Analyses

In [1]:

#install.packages("remotes")
#remotes::install_github("DevPsyLab/petersenlab")

Intro

Preamble

Install Libraries

Load Libraries

In [2]:

library("petersenlab") #located here: https://github.com/DevPsyLab/petersenlab
library("knitr")
library("magick")

Linking to ImageMagick 6.9.12.98
Enabled features: fontconfig, freetype, fftw, heic, lcms, pango, raw, webp, x11
Disabled features: cairo, ghostscript, rsvg

Using 4 threads

library("grid")
library("patchwork")
library("tidyverse")

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.2     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Load Data

In [3]:

longitudinalStudies <- read_csv("../Data/longitudinalStudies.csv") #read_csv("./Data/longitudinalStudies.csv")

Rows: 46 Columns: 17
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): study, studyLabel, designType, sampleType, country, continent
dbl (8): sampleSize, yearStart, yearMostRecentAssessment, yearEnd, ageStartM...
lgl (3): prenatalRecruitment, active, interventionPrevention

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

longitudinalExternalizingStudies <- read_csv("../Data/longitudinalExternalizingStudies.csv") #read_csv("./Data/longitudinalExternalizingStudies.csv")

Rows: 20 Columns: 22
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (14): sample, sampleLabel, study, year, studyLabel, measurementOccasions...
dbl  (6): sampleSize, ageStartMin, ageStartMax, ageEndMin, ageEndMax, maxTim...
lgl  (2): registry, developmentalScaling

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

externalizingFacets <- read_csv("../Data/externalizingFacets.csv") #read_csv("./Data/externalizingFacets.csv")

Rows: 30 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): facet, facetLabel, facetDefinition, positiveOpposite

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Plots

Cognitive Trajectories

In [4]:

# Read images
lifespanCognitiveTrajectoriesFigA <- magick::image_read("../Figures/Borelli2018_figure1.jpg") #magick::image_read("./Figures/Borelli2018_figure1.jpg")
lifespanCognitiveTrajectoriesFigB <- magick::image_read("../Figures/Abrous2026_figure1.jpg") #magick::image_read("./Figures/Abrous2026_figure1.jpg")

# Convert images to patchwork-compatible objects
lifespanCognitiveTrajectoriesPanelA <- patchwork::wrap_elements(
  panel = grid::rasterGrob(as.raster(lifespanCognitiveTrajectoriesFigA), interpolate = TRUE)
) +
  labs(tag = "A")

lifespanCognitiveTrajectoriesPanelB <- patchwork::wrap_elements(
  panel = grid::rasterGrob(as.raster(lifespanCognitiveTrajectoriesFigB), interpolate = TRUE)
) +
  labs(tag = "B")

# Combine panels
lifespanCognitiveTrajectoriesPanelA + lifespanCognitiveTrajectoriesPanelB +
  patchwork::plot_layout(widths = c(53, 47)) &
  theme(
    plot.tag = element_text(face = "bold", size = 14),
    plot.margin = margin(0, 0, 0, 0)
  )

Different Cognitive Trajectories of Aging

Externalizing Trajectories

In [5]:

# Read images
externalizingByAgeFigA <- magick::image_read("../Figures/Sivertsson2024_figure1.png") #magick::image_read("./Figures/Sivertsson2024_figure1.png")
externalizingByAgeFigB <- magick::image_read("../Figures/developmentalTaxonomy.png") #magick::image_read("./Figures/developmentalTaxonomy.png")
externalizingByAgeFigC <- magick::image_read("../Figures/Petersen2015_adapted.png") #magick::image_read("./Figures/Petersen2015_adapted.png")

# Convert images to patchwork-compatible objects
externalizingByAgePanelA <- patchwork::wrap_elements(
  panel = grid::rasterGrob(as.raster(externalizingByAgeFigA), interpolate = TRUE)
) +
  labs(tag = "A")

externalizingByAgePanelB <- patchwork::wrap_elements(
  panel = grid::rasterGrob(as.raster(externalizingByAgeFigB), interpolate = TRUE)
) +
  labs(tag = "B")

externalizingByAgePanelC <- patchwork::wrap_elements(
  panel = grid::rasterGrob(as.raster(externalizingByAgeFigC), interpolate = TRUE)
) +
  labs(tag = "C")

