Introduction to ggplot2

Author

Emorie D Beck

Code

library(plyr)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.3     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::arrange()   masks plyr::arrange()
✖ purrr::compact()   masks plyr::compact()
✖ dplyr::count()     masks plyr::count()
✖ dplyr::desc()      masks plyr::desc()
✖ dplyr::failwith()  masks plyr::failwith()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::id()        masks plyr::id()
✖ dplyr::lag()       masks stats::lag()
✖ dplyr::mutate()    masks plyr::mutate()
✖ dplyr::rename()    masks plyr::rename()
✖ dplyr::summarise() masks plyr::summarise()
✖ dplyr::summarize() masks plyr::summarize()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Code

knitr::include_graphics("https://github.com/rstudio/hex-stickers/raw/master/thumbs/ggplot2.png")

What is ggplot2 trying to do?

Create a grammar of graphics
Aims to help draw connections across diverse plots
Create order in the chaos of complicated plots

From Wickham (2010):

A grammar of graphics is a tool that enables us to concisely describe the components of a graphic.

What are the core elements of ggplot2 grammar?

Mappings: base layer
Scales: control and modify your mappings
Geoms: plot elements
Facets: panel your plot
Grobs: things that aren’t geoms that we want to layer on like text, arrows, other things
Themes: style your figure

But first, our data

These are some Experience Sampling Method data I collected during my time in graduate school
Specifically, these include data from Beck & Jackson (2022)
In that paper I built personalized machine learning models of behaviors and experiences from sets of:
- psychological
- situational
- and time variables

Code

load(url("https://github.com/emoriebeck/psc290-data-viz-2022/raw/main/02-week2-ggplot2/01-data/ipcs_data.RData"))
ipcs_data

Code

ipcs_data <- ipcs_data %>%
  group_by(SID) %>%
  mutate(beep = 1:n()) %>%
  ungroup()

Mappings

The first thing we call with ggplot2 is always the ggplot() function, which has two core arguments:
- data: your data object (can also be piped)

Code

ggplot(
  data = ipcs_data
)

mapping: your aesthetic mappings for the plot, wrapped in aes()

Code

ggplot(
  data = ipcs_data
  , mapping = aes(x = linear, y = happy)
)

How many aesthetic mappings are there?
- x
- y
- col / color
- fill
- shape
- size
- linetype
- xmin / xmax
- ymin / ymax
- alpha
There are lots of geom-specific ones, too
Use ?geom_whatever() to get more info on a specific geom

Scales

Every mapping is a scale
Scales can be lots of different things
In ggplot2 language, some core ones are:
- continuous
- discrete
- manual
- ordinal
- binned
- date
- brewer
All of these have specific arguments based on the type of scale

`continuous`

Let’s try the continuous scale with our y mapping
We’ll use the following three arguments
- limits: vector length 2
- breaks: vector of any length
- labels: numeric or character vector

Code

ggplot(
  data = ipcs_data
  , mapping = aes(x = linear, y = happy)
) + 
  scale_y_continuous(
    limits = c(1, 5)
    , breaks = seq(1, 5, by = 2)
    , labels = c("one", "three", "five")
  )

Geoms

We’ll loop back to scales after talking about some geoms
It’s not possible to go through all the possible geoms (that’s what the rest of the class is for!)
We’ll focus on some basic ones for now
- geom_point()
- geom_jitter()
- geom_smooth()
- geom_hline() / geom_vline()
- geom_bar()
- geom_boxplot()
- geom_density()
- geom_histogram()

##geom_point()

Your basic scatterplot!

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(
    x = purposeful
    , y = happy
    )) + 
    geom_point() + 
    theme_classic() # I just hate grey backgrounds

Let’s add color:

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(
    x = purposeful
    , y = happy
    , color = happy
    )) + 
    geom_point() + 
    theme_classic() # I just hate grey backgrounds

And change the scale using built-in types.

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(
    x = purposeful
    , y = happy
    , color = happy
    )) + 
    scale_color_continuous(type = "viridis") +
    geom_point() + 
    theme_classic() # I just hate grey backgrounds

`geom_jitter()`

Sometimes we have data that have lots of repeating values, especially with ordinal response scales where the variables aren’t composited / latent
jitter adds random noise to the point to allow you to see more of the points

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(
    x = purposeful
    , y = happy
    )) + 
    geom_jitter() + 
    theme_classic() # I just hate grey backgrounds

This may be too much jitter

Sometimes we have data that have lots of repeating values, especially with ordinal response scales where the variables aren’t composited / latent
jitter adds random noise to the point to allow you to see more of the points

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(
    x = purposeful
    , y = happy
    )) + 
    geom_jitter(width = .1, height = .1) + 
    theme_classic() # I just hate grey backgrounds

`alpha`

Alpha can help us understand how many points are stacked when using jitter (or other overlapping data)

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy, alpha = .25)) + 
    geom_jitter(
      width = .1
      , height = .1
      # , alpha = .25
      ) + 
    theme_classic() # I just hate grey backgrounds

`geom_smooth()`

geom_smooth() allows you to apply statistical functions to your data
There are other ways to do this that we won’t cover today
Core arguments are:
- method: “loess”, “lm”, “glm”, “gam”
- formula: e.g., y ~ x or y ~ poly(x, 2)
- se: display standard error of estimate (T/ F)
- aes() wrapped aesthetics or directly mapped aesthetics

Remember: it’s a LAYERED grammar of graphics, so let’s layer!

