Manejo de fechas en R

Vásquez Guerra Carlos Fernando

12/8/2020

El manejo de fechas es tan importante como aprender el manejo de factores u otros tipos de datos de uso común en un análisis de datos, por lo que aquí se deja un pequeño resumen con ejemplos del manejo de estas con las funciones básicas de R además del uso de la librería chron, lubridate,zoo y xts.

Creación de fechas

class(as.Date("2020/01/23"))

[1] "Date"

Date

Se tienen diferentes formas de crear fechas:

as.Date() : Manejar fechas pero sin tiempos.
chron() : Manejar fechas pero con tiempos.
as.POSIXct() : Manejar fechas pero con tiempos y zona horaria.

as.Date('1998-09-15')

[1] "1998-09-15"

as.Date('1998/9/15')

[1] "1998-09-15"

Formato por defecto: AAAA, M(M), D(D)

Si se tiene otro formato de entrada, se puede utilizar las siguientes combinaciones para indicar dicho formato

Símbolo	Descripción
%d	Día en número
%m	Mes en número abreviado
%b	Mes abreviado
%B	Mes en nombre completo
%y	Año en 2 dígitos
%Y	Año en 4 dígitos

Aquí algunos ejemplos:

as.Date('March/15: 1992', format = '%B/%d: %Y')

[1] "1992-03-15"

as.Date("September 15, 1998", format = "%B %d, %Y")

[1] "1998-09-15"

as.Date("15JUN1998", format = "%d%b%Y")

[1] "1998-06-15"

La fecha establecida, en general, de origen para el manejo de fechas es el 1 de Enero de 1970 (Unix Epoch), y con este día podemos obtener la representación numérica que entiende el ordenador (Epoch Time). Con días antes obtendremos números negativos y posteriores positivos

as.numeric(as.Date("January 2, 1970", format = "%B %d, %Y"))

[1] 1

as.numeric(as.Date("December 31, 1969", format = "%B %d, %Y"))

[1] -1

Chron

Ahora con chron, en este caso, las fechas y los tiempos se deben dar de manera separada

library(chron)
#Primero las fechas
days <- dates(c("02/27/92", "02/27/92", "01/14/92",
               "02/28/92", "02/01/92"))
#Luego los horarios
times <- times(c("23:03:20", "22:29:56", "01:03:30",
               "18:21:03", "16:56:26"))
#Ahora sí:
chron(dates = days, times = times)

[1] (02/27/92 23:03:20) (02/27/92 22:29:56) (01/14/92 01:03:30)
[4] (02/28/92 18:21:03) (02/01/92 16:56:26)

E igual se puede establecer el formato de entrada de acuerdo a la siguiente nomenclatura:

Símbolo	Descripción
m	Mes en número
d	día
y	Año con 4 dígitos
mon, month	Mes abreviado o completo
h	hora
m	minutos
s	segundos

days <- c("March-27-1992", "September-27-1992", "August-14-1992",
               "Febrary-28-1992", "January-01-1992")
times <- c("23-03-20", "22-29-56", "01-03-30",
               "18-21-03", "16-56-26")
chron(days, times, format = c("month-d-y", "h-m-s"))

[1] (March-27-1992 23-03-20)     (September-27-1992 22-29-56)
[3] (August-14-1992 01-03-30)    (February-28-1992 18-21-03) 
[5] (January-01-1992 16-56-26)

chron, internamente, almacena sus valores como una fracción del número de días desde el día Unix Epoch.

POSIX

POSIX es un formato, principalmente para sistemas UNIX, para representar fechas junto con tiempos (tal como un objeto chron) pero que permite agregar zonas horarias. La clase POSIX almacena el tiempo más cercano en segundos y no en fracción del día, por lo que se logra tener una mejor precisión sobre el tiempo.

Hay dos clases en R para este tipo de representaciones, las cuales difieren en su forma de almacenamiento interno. En general se utiliza la primera clase, a menos que se requiera lo contrario.

POSIXct: Se almacenan los valores mediante el número de segundos desde el 1 Enero de 1970.
POSIXlt: Se almacenan los valores en una lista con diferentes elementos correspondientes a los segundos, minutos, horas, días, meses, año, entre otros.

Se tienen distintas maneras aceptables de formato:

cat(paste("y/m/d: ", as.POSIXct("2020/8/16"),
          "\ny-m-d: ", as.POSIXct("2020-8-16"),
          "\ndate hour:minutes : ", as.POSIXct("2020/8/16 09:10"),
          "\ndate hour:minutes:seconds : ", as.POSIXct("2020-8-16 09:10:54")))

y/m/d:  2020-08-16 
y-m-d:  2020-08-16 
date hour:minutes :  2020-08-16 09:10:00 
date hour:minutes:seconds :  2020-08-16 09:10:54

Una ventaja que se consigue con este tipo de datos, es que si tiene el valor numérico de las fechas, se puede modificar la clase para obtener los valores en formato fecha directamente.

# Se pudo utiliar la función class
fechas_num <- c(1608055200, 1608057000, 1608058800, 1608060600, 1608062400)
(fechas_num <- structure(fechas_num,class=c('POSIXt','POSIXct')))

[1] "2020-12-15 12:00:00 CST" "2020-12-15 12:30:00 CST"
[3] "2020-12-15 13:00:00 CST" "2020-12-15 13:30:00 CST"
[5] "2020-12-15 14:00:00 CST"

Para ingresar datos con otro formato se utilizan las funciones strptime y strftime, donde la diferencia radica en que la segunda permite modificar la zona horaria. Los posibles quedan determinados mediante las combinaciones de la siguiente tabla; aunque hay que tener en cuenta que no se tomará literalmente los caracteres no sean especiales para el formato. (Por cuestiones de formato del documento se derajá en ingles el significado de cada símbolo).

Símbolo	Descripción	Símbolo	Descripción
%a	Abbreviated weekday	%A	Full weekday
%b	Abbreviated month	%B	Full month
%c	Locale-specific date and time	%d	Decimal date
%H	Decimal hours (24 hour)	%I	Decimal hours (12 hour)
%j	Decimal day of the year	%m	Decimal month
%M	Decimal minute	%p	Locale-specific AM/PM
%S	Decimal second	%U	Decimal week of the year (starting on Sunday)
%w	Decimal Weekday (0=Sunday)	%W	Decimal week of the year (starting on Monday)
%x	Locale-specific Date	%X	Locale-specific Time
%y	2-digit year	%Y	4-digit year
%z	Offset from GMT	%Z	Time zone (character)

strptime('2005/Sep/20:07:04:41',format='%Y/%b/%d:%S:%H:%M')

[1] "2005-09-20 04:41:07 CDT"

Manipulación de fechas

Extracción de componentes

Podemos extraer cosas importantes

Día de la semana

fechitas <- c(birthday = as.Date("September 15, 1998", format = "%B %d, %Y"),
              begin = as.Date("January 2, 1970", format = "%B %d, %Y"),
              other = as.Date("15JUN1998", format = "%d%b%Y"),
              other2 = as.Date('1998-09-15'),
              today = as.Date("2020/12/8")) 

weekdays(fechitas)

 birthday     begin     other    other2     today 
"Tuesday"  "Friday"  "Monday" "Tuesday" "Tuesday"

Día del mes

months(fechitas)

   birthday       begin       other      other2       today 
"September"   "January"      "June" "September"  "December"

Cuatrimestre del año

quarters(fechitas)

[1] "Q3" "Q1" "Q2" "Q3" "Q4"

Operaciones

Básicas

mean: Fecha intermedia

mean(fechitas)

[1] "1997-05-12"

mean(c(as.Date("2020/01/01"), as.Date("2020/12/31")))

[1] "2020-07-01"

range: Rango de las fechas (Fecha más antigua, Fecha reciente)

range(fechitas)

[1] "1970-01-02" "2020-12-08"

sum: Suma de fechas

as.Date("2020/12/8") + 1

[1] "2020-12-09"

Resta

as.Date("2020/12/8") - 8

[1] "2020-11-30"

Vamos a hacer cortes entre los años que abarcan el rango de nuestras observaciones

f <- cut(fechitas, "year")
levels(f)

