.Rhistory

df <- df %>%  add_column(ID = rep(0,nrow(df)), .after = 1)   #insert ID column
df$ID[1] <- 1
for (t in 2:nrow(df))
{
if (   df$SubjectID[t]!= df$SubjectID[t-1]  )  {
df$ID[t] <- df$ID[t-1]+1 }
else {
df$ID[t] <- df$ID[t-1]
}
}
##--------------------- 1. make Age from year/month -----------------------##
df$AGE <- round( df$AGEYR + df$AGEMT/12, digits=1)
df$age_group <-NA
df$age_group[df$AGE< 2] <- 'infants'
df$age_group[(df$AGE >= 2) & (df$AGE< 12) ] <- 'children'
df$age_group[(df$AGE >= 12) & (df$AGE< 18)] <- 'adolescents'
df$age_group[(df$AGE >= 18)] <- 'young adults'
#Subject_amount[ length(Subject_amount) +1] <-   n_distinct(df$ID)
demo_by_age_initial <- df %>% group_by(age_group) %>% summarise ( num = n_distinct(ID) )
n_distinct(df$ID)
######################### orginal data summary #######################
summary ( (df$adductlevel>=0)& (df$adductlevel < 100))
nrow(df)
summary (df$dose >=0)
nrow ( df[( is.na(df$dose) & is.na(df$DV)), ])
write_csv(df,'cleaned_data.csv', na='.' )
n_distinct(df$ID[df$adductlevel==9999])
unique(df$SubjectID [df$adductlevel==9999])
########### ----------- 1. remove ID with no dose ----------############
##---------------- 1. add EVID   ------------------##
df <- df %>%  add_column(EVID = rep(0,nrow(df)), .after = match("dose",names(df)) )  #insert EVID column
df$EVID  <- ifelse(df$dose> 0, 1,0 )
df$EVID[is.na(df$EVID)]  <- 0
n_distinct(df$ID)
nrow(df)
##-------------------1. remove rows without neither dose or DV   ------------------##
df$DV <- df$adductlevel
df  <- df[ !( is.na(df$dose) & is.na(df$DV)), ]
aa  <-  df[ ( is.na(df$dose) & is.na(df$DV)), ]
Subject_amount[ length(Subject_amount) +1] <- n_distinct(df$ID)
n_distinct(df$ID)
nrow(Odf)-  nrow(df)
View(df)
df$DV <- df$adductlevel
##  data cleaning and analysis of the pediatric data
##------------------------- start --------------------####
library(ggplot2)
library(readr)
library(tibble)
library(dplyr)
library(readxl)
##------------------------- read original dataset --------------------####
path <- 'O:\\G_Drive\\code\\R_Script\\0_Projects\\data_cleaning\\Copy of Group A analysis merged data for popPK-pinkedited.xlsx'    # original dataset location
setwd(dirname(path))         # set working directory
if (!file.exists(path) )   {winDialog( "ok", "File Dose Not Exist") }
Odf <- read_excel (path)
n_distinct(Odf$SubjectID )
########################################################
##---------------------------------------------------##
##----------------  Part 1    ------------------##
##--------------------------------------------------##
df <- Odf
df$doseperkg <- round(df$doseperkg,1)
df$DV<- df$adductlevel              # addcut level to DV
Subject_amount <-  c()             # computation of total subject amount in dataset
Subject_amount[ length(Subject_amount) +1] <-  n_distinct(df$SubjectID)
##---------------------1. add original row number ------------######
df$original_row_NO <- seq.int(nrow(df))
df <- df[df$original_row_NO!= 1794,]  # CNMA022 first dose one month early
## ------------------- 1. add individule ID from 1 2,3, no letter in ID--------------##
df <- df %>%  add_column(ID = rep(0,nrow(df)), .after = 1)   #insert ID column
df$ID[1] <- 1
for (t in 2:nrow(df))
{
if (   df$SubjectID[t]!= df$SubjectID[t-1]  )  {
df$ID[t] <- df$ID[t-1]+1 }
else {
df$ID[t] <- df$ID[t-1]
}
}
##--------------------- 1. make Age from year/month -----------------------##
df$AGE <- round( df$AGEYR + df$AGEMT/12, digits=1)
df$age_group <-NA
df$age_group[df$AGE< 2] <- 'infants'
df$age_group[(df$AGE >= 2) & (df$AGE< 12) ] <- 'children'
df$age_group[(df$AGE >= 12) & (df$AGE< 18)] <- 'adolescents'
df$age_group[(df$AGE >= 18)] <- 'young adults'
#Subject_amount[ length(Subject_amount) +1] <-   n_distinct(df$ID)
demo_by_age_initial <- df %>% group_by(age_group) %>% summarise ( num = n_distinct(ID) )
