cat yx_phe.txt|awk '{if(NR==1){print$0} else if($5==1001) {print$0}}'|sed "s/\-9999/NA/g" >DX.PHE
cat yx_phe.txt|awk '{if(NR==1){print$0} else if($5==1003) {print$0}}'|sed "s/\-9999/NA/g" >HX.PHE
cat yx_phe.txt|awk '{if(NR==1){print$0} else if($5==1011) {print$0}}'|sed "s/\-9999/NA/g" >MX.PHE
cat tb_phe.txt|awk '{if($5==2001) {print$0}}'|sed "s/\-9999/NA/g" >>MX.PHE
cat zd_phe.txt|awk '{if($5==3001) {print$0}}'|sed "s/\-9999/NA/g" >>MX.PHE
cat zd_phe.txt|awk '{if(NR==1){print$0} else if($5==3002) {print$0}}'|sed "s/\-9999/NA/g" >JX.PHE
R语言
setwd("D:/桌面/研三下学期工作/马老师画图/基金申请")
library(tidyverse)
library(ggplot2)
data1=read.table(file="HX.PHE",sep="\t",header = T)
data1$group="JX" #用华系来替代加系大白
data2=read.table(file="DX.PHE",sep="\t",header = T)
data2$group="DX"
data3=read.table(file="MX.PHE",sep="\t",header = T)
data3$group="MX"
data4=rbind(data1,data2)
data=rbind(data3,data4)
dataBackfat=data %>% drop_na(Backfat) #去掉重复值
dataBackfat_mean <- dataBackfat %>% group_by(group) %>% dplyr::summarise(sd=sd(Backfat), value=mean(Backfat))
dataBackfat_mean <- as.data.frame(dataBackfat_mean)
p<-ggplot(dataBackfat_mean, aes(x=group, y=value)) + geom_col(position = 'dodge',width = 0.5,fill="#B5838D")+ geom_errorbar(aes(ymin=value-sd, ymax=value+sd),width = 0.2,size=0.66)+theme(axis.text.x = element_text(size=28,face="bold", hjust = 0.5, vjust = 0.5),axis.text.y = element_text(size=12,face="bold", hjust = 0.5, vjust = 0.5),axis.title.y = element_text(face = "bold"),axis.title.x = element_text(face = "bold"))+theme_bw(base_size = 16)+geom_text(aes(label=round(value,2)), hjust = 1.1,vjust = -0.1,size=6)+
theme(panel.grid = element_blank(), axis.line = element_line(colour = 'black',size=1),panel.background = element_rect(fill = "transparent"))+
labs(x = '', y = expression("Backfat (mm)")) +
scale_y_continuous(breaks=seq(0,15,3),limits=c(0,21),expand=(c(0.02,0)))
ggsave(p,file="Backfat.tiff",width=8,height=8)
#-----------------------------------------------------------
dataCorrected_100kg_ADG=data %>% drop_na(Corrected_100kg_ADG) #去掉重复值
dataCorrected_100kg_ADG_mean <- dataCorrected_100kg_ADG %>% group_by(group) %>% dplyr::summarise(sd=sd(Corrected_100kg_ADG), value=mean(Corrected_100kg_ADG))
dataCorrected_100kg_ADG_mean <- as.data.frame(dataCorrected_100kg_ADG_mean)
p<-ggplot(dataCorrected_100kg_ADG_mean, aes(x=group, y=value)) + geom_col(position = 'dodge',width = 0.5,fill="#FFB4A2")+ geom_errorbar(aes(ymin=value-sd, ymax=value+sd),width = 0.2,size=0.66)+theme(axis.text.x = element_text(size=28,face="bold", hjust = 0.5, vjust = 0.5),axis.text.y = element_text(size=12,face="bold", hjust = 0.5, vjust = 0.5),axis.title.y = element_text(face = "bold"),axis.title.x = element_text(face = "bold"))+theme_bw(base_size = 16)+geom_text(aes(label=round(value,2)), hjust = 1.1,vjust = -0.1,size=6)+
theme(panel.grid = element_blank(), axis.line = element_line(colour = 'black',size=1),panel.background = element_rect(fill = "transparent"))+