 [1] "1970-01-01" "1971-01-01" "1972-01-01" "1973-01-01" "1974-01-01"
 [6] "1975-01-01" "1976-01-01" "1977-01-01" "1978-01-01" "1979-01-01"
[11] "1980-01-01" "1981-01-01" "1982-01-01" "1983-01-01" "1984-01-01"
[16] "1985-01-01" "1986-01-01" "1987-01-01" "1988-01-01" "1989-01-01"
[21] "1990-01-01" "1991-01-01" "1992-01-01" "1993-01-01" "1994-01-01"
[26] "1995-01-01" "1996-01-01" "1997-01-01" "1998-01-01" "1999-01-01"
[31] "2000-01-01" "2001-01-01" "2002-01-01" "2003-01-01" "2004-01-01"
[36] "2005-01-01" "2006-01-01" "2007-01-01" "2008-01-01" "2009-01-01"
[41] "2010-01-01" "2011-01-01" "2012-01-01" "2013-01-01" "2014-01-01"
[46] "2015-01-01" "2016-01-01" "2017-01-01" "2018-01-01" "2019-01-01"
[51] "2020-01-01"

[1] 1998-01-01 1970-01-01 1998-01-01 1998-01-01 2020-01-01
51 Levels: 1970-01-01 1971-01-01 1972-01-01 1973-01-01 ... 2020-01-01

Filtros y secuencias

Se pueden hacer filtros con fechas

fechitas[fechitas < as.Date("2020-12-08")]

    birthday        begin        other       other2 
"1998-09-15" "1970-01-02" "1998-06-15" "1998-09-15"

Y secuencia de fechas:

f <- as.Date("2020-12-01")
e <- as.Date("2020-12-31")
seq(from=f, to=e, by=1)

 [1] "2020-12-01" "2020-12-02" "2020-12-03" "2020-12-04" "2020-12-05"
 [6] "2020-12-06" "2020-12-07" "2020-12-08" "2020-12-09" "2020-12-10"
[11] "2020-12-11" "2020-12-12" "2020-12-13" "2020-12-14" "2020-12-15"
[16] "2020-12-16" "2020-12-17" "2020-12-18" "2020-12-19" "2020-12-20"
[21] "2020-12-21" "2020-12-22" "2020-12-23" "2020-12-24" "2020-12-25"
[26] "2020-12-26" "2020-12-27" "2020-12-28" "2020-12-29" "2020-12-30"
[31] "2020-12-31"

Otra forma de hacer lo anterior es de la siguiente manera

# También se puede por años y meses.
seq(from=f, by="day", length.out=31)

 [1] "2020-12-01" "2020-12-02" "2020-12-03" "2020-12-04" "2020-12-05"
 [6] "2020-12-06" "2020-12-07" "2020-12-08" "2020-12-09" "2020-12-10"
[11] "2020-12-11" "2020-12-12" "2020-12-13" "2020-12-14" "2020-12-15"
[16] "2020-12-16" "2020-12-17" "2020-12-18" "2020-12-19" "2020-12-20"
[21] "2020-12-21" "2020-12-22" "2020-12-23" "2020-12-24" "2020-12-25"
[26] "2020-12-26" "2020-12-27" "2020-12-28" "2020-12-29" "2020-12-30"
[31] "2020-12-31"

Se puede adecuar el parámetro by para obtener secuencias de días ("day"), semanas ("week"), meses ("month"), trimestres ("quarter") o años ("year"). Sí se agrega el sufijo s a cualquiera de los anteriores se puede obtener una secuencia más grande a una unidad correspondiente

seq(from=f, by="2 months", length.out=31)

 [1] "2020-12-01" "2021-02-01" "2021-04-01" "2021-06-01" "2021-08-01"
 [6] "2021-10-01" "2021-12-01" "2022-02-01" "2022-04-01" "2022-06-01"
[11] "2022-08-01" "2022-10-01" "2022-12-01" "2023-02-01" "2023-04-01"
[16] "2023-06-01" "2023-08-01" "2023-10-01" "2023-12-01" "2024-02-01"
[21] "2024-04-01" "2024-06-01" "2024-08-01" "2024-10-01" "2024-12-01"
[26] "2025-02-01" "2025-04-01" "2025-06-01" "2025-08-01" "2025-10-01"
[31] "2025-12-01"

Hay que recordar que se puede hacer secuencias con objetos POSIXct

seq(as.POSIXct("2010-01-01 00:00"), as.POSIXct("2012-01-01 00:00"), by = "30 min")[1:10]

 [1] "2010-01-01 00:00:00 CST" "2010-01-01 00:30:00 CST"
 [3] "2010-01-01 01:00:00 CST" "2010-01-01 01:30:00 CST"
 [5] "2010-01-01 02:00:00 CST" "2010-01-01 02:30:00 CST"
 [7] "2010-01-01 03:00:00 CST" "2010-01-01 03:30:00 CST"
 [9] "2010-01-01 04:00:00 CST" "2010-01-01 04:30:00 CST"

Difftime

Véase que es lo que pasa en la siguiente operación

as.Date("2020/12/8") - as.Date("2020/12/7")

Time difference of 1 days

Las diferencias entre fechas son un nuevo tipo de objeto

class(as.Date("2020/12/8") - as.Date("2020/12/7"))

[1] "difftime"

Y podemos decir como es medida esa diferencia:

f1 <- as.Date("2020/12/8")
f2 <- as.Date("2020/12/7")
difftime(f1,f2, units = "secs")

Time difference of 86400 secs

cat(paste("Minutos: ",difftime(f1,f2, units = "mins"),
"\nHoras: ",difftime(f1,f2, units = "hours"),
"\nDías: ",difftime(f1,f2, units = "days"),
"\nSemanas",difftime(f1,f2, units = "weeks")))

Minutos:  1440 
Horas:  24 
Días:  1 
Semanas 0.142857142857143

lubridate

Nunca será innecesaria una implementación que agregue funciones interesantes que las funciones del paquete base no abarquen y en este caso para el manera de fechas tenemos el pequete lubridate. Para más información se recomienda leer el capitulo pertinente del libro R for Data Science.

Tenemos funciones como today() y now() que nos devuelven la fecha actual.

library(lubridate)
cat(paste("Fecha actual: ",today(),
          "\nFecha actual con horario: ",now()))

Fecha actual:  2021-01-10 
Fecha actual con horario:  2021-01-10 23:55:15

En este caso, tenemos maneras más flexibles de crear fechas indicando el orden en que deseamos tener la fecha con y, m o d

cat(paste("ymd: ",ymd("2020-12-01"),
          "\nmdy: ", mdy("December 1st, 2020"),
          "\ndmy: ", dmy("01-Dec-2020")))

ymd:  2020-12-01 
mdy:  2020-12-01 
dmy:  2020-12-01

Además de poder hacerlas con un objeto numérico:

ymd(20201201)

[1] "2020-12-01"

Para crear las fechas con las horas, se utilizan funciones muy similares a las anteriores sólo que ahora se agrega un _ y el formato de fecha con h, m y s

cat(paste("Con hora, minutos y segundos: ",ydm_hms("2020-12-01 15:50:56"),
          "\nCon hora y minutos: ", ydm_hm("2020-12-01 15:50"),
          "\nCon hora: ", ydm_h("2020-12-01 15")))

Con hora, minutos y segundos:  2020-01-12 15:50:56 
Con hora y minutos:  2020-01-12 15:50:00 
Con hora:  2020-01-12 15:00:00

Donde la zona horaria puede ser fácil agregada

ydm_hms("2020-12-01 15:50:56", tz = "UTC")

[1] "2020-01-12 15:50:56 UTC"

lubridate añade lapsos de tiempos más allá de los que se pueden obtener con difftime.