# Combine panels
externalizingByAgePanelA /
(externalizingByAgePanelB + externalizingByAgePanelC) +
  patchwork::plot_layout(
    heights = c(1, 1)
  ) &
  theme(
    plot.tag = element_text(face = "bold", size = 14),
    plot.margin = margin(0, 0, 0, 0)
  )

Externalizing Behavior Across Development

Longitudinal Studies

In [6]:

# prepare data for plot
longitudinalStudies_plotData <- bind_rows(

  # ---- start cross-section ----
  longitudinalStudies %>%
    mutate(
      segment_group = "cross-section",
      segment_type = "start_crossSection",
      ageStart = ageStartMin,
      ageEnd = ageStartMax
    ) %>%
    select(study, studyLabel, ageStart, ageEnd, segment_group, segment_type),

  # ---- core (ONLY if true longitudinal cohort span exists) ----
  longitudinalStudies %>%
    mutate(has_cor = ageStartMax < ageEndMin) %>%
    filter(has_cor) %>%
    mutate(
      segment_group = "core",
      segment_type = "core",
      ageStart = ageStartMax,
      ageEnd = ageEndMin
    ) %>%
    select(study, studyLabel, ageStart, ageEnd, segment_group, segment_type),

  # ---- end cross-section ----
  longitudinalStudies %>%
    mutate(
      segment_group = "cross-section",
      segment_type = "end_crossSection",
      ageStart = ageEndMin,
      ageEnd = ageEndMax
    ) %>%
    select(study, studyLabel, ageStart, ageEnd, segment_group, segment_type)
)

# Merge active status
longitudinalStudies_plotData <- longitudinalStudies_plotData %>%
  left_join(
    longitudinalStudies %>% select(study, active),
    by = c("study")
  )

# Order studies for plot
study_order <- longitudinalStudies %>%
  arrange(ageEndMax, ageStartMin) %>%
  pull(studyLabel)

longitudinalStudies_plotData <- longitudinalStudies_plotData %>%
  mutate(studyLabelFactor = factor(studyLabel, levels = study_order))

# Prenatal enrollment
prenatal_df <- longitudinalStudies %>%
  filter(prenatalRecruitment == TRUE) %>%
  mutate(
    age = 0,
    studyLabelFactor = factor(studyLabel, levels = study_order)
  )

In [7]:

plot_longitudinalStudies <- ggplot2::ggplot(
  data = longitudinalStudies_plotData,
  mapping = aes(
    y = studyLabelFactor
  )
) +
  geom_segment(
    aes(
      x = ageStart,
      xend = ageEnd,
      yend = studyLabelFactor,
      linetype = segment_group,
      color = active
    ),
    linewidth = 1
  ) +
  scale_linetype_manual(
    values = c(
      "cross-section" = "11",
      "core" = "solid"
    ),
    guide = "none"
  ) +
  scale_color_manual(
    values = c(
      "TRUE" = "black",
      "FALSE" = "gray70"
    ),
    guide = "none"
  ) +
  geom_point(
    data = prenatal_df,
    aes(
      x = age,
      y = studyLabelFactor,
      color = active
    ),
    shape = 8,
    size = 3
  ) +
  labs(
    x = "Age (years)",
    y = NULL
  ) +
  theme_classic()

plot_longitudinalStudies

(Approximate) Ages Spanned by Various Lengthy Longitudinal Studies

Longitudinal Externalizing Studies

In [8]:

# Prepare plot data
longitudinalExternalizingStudies_plotData <- bind_rows(

  # start cross-sectional span
  longitudinalExternalizingStudies %>%
    mutate(
      segment_group = "cross-section",
      ageStart = ageStartMin,
      ageEnd = ageStartMax
    ) %>%
    select(
      sample,
      sampleLabel,
      study,
      studyLabel,
      ageStart,
      ageEnd,
      segment_group
    ),