`se = F`

`method = "lm"`

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_smooth(
      method = "lm"
      , formula = y ~ x
      , se = F
      , color = "blue"
    ) + 
    theme_classic() # I just hate grey backgrounds

`method = "loess"`

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_smooth(
      method = "loess"
      , formula = y ~ x
      , se = F
      , color = "blue"
    ) + 
    theme_classic() # I just hate grey backgrounds

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: pseudoinverse used at 4.015

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: neighborhood radius 1.015

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: reciprocal condition number 0

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: There are other near singularities as well. 1

`se=T`

And we can add standard error ribbons

`method = "lm"`

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_smooth(
      method = "lm"
      , formula = y ~ x
      , se = T
      , color = "blue"
    ) + 
    theme_classic() # I just hate grey backgrounds

`method = "loess"`

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_smooth(
      method = "loess"
      , formula = y ~ x
      , se = T
      , color = "blue"
    ) + 
    theme_classic() # I just hate grey backgrounds

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: pseudoinverse used at 4.015

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: neighborhood radius 1.015

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: reciprocal condition number 0

Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
: There are other near singularities as well. 1

Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
4.015

Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
1.015

Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
number 0

Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near
singularities as well. 1

`geom_hline()`/`geom_vline()`

Sometimes, we will want to place lines at various intercepts
We’ll get into specific use cases as the course progresses
geom_hline(): horizontal lines have yintercept mappings
geom_vline(): vertical lines have xintercept mappings

`geom_hline()`

Horizontal lines have yintercept mappings

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_hline(
      aes(yintercept = mean(happy, na.rm = T))
      , linetype = "dashed"
      ) + 
    geom_smooth(method = "lm", formula = y ~ x, se = F, color = "blue") + 
    theme_classic() # I just hate grey backgrounds

`geom_vline()`

Vertical lines have xintercept mappings

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_vline(
      aes(xintercept = mean(purposeful, na.rm = T))
      , linetype = "dashed"
      ) + 
    geom_smooth(method = "lm", formula = y ~ x, se = F, color = "blue") + 
    theme_classic() # I just hate grey backgrounds

`geom_bar()`

Bar graphs can be useful for showing relative differences
My hot take is that they are rarely that useful
- (This is mostly because of how we perceive errorbars and differences, which we’ll talk more about in a few weeks!)
But let’s look at using them for frequency and means / se’s

Frequency

How often did our participant have an argument, interact with others, study, and feel tired?

Code

ipcs_data %>%
  filter(SID == "216") %>% 
  select(SID, Full_Date, argument, interacted, study, tired) %>%
  pivot_longer(
    cols = argument:tired
    , names_to = "item"
    , values_to = "value"
    , values_drop_na = T
  ) %>%
  group_by(item) %>%
  summarize(value = sum(value == 1)) %>%
  ggplot(aes(x = item, fill = item, y = value)) + 
    geom_col(color = "black") + 
    theme_classic()

Mean differences

Were there mean-level in our continuous variables?

Code

ipcs_data %>%
  filter(SID %in% c("216")) %>%
  select(SID, Full_Date, happy, purposeful, afraid, attentive) %>%
  pivot_longer(
    cols = c(-SID, -Full_Date)
    , names_to = "item"
    , values_to = "value"
    , values_drop_na = T
  ) %>%
  group_by(item) %>%
  summarize(
    mean = mean(value)
    , ci = 1.96*(sd(value)/sqrt(n()))
    ) %>%
  ggplot(aes(x = item, fill = item, y = mean)) + 
    geom_col(color = "black") + 
    geom_errorbar(
      aes(ymin = mean - ci, ymax = mean + ci)
      , position = position_dodge(width = .1)
      , width = .1
      , stat = "identity"
    ) + 
    theme_classic()

`geom_boxplot()`

Sometimes called box and whisker plots
A method for summarizing a distribution of data without showing raw data
Box instead shows 25th, 50th, and 75th percentile (quartiles)
Whiskers show 1.5 * interquartile range (75%tile-25%tile)
More fun when we want to compare distributions across variables (IMO)

One boxplot

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(aes(y = SID, x = happy)) + 
    geom_boxplot(width = .5) + 
    theme_classic()

Multiple boxplots

Multiple Participants

Later, we’ll also talk about how to order the boxplots (and other axes) by means, medians, etc.

Code

ipcs_data %>%
  filter(SID %in% c("216", "211", "174", "150", "171")) %>%
  ggplot(aes(
    y = SID, x = happy
    , fill = SID
    )) + 
    geom_boxplot(width = .5) + 
    theme_classic()

Multiple Variables

Code

ipcs_data %>%
  filter(SID %in% c("216")) %>%
  select(SID, Full_Date, happy, purposeful, afraid, attentive) %>%
  pivot_longer(
    cols = c(-SID, -Full_Date)
    , names_to = "item"
    , values_to = "value"
  ) %>%
  ggplot(aes(
    y = item
    , x = value
    , fill = item
    )) + 
    geom_boxplot(width = .5) + 
    theme_classic()

Advanced!

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    scale_x_continuous(limits = c(1,7), breaks = seq(1,5,2)) + 
    scale_y_continuous(limits = c(1,7), breaks = seq(1,5,2)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_boxplot(aes(
      x = 6
      , y = happy
      )) + 
    geom_boxplot(aes(
      y = 6
      , x = purposeful
      )) +
    geom_smooth(
      method = "lm"
      , formula = y ~ x
      , se = F
      , color = "blue"
      ) + 
    theme_classic() # I just hate grey backgrounds

Warning: Removed 1 rows containing missing values (`geom_point()`).

