[r] Scatter plot with error bars

How can I generate the following plot in R? Points, shown in the plot are the averages, and their ranges correspond to minimal and maximal values. I have data in two files (below is an example).

x   y
1   0.8773
1   0.8722
1   0.8816
1   0.8834
1   0.8759
1   0.8890
1   0.8727
2   0.9047
2   0.9062
2   0.8998
2   0.9044
2   0.8960
..  ...

I put together start to finish code of a hypothetical experiment with ten measurement replicated three times. Just for fun with the help of other stackoverflowers. Thank you... Obviously loops are an option as applycan be used but I like to see what happens.

#Create fake data
x <-rep(1:10, each =3)
y <- rnorm(30, mean=4,sd=1)

#Loop to get standard deviation from data
sd.y = NULL
for(i in 1:10){
  sd.y[i] <- sd(y[(1+(i-1)*3):(3+(i-1)*3)])
}
sd.y<-rep(sd.y,each = 3)

#Loop to get mean from data
mean.y = NULL
for(i in 1:10){
  mean.y[i] <- mean(y[(1+(i-1)*3):(3+(i-1)*3)])
}
mean.y<-rep(mean.y,each = 3)

#Put together the data to view it so far
data <- cbind(x, y, mean.y, sd.y)

#Make an empty matrix to fill with shrunk data
data.1 = matrix(data = NA, nrow=10, ncol = 4)
colnames(data.1) <- c("X","Y","MEAN","SD")

#Loop to put data into shrunk format
for(i in 1:10){
  data.1[i,] <- data[(1+(i-1)*3),]
}

#Create atomic vectors for arrows
x <- data.1[,1]
mean.exp <- data.1[,3]
sd.exp <- data.1[,4]

#Plot the data
plot(x, mean.exp, ylim = range(c(mean.exp-sd.exp,mean.exp+sd.exp)))
abline(h = 4)
arrows(x, mean.exp-sd.exp, x, mean.exp+sd.exp, length=0.05, angle=90, code=3)

#some example data
set.seed(42)
df <- data.frame(x = rep(1:10,each=5), y = rnorm(50))

#calculate mean, min and max for each x-value
library(plyr)
df2 <- ddply(df,.(x),function(df) c(mean=mean(df$y),min=min(df$y),max=max(df$y)))

#plot error bars
library(Hmisc)
with(df2,errbar(x,mean,max,min))
grid(nx=NA,ny=NULL)

Another (easier - at least for me) way to do this is below.

install.packages("ggplot2movies")

data(movies, package="ggplot2movies")

rating_by_len = tapply(movies$length,
                       movies$rating,
                       mean)

plot(names(rating_by_len), rating_by_len, ylim=c(0, 200)
     ,xlab = "Rating", ylab = "Length", main="Average Rating by Movie Length", pch=21)

sds = tapply(movies$length, movies$rating, sd)
upper = rating_by_len + sds
lower = rating_by_len - sds
segments(x0=as.numeric(names(rating_by_len)), 
         y0=lower, 
         y1=upper)

Hope that helps.

Using ggplot and a little dplyr for data manipulation:

set.seed(42)
df <- data.frame(x = rep(1:10,each=5), y = rnorm(50))

library(ggplot2)
library(dplyr)

df.summary <- df %>% group_by(x) %>%
    summarize(ymin = min(y),
              ymax = max(y),
              ymean = mean(y))

ggplot(df.summary, aes(x = x, y = ymean)) +
    geom_point(size = 2) +
    geom_errorbar(aes(ymin = ymin, ymax = ymax))

If there's an additional grouping column (OP's example plot has two errorbars per x value, saying the data is sourced from two files), then you should get all the data in one data frame at the start, add the grouping variable to the dplyr::group_by call (e.g., group_by(x, file) if file is the name of the column) and add it as a "group" aesthetic in the ggplot, e.g., aes(x = x, y = ymean, group = file).

To summarize Laryx Decidua's answer:

define and use a function like the following

plot.with.errorbars <- function(x, y, err, ylim=NULL, ...) {
  if (is.null(ylim))
    ylim <- c(min(y-err), max(y+err))
  plot(x, y, ylim=ylim, pch=19, ...)
  arrows(x, y-err, x, y+err, length=0.05, angle=90, code=3)
}

where one can override the automatic ylim, and also pass extra parameters such as main, xlab, ylab.