Read-in phenotype file
#Read-in the phenotype file
pheno <- read.csv("C:/Users/ca16591/Dropbox/Bristol/bf.winsor.complete.no.missings.bw.phenofile.alspac.csv")
CreateTableOne(data = pheno)
##
## Overall
## n 7922
## aln (mean (sd)) 41429.18 (6949.03)
## alnqlet (mean (sd)) 41429.18 (6949.03)
## psych.meds (mean (sd)) 0.07 (0.25)
## anx.score (mean (sd)) 14.30 (8.88)
## anx.dichot (mean (sd)) 0.15 (0.36)
## sex (mean (sd)) 0.51 (0.50)
## mat.ses (mean (sd)) 0.41 (0.49)
## mat.age (mean (sd)) 28.88 (4.60)
## mat.smoke (mean (sd)) 0.40 (0.78)
## gest.age (mean (sd)) 39.59 (1.61)
## bw (mean (sd)) 3459.12 (504.44)
#Vector of variables to summarize
myVars <- c("psych.meds", "anx.score", "anx.dichot", "sex", "mat.ses",
"mat.age", "mat.smoke", "gest.age", "bw")
#Vector of factor variables to summarize
CatVars <- c("psych.meds", "anx.dichot", "sex", "mat.ses",
"mat.smoke")
Descriptives before winsorizing
tab.complete <- CreateTableOne(vars=myVars, data=pheno, factorVars = CatVars)
print(tab.complete, showAllLevels =TRUE)
##
## level Overall
## n 7922
## psych.meds (%) 0 7385 (93.2)
## 1 537 ( 6.8)
## anx.score (mean (sd)) 14.30 (8.88)
## anx.dichot (%) 0 6702 (84.6)
## 1 1220 (15.4)
## sex (%) 0 3893 (49.1)
## 1 4029 (50.9)
## mat.ses (%) 0 4703 (59.4)
## 1 3219 (40.6)
## mat.age (mean (sd)) 28.88 (4.60)
## mat.smoke (%) 0 6207 (78.4)
## 1 269 ( 3.4)
## 2 1446 (18.3)
## gest.age (mean (sd)) 39.59 (1.61)
## bw (mean (sd)) 3459.12 (504.44)
summary(tab.complete)
##
## ### Summary of continuous variables ###
##
## strata: Overall
## n miss p.miss mean sd median p25 p75 min max skew kurt
## anx.score 7922 0 0 14 9 13 8 19 0 48 0.85 0.43
## mat.age 7922 0 0 29 5 29 26 32 15 45 0.09 0.01
## gest.age 7922 0 0 40 2 40 39 41 26 45 -1.26 4.33
## bw 7922 0 0 3459 504 3460 3160 3780 815 5640 -0.21 0.89
##
## =======================================================================================
##
## ### Summary of categorical variables ###
##
## strata: Overall
## var n miss p.miss level freq percent cum.percent
## psych.meds 7922 0 0.0 0 7385 93.2 93.2
## 1 537 6.8 100.0
##
## anx.dichot 7922 0 0.0 0 6702 84.6 84.6
## 1 1220 15.4 100.0
##
## sex 7922 0 0.0 0 3893 49.1 49.1
## 1 4029 50.9 100.0
##
## mat.ses 7922 0 0.0 0 4703 59.4 59.4
## 1 3219 40.6 100.0
##
## mat.smoke 7922 0 0.0 0 6207 78.4 78.4
## 1 269 3.4 81.7
## 2 1446 18.3 100.0
##
Read-in the winsorzed phenotype file
Descriptives (before and) after winsoring
tab.complete <- CreateTableOne(vars=myVars, data=winsored, factorVars = CatVars)
print(tab.complete, showAllLevels =TRUE)
##
## level Overall
## n 7922
## psych.meds (%) 0 7385 (93.2)
## 1 537 ( 6.8)
## anx.score (mean (sd)) 14.30 (8.88)
## anx.contwins (mean (sd)) 14.28 (8.82)
## anx.dichot (%) 0 6702 (84.6)
## 1 1220 (15.4)
## sex (%) 0 3893 (49.1)
## 1 4029 (50.9)
## mat.ses (%) 0 4703 (59.4)
## 1 3219 (40.6)
## mat.age (mean (sd)) 28.88 (4.60)
## mat.age.wins (mean (sd)) 28.88 (4.60)
## mat.smoke (%) 0 6207 (78.4)
## 1 269 ( 3.4)
## 2 1446 (18.3)
## gest.age (mean (sd)) 39.59 (1.61)
## gest.wins (mean (sd)) 39.61 (1.53)
## bw (mean (sd)) 3459.12 (504.44)
## bw.wins (mean (sd)) 3460.53 (497.40)
summary(tab.complete)
##
## ### Summary of continuous variables ###
##
## strata: Overall
## n miss p.miss mean sd median p25 p75 min max skew
## anx.score 7922 0 0 14 9 13 8 19 0 48 0.85
## anx.contwins 7922 0 0 14 9 13 8 19 0 41 0.81
## mat.age 7922 0 0 29 5 29 26 32 15 45 0.09
## mat.age.wins 7922 0 0 29 5 29 26 32 15 43 0.09
## gest.age 7922 0 0 40 2 40 39 41 26 45 -1.26
## gest.wins 7922 0 0 40 2 40 39 41 35 44 -0.72
## bw 7922 0 0 3459 504 3460 3160 3780 815 5640 -0.21
## bw.wins 7922 0 0 3461 497 3460 3160 3780 1946 4972 -0.11
## kurt
## anx.score 0.428
## anx.contwins 0.241
## mat.age 0.014
## mat.age.wins -0.009
## gest.age 4.331
## gest.wins 0.712
## bw 0.893
## bw.wins 0.312
##
## =======================================================================================
##
## ### Summary of categorical variables ###
##
## strata: Overall
## var n miss p.miss level freq percent cum.percent
## psych.meds 7922 0 0.0 0 7385 93.2 93.2
## 1 537 6.8 100.0
##
## anx.dichot 7922 0 0.0 0 6702 84.6 84.6
## 1 1220 15.4 100.0
##
## sex 7922 0 0.0 0 3893 49.1 49.1
## 1 4029 50.9 100.0
##
## mat.ses 7922 0 0.0 0 4703 59.4 59.4
## 1 3219 40.6 100.0
##
## mat.smoke 7922 0 0.0 0 6207 78.4 78.4
## 1 269 3.4 81.7
## 2 1446 18.3 100.0
##