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BackFedDataAnalytics/analyticsRecord06132025.R

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2025-06-16 09:27:34 +08:00
library(readr)
#library(data.table)
library(Cairo)
library(ggplot2)
library(stringi)
library(stringr)
library(datetime)
library(dplyr)
library(ggthemes)
plotfunction<- function(dfData,independentVariableName,dependentVariableNames,titleNames) {
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = dfData00[,2], y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionVector仪器序列号<- function(ik,independentVariableList,dfData,independentVariableName,dependentVariableNames,titleNames) {
dfData<-dfData[(dfData$仪器序列号 %in% c(independentVariableList[independentVariableList[,2]==ik,][,1])),]
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = dfData00[,2], y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionVector批次名称<- function(ik,independentVariableList,dfData,independentVariableName,dependentVariableNames,titleNames) {
dfData<-dfData[(dfData$批次名称 %in% c(independentVariableList[independentVariableList[,2]==ik,][,1])),]
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = dfData00[,2], y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionVector批次名称Log<- function(ik,independentVariableList,dfData,independentVariableName,dependentVariableNames,titleNames) {
dfData<-dfData[(dfData$批次名称 %in% c(independentVariableList[independentVariableList[,2]==ik,][,1])),]
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = dfData00[,2], y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
scale_y_log10()+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionVector项目名称<- function(ik,independentVariableList,dfData,independentVariableName,dependentVariableNames,titleNames) {
dfData<-dfData[(dfData$项目名称 %in% c(independentVariableList[independentVariableList[,2]==ik,][,1])),]
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = dfData00[,2], y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionVector项目名称Log<- function(ik,independentVariableList,dfData,independentVariableName,dependentVariableNames,titleNames) {
dfData<-dfData[(dfData$项目名称 %in% c(independentVariableList[independentVariableList[,2]==ik,][,1])),]
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = dfData00[,2], y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
scale_y_log10()+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionLog<- function(dfData,independentVariableName,dependentVariableNames,titleNames) {
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = dfData00[,2], y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
scale_y_log10()+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionVector仪器序列号Log<- function(ik,independentVariableList,dfData,independentVariableName,dependentVariableNames,titleNames) {
dfData<-dfData[(dfData$仪器序列号 %in% c(independentVariableList[independentVariableList[,2]==ik,][,1])),]
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = dfData00[,2], y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
scale_y_log10()+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
panelPlotByDayAndLocation<- function(dfData,choosenList,independentVariableName,panelName,dependentVariableNames,titleNames) {
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,panelName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,panelName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
dfData00<-dfData00[dfData00$省编号 %in% choosenList,]
dfData00$省编号<-factor(dfData00$省编号,levels = choosenList)
plot1<-ggplot(dfData00, aes(x = testDay, y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
facet_wrap( ~ 省编号,ncol=2) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
panel.border = element_rect(
color = "black", # 边框颜色
size = 1., # 边框粗细
linetype = "solid", # 线型（solid/dashed/dotted）
fill = NA # 填充色（NA为透明）
),
legend.position = "right")
plot1
}
panelPlotByMonthAndLocation<- function(dfData,choosenList,independentVariableName,panelName,dependentVariableNames,titleNames) {
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,panelName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,panelName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
dfData00<-dfData00[dfData00$省编号 %in% choosenList,]
dfData00$省编号<-factor(dfData00$省编号,levels = choosenList)
plot1<-ggplot(dfData00, aes(x = testMonth, y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
facet_wrap( ~ 省编号,ncol=2) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
panel.border = element_rect(
color = "black", # 边框颜色
size = 1., # 边框粗细
linetype = "solid", # 线型（solid/dashed/dotted）
fill = NA # 填充色（NA为透明）
),
legend.position = "right")
plot1
}
pointPlotfunction批次<- function(dfData,independentVariableName,dependentVariableName,titleNames) {
