This vignette gives examples of how to read data in various formats
in the zoo package using the read.zoo()
function. The function read.zoo() function expects either a
text file (or text connection) as input or data frame. The
former case is handled by first using read.table() to
produce the data frame. (Instead of a text file, the
text argument can be used to read a text string that is
already stored in R which is used in the examples of this vignette.)
Subsequently, read.zoo() provides a wide collection of
convenience functionality to turn that data frame into a
zoo series with a specific structure and a specific time
index. In this vignette, an overview is provided of the wide variety of
cases that can be handled with read.zoo(). All examples
assume that zoo is already loaded and (if necessary) that
the chron package has been loaded as well.
Note that functions read.csv.zoo(),
read.csv2.zoo(), read.delim.zoo(), and
read.delim2.zoo() are available that call the respective
read.*() function instead of read.table() and
subsequently read.zoo(). However, these convenience
interfaces are not employed in this vignette in order to demonstrate
setting all arguments `by hand’.
Input class: Text file/connection (space-separated with header).
Input index: integer.
Output class: Multivariate zoo
series.
Output index: integer.
Strategy: No transformation of time index needed,
hence only a simple call to read.zoo().
Lines <- "
time latitude longitude altitude distance heartrate
1277648884 0.304048 -0.793819 260 0.000000 94
1277648885 0.304056 -0.793772 262 4.307615 95
1277648894 0.304075 -0.793544 263 25.237911 103
1277648902 0.304064 -0.793387 256 40.042988 115
"
z <- read.zoo(text = Lines, header = TRUE)
z## latitude longitude altitude distance heartrate
## 1277648884 0.304048 -0.793819 260 0.000000 94
## 1277648885 0.304056 -0.793772 262 4.307615 95
## 1277648894 0.304075 -0.793544 263 25.237911 103
## 1277648902 0.304064 -0.793387 256 40.042988 115Input class: data.frame.
Input index: factor with labels
indicating AM/PM times but no date.
Output class: Multivariate zoo
series.
Output index: times (from
chron).
Strategy: The idea is to add some dummy date (here
1970-01-01) to the character lables, then transform to
chron and extract the times.
Caveat: The AM/PM labels are locale-dependent (e.g., could be am/pm or a.m./p.m. in other English locales). Hence, it needs to be checked whether the attribute is read correctly in the current locale. Here, a C locale is used to assure reproducibility.
DF <- structure(list(
Time = structure(1:5, levels = c("7:10:03 AM", "7:10:36 AM",
"7:11:07 AM", "7:11:48 AM", "7:12:25 AM"), class = "factor"),
Bid = c(6118.5, 6118.5, 6119.5, 6119, 6119),
Offer = c(6119.5, 6119.5, 6119.5, 6120, 6119.5)),
names = c("Time", "Bid", "Offer"), row.names = c(NA, -5L),
class = "data.frame")
DF## Time Bid Offer
## 1 7:10:03 AM 6118.5 6119.5
## 2 7:10:36 AM 6118.5 6119.5
## 3 7:11:07 AM 6119.5 6119.5
## 4 7:11:48 AM 6119.0 6120.0
## 5 7:12:25 AM 6119.0 6119.5z <- read.zoo(DF, FUN = function(x)
times(as.chron(paste("1970-01-01", x), format = "%Y-%m-%d %I:%M:%S %p")))
z## Bid Offer
## 07:10:03 6118.5 6119.5
## 07:10:36 6118.5 6119.5
## 07:11:07 6119.5 6119.5
## 07:11:48 6119.0 6120.0
## 07:12:25 6119.0 6119.5Input class: Text file/connection (semicolon-separated with header).
Input index: factors with labels
indicating dates (column 1) and times (column 2).
Output class: Multivariate zoo series,
with separate columns for each date.
Output index: times (from
chron).
Strategy: Split the data based on date (column 1)
and process times (column 2) to times. Enhance column names
at the end.
