Reading Multiple CSV files and perform a logistic regression for all those files separately

base R answer

# Replace '...' with the path to the directory with your files.
files <- list.files(..., pattern="\.csv$", full.names=TRUE)
files <- setNames(files, basename(files))

results <- lapply(files,  function(x) {
  df <- read.csv(x, stringsAsFactors=FALSE)
  result <- glm(
    Event ~ T-ctrl + T_out + RH_out + T_stp_cool + T_stp_heat + Humi,
    data=df, family=binomial(link="logit")
  )
  return(result)
})

data.table will speed things up a bit if the files are big and/or you have a lot of files.

library("data.table")

# Replace '...' with the path to the directory with your files.
files <- list.files(..., pattern="\.csv$", full.names=TRUE)
files <- setNames(files, basename(files))

results <- lapply(files,  function(x) {
  DT <- fread(x, sep=",")
  result <- glm(
    Event ~ T-ctrl + T_out + RH_out + T_stp_cool + T_stp_heat + Humi,
    data=DT, family=binomial(link="logit")
  )
  return(result)
})