Extracciones

Vamos a tomar el ejemplo dado por ymd(20201201) para obtener algunas componentes con distintas funciones que nos proporciona lubridate.

fecha <- ymd(20201201)
cat(paste("Fecha: ", fecha,
          "\nAño: ", year(fecha),
          "\nMes: ", month(fecha),
          "\nDía: ", mday(fecha),
          "\nDía del año: ", yday(fecha),
          "\nDía de la semana: ", wday(fecha)))

Fecha:  2020-12-01 
Año:  2020 
Mes:  12 
Día:  1 
Día del año:  336 
Día de la semana:  3

Además se puede agregar el nombre abreviado (o no) de algunas componentes

cat(paste("Mes: ", month(fecha, label = TRUE, abbr = TRUE),
          "\nDía de la semana: ", wday(fecha, label = TRUE, abbr = FALSE)))

Mes:  Dec 
Día de la semana:  Tuesday

Duraciones

Las duraciones son cantidades exactas en segundos

tod_yes <- as.Date("2020/12/8") - as.Date("2020/12/7")
as.duration(tod_yes)

[1] "86400s (~1 days)"

Además de que se tienen otros constructores:

cat(paste("Segundos: ",dseconds(10),
          "\nMinutos: ",dminutes(10),
          "\nHoras: ", dhours(10),
          "\nDías: ",ddays(10),
          "\nSemanas: ", dweeks(10),
          "\nAños: ",dyears(10)))

Segundos:  10s 
Minutos:  600s (~10 minutes) 
Horas:  36000s (~10 hours) 
Días:  864000s (~1.43 weeks) 
Semanas:  6048000s (~10 weeks) 
Años:  315576000s (~10 years)

Y con esto ya se pueden realizar operaciones:

dyears(c(10, 20, 30)) + dweeks(c(5, 6, 7)) + dhours(c(40, 50, 60))

[1] "318744000s (~10.1 years)"  "634960800s (~20.12 years)"
[3] "951177600s (~30.14 years)"

Aunque al estar tratando con segundos exactos, podemos tener resultados inesperados debido a los cambios de horario que ya están considerados en estas funciones

(another_day <- ymd_hms("2016-03-12 13:00:00", tz = "America/New_York"))

[1] "2016-03-12 13:00:00 EST"

another_day + ddays(1)

[1] "2016-03-13 14:00:00 EDT"

Periodos

Los periodos son unidades entendibles para el humano como las semanas y los meses, es decir que no se trabaha con duraciones fijas en segundos

cat(paste("Segundos: ",seconds(10),
          "\nMinutos: ",minutes(10),
          "\nHoras: ", hours(10),
          "\nDías: ",days(10),
          "\nSemanas: ", weeks(10),
          "\nAños: ",years(10)))

Segundos:  10S 
Minutos:  10M 0S 
Horas:  10H 0M 0S 
Días:  10d 0H 0M 0S 
Semanas:  70d 0H 0M 0S 
Años:  10y 0m 0d 0H 0M 0S

Y de igual manera se pueden tener operaciones, por ejemplo, véase que lo siguiente corresponde a que el 2016 es un año bisiesto y utilizando durations es menos entendible por el uso de segundos exactos, lo cual no sucede al usar periodos

ymd("2016-01-01") + dyears(1)

[1] "2016-12-31 06:00:00 UTC"

ymd("2016-01-01") + years(1)

[1] "2017-01-01"

O con los horarios de verano

five_pm <- ymd_hms("2016-03-12 17:00:00", tz = "America/New_York")
five_pm + ddays(1)

[1] "2016-03-13 18:00:00 EDT"

five_pm + days(1)

[1] "2016-03-13 17:00:00 EDT"

Intervalos

Este tipo de lapsos nos ayuda a tener coherencia con nuestra percepción al realizar operaciones con las fechas, como years(1) / days(1). Si el año es 2016 (un año bisiesto), desearíamos obtener 365 y no 366. Si bien lubridate no puede dar una respuesta certera, puede dar una estimación con una advertencia:

years(1)/days(1)

[1] 365.25

Si se desea un resultado más preciso, se utiliza un intervalo el cual indica la duración con un punto de inicio

(next_year <- today() + years(1))

[1] "2022-01-10"

today() %--% next_year

[1] 2021-01-10 UTC--2022-01-10 UTC

(today() %--% next_year) / ddays(1)

[1] 365

Y para saber cuántos periodos son abarcados por un intervalo, se utiliza la división de enteros:

(today() %--% next_year) %/% ddays(1)

[1] 365

Hay usos más interesantes que proporciona esta paquetería, aquí se deja una pequeña lista que pueden ser útiles al momento de trabajar con fechas en un data frame:

make_date() y make_datatime() para crear fechas si se tienen separados los componentes para la fecha.
as_datatime() y as_data() para cambiar entre fecha y fecha-hora.
floor_date(), ceiling_date() y round_date() redondean la fecha a una unidad cercana del tiempo, por ejemplo podemos utilizar esto para ajustar un conjunto de fechas a sólo datos semanales.
Se puede modificar una componente utilizando las funciones del punto 3 y asignando un nuevo valor, aunque también se puede utilizar la función update() dando la ventaja de cambiar múltiples valores a la vez.

Zoo y xts

Cuando se esta trabajando con una serie de tiempo se acostumbra el uso de los objetos ts proporcionados por el constructor stats::ts(), aunque otra alternativa más amigable para la manipulación de fechas en series de tiempo la proporciona xts, la cual es una librería derivada de zoo. Para una introducción sobre series de tiempo en R, se puede consultar el siguiente link.

Además de proporcionar funciones y filtros para manipular de manera más sencilla que con un objeto ts, se tienen algunas ventajas sobre los objetos ts ya que estos, al distribuir sus tiempos de manera uniforme y de igual longitud, pueden conllevar a problemas cuando no se tenga esta estructura en los datos, como en datos financieros. Con los objetos zoo y xts no se tendrá problema al trata con tiempos espaciados de manera irregular.

Para objetos zoo y xts, se deben dar dos componentes fundamentales: coredata e index, los cuales son suministrados en los argumentos x y order.by respectivamente en los constructores zoo::zoo() y xts::xts(). Se tienen otros argumentos comunes como la frecuencia en objetos ts.

library(zoo)
library(xts)
set.seed(10)
#coredata
data <- rgamma(n = 10, shape = 10, scale = 0.5)
# index
dates <- seq(as.Date("2021-01-01"), length = 10, by = "days")
zoo::zoo(x = data, order.by = dates)

2021-01-01 2021-01-02 2021-01-03 2021-01-04 2021-01-05 2021-01-06 2021-01-07 
  4.778934   4.470291   3.659127   5.214769   5.369706   3.070661   4.477540 
2021-01-08 2021-01-09 2021-01-10 
  4.362963   5.212754   4.196428

xts::xts(x = data, order.by = dates)

               [,1]
2021-01-01 4.778934
2021-01-02 4.470291
2021-01-03 3.659127
2021-01-04 5.214769
2021-01-05 5.369706
2021-01-06 3.070661
2021-01-07 4.477540
2021-01-08 4.362963
2021-01-09 5.212754
2021-01-10 4.196428

Además, se permite agregar metadata directamente en el constructor xts().

one_more_data <- lubridate::today()
xts_one <- xts::xts(x = data, order.by = dates, meta = one_more_data)
str(xts_one)

An 'xts' object on 2021-01-01/2021-01-10 containing:
  Data: num [1:10, 1] 4.78 4.47 3.66 5.21 5.37 ...
  Indexed by objects of class: [Date] TZ: UTC
  xts Attributes:  
List of 1
 $ meta: Date[1:1], format: "2021-01-10"

Conversiones

De igual manera, para crear un objeto xts, se puede utiliza la función as.xts() la cual soporta objetos de tipo timeSeries, ts, irts, fts, matrix, data.frame y zoo.

(APxst <- as.xts(AirPassengers) %>% head())

         [,1]
Jan 1949  112
Feb 1949  118
Mar 1949  132
Apr 1949  129
May 1949  121
Jun 1949  135

Y se puede convertir un objeto xts en diferentes formatos sin ningún problema. Esto es muy útil ya que pueden existir funciones que R que sólo admitan objetos ts.

cat(paste("as.matrix(): ", class(as.matrix(APxst)),
          "\nas.ts():", class(as.ts(APxst))))

as.matrix():  matrix 
as.ts(): ts

Extración

Los componentes de un objeto zoo o xts se pueden obtener con las siguientes funciones. La primera regresará una matriz y la segunda un vector tipo Date.

zoo::coredata(xts_one)

          [,1]
 [1,] 4.778934
 [2,] 4.470291
 [3,] 3.659127
 [4,] 5.214769
 [5,] 5.369706
 [6,] 3.070661
 [7,] 4.477540
 [8,] 4.362963
 [9,] 5.212754
[10,] 4.196428

zoo::index(xts_one)

 [1] "2021-01-01" "2021-01-02" "2021-01-03" "2021-01-04" "2021-01-05"
 [6] "2021-01-06" "2021-01-07" "2021-01-08" "2021-01-09" "2021-01-10"

xts tiene algunas funciones para extraer periodos extremos en la serie los cuales se pueden combinar para obtener información interesante. Los periodos pueden ser secs, seconds, mins, minutes, hours, days, weeks, months, quarters, y years. Las funciones son las siguientes:

last : Obtiene el último periodo solicitado.
first: Obtiene el primer periodo solicitado.