`geom_histogram()` & `geom_density()`

Useful for showing raw / smoothed distributions of data

Histogram

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(aes(y = happy)) + 
    geom_histogram(
      fill = "grey"
      , color = "black"
    ) + 
    coord_flip() +
    theme_classic()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Density Distribution

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(aes(x = happy, y = ..density..)) + 
    geom_histogram(
      fill = "grey"
      , color = "black"
    ) + 
    theme_classic()

Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Multiple histograms / density distributions

We can compare multiple participants

Code

ipcs_data %>%
  filter(SID %in% c("216", "211", "174", "150", "171")) %>%
  ggplot(aes(
     y = happy
    , fill = SID
    )) + 
    geom_density(alpha = .2) + 
    coord_flip() +
    theme_classic()

Plot Appearance Beyond Mappings

So far, we have only changed appearance via the scale_() functions
But that doesn’t change things like axis, text, title, and more
Nor does it help when we want to split the plot into multiple panels
Let’s do those next!

Facets

Often, we have lots of other reasons we need to reproduce the same plot multiple times
- multiple variables
- multiple people
- multiple conditions
- etc.
There are more ways to do this than we’ll cover today, like piecing plots together and more

The core of directly faceting within ggplot is that you have to facet according to variables in your data set
This is part of why we covered moving your data to long
Say that you want to facet by variable, for example, but your data is in wide form
- Facets couldn’t handle that

Code

ipcs_data %>%
  filter(SID == "216") %>%
  select(SID, beep, afraid:content) %>%
  pivot_longer(
    cols = afraid:content
    , names_to = "item"
    , values_to = "value"
  )

In ggplot2, there are two core faceting functions * facet_grid() + snaps figures in a grid; no wrapping + especially useful for 1-2 faceting variables * facet_wrap() + treats each facet a separate + wraps according to nrow and ncol arguments

`facet_grid()`

Core arguments:

rows, cols: list of variables or formula, e.g., x ~ y
scales: same x or y scale on all facets?
space: same space for unequal length x or y facets?
switch: move labels from left to right or top to bottom?
drop: drop unused factor levels

Code

ipcs_data %>%
  filter(SID == "216") %>%
  select(SID, beep, afraid:content) %>%
  pivot_longer(
    cols = afraid:content
    , names_to = "item"
    , values_to = "value"
  ) %>%
  ggplot(aes(x = beep, y = value, group = item)) + 
    geom_point() + 
    geom_line() + 
    facet_grid(item~.) +
    theme_classic()

`facet_wrap()`

Core arguments:

facets: barequoted or one-sided formula, e.g., ~ x + y
nrow / ncol: number of rows and columns
scales: same x or y scale on all facets?
switch: move labels from left to right or top to bottom?
drop: drop unused factor levels
dir: horizontal or vertical
strip.position: where to put the labels

Code

ipcs_data %>%
  filter(SID == "216") %>%
  select(SID, beep, afraid:content) %>%
  pivot_longer(
    cols = afraid:content
    , names_to = "item"
    , values_to = "value"
  ) %>%
  ggplot(aes(x = beep, y = value, group = item)) + 
    geom_point() + 
    geom_line() + 
    facet_wrap(
      ~item
      , ncol = 1
      , strip.position = "right"
      ) +
    theme_classic()

Change scale and space

Code

ipcs_data %>%
  filter(SID == "216") %>%
  select(SID, beep, afraid:content) %>%
  pivot_longer(
    cols = afraid:content
    , names_to = "item"
    , values_to = "value"
  ) %>%
  ggplot(aes(x = beep, y = value, group = item)) + 
    geom_point() + 
    geom_line() + 
    facet_grid(
      item ~ . 
      , scales = "free_y"
      , space = "free_y"
      ) +
    theme_classic()

Labels & Titles

APA style says titles are bad
Common sense says titles help understanding
Ask for forgiveness, not permission

Remember this?

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_smooth(
      method = "lm"
      , formula = y ~ x
      , se = T
      , color = "blue"
    ) + 
    theme_classic() # I just hate grey backgrounds

We can add labels and a title

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_smooth(
      method = "lm"
      , formula = y ~ x
      , se = T
      , color = "blue"
    ) + 
    labs(
      x = "Momentary Purpose (1-5)"
      , y = "Momentary Happiness (1-5)"
      , title = "Zero-Order Associations 
                  Between Momentary Happiness and Purpose"
    ) + 
    theme_classic() # I just hate grey backgrounds

Labels also apply to other mappings like color

Code

ipcs_data %>%
  filter(SID %in% c("216", "211", "174", "150", "171")) %>%
  ggplot(aes(
     y = happy
    , fill = SID
    )) + 
    geom_density(alpha = .2) + 
    coord_flip() +
    labs(
      x = "Momentary Happiness"
      , y = "Smoothed Density"
      , fill = "Participant\nID"
    ) + 
    theme_classic()

You can also use labels to remove axis labels

Code

ipcs_data %>%
  filter(SID %in% c("216")) %>%
  select(SID, Full_Date, happy, purposeful, afraid, attentive) %>%
  pivot_longer(
    cols = c(-SID, -Full_Date)
    , names_to = "item"
    , values_to = "value"
  ) %>%
  ggplot(aes(
    y = item
    , x = value
    , fill = item
    )) +
    geom_boxplot(width = .5) + 
    labs(
      x = "Momentart Rating (1-5)"
      , y = NULL
      , fill = "Item"
    ) + 
    theme_classic()