  # core longitudinal span
  longitudinalExternalizingStudies %>%
    mutate(has_core = ageStartMax < ageEndMin) %>%
    filter(has_core) %>%
    mutate(
      segment_group = "core",
      ageStart = ageStartMax,
      ageEnd = ageEndMin
    ) %>%
    select(
      sample,
      sampleLabel,
      study,
      studyLabel,
      ageStart,
      ageEnd,
      segment_group
    ),

  # end cross-sectional span
  longitudinalExternalizingStudies %>%
    mutate(
      segment_group = "cross-section",
      ageStart = ageEndMin,
      ageEnd = ageEndMax
    ) %>%
    select(
      sample,
      sampleLabel,
      study,
      studyLabel,
      ageStart,
      ageEnd,
      segment_group
    )
)

# Merge registry/developmental scaling info
longitudinalExternalizingStudies_plotData <- longitudinalExternalizingStudies_plotData %>%
  left_join(
    longitudinalExternalizingStudies %>%
      select(study, registry, developmentalScaling),
    by = "study"
  ) %>%
  mutate(
    plotColor = case_when(
      developmentalScaling ~ "developmentalScaling",
      registry ~ "registry",
      TRUE ~ "standard"
    )
  )

# Study ordering
longitudinalExternalizingStudies_order <- longitudinalExternalizingStudies %>%
  arrange(ageEndMax, ageStartMin) %>%
  pull(studyLabel)

# Add numeric study positions
study_positions <- tibble(
  studyLabel = longitudinalExternalizingStudies_order,
  study_num = seq_along(longitudinalExternalizingStudies_order)
)

# Main plotting data
longitudinalExternalizingStudies_plotData <- longitudinalExternalizingStudies_plotData %>%
  left_join(study_positions, by = "studyLabel") %>%
  mutate(
    line_y = study_num + 0.25
  )

# Measurement occasions

longitudinalExternalizingStudies_measurementOccasions <- longitudinalExternalizingStudies %>%
  filter(!is.na(measurementOccasions)) %>%
  separate_rows(
    measurementOccasions,
    sep = ";"
  ) %>%
  mutate(
    measurementOccasions = as.numeric(str_trim(measurementOccasions))
  ) %>%
  left_join(study_positions, by = "studyLabel") %>%
  mutate(
    point_y = study_num + 0.45,
    plotColor = case_when(
      developmentalScaling ~ "developmentalScaling",
      registry ~ "registry",
      TRUE ~ "standard"
    )
  )

# Sample labels
longitudinalExternalizingStudies_sampleLabels <- longitudinalExternalizingStudies %>%
  left_join(study_positions, by = "studyLabel") %>%
  mutate(
    label_x = ageStartMin,
    label_y = study_num,
    plotColor = case_when(
      developmentalScaling ~ "developmentalScaling",
      registry ~ "registry",
      TRUE ~ "standard"
    )
  )

In [9]:

plot_longitudinalExternalizingStudies <- ggplot2::ggplot() +
  geom_segment(
    data = longitudinalExternalizingStudies_plotData,
    aes(
      x = ageStart,
      xend = ageEnd,
      y = line_y,
      yend = line_y,
      linetype = segment_group,
      color = plotColor
    ),
    linewidth = 1
  ) +
  geom_point(
    data = longitudinalExternalizingStudies_measurementOccasions,
    aes(
      x = measurementOccasions,
      y = point_y,
      color = plotColor
    ),
    size = 1.5
  ) +
  geom_text(
    data = longitudinalExternalizingStudies_sampleLabels,
    aes(
      x = label_x,
      y = label_y,
      label = sampleLabel,
      color = plotColor
    ),
    hjust = 0,
    vjust = 0.5,
    size = 2.8
  ) +
  scale_y_continuous(
    breaks = study_positions$study_num,
    labels = study_positions$studyLabel
  ) +
  scale_linetype_manual(
    values = c(
      "cross-section" = "11",
      "core" = "solid"
    ),
    guide = "none"
  ) +
  scale_color_manual(
    values = c(
      standard = "black",
      registry = "gray70",
      developmentalScaling = "#2E7D32"
    ),
    guide = "none"
  ) +
  coord_cartesian(
    clip = "off"
  ) +
  labs(
    x = "Age (years)",
    y = NULL
  ) +
  theme_classic()

plot_longitudinalExternalizingStudies

(Approximate) Ages Spanned by Externalizing Trajectories in Various Lengthy Longitudinal Studies Examining Individuals’ Trajectories of Externalizing Behavior

Tables

Externalizing Facets

In [10]:

knitr::kable(
  externalizingFacets %>% select(facetLabel, facetDefinition, positiveOpposite),
  col.names = c("Facet","Definition","Potential Positive Opposite")
)

In [11]:

Externalizing Facets Identified in Content Analysis of Externalizing Measures.

