Import CSV and merge time series as XTS without unambig date/diff length/bad entry

I have the following data to import from CSV to an xts, as the date-price are inconsistent in the data seek to merge. When using merge/merge.xts there are a variety of errors below. What is best methodology?

Date 2y Date 10y Date BankRate
02/08/2016 0.21 02/08/2016 0.815 02/08/2016 0.5
01/08/2016 0.164 01/08/2016 0.73 01/08/2016 0.5
...
09/06/2000 6.145 12/06/2000 5.108 05/01/2000 5.5
08/06/2000 6.169 09/06/2000 5.118 04/01/2000 5.5
07/06/2000 6.122 08/06/2000 5.162 03/01/2000 5.5

1. Using as.xts(read.zoo(...)) gives error index has 2621 bad entries at data rows: 3 4 8...
2. Using
   grid <- read.csv("gridukyield.csv", header=T)
   grid$Date <- as.Date(grid$Date, "%d/%m/%Y")
   UK2y <- grid[,c("Date","X2y")]
   UK2y <- as.zoo(UK2y)
   gives error charToDate(x) strg not in a unambig format
3. Using
   dates <- xts(Date = seq(as.Date("2000-01-01"), as.Date("2016-08-01"), by='1 day'))
   dates <- dates[isWeekday(dates)]
   group <- merge(dates, UK2y, by="Date", all = T )
   gives error improper length of one or more arguments to merge.xts

What is best methodology?