library(dplyr) library(ggplot2) library(data.table) ######### Necropsy data ######### Maternal and litter outcomes setwd("C:/Users/blakebe/Documents/CD1 PFOA GenX/R CSV files") datapoints<-read.csv("Combined necropsy data E11.5 and E17.5_updated.csv") names(datapoints) df.preg<-datapoints%>%filter(pregnant=="Yes") df.nonpreg<-datapoints%>%filter(pregnant=="No") nonpregs<-df.nonpreg%>% group_by(timepoint,group)%>%summarise(length(unique(dam.id))) names(df.preg) df.drop<-df.preg[,-which(names(df.preg) %in% c("block","breeding.group","dam.id","chem","dose","kid","kid.p.wt","pregnant"))] names(df.drop) summary.stats<-df.drop%>% group_by(timepoint,group)%>%summarise_all(funs(mean,sd),na.rm=TRUE) summary.stats df.drop %>% group_by(timepoint,group) %>% summarise(length(na.omit(liv.p.wt))) df.drop %>% group_by(timepoint,group) %>% summarise(length(na.omit(d.wt.17))) count<-function(x){length(na.omit(x))} summary.nobs<-df.drop%>%group_by(timepoint,group)%>% summarise_all(funs(count)) summary.stats.all<-merge(summary.stats, summary.nobs, all.x=T) summary.stats.ordered<-summary.stats.all[,order(colnames(summary.stats.all))] #write.csv(summary.stats.ordered, "Maternal and litter necropsy data summary statistics mean SD nobs.csv",row.names = F) e11<-subset(df.drop, timepoint=="E11.5") summary(lm(d.wt.0~group,data=e11)) summary(lm(d.wt.11~group,data=e11)) summary(lm(d.wt.p.11~group,data=e11)) summary(lm(liv~group, data=e11)) summary(lm(liv.p.wt~group, data=e11)) summary(lm(d.wt.0~group, data=e11)) summary(lm(implant.n~group, data=e11)) summary(lm(litter.n~group, data=e11)) summary(lm(resorp.n~group, data=e11)) library(car) library(multcomp) Anova(aov(d.wt.0~group,data=e11)) Anova(aov(d.wt.11~group,data=e11)) Anova(aov(d.wt.p.11~group,data=e11)) Anova(aov(liv~group, data=e11)) Anova(aov(liv.p.wt~group, data=e11)) Anova(aov(implant.n~group, data=e11)) Anova(aov(litter.n~group, data=e11)) Anova(aov(resorp.n~group, data=e11)) summary(glht(aov(d.wt.0~group,data=e11), linfct=mcp(group="Tukey"))) summary(glht(aov(d.wt.11~group,data=e11), linfct=mcp(group="Tukey"))) summary(glht(aov(d.wt.p.11~group,data=e11), linfct=mcp(group="Tukey"))) summary(glht(aov(liv~group,data=e11), linfct=mcp(group="Tukey"))) summary(glht(aov(liv.p.wt~group,data=e11), linfct=mcp(group="Tukey"))) summary(glht(aov(implant.n~group,data=e11), linfct=mcp(group="Tukey"))) summary(glht(aov(litter.n~group,data=e11), linfct=mcp(group="Tukey"))) summary(glht(aov(resorp.n~group,data=e11), linfct=mcp(group="Tukey"))) e17<-subset(df.drop, timepoint=="E17.5") summary(lm(d.wt.0~group,data=e17)) summary(lm(d.wt.17~group,data=e17)) summary(lm(d.wt.pt.17~group,data=e17)) summary(lm(liv~group, data=e17)) summary(lm(liv.p.wt~group, data=e17)) summary(lm(d.wt.0~group, data=e17)) summary(lm(implant.n~group, data=e17)) summary(lm(litter.n~group, data=e17)) summary(lm(resorp.n~group, data=e17)) Anova(aov(d.wt.0~group,data=e17)) Anova(aov(d.wt.17~group,data=e17)) Anova(aov(d.wt.pt.17~group,data=e17)) Anova(aov(liv~group, data=e17)) Anova(aov(liv.p.wt~group, data=e17)) Anova(aov(implant.n~group, data=e17)) Anova(aov(litter.n~group, data=e17)) Anova(aov(resorp.n~group, data=e17)) summary(glht(aov(d.wt.0~group,data=e17), linfct=mcp(group="Tukey"))) summary(glht(aov(d.wt.17~group,data=e17), linfct=mcp(group="Tukey"))) summary(glht(aov(d.wt.pt.17~group,data=e17), linfct=mcp(group="Tukey"))) summary(glht(aov(liv~group,data=e17), linfct=mcp(group="Tukey"))) summary(glht(aov(liv.p.wt~group,data=e17), linfct=mcp(group="Tukey"))) summary(glht(aov(implant.n~group,data=e17), linfct=mcp(group="Tukey"))) summary(glht(aov(litter.n~group,data=e17), linfct=mcp(group="Tukey"))) summary(glht(aov(resorp.n~group,data=e17), linfct=mcp(group="Tukey"))) library(sjPlot) library(sjlabelled) library(sjmisc) library(ggplot2) set_theme(base = theme_bw()) m1<-glm(d.wt.p.11~group+litter.n, data=e11) labels.11<-c("Litter size","High GenX","High PFOA","Low PFOA","Low GenX") plot_model(m1, title="Change in maternal weight (%) at E11.5", axis.labels=labels.11, show.values=T, value.offset = 0.4) m1<-glm(d.wt.11~group+litter.n,data=e11) coef(m1) confint(m1) plot_model(m1) m1<-glm(d.wt.pt.17~group+litter.n,data=e17) m1<-glm(d.wt.pt.17~group+litter.n, data=e17) labels.17<-c("Litter size","High GenX","High PFOA","Low PFOA","Low GenX") plot_model(m1, title="Change in maternal weight (%) at E17.5", axis.labels=labels.17, show.values=T, value.offset = 0.4) e17$weight.noliv<-e17$d.wt.17-e17$liv e11$weight.noliv<-e11$d.wt.17-e11$liv m1<-glm(weight.noliv~group+litter.n, data=e17) labels.17<-c("Litter size","High GenX","High PFOA","Low PFOA","Low GenX") plot_model(m1, title="Maternal weight (g) at E17.5 excluding liver", axis.labels=labels.17, show.values=T, value.offset = 0.4) m1<-glm(d.wt.17~group+litter.n, data=e17) labels.17<-c("Litter size","High GenX","High PFOA","Low PFOA","Low GenX") plot_model(m1, title="Maternal weight (g) at E17.5", axis.labels=labels.17, show.values=T, value.offset = 0.4) e17$weight.noliv<-e17$d.wt.17-e17$liv e11$weight.noliv<-e11$d.wt.11-e11$liv m1<-glm(weight.noliv~group+litter.n, data=e11) labels.11<-c("Litter size","High GenX","High PFOA","Low PFOA","Low GenX") plot_model(m1, title="Maternal weight (g) at E11.5 excluding liver", axis.labels=labels.11, show.values=T, value.offset = 0.4) m1<-glm(d.wt.11~group+litter.n, data=e11) labels.11<-c("Litter size","High GenX","High PFOA","Low PFOA","Low GenX") plot_model(m1, title="Maternal weight (g) at E11.5", axis.labels=labels.11, show.values=T, value.offset = 0.4)