Themes

Basic, Built-in Themes

There are lots of themes you can use in ggplot that are pre-built into the package
Try tying theme_ into your R console, and look at the functions that pop up
Some stand-out ones are:
- theme_classic() (what we’ve been using)
- theme_bw()
- theme_minimal() (but is there a theme_maximal?)
- theme_void

Advanced Themes

Custom themes are one of the best ways to “hack” your ggplots
You will not remember all of them
You will have to google them all time
Here’s the site: https://ggplot2.tidyverse.org/reference/theme.html
Rather than give details on a bunch of these, I’m going to demonstrate theme modifications I often use

Smoothed Regression Line

Code

ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = purposeful, y = happy)) + 
    geom_jitter(width = .1, height = .1, alpha = .25) + 
    geom_smooth(
      method = "lm"
      , formula = y ~ x
      , se = T
      , color = "blue"
    ) + 
    labs(
      x = "Momentary Purpose (1-5)"
      , y = "Momentary Happiness (1-5)"
      , title = "Zero-Order Associations Between Momentary Happiness and Purpose"
    ) + 
    theme_classic() + 
    theme(
      plot.title = element_text(
        face = "bold"
        , size = rel(1.1)
        , hjust = .5
        )
      , axis.title = element_text(
        face = "bold"
        , size = rel(1.1)
        )
      , axis.text = element_text(
        face = "bold"
        , size = rel(1.2)
        )
    )

Bar Chart

Code

ipcs_data %>%
  filter(SID %in% c("216")) %>%
  select(SID, Full_Date, happy, purposeful, afraid, attentive) %>%
  pivot_longer(
    cols = c(-SID, -Full_Date)
    , names_to = "item"
    , values_to = "value"
    , values_drop_na = T
  ) %>%
  group_by(item) %>%
  summarize(
    mean = mean(value)
    , ci = 1.96*(sd(value)/sqrt(n()))
    ) %>%
  ggplot(aes(x = item, fill = item, y = mean)) + 
    geom_col(color = "black") + 
    geom_errorbar(
      aes(ymin = mean - ci, ymax = mean + ci)
      , position = position_dodge(width = .1)
      , width = .1
      , stat = "identity"
    ) + 
    labs(
      x = NULL
      , y = "Mean Momentary Rating (CI)\n[Range 1-5]"
      , title = "Descriptive Statistics of Momentary Emotion Ratings"
    ) + 
    theme_classic() + 
    theme(
      legend.position = "none"
      , plot.title = element_text(face = "bold", size = rel(1.1), hjust = .5)
      , axis.title = element_text(face = "bold", size = rel(1.1))
      , axis.text = element_text(face = "bold", size = rel(1.2))
  )

Time Series

Code

ipcs_data %>%
  filter(SID == "216") %>%
  select(SID, beep, afraid:content) %>%
  pivot_longer(
    cols = afraid:content
    , names_to = "item"
    , values_to = "value"
  ) %>%
  ggplot(aes(x = beep, y = value, group = item)) + 
    geom_line(aes(color = item)) + 
    geom_point(size = 1) + 
    facet_grid(item~.) +
    labs(
      x = "ESM Beep (#)"
      , y = "Rated Momentary Value (1-5)"
      , title = "Time Series of Four Momentary Emotion Items for Participant 216"
      , color = NULL
      ) + 
    theme_classic() + 
    theme(
      legend.position = "bottom"
      , legend.text = element_text(face = "bold", size = rel(1.1))
      , plot.title = element_text(face = "bold", size = rel(1.1), hjust = .5)
      , axis.title = element_text(face = "bold", size = rel(1.1))
      , axis.text = element_text(face = "bold", size = rel(1.2))
      , strip.background = element_rect(color = "black", fill = "cornflowerblue")
      , strip.text = element_text(face = "bold", size = rel(1.2), color = "white")
    )