labs(x = '', y = expression("ADG100 (kg)")) +
scale_y_continuous(breaks=seq(0,0.8,0.2),limits=c(0,1.2),expand=(c(0.02,0)))
ggsave(p,file="Corrected_100kg_ADG.tiff",width=8,height=8)
#--------------------------------------------------------------
dataCorrected_115kg_ADG=data %>% drop_na(Corrected_115kg_ADG) #去掉重复值
dataCorrected_115kg_ADG_mean <- dataCorrected_115kg_ADG %>% group_by(group) %>% dplyr::summarise(sd=sd(Corrected_115kg_ADG), value=mean(Corrected_115kg_ADG))
dataCorrected_115kg_ADG_mean <- as.data.frame(dataCorrected_115kg_ADG_mean)
p<-ggplot(dataCorrected_115kg_ADG_mean, aes(x=group, y=value)) + geom_col(position = 'dodge',width = 0.5,fill="#FFCDB2")+ geom_errorbar(aes(ymin=value-sd, ymax=value+sd),width = 0.2,size=0.66)+theme(axis.text.x = element_text(size=28,face="bold", hjust = 0.5, vjust = 0.5),axis.text.y = element_text(size=12,face="bold", hjust = 0.5, vjust = 0.5),axis.title.y = element_text(face = "bold"),axis.title.x = element_text(face = "bold"))+theme_bw(base_size = 16)+geom_text(aes(label=round(value,2)), hjust = 1.1,vjust = -0.1,size=6)+
theme(panel.grid = element_blank(), axis.line = element_line(colour = 'black',size=1),panel.background = element_rect(fill = "transparent"))+
labs(x = '', y = expression("ADG115 (kg)")) +
scale_y_continuous(breaks=seq(0,0.8,0.2),limits=c(0,1.2),expand=(c(0.02,0)))
ggsave(p,file="Corrected_115kg_ADG.tiff",width=8,height=8)
#------------------------------------------------------------------
data1tai=subset(data,data$Birth_Parity==1)
data1taiTotal_Born=data1tai %>% drop_na(Total_Born) #去掉重复值
data1taiTotal_Born_mean <- data1taiTotal_Born %>% group_by(group) %>% dplyr::summarise(sd=sd(Total_Born), value=mean(Total_Born))
data1taiTotal_Born_mean <- as.data.frame(data1taiTotal_Born_mean)
p<-ggplot(data1taiTotal_Born_mean, aes(x=group, y=value)) + geom_col(position = 'dodge',width = 0.5,fill="#D2B48C")+ geom_errorbar(aes(ymin=value-sd, ymax=value+sd),width = 0.2,size=0.66)+theme(axis.text.x = element_text(size=28,face="bold", hjust = 0.5, vjust = 0.5),axis.text.y = element_text(size=12,face="bold", hjust = 0.5, vjust = 0.5),axis.title.y = element_text(face = "bold"),axis.title.x = element_text(face = "bold"))+theme_bw(base_size = 16)+geom_text(aes(label=round(value,2)), hjust = 1.1,vjust = -0.1,size=6)+
theme(panel.grid = element_blank(), axis.line = element_line(colour = 'black',size=1),panel.background = element_rect(fill = "transparent"))+
labs(x = '', y = expression("Total_Born")) +
scale_y_continuous(breaks=seq(0,25,5),limits=c(0,30),expand=(c(0.02,0)))
ggsave(p,file="Total_Born.tiff",width=8,height=8)
#------------------------------------------------------------------
data1taiBorn_Alive=data1tai %>% drop_na(Born_Alive) #去掉重复值
data1taiBorn_Alive_mean <- data1taiBorn_Alive %>% group_by(group) %>% dplyr::summarise(sd=sd(Born_Alive), value=mean(Born_Alive))