n_distinct(df$ID)
######################### orginal data summary #######################
summary ( (df$adductlevel>=0)& (df$adductlevel < 100))
nrow(df)
summary (df$dose >=0)
nrow ( df[( is.na(df$dose) & is.na(df$DV)), ])
write_csv(df,'cleaned_data.csv', na='.' )
n_distinct(df$ID[df$adductlevel==9999])
unique(df$SubjectID [df$adductlevel==9999])
##  data cleaning and analysis of the pediatric data
##------------------------- start --------------------####
library(ggplot2)
library(readr)
library(tibble)
library(dplyr)
library(readxl)
##------------------------- read original dataset --------------------####
path <- 'O:\\G_Drive\\code\\R_Script\\0_Projects\\data_cleaning\\Copy of Group A analysis merged data for popPK-pinkedited.xlsx'    # original dataset location
setwd(dirname(path))         # set working directory
if (!file.exists(path) )   {winDialog( "ok", "File Dose Not Exist") }
Odf <- read_excel (path)
n_distinct(Odf$SubjectID )
########################################################
##---------------------------------------------------##
##----------------  Part 1    ------------------##
##--------------------------------------------------##
df <- Odf
df$doseperkg <- round(df$doseperkg,1)
df$DV<- df$adductlevel              # addcut level to DV
Subject_amount <-  c()             # computation of total subject amount in dataset
Subject_amount[ length(Subject_amount) +1] <-  n_distinct(df$SubjectID)
##---------------------1. add original row number ------------######
df$original_row_NO <- seq.int(nrow(df))
df <- df[df$original_row_NO!= 1794,]  # CNMA022 first dose one month early
##  data cleaning and analysis of the pediatric data
##------------------------- start --------------------####
library(ggplot2)
library(readr)
library(tibble)
library(dplyr)
library(readxl)
##------------------------- read original dataset --------------------####
path <- 'O:\\G_Drive\\code\\R_Script\\0_Projects\\data_cleaning\\Copy of Group A analysis merged data for popPK-pinkedited.xlsx'    # original dataset location
setwd(dirname(path))         # set working directory
if (!file.exists(path) )   {winDialog( "ok", "File Dose Not Exist") }
Odf <- read_excel (path)
n_distinct(Odf$SubjectID )
df <- Odf
df$doseperkg <- round(df$doseperkg,1)
df$DV<- df$adductlevel              # addcut level to DV
Subject_amount <-  c()             # computation of total subject amount in dataset
Subject_amount[ length(Subject_amount) +1] <-  n_distinct(df$SubjectID)
df$original_row_NO <- seq.int(nrow(df))
df <- df[df$original_row_NO!= 1794,]     # CNMA022 first dose one month early
##  data cleaning and analysis of the pediatric data
##------------------------- start --------------------####
library(ggplot2)
library(readr)
library(tibble)
library(dplyr)
library(readxl)
##------------------------- read original dataset --------------------####
path <- 'O:\\G_Drive\\code\\R_Script\\0_Projects\\data_cleaning\\Copy of Group A analysis merged data for popPK-pinkedited.xlsx'    # original dataset location
setwd(dirname(path))         # set working directory
if (!file.exists(path) )   {winDialog( "ok", "File Dose Not Exist") }
Odf <- read_excel (path)
n_distinct(Odf$SubjectID )
########################################################
##---------------------------------------------------##
##----------------  Part 1    ------------------##
##--------------------------------------------------##
df <- Odf
df$doseperkg <- round(df$doseperkg,1)
df$DV<- df$adductlevel              # addcut level to DV
Subject_amount <-  c()             # computation of total subject amount in dataset
Subject_amount[ length(Subject_amount) +1] <-  n_distinct(df$SubjectID)
df$original_row_NO <- seq.int(nrow(df))
View(df)
df <- df[df$original_row_NO!= 1794,]     # CNMA022 first dose one month early