bad_year <- as.Date("2020-01-01") + 0:365
bad_year_xts <- xts(x = seq_along(bad_year), order.by = bad_year)

# Del último mes del 2020, se toma la primera semana y todos los días excepto los últimos 3
xts::last(xts::first(xts::last(bad_year_xts, "1 month"), "1 week"), "-3 days")

           [,1]
2020-12-01  336
2020-12-02  337
2020-12-03  338

Filtros

Referente a los filtros, estos son más sencillos que en un objeto ts, ya que se puede especificar el año, o el mes, el día, etc., mediante los formatos internacionales estandarizados ISO-8601; es decir: AAAA-MM-DD o AAAAMMDD y sus reducciones para obtener sólo un mes, un año o un día en específico

dates <- as.Date("2015-01-01") + 0:1000
some_days_2015_2017 <- xts(x = seq_along(dates), order.by = dates)

Sólo un año

some_days_2015_2017["2016"]

           [,1]
2016-01-01  366
2016-01-02  367
2016-01-03  368
2016-01-04  369
2016-01-05  370
2016-01-06  371
2016-01-07  372
2016-01-08  373
2016-01-09  374
2016-01-10  375
2016-01-11  376
2016-01-12  377
2016-01-13  378
2016-01-14  379
2016-01-15  380
2016-01-16  381
2016-01-17  382
2016-01-18  383
2016-01-19  384
2016-01-20  385
2016-01-21  386
2016-01-22  387
2016-01-23  388
2016-01-24  389
2016-01-25  390
2016-01-26  391
2016-01-27  392
2016-01-28  393
2016-01-29  394
2016-01-30  395
2016-01-31  396
2016-02-01  397
2016-02-02  398
2016-02-03  399
2016-02-04  400
2016-02-05  401
2016-02-06  402
2016-02-07  403
2016-02-08  404
2016-02-09  405
2016-02-10  406
2016-02-11  407
2016-02-12  408
2016-02-13  409
2016-02-14  410
2016-02-15  411
2016-02-16  412
2016-02-17  413
2016-02-18  414
2016-02-19  415
2016-02-20  416
2016-02-21  417
2016-02-22  418
2016-02-23  419
2016-02-24  420
2016-02-25  421
2016-02-26  422
2016-02-27  423
2016-02-28  424
2016-02-29  425
2016-03-01  426
2016-03-02  427
2016-03-03  428
2016-03-04  429
2016-03-05  430
2016-03-06  431
2016-03-07  432
2016-03-08  433
2016-03-09  434
2016-03-10  435
2016-03-11  436
2016-03-12  437
2016-03-13  438
2016-03-14  439
2016-03-15  440
2016-03-16  441
2016-03-17  442
2016-03-18  443
2016-03-19  444
2016-03-20  445
2016-03-21  446
2016-03-22  447
2016-03-23  448
2016-03-24  449
2016-03-25  450
2016-03-26  451
2016-03-27  452
2016-03-28  453
2016-03-29  454
2016-03-30  455
2016-03-31  456
2016-04-01  457
2016-04-02  458
2016-04-03  459
2016-04-04  460
2016-04-05  461
2016-04-06  462
2016-04-07  463
2016-04-08  464
2016-04-09  465
2016-04-10  466
2016-04-11  467
2016-04-12  468
2016-04-13  469
2016-04-14  470
2016-04-15  471
2016-04-16  472
2016-04-17  473
2016-04-18  474
2016-04-19  475
2016-04-20  476
2016-04-21  477
2016-04-22  478
2016-04-23  479
2016-04-24  480
2016-04-25  481
2016-04-26  482
2016-04-27  483
2016-04-28  484
2016-04-29  485
2016-04-30  486
2016-05-01  487
2016-05-02  488
2016-05-03  489
2016-05-04  490
2016-05-05  491
2016-05-06  492
2016-05-07  493
2016-05-08  494
2016-05-09  495
2016-05-10  496
2016-05-11  497
2016-05-12  498
2016-05-13  499
2016-05-14  500
2016-05-15  501
2016-05-16  502
2016-05-17  503
2016-05-18  504
2016-05-19  505
2016-05-20  506
2016-05-21  507
2016-05-22  508
2016-05-23  509
2016-05-24  510
2016-05-25  511
2016-05-26  512
2016-05-27  513
2016-05-28  514
2016-05-29  515
2016-05-30  516
2016-05-31  517
2016-06-01  518
2016-06-02  519
2016-06-03  520
2016-06-04  521
2016-06-05  522
2016-06-06  523
2016-06-07  524
2016-06-08  525
2016-06-09  526
2016-06-10  527
2016-06-11  528
2016-06-12  529
2016-06-13  530
2016-06-14  531
2016-06-15  532
2016-06-16  533
2016-06-17  534
2016-06-18  535
2016-06-19  536
2016-06-20  537
2016-06-21  538
2016-06-22  539
2016-06-23  540
2016-06-24  541
2016-06-25  542
2016-06-26  543
2016-06-27  544
2016-06-28  545
2016-06-29  546
2016-06-30  547
2016-07-01  548
2016-07-02  549
2016-07-03  550
2016-07-04  551
2016-07-05  552
2016-07-06  553
2016-07-07  554
2016-07-08  555
2016-07-09  556
2016-07-10  557
2016-07-11  558
2016-07-12  559
2016-07-13  560
2016-07-14  561
2016-07-15  562
2016-07-16  563
2016-07-17  564
2016-07-18  565
2016-07-19  566
2016-07-20  567
2016-07-21  568
2016-07-22  569
2016-07-23  570
2016-07-24  571
2016-07-25  572
2016-07-26  573
2016-07-27  574
2016-07-28  575
2016-07-29  576
2016-07-30  577
2016-07-31  578
2016-08-01  579
2016-08-02  580
2016-08-03  581
2016-08-04  582
2016-08-05  583
2016-08-06  584
2016-08-07  585
2016-08-08  586
2016-08-09  587
2016-08-10  588
2016-08-11  589
2016-08-12  590
2016-08-13  591
2016-08-14  592
2016-08-15  593
2016-08-16  594
2016-08-17  595
2016-08-18  596
2016-08-19  597
2016-08-20  598
2016-08-21  599
2016-08-22  600
2016-08-23  601
2016-08-24  602
2016-08-25  603
2016-08-26  604
2016-08-27  605
2016-08-28  606
2016-08-29  607
2016-08-30  608
2016-08-31  609
2016-09-01  610
2016-09-02  611
2016-09-03  612
2016-09-04  613
2016-09-05  614
2016-09-06  615
2016-09-07  616
2016-09-08  617
2016-09-09  618
2016-09-10  619
2016-09-11  620
2016-09-12  621
2016-09-13  622
2016-09-14  623
2016-09-15  624
2016-09-16  625
2016-09-17  626
2016-09-18  627
2016-09-19  628
2016-09-20  629
2016-09-21  630
2016-09-22  631
2016-09-23  632
2016-09-24  633
2016-09-25  634
2016-09-26  635
2016-09-27  636
2016-09-28  637
2016-09-29  638
2016-09-30  639
2016-10-01  640
2016-10-02  641
2016-10-03  642
2016-10-04  643
2016-10-05  644
2016-10-06  645
2016-10-07  646
2016-10-08  647
2016-10-09  648
2016-10-10  649
2016-10-11  650
2016-10-12  651
2016-10-13  652
2016-10-14  653
2016-10-15  654
2016-10-16  655
2016-10-17  656
2016-10-18  657
2016-10-19  658
2016-10-20  659
2016-10-21  660
2016-10-22  661
2016-10-23  662
2016-10-24  663
2016-10-25  664
2016-10-26  665
2016-10-27  666
2016-10-28  667
2016-10-29  668
2016-10-30  669
2016-10-31  670
2016-11-01  671
2016-11-02  672
2016-11-03  673
2016-11-04  674
2016-11-05  675
2016-11-06  676
2016-11-07  677
2016-11-08  678
2016-11-09  679
2016-11-10  680
2016-11-11  681
2016-11-12  682
2016-11-13  683
2016-11-14  684
2016-11-15  685
2016-11-16  686
2016-11-17  687
2016-11-18  688
2016-11-19  689
2016-11-20  690
2016-11-21  691
2016-11-22  692
2016-11-23  693
2016-11-24  694
2016-11-25  695
2016-11-26  696
2016-11-27  697
2016-11-28  698
2016-11-29  699
2016-11-30  700
2016-12-01  701
2016-12-02  702
2016-12-03  703
2016-12-04  704
2016-12-05  705
2016-12-06  706
2016-12-07  707
2016-12-08  708
2016-12-09  709
2016-12-10  710
2016-12-11  711
2016-12-12  712
2016-12-13  713
2016-12-14  714
2016-12-15  715
2016-12-16  716
2016-12-17  717
2016-12-18  718
2016-12-19  719
2016-12-20  720
2016-12-21  721
2016-12-22  722
2016-12-23  723
2016-12-24  724
2016-12-25  725
2016-12-26  726
2016-12-27  727
2016-12-28  728
2016-12-29  729
2016-12-30  730
2016-12-31  731

