Week 2 (Slides) - Cognitive Perspectives and Introduction to ggplot2

Emorie D Beck

Part 1: Good/Bad Data Visualizations and Reading Discussion

The cognitive and psychological foundation of data visualization

  • We are better at processing visual information than verbal information (includes reading)? Yes!
  • Why? Preattentive processing

Visual Variables

  • Position (x; y)
  • Size (size)
  • Shape (shape; linetype)
  • Lightness (color; fill)
  • Color hue (color; fill)
  • Orientation (angle)
  • Texture (linetype; shape)
  • Color saturation (color; fill)
  • Arrangement (scale_[mapping]_[type])
  • Crispness (e.g., geom_point v. geom_jitter)
  • Resolution (ggsave)
  • Transparency (alpha)

6 Common Types of Visual Aesthetics

Gestalt Principles

  • Gestalt psychology emerges from the observation that we perceive elements of the world holistically
  • Can be leveraged to allow simple perceptions of complex patterns

Gestalt Principles

The Cognitive Psychology of Data Visualization

Principles for Efficient Visualization

  1. Use visualizations to allow viewers to powerfully compute statistics
  2. Avoid visual processing limits: making comparisons
  3. Control comparison with visual grouping cues
  4. Guide viewer to the most important comparison
  5. Avoid taxing limited working memory

Visualizing Data in ggplot2

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
load(url("https://github.com/emoriebeck/psc290-data-viz-2022/raw/main/02-week2-ggplot2/01-data/ipcs_data.RData"))
ipcs_data %>% 
  print(n = 6)
# A tibble: 4,222 × 70
  SID   Full_Date    afraid angry attentive content excited goaldir guilty happy
  <chr> <chr>         <dbl> <dbl>     <dbl>   <dbl>   <dbl>   <dbl>  <dbl> <dbl>
1 02    2018-10-22 …      1     2         4       4       2       5      2     3
2 02    2018-10-22 …      1     1         4       3       2       5      1     3
3 02    2018-10-23 …      2     1         2       3       1       2      2     3
4 02    2018-10-23 …      2     2         4       3       2       4      1     3
5 02    2018-10-23 …      2     1         4       4       3       4      1     3
6 02    2018-10-24 …      2     1         4       4       2       4      1     3
# ℹ 4,216 more rows
# ℹ 60 more variables: proud <dbl>, purposeful <dbl>,
#   agreeableness_Compassion <dbl>, agreeableness_Respectfulness <dbl>,
#   agreeableness_Trust <dbl>, conscientiousness_Organization <dbl>,
#   conscientiousness_Productiveness <dbl>,
#   conscientiousness_Responsibility <dbl>, extraversion_Assertiveness <dbl>,
#   extraversion_Energy.Level <dbl>, extraversion_Sociability <dbl>, …

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)
ggplot(
  data = ipcs_data
)

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)
    • mapping: you aesthetic mappings for the plot, wrapped in aes() 
ggplot(
  data = ipcs_data
  , mapping = aes(x = linear, y = happy)
)

Mappings

  • 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

Scales

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
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()

Geoms: geom_point()

Your basic scatterplot!

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

Geoms: geom_point()

Your basic scatterplot!

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

Geoms: geom_point()

Your basic scatterplot!

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

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

Geoms: 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
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

Geoms: 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
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

Geoms: geom_jitter()

alpha

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

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

Geoms: 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

Geoms: geom_smooth()

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

method = "lm"

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"

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

Geoms: geom_smooth()

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

method = "lm"

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"

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

Geoms:

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

Geoms:

geom_hline()/geom_vline()

geom_hline(): horizontal lines have yintercept mappings

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

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

Geoms: geom_line()

  • Line graphs are great for showing relationships among variables as well as showing changes over time
  • Be careful, geom_line() in ggplot2 is pretty literal
  • We’ll come back to this several times, so I’ll just show one example of a time series
ipcs_data %>%
  filter(SID == "216") %>%
  ggplot(mapping = aes(x = beep, y = happy)) + 
    geom_line(color = "blue") + 
    geom_point(size = 1) + 
    theme_classic() # I just hate grey backgrounds

Geoms: 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

Geoms: geom_bar()

Frequency

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

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()

Geoms: geom_bar()

Mean differences

Were there mean-level in our continuous variables?

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()

Geoms: 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)

Geoms: geom_boxplot()

One boxplot

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

Geoms: geom_boxplot()

Multiple boxplots

  • We can compare multiple participants
  • Later, we’ll also talk about how to order the boxplots (and other axes) by means, medians, etc.
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()

Geoms: geom_boxplot()

Multiple boxplots

  • We can also compare multiple variables
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()

Geoms: geom_boxplot()

Advanced!

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

Geoms:

geom_histogram() & geom_density()

  • Useful for showing raw / smoothed distributions of data

Histogram

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

Density Distribution

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

Geoms:

geom_histogram() & geom_density()

Multiple histograms / density distributions

  • We can compare multiple participants
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: Facets and Themes

Plot Appearance

  • 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!

Plot Apperance: 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

Appearance: Facets

  • 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
ipcs_data %>%
  filter(SID == "216") %>%
  select(SID, beep, afraid:content) %>%
  pivot_longer(
    cols = afraid:content
    , names_to = "item"
    , values_to = "value"
  ) %>%
  print(n = 8)
# A tibble: 432 × 4
  SID    beep item      value
  <chr> <int> <chr>     <dbl>
1 216       1 afraid        1
2 216       1 angry         2
3 216       1 attentive     3
4 216       1 content       4
5 216       2 afraid        2
6 216       2 angry         2
7 216       2 attentive     3
8 216       2 content       4
# ℹ 424 more rows

Appearance: Facets

  • In ggplot2, there are two core faceting functions
    • facet_grid()
    • facet_wrap()
  • 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

Appearance: Facets

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

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

Appearance: Facets

facet_grid()

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()

Appearance: Facets

facet_wrap()

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()

Appearance: Facets

facet_wrap(): Change scale and space

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()

Appearance: Labels & Titles

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

Appearance: Labels & Titles

Remember this?

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

Appearance: Labels & Titles

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

Appearance: Labels & Titles

Labels also apply to other mappings like color

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()

Appearance: Labels & Titles

You can also use labels to remove axis labels

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()

Appearance: Themes (Basic)

  • 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

Appearance: Themes (Advanced)

  • 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

Appearance: Themes

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)
        )
    )

Appearance: Themes

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))
  )

Appearance: Themes

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")
    )

PSC 290 - Data Visualization