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Correlation and partial correlation tables

Source: vignettes/correlation.Rmd
correlation.Rmd

Overview

tbl_correlation() formats a tidy data frame of (partial) correlation results into a publication-ready table. It expects pre-computed coefficients as a tidy data frame with one row per predictor x outcome pair.

Column-name arguments take quoted strings. Defaults match a typical correlation results frame with columns named variable, outcome, r, p:

head(clerk_cor_example)
#>      variable  outcome   n      r     p
#> 1         hdl tmt_time 270  0.190 0.008
#> 2     glucose tmt_time 254 -0.229 0.398
#> 3         bmi tmt_time 290 -0.086 0.013
#> 4       waist tmt_time 253  0.279 0.002
#> 5 systolic_bp tmt_time 242  0.195 0.379
#> 6         bdi tmt_time 281  0.096 0.109

Basic long-format table

Since clerk_cor_example already matches all defaults, a minimal call suffices:

tbl_correlation(
  clerk_cor_example,
  output = "gt"
) |>
  clerk_render(title = "Partial correlations (age + sex controlled)")
Partial correlations (age + sex controlled)
Outcome r p
All variables
hdl tmt_time +0.190 0.008
glucose tmt_time -0.229 0.398
bmi tmt_time -0.086 0.013
waist tmt_time +0.279 0.002
systolic_bp tmt_time +0.195 0.379
bdi tmt_time +0.096 0.109
panas_neg tmt_time +0.157 0.006
life_satisfaction tmt_time +0.247 0.117
hdl verbal_fluency +0.078 0.072
glucose verbal_fluency +0.216 0.003
bmi verbal_fluency +0.022 0.207
waist verbal_fluency +0.047 0.185
systolic_bp verbal_fluency -0.105 0.077
bdi verbal_fluency -0.176 0.070
panas_neg verbal_fluency +0.232 0.117
life_satisfaction verbal_fluency +0.201 0.234

Including sample size

Pass the column name as a string to n:

tbl_correlation(
  clerk_cor_example,
  n      = "n",
  output = "gt"
) |>
  clerk_render(title = "Partial correlations (age + sex controlled)")
Partial correlations (age + sex controlled)
Outcome n r p
All variables
hdl tmt_time 270 +0.190 0.008
glucose tmt_time 254 -0.229 0.398
bmi tmt_time 290 -0.086 0.013
waist tmt_time 253 +0.279 0.002
systolic_bp tmt_time 242 +0.195 0.379
bdi tmt_time 281 +0.096 0.109
panas_neg tmt_time 289 +0.157 0.006
life_satisfaction tmt_time 293 +0.247 0.117
hdl verbal_fluency 282 +0.078 0.072
glucose verbal_fluency 276 +0.216 0.003
bmi verbal_fluency 291 +0.022 0.207
waist verbal_fluency 253 +0.047 0.185
systolic_bp verbal_fluency 293 -0.105 0.077
bdi verbal_fluency 264 -0.176 0.070
panas_neg verbal_fluency 265 +0.232 0.117
life_satisfaction verbal_fluency 266 +0.201 0.234

Domain grouping 

tbl_correlation(
  clerk_cor_example,
  n       = "n",
  domains = list(
    "Metabolic"      = c("hdl", "glucose", "bmi"),
    "Anthropometric" = c("waist", "systolic_bp"),
    "Mental health"  = c("bdi", "panas_neg", "life_satisfaction")
  ),
  output = "gt"
) |>
  clerk_render(title = "Partial correlations (age + sex controlled)")
#> Warning in `[<-.factor`(`*tmp*`, is.na(tbl[["domain"]]), value = "Other"):
#> invalid factor level, NA generated
Partial correlations (age + sex controlled)
Outcome n r p
Metabolic
hdl tmt_time 270 +0.190 0.008
glucose tmt_time 254 -0.229 0.398
bmi tmt_time 290 -0.086 0.013
hdl verbal_fluency 282 +0.078 0.072
glucose verbal_fluency 276 +0.216 0.003
bmi verbal_fluency 291 +0.022 0.207
Anthropometric
waist tmt_time 253 +0.279 0.002
systolic_bp tmt_time 242 +0.195 0.379
waist verbal_fluency 253 +0.047 0.185
systolic_bp verbal_fluency 293 -0.105 0.077
Mental health
bdi tmt_time 281 +0.096 0.109
panas_neg tmt_time 289 +0.157 0.006
life_satisfaction tmt_time 293 +0.247 0.117
bdi verbal_fluency 264 -0.176 0.070
panas_neg verbal_fluency 265 +0.232 0.117
life_satisfaction verbal_fluency 266 +0.201 0.234

