I want to create a matrix from my data. My data consists of two columns, date and my observations for each date. I want the matrix to have year as rows and days as columns, e.g. :
17 18 19 20 ... 31
1904 x11 x12 ...
1905
1906
.
.
.
2019
The days in this case is for December each year. I would like missing values to equal NA.
Here's a sample of my data:
> head(cdata)
# A tibble: 6 x 2
Datum Snödjup
<dttm> <dbl>
1 1904-12-01 00:00:00 0.02
2 1904-12-02 00:00:00 0.02
3 1904-12-03 00:00:00 0.01
4 1904-12-04 00:00:00 0.01
5 1904-12-12 00:00:00 0.02
6 1904-12-13 00:00:00 0.02
I figured that the first thing I need to do is to split the date into year, month and day (European formatting, YYYY-MM-DD) so I did that and got rid of the date column (the one that says Datum) and also got rid of the unrelevant days, namely the ones < 17.
cdata %>%
dplyr::mutate(year = lubridate::year(Datum),
month = lubridate::month(Datum),
day = lubridate::day(Datum))
select(cd, -c(Datum))
cu <- cd[which(cd$day > 16
& cd$day < 32
& cd$month == 12),]
and now it looks like this:
> cu
# A tibble: 1,284 x 4
Snödjup year month day
<dbl> <dbl> <dbl> <int>
1 0.01 1904 12 26
2 0.01 1904 12 27
3 0.01 1904 12 28
4 0.12 1904 12 29
5 0.12 1904 12 30
6 0.15 1904 12 31
7 0.07 1906 12 17
8 0.05 1906 12 18
9 0.05 1906 12 19
10 0.04 1906 12 20
# … with 1,274 more rows
Now I need to fit my data into a matrix with missing values as NA. Is there anyway to do this?