##  data cleaning and analysis of the pediatric data
##------------------------- start --------------------####
library(ggplot2)
library(readr)
library(tibble)
library(dplyr)
library(readxl)
##------------------------- read original dataset --------------------####
path <- 'O:\\G_Drive\\code\\R_Script\\0_Projects\\data_cleaning\\Copy of Group A analysis merged data for popPK-pinkedited.xlsx'    # original dataset location
setwd(dirname(path))         # set working directory
if (!file.exists(path) )   {winDialog( "ok", "File Dose Not Exist") }
Odf <- read_excel (path)
n_distinct(Odf$SubjectID )
########################################################
##---------------------------------------------------##
##----------------  Part 1    ------------------##
##--------------------------------------------------##
df <- Odf
df$doseperkg <- round(df$doseperkg,1)
df$DV<- df$adductlevel              # addcut level to DV
Subject_amount <-  c()             # computation of total subject amount in dataset
Subject_amount[ length(Subject_amount) +1] <-  n_distinct(df$SubjectID)
##---------------------1. add original row number ------------######
df$original_row_NO <- seq.int(nrow(df))
## ------------------- 1. add individule ID from 1 2,3, no letter in ID--------------##
df <- df %>%  add_column(ID = rep(0,nrow(df)), .after = 1)   #insert ID column
df$ID[1] <- 1
for (t in 2:nrow(df))
{
if (   df$SubjectID[t]!= df$SubjectID[t-1]  )  {
df$ID[t] <- df$ID[t-1]+1 }
else {
df$ID[t] <- df$ID[t-1]
}
}
##--------------------- 1. make Age from year/month -----------------------##
df$AGE <- round( df$AGEYR + df$AGEMT/12, digits=1)
df$age_group <-NA
df$age_group[df$AGE< 2] <- 'infants'
df$age_group[(df$AGE >= 2) & (df$AGE< 12) ] <- 'children'
df$age_group[(df$AGE >= 12) & (df$AGE< 18)] <- 'adolescents'
df$age_group[(df$AGE >= 18)] <- 'young adults'
#Subject_amount[ length(Subject_amount) +1] <-   n_distinct(df$ID)
demo_by_age_initial <- df %>% group_by(age_group) %>% summarise ( num = n_distinct(ID) )
n_distinct(df$ID)
######################### orginal data summary #######################
summary ( (df$adductlevel>=0)& (df$adductlevel < 100))
nrow(df)
summary (df$dose >=0)
nrow ( df[( is.na(df$dose) & is.na(df$DV)), ])
write_csv(df,'cleaned_data.csv', na='.' )
n_distinct(df$ID[df$adductlevel==9999])
unique(df$SubjectID [df$adductlevel==9999])
########### ----------- 1. remove ID with no dose ----------############
##---------------- 1. add EVID   ------------------##
df <- df %>%  add_column(EVID = rep(0,nrow(df)), .after = match("dose",names(df)) )  #insert EVID column
df$EVID  <- ifelse(df$dose> 0, 1,0 )
df$EVID[is.na(df$EVID)]  <- 0
n_distinct(df$ID)
nrow(df)
##-------------------1. remove rows without neither dose or DV   ------------------##
df$DV <- df$adductlevel
df  <- df[ !( is.na(df$dose) & is.na(df$DV)), ]
Subject_amount[ length(Subject_amount) +1] <- n_distinct(df$ID)
n_distinct(df$ID)
nrow(Odf)-  nrow(df)
## ----------- 1. remove ID with no dose ----------############
remove_No_dose  <- function (df)  {
if ( all(df$EVID == 0) ) {
df$original_row_NO  <- 999999    }
return(df)
}
df  <- df  %>% group_by(ID) %>%  do( remove_No_dose(.))
df1   <- df  [df$original_row_NO!=999999,]
setdiff( unique(Odf$SubjectID),unique(df1$SubjectID))
### ----------- 1. no DV after first dose ----------############
### input table has least one ID
remove0DV  <- function (df)  {
firstEVID <-  which(df$EVID==1)[1]  # first EVID row number
df2  <- df[(firstEVID+1):nrow(df),]   # rows bigger than first evid
if ( all(is.na(df2$DV)) ) {
df$original_row_NO  <- 999999        # remove this line
return(df)
}      else {                                                                                             return (df)
}
}
df2 <-  df1 %>% group_by(ID) %>%  do( remove0DV(.))