FDR correction

Set fdr = TRUE to add a BH-corrected p_fdr column. Use fdr_within to correct separately within each outcome:

tbl_correlation(
  clerk_cor_example,
  fdr        = TRUE,
  fdr_within = "outcome",
  output     = "gt"
) |>
  clerk_render(
    title    = "Partial correlations (age + sex controlled)",
    footnote = "FDR correction applied within each outcome (BH)."
  )
Partial correlations (age + sex controlled)
Outcome r p p (FDR)
All variables
hdl tmt_time +0.190 0.008 0.021
glucose tmt_time -0.229 0.398 0.398
bmi tmt_time -0.086 0.013 0.026
waist tmt_time +0.279 0.002 0.016
systolic_bp tmt_time +0.195 0.379 0.398
bdi tmt_time +0.096 0.109 0.156
panas_neg tmt_time +0.157 0.006 0.021
life_satisfaction tmt_time +0.247 0.117 0.156
hdl verbal_fluency +0.078 0.072 0.154
glucose verbal_fluency +0.216 0.003 0.024
bmi verbal_fluency +0.022 0.207 0.234
waist verbal_fluency +0.047 0.185 0.234
systolic_bp verbal_fluency -0.105 0.077 0.154
bdi verbal_fluency -0.176 0.070 0.154
panas_neg verbal_fluency +0.232 0.117 0.187
life_satisfaction verbal_fluency +0.201 0.234 0.234
FDR correction applied within each outcome (BH).

Wide (pivoted) format

Set pivot = TRUE to show one column per outcome, with r (p) values in each cell:

tbl_correlation(
  clerk_cor_example,
  domains = list(
    "Metabolic"    = c("hdl", "glucose", "bmi"),
    "Mental health"= c("bdi", "panas_neg")
  ),
  fdr    = TRUE,
  pivot  = TRUE,
  output = "gt"
) |>
  clerk_render(
    title    = "Partial correlations (age + sex controlled)",
    footnote = "Values shown as r (p FDR). FDR-corrected p-value (BH)."
  )
#> Warning in `[<-.factor`(`*tmp*`, is.na(tbl[["domain"]]), value = "Other"):
#> invalid factor level, NA generated
Partial correlations (age + sex controlled)
tmt_time verbal_fluency
Metabolic
hdl +0.190 (0.032) +0.078 (0.154)
glucose -0.229 (0.398) +0.216 (0.024)
bmi -0.086 (0.042) +0.022 (0.255)
NA
waist +0.279 (0.024) +0.047 (0.247)
systolic_bp +0.195 (0.398) -0.105 (0.154)
life_satisfaction +0.247 (0.170) +0.201 (0.267)
Mental health
bdi +0.096 (0.170) -0.176 (0.154)
panas_neg +0.157 (0.032) +0.232 (0.170)
Values shown as r (p FDR). FDR-corrected p-value (BH).

Custom column names

When your results frame uses different column names, pass them as strings:

my_results <- data.frame(
  predictor_var = c("bmi", "hdl"),
  outcome_var   = c("tmt_time", "tmt_time"),
  rho           = c(0.12, -0.08),
  pval          = c(0.021, 0.140),
  sample_n      = c(249, 249)
)

tbl_correlation(
  my_results,
  predictor = "predictor_var",
  outcome   = "outcome_var",
  r         = "rho",
  p         = "pval",
  n         = "sample_n",
  fdr       = TRUE,
  output    = "gt"
) |>
  clerk_render(title = "Partial correlations (age + sex controlled)")

HTML output 

tbl_correlation(
  clerk_cor_example,
  domains = list(
    "Metabolic"    = c("hdl", "glucose", "bmi"),
    "Mental health"= c("bdi", "panas_neg")
  ),
  fdr    = TRUE,
  output = "html"
) |>
  clerk_render()
#> Warning in `[<-.factor`(`*tmp*`, is.na(tbl[["domain"]]), value = "Other"):
#> invalid factor level, NA generated