I typically create my dataset using the following code, where I sample individual estimates from the pkpd dataset (data_pkpd):
NSIM=100
idata_SIM0<- data.frame(expand.idata(ID=c(1:nsim)) %>%
mutate(GRP=1) %>%
# mutate(WT=rep(each=200,seq(41,120,1))) %>%
mutate_random(CL ~ sample(data_pkpd$ICL, size=nsim, replace = TRUE))%>%
mutate_random(Q ~ sample(data_pkpd$IQ, size=nsim, replace = TRUE))%>%
mutate_random(V2 ~ sample(data_pkpd$IV2, size=nsim, replace = TRUE))%>%
mutate_random(V3 ~ sample(data_pkpd$IV3, size=nsim, replace = TRUE))%>%
mutate_random(V7 ~ sample(data_pkpd$IV7, size=nsim, replace = TRUE))%>%
mutate_random(Q2 ~ sample(data_pkpd$IQ2, size=nsim, replace = TRUE))%>%
mutate_random(KA ~ sample(data_pkpd$IKA, size=nsim, replace = TRUE))%>%
mutate_random(F1 ~ sample(data_pkpd$IF1, size=nsim, replace = TRUE))%>%
mutate_random(BL_PD ~ sample(data_pkpd$BL_PD, size=nsim, replace = TRUE))%>%
mutate(time=0))
However, mutate_random is a part of dmutate package and I am unable to use it for the current analysis. Is there any alternative approach to create individual dataset with sampling using tidyr package?
Thank you!
Are you just wanting to create a random dataframe? If so, you can try
data.frame(replicate(5,sample(0:100,10,rep=TRUE)))
The first number (5) is the number of columns, the range (0:100) is the sample range aka the possible numbers, the last number (10) is the number of rows.
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