data1taiBorn_Alive_mean <- as.data.frame(data1taiBorn_Alive_mean)
p<-ggplot(data1taiBorn_Alive_mean, aes(x=group, y=value)) + geom_col(position = 'dodge',width = 0.5,fill="#6D6874")+ geom_errorbar(aes(ymin=value-sd, ymax=value+sd),width = 0.2,size=0.66)+theme(axis.text.x = element_text(size=28,face="bold", hjust = 0.5, vjust = 0.5),axis.text.y = element_text(size=12,face="bold", hjust = 0.5, vjust = 0.5),axis.title.y = element_text(face = "bold"),axis.title.x = element_text(face = "bold"))+theme_bw(base_size = 16)+geom_text(aes(label=round(value,2)), hjust = 1.1,vjust = -0.1,size=6)+
theme(panel.grid = element_blank(), axis.line = element_line(colour = 'black',size=1),panel.background = element_rect(fill = "transparent"))+
labs(x = '', y = expression("Born_Alive")) +
scale_y_continuous(breaks=seq(0,25,5),limits=c(0,30),expand=(c(0.02,0)))
ggsave(p,file="Born_Alive.tiff",width=8,height=8)
*****************独立样本T检验*********************
#1.第一胎总产仔数
data1taiTotal_BornDX=subset(data1taiTotal_Born,data1taiTotal_Born$group=="DX")
data1taiTotal_BornJX=subset(data1taiTotal_Born,data1taiTotal_Born$group=="JX")
data1taiTotal_BornMX=subset(data1taiTotal_Born,data1taiTotal_Born$group=="MX")
t.test(data1taiTotal_BornDX$Total_Born,data1taiTotal_BornJX$Total_Born)
Welch Two Sample t-test
data: data1taiTotal_BornDX$Total_Born and data1taiTotal_BornJX$Total_Born
t = 3.0734, df = 15.197, p-value = 0.007631
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1.179559 6.498179
sample estimates:
mean of x mean of y
16.33887 12.50000
t.test(data1taiTotal_BornDX$Total_Born,data1taiTotal_BornMX$Total_Born)
Welch Two Sample t-test
data: data1taiTotal_BornDX$Total_Born and data1taiTotal_BornMX$Total_Born
t = 22.035, df = 3803.3, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
2.941625 3.516230
sample estimates:
mean of x mean of y
16.33887 13.10994
t.test(data1taiTotal_BornJX$Total_Born,data1taiTotal_BornMX$Total_Born)
Welch Two Sample t-test
data: data1taiTotal_BornJX$Total_Born and data1taiTotal_BornMX$Total_Born
t = -0.48814, df = 15.22, p-value = 0.6324
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.269895 2.050012
sample estimates:
mean of x mean of y
12.50000 13.10994
#2.第一胎产活仔数
data1taiBorn_AliveDX=subset(data1taiBorn_Alive,data1taiBorn_Alive$group=="DX")
data1taiBorn_AliveJX=subset(data1taiBorn_Alive,data1taiBorn_Alive$group=="JX")
data1taiBorn_AliveMX=subset(data1taiBorn_Alive,data1taiBorn_Alive$group=="MX")
t.test(data1taiBorn_AliveDX$Born_Alive,data1taiBorn_AliveJX$Born_Alive)
Welch Two Sample t-test
data: data1taiBorn_AliveDX$Born_Alive and data1taiBorn_AliveJX$Born_Alive
t = 2.4272, df = 15.2, p-value = 0.02809
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.362845 5.543573
sample estimates:
mean of x mean of y
15.01571 12.06250
t.test(data1taiBorn_AliveDX$Born_Alive,data1taiBorn_AliveMX$Born_Alive)
Welch Two Sample t-test
data: data1taiBorn_AliveDX$Born_Alive and data1taiBorn_AliveMX$Born_Alive
