---
title: "Dates and Times"
---


## Packages for this section
```{r dates-and-times-1}
library(tidyverse)
# library(lubridate)
```

`lubridate` is the package that handles dates and times, but is now part of the `tidyverse`, so no need to load separately.

## Dates
- Dates represented on computers as “days since an origin”,
typically Jan 1, 1970, with a negative date being before the origin: 

```{r dates-and-times-2, paged.print=T}
mydates <- c("1970-01-01", "2007-09-04", "1931-08-05")
(somedates <- tibble(text = mydates) %>%
  mutate(
    d = as.Date(text),
    numbers = as.numeric(d)
  ))
```

## Doing arithmetic with dates

- Dates are "actually" numbers, so can add and subtract (difference is 2007 date in `d` minus others): 

```{r dates-and-times-3, paged.print=T}
somedates %>% mutate(plus30 = d + 30, diffs = d[2] - d)
```

## Reading in dates from a file
- `read_csv` and the others can guess that you have dates, if you
format them as year-month-day, like column 1 of this `.csv`:

```
date,status,dunno
2011-08-03,hello,August 3 2011
2011-11-15,still here,November 15 2011
2012-02-01,goodbye,February 1 2012
```

- Then read them in:

```{r dates-and-times-4}
my_url <- "http://ritsokiguess.site/datafiles/mydates.csv"
ddd <- read_csv(my_url)
```

- read_csv guessed that the 1st column is dates, but not 3rd.

## The data as read in

```{r dates-and-times-5, paged.print=T}
ddd
```

## Dates in other formats 

- Preceding shows that dates should be stored as text in format
yyyy-mm-dd (ISO standard).
- To deal with dates in other formats, use package `lubridate` and
convert. For example, dates in US format with month first:
```{r dates-and-times-6, paged.print=T}
tibble(usdates = c("05/27/2012", "01/03/2016", "12/31/2015")) %>%
  mutate(iso = mdy(usdates))
```

## Trying to read these as UK dates 

```{r dates-and-times-7, paged.print=T}
tibble(usdates = c("05/27/2012", "01/03/2016", "12/31/2015")) %>%
  mutate(uk = dmy(usdates))
```

- For UK-format dates with month second, one of these dates is legit,
but the other two make no sense.

## Our data frame's last column:

- Back to this: 

```{r dates-and-times-8, paged.print=F}
ddd
```

- Month, day,  year in that order.

## so interpret as such

```{r dates-and-times-9, paged.print=T}
(ddd %>% mutate(date2 = mdy(dunno)) -> d4)
```


## Are they really the same? 

- Column `date2` was correctly converted from column `dunno`: 

```{r dates-and-times-10, paged.print=F}
d4 %>% mutate(equal = identical(date, date2))
```

- The two columns of dates are all the same.

## Making dates from pieces
Starting from this file:

```
year month day
1970 1 1
2007 9 4
1940 4 15
```

```{r dates-and-times-11}
my_url <- "http://ritsokiguess.site/datafiles/pieces.txt"
dates0 <- read_delim(my_url, " ")
```

## Making some dates 

```{r dates-and-times-12}
dates0
dates0 %>%
  unite(dates, day, month, year)%>%
  mutate(d = dmy(dates)) -> newdates
```


## The results

```{r dates-and-times-13, paged.print=T}
newdates
```

- `unite` glues things together with an underscore between them (if you
don’t specify anything else). Syntax: first thing is new column to be
created, other columns are what to make it out of.
- `unite` makes the original variable columns year, month, day
*disappear*.
- The column `dates` is text, while `d` is a real date.

## Extracting information from dates 

```{r dates-and-times-14, paged.print=T}
newdates %>%
  mutate(
    mon = month(d),
    day = day(d),
    weekday = wday(d, label = TRUE)
  )
```

## Dates and times
- Standard format for times is to put the time after the date, hours,
minutes, seconds:
```{r dates-and-times-15, paged.print=F}
(dd <- tibble(text = c(
  "1970-01-01 07:50:01", "2007-09-04 15:30:00",
  "1940-04-15 06:45:10", "2016-02-10 12:26:40"
)))
```

## Converting text to date-times: 

- Then get from this text using `ymd_hms`:
```{r dates-and-times-16, paged.print=F}
dd %>% mutate(dt = ymd_hms(text))
```


## Timezones

- Default timezone is “Universal Coordinated Time”. Change it via `tz=`
and the name of a timezone: 

```{r dates-and-times-17, paged.print=T}
dd %>% 
  mutate(dt = ymd_hms(text, tz = "America/Toronto")) -> dd
dd %>% mutate(zone = tz(dt))
```

## Extracting time parts
- As you would expect: 
```{r dates-and-times-18, paged.print=F}
dd %>%
  select(-text) %>%
  mutate(
    h = hour(dt),
    sec = second(dt),
    min = minute(dt),
    zone = tz(dt)
  )
```

## Same times, but different time zone: 

```{r dates-and-times-19, paged.print=T}
dd %>%
  select(dt) %>%
  mutate(oz = with_tz(dt, "Australia/Sydney"))
```

In more detail: 

```{r dates-and-times-20}
dd %>%
  mutate(oz = with_tz(dt, "Australia/Sydney")) %>%
  pull(oz)
```


## How long between date-times?
- We may need to calculate the time between two events. For example,
these are the dates and times that some patients were admitted to
and discharged from a hospital:

```
admit,discharge
1981-12-10 22:00:00,1982-01-03 14:00:00
2014-03-07 14:00:00,2014-03-08 09:30:00
2016-08-31 21:00:00,2016-09-02 17:00:00
```

## Do they get read in as date-times? 

- These ought to get read in and converted to date-times:

```{r dates-and-times-21}
my_url <- "http://ritsokiguess.site/datafiles/hospital.csv"
stays <- read_csv(my_url)
stays
```

-  and so it proves.

## Subtracting the date-times

- In the obvious way, this gets us an answer:
```{r dates-and-times-22}
stays %>% mutate(stay = discharge - admit)
```

- Number of hours; hard to interpret. 

## Days

- Fractional number of days would
be better: 

```{r dates-and-times-23, paged.print=T}
stays %>% 
  mutate(
    stay_days = as.period(admit %--% discharge) / days(1))
```

## Completed days

- Pull out with `day()` etc, as for a date-time:

```{r dates-and-times-24, error=TRUE}
stays %>% 
  mutate(
    stay = as.period(admit %--% discharge),
    stay_days = day(stay),
    stay_hours = hour(stay)
    ) %>%
  select(starts_with("stay"))
```


## Comments
- Date-times are stored internally as seconds-since-something, so that
subtracting two of them will give, internally, a number of seconds.
- Just subtracting the date-times is displayed as a time (in units that R
chooses for us).
- Convert to fractional times via a "period", then divide by `days(1)`, `months(1)` etc.
- These ideas useful for calculating time from a start point until an event
happens (in this case, a patient being discharged from hospital).