Facet	Definition	Potential Positive Opposite
Apathy/Indolence/Disengagement	Unconcerned about achievement, low motivation, low concern about how one’s abilities or actions are evaluated, specific to contexts in which others have expectations for performance (e.g., school and work), specifically low effort rather than performance	Diligence/Achievement motivation
Attention seeking/Exhibitionism	Exaggerated or dramatic expressions, interrupting others, inappropriate or provocative behaviors, provoking reactions	Humility/Appropriate self-expression
Behavioral addiction	Impulsive/compulsive engagement in rewarding non-substance behaviors (e.g., gambling, video games, risky sex, pornography, food, the internet, mobile devices, shopping)	Self-regulated engagement
Blame externalization	Attribute cause of problems or failures to external factors, falsely claiming victimhood, blaming others	Personal responsibility/Accountability
Boredom proneness	Expressing boredom often, difficulty maintaining interest, lack of engagement	Curiosity/Sustained engagement
Callous aggression	Inflicting harm without feeling remorse or concern, usually proactive/intentional	Compassionate protection/Benevolence
Callousness/Disregard for others/Selfishness/Egocentricity	Emotional detachment, indifference to well-being of others	Empathy/Compassion/Concern for others/Helping others/Sharing
Deceitfulness/Fraud/Lying/Cheating/Dishonesty/Sneakiness/Exploitation	Keeping the truth hidden, especially to get an advantage (e.g., lying, withholding information, manipulation, making false promises)	Honesty/Integrity/Fairness
Deviant peer affiliation	Affiliating with peers who engage in deviant or delinquent behaviors	Prosocial peer affiliations
Destructive aggression/Destruction of property	Destruction of property, self, or others, objects	Constructiveness/Care for property
Distractibility/Inattention/Attention dysregulation/Forgetfulness/Daydreaming	Difficulty maintaining focus, attention, or concentration, frequent attention shifts	Attentional control/Sustained attention
Domineering/Bossiness	Asserting control, power, and authority in an overbearing or oppressive manner	Collaborativeness
Excitement seeking/Risk taking/Sensation seeking	Tendency to impulsively pursue new and different sensations, especially ones that are intense, exhilarating, thrilling, unsafe, etc. (e.g., risk taking, preference for novelty, intensity)	Prudent exploration/Measured novelty seeking
Grandiosity/Overconfidence	Exaggerated sense of self-importance, sense of superiority or specialness	Realistic self-confidence
Hostility/Rudeness	Hostile behavior, unfriendliness, resentment, antagonism, ranging from mild irritation to intense rage, interpret others’ behavior as hostile	Warmth/Friendliness/Kindness
Hyperactivity	High levels of physical activity, restlessness, difficulty engaging in quiet activities	Behavioral regulation
Impulsivity/Disinhibition/Impatient urgency	Tendency to act without thinking, disinhibited engagement	Planful control
Irresponsibility/Unreliability/Disorganization	Failing to meet obligations, avoiding addressing difficulties, inconsistency, lack of organization and planning	Responsibility/Conscientiousness/Dependability/Reliability/Organization
Manipulativeness/Psychological aggression	Tendency to influence or control others to achieve one’s own goals or desires, e.g., playing the victim, flattery, gaslighting, guilt-tripping, psychological aggression	Sincerity/Authenticity
Mistrust/Suspiciousness/Paranoia	Lack of confidence in the reliability, intentions of others, difficulty forming close relationships with others, difficulty forgiving	Interpersonal trust (appropriately calibrated)
Oppositionality/Stubbornness	Persistent and defiant attitude, tendency to challenge authority, refusal to comply, argumentativeness (could include while complying), etc.	Cooperativeness
Physical aggression	Use of physical force or violence to harm others or their property	Respect for others’ bodies/Helping others
Punishment insensitivity	Does not change behavior after punishment, high tolerance for negative outcomes	Responsive to consequences
Rebelliousness/Rule-breaking/Noncompliance	Doing things differently than one is asked to do (e.g., breaking house rules)	Compliance/Civic responsibility
Relational aggression	Aggression intended to harm others through deliberate manipulation of their social standing and relationships (e.g., exclusion, speaking negatively of another, etc.)	Inclusion
Sexual aggression	Engaging in sexual activity with someone who does not or cannot consent to that behavior, or threatening to do so (e.g., harassment)	Respect for consent
Impulsive sexual behavior/Risky sexual behavior	Engaging in risky sexual activity (e.g., sex without contraceptives)	Responsible sexual decision-making
(Harmful) Substance Use/Problems	Harmful use of any drugs (e.g., alcohol, tobacco, illegal drugs)	Healthy choices around substance use
Theft/Stealing	Unauthorized taking of someone else’s property without their knowledge or consent	Respect for people’s possessions
Verbal Aggression	Communication with an intention to harm an individual through words, tone, or manner	Respectful communication