df2  <- df2 [df2$original_row_NO!=999999,]
nrow(df1) - nrow(df2)
setdiff( unique(df1$SubjectID),unique(df2$SubjectID))
Total_removedID <-  setdiff( unique(df$SubjectID),unique(df2$SubjectID))
df[df$SubjectID %in% Total_removedID, ] %>% group_by(age_group) %>%   summarise ( num  = n_distinct(ID)   )
n_distinct(df2$ID)
nrow(Odf)-  nrow(df2)
nrow(df) -  nrow(df2)
##----------------- 5. Add time --------------------------------##
df2$Time <- df2$Time *24
df2$Time <- round(df2$Time,3)
df2 <- as.data.frame(df2)
df2 <- df2 %>%  add_column(TIME = rep(NA,nrow(df2)), .after = match("time",names(df2)))   #insert ID column
addtime <- function (df)   {
a  <- which(df$dose>0)[1]
df$TIME[a] <- 0
for (n in seq(a, (nrow(df)-1)  ) )   {
df$TIME[n+1]<- df$TIME[n] + df$Time[n+1]- df$Time[n]
}
df$TIME[is.na(df$TIME)] <- c(-1)
return (df)
}
df2  <- df2 %>% group_by(ID) %>%  do(addtime(.))
n_distinct(df2$ID)
######################  make redundant baseline   ##############################
baseline_clean <-  function (tb)  {
if ( nrow (  tb[tb$TIME==-1, ] )>=2 )  {   # more than one minus
dose_row <- which(tb$TIME==0)
tb$predoseDV[1:(dose_row-2)] <- 'extrabase'
return (tb)
} else {
cat ( 'ID',unique(tb$ID),'\n' )
return (tb)}
}
df2$predoseDV  <- NA
df3   <-  df2 %>% group_by(ID) %>%  do( baseline_clean(.))
write_csv(df3,'test.csv')
n_distinct(df3$ID)
nrow(df2)- nrow(df3)
nrow(df3[df3$TIME==-1,])
n_distinct (df3$ID[df3$TIME==-1])
df3 %>% group_by(age_group) %>%  summarise ( num = n_distinct(ID) )
nrow(Odf)-nrow(df3)
summary(df3[ df3$DV >=0, ] )
summary (df3$adductlevel>=0)
summary (df3$dose >=0)
ddd <-  df3[ ( is.na(df3$dose) & is.na(df3$DV)), ]
###############################################################
##################### plot baseline ###############################
################ plot before conversion ###############
plotdf<-   df3[(df3$TIME==-1),]
plotdf$SubjectID[plotdf$DV<0.005] <- NA
ggplot( plotdf,aes(x=ID,y=DV))+ geom_point()+
xlab(" Subject ID") +
ylab('APAP-adduct (nmol/ml)') +
ggtitle('Adduct Baseline') +
theme(plot.title = element_text(hjust = 0.5))+
geom_text (aes (label=SubjectID), size=2,hjust=-0.2,vjust=2)+
geom_hline(yintercept = c(0.03),linetype='dashed',colour = "red", size = 1) +           geom_hline(yintercept = c(0.006),linetype='solid',colour = "red", size = 1,alpha=0.5) +
scale_y_continuous(limits=c(0, 0.037), breaks=seq(0,0.04, by = 0.005)  )
################ plot aftter conversion ###############
plotdf2 <-   df3[(df3$TIME==-1),]
plotdf2$DV[plotdf2$DV<(0.03*0.2)] <- 0
ggplot( plotdf2, aes(x=DV)) +
geom_histogram()+
ggtitle('Baseline after Conversion') +
ylab('Number of Subjects')+
theme(plot.title = element_text(hjust = 0.5))+
ylim(0,80)  +
geom_vline(xintercept = c(0.03),linetype='dashed',colour = "red", size = 1) +           geom_vline(xintercept = c(0.006),linetype='solid',colour = "red", size = 1,alpha=0.5) +
scale_x_continuous(name = "APAP-adduct (nmol/ml)", limits=c(-0.001, 0.037), breaks=seq(0,0.04, by = 0.005)  )
###-----------2. remove the last dosing  record -------- ##########
remove_last_dosing <- function (grouped_ID)  {
if    (  !is.na(tail(grouped_ID$DV,1)) )  {    #last records is missming
return (grouped_ID)
}  else {
grouped_ID  <- grouped_ID[-nrow(grouped_ID),]
remove_last_dosing (grouped_ID)     # recursion
}
}
df4 <- df3 %>%  group_by(ID) %>% do (remove_last_dosing(.))