Sólo un mes

some_days_2015_2017["2016-05"]

           [,1]
2016-05-01  487
2016-05-02  488
2016-05-03  489
2016-05-04  490
2016-05-05  491
2016-05-06  492
2016-05-07  493
2016-05-08  494
2016-05-09  495
2016-05-10  496
2016-05-11  497
2016-05-12  498
2016-05-13  499
2016-05-14  500
2016-05-15  501
2016-05-16  502
2016-05-17  503
2016-05-18  504
2016-05-19  505
2016-05-20  506
2016-05-21  507
2016-05-22  508
2016-05-23  509
2016-05-24  510
2016-05-25  511
2016-05-26  512
2016-05-27  513
2016-05-28  514
2016-05-29  515
2016-05-30  516
2016-05-31  517

Un día

some_days_2015_2017["20160808"]

           [,1]
2016-08-08  586

También es posible hacer un rango de fechas separando estas con una diagonal /

some_days_2015_2017["20160201/20160220"]

           [,1]
2016-02-01  397
2016-02-02  398
2016-02-03  399
2016-02-04  400
2016-02-05  401
2016-02-06  402
2016-02-07  403
2016-02-08  404
2016-02-09  405
2016-02-10  406
2016-02-11  407
2016-02-12  408
2016-02-13  409
2016-02-14  410
2016-02-15  411
2016-02-16  412
2016-02-17  413
2016-02-18  414
2016-02-19  415
2016-02-20  416

#Todos los datos a partir del primero de febrero
some_days_2015_2017["20160201/"]