plot2 <- ggplot(df20Ploted)+geom_point(aes(dfData[,independentVariableName], dfData[,dependentVariableName],color=as.character(批次名称)))+
# geom_line(aes(testDay,浓度1移动均值),color="blue")+
# geom_line(aes(testDay,浓度1累计均值),color="green",linewidth=1.)+
# geom_line(aes(testDay,浓度1允许波动范围),color="red",linewidth=1.)+
# xlim(xmin, xmax) + ylim(ymin, ymax)+theme(legend.position ="right")+
labs(
title = titleNames[1], # 主标题
x = titleNames[2], # X轴标签
y = titleNames[3], # Y轴标签
color = titleNames[4] # 图例标题
)+
# scale_x_date(
# date_labels = "%Y-%m-%d", # 格式符组合
# date_breaks = "1 month" # 标签间隔（如 "2 weeks"）
# )+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90),
legend.position = "right")
plot2
}
pointPlotfunction批次Log<- function(dfData,independentVariableName,dependentVariableName,titleNames) {
plot2 <- ggplot(df20Ploted)+geom_point(aes(dfData[,independentVariableName], dfData[,dependentVariableName],color=as.character(批次名称)))+
# geom_line(aes(testDay,浓度1移动均值),color="blue")+
# geom_line(aes(testDay,浓度1累计均值),color="green",linewidth=1.)+
# geom_line(aes(testDay,浓度1允许波动范围),color="red",linewidth=1.)+
# xlim(xmin, xmax) + ylim(ymin, ymax)+theme(legend.position ="right")+
labs(
title = titleNames[1], # 主标题
x = titleNames[2], # X轴标签
y = titleNames[3], # Y轴标签
color = titleNames[4] # 图例标题
)+
# scale_x_date(
# date_labels = "%Y-%m-%d", # 格式符组合
# date_breaks = "1 month" # 标签间隔（如 "2 weeks"）
# )+
scale_y_log10()+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90),
legend.position = "right")
plot2
}
pointPlotfunction仪器<- function(dfData,independentVariableName,dependentVariableName,titleNames) {
plot2 <- ggplot(df20Ploted)+geom_point(aes(dfData[,independentVariableName], dfData[,dependentVariableName],color=as.character(仪器序列号)))+
# geom_line(aes(testDay,浓度1移动均值),color="blue")+
# geom_line(aes(testDay,浓度1累计均值),color="green",linewidth=1.)+
# geom_line(aes(testDay,浓度1允许波动范围),color="red",linewidth=1.)+
# xlim(xmin, xmax) + ylim(ymin, ymax)+theme(legend.position ="right")+
labs(
title = titleNames[1], # 主标题
x = titleNames[2], # X轴标签
y = titleNames[3], # Y轴标签
color = titleNames[4] # 图例标题
)+
# scale_x_date(
# date_labels = "%Y-%m-%d", # 格式符组合
# date_breaks = "1 month" # 标签间隔（如 "2 weeks"）
# )+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90),
legend.position = "right")
plot2
}
pointPlotfunction仪器Log<- function(dfData,independentVariableName,dependentVariableName,titleNames) {
plot2 <- ggplot(df20Ploted)+geom_point(aes(dfData[,independentVariableName], dfData[,dependentVariableName],color=as.character(仪器序列号)))+
# geom_line(aes(testDay,浓度1移动均值),color="blue")+
# geom_line(aes(testDay,浓度1累计均值),color="green",linewidth=1.)+
# geom_line(aes(testDay,浓度1允许波动范围),color="red",linewidth=1.)+
# xlim(xmin, xmax) + ylim(ymin, ymax)+theme(legend.position ="right")+
labs(
title = titleNames[1], # 主标题
x = titleNames[2], # X轴标签
y = titleNames[3], # Y轴标签
color = titleNames[4] # 图例标题
)+
# scale_x_date(
# date_labels = "%Y-%m-%d", # 格式符组合
# date_breaks = "1 month" # 标签间隔（如 "2 weeks"）
# )+
scale_y_log10()+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90),
legend.position = "right")
plot2
}
pointPlotfunction样本<- function(dfData,independentVariableName,dependentVariableName,titleNames) {
plot2 <- ggplot(df20Ploted)+geom_point(aes(dfData[,independentVariableName], dfData[,dependentVariableName],color=as.character(样本类型)))+
# geom_line(aes(testDay,浓度1移动均值),color="blue")+
# geom_line(aes(testDay,浓度1累计均值),color="green",linewidth=1.)+
# geom_line(aes(testDay,浓度1允许波动范围),color="red",linewidth=1.)+
# xlim(xmin, xmax) + ylim(ymin, ymax)+theme(legend.position ="right")+
labs(
title = titleNames[1], # 主标题
x = titleNames[2], # X轴标签
y = titleNames[3], # Y轴标签
color = titleNames[4] # 图例标题
)+
# scale_x_date(
# date_labels = "%Y-%m-%d", # 格式符组合
# date_breaks = "1 month" # 标签间隔（如 "2 weeks"）
# )+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90),
legend.position = "right")
plot2
}
pointPlotfunction样本Log<- function(dfData,independentVariableName,dependentVariableName,titleNames) {
plot2 <- ggplot(df20Ploted)+geom_point(aes(dfData[,independentVariableName], dfData[,dependentVariableName],color=as.character(样本类型)))+
# geom_line(aes(testDay,浓度1移动均值),color="blue")+
# geom_line(aes(testDay,浓度1累计均值),color="green",linewidth=1.)+
# geom_line(aes(testDay,浓度1允许波动范围),color="red",linewidth=1.)+
# xlim(xmin, xmax) + ylim(ymin, ymax)+theme(legend.position ="right")+
labs(
title = titleNames[1], # 主标题
x = titleNames[2], # X轴标签
y = titleNames[3], # Y轴标签
color = titleNames[4] # 图例标题
)+
# scale_x_date(
# date_labels = "%Y-%m-%d", # 格式符组合
# date_breaks = "1 month" # 标签间隔（如 "2 weeks"）
# )+
scale_y_log10()+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90),
