Data handling on multiple Heart rate files - heartbeat

Question

I have been collecting the Heart rates of 12 calves who each received an anesthetic through four different routes of administration. I now have 48 txt files of this format:
Time HRbpm
0:00:01.7 97
0:00:02.3 121
0:00:02.8 15
... ...
HR was recorded for around 2hours. The Time column was dependent of the monitor, resulting in inconsistent time intervals between two measures.
The txt files are named as follows: 6133_IM_27.00.txt
With 6133 being the ID, IM the route and 27.00 the time (min:min.s:s) at which the treatment was injected.
My first goal is to have all the HR data so I can do an outlier analysis.
Then, I would like to include all this data in a single data frame that would look like this:
data.frame(ID=c(6133,6133,6133,6133,"...",6134,6134,"..."),
Route = c("IM","IM","IM","IM","...","SC","SC","..."),
time=c(0, 10, 20, 30,"...",0,10,"..."),
HR=c(160, 150, 145, 130,"...",162,158,"..."))
Time column going from 0 to 120 in 10min increments.
Each HR of this df would represent the mean of the HR values for the preceding minute for a given time (e.g. for time = 30, HR would represent the mean between 29 and 30 minutes for a given ID/Route combination).
I'm fairly new to R, so I've been having trouble just knowing by what angle starting on that problem. Any help would be welcome.
Thanks,
Thomas

Answer 1

For anyone who stumbles on this post, here's what I've done, seems to be working.
library(plyr)
library(reshape)
library(ggplot2)
setwd("/directory")
filelist = list.files(pattern = ".*.txt")
datalist = lapply(filelist, read.delim)
for (i in 1:length(datalist))
{datalist[[i]][3] = filelist[i]}
df = do.call("rbind", datalist)
attach(df)
out_lowHR = quantile(HRbpm,0.25)-1.5*IQR(HRbpm)
out_highHR = quantile(HRbpm,0.75)+1.5*IQR(HRbpm) #outliers thresholds: 60 and 200
dfc = subset(df,HRbpm>=60 & HRbpm<=200)
(length(df$HRbpm)-length(dfc$HRbpm))/length(df$HRbpm)*100 #8.6% of values excluded
df = dfc
df$ID = substr(df$V3,4,7)
df$ROA = substr(df$V3,9,11)
df$ti = substr(df$V3,13,17)
df$Time = as.POSIXct(as.character(df$Time), format="%H:%M:%S")
df$ti = as.POSIXct(as.character(df$ti), format="%M.%S")
df$t = as.numeric(df$Time-df$ti)
m=60
meanHR = ddply(df, c("ROA","ID"), summarise,
mean0 = mean(HRbpm[t>-60*m & t <=0]),
mean10 = mean(HRbpm[t>9*m & t <=10*m]),
mean20 = mean(HRbpm[t>19*m & t <=20*m]),
mean30 = mean(HRbpm[t>29*m & t <=30*m]),
mean45 = mean(HRbpm[t>44*m & t <=45*m]),
mean60 = mean(HRbpm[t>59*m & t <=60*m]),
mean90 = mean(HRbpm[t>89*m & t <=90*m]),
mean120 = mean(HRbpm[t>119*m & t <=120*m]))
meanHR = melt(meanHR)
meanHR$time = as.numeric(gsub("mean", "", meanHR$variable))
ggplot(meanHR, aes(x = time, y = value, col = ROA))+
geom_smooth()+
theme_classic()