           [,1]
2016-02-01  397
2016-02-02  398
2016-02-03  399
2016-02-04  400
2016-02-05  401
2016-02-06  402
2016-02-07  403
2016-02-08  404
2016-02-09  405
2016-02-10  406
2016-02-11  407
2016-02-12  408
2016-02-13  409
2016-02-14  410
2016-02-15  411
2016-02-16  412
2016-02-17  413
2016-02-18  414
2016-02-19  415
2016-02-20  416
2016-02-21  417
2016-02-22  418
2016-02-23  419
2016-02-24  420
2016-02-25  421
2016-02-26  422
2016-02-27  423
2016-02-28  424
2016-02-29  425
2016-03-01  426
2016-03-02  427
2016-03-03  428
2016-03-04  429
2016-03-05  430
2016-03-06  431
2016-03-07  432
2016-03-08  433
2016-03-09  434
2016-03-10  435
2016-03-11  436
2016-03-12  437
2016-03-13  438
2016-03-14  439
2016-03-15  440
2016-03-16  441
2016-03-17  442
2016-03-18  443
2016-03-19  444
2016-03-20  445
2016-03-21  446
2016-03-22  447
2016-03-23  448
2016-03-24  449
2016-03-25  450
2016-03-26  451
2016-03-27  452
2016-03-28  453
2016-03-29  454
2016-03-30  455
2016-03-31  456
2016-04-01  457
2016-04-02  458
2016-04-03  459
2016-04-04  460
2016-04-05  461
2016-04-06  462
2016-04-07  463
2016-04-08  464
2016-04-09  465
2016-04-10  466
2016-04-11  467
2016-04-12  468
2016-04-13  469
2016-04-14  470
2016-04-15  471
2016-04-16  472
2016-04-17  473
2016-04-18  474
2016-04-19  475
2016-04-20  476
2016-04-21  477
2016-04-22  478
2016-04-23  479
2016-04-24  480
2016-04-25  481
2016-04-26  482
2016-04-27  483
2016-04-28  484
2016-04-29  485
2016-04-30  486
2016-05-01  487
2016-05-02  488
2016-05-03  489
2016-05-04  490
2016-05-05  491
2016-05-06  492
2016-05-07  493
2016-05-08  494
2016-05-09  495
2016-05-10  496
2016-05-11  497
2016-05-12  498
2016-05-13  499
2016-05-14  500
2016-05-15  501
2016-05-16  502
2016-05-17  503
2016-05-18  504
2016-05-19  505
2016-05-20  506
2016-05-21  507
2016-05-22  508
2016-05-23  509
2016-05-24  510
2016-05-25  511
2016-05-26  512
2016-05-27  513
2016-05-28  514
2016-05-29  515
2016-05-30  516
2016-05-31  517
2016-06-01  518
2016-06-02  519
2016-06-03  520
2016-06-04  521
2016-06-05  522
2016-06-06  523
2016-06-07  524
2016-06-08  525
2016-06-09  526
2016-06-10  527
2016-06-11  528
2016-06-12  529
2016-06-13  530
2016-06-14  531
2016-06-15  532
2016-06-16  533
2016-06-17  534
2016-06-18  535
2016-06-19  536
2016-06-20  537
2016-06-21  538
2016-06-22  539
2016-06-23  540
2016-06-24  541
2016-06-25  542
2016-06-26  543
2016-06-27  544
2016-06-28  545
2016-06-29  546
2016-06-30  547
2016-07-01  548
2016-07-02  549
2016-07-03  550
2016-07-04  551
2016-07-05  552
2016-07-06  553
2016-07-07  554
2016-07-08  555
2016-07-09  556
2016-07-10  557
2016-07-11  558
2016-07-12  559
2016-07-13  560
2016-07-14  561
2016-07-15  562
2016-07-16  563
2016-07-17  564
2016-07-18  565
2016-07-19  566
2016-07-20  567
2016-07-21  568
2016-07-22  569
2016-07-23  570
2016-07-24  571
2016-07-25  572
2016-07-26  573
2016-07-27  574
2016-07-28  575
2016-07-29  576
2016-07-30  577
2016-07-31  578
2016-08-01  579
2016-08-02  580
2016-08-03  581
2016-08-04  582
2016-08-05  583
2016-08-06  584
2016-08-07  585
2016-08-08  586
2016-08-09  587
2016-08-10  588
2016-08-11  589
2016-08-12  590
2016-08-13  591
2016-08-14  592
2016-08-15  593
2016-08-16  594
2016-08-17  595
2016-08-18  596
2016-08-19  597
2016-08-20  598
2016-08-21  599
2016-08-22  600
2016-08-23  601
2016-08-24  602
2016-08-25  603
2016-08-26  604
2016-08-27  605
2016-08-28  606
2016-08-29  607
2016-08-30  608
2016-08-31  609
2016-09-01  610
2016-09-02  611
2016-09-03  612
2016-09-04  613
2016-09-05  614
2016-09-06  615
2016-09-07  616
2016-09-08  617
2016-09-09  618
2016-09-10  619
2016-09-11  620
2016-09-12  621
2016-09-13  622
2016-09-14  623
2016-09-15  624
2016-09-16  625
2016-09-17  626
2016-09-18  627
2016-09-19  628
2016-09-20  629
2016-09-21  630
2016-09-22  631
2016-09-23  632
2016-09-24  633
2016-09-25  634
2016-09-26  635
2016-09-27  636
2016-09-28  637
2016-09-29  638
2016-09-30  639
2016-10-01  640
2016-10-02  641
2016-10-03  642
2016-10-04  643
2016-10-05  644
2016-10-06  645
2016-10-07  646
2016-10-08  647
2016-10-09  648
2016-10-10  649
2016-10-11  650
2016-10-12  651
2016-10-13  652
2016-10-14  653
2016-10-15  654
2016-10-16  655
2016-10-17  656
2016-10-18  657
2016-10-19  658
2016-10-20  659
2016-10-21  660
2016-10-22  661
2016-10-23  662
2016-10-24  663
2016-10-25  664
2016-10-26  665
2016-10-27  666
2016-10-28  667
2016-10-29  668
2016-10-30  669
2016-10-31  670
2016-11-01  671
2016-11-02  672
2016-11-03  673
2016-11-04  674
2016-11-05  675
2016-11-06  676
2016-11-07  677
2016-11-08  678
2016-11-09  679
2016-11-10  680
2016-11-11  681
2016-11-12  682
2016-11-13  683
2016-11-14  684
2016-11-15  685
2016-11-16  686
2016-11-17  687
2016-11-18  688
2016-11-19  689
2016-11-20  690
2016-11-21  691
2016-11-22  692
2016-11-23  693
2016-11-24  694
2016-11-25  695
2016-11-26  696
2016-11-27  697
2016-11-28  698
2016-11-29  699
2016-11-30  700
2016-12-01  701
2016-12-02  702
2016-12-03  703
2016-12-04  704
2016-12-05  705
2016-12-06  706
2016-12-07  707
2016-12-08  708
2016-12-09  709
2016-12-10  710
2016-12-11  711
2016-12-12  712
2016-12-13  713
2016-12-14  714
2016-12-15  715
2016-12-16  716
2016-12-17  717
2016-12-18  718
2016-12-19  719
2016-12-20  720
2016-12-21  721
2016-12-22  722
2016-12-23  723
2016-12-24  724
2016-12-25  725
2016-12-26  726
2016-12-27  727
2016-12-28  728
2016-12-29  729
2016-12-30  730
2016-12-31  731
2017-01-01  732
2017-01-02  733
2017-01-03  734
2017-01-04  735
2017-01-05  736
2017-01-06  737
2017-01-07  738
2017-01-08  739
2017-01-09  740
2017-01-10  741
2017-01-11  742
2017-01-12  743
2017-01-13  744
2017-01-14  745
2017-01-15  746
2017-01-16  747
2017-01-17  748
2017-01-18  749
2017-01-19  750
2017-01-20  751
2017-01-21  752
2017-01-22  753
2017-01-23  754
2017-01-24  755
2017-01-25  756
2017-01-26  757
2017-01-27  758
2017-01-28  759
2017-01-29  760
2017-01-30  761
2017-01-31  762
2017-02-01  763
2017-02-02  764
2017-02-03  765
2017-02-04  766
2017-02-05  767
2017-02-06  768
2017-02-07  769
2017-02-08  770
2017-02-09  771
2017-02-10  772
2017-02-11  773
2017-02-12  774
2017-02-13  775
2017-02-14  776
2017-02-15  777
2017-02-16  778
2017-02-17  779
2017-02-18  780
2017-02-19  781
2017-02-20  782
2017-02-21  783
2017-02-22  784
2017-02-23  785
2017-02-24  786
2017-02-25  787
2017-02-26  788
2017-02-27  789
2017-02-28  790
2017-03-01  791
2017-03-02  792
2017-03-03  793
2017-03-04  794
2017-03-05  795
2017-03-06  796
2017-03-07  797
2017-03-08  798
2017-03-09  799
2017-03-10  800
2017-03-11  801
2017-03-12  802
2017-03-13  803
2017-03-14  804
2017-03-15  805
2017-03-16  806
2017-03-17  807
2017-03-18  808
2017-03-19  809
2017-03-20  810
2017-03-21  811
2017-03-22  812
2017-03-23  813
2017-03-24  814
2017-03-25  815
2017-03-26  816
2017-03-27  817
2017-03-28  818
2017-03-29  819
2017-03-30  820
2017-03-31  821
2017-04-01  822
2017-04-02  823
2017-04-03  824
2017-04-04  825
2017-04-05  826
2017-04-06  827
2017-04-07  828
2017-04-08  829
2017-04-09  830
2017-04-10  831
2017-04-11  832
2017-04-12  833
2017-04-13  834
2017-04-14  835
2017-04-15  836
2017-04-16  837
2017-04-17  838
2017-04-18  839
2017-04-19  840
2017-04-20  841
2017-04-21  842
2017-04-22  843
2017-04-23  844
2017-04-24  845
2017-04-25  846
2017-04-26  847
2017-04-27  848
2017-04-28  849
2017-04-29  850
2017-04-30  851
2017-05-01  852
2017-05-02  853
2017-05-03  854
2017-05-04  855
2017-05-05  856
2017-05-06  857
2017-05-07  858
2017-05-08  859
2017-05-09  860
2017-05-10  861
2017-05-11  862
2017-05-12  863
2017-05-13  864
2017-05-14  865
2017-05-15  866
2017-05-16  867
2017-05-17  868
2017-05-18  869
2017-05-19  870
2017-05-20  871
2017-05-21  872
2017-05-22  873
2017-05-23  874
2017-05-24  875
2017-05-25  876
2017-05-26  877
2017-05-27  878
2017-05-28  879
2017-05-29  880
2017-05-30  881
2017-05-31  882
2017-06-01  883
2017-06-02  884
2017-06-03  885
2017-06-04  886
2017-06-05  887
2017-06-06  888
2017-06-07  889
2017-06-08  890
2017-06-09  891
2017-06-10  892
2017-06-11  893
2017-06-12  894
2017-06-13  895
2017-06-14  896
2017-06-15  897
2017-06-16  898
2017-06-17  899
2017-06-18  900
2017-06-19  901
2017-06-20  902
2017-06-21  903
2017-06-22  904
2017-06-23  905
2017-06-24  906
2017-06-25  907
2017-06-26  908
2017-06-27  909
2017-06-28  910
2017-06-29  911
2017-06-30  912
2017-07-01  913
2017-07-02  914
2017-07-03  915
2017-07-04  916
2017-07-05  917
2017-07-06  918
2017-07-07  919
2017-07-08  920
2017-07-09  921
2017-07-10  922
2017-07-11  923
2017-07-12  924
2017-07-13  925
2017-07-14  926
2017-07-15  927
2017-07-16  928
2017-07-17  929
2017-07-18  930
2017-07-19  931
2017-07-20  932
2017-07-21  933
2017-07-22  934
2017-07-23  935
2017-07-24  936
2017-07-25  937
2017-07-26  938
2017-07-27  939
2017-07-28  940
2017-07-29  941
2017-07-30  942
2017-07-31  943
2017-08-01  944
2017-08-02  945
2017-08-03  946
2017-08-04  947
2017-08-05  948
2017-08-06  949
2017-08-07  950
2017-08-08  951
2017-08-09  952
2017-08-10  953
2017-08-11  954
2017-08-12  955
2017-08-13  956
2017-08-14  957
2017-08-15  958
2017-08-16  959
2017-08-17  960
2017-08-18  961
2017-08-19  962
2017-08-20  963
2017-08-21  964
2017-08-22  965
2017-08-23  966
2017-08-24  967
2017-08-25  968
2017-08-26  969
2017-08-27  970
2017-08-28  971
2017-08-29  972
2017-08-30  973
2017-08-31  974
2017-09-01  975
2017-09-02  976
2017-09-03  977
2017-09-04  978
2017-09-05  979
2017-09-06  980
2017-09-07  981
2017-09-08  982
2017-09-09  983
2017-09-10  984
2017-09-11  985
2017-09-12  986
2017-09-13  987
2017-09-14  988
2017-09-15  989
2017-09-16  990
2017-09-17  991
2017-09-18  992
2017-09-19  993
2017-09-20  994
2017-09-21  995
2017-09-22  996
2017-09-23  997
2017-09-24  998
2017-09-25  999
2017-09-26 1000
2017-09-27 1001

O bien utilizando rangos normales

dates <- seq(as.Date("2016-01-01"), as.Date("2016-01-10"), by = "day")
some_days_2015_2017[dates]

           [,1]
2016-01-01  366
2016-01-02  367
2016-01-03  368
2016-01-04  369
2016-01-05  370
2016-01-06  371
2016-01-07  372
2016-01-08  373
2016-01-09  374
2016-01-10  375

Otro ejemplo

some_days_2015_2017[as.POSIXct(dates)]

           [,1]
2016-01-01  366
2016-01-02  367
2016-01-03  368
2016-01-04  369
2016-01-05  370
2016-01-06  371
2016-01-07  372
2016-01-08  373
2016-01-09  374
2016-01-10  375

Además se pueden utilizar atributos index para realizar los filtros

dates <- as.Date("2021-01-01") + 0:364
xts2021 <- xts(x = rnorm(365), order.by = dates)

dates <- seq(as.Date("2021-01-01"), as.Date("2021-01-31"), by = "day")
January2021 <- xts(x = rnorm(31), order.by = as.POSIXct(dates))

xts2021[zoo::index(January2021)]