legend.position = "right")
plot2
}
pointPlotfunction项目<- function(dfData,independentVariableName,dependentVariableName,titleNames) {
plot2 <- ggplot(df20Ploted)+geom_point(aes(dfData[,independentVariableName], dfData[,dependentVariableName],color=as.character(项目名称)))+
# geom_line(aes(testDay,浓度1移动均值),color="blue")+
# geom_line(aes(testDay,浓度1累计均值),color="green",linewidth=1.)+
# geom_line(aes(testDay,浓度1允许波动范围),color="red",linewidth=1.)+
# xlim(xmin, xmax) + ylim(ymin, ymax)+theme(legend.position ="right")+
labs(
title = titleNames[1], # 主标题
x = titleNames[2], # X轴标签
y = titleNames[3], # Y轴标签
color = titleNames[4] # 图例标题
)+
# scale_x_date(
# date_labels = "%Y-%m-%d", # 格式符组合
# date_breaks = "1 month" # 标签间隔（如 "2 weeks"）
# )+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90),
legend.position = "right")
plot2
}
pointPlotfunction项目Log<- function(dfData,independentVariableName,dependentVariableName,titleNames) {
plot2 <- ggplot(df20Ploted)+geom_point(aes(dfData[,independentVariableName], dfData[,dependentVariableName],color=as.character(项目名称)))+
# geom_line(aes(testDay,浓度1移动均值),color="blue")+
# geom_line(aes(testDay,浓度1累计均值),color="green",linewidth=1.)+
# geom_line(aes(testDay,浓度1允许波动范围),color="red",linewidth=1.)+
# xlim(xmin, xmax) + ylim(ymin, ymax)+theme(legend.position ="right")+
labs(
title = titleNames[1], # 主标题
x = titleNames[2], # X轴标签
y = titleNames[3], # Y轴标签
color = titleNames[4] # 图例标题
)+
# scale_x_date(
# date_labels = "%Y-%m-%d", # 格式符组合
# date_breaks = "1 month" # 标签间隔（如 "2 weeks"）
# )+
scale_y_log10()+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 10,face = "bold"),
axis.title.y = element_text(size = 10,face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90),
legend.position = "right")
plot2
}
plotfunctionByDay<- function(dfData,independentVariableName,dependentVariableNames,titleNames) {
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = testDay, y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionBy区域<- function(dfData,independentVariableName,dependentVariableNames,titleNames) {
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = 省编号, y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionBy仪器<- function(dfData,independentVariableName,dependentVariableNames,titleNames) {
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = 仪器序列号, y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
plotfunctionBy仪器Log<- function(dfData,independentVariableName,dependentVariableNames,titleNames) {
lth1<-length(dependentVariableNames)
dfData00<-data.frame()
for(i in 1:lth1){
dfData0<-cbind(dependentVariableNames[i],dfData[,c(independentVariableName,dependentVariableNames[i])])
colnames(dfData0)<-c("Group",c(independentVariableName,"Count"))
dfData00<-rbind(dfData00,dfData0)
}
plot1<-ggplot(dfData00, aes(x = 仪器序列号, y = Count, fill = Group)) +
geom_col(
position = position_dodge(width = 0.4), # 控制条间距
width = 0.7 # 条宽度
) +
geom_line(
aes(group = Group, color = Group),
position = position_dodge(width = 0.4),
size = 1,
linetype="dashed",
show.legend=FALSE,
alpha = 1.0
) +
scale_color_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
geom_text(
aes(label = round(Count,2)), # 标签内容
position = position_dodge(width = 0.4), # 与柱子位置一致
vjust = -0.4, # 垂直位置（负值向上）
color = "black", # 标签颜色
size = 1.2 # 字体大小
) +
labs(
title = titleNames[1],
x = titleNames[2],
y = titleNames[3],
fill = titleNames[4]
) +
scale_fill_manual(values = c("red", "blue","green","yellow")) + # 自定义颜色
scale_y_log10()+
theme_minimal() + theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 8, face = "bold",angle=90) ,
legend.position = "right")
plot1
#return (list(plotdfDataNumber,b23,b33,b43,b53))
}
running_avg <- function(x, window = 3) {
sapply(seq_along(x), function(i) {
start <- max(1, i - window + 1)
mean(x[start:i],na.rm=TRUE)
})
}
lumping_avg<- function(x,window = 100) {
sapply(seq_along(x), function(i) {
start <- max(1, i - window + 1)
mean(x[start:i],na.rm=TRUE)
})
}
lumping_sd<- function(x,window = 100) {
sapply(seq_along(x), function(i) {
start <- max(1, i - window + 1)
sd(x[start:i],na.rm=TRUE)
})
}
rep6<-rep(1,6)
rep50<-rep(1,50)
rep0<-rep6
for(i in 1:1000) rep0<-c(rep0,rep6+i)
rep00<-rep50
for(i in 1:100) rep00<-c(rep00,rep50+i)
if(FALSE){
csv_files <- list.files(pattern = "\\UTF.csv$")
df00<-data.frame()
for(i in 1:length(csv_files)) {
#guess_encoding("historyRecord20.csv") # [[1]]$encoding
#df2 <- fread("historyRecord20250604175205UTF.csv",encoding = "UTF-8",fill = TRUE)
df2 <- read.csv(csv_files[i],encoding = "UTF-8",fill = TRUE)
df2$批次名称<-as.character(df2$批次名称)
df2<-df2[!is.na(df2$批次名称),]
df2$是否阳性<-df2$结论
df2$是否阳性[df2$是否阳性=="阳性"]<-1.0
df2$是否阳性[df2$是否阳性=="阴性"]<-0.0
df2$是否阳性[df2$是否阳性=="无效"]<-0.5
df2$是否阳性<-as.numeric(df2$是否阳性)