--- title: "Introduction to ggplot2" author: "Emorie D Beck" format: html: code-tools: true code-copy: true code-line-numbers: true code-link: true theme: united highlight-style: tango df-print: paged code-fold: show toc: true toc-float: true --- ```{r} library(plyr) library(tidyverse) ``` ```{r, fig.align='center'} knitr::include_graphics("https://github.com/rstudio/hex-stickers/raw/master/thumbs/ggplot2.png") ``` ## What is ggplot2 trying to do? * Create a grammar of graphics * Aims to help draw connections across diverse plots * Create order in the chaos of complicated plots From Wickham (2010): >A grammar of graphics is a tool that enables us to concisely describe the components of a graphic. ## What are the core elements of ggplot2 grammar? * **Mappings**: base layer * **Scales**: control and modify your mappings * **Geoms**: plot elements * **Facets**: panel your plot * **Grobs**: things that aren't geoms that we want to layer on like text, arrows, other things * **Themes**: style your figure ## But first, our data * These are some Experience Sampling Method data I collected during my time in graduate school * Specifically, these include data from Beck \& Jackson (2022) * In that paper I built personalized machine learning models of behaviors and experiences from sets of: + psychological + situational + and time variables ```{r, echo = T} load(url("https://github.com/emoriebeck/psc290-data-viz-2022/raw/main/02-week2-ggplot2/01-data/ipcs_data.RData")) ipcs_data ``` ```{r} ipcs_data <- ipcs_data %>% group_by(SID) %>% mutate(beep = 1:n()) %>% ungroup() ``` # Mappings * The first thing we call with `ggplot2` is always the `ggplot()` function, which has two core arguments: + `data`: your data object (can also be piped) ```{r, echo = T, fig.dim= c(7,3.5)} ggplot( data = ipcs_data ) ``` + `mapping`: your aesthetic mappings for the plot, wrapped in `aes()` ```{r, echo = T, fig.dim= c(5,3.5)} ggplot( data = ipcs_data , mapping = aes(x = linear, y = happy) ) ``` * How many aesthetic mappings are there? + `x` + `y` + `col` / `color` + `fill` + `shape` + `size` + `linetype` + `xmin` / `xmax` + `ymin` / `ymax` + `alpha` * There are lots of geom-specific ones, too * Use ?geom_whatever() to get more info on a specific geom # Scales * Every mapping is a scale * Scales can be lots of different things * In ggplot2 language, some core ones are: + `continuous` + `discrete` + `manual` + `ordinal` + `binned` + `date` + `brewer` * All of these have specific arguments based on the type of scale ## `continuous` * Let's try the `continuous` scale with our `y` mapping * We'll use the following three arguments + `limits`: vector length 2 + `breaks`: vector of any length + `labels`: numeric or character vector ```{r, echo = T, fig.align='center',fig.dim= c(5,3.5)} ggplot( data = ipcs_data , mapping = aes(x = linear, y = happy) ) + scale_y_continuous( limits = c(1, 5) , breaks = seq(1, 5, by = 2) , labels = c("one", "three", "five") ) ``` # Geoms * We'll loop back to scales after talking about some geoms * It's not possible to go through all the possible geoms (that's what the rest of the class is for!) * We'll focus on some basic ones for now + `geom_point()` + `geom_jitter()` + `geom_smooth()` + `geom_hline()` / `geom_vline()` + `geom_bar()` + `geom_boxplot()` + `geom_density()` + `geom_histogram()` ##`geom_point()` Your basic scatterplot! ```{r, echo = T, fig.show='show', fig.align='center', fig.dim= c(6,4)} ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes( x = purposeful , y = happy )) + geom_point() + theme_classic() # I just hate grey backgrounds ``` Let's add color: ```{r, echo = T, fig.show='show', fig.align='center', fig.dim= c(6,4)} ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes( x = purposeful , y = happy , color = happy )) + geom_point() + theme_classic() # I just hate grey backgrounds ``` And change the scale using built-in types. ```{r, echo = T, out.height="50%", fig.align='center', fig.dim= c(5,3.5)} #| code-line-numbers: "8" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes( x = purposeful , y = happy , color = happy )) + scale_color_continuous(type = "viridis") + geom_point() + theme_classic() # I just hate grey backgrounds ``` ## `geom_jitter()` * Sometimes we have data that have lots of repeating values, especially with ordinal response scales where the variables aren't composited / latent * jitter adds random noise to the point to allow you to see more of the points ```{r, echo = T, fig.align='center', fig.dim= c(5,3.5)} ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes( x = purposeful , y = happy )) + geom_jitter() + theme_classic() # I just hate grey backgrounds ``` This may be too much jitter * Sometimes we have data that have lots of repeating values, especially with ordinal response scales where the variables aren't composited / latent * jitter adds random noise to the point to allow you to see more of the points ```{r, echo = T, fig.align='center', fig.dim= c(5,3.5)} ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes( x = purposeful , y = happy )) + geom_jitter(width = .1, height = .1) + theme_classic() # I just hate grey backgrounds ``` ### `alpha` Alpha can help us understand how many points are stacked when using jitter (or other overlapping data) ```{r, echo = T, fig.show='show', fig.align='center', fig.dim= c(5,3.5)} #| code-line-numbers: "|7" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy, alpha = .25)) + geom_jitter( width = .1 , height = .1 # , alpha = .25 ) + theme_classic() # I just hate grey backgrounds ``` ## `geom_smooth()` * `geom_smooth()` allows you to apply statistical functions to your data * There are other ways to do this that we won't cover today * Core arguments are: + `method`: "loess", "lm", "glm", "gam" + `formula`: e.g., `y ~ x` or `y ~ poly(x, 2)` + `se`: display standard error of estimate (`T`/ `F`) + `aes()` wrapped aesthetics or directly mapped aesthetics Remember: it's a **LAYERED** grammar of graphics, so let's layer! ### `se = F` #### `method = "lm"` ```{r, echo = T, fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "|5-9" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_smooth( method = "lm" , formula = y ~ x , se = F , color = "blue" ) + theme_classic() # I just hate grey backgrounds ``` #### `method = "loess"` ```{r, echo = T, fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "5-9" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_smooth( method = "loess" , formula = y ~ x , se = F , color = "blue" ) + theme_classic() # I just hate grey backgrounds ``` ### `se=T` And we can add standard error ribbons #### `method = "lm"` ```{r, echo = T, fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "5-9" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_smooth( method = "lm" , formula = y ~ x , se = T , color = "blue" ) + theme_classic() # I just hate grey backgrounds ``` #### `method = "loess"` ```{r, echo = T, fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "5-9" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_smooth( method = "loess" , formula = y ~ x , se = T , color = "blue" ) + theme_classic() # I just hate grey backgrounds ``` ## `geom_hline()`/`geom_vline()` * Sometimes, we will want to place lines at various intercepts * We'll get into specific use cases as the course progresses * `geom_hline()`: horizontal lines have `yintercept` mappings * `geom_vline()`: vertical lines have `xintercept` mappings ### `geom_hline()` Horizontal lines have `yintercept` mappings ```{r, echo = T, fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "5-8" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_hline( aes(yintercept = mean(happy, na.rm = T)) , linetype = "dashed" ) + geom_smooth(method = "lm", formula = y ~ x, se = F, color = "blue") + theme_classic() # I just hate grey backgrounds ``` ### `geom_vline()` Vertical lines have `xintercept` mappings ```{r, echo = T, fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "5-8" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_vline( aes(xintercept = mean(purposeful, na.rm = T)) , linetype = "dashed" ) + geom_smooth(method = "lm", formula = y ~ x, se = F, color = "blue") + theme_classic() # I just hate grey backgrounds ``` ## `geom_bar()` * Bar graphs can be useful for showing relative differences * My hot take is that they are rarely that useful + (This is mostly because of how we perceive errorbars and differences, which we'll talk more about in a few weeks!) * But let's look at using them for frequency and means / se's ### Frequency How often did our participant have an argument, interact with others, study, and feel tired? ```{r, echo = T, fig.show = 'show', fig.align='center', fig.dim= c(6,4)} ipcs_data %>% filter(SID == "216") %>% select(SID, Full_Date, argument, interacted, study, tired) %>% pivot_longer( cols = argument:tired , names_to = "item" , values_to = "value" , values_drop_na = T ) %>% group_by(item) %>% summarize(value = sum(value == 1)) %>% ggplot(aes(x = item, fill = item, y = value)) + geom_col(color = "black") + theme_classic() ``` ### Mean differences Were there mean-level in our continuous variables? ```{r, echo = T, fig.show = 'show', fig.align='center', fig.dim= c(6,4)} #| code-line-numbers: "|3-9|10-14|16|17-22" ipcs_data %>% filter(SID %in% c("216")) %>% select(SID, Full_Date, happy, purposeful, afraid, attentive) %>% pivot_longer( cols = c(-SID, -Full_Date) , names_to = "item" , values_to = "value" , values_drop_na = T ) %>% group_by(item) %>% summarize( mean = mean(value) , ci = 1.96*(sd(value)/sqrt(n())) ) %>% ggplot(aes(x = item, fill = item, y = mean)) + geom_col(color = "black") + geom_errorbar( aes(ymin = mean - ci, ymax = mean + ci) , position = position_dodge(width = .1) , width = .1 , stat = "identity" ) + theme_classic() ``` ## `geom_boxplot()` * Sometimes called box and whisker plots * A method for summarizing a distribution of data without showing raw data * Box instead shows 25th, 50th, and 75th percentile (quartiles) * Whiskers show 1.5 * interquartile range (75%tile-25%tile) * More fun when we want to compare distributions across variables (IMO) ### One boxplot ```{r , echo = T, fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "4" ipcs_data %>% filter(SID == "216") %>% ggplot(aes(y = SID, x = happy)) + geom_boxplot(width = .5) + theme_classic() ``` ### Multiple boxplots #### Multiple Participants * Later, we'll also talk about how to order the boxplots (and other axes) by means, medians, etc. ```{r , echo = T, fig.show='show', fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "|5" ipcs_data %>% filter(SID %in% c("216", "211", "174", "150", "171")) %>% ggplot(aes( y = SID, x = happy , fill = SID )) + geom_boxplot(width = .5) + theme_classic() ``` #### Multiple Variables ```{r , echo = T, fig.show='show', fig.align='center', fig.dim= c(5,3.5)} #| code-line-numbers: "|5-8|10,12" ipcs_data %>% filter(SID %in% c("216")) %>% select(SID, Full_Date, happy, purposeful, afraid, attentive) %>% pivot_longer( cols = c(-SID, -Full_Date) , names_to = "item" , values_to = "value" ) %>% ggplot(aes( y = item , x = value , fill = item )) + geom_boxplot(width = .5) + theme_classic() ``` ### Advanced! ```{r, echo = T, fig.show='show', fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "4-14" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + scale_x_continuous(limits = c(1,7), breaks = seq(1,5,2)) + scale_y_continuous(limits = c(1,7), breaks = seq(1,5,2)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_boxplot(aes( x = 6 , y = happy )) + geom_boxplot(aes( y = 6 , x = purposeful )) + geom_smooth( method = "lm" , formula = y ~ x , se = F , color = "blue" ) + theme_classic() # I just hate grey backgrounds ``` ## `geom_histogram()` \& `geom_density()` * Useful for showing raw / smoothed distributions of data ### Histogram ```{r , echo = T, fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "4-7" ipcs_data %>% filter(SID == "216") %>% ggplot(aes(y = happy)) + geom_histogram( fill = "grey" , color = "black" ) + coord_flip() + theme_classic() ``` ### Density Distribution ```{r , echo = T, fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "4-7" ipcs_data %>% filter(SID == "216") %>% ggplot(aes(x = happy, y = ..density..)) + geom_histogram( fill = "grey" , color = "black" ) + theme_classic() ``` ### Multiple histograms / density distributions * We can compare multiple participants ```{r , echo = T, fig.show='show', fig.align='center', fig.dim= c(6,4)} #| code-line-numbers: "|5" ipcs_data %>% filter(SID %in% c("216", "211", "174", "150", "171")) %>% ggplot(aes( y = happy , fill = SID )) + geom_density(alpha = .2) + coord_flip() + theme_classic() ``` # Plot Appearance Beyond Mappings * So far, we have only changed appearance via the `scale_()` functions * But that doesn't change things like axis, text, title, and more * Nor does it help when we want to split the plot into multiple panels * Let's do those next! ## Facets * Often, we have lots of other reasons we need to reproduce the same plot multiple times + multiple variables + multiple people + multiple conditions + etc. * There are more ways to do this than we'll cover today, like piecing plots together and more ```{r, echo = F,fig.align='center', fig.dim= c(5,5)} ipcs_data %>% filter(SID %in% c("216", "211", "174", "150")) %>% ggplot(aes(x = beep, y = happy, group = SID)) + geom_point() + geom_line() + facet_wrap(~SID, ncol = 1) + theme_classic() ``` * The core of directly faceting within ggplot is that you have to facet according to variables in your data set * This is part of why we covered moving your data to long * Say that you want to facet by variable, for example, but your data is in wide form + Facets couldn't handle that ```{r, echo = T} ipcs_data %>% filter(SID == "216") %>% select(SID, beep, afraid:content) %>% pivot_longer( cols = afraid:content , names_to = "item" , values_to = "value" ) ``` In `ggplot2`, there are two core faceting functions * `facet_grid()` + snaps figures in a grid; no wrapping + especially useful for 1-2 faceting variables * `facet_wrap()` + treats each facet a separate + wraps according to `nrow` and `ncol` arguments ### `facet_grid()` Core arguments: * `rows`, `cols`: list of variables or