t = 21.403, df = 3871.2, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
2.716088 3.263869
sample estimates:
mean of x mean of y
15.01571 12.02573
t.test(data1taiBorn_AliveJX$Born_Alive,data1taiBorn_AliveMX$Born_Alive)
Welch Two Sample t-test
data: data1taiBorn_AliveJX$Born_Alive and data1taiBorn_AliveMX$Born_Alive
t = 0.030221, df = 15.199, p-value = 0.9763
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.553558 2.627096
sample estimates:
mean of x mean of y
12.06250 12.02573
#3.结测背膘厚
dataBackfatDX=subset(dataBackfat,dataBackfat$group=="DX")
dataBackfatJX=subset(dataBackfat,dataBackfat$group=="JX")
dataBackfatMX=subset(dataBackfat,dataBackfat$group=="MX")
t.test(dataBackfatDX$Backfat,dataBackfatJX$Backfat)
Welch Two Sample t-test
data: dataBackfatDX$Backfat and dataBackfatJX$Backfat
t = -1.6333, df = 451.56, p-value = 0.1031
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.42526136 0.03923262
sample estimates:
mean of x mean of y
10.90183 11.09484
t.test(dataBackfatDX$Backfat,dataBackfatMX$Backfat)
Welch Two Sample t-test
data: dataBackfatDX$Backfat and dataBackfatMX$Backfat
t = -18.123, df = 26937, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5884693 -0.4736030
sample estimates:
mean of x mean of y
10.90183 11.43287
t.test(dataBackfatJX$Backfat,dataBackfatMX$Backfat)
Welch Two Sample t-test
data: dataBackfatJX$Backfat and dataBackfatMX$Backfat
t = -2.881, df = 438.73, p-value = 0.004159
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5686176 -0.1074259
sample estimates:
mean of x mean of y
11.09484 11.43287
#4.校正达100kg平均日增重
dataCorrected_100kg_ADGDX=subset(dataCorrected_100kg_ADG,dataCorrected_100kg_ADG$group=="DX")
dataCorrected_100kg_ADGJX=subset(dataCorrected_100kg_ADG,dataCorrected_100kg_ADG$group=="JX")
dataCorrected_100kg_ADGMX=subset(dataCorrected_100kg_ADG,dataCorrected_100kg_ADG$group=="MX")
t.test(dataCorrected_100kg_ADGDX$Corrected_100kg_ADG,dataCorrected_100kg_ADGJX$Corrected_100kg_ADG)
Welch Two Sample t-test
data: dataCorrected_100kg_ADGDX$Corrected_100kg_ADG and dataCorrected_100kg_ADGJX$Corrected_100kg_ADG
t = -13.62, df = 437.23, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.04005693 -0.02995401
sample estimates:
mean of x mean of y
0.5900695 0.6250750
t.test(dataCorrected_100kg_ADGDX$Corrected_100kg_ADG,dataCorrected_100kg_ADGMX$Corrected_100kg_ADG)
Welch Two Sample t-test
data: dataCorrected_100kg_ADGDX$Corrected_100kg_ADG and dataCorrected_100kg_ADGMX$Corrected_100kg_ADG
t = -39.47, df = 30214, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.02644276 -0.02394074
sample estimates:
mean of x mean of y
0.5900695 0.6152613
t.test(dataCorrected_100kg_ADGJX$Corrected_100kg_ADG,dataCorrected_100kg_ADGMX$Corrected_100kg_ADG)
Welch Two Sample t-test
data: dataCorrected_100kg_ADGJX$Corrected_100kg_ADG and dataCorrected_100kg_ADGMX$Corrected_100kg_ADG
t = 3.7859, df = 452.37, p-value = 0.0001738