                  [,1]
2021-01-01  0.98744470
2021-01-02  0.74139013
2021-01-03  0.08934727
2021-01-04 -0.95494386
2021-01-05 -0.19515038
2021-01-06  0.92552126
2021-01-07  0.48297852
2021-01-08 -0.59631064
2021-01-09 -2.18528684
2021-01-10 -0.67486594
2021-01-11 -2.11906119
2021-01-12 -1.26519802
2021-01-13 -0.37366156
2021-01-14 -0.68755543
2021-01-15 -0.87215883
2021-01-16 -0.10176101
2021-01-17 -0.25378053
2021-01-18 -1.85374045
2021-01-19 -0.07794607
2021-01-20  0.96856634
2021-01-21  0.18492596
2021-01-22 -1.37994358
2021-01-23 -1.43551436
2021-01-24  0.36208723
2021-01-25 -1.75908675
2021-01-26 -0.32454401
2021-01-27 -0.65156299
2021-01-28  1.08655140
2021-01-29 -0.76254488
2021-01-30 -0.82866254
2021-01-31  0.83447390

Algo muy útil que tienen este tipo de objetos es que se puede obtener información intraday. Para hacer esto se agrega una notación T/T para indicar el tiempo

simulate_finance <- tibble::tribble(~Date, ~Time, ~Open, ~High,  ~Low, ~Close,
                                    20150408,"09:17:00", 7.10, 7.14, 7.17, 7.18,
                                    20150408,"09:18:00", 7.11, 7.10, 7.10, 7.13,
                                    20150408,"09:19:00", 7.11, 7.12, 7.17, 7.11,
                                    20150408,"09:20:00", 7.16, 7.10, 7.17, 7.12,
                                    20150408,"09:21:00", 7.19, 7.17, 7.18, 7.14,
                                    20150408,"09:22:00", 7.13, 7.20, 7.11, 7.14,
                                    20150409,"09:17:00", 7.14, 7.16, 7.18, 7.19,
                                    20150409,"09:18:00", 7.20, 7.12, 7.15, 7.10,
                                    20150409,"09:19:00", 7.12, 7.20, 7.19, 7.11,
                                    20150409,"09:20:00", 7.13, 7.16, 7.19, 7.18,
                                    20150409,"09:21:00", 7.11, 7.11, 7.12, 7.11,
                                    20150409,"09:22:00", 7.14, 7.19, 7.12, 7.17)
dates <- as.POSIXct(paste(simulate_finance$Date, simulate_finance$Time), format="%Y%m%d %H:%M:%S")
simulateF_xts <- xts(simulate_finance[, -(1:2)], order.by=dates)

#Obtenemos toda la información entre las 09:18 AM y las 09:21 AM
simulateF_xts["T09:18/T09:21"]

                    Open High  Low Close
2015-04-08 09:18:00 7.11 7.10 7.10  7.13
2015-04-08 09:19:00 7.11 7.12 7.17  7.11
2015-04-08 09:20:00 7.16 7.10 7.17  7.12
2015-04-08 09:21:00 7.19 7.17 7.18  7.14
2015-04-09 09:18:00 7.20 7.12 7.15  7.10
2015-04-09 09:19:00 7.12 7.20 7.19  7.11
2015-04-09 09:20:00 7.13 7.16 7.19  7.18
2015-04-09 09:21:00 7.11 7.11 7.12  7.11

Otro ejemplo

dates <- seq(as.POSIXct("2010-01-01 00:00"), as.POSIXct("2012-01-01 00:00"), by = "30 min")
intra_example <- xts(x = seq_along(dates), order.by = dates)