formula, e.g., `x ~ y` * `scales`: same x or y scale on all facets? * `space`: same space for unequal length x or y facets? * `switch`: move labels from left to right or top to bottom? * `drop`: drop unused factor levels ```{r , echo = T, fig.show='show', fig.align='center'} #| code-line-numbers: "|12" ipcs_data %>% filter(SID == "216") %>% select(SID, beep, afraid:content) %>% pivot_longer( cols = afraid:content , names_to = "item" , values_to = "value" ) %>% ggplot(aes(x = beep, y = value, group = item)) + geom_point() + geom_line() + facet_grid(item~.) + theme_classic() ``` ### `facet_wrap()` Core arguments: * `facets`: barequoted or one-sided formula, e.g., `~ x + y` * `nrow` / `ncol`: number of rows and columns * `scales`: same x or y scale on all facets? * `switch`: move labels from left to right or top to bottom? * `drop`: drop unused factor levels * `dir`: horizontal or vertical * `strip.position`: where to put the labels ```{r , echo = T, fig.show='show', fig.align='center'} #| code-line-numbers: "|12-16" ipcs_data %>% filter(SID == "216") %>% select(SID, beep, afraid:content) %>% pivot_longer( cols = afraid:content , names_to = "item" , values_to = "value" ) %>% ggplot(aes(x = beep, y = value, group = item)) + geom_point() + geom_line() + facet_wrap( ~item , ncol = 1 , strip.position = "right" ) + theme_classic() ``` #### Change scale and space ```{r , echo = T, fig.show='show', fig.align='center'} #| code-line-numbers: "|12-16" ipcs_data %>% filter(SID == "216") %>% select(SID, beep, afraid:content) %>% pivot_longer( cols = afraid:content , names_to = "item" , values_to = "value" ) %>% ggplot(aes(x = beep, y = value, group = item)) + geom_point() + geom_line() + facet_grid( item ~ . , scales = "free_y" , space = "free_y" ) + theme_classic() ``` ## Labels & Titles * APA style says titles are bad * Common sense says titles help understanding * Ask for forgiveness, not permission Remember this? ```{r, echo = T, fig.show = 'show', fig.align='center', fig.dim= c(6,4)} #| code-line-numbers: "5-9" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_smooth( method = "lm" , formula = y ~ x , se = T , color = "blue" ) + theme_classic() # I just hate grey backgrounds ``` We can add labels and a title ```{r, echo = T, fig.show = 'show', fig.align='center', fig.dim= c(6,4)} #| code-line-numbers: "|11-15" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_smooth( method = "lm" , formula = y ~ x , se = T , color = "blue" ) + labs( x = "Momentary Purpose (1-5)" , y = "Momentary Happiness (1-5)" , title = "Zero-Order Associations Between Momentary Happiness and Purpose" ) + theme_classic() # I just hate grey backgrounds ``` Labels also apply to other mappings like color ```{r , echo = T, fig.show='show', fig.align='center', fig.dim= c(6,4)} #| code-line-numbers: "|9-13" ipcs_data %>% filter(SID %in% c("216", "211", "174", "150", "171")) %>% ggplot(aes( y = happy , fill = SID )) + geom_density(alpha = .2) + coord_flip() + labs( x = "Momentary Happiness" , y = "Smoothed Density" , fill = "Participant\nID" ) + theme_classic() ``` You can also use labels to **remove** axis labels ```{r , echo = T, fig.show='show', fig.align='center', fig.dim= c(6,4)} #| code-line-numbers: "|5-8|15-19" ipcs_data %>% filter(SID %in% c("216")) %>% select(SID, Full_Date, happy, purposeful, afraid, attentive) %>% pivot_longer( cols = c(-SID, -Full_Date) , names_to = "item" , values_to = "value" ) %>% ggplot(aes( y = item , x = value , fill = item )) + geom_boxplot(width = .5) + labs( x = "Momentart Rating (1-5)" , y = NULL , fill = "Item" ) + theme_classic() ``` ## Themes ### Basic, Built-in Themes * There are lots of themes you can use in ggplot that are pre-built into the package * Try tying `theme_` into your R console, and look at the functions that pop up * Some stand-out ones are: + `theme_classic()` (what we've been using) + `theme_bw()` + `theme_minimal()` (but is there a theme_maximal?) + `theme_void` ### Advanced Themes * Custom themes are one of the best ways to "hack" your ggplots * You will not remember all of them * You will have to google them all time * Here's the site: [https://ggplot2.tidyverse.org/reference/theme.html](https://ggplot2.tidyverse.org/reference/theme.html) * Rather than give details on a bunch of these, I'm going to demonstrate theme modifications I often use #### Smoothed Regression Line ```{r, echo = T, fig.show = 'show', fig.align='center', fig.dim= c(4,2.75)} #| code-line-numbers: "17-31" ipcs_data %>% filter(SID == "216") %>% ggplot(mapping = aes(x = purposeful, y = happy)) + geom_jitter(width = .1, height = .1, alpha = .25) + geom_smooth( method = "lm" , formula = y ~ x , se = T , color = "blue" ) + labs( x = "Momentary Purpose (1-5)" , y = "Momentary Happiness (1-5)" , title = "Zero-Order Associations Between Momentary Happiness and Purpose" ) + theme_classic() + theme( plot.title = element_text( face = "bold" , size = rel(1.1) , hjust = .5 ) , axis.title = element_text( face = "bold" , size = rel(1.1) ) , axis.text = element_text( face = "bold" , size = rel(1.2) ) ) ``` #### Bar Chart ```{r, echo = T, fig.show = 'show', fig.align='center', fig.dim= c(5,3.5)} #| code-line-numbers: "|23-27|29-34" ipcs_data %>% filter(SID %in% c("216")) %>% select(SID, Full_Date, happy, purposeful, afraid, attentive) %>% pivot_longer( cols = c(-SID, -Full_Date) , names_to = "item" , values_to = "value" , values_drop_na = T ) %>% group_by(item) %>% summarize( mean = mean(value) , ci = 1.96*(sd(value)/sqrt(n())) ) %>% ggplot(aes(x = item, fill = item, y = mean)) + geom_col(color = "black") + geom_errorbar( aes(ymin = mean - ci, ymax = mean + ci) , position = position_dodge(width = .1) , width = .1 , stat = "identity" ) + labs( x = NULL , y = "Mean Momentary Rating (CI)\n[Range 1-5]" , title = "Descriptive Statistics of Momentary Emotion Ratings" ) + theme_classic() + theme( legend.position = "none" , plot.title = element_text(face = "bold", size = rel(1.1), hjust = .5) , axis.title = element_text(face = "bold", size = rel(1.1)) , axis.text = element_text(face = "bold", size = rel(1.2)) ) ``` #### Time Series ```{r , echo = T, fig.show='show', fig.align='center'} #| code-line-numbers: "|13-18|20-29|21-22|26-27" ipcs_data %>% filter(SID == "216") %>% select(SID, beep, afraid:content) %>% pivot_longer( cols = afraid:content , names_to = "item" , values_to = "value" ) %>% ggplot(aes(x = beep, y = value, group = item)) + geom_line(aes(color = item)) + geom_point(size = 1) + facet_grid(item~.) + labs( x = "ESM Beep (#)" , y = "Rated Momentary Value (1-5)" , title = "Time Series of Four Momentary Emotion Items for Participant 216" , color = NULL ) + theme_classic() + theme( legend.position = "bottom" , legend.text = element_text(face = "bold", size = rel(1.1)) , plot.title = element_text(face = "bold", size = rel(1.1), hjust = .5) , axis.title = element_text(face = "bold", size = rel(1.1)) , axis.text = element_text(face = "bold", size = rel(1.2)) , strip.background = element_rect(color = "black", fill = "cornflowerblue") , strip.text = element_text(face = "bold", size = rel(1.2), color = "white") ) ```