df2$是否阳性[is.na(df2$是否阳性)]<-0.5
df2$结论[df2$结论=="阳性"]<-1.0
df2$结论[df2$结论=="阴性"]<-0.0
df2$结论<-as.numeric(df2$结论)
df2$是否有效<-1
df2$是否有效[df2$是否阳性==0.5]<-0
df00<-rbind(df00,df2)
}
write.csv(df00,file="history06092025.csv")
df2<-df00
}
if(FALSE){
series<-read.csv("荧光层析设备1.csv",encoding = "UTF-8",fill = TRUE)
series1<-read.csv("FIC-Q100N.csv",encoding = "UTF-8",fill = TRUE)
series0<-series$仪器序列号[series$仪器序列号 %in% 仪器号List$仪器序列号]
series00<-series$仪器编号[series$仪器编号 %in% series1$仪器编号]
colnames(series[,1:13])
colnames(series1[,3:11])
仪器编号和序列号<-merge(series[,1:13],series1[,3:11],by="仪器编号",all=FALSE)
仪器编号和序列号all<-merge(series[,1:13],series1[,3:11],by="仪器编号",all=TRUE)
colnames(仪器编号和序列号)
仪器编号和序列号$SIM卡号<- gsub("\t", " ", 仪器编号和序列号$SIM卡号)
仪器编号和序列号$返利表序号<- gsub("\n", "_", 仪器编号和序列号$返利表序号)
仪器编号和序列号all$SIM卡号<- gsub("\t", " ", 仪器编号和序列号all$SIM卡号)
仪器编号和序列号all$返利表序号<- gsub("\n", "_", 仪器编号和序列号all$返利表序号)
#仪器编号和序列号[仪器编号和序列号$仪器序列号=="781be43bb68f05bb",]
cname0<-c("仪器编号" , "仪器序列号" ,
"SIM卡号" , "发货时间" , "仪器类型" ,
"总测试量" , "最后一次开机时间" ,"时间差" ,
"开机地点" , "网络类型" , "ip地址" ,
"用户" , "用户CRM" , "返利表序号" ,
"申请日期" , "区域" , "客户编码" ,
"代理商名称" , "用户名称" , "规格" ,
"状态")
write.csv(仪器编号和序列号all[,cname0],file="仪器编号和序列号all.csv")
write.csv(仪器编号和序列号[,cname0],file="仪器编号和序列号.csv")
}
仪器编号和序列号all<-read.csv("仪器编号和序列号all.csv",encoding = "UTF-8",fill = TRUE)
仪器编号和序列号<-read.csv("仪器编号和序列号.csv",encoding = "UTF-8",fill = TRUE)
df2 <- read.csv("history06092025.csv",encoding = "UTF-8",fill = TRUE)
df2<-distinct(df2)
df2$省编号<-(df2$省市编号 %/% 10000)*10000
df2$省名<-str_sub(df2$详细地址,1,3)
df2$省名[str_length(df2$省名)<3]<-NA
Numberof项目号<-length(unique(df2$项目号))
Numberof批次号<-length(unique(df2$批次号))
Numberof样品编号<-length(unique(df2$样品编号))
Numberof项目名称<-length(unique(df2$项目名称))
Numberof批次名称<-length(unique(df2$批次名称))
Numberof省编号<-length(unique(df2$省编号))
Numberof省名<-length(unique(df2$省名))
Numberof省市编号<-length(unique(df2$省市编号))
Numberof仪器序列号<-length(unique(df2$仪器序列号))
Numberof详细地址<-length(unique(df2$详细地址))
Numberof仪器备注名称<-length(unique(df2$仪器备注名称))
Numberof仪器投放区域<-length(unique(df2$仪器投放区域))
Numberof样本类型<-length(unique(df2$样本类型))
省编号and省名<-cbind(df2$省编号,df2$省名)
省编号and省名<-省编号and省名[!duplicated(省编号and省名),]
summaryTable<-stri_join("Numberof测试: ",as.character(nrow(df2)),
"\nNumberof项目名称: ", as.character(Numberof项目名称),
"\nNumberof批次号: ",as.character(Numberof批次号), " \n",
"Numberof样品编号： ",as.character(Numberof样品编号)," \n",
"Numberof批次名称: ",as.character(Numberof批次名称)," \n",
"Numberof省编号: ",as.character(Numberof省编号)," \n",
"Numberof省市编号: ",as.character(Numberof省市编号)," \n",
"Numberof仪器序列号: ",as.character(Numberof仪器序列号)," \n",
"Numberof详细地址: ",as.character(Numberof详细地址)," \n",
"Numberof样本类型: ",as.character(Numberof样本类型)," \n",sep="")
summary(df2$结论)
summary(df2$是否阳性)
summary(df2$是否有效)
#df2$仪器序列号_批次名称<-str_c(df2$仪器序列号,'_',df2$批次名称)
colnames(df2)
#colnames(df00)
#df200<-df20
summary(df2$仪器序列号)
summary(df2$批次名称)
df20<-df2 #[,c(1:11,16,17,20)]
df20$省编号<-str_c("省编号: ",df20$省编号)
df20<-df20[complete.cases(df20$测试时间), ]
colnames(df20)
df20$浓度1<-as.numeric(df20$浓度1)
df20$C值<-as.numeric(df20$C值)
df20$T值<-as.numeric(df20$T值)
df20$ToverC值<-df20$T值/df20$C值
df20$ToverC值[df20$是否有效==0]<-NA
summary(as.numeric(df20$结论))
summary(df20$浓度1)
summary(df20$ToverC值)
summary(df20$testTimeFromeBegining)
colnames(df20)
df20$testMonth<-str_sub(df20$测试时间,1,7)
#df20$testMonthinNumber<-as.numeric(str_c(str_sub(df20$测试时间,1,4),str_sub(df20$测试时间,6,7)))
df20$testDay<-as.Date(str_sub(df20$测试时间,1,10))
df20<-df20[df20$testDay<=Sys.Date(), ]
df20<-df20[complete.cases(df20$testDay), ]
summary(df20$testDay)
df20$testTime<-as.datetime(str_sub(df20$测试时间,1,19),format='%Y-%m-%d %H:%M:%S')
duration<-(max(df20$testTime,na.rm = TRUE)-min(df20$testTime,na.rm = TRUE))/(60.*60.*24.)
df20$testTimeFromeToday<-(max(df20$testTime,na.rm = TRUE)-df20$testTime)/(60.*60.*24.)+1.
#max0<-max(df20$testTime,na.rm=TRUE)
#min0<-min(df20$testTime,na.rm=TRUE)
#unique(dfBy样本$样本类型)
#max0-min0
#max(df20$testDay,na.rm=TRUE)
#min(df20$testDay,na.rm=TRUE)
summary(df20$testTimeFromeToday)
dayMax<-max(df20$testTimeFromeToday,na.rm = TRUE)
df2000<-df20
df20<-df20[df20$testTimeFromeToday<=730,]
#df20<-df20[complete.cases(df20$测试时间), ]
lastDay<-max(df20$testDay,na.rm = TRUE)
df20dayLast21<-df20[lastDay-df20$testDay<21,]
dfByDay <- df2000 %>% group_by(testMonth,testDay) %>% summarise(
测试数 = n(),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
样本数=length(unique(样本类型)),
项目数=length(unique(项目名称)),
仪器数=length(unique(仪器序列号)),
批次数=length(unique(批次名称)),
市数=length(unique(省市编号)),
省数=length(unique(省编号))
)
dfByDay$阳性率<-dfByDay$阳性数/dfByDay$有效数
dfByDay$有效率<-dfByDay$有效数/dfByDay$测试数
dfByDay$阳性数<-as.integer(dfByDay$阳性数)
dfByDay$测试数<-as.integer(dfByDay$测试数)
dfByDay$有效数<-as.integer(dfByDay$有效数)
dfByDay$省数<-as.integer(dfByDay$省数)
