r - Looping linear models for multiple files in directory -

i have folder 26 .csv files in it. each file has 2 columns headers do2 , time_min , have @ least 300+ rows.

i want make scatterplot x=time_min , y=do2, make linear model of each, take coefficient , r^2 each of 26 models , put in table.

this i've written far code goes. know can copy , paste know there has smarter way.

setwd("~/documents/masters/data/r/35789/35789_ucrit")  #the file want coefficients , r^2 go ue_slope <- read.csv("~/documents/masters/data/r/35789/35789_ue_slope.csv")  temp = list.files(pattern="*.csv") (i in 1:length(temp))(assign(temp[i], read.csv(temp[i])))  #seal# names files directory, 1-26 plot(do2 ~ time_min, data = seal1) model1 <- lm(do2 ~ time_min, data = seal1.csv) ue_slope <- rbind(ue_slope, data.frame("slope"=coef(model1)[[2]], "r.2"=summary(model1)$r.squared)) 

we first define function, reads "csv" file, fits linear model , obtains summary statistics.

f <- function (file) {   ## read file   dat <- read.csv(file)   ## fit model   fit <- lm(do2 ~ time_min, data = dat)   slope <- coef(fit)[2]   ## make plot??   plot(do2 ~ time_min, data = dat, main = file)  ## use file names title   abline(fit)  ## overlay fitted regression line   ## note, not using `summary.lm` expensive   ## r-squared can computed   rss <- crossprod(fit$residuals)[1]   tss <- crossprod(dat$do2 - mean(dat$do2))[1]   r2 <- 1 - rss / tss   ## return vector   c("slope" = slope, "r.2" = r2)   } 

now, loop through files, applying f:

temp <- list.files(pattern = "*.csv") pdf("whatever.pdf") result <- t(sapply(temp, f)) dev.off() 

sapply cbind ends flat matrix; use t() make tall matrix. pdf() , dev,off() opens / closes pdf file , plots made on file. looks necessary have 26 figures, not easy display them in panel fashion on screen. using pdf file, can have 1 plot per page. pdf file in current working directory.