# Ahora, todos los datos entre las 08:00AM y las 10:00 AM
intra_example["T08:00/T10:00"]

                     [,1]
2010-01-01 08:00:00    17
2010-01-01 08:30:00    18
2010-01-01 09:00:00    19
2010-01-01 09:30:00    20
2010-01-01 10:00:00    21
2010-01-02 08:00:00    65
2010-01-02 08:30:00    66
2010-01-02 09:00:00    67
2010-01-02 09:30:00    68
2010-01-02 10:00:00    69
2010-01-03 08:00:00   113
2010-01-03 08:30:00   114
2010-01-03 09:00:00   115
2010-01-03 09:30:00   116
2010-01-03 10:00:00   117
2010-01-04 08:00:00   161
2010-01-04 08:30:00   162
2010-01-04 09:00:00   163
2010-01-04 09:30:00   164
2010-01-04 10:00:00   165
2010-01-05 08:00:00   209
2010-01-05 08:30:00   210
2010-01-05 09:00:00   211
2010-01-05 09:30:00   212
2010-01-05 10:00:00   213
2010-01-06 08:00:00   257
2010-01-06 08:30:00   258
2010-01-06 09:00:00   259
2010-01-06 09:30:00   260
2010-01-06 10:00:00   261
2010-01-07 08:00:00   305
2010-01-07 08:30:00   306
2010-01-07 09:00:00   307
2010-01-07 09:30:00   308
2010-01-07 10:00:00   309
2010-01-08 08:00:00   353
2010-01-08 08:30:00   354
2010-01-08 09:00:00   355
2010-01-08 09:30:00   356
2010-01-08 10:00:00   357
2010-01-09 08:00:00   401
2010-01-09 08:30:00   402
2010-01-09 09:00:00   403
2010-01-09 09:30:00   404
2010-01-09 10:00:00   405
2010-01-10 08:00:00   449
2010-01-10 08:30:00   450
2010-01-10 09:00:00   451
2010-01-10 09:30:00   452
2010-01-10 10:00:00   453
2010-01-11 08:00:00   497
2010-01-11 08:30:00   498
2010-01-11 09:00:00   499
2010-01-11 09:30:00   500
2010-01-11 10:00:00   501
2010-01-12 08:00:00   545
2010-01-12 08:30:00   546
2010-01-12 09:00:00   547
2010-01-12 09:30:00   548
2010-01-12 10:00:00   549
2010-01-13 08:00:00   593
2010-01-13 08:30:00   594
2010-01-13 09:00:00   595
2010-01-13 09:30:00   596
2010-01-13 10:00:00   597
2010-01-14 08:00:00   641
2010-01-14 08:30:00   642
2010-01-14 09:00:00   643
2010-01-14 09:30:00   644
2010-01-14 10:00:00   645
2010-01-15 08:00:00   689
2010-01-15 08:30:00   690
2010-01-15 09:00:00   691
2010-01-15 09:30:00   692
2010-01-15 10:00:00   693
2010-01-16 08:00:00   737
2010-01-16 08:30:00   738
2010-01-16 09:00:00   739
2010-01-16 09:30:00   740
2010-01-16 10:00:00   741
2010-01-17 08:00:00   785
2010-01-17 08:30:00   786
2010-01-17 09:00:00   787
2010-01-17 09:30:00   788
2010-01-17 10:00:00   789
2010-01-18 08:00:00   833
2010-01-18 08:30:00   834
2010-01-18 09:00:00   835
2010-01-18 09:30:00   836
2010-01-18 10:00:00   837
2010-01-19 08:00:00   881
2010-01-19 08:30:00   882
2010-01-19 09:00:00   883
2010-01-19 09:30:00   884
2010-01-19 10:00:00   885
2010-01-20 08:00:00   929
2010-01-20 08:30:00   930
2010-01-20 09:00:00   931
2010-01-20 09:30:00   932
2010-01-20 10:00:00   933
2010-01-21 08:00:00   977
2010-01-21 08:30:00   978
2010-01-21 09:00:00   979
2010-01-21 09:30:00   980
2010-01-21 10:00:00   981
2010-01-22 08:00:00  1025
2010-01-22 08:30:00  1026
2010-01-22 09:00:00  1027
2010-01-22 09:30:00  1028
2010-01-22 10:00:00  1029
2010-01-23 08:00:00  1073
2010-01-23 08:30:00  1074
2010-01-23 09:00:00  1075
2010-01-23 09:30:00  1076
2010-01-23 10:00:00  1077
2010-01-24 08:00:00  1121
2010-01-24 08:30:00  1122
2010-01-24 09:00:00  1123
2010-01-24 09:30:00  1124
2010-01-24 10:00:00  1125
2010-01-25 08:00:00  1169
2010-01-25 08:30:00  1170
2010-01-25 09:00:00  1171
2010-01-25 09:30:00  1172
2010-01-25 10:00:00  1173
2010-01-26 08:00:00  1217
2010-01-26 08:30:00  1218
2010-01-26 09:00:00  1219
2010-01-26 09:30:00  1220
2010-01-26 10:00:00  1221
2010-01-27 08:00:00  1265
2010-01-27 08:30:00  1266
2010-01-27 09:00:00  1267
2010-01-27 09:30:00  1268
2010-01-27 10:00:00  1269
2010-01-28 08:00:00  1313
2010-01-28 08:30:00  1314
2010-01-28 09:00:00  1315
2010-01-28 09:30:00  1316
2010-01-28 10:00:00  1317
2010-01-29 08:00:00  1361
2010-01-29 08:30:00  1362
2010-01-29 09:00:00  1363
2010-01-29 09:30:00  1364
2010-01-29 10:00:00  1365
2010-01-30 08:00:00  1409
2010-01-30 08:30:00  1410
2010-01-30 09:00:00  1411
2010-01-30 09:30:00  1412
2010-01-30 10:00:00  1413
2010-01-31 08:00:00  1457
2010-01-31 08:30:00  1458
2010-01-31 09:00:00  1459
2010-01-31 09:30:00  1460
2010-01-31 10:00:00  1461
2010-02-01 08:00:00  1505
2010-02-01 08:30:00  1506
2010-02-01 09:00:00  1507
2010-02-01 09:30:00  1508
2010-02-01 10:00:00  1509
2010-02-02 08:00:00  1553
2010-02-02 08:30:00  1554
2010-02-02 09:00:00  1555
2010-02-02 09:30:00  1556
2010-02-02 10:00:00  1557
2010-02-03 08:00:00  1601
2010-02-03 08:30:00  1602
2010-02-03 09:00:00  1603
2010-02-03 09:30:00  1604
2010-02-03 10:00:00  1605
2010-02-04 08:00:00  1649
2010-02-04 08:30:00  1650
2010-02-04 09:00:00  1651
2010-02-04 09:30:00  1652
2010-02-04 10:00:00  1653
2010-02-05 08:00:00  1697
2010-02-05 08:30:00  1698
2010-02-05 09:00:00  1699
2010-02-05 09:30:00  1700
2010-02-05 10:00:00  1701
2010-02-06 08:00:00  1745
2010-02-06 08:30:00  1746
2010-02-06 09:00:00  1747
2010-02-06 09:30:00  1748
2010-02-06 10:00:00  1749
2010-02-07 08:00:00  1793
2010-02-07 08:30:00  1794
2010-02-07 09:00:00  1795
2010-02-07 09:30:00  1796
2010-02-07 10:00:00  1797
2010-02-08 08:00:00  1841
2010-02-08 08:30:00  1842
2010-02-08 09:00:00  1843
2010-02-08 09:30:00  1844
2010-02-08 10:00:00  1845
2010-02-09 08:00:00  1889
2010-02-09 08:30:00  1890
2010-02-09 09:00:00  1891
2010-02-09 09:30:00  1892
2010-02-09 10:00:00  1893
2010-02-10 08:00:00  1937
2010-02-10 08:30:00  1938
2010-02-10 09:00:00  1939
2010-02-10 09:30:00  1940
2010-02-10 10:00:00  1941
2010-02-11 08:00:00  1985
2010-02-11 08:30:00  1986
2010-02-11 09:00:00  1987
2010-02-11 09:30:00  1988
2010-02-11 10:00:00  1989
2010-02-12 08:00:00  2033
2010-02-12 08:30:00  2034
2010-02-12 09:00:00  2035
2010-02-12 09:30:00  2036
2010-02-12 10:00:00  2037
2010-02-13 08:00:00  2081
2010-02-13 08:30:00  2082
2010-02-13 09:00:00  2083
2010-02-13 09:30:00  2084
2010-02-13 10:00:00  2085
2010-02-14 08:00:00  2129
2010-02-14 08:30:00  2130
2010-02-14 09:00:00  2131
2010-02-14 09:30:00  2132
2010-02-14 10:00:00  2133
2010-02-15 08:00:00  2177
2010-02-15 08:30:00  2178
2010-02-15 09:00:00  2179
2010-02-15 09:30:00  2180
2010-02-15 10:00:00  2181
2010-02-16 08:00:00  2225
2010-02-16 08:30:00  2226
2010-02-16 09:00:00  2227
2010-02-16 09:30:00  2228
2010-02-16 10:00:00  2229
2010-02-17 08:00:00  2273
2010-02-17 08:30:00  2274
2010-02-17 09:00:00  2275
2010-02-17 09:30:00  2276
2010-02-17 10:00:00  2277
2010-02-18 08:00:00  2321
2010-02-18 08:30:00  2322
2010-02-18 09:00:00  2323
2010-02-18 09:30:00  2324
2010-02-18 10:00:00  2325
2010-02-19 08:00:00  2369
2010-02-19 08:30:00  2370
2010-02-19 09:00:00  2371
2010-02-19 09:30:00  2372
2010-02-19 10:00:00  2373
2010-02-20 08:00:00  2417
2010-02-20 08:30:00  2418
2010-02-20 09:00:00  2419
2010-02-20 09:30:00  2420
2010-02-20 10:00:00  2421
2010-02-21 08:00:00  2465
2010-02-21 08:30:00  2466
2010-02-21 09:00:00  2467
2010-02-21 09:30:00  2468
2010-02-21 10:00:00  2469
2010-02-22 08:00:00  2513
2010-02-22 08:30:00  2514
2010-02-22 09:00:00  2515
2010-02-22 09:30:00  2516
2010-02-22 10:00:00  2517
2010-02-23 08:00:00  2561
2010-02-23 08:30:00  2562
2010-02-23 09:00:00  2563
2010-02-23 09:30:00  2564
2010-02-23 10:00:00  2565
2010-02-24 08:00:00  2609
2010-02-24 08:30:00  2610
2010-02-24 09:00:00  2611
2010-02-24 09:30:00  2612
2010-02-24 10:00:00  2613
2010-02-25 08:00:00  2657
2010-02-25 08:30:00  2658
2010-02-25 09:00:00  2659
2010-02-25 09:30:00  2660
2010-02-25 10:00:00  2661
2010-02-26 08:00:00  2705
2010-02-26 08:30:00  2706
2010-02-26 09:00:00  2707
2010-02-26 09:30:00  2708
2010-02-26 10:00:00  2709
2010-02-27 08:00:00  2753
2010-02-27 08:30:00  2754
2010-02-27 09:00:00  2755
2010-02-27 09:30:00  2756
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2011-12-27 08:00:00 34817
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2011-12-28 08:00:00 34865
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2011-12-29 08:00:00 34913
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2011-12-31 08:00:00 35009
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2011-12-31 09:00:00 35011
2011-12-31 09:30:00 35012
2011-12-31 10:00:00 35013

Finalmente, se dejan aquí un conjunto de funciones o recomendaciones que pueden ser de utilidad en ciertos casos

Para leer y escribir archivos más adecuados para series de tiempo (los cuales pueden estar en formato csv) son read.zoo() y write.zoo(). Un ejemplo de esto, ya que se puede establecer algunas opciones desde la carga o escritura de los archivos, son read.zoo(file, index.column = 0, sep = ",", format = "%m/%d/%Y") y write.zoo(object_xts, sep = ",", file = tmp). Después de cargar el archivo con read.zoo() se recomienda utilizar as.xts().
Ya que se pueden crear filtros, es posible remplazar valores de ciertas fechas con NA, valores numéricos, otras fechas, etc. Lo cual puede ayudar a crear valores perdidos controlados o en la imputación de datos.
Al igual que en lubridate, se pueden hacer las operaciones básicas (suma, resta, multiplicación y división). ¿Qué pasa con los índices?
Para las operaciones básicas, se realizan intersecciones. Se puede utilizar la función merge, con las distintas formas de unión, para unir fechas.
zoo cuenta con muchas funciones para tratar los valores perdido, como zoo::na.aggregate(), zoo::na.fill(), zoo::na.locf() , zoo::na.approx(), etc. Estas últimas dos rellenan los valores perdidos con la última (zoo::na.locf()) o la siguiente observación (zoo::na.locf()) y con un ajuste lineal.
Existe la función xts::lag.xts() y se puede utilizar la función diff().

Recursos adicionales

Aquí se dejan algunos enlaces interesantes que pueden ser de utilidad o pueden ayudar a tener un mayor conocimiento en toda esta interesante herramienta