nrow(df3) - nrow(df4)
# xxx  <- setdiff(df3,df4)
n_distinct(df4$ID)
summary(df4$DV >=0)
summary (df4$dose>=0)
################## add dose column dv row  ###############
pattern <- function(df)  {
n <- 1
fulldf<- list()
df$last_dose <- NA
df$last_doseperkg <- NA
for (t in 1:nrow(df))   {
if ( !is.na(df$dose[t]) & !is.na(df$DV[t+1])  )   {
df$TIME[t+1] <- df$TIME[t+1] - df$TIME[t]
df$last_dose[t+1] <- df$dose[t]
df$last_doseperkg[t+1] <- df$doseperkg[t]
df$dosageform[t+1] <- df$dosageform[t]
df$TIME[t]<- 0
fulldf[[n]]  <- df[(t:(t+1)),]
n <- n+1
}
}
return ( bind_rows(fulldf)  )
}
a  <- df4  %>% group_by(ID) %>% do(pattern(.) )   ##### data
a <- na.omit ( a [,c('original_row_NO','last_dose','last_doseperkg')] )
df5 <- merge(x = df4, y = a, by = "original_row_NO", all = TRUE)
##---------------------- 4 .formulation -------------------------##
##################### add formulation information #########################
formu_fun <-  function (tb)  {
tb$formulationNo <- NA     # new column for Formulation numbers
iv <- c('H01','H02')       # 1
tb$formulation[tb$dosageform %in% iv] <- 'iv'
tb$formulationNo[tb$dosageform %in% iv] <-  1
tablet_mono <- c('C01','C02','C03','C04','C05')    # 2
tb$formulation[tb$dosageform %in% tablet_mono] <- 'tablet_mono'
tb$formulationNo[tb$dosageform %in% tablet_mono] <-  2
suppository <- c('B01','B02','B03','B04','B05')  # 3
tb$formulation[tb$dosageform %in% suppository] <- 'suppository'
tb$formulationNo[tb$dosageform %in% suppository] <- 3
elixir <- c('A01')        # 4
tb$formulation[tb$dosageform %in% elixir] <- 'elixir'
tb$formulationNo[tb$dosageform %in% elixir] <- 4
solution <- c('A02')      # 5
tb$formulation[tb$dosageform %in% solution] <- 'solution'
tb$formulationNo[tb$dosageform %in% solution] <- 5
tablet_comb <- c('E01','E05','E06','E10','E12','E14','E18','E19','F05')  # 6
tb$formulation[tb$dosageform %in% tablet_comb] <- 'tablet_comb'
tb$formulationNo[tb$dosageform %in% tablet_comb] <-  6
liquid_comb <- c('D01','D03','D06')     # 7
tb$formulation[tb$dosageform %in% liquid_comb] <- 'liquid_comb'
tb$formulationNo[tb$dosageform %in% liquid_comb] <- 7
other <- c('A03','A04','F07','G13')  # 8
tb$formulation[tb$dosageform %in% other] <- 'other'
tb$formulationNo[tb$dosageform %in% other] <- 7
tb$formulationfactor <-  factor(tb$formulationNo,labels=c(
'iv','tablet_mono','suppository',
'elixir','solution','tablet_comb','liquid_comb' ))
return (tb)
}
df6 <- formu_fun(df5)
# test formulation
#   outersect <- function(x, y) {
#   cat ('only left:')
#   print (setdiff(x, y))
#   cat ('only  right:')
#   print    ( setdiff(y, x))
#   return(  sort(c(setdiff(x, y), setdiff(y, x)))           )
# }
#
#   nrow ( df6[is.na(df6$formulation),])
#   nrow ( df6[df6$EVID==0,])
#   outersect(  df6$original_row_NO[df6$EVID==0],
#               df6$original_row_NO[is.na(df6$formulation)] )
#
##########################################################################
##---------------------- 4 .Final NONMEM format -------------------------##
###########################################################################
df_NM  <- df6
df_NM$AMT <- round(df_NM$dose*1000/151,digits=1)  #dose mg -> molar
df_NM$RATE <-  ifelse(df_NM$formulationNo==1,df_NM$AMT/0.25,0  )
df_NM <- rename(df_NM,WT=WEIGHT)             # rename weight -> WT
##--------------------- 4. add Compartment -----------------------##
df_NM$CMT[df_NM$formulationNo==1] <- 2
df_NM$CMT[df_NM$EVID==0 ] <- 2
df_NM$MDV <-  ifelse(df_NM$EVID==1,1,0)
names(df_NM)
df_NM2  <- select(df_NM,ID,TIME,AMT,RATE,CMT,EVID,DV,MDV,WT,formulationNo,
AGE,original_row_NO,age_group)
############# iv group ####################
#   All subjects containing one iv dosing
iv_ID <- df6$ID[df6$formulation=='iv']
iv_ID <- unique(  (  na.omit (iv_ID )) )
n_distinct(iv_ID)
unique(iv_ID)
df_iv_ID <- df6[df6$ID %in% iv_ID, ]
## all subjects contianing only iv id
all_iv  <- function(tb)  {                           # return all iv group
if (all(tb$formulationNo==1,na.rm = TRUE)) {
return(tb)
}  else {return (data.frame())}
}
df_iv_only <-  df6 %>% group_by(ID)  %>% do(all_iv(.))