dfByDay <- dfByDay[order(dfByDay$testDay),]
#dfByDay<-dfByDay[lastDay-dfByDay$testDay<=21,]
write.csv(dfByDay,file="按日统计0.csv")
dfByMonth <- df2000 %>% group_by(testMonth) %>% summarise(
测试数 = n(),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
样本数=length(unique(样本类型)),
项目数=length(unique(项目名称)),
仪器数=length(unique(仪器序列号)),
批次数=length(unique(批次名称)),
市数=length(unique(省市编号)),
省数=length(unique(省编号))
)
dfByMonth$阳性率<-dfByMonth$阳性数/dfByMonth$有效数
dfByMonth$有效率<-dfByMonth$有效数/dfByMonth$测试数
dfByMonth$阳性数<-as.integer(dfByMonth$阳性数)
dfByMonth$测试数<-as.integer(dfByMonth$测试数)
dfByMonth$有效数<-as.integer(dfByMonth$有效数)
dfByMonth$省数<-as.integer(dfByMonth$省数)
dfByMonth <- dfByMonth[order(dfByMonth$testMonth),]
dfByMonth <- dfByMonth[nrow(dfByMonth):1,]
write.csv(dfByMonth,file="按月统计0.csv")
dfBy项目 <- df2000 %>% group_by(项目名称) %>% summarise(
仪器数=length(unique(仪器序列号)),
批次数=length(unique(批次名称)),
样本数=length(unique(样本类型)),
省数=length(unique(省编号)),
市数=length(unique(省市编号)),
测试数 = n(),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
测试开始 = round(max(testTimeFromeToday,na.rm = TRUE),2),
测试截止 = round(min(testTimeFromeToday,na.rm = TRUE),2)
)
dfBy项目$阳性率<-dfBy项目$阳性数/dfBy项目$有效数
dfBy项目$有效率<-dfBy项目$有效数/dfBy项目$测试数
dfBy项目$阳性数<-as.integer(dfBy项目$阳性数)
dfBy项目$测试数<-as.integer(dfBy项目$测试数)
dfBy项目$有效数<-as.integer(dfBy项目$有效数)
dfBy项目 <- dfBy项目[order(-dfBy项目$测试数),]
write.csv(dfBy项目,file="按项目统计0.csv")
dfBy样本 <- df2000 %>% group_by(样本类型) %>% summarise(
仪器数=length(unique(仪器序列号)),
批次数=length(unique(批次名称)),
项目数=length(unique(项目名称)),
省数=length(unique(省编号)),
市数=length(unique(省市编号)),
测试数 = n(),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
测试开始 = round(max(testTimeFromeToday,na.rm = TRUE),2),
测试截止 = round(min(testTimeFromeToday,na.rm = TRUE),2)
)
dfBy样本$阳性率<-dfBy样本$阳性数/dfBy样本$有效数
dfBy样本$有效率<-dfBy样本$有效数/dfBy样本$测试数
dfBy样本$阳性数<-as.integer(dfBy样本$阳性数)
dfBy样本$测试数<-as.integer(dfBy样本$测试数)
dfBy样本$有效数<-as.integer(dfBy样本$有效数)
dfBy样本 <- dfBy样本[order(-dfBy样本$测试数),]
write.csv(dfBy样本,file="按样本统计0.csv")
dfBy区域 <- df2000 %>% group_by(省编号) %>% summarise(
仪器数=length(unique(仪器序列号)),
批次数=length(unique(批次名称)),
样本数=length(unique(样本类型)),
项目数=length(unique(项目名称)),
市数=length(unique(省市编号)),
测试数 = n(),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
测试开始 = round(max(testTimeFromeToday,na.rm = TRUE),2),
测试截止 = round(min(testTimeFromeToday,na.rm = TRUE),2)
)
dfBy区域$阳性率<-dfBy区域$阳性数/dfBy区域$有效数
dfBy区域$有效率<-dfBy区域$有效数/dfBy区域$测试数
dfBy区域$阳性数<-as.integer(dfBy区域$阳性数)
dfBy区域$测试数<-as.integer(dfBy区域$测试数)
dfBy区域$有效数<-as.integer(dfBy区域$有效数)
dfBy区域 <- dfBy区域[order(-dfBy区域$测试数),]
write.csv(dfBy区域,file="按区域统计0.csv")
dfBy批次 <- df2000 %>% group_by(批次名称) %>% summarise(
仪器数=length(unique(仪器序列号)),
测试数 = n(),
省数=length(unique(省编号)),
市数=length(unique(省市编号)),
样本数=length(unique(样本类型)),
项目数=length(unique(项目名称)),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
最早测试=min(testDay,na.rm = TRUE),
最近测试=max(testDay,na.rm = TRUE),
测试开始 = round(max(testTimeFromeToday,na.rm = TRUE),2),
测试截止 = round(min(testTimeFromeToday,na.rm = TRUE),2)
)
dfBy批次$阳性率<-dfBy批次$阳性数/dfBy批次$有效数
dfBy批次$有效率<-dfBy批次$有效数/dfBy批次$测试数
dfBy批次$阳性数<-as.integer(dfBy批次$阳性数)
dfBy批次$测试数<-as.integer(dfBy批次$测试数)
dfBy批次$有效数<-as.integer(dfBy批次$有效数)
dfBy批次$省数<-as.integer(dfBy批次$省数)
dfBy批次 <- dfBy批次[order(-dfBy批次$测试数),]
write.csv(dfBy批次,file="按批次统计0.csv")
df20001<-cbind(df2000,IDIndex=1:nrow(df2000))
dfBy仪器 <- df20001 %>% group_by(仪器序列号) %>% summarise(
批次数=length(unique(批次名称)),
测试数 = n(),
样本数=length(unique(样本类型)),
项目数=length(unique(项目名称)),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
earliestIndex=min(IDIndex),
medianIndex=median(IDIndex),
latestIndex=max(IDIndex),
最早测试=min(testDay,na.rm = TRUE),
最近测试=max(testDay,na.rm = TRUE),
测试开始 = round(max(testTimeFromeToday,na.rm = TRUE),2),
测试截止 = round(min(testTimeFromeToday,na.rm = TRUE),2)
)
dfBy仪器$阳性率<-dfBy仪器$阳性数/dfBy仪器$有效数
dfBy仪器$有效率<-dfBy仪器$有效数/dfBy仪器$测试数
dfBy仪器$阳性数<-as.integer(dfBy仪器$阳性数)
dfBy仪器$测试数<-as.integer(dfBy仪器$测试数)
dfBy仪器$有效数<-as.integer(dfBy仪器$有效数)
dfBy仪器$最早测试地点<-df2000$详细地址[dfBy仪器$earliestIndex]
dfBy仪器$中期测试地点<-df2000$详细地址[dfBy仪器$medianIndex]
dfBy仪器$最后测试地点<-df2000$详细地址[dfBy仪器$latestIndex]
dfBy仪器 <- dfBy仪器[order(-dfBy仪器$测试数),]
write.csv(dfBy仪器,file="按仪器统计0.csv")
cname0<-c( "仪器序列号" ,"仪器编号" ,"最早测试地点","中期测试地点", "最后测试地点",
"开机地点" , "批次数" , "测试数" , "样本数" , "项目数" ,
"阳性数", "有效数" , "最早测试" , "最近测试" , "测试开始" ,
"测试截止", "阳性率" , "有效率",
"SIM卡号" , "发货时间" , "仪器类型" ,
"总测试量" , "最后一次开机时间" ,"时间差" ,
"网络类型" , "ip地址" ,
"用户" , "用户CRM" , "返利表序号" ,
"申请日期" , "区域" , "客户编码" ,
"代理商名称" , "用户名称" , "规格" ,
"状态")
colnames(dfBy仪器)
dfBy仪器andInf<-merge(dfBy仪器,仪器编号和序列号,by="仪器序列号",all.x=TRUE,all.y=FALSE)
dfBy仪器andInf <- dfBy仪器andInf[order(-dfBy仪器andInf$测试截止),][,cname0]
write.csv(dfBy仪器andInf,file="仪器统计andInf.csv")