Session Info

In [12]:

sessionInfo()

R version 4.6.1 (2026-06-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

time zone: UTC
tzcode source: system (glibc)

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] lubridate_1.9.5   forcats_1.0.1     stringr_1.6.0     dplyr_1.2.1      
 [5] purrr_1.2.2       readr_2.2.0       tidyr_1.3.2       tibble_3.3.1     
 [9] ggplot2_4.0.3     tidyverse_2.0.0   patchwork_1.3.2   magick_2.9.1     
[13] knitr_1.51        petersenlab_1.2.3

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1    psych_2.6.5         viridisLite_0.4.3  
 [4] farver_2.1.2        S7_0.2.2            fastmap_1.2.0      
 [7] digest_0.6.39       rpart_4.1.27        timechange_0.4.0   
[10] lifecycle_1.0.5     cluster_2.1.8.2     magrittr_2.0.5     
[13] compiler_4.6.1      rlang_1.3.0         Hmisc_5.2-6        
[16] tools_4.6.1         yaml_2.3.12         data.table_1.18.6.1
[19] labeling_0.4.3      htmlwidgets_1.6.4   bit_4.6.0          
[22] mnormt_2.1.2        plyr_1.8.9          RColorBrewer_1.1-3 
[25] foreign_0.8-91      withr_3.0.3         nnet_7.3-20        
[28] stats4_4.6.1        lavaan_0.7-2        xtable_1.8-8       
[31] colorspace_2.1-3    scales_1.4.0        MASS_7.3-65        
[34] cli_3.6.6           mvtnorm_1.4-2       crayon_1.5.3       
[37] rmarkdown_2.31      reformulas_0.4.4    generics_0.1.4     
[40] otel_0.2.0          rstudioapi_0.19.0   tzdb_0.5.0         
[43] reshape2_1.4.5      minqa_1.2.8         DBI_1.3.0          
[46] splines_4.6.1       parallel_4.6.1      base64enc_0.1-6    
[49] mitools_2.4         vctrs_0.7.3         boot_1.3-32        
[52] Matrix_1.7-5        jsonlite_2.0.0      hms_1.1.4          
[55] bit64_4.8.4         Formula_1.2-6       htmlTable_2.5.0    
[58] glue_1.8.1          nloptr_2.2.1        stringi_1.8.9      
[61] gtable_0.3.6        quadprog_1.5-8      lme4_2.0-6         
[64] pillar_1.11.1       htmltools_0.5.9     R6_2.6.1           
[67] Rdpack_2.6.6        mix_1.0-13          vroom_1.7.1        
[70] evaluate_1.0.5      pbivnorm_0.6.0      lattice_0.22-9     
[73] rbibutils_2.4.1     backports_1.5.1     Rcpp_1.1.2         
[76] gridExtra_2.3.1     nlme_3.1-169        checkmate_2.3.4    
[79] xfun_0.60           pkgconfig_2.0.3    

Rstudio Version

In [13]:

#rstudioapi::versionInfo()