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srs_corr <- c("srs_awr_t", "srs_cog_t", "srs_com_t", "srs_mot_t", "srs_rrb_t", "srs_sci_t", "srs_total_t", "srs_total_social_t")
srs_subset_data <- adi_ados_merge[ , srs_corr]
srs_correlation_matrix <- cor(srs_subset_data, use = "pairwise.complete.obs")
srs_pmat <- cor.mtest(srs_correlation_matrix, use = "all")
corrplot(srs_correlation_matrix,
        method="shade",
        type="lower",
        p.mat = srs_pmat$p,
        order = "original",
        insig = "label_sig",
        sig.level = c(.001, .01, .05),
        pch.cex = 1.4,
        pch.col = "black",
        tl.col="black",
        tl.cex=1,
        addCoef.col = "black",
        tl.srt=45, # Text label rotation
        diag = F,
        outline= T)
        
> get_descriptives(srs_subset_data)
# A tibble: 8 × 7
  variable           count  mean    sd median   min   max
  <chr>              <int> <dbl> <dbl>  <dbl> <dbl> <dbl>
1 srs_awr_t            447  68.4  9.92   68    43    94  
2 srs_cog_t            447  66.8  9.60   68    44    96  
3 srs_com_t            447  71.8 11.2    72.5  43    98  
4 srs_mot_t            447  66.4 12.0    66    39   101  
5 srs_rrb_t            447  70.9 14.6    70    42   110  
6 srs_sci_t            447  70.7 10.2    71    46    96  
7 srs_total_social_t   447  69.4  9.34   69.5  46.5  93.8
8 srs_total_t          447  71.2 10.8    71    45    98  




> srs_correlation_matrix
                   srs_awr_t srs_cog_t srs_com_t srs_mot_t srs_rrb_t srs_sci_t srs_total_t
srs_awr_t          1.0000000 0.5617556 0.6371749 0.4163185 0.5145037 0.7109790   0.6898341
srs_cog_t          0.5617556 1.0000000 0.7295800 0.6345804 0.6993044 0.8546356   0.8529904
srs_com_t          0.6371749 0.7295800 1.0000000 0.7110870 0.7482974 0.9445597   0.9330537
srs_mot_t          0.4163185 0.6345804 0.7110870 1.0000000 0.6609307 0.8333061   0.8219971
srs_rrb_t          0.5145037 0.6993044 0.7482974 0.6609307 1.0000000 0.7864382   0.8748011
srs_sci_t          0.7109790 0.8546356 0.9445597 0.8333061 0.7864382 1.0000000   0.9865161
srs_total_t        0.6898341 0.8529904 0.9330537 0.8219971 0.8748011 0.9865161   1.0000000
srs_total_social_t 0.8160254 0.8727226 0.9293317 0.7157194 0.7680642 0.9799330   0.9661011
                   srs_total_social_t
srs_awr_t                   0.8160254
srs_cog_t                   0.8727226
srs_com_t                   0.9293317
srs_mot_t                   0.7157194
srs_rrb_t                   0.7680642
srs_sci_t                   0.9799330
srs_total_t                 0.9661011
srs_total_social_t          1.0000000


> srs_pmat
$p
                    srs_awr_t  srs_cog_t   srs_com_t  srs_mot_t srs_rrb_t    srs_sci_t  srs_total_t
srs_awr_t          0.00000000 0.73178174 0.938212488 0.06921226 0.2708301 0.9034103758 0.6341653918
srs_cog_t          0.73178174 0.00000000 0.276264459 0.70487581 0.4367219 0.0891439960 0.0888586522
srs_com_t          0.93821249 0.27626446 0.000000000 0.36896056 0.2875269 0.0011266333 0.0025683437
srs_mot_t          0.06921226 0.70487581 0.368960560 0.00000000 0.4615671 0.1654115225 0.1657475924
srs_rrb_t          0.27083006 0.43672193 0.287526933 0.46156710 0.0000000 0.3535799419 0.0709010964
srs_sci_t          0.90341038 0.08914400 0.001126633 0.16541152 0.3535799 0.0000000000 0.0004549992
srs_total_t        0.63416539 0.08885865 0.002568344 0.16574759 0.0709011 0.0004549992 0.0000000000
srs_total_social_t 0.26521592 0.09020456 0.013326231 0.99674917 0.6804714 0.0106513499 0.0377275450
                   srs_total_social_t
srs_awr_t                  0.26521592
srs_cog_t                  0.09020456
srs_com_t                  0.01332623
srs_mot_t                  0.99674917
srs_rrb_t                  0.68047144
srs_sci_t                  0.01065135
srs_total_t                0.03772754
srs_total_social_t         0.00000000