n_distinct(df_iv_only$ID)
unique(df_iv_only$ID)
outersect(   unique(iv_ID),   unique(df_iv_only$ID)  )
unique(df_iv_only$ID)
n_distinct(df_iv_only$ID)
df_iv_only
I
df_iv_only
View(df_iv_only)
detach("package:aaasibo", unload=TRUE)
x  <- c(-5:5, NA)
if_else(x < 0, NA_integer_, x)
x
if_else(x < 0, "negative", "positive", "missing")
ifelse(x < 0, "negative", "positive", "missing")
ifelse(x < 0, "negative", "positive", "missing")
x  <- c(-5:5, NA)
if_else(x < 0, "negative", "positive", "missing")
ifelse(x < 0, "negative", "positive", "missing")
ifelse(x < 0, "negative", "positive")
ifelse(x < 0, "negative", "positive")
ifelse(x < 0, "negative", "positive")
x <- factor(sample(letters[1:5], 10, replace = TRUE))
x
ifelse(x %in% c("a", "b", "c"), x, factor(NA))
if_else(x %in% c("a", "b", "c"), x, factor(NA))
x  <- c(-5:5, NA)
if_else(x < 0, "negative", "positive", "missing")
ifelse(x < 0, "negative", "positive")
x  <- c(-5:5, NA)
x
if_else(x < 0, "negative", "positive", "missing")
ifelse(x < 0, "negative", "positive")
path <- 'O:\\G_Drive\\code\\R_Script\\0_Projects\\data_cleaning\\Copy of Group A analysis merged data for popPK-pinkedited.xlsx'    # original dataset location
setwd(dirname(path))
df_iv_only$LLQQ  [!(df_iv_only$ID %in% c(99,98,100,101,104,112)),]   <- above_LLOQ
df_iv_only$LLQQ  [!(df_iv_only$ID %in% c(99,98,100,101,104,112)),]   <- 'above_LLOQ'
unique(df_iv_only$ID)
library("aaasibo", lib.loc="C:/Program Files/R/R/library")
outersect(  unique(df_iv_only$ID),c(99,98,100,101,104))
outersect(  unique(df_iv_only$ID),c(99,98,100,101,104,112))
df_iv_only
df_iv_only$LLQQ [ df_iv_only$ID %in% c(99,98,100,101,104)]  <- 'above_LLOQ'
outersect(  unique(df_iv_only$ID),c(99,98,100,101,104,112))
library("aaasibo", lib.loc="C:/Program Files/R/R/library")
detach("package:aaasibo", unload=TRUE)
library(aaasibo)
outersect()
outersect
outersect()
library("aaasibo", lib.loc="C:/Program Files/R/R/library")
outersect()
library("aaasibo", lib.loc="C:/Program Files/R/R/library")
con_para
con_para()
library(aaasibo)
con_para()
x <- "a=10 ,
b=3,
c=1.86E+04,
d=a-b,
e=3+d,
g=c/100 "
con_para(x)
aaa  <- con_para(x)
aaa
x <- "a=10 ,
b=3,
c=1.86E+04,
d=a-b,
e=3+d,
g=c/100 "
aaa  <- con_para(x)