dfByDayAndLocation <- df2000 %>% group_by(testDay,testMonth,省编号) %>% summarise(
测试数 = n(),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
样本数=length(unique(样本类型)),
项目数=length(unique(项目名称)),
仪器数=length(unique(仪器序列号)),
批次数=length(unique(批次名称)),
市数=length(unique(省市编号)),
省数=length(unique(省编号))
)
dfByDayAndLocation$阳性率<-dfByDayAndLocation$阳性数/dfByDayAndLocation$有效数
dfByDayAndLocation$有效率<-dfByDayAndLocation$有效数/dfByDayAndLocation$测试数
dfByDayAndLocation$阳性数<-as.integer(dfByDayAndLocation$阳性数)
dfByDayAndLocation$测试数<-as.integer(dfByDayAndLocation$测试数)
dfByDayAndLocation$有效数<-as.integer(dfByDayAndLocation$有效数)
dfByDayAndLocation <- dfByDayAndLocation[order(dfByDayAndLocation$testDay),]
dfByMonthAndLocation <- df20 %>% group_by(testMonth,省编号) %>% summarise(
测试数 = n(),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
样本数=length(unique(样本类型)),
项目数=length(unique(项目名称)),
仪器数=length(unique(仪器序列号)),
批次数=length(unique(批次名称)),
市数=length(unique(省市编号))
)
dfByMonthAndLocation$阳性率<-dfByMonthAndLocation$阳性数/dfByMonthAndLocation$有效数
dfByMonthAndLocation$有效率<-dfByMonthAndLocation$有效数/dfByMonthAndLocation$测试数
dfByMonthAndLocation$阳性数<-as.integer(dfByMonthAndLocation$阳性数)
dfByMonthAndLocation$测试数<-as.integer(dfByMonthAndLocation$测试数)
dfByMonthAndLocation$有效数<-as.integer(dfByMonthAndLocation$有效数)
dfByMonthAndLocation <- dfByMonthAndLocation[order(dfByMonthAndLocation$testMonth),]
cairo_pdf(paste("和迈dataAnalytics06132025",".pdf",sep=""), width = 8, height = 6,family = "SimHei" )
plotSummaryTable<-ggplot() +geom_text(aes(x = 100, y = 40,
label = summaryTable),
stat = "unique",
fontface = "bold",
color = "black", # 标签颜色
size = 5.0 )+ # 字体大小
xlim(0,200)+ylim(0,100)+
theme_minimal() + theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank())
plotSummaryTable
dfByDay1<-dfByDay[lastDay-dfByDay$testDay<42,]
plotfunction(dfData=dfByDay1,independentVariableName="testDay",
dependentVariableNames=c("测试数","有效数","阳性数"),titleNames=c("最后六周按日测试统计","测试时间","统计数值",""))
plotfunction(dfData=dfByDay1,independentVariableName="testDay",
dependentVariableNames=c("有效率","阳性率"),titleNames=c("最后六周按日测试统计","测试时间","统计数值",""))
plotfunction(dfData=dfByDay1,independentVariableName="testDay",
dependentVariableNames=c("项目数","仪器数","批次数"),titleNames=c("最后六周按日测试统计","测试时间","统计数值",""))
plotfunction(dfData=dfByDay1,independentVariableName="testDay",
dependentVariableNames=c("样本数","市数","省数"),titleNames=c("最后六周按日测试统计","测试时间","统计数值",""))
dfByDayAndLocation1<-dfByDayAndLocation[lastDay-dfByDayAndLocation$testDay<42,]
panelPlotByDayAndLocation(dfData=dfByDayAndLocation1,choosenList=dfBy区域$省编号[1:6],independentVariableName="testDay",panelName="省编号",
dependentVariableNames=c("测试数","有效数","阳性数"),titleNames=c("最后六周按日测试统计","测试时间","统计数值",""))
panelPlotByDayAndLocation(dfData=dfByDayAndLocation1,choosenList=dfBy区域$省编号[1:6],independentVariableName="testDay",panelName="省编号",
dependentVariableNames=c("有效率","阳性率"),titleNames=c("最后六周按日测试统计","测试时间","统计数值",""))
panelPlotByDayAndLocation(dfData=dfByDayAndLocation1,choosenList=dfBy区域$省编号[1:6],independentVariableName="testDay",panelName="省编号",
dependentVariableNames=c("项目数","仪器数","批次数"),titleNames=c("最后六周按日测试统计","测试时间","统计数值",""))
panelPlotByDayAndLocation(dfData=dfByDayAndLocation1,choosenList=dfBy区域$省编号[1:6],independentVariableName="testDay",panelName="省编号",
dependentVariableNames=c("样本数","市数","省数"),titleNames=c("最后六周按日测试统计","测试时间","统计数值",""))
dfByMonth1<-dfByMonth[1:30,]
plotfunction(dfData=dfByMonth1,independentVariableName="testMonth",
dependentVariableNames=c("测试数","有效数","阳性数"),titleNames=c("按月测试统计","测试时间","统计数值",""))
plotfunction(dfData=dfByMonth1,independentVariableName="testMonth",
dependentVariableNames=c("有效率","阳性率"),titleNames=c("按月测试统计","测试时间","统计数值",""))
plotfunction(dfData=dfByMonth1,independentVariableName="testMonth",
dependentVariableNames=c("项目数","仪器数","批次数"),titleNames=c("按月测试统计","测试时间","统计数值",""))
plotfunction(dfData=dfByMonth1,independentVariableName="testMonth",
dependentVariableNames=c("样本数","市数","省数"),titleNames=c("按月测试统计","测试时间","统计数值",""))
panelPlotByMonthAndLocation(dfData=dfByMonthAndLocation,choosenList=dfBy区域$省编号[1:6],independentVariableName="testMonth",panelName="省编号",
dependentVariableNames=c("测试数","有效数","阳性数"),titleNames=c("按月分省测试统计","测试时间","统计数值",""))
panelPlotByMonthAndLocation(dfData=dfByMonthAndLocation,choosenList=dfBy区域$省编号[1:6],independentVariableName="testMonth",panelName="省编号",
dependentVariableNames=c("有效率","阳性率"),titleNames=c("按月分省测试统计","测试时间","统计数值",""))
panelPlotByMonthAndLocation(dfData=dfByMonthAndLocation,choosenList=dfBy区域$省编号[1:6],independentVariableName="testMonth",panelName="省编号",
dependentVariableNames=c("项目数","仪器数","批次数"),titleNames=c("按月分省测试统计","测试时间","统计数值",""))
panelPlotByMonthAndLocation(dfData=dfByMonthAndLocation,choosenList=dfBy区域$省编号[1:6],independentVariableName="testMonth",panelName="省编号",
