#########Import your file#########
> culti <- read.csv("cultivation2.csv", header = TRUE, sep = ",")
> culti$date <- as.Date(culti$hdate)
> cult2 <- data.frame(culti$date, culti$site, culti$code)
> colnames(cult2) <- c("date", "site", "crop")
> cutiec1 <- cult2 %>%
filter(site == 1)
###########Do not forget to check your structure of data#######
> str(cutiec1) ("It should be character otherwise it would not work")
'data.frame': 11 obs. of 3 variables:
$ date: Date, format: "2009-12-12" ...
$ site: int 1 1 1 1 1 1 1 1 1 1 ...
$ crop: chr "CC" "SM" "WW" "WR" ...
####Change date to character format all rows
> cutiec1$date <- as.character(cutiec1$date)
> cutiec1$site <- as.character(cutiec1$site)
###option 1#####
> cutiec1[nrow(cutiec1)+ 1, ] <- c("2018-09-29")
but this would create problems like this
date site crop
1 2009-08-02 1 <NA>
2 2009-12-12 1 CC
3 2010-10-14 1 SM
4 2011-07-28 1 WW
5 2012-07-20 1 WR
6 2013-08-04 1 WW
7 2013-12-11 1 CC
8 2014-10-09 1 SM
9 2015-07-22 1 WW
10 2016-11-01 1 GM
11 2017-07-30 1 WW
12 2018-07-09 1 WR
13 2018-09-29 2018-09-29 2018-09-29
Hence follow this ---
############Option 2##########
> cutiec1 <- cutiec1 %>% add_row(date="2009-08-02", site="1", .before = 1)
> cutiec1 <- cutiec1 %>% add_row(date="2018-09-29", site="1", .after = 12)
######then character date to POSIXct date
> cutiec1$date <- as.POSIXct(cutiec1$date, tz = "UTC")
#########Then convert POSIXct date to date format
> cutiec1$date <- as.Date(cutiec1$date)
Then do "pad". It will creat date from 2009-08-02 to 2018-09-29 at interval of day
> cutiec12 <- pad(cutiec1, interval = "day",)
It shows 3346 variables for all 9 years on daily basis.
#############################Done##############################################
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