[R] extracting bootstrap statistics by group with loop

[Marna Wagley]

Hi R users,
I was trying to extract the bootstrap mean and its SE by group but I have
been doing  it by separating the group manually. The data set is big so
doing it manually is a kind of tedious task. I am wondering whether there
is a possibility to do it by creating a loop. I am weak in writing loop
functions. I am attaching an example data and how I performed the
analysis, see below.
Thanks for your help.
Sincerely,
MW
####
library(boot)
DaT<-structure(list(bothTimes = c(0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L),
total = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Area = c("A",
"A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "A", "A",
"A", "A", "A", "A", "B", "B", "B", "B", "B", "B"), Year = c(2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L)), class = "data.frame", row.names =
c(NA, -24L))

head(DaT)
R=100
bootprop <- function(data, index){
  d <- data[index, ]
  sum(d[["bothTimes"]], na.rm = TRUE)/sum(d[["total"]], na.rm = TRUE)#
}

###################
#2015
###################
#-----Year2015_pooled
Y2015_pooled<-subset(DaT, DaT$Year=="2015")
Y2015_pooled_boot <- boot(Y2015_pooled, bootprop, R)
boot_Y2015_pooled<-data.frame(Year="2015", Area= "Pooled", bootMean=
Y2015_pooled_boot$t0, SE=sd(Y2015_pooled_boot$t))
#-----Year2015_AreaA
Y2015_A<-subset(DaT, DaT$Year=="2015" & DaT$Area=="A")
Y2015_A_boot <- boot(Y2015_A, bootprop, R)
boot_Y2015_A<-data.frame(Year="2015", Area= "A", bootMean= Y2015_A_boot$t0,
SE=sd(Y2015_A_boot$t))
#----Year2015_AreaB
Y2015_B<-subset(DaT, DaT$Year=="2015" & DaT$Area=="B")
Y2015_B_boot <- boot(Y2015_B, bootprop, R)
boot_Y2015_B<-data.frame(Year="2015", Area= "B", bootMean= Y2015_B_boot$t0,
SE=sd(Y2015_B_boot$t))
###################
#2016
###################
#-----Year2016_pooled
Y2016_pooled<-subset(DaT, DaT$Year=="2016")
Y2016_pooled_boot <- boot(Y2016_pooled, bootprop, R)
boot_Y2016_pooled<-data.frame(Year="2016", Area= "Pooled", bootMean=
Y2016_pooled_boot$t0, SE=sd(Y2016_pooled_boot$t))

#-----Year2016_AreaA
Y2016_A<-subset(DaT, DaT$Year=="2016" & DaT$Area=="A")
Y2016_A_boot <- boot(Y2016_A, bootprop, R)

boot_Y2016_A<-data.frame(Year="2016", Area= "A", bootMean= Y2016_A_boot$t0,
SE=sd(Y2016_A_boot$t))
#----Year2016_AreaB
Y2016_B<-subset(DaT, DaT$Year=="2016" & DaT$Area=="B")
Y2016_B_boot <- boot(Y2016_B, bootprop, R)
boot_Y2016_B<-data.frame(Year="2016", Area= "B", bootMean= Y2016_B_boot$t0,
SE=sd(Y2016_B_boot$t))

## output data.matrix
BootMean_All<-rbind(boot_Y2015_pooled,boot_Y2015_A,boot_Y2015_B,boot_Y2016_pooled,boot_Y2016_A,boot_Y2016_B)
BootMean_All

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