I seem to have some trouble converting my data frame data into a time series. I have a typical data set consisting of date, export quantity, GDP, FDI etc.
# A tibble: 252 x 10
Date `Maize Exports (m/t)` `Rainfall (mm)` `Temperature ©` `Exchange rate (R/$)` `Maize price (R)` `FDI (Million R)` GDP (Million~1 Oil p~2 Infla~3
<dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2000-05-01 00:00:00 21000 30.8 14.4 0.144 678. 4337 9056 192. 5.1
2 2000-06-01 00:00:00 54000 14.9 14.0 0.147 583. -4229 9056 205. 5.1
3 2000-07-01 00:00:00 134000 11.1 12.6 0.144 518. -4229 8841 196. 5.9
4 2000-08-01 00:00:00 213000 6.1 15.3 0.143 526. -4229 8841 205. 6.8
5 2000-09-01 00:00:00 123000 38.5 17.8 0.138 576. 6315 8841 234. 6.8
6 2000-10-01 00:00:00 94000 61.9 20.1 0.132 636. 6315 4487 231. 7.1
7 2000-11-01 00:00:00 192000 93.9 19.9 0.129 685. 6315 4487 250. 7.1
8 2000-12-01 00:00:00 134000 85.6 22.3 0.132 747. -2143 4487 192. 7
9 2001-01-01 00:00:00 133000 92.4 23.4 0.0875 1066. -5651 7365 226. 5
10 2001-02-01 00:00:00 168000 51 22.0 0.0879 1042. -5651 7365 233. 5.9
I've installed the right packages (readxl), I've used the as.Date function so ensure my Date is recognized as such, and I've used the as.ts function to convert the dataset. However, after using the as.ts function, the date column is all muddled up into a random number and not a date anymore. What am I doing wrong? Please help!
Date Maize Exports (m/t) Rainfall (mm) Temperature © Exchange rate (R/$) Maize price (R) FDI (Million R) GDP (Million R) Oil prices (R/barrel)
[1,] 957139200 21000 30.8 14.36 0.1435235 677.88 4337 9056 192.35
[2,] 959817600 54000 14.9 13.96 0.1474926 583.48 -4229 9056 205.36
[3,] 962409600 134000 11.1 12.61 0.1437298 518.10 -4229 8841 196.38
[4,] 965088000 213000 6.1 15.27 0.1433075 525.59 -4229 8841 204.66
[5,] 967766400 123000 38.5 17.83 0.1382170 576.08 6315 8841 233.64
[6,] 970358400 94000 61.9 20.10 0.1322751 635.79 6315 4487 231.27
CodePudding user response：
In short nothing is wrong - and while this response should really be a comment, I wanted to use a full answer to have a bit more space to explain.
Behind each date is a numeric value tethered to an origin, so this is just R's way of handling it. And since you imported from excel originally, those origins may not line up if you tried to cross check it (see below).
You didn't make your question reproducible, but I put some similar data together to demonstrate what's going on:
Data
df <- data.frame(date = as.Date(c("2000-05-01",
"2000-06-01",
"2000-07-01",
"2000-08-01",
"2000-09-01",
"2000-10-01",
"2000-11-01")),
maize = c(21, 54, 132, 213, 123, 94, 192) * 1000,
rainfall = c(30, 14, 11, 6, 38, 61, 93))
tb <- tidyr::as_tibble(df)
Turning this into a time series object using as.ts()
tb_ts <- as.ts(tb)
# Time Series:
# Start = 1
# End = 7
# Frequency = 1
# date maize rainfall
# 1 11078 21000 30
# 2 11109 54000 14
# 3 11139 132000 11
# 4 11170 213000 6
# 5 11201 123000 38
# 6 11231 94000 61
# 7 11262 192000 93
Since I created these data in R, the "origin" is January 1, 1970, and we can see this in numerical dates from the time series object and convert them back into date formats:
as.Date(tb_ts[1:7], origin = '1970-01-01')
# [1] "2000-05-01" "2000-06-01" "2000-07-01" "2000-08-01"
# [5] "2000-09-01" "2000-10-01" "2000-11-01"
Note that if you import data from Excel, Excel's origin is December 30th, 1899 (i.e., as.Date(xx, origin = "1899-12-30")), so if you tried that you get the wrong dates:
as.Date(tb_ts[1:7], origin = "1899-12-30")
# [1] "1930-04-30" "1930-05-31" "1930-06-30" "1930-07-31"
# [5] "1930-08-31" "1930-09-30" "1930-10-31
CodePudding user response：
The function worked as it's supposed to. Keeping the date format you're familiar with isn't practical for execution, so it converts the dates to a different value, usually something like the number of days (or minutes or seconds) since a certain year, usually Jan. 1 1970. For example, here is a little set to make the point:
# a test vector of dates
> del1 <- seq(as.Date("2012-04-01"), length.out=4, by=30)
# looks like
> del1
[1] "2012-04-01" "2012-05-01" "2012-05-31" "2012-06-30"
# use the as.ts
> as.ts(del1)
Time Series:
Start = 1
End = 4
Frequency = 1
[1] 15431 15461 15491 15521
So you can see the dates, which are 30 days apart, are converted to a series of values that are 30 integers apart.