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.004719525 0.014907913
sample estimates:
mean of x mean of y
0.6250750 0.6152613
#4.校正达115kg平均日增重
dataCorrected_115kg_ADGDX=subset(dataCorrected_115kg_ADG,dataCorrected_115kg_ADG$group=="DX")
dataCorrected_115kg_ADGJX=subset(dataCorrected_115kg_ADG,dataCorrected_115kg_ADG$group=="JX")
dataCorrected_115kg_ADGMX=subset(dataCorrected_115kg_ADG,dataCorrected_115kg_ADG$group=="MX")
t.test(dataCorrected_115kg_ADGDX$Corrected_115kg_ADG,dataCorrected_115kg_ADGJX$Corrected_115kg_ADG)
Welch Two Sample t-test
data: dataCorrected_115kg_ADGDX$Corrected_115kg_ADG and dataCorrected_115kg_ADGJX$Corrected_115kg_ADG
t = -12.872, df = 436.51, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.04840503 -0.03558090
sample estimates:
mean of x mean of y
0.613794 0.655787
t.test(dataCorrected_115kg_ADGDX$Corrected_115kg_ADG,dataCorrected_115kg_ADGMX$Corrected_115kg_ADG)
Welch Two Sample t-test
data: dataCorrected_115kg_ADGDX$Corrected_115kg_ADG and dataCorrected_115kg_ADGMX$Corrected_115kg_ADG
t = -36.839, df = 28898, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.02515984 -0.02261780
sample estimates:
mean of x mean of y
0.6137940 0.6376828
t.test(dataCorrected_115kg_ADGJX$Corrected_115kg_ADG,dataCorrected_115kg_ADGMX$Corrected_115kg_ADG)
Welch Two Sample t-test
data: dataCorrected_115kg_ADGJX$Corrected_115kg_ADG and dataCorrected_115kg_ADGMX$Corrected_115kg_ADG
t = 5.5586, df = 433.58, p-value = 4.759e-08
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.01170277 0.02450552
sample estimates:
mean of x mean of y
0.6557870 0.6376828
setwd("D:/桌面/研三下学期工作/马老师画图/基金申请")
library(tidyverse)
library(ggplot2)
#1.结测背膘厚
data1=read.table(file="HX.PHE",sep="\t",header = T)
data1$group="JX" #用华系来替代加系大白
data2=read.table(file="DX.PHE",sep="\t",header = T)
data2$group="DX"
data3=read.table(file="MX.PHE",sep="\t",header = T)
data3$group="MX"
data1=data1 %>% drop_na(Backfat)
data2=data2 %>% drop_na(Backfat)
data3=data3 %>% drop_na(Backfat)
data1=data1 %>% sample_n(400, replace = FALSE) #不放回的随机抽取400行
data2=data2 %>% sample_n(400, replace = FALSE) #不放回的随机抽取400行
data3=data3 %>% sample_n(400, replace = FALSE) #不放回的随机抽取400行
data1=subset(data1,data1$Backfat>quantile(data1$Backfat, 0.01)&data1$Backfat<quantile(data1$Backfat, 0.99)) #去掉异常值
data2=subset(data2,data2$Backfat>quantile(data2$Backfat, 0.01)&data2$Backfat<quantile(data2$Backfat, 0.99))
data3=subset(data3,data3$Backfat>quantile(data3$Backfat, 0.01)&data3$Backfat<quantile(data3$Backfat, 0.99))
data4=rbind(data1,data2)
data=rbind(data3,data4)
dataBackfat=data %>% drop_na(Backfat) #去掉缺失值
dataBackfat_mean <- dataBackfat %>% group_by(group) %>% dplyr::summarise(sd=sd(Backfat), value=mean(Backfat))
dataBackfat_mean <- as.data.frame(dataBackfat_mean)
