## Introduction

In the examples below, we will be working with a random sample of 10,000 public Facebook posts by members of the U.S. Congress. 

For a review of the log scale, see for example this [video](https://www.youtube.com/watch?v=sBhEi4L91Sg).

Loading packages:

```{r}
library("tidyverse")
library("lubridate")
library("scales")
```

Reading in the data and some initial processing:

```{r}
df <- read.csv("data/fb-congress-data.csv", stringsAsFactors = FALSE)

# Transform date column to datetime
df$date <- as_date(df$date)

# Dropping some very uncommon post types
nrow(df)
df <- df %>%
  filter(!post_type %in% c("music", "note"))
nrow(df)
```


## First (time series) plots

After creating a base layer, `geom_line()` can be used for line plots such as time series. Plotting daily posts over time:

```{r}
counts <- df %>%
  group_by(date) %>% 
  summarise(posts = n())

# Base layer
p <- ggplot(data = counts, mapping = aes(x = date, y = posts))  # 'data = ...' and 'mapping = ...' are often omitted

# Line plot of the posts per day
p + geom_line()
```

Note: Aesthetic mappings `aes` describe which columns/variables in the data are mapped to which variables/features in the plot. Thus, in `aes` we list parts of the data that shall be used in the plot. If we instead want to set plot features like e.g. the size of points in a scatter plot, colours which do not depend on the data, etc., then these would be set outside of an aesthetic mapping.

Two separate time series by party, now also aggregated to monthly:

```{r}
# Obtain a new data frame with monthly counts of posts per party
counts <- df %>%
  filter(party != "Independent") %>% 
  group_by(month = ceiling_date(date, "month"), party) %>% 
  summarise(posts = n())

p <- ggplot(counts, aes(x = month, 
                        y = posts, group = party))
p + geom_line(aes(color = party)) + # (additional) aesthetic mapping here layer specific rather than global via ggplot()
  scale_color_manual(values=c("blue", "red"))
```


## Univariate analysis for a single continuous variable

```{r}
# Base layer
p <- ggplot(df, aes(x=likes_count))

# Histogram
p + geom_histogram()

# Smoothed density estimate
p + geom_density() + scale_x_continuous("likes count", labels = comma)

# The same log scale (note: the labels = comma option prevents scientific
# notation of numbers as 1e+00, 1e+01 and uses the `scales` package)
p + geom_histogram() + scale_x_log10("likes count", labels = comma)
p + geom_density() + scale_x_log10("likes count", labels = comma)

# Why does this line of code drops some observations?
```

## Univariate analysis for a single categorical variable

```{r}
p <- ggplot(df, aes(x=post_type)) + xlab("post type")

# Bar chart
p + geom_bar() ## number of posts by type

# Bar chart (horizontal)
p + geom_bar() + coord_flip()
```


## Bivariate analysis for two continuous variables

```{r}
# Base layer
p <- ggplot(df, aes(x = likes_count, y = comments_count)) + xlab("Likes count") +
  ylab("Comments count")

# Scatter plot: Relationship between number of likes and number of comments
p + geom_point()  

# With smoothed conditional means
p + geom_point() + stat_smooth(na.rm = TRUE) 

# With restricted axes
p + geom_point() + xlim(0, 25000) + ylim(0, 2500)

# Particularities of integer variables in scatter plots
p + geom_point() + xlim(0, 10) + ylim(0, 10)
  
# With log scales
p + geom_point() + scale_x_log10(labels = comma) + scale_y_log10(labels = comma) 
p + geom_point() + scale_x_log10(labels = comma) + scale_y_log10(labels = comma) + 
    stat_smooth()
```