What is ggplot2 trying to do?

What are the core elements of ggplot2 grammar?

But first, our data

Mappings

Scales

continuous

Geoms

geom_jitter()

alpha

geom_smooth()

se = F

method = "lm"

method = "loess"

se=T

method = "lm"

method = "loess"

geom_hline()/geom_vline()

geom_hline()

geom_vline()

geom_bar()

Frequency

Mean differences

geom_boxplot()

One boxplot

Multiple boxplots

Multiple Participants

Multiple Variables

Advanced!

geom_histogram() & geom_density()

Histogram

Density Distribution

Multiple histograms / density distributions

Plot Appearance Beyond Mappings

Facets

facet_grid()

facet_wrap()

Change scale and space

Labels & Titles

Themes

Basic, Built-in Themes

Advanced Themes

Smoothed Regression Line

Bar Chart

Time Series

`continuous`

`geom_jitter()`

`alpha`

`geom_smooth()`

`se = F`

`method = "lm"`

`method = "loess"`

`se=T`

`method = "lm"`

`method = "loess"`

`geom_hline()`/`geom_vline()`

`geom_hline()`

`geom_vline()`

`geom_bar()`

`geom_boxplot()`

`geom_histogram()` & `geom_density()`

`facet_grid()`

`facet_wrap()`