p<-ggplot(dataBackfat_mean, aes(x=group, y=value)) + geom_col(position = 'dodge',width = 0.5,fill="#B5838D")+ geom_errorbar(aes(ymin=value-sd, ymax=value+sd),width = 0.2,size=0.66)+theme(axis.text.x = element_text(size=28,face="bold", hjust = 0.5, vjust = 0.5),axis.text.y = element_text(size=12,face="bold", hjust = 0.5, vjust = 0.5),axis.title.y = element_text(face = "bold"),axis.title.x = element_text(face = "bold"))+theme_bw(base_size = 16)+geom_text(aes(label=round(value,2)), hjust = 1.1,vjust = -0.1,size=6)+
theme(panel.grid = element_blank(), axis.line = element_line(colour = 'black',size=1),panel.background = element_rect(fill = "transparent"))+
labs(x = '', y = expression("Backfat")) +
scale_y_continuous(breaks=seq(0,15,3),limits=c(0,21),expand=(c(0.02,0)))
ggsave(p,file="BackfatQC+SJ.tiff",width=8,height=8)
dataBackfatDX=subset(dataBackfat,dataBackfat$group=="DX")
dataBackfatJX=subset(dataBackfat,dataBackfat$group=="JX")
dataBackfatMX=subset(dataBackfat,dataBackfat$group=="MX")
t.test(dataBackfatDX$Backfat,dataBackfatJX$Backfat)
Welch Two Sample t-test
data: dataBackfatDX$Backfat and dataBackfatJX$Backfat
t = -0.84449, df = 764.34, p-value = 0.3987
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.4750474 0.1892673
sample estimates:
mean of x mean of y
10.93056 11.07345
t.test(dataBackfatDX$Backfat,dataBackfatMX$Backfat)
Welch Two Sample t-test
data: dataBackfatDX$Backfat and dataBackfatMX$Backfat
t = -1.8865, df = 775.46, p-value = 0.0596
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.67359868 0.01339067
sample estimates:
mean of x mean of y
10.93056 11.26067
t.test(dataBackfatJX$Backfat,dataBackfatMX$Backfat)
Welch Two Sample t-test
data: dataBackfatJX$Backfat and dataBackfatMX$Backfat
t = -1.1496, df = 774.49, p-value = 0.2507
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5068983 0.1324704
sample estimates:
mean of x mean of y
11.07345 11.26067
#2.平均115日增重
data1=read.table(file="HX.PHE",sep="\t",header = T)
data1$group="JX" #用华系来替代加系大白
data2=read.table(file="DX.PHE",sep="\t",header = T)
data2$group="DX"
data3=read.table(file="MX.PHE",sep="\t",header = T)
data3$group="MX"
data1=data1 %>% drop_na(Corrected_115kg_ADG)
data2=data2 %>% drop_na(Corrected_115kg_ADG)
data3=data3 %>% drop_na(Corrected_115kg_ADG)
data1=data1 %>% sample_n(400, replace = FALSE) #不放回的随机抽取400行
data2=data2 %>% sample_n(800, replace = FALSE) #不放回的随机抽取400行
data3=data3 %>% sample_n(800, replace = FALSE) #不放回的随机抽取400行
data1=subset(data1,data1$Corrected_115kg_ADG>quantile(data1$Corrected_115kg_ADG, 0.001)&data1$Corrected_115kg_ADG<quantile(data1$Corrected_115kg_ADG, 0.999)) #去掉异常值
data2=subset(data2,data2$Corrected_115kg_ADG>quantile(data2$Corrected_115kg_ADG, 0.001)&data2$Corrected_115kg_ADG<quantile(data2$Corrected_115kg_ADG, 0.92))
data3=subset(data3,data3$Corrected_115kg_ADG>quantile(data3$Corrected_115kg_ADG, 0.1)&data3$Corrected_115kg_ADG<quantile(data3$Corrected_115kg_ADG, 0.9999))
data4=rbind(data1,data2)
data=rbind(data3,data4)
dataCorrected_115kg_ADG=data %>% drop_na(Corrected_115kg_ADG) #去掉缺失值