dependentVariableNames=c("样本数","市数"),titleNames=c("按月分省测试统计","测试时间","统计数值",""))
plotfunction(dfData=dfBy区域,independentVariableName="省编号",
dependentVariableNames=c("测试数","有效数","阳性数"),titleNames=c("按省测试统计","省编号","统计数值",""))
plotfunction(dfData=dfBy区域,independentVariableName="省编号",
dependentVariableNames=c("有效率","阳性率"),titleNames=c("按省测试统计","省编号","统计数值",""))
plotfunction(dfData=dfBy区域,independentVariableName="省编号",
dependentVariableNames=c("项目数","仪器数","批次数"),titleNames=c("按省测试统计","省编号","统计数值",""))
plotfunction(dfData=dfBy区域,independentVariableName="省编号",
dependentVariableNames=c("样本数","市数"),titleNames=c("按省测试统计","省编号","统计数值",""))
plotfunctionLog(dfData=dfBy区域,independentVariableName="省编号",
dependentVariableNames=c("测试开始","测试截止"),titleNames=c("按省测试统计","省编号","多少天之前",""))
仪器号List0<-cbind(dfBy仪器,GroupID=rep00[1:length(dfBy仪器$仪器序列号)])
仪器号List<-仪器号List0[,c(1,ncol(仪器号List0))]
dfBy仪器$仪器序列号<-factor(dfBy仪器$仪器序列号,levels = 仪器号List[,1])
plotfunctionVector仪器序列号(ik=1,independentVariableList=仪器号List,dfData=dfBy仪器,independentVariableName="仪器序列号",
dependentVariableNames=c("测试数","有效数","阳性数"),titleNames=c("按仪器测试统计","仪器序列号","统计数值",""))
plotfunctionVector仪器序列号(ik=1,independentVariableList=仪器号List,dfData=dfBy仪器,independentVariableName="仪器序列号",
dependentVariableNames=c("项目数","样本数","批次数"),titleNames=c("按仪器测试统计","仪器序列号","统计数值",""))
plotfunctionVector仪器序列号(ik=1,independentVariableList=仪器号List,dfData=dfBy仪器,independentVariableName="仪器序列号",
dependentVariableNames=c("有效率","阳性率"),titleNames=c("按仪器测试统计","仪器序列号","统计数值",""))
plotfunctionVector仪器序列号Log(ik=1,independentVariableList=仪器号List,dfData=dfBy仪器,independentVariableName="仪器序列号",
dependentVariableNames=c("测试开始","测试截止"),titleNames=c("按仪器测试统计","仪器序列号","多少天之前",""))
if(FALSE) {
dfBy仪器00 <- df2000 %>% group_by(仪器序列号,省市编号,详细地址,仪器备注名称,仪器投放区域) %>% summarise(
批次数=length(unique(批次名称)),
测试数 = n(),
样本数=length(unique(样本类型)),
项目数=length(unique(项目名称)),
阳性数=sum(结论,na.rm = TRUE),
有效数=sum(是否有效,na.rm = TRUE),
earliestIndex=min(IDIndex),
medianIndex=median(IDIndex),
latestIndex=max(IDIndex),
最早测试=min(testDay,na.rm = TRUE),
最近测试=max(testDay,na.rm = TRUE),
测试开始 = round(max(testTimeFromeToday,na.rm = TRUE),2),
测试截止 = round(min(testTimeFromeToday,na.rm = TRUE),2)
)
dfBy仪器00$阳性率<-dfBy仪器00$阳性数/dfBy仪器00$有效数
dfBy仪器00$有效率<-dfBy仪器00$有效数/dfBy仪器00$测试数
dfBy仪器00$阳性数<-as.integer(dfBy仪器00$阳性数)
dfBy仪器00$测试数<-as.integer(dfBy仪器00$测试数)
dfBy仪器00$有效数<-as.integer(dfBy仪器00$有效数)
dfBy仪器1<-merge(dfBy仪器00[,1:5],dfBy仪器,by="仪器序列号",all=FALSE)
write.csv(dfBy仪器1,file="仪器统计1.csv")
#仪器号MissedFromTwoYearsAgo<-仪器号[!(仪器号 %in% 仪器号00)]
}
#样本号List<-unique(dfBy样本$样本类型)
plotfunction(dfData=dfBy样本,independentVariableName="样本类型",
dependentVariableNames=c("测试数","有效数","阳性数"),titleNames=c("按样本类型测试统计","样本类型","统计数值",""))
plotfunction(dfData=dfBy样本,independentVariableName="样本类型",
dependentVariableNames=c("项目数","批次数","省数"),titleNames=c("按样本类型测试统计","样本类型","统计数值",""))
plotfunction(dfData=dfBy样本,independentVariableName="样本类型",
dependentVariableNames=c("有效率","阳性率"),titleNames=c("按样本类型测试统计","样本类型","统计数值",""))
plotfunctionLog(dfData=dfBy样本,independentVariableName="样本类型",
dependentVariableNames=c("测试开始","测试截止"),titleNames=c("按样本类型测试统计","样本类型","多少天之前",""))
批次号List0<-cbind(dfBy批次,GroupID=rep00[1:length(dfBy批次$批次名称)])
批次号List<-批次号List0[,c(1,ncol(批次号List0))]
dfBy批次$批次名称<-factor(dfBy批次$批次名称,levels = 批次号List[,1])
plotfunctionVector批次名称(ik=1,independentVariableList=批次号List,dfData=dfBy批次,independentVariableName="批次名称",
dependentVariableNames=c("测试数","有效数","阳性数"),titleNames=c("按批次测试统计","批次名称","统计数值",""))
plotfunctionVector批次名称(ik=1,independentVariableList=批次号List,dfData=dfBy批次,independentVariableName="批次名称",
dependentVariableNames=c("项目数","样本数","省数"),titleNames=c("按试剂批次测试统计","批次名称","统计数值",""))
plotfunctionVector批次名称(ik=1,independentVariableList=批次号List,dfData=dfBy批次,independentVariableName="批次名称",
dependentVariableNames=c("有效率","阳性率"),titleNames=c("按试剂批次测试统计","批次名称","统计数值",""))
plotfunctionVector批次名称Log(ik=1,independentVariableList=批次号List,dfData=dfBy批次,independentVariableName="批次名称",
dependentVariableNames=c("测试开始","测试截止"),titleNames=c("按试剂批次测试统计","批次名称","多少天之前",""))
项目号List0<-cbind(dfBy项目,GroupID=rep00[1:length(dfBy项目$项目名称)])
项目号List<-项目号List0[,c(1,ncol(项目号List0))]
dfBy项目$项目名称<-factor(dfBy项目$项目名称,levels = 项目号List[,1])
plotfunctionVector项目名称(ik=1,independentVariableList=项目号List,dfData=dfBy项目,independentVariableName="项目名称",
dependentVariableNames=c("测试数","有效数","阳性数"),titleNames=c("按项目测试统计","项目名称","统计数值",""))
plotfunctionVector项目名称(ik=1,independentVariableList=项目号List,dfData=dfBy项目,independentVariableName="项目名称",
dependentVariableNames=c("批次数","样本数","省数"),titleNames=c("按试剂项目测试统计","项目名称","统计数值",""))
