Convert data frame with date column to timeseries

Your DATE column may represent a date, but it is actually either a character, factor, integer, or a numeric vector.

First, you need to convert the DATE column to a Date object. Then you can create an xts object from the CLOSE and DATE columns of your PRICE data.frame. Finally, you can use the xts object to calculate returns and the Calmar ratio.

PRICE <- structure(list(
  DATE = c(20070103L, 20070104L, 20070105L, 20070108L, 20070109L,
           20070110L, 20070111L, 20070112L, 20070115L),
  CLOSE = c(54.7, 54.77, 55.12, 54.87, 54.86, 54.27, 54.77, 55.36, 55.76)),
  .Names = c("DATE", "CLOSE"), class = "data.frame",
  row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9"))

library(PerformanceAnalytics)  # loads/attaches xts
# Convert DATE to Date class
PRICE$DATE <- as.Date(as.character(PRICE$DATE),format="%Y%m%d")
# create xts object
x <- xts(PRICE$CLOSE,PRICE$DATE)
CalmarRatio(Return.calculate(x))
#                  [,1]
# Calmar Ratio 52.82026

Most people find working with the time series class to be a big pain. You should consider using the zoo class from package zoo. It will not complain about missing times , only about duplicates. The PerformanceAnalytics functions are almost certainly going to be expecting 'zoo' or its descendant class 'xts'.

pricez <- read.zoo(text="   DATE  CLOSE
 1    20070103 54.700
 2    20070104 54.770
 3    20070105 55.120
 4    20070108 54.870
 5    20070109 54.860
 6    20070110 54.270
 7    20070111 54.770
 8    20070112 55.360
 9    20070115 55.760
 ")
 index(pricez) <- as.Date(as.character(index(pricez)), format="%Y%m%d")
 pricez
2007-01-03 2007-01-04 2007-01-05 2007-01-08 2007-01-09 2007-01-10 2007-01-11 2007-01-12 2007-01-15 
     54.70      54.77      55.12      54.87      54.86      54.27      54.77      55.36      55.76