by(Date, `Product line`, `Retailer type`, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = `Retailer type`)) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  facet_grid(`Product line`~`Order method type`) +
  theme_bw() + theme(legend.position = "bottom") +
  ggtitle("Revenue, faceted on Order method and Product line")
```

## Experiment

```{r}
sales %>% group_by(Date, `Product line`, `Retailer type`, `Order method type`) %>% 
  summarize(SumRev = sum(Revenue)) %>% 
  ggplot(aes(Date, SumRev, color = `Order method type`)) + geom_line() +
  scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
  facet_grid(`Product line`~`Retailer type`) +
  theme_bw() + theme(legend.position = "bottom") +
  ggtitle("Revenue, faceted on Retailer type and Product line")
```

## Scatterplot matrices

Can't create with faceting in `ggplot2`

Options:

`plot()`

`lattice::splom()`

`GGally::ggpairs()`

## Scatterplot matrices

```{r, fig.width = 5, fig.height = 5}
plot(iris)
```

## Scatterplot matrices

```{r, fig.width = 5, fig.height = 5}
lattice::splom(iris)
```

## Scatterplot matrices

```{r, fig.width = 5, fig.height = 5}
lattice::splom(iris[,1:4], group = iris$Species,
               par.settings = list(superpose.symbol = list(pch = 16, cex = .5)), 
               axis.text.cex = .5, axis.text.col = "grey50", 
               axis.line.tck = .5, auto.key = TRUE)
```

## Scatterplot matrices

```{r, fig.width = 5, fig.height = 5}
GGally::ggpairs(iris, mapping = ggplot2::aes(color = Species))
```