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library(haven)

Warning: package ‘haven’ was built under R version 4.2.2

library(tidyverse)

Warning: package ‘tidyverse’ was built under R version 4.2.2Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ────────────────────────────────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.5.0 
✔ readr   2.1.3      ✔ forcats 0.5.2 Warning: package ‘ggplot2’ was built under R version 4.2.2Warning: package ‘tibble’ was built under R version 4.2.2Warning: package ‘tidyr’ was built under R version 4.2.2Warning: package ‘readr’ was built under R version 4.2.2Warning: package ‘purrr’ was built under R version 4.2.2Warning: package ‘dplyr’ was built under R version 4.2.2Warning: package ‘stringr’ was built under R version 4.2.2Warning: package ‘forcats’ was built under R version 4.2.2── Conflicts ───────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(EGAnet)

Warning: package ‘EGAnet’ was built under R version 4.2.2
EGAnet (version 1.2.3) 
For help getting started, type browseVignettes("EGAnet")
 
For bugs and errors, submit an issue to <https://github.com/hfgolino/EGAnet/issues>

library(lavaan)

Warning: package ‘lavaan’ was built under R version 4.2.2This is lavaan 0.6-13
lavaan is FREE software! Please report any bugs.

library(bootnet)

Warning: package ‘bootnet’ was built under R version 4.2.2Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'mgm':
  method         from  
  print.resample modelr
This is bootnet 1.5
For questions and issues, please see github.com/SachaEpskamp/bootnet.

acute.clean <- Acute_Care_Years %>%
  select(.,
    DXMOOD,
    DXANXIETY,
    DXBEHAVIOR,
    DXPSYCHOTIC,
    DXMIOTHER,
    SELFINJ,
    DISBEH,
    UNCPBEH,
    HCCARVAS,
    HCDIAB,
    HCHIBP,
    HCHICHL,
    HCDYSPH,
    HCPRESULC,
    HCALZHEIM,
    HCORALDENTAL,
    HCSLEEPAPNEA,
    HEALTH,
    PRIMARY_VISIT_Total,
    OUTPATIENT_COUNSELING_TOTAL,
    INPATIENT_MH_TOTAL,
    ER_Total,
    scr_1A_raw_total,
    scr_1B_raw_total)%>%
  na_if(., -9) %>%
  na_if(., -99) %>% 
  na_if(., -999) %>%
  na_if(., 98) %>%
  na_if(., 99)%>%
  na.omit

model.health <- acute.clean %>%
  select(.,
       DXMOOD,
    DXANXIETY,
    DXBEHAVIOR,
    DXPSYCHOTIC,
    DXMIOTHER,
    SELFINJ,
    DISBEH,
    UNCPBEH,
    HCCARVAS,
    HCDIAB,
    HCHIBP,
    HCHICHL,
    HCDYSPH,
    HCPRESULC,
    HCALZHEIM,
    HCORALDENTAL,
    HCSLEEPAPNEA)

ega.health <- EGA(data = model.health, model = "glasso")

summary(ega.health)

Number of communities: 4 

      DXMOOD    DXANXIETY   DXBEHAVIOR  DXPSYCHOTIC    DXMIOTHER      SELFINJ 
           2            2            4            2            2            4 
      DISBEH      UNCPBEH     HCCARVAS       HCDIAB       HCHIBP      HCHICHL 
           4            4            1            1            1            1 
     HCDYSPH    HCPRESULC    HCALZHEIM HCORALDENTAL HCSLEEPAPNEA 
           3            3            3            3            1 

Methods:
                                                         
Correlations =          auto (from qgraph)               
Model =                 glasso                           
Algorithm =             walktrap                         
Unidimensional Method = louvain with consensus clustering

centab <- centralityTable(health=ega.health$network, standardized = TRUE,  relative = FALSE, weighted = TRUE, signed = TRUE)
centab

EI.health <- filter(centab, measure == "ExpectedInfluence")
EI.health

EI.health.order <- EI.health[order(EI.health$value),]
EI.health.order

EI_plot <- ggplot(EI.health.order, aes (x=value, y = node))+ geom_point()

print(EI_plot)