plotfunctionVector项目名称(ik=1,independentVariableList=项目号List,dfData=dfBy项目,independentVariableName="项目名称",
dependentVariableNames=c("有效率","阳性率"),titleNames=c("按试剂项目测试统计","项目名称","统计数值",""))
plotfunctionVector项目名称Log(ik=1,independentVariableList=项目号List,dfData=dfBy项目,independentVariableName="项目名称",
dependentVariableNames=c("测试开始","测试截止"),titleNames=c("按试剂项目测试统计","项目名称","多少天之前",""))
dev.off()
df20$浓度1<-as.numeric(df20$浓度1)
df20$C值<-as.numeric(df20$C值)
df20$T值<-as.numeric(df20$T值)
df20$ToverC值<-df20$T值/df20$C值
df20$ToverC值[df20$是否有效==0]<-NA
summary(as.numeric(df20$结论))
summary(df20$浓度1)
summary(df20$ToverC值)
summary(df20$testTimeFromeToday)
colnames(df20)
#df201<-df20
# for(i in 1:5){
cairo_pdf(paste("和迈dataAnalyticsForRandD","_a06132025.pdf",sep=""), width = 8, height = 6,family = "SimHei" )
plotSummaryTable
# df20<-df201[(df201$项目名称 %in% dfBy项目top4[i]),]
# plot项目名称Title<-ggplot() +geom_text(aes(x = 100, y = 60,
# label = paste("项目名称： ",dfBy项目名称top5[i],sep="")),
# stat = "unique",
# fontface = "bold",
# color = "black", # 标签颜色
# size = 8.0 )+ # 字体大小
# xlim(0,200)+ylim(0,100)+
# theme_minimal() + theme(axis.text = element_blank(),
# axis.ticks = element_blank(),
# axis.title = element_blank())
#
# plot项目名称Title
df20 <- df20[order(-df20$testTimeFromeToday),]
df20 <- df20 %>% group_by(批次名称) %>% transform(
浓度1移动均值 = running_avg(浓度1,20),
C值移动均值 = running_avg(C值,20),
T值移动均值 = running_avg(T值,20),
ToverC值移动均值 = running_avg(ToverC值,20),
结论移动均值 = running_avg(结论,20)
)
# df20$浓度1移动均值<-running_avg(df20$浓度1, 20)
# df20$C值移动均值<-running_avg(df20$C值, 20)
# df20$T值移动均值<-running_avg(df20$T值, 20)
# df20$ToverC值移动均值<-running_avg(df20$ToverC值, 20)
# df20$结论移动均值<-running_avg(df20$结论, 20)
df20$浓度1累计均值<-lumping_avg(df20$浓度1)
df20$C值累计均值<-lumping_avg(df20$C值)
df20$T值累计均值<-lumping_avg(df20$T值)
df20$ToverC值累计均值<-lumping_avg(df20$ToverC值)
df20$结论累计均值<-lumping_avg(df20$结论)
# df20$浓度1累计标差<-lumping_sd(df20$浓度1)
# df20$C值累计标差<-lumping_sd(df20$C值)
# df20$T值累计标差<-lumping_sd(df20$T值)
# df20$ToverC值累计标差<-lumping_sd(df20$ToverC值)
# df20$结论累计标差<-lumping_sd(df20$结论移动均值)
df20$浓度1累计标差<-lumping_sd(df20$浓度1移动均值)
df20$C值累计标差<-lumping_sd(df20$C值移动均值)
df20$T值累计标差<-lumping_sd(df20$T值移动均值)
df20$ToverC值累计标差<-lumping_sd(df20$ToverC值移动均值)
df20$结论累计标差<-lumping_sd(df20$结论移动均值)
df20$浓度1允许波动范围<-df20$浓度1累计均值+2*df20$浓度1累计标差
df20$C值允许波动范围<-df20$C值累计均值+2*df20$C值累计标差
df20$T值允许波动范围<-df20$T值累计均值+2*df20$T值累计标差
df20$ToverC值允许波动范围<-df20$ToverC值累计均值+2*df20$ToverC值累计标差
df20$结论允许波动范围<-df20$结论累计均值+2*df20$结论累计标差
summary(df20$浓度1移动均值)
summary(df20$running_avg)
summary(df20$浓度1累计均值)
summary(df20$浓度1累计标差)
summary(df20$结论累计均值)
df200<-df20
df20<-df200[df200$项目名称 %in% 项目号List[1,1], ]
#df20Ploted<-df20[df20$批次名称 %in% 批次号List$批次名称[批次号List[,2]==1],]
df20Ploted<-df20[df20$批次名称 %in% 批次号List$批次名称[1:5],]
pointPlotfunction批次(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="浓度1",titleNames=c("浓度","测试日期","浓度1",""))
pointPlotfunction批次Log(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="C值",titleNames=c("C值","测试日期","C值",""))
pointPlotfunction批次Log(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="T值",titleNames=c("T值","测试日期","T值",""))
pointPlotfunction批次(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="ToverC值",titleNames=c("T/C值","测试日期","T/C值",""))
pointPlotfunction批次(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="结论移动均值",titleNames=c("结论/阳性率","测试日期","结论/阳性率",""))
df20Ploted<-df20[df20$仪器序列号 %in% 仪器号List$仪器序列号[1:5],]
pointPlotfunction仪器(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="浓度1",titleNames=c("浓度","测试日期","浓度1",""))
pointPlotfunction仪器Log(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="C值",titleNames=c("C值","测试日期","C值",""))
pointPlotfunction仪器Log(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="T值",titleNames=c("T值","测试日期","T值",""))
pointPlotfunction仪器(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="ToverC值",titleNames=c("T/C值","测试日期","T/C值",""))
pointPlotfunction仪器(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="结论移动均值",titleNames=c("结论/阳性率","测试日期","结论/阳性率",""))
df20Ploted<-df20[df20$样本类型 %in% unique(df20$样本类型)[1:2],]
pointPlotfunction样本(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="浓度1",titleNames=c("浓度","测试日期","浓度1",""))
pointPlotfunction样本Log(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="C值",titleNames=c("C值","测试日期","C值",""))
pointPlotfunction样本Log(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="T值",titleNames=c("T值","测试日期","T值",""))
pointPlotfunction样本(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="ToverC值",titleNames=c("T/C值","测试日期","T/C值",""))
pointPlotfunction样本(dfData=df20Ploted,independentVariableName="testDay",
dependentVariableName="结论移动均值",titleNames=c("结论/阳性率","测试日期","结论/阳性率",""))
dev.off()
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