## Bivariate analysis for one continuous variable and one categorical variable

```{r}
# Number of likes by type of post as a box blot
p <- ggplot(df, aes(x = post_type, y = likes_count)) + xlab("Post type") +
  ylab("Likes count")
p + geom_boxplot()

# Box plot and violin plot with log scale
p + geom_boxplot() + scale_y_log10(labels = comma)
p + geom_violin() + scale_y_log10(labels = comma)

# Density plot for log like distributions for different parties
p <- ggplot(df, aes(x = likes_count))
p + geom_density(aes(color = party)) + scale_x_log10("likes count", labels = comma)
```


## Bivariate analysis for two categorical variables

```{r}
counts <- df %>%
  filter(party != "Independent") %>% 
  group_by(post_type, party) %>% 
  summarise(posts = n())

p <- ggplot(counts, aes(x = party, y = post_type)) + ylab("post type")
p + geom_tile(aes(fill = posts))

```


## Multivariate analysis for three continuous variables

```{r}
p <- ggplot(df, aes(x = likes_count, y = comments_count, color = log(angry_count))) +
  xlab("Likes count") + ylab("Comments count")

p + geom_point()

p + geom_point() + scale_y_log10(labels = comma) + scale_x_log10(labels = comma) + 
 stat_smooth(method = "lm")
```


## Multivariate analysis for two continuous variables and one categorical variable

```{r}

# Grid of plots: 2x4, by post type
p <- ggplot(df, aes(x = likes_count, y = comments_count)) + xlab("Likes count") +
  ylab("Comments count")
p + geom_point() + scale_x_log10(labels = comma) + scale_y_log10(labels = comma) + 
        facet_wrap(~post_type, nrow = 2) 


# geom_text() allows to use party names instead of points
p <- ggplot(df[df$likes_count>10000, ], 
            aes(x = likes_count, y = comments_count, label = party)) +
  xlab("Likes count") +
  ylab("Comments count")
p + geom_text() + scale_x_log10(labels = comma) + scale_y_log10(labels = comma)
```

Other examples:

```{r}
## Scatter plot with dots colored by type of post
p <- ggplot(df[df$likes_count>5000, ], 
            aes(x = likes_count, y = comments_count)) +
  scale_x_log10("Likes count", labels = comma) +
  scale_y_log10("Comments count", labels = comma)
p + geom_point(aes(color = post_type))

## Same for point shape
p + geom_point(aes(shape = post_type))

## Combining both (now different shapes also have different colors)
p + geom_point(aes(shape = post_type, color = post_type))
```

## Dealing with cases where a lot of points are in some areas

Jittering points can avoid "overplotting", however, can also easily be misleading:

```{r}
p <- ggplot(df, aes(x = party, y = comments_count)) + ylab("comments count")
p + geom_point()

# vs
p + geom_jitter(position = position_jitter(width = .1, height=.1))
```

```{r}
# Baseline
p <- ggplot(df, aes(x = likes_count, y = comments_count)) +
  scale_x_log10("Likes count", labels = comma) +
  scale_y_log10("Comments count", labels = comma) 
p + geom_point()

## Points could also be jittered in scatter plots (yet, obscures the log values here and would be misleading)
p + geom_jitter(position = position_jitter(width = .5, height =.5))

## Transparency
p + geom_jitter(position = position_jitter(width = .5, height = .5), alpha = 1/25)

## Hexbin (if error: run install.packages("hexbin"))
p <- ggplot(df[df$likes_count>0 & df$comments_count>0,], 
        aes(x=likes_count, y = comments_count))
p + geom_hex() + scale_fill_continuous(trans="log10")

# Generally, plotting binned means (either over deciles or over fixed (log) intervals like here)
# can help with plots that contain a lot of points/observations.
# Furthermore, geom_rug() is another option to indicate where most mass is
p <- ggplot(df, aes(x = likes_count, y = comments_count)) +
  scale_x_log10("Likes count", labels = comma) +
  scale_y_log10("Comments count", labels = comma) 
p + geom_point() + geom_rug(color = "grey", alpha = 0.6) +
   stat_summary_bin(fun = 'mean', bins = 20, color = 'green', size = 2, geom = 'point')

```