dataCorrected_115kg_ADG_mean <- dataCorrected_115kg_ADG %>% group_by(group) %>% dplyr::summarise(sd=sd(Corrected_115kg_ADG), value=mean(Corrected_115kg_ADG))
dataCorrected_115kg_ADG_mean <- as.data.frame(dataCorrected_115kg_ADG_mean)
dataCorrected_115kg_ADGDX=subset(dataCorrected_115kg_ADG,dataCorrected_115kg_ADG$group=="DX")
dataCorrected_115kg_ADGJX=subset(dataCorrected_115kg_ADG,dataCorrected_115kg_ADG$group=="JX")
dataCorrected_115kg_ADGMX=subset(dataCorrected_115kg_ADG,dataCorrected_115kg_ADG$group=="MX")
t.test(dataCorrected_115kg_ADGJX$Corrected_115kg_ADG,dataCorrected_115kg_ADGMX$Corrected_115kg_ADG)
p<-ggplot(dataCorrected_115kg_ADG_mean, aes(x=group, y=value)) + geom_col(position = 'dodge',width = 0.5,fill="#FFCDB2")+ geom_errorbar(aes(ymin=value-sd, ymax=value+sd),width = 0.2,size=0.66)+theme(axis.text.x = element_text(size=28,face="bold", hjust = 0.5, vjust = 0.5),axis.text.y = element_text(size=12,face="bold", hjust = 0.5, vjust = 0.5),axis.title.y = element_text(face = "bold"),axis.title.x = element_text(face = "bold"))+theme_bw(base_size = 16)+geom_text(aes(label=round(value,3)), hjust = 1.1,vjust = -0.1,size=6)+
theme(panel.grid = element_blank(), axis.line = element_line(colour = 'black',size=1),panel.background = element_rect(fill = "transparent"))+
labs(x = '', y = expression("ADG115 (kg)")) +
scale_y_continuous(breaks=seq(0,0.8,0.2),limits=c(0,1.2),expand=(c(0.02,0)))
ggsave(p,file="Corrected_115kg_ADG.tiff",width=8,height=8)
dataCorrected_115kg_ADGDX=subset(dataCorrected_115kg_ADG,dataCorrected_115kg_ADG$group=="DX")
dataCorrected_115kg_ADGJX=subset(dataCorrected_115kg_ADG,dataCorrected_115kg_ADG$group=="JX")
dataCorrected_115kg_ADGMX=subset(dataCorrected_115kg_ADG,dataCorrected_115kg_ADG$group=="MX")
t.test(dataCorrected_115kg_ADGDX$Corrected_115kg_ADG,dataCorrected_115kg_ADGJX$Corrected_115kg_ADG)
Welch Two Sample t-test
data: dataCorrected_115kg_ADGDX$Corrected_115kg_ADG and dataCorrected_115kg_ADGJX$Corrected_115kg_ADG
t = -10.61, df = 749.61, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.05018711 -0.03451535
sample estimates:
mean of x mean of y
0.6136988 0.6560500
t.test(dataCorrected_115kg_ADGDX$Corrected_115kg_ADG,dataCorrected_115kg_ADGMX$Corrected_115kg_ADG)
Welch Two Sample t-test
data: dataCorrected_115kg_ADGDX$Corrected_115kg_ADG and dataCorrected_115kg_ADGMX$Corrected_115kg_ADG
t = -6.4955, df = 777.24, p-value = 1.476e-10
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.03121150 -0.01672463
sample estimates:
mean of x mean of y
0.6136988 0.6376669
t.test(dataCorrected_115kg_ADGJX$Corrected_115kg_ADG,dataCorrected_115kg_ADGMX$Corrected_115kg_ADG)
Welch Two Sample t-test
data: dataBackfatJX$Backfat and dataBackfatMX$Backfat
t = -1.1496, df = 774.49, p-value = 0.2507
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5068983 0.1324704
sample estimates:
mean of x mean of y
11.07345 11.26067
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