Lines <- "
Date;Time;Close
01/09/2009;10:00;56567
01/09/2009;10:05;56463
01/09/2009;10:10;56370
01/09/2009;16:45;55771
01/09/2009;16:50;55823
01/09/2009;16:55;55814
02/09/2009;10:00;55626
02/09/2009;10:05;55723
02/09/2009;10:10;55659
02/09/2009;16:45;55742
02/09/2009;16:50;55717
02/09/2009;16:55;55385
"
f <- function(x) times(paste(x, 0, sep = ":"))
z <- read.zoo(text = Lines, header = TRUE, sep = ";",
split = 1, index = 2, FUN = f)
colnames(z) <- sub("X(..).(..).(....)", "\\3-\\2-\\1", colnames(z))
z## 01/09/2009 02/09/2009
## 10:00:00 56567 55626
## 10:05:00 56463 55723
## 10:10:00 56370 55659
## 16:45:00 55771 55742
## 16:50:00 55823 55717
## 16:55:00 55814 55385Input class: Text file/connection (space-separated with header).
Input index: factors with labels
indicating dates (column 1) and times (column 2).
Output class: Multivariate zoo
series.
Output index: chron (from
chron).
Strategy: Indicate vector of two columns in
index, which is subsequently processed by a
FUN taking two arguments and returning a chron
time/date.
Lines <- "
Date Time O H L C
1/2/2005 17:05 1.3546 1.3553 1.3546 1.35495
1/2/2005 17:10 1.3553 1.3556 1.3549 1.35525
1/2/2005 17:15 1.3556 1.35565 1.35515 1.3553
1/2/2005 17:25 1.355 1.3556 1.355 1.3555
1/2/2005 17:30 1.3556 1.3564 1.35535 1.3563
"
f <- function(d, t) as.chron(paste(as.Date(chron(d)), t))
z <- read.zoo(text = Lines, header = TRUE, index = 1:2, FUN = f)
z## O H L C
## (01/02/05 17:05:00) 1.3546 1.35530 1.35460 1.35495
## (01/02/05 17:10:00) 1.3553 1.35560 1.35490 1.35525
## (01/02/05 17:15:00) 1.3556 1.35565 1.35515 1.35530
## (01/02/05 17:25:00) 1.3550 1.35560 1.35500 1.35550
## (01/02/05 17:30:00) 1.3556 1.35640 1.35535 1.35630Input class: Text file/connection (space-separated with non-matching header).
Input index: factors with labels
indicating dates (column 6) and unneeded weekdays (column 5) and times
(column 7).
Output class: Multivariate zoo
series.
Output index: Date.
Strategy: First, skip the header line,
remove unneeded columns by setting colClasses to
"NULL", and set suitable col.names. Second,
convert the date column to a Date index using
format. Finally, aggregate over duplicate dates, keeping
only the last observation.
Lines <-
" views number timestamp day time
1 views 910401 1246192687 Sun 6/28/2009 12:38
2 views 921537 1246278917 Mon 6/29/2009 12:35
3 views 934280 1246365403 Tue 6/30/2009 12:36
4 views 986463 1246888699 Mon 7/6/2009 13:58
5 views 995002 1246970243 Tue 7/7/2009 12:37
6 views 1005211 1247079398 Wed 7/8/2009 18:56
7 views 1011144 1247135553 Thu 7/9/2009 10:32
8 views 1026765 1247308591 Sat 7/11/2009 10:36
9 views 1036856 1247436951 Sun 7/12/2009 22:15
10 views 1040909 1247481564 Mon 7/13/2009 10:39
11 views 1057337 1247568387 Tue 7/14/2009 10:46
12 views 1066999 1247665787 Wed 7/15/2009 13:49
13 views 1077726 1247778752 Thu 7/16/2009 21:12
14 views 1083059 1247845413 Fri 7/17/2009 15:43
15 views 1083059 1247845824 Fri 7/17/2009 18:45
16 views 1089529 1247914194 Sat 7/18/2009 10:49
"
cl <- c("NULL", "numeric", "character")[c(1, 1, 2, 2, 1, 3, 1)]
cn <- c(NA, NA, "views", "number", NA, NA, NA)
z <- read.zoo(text = Lines, skip = 1, col.names = cn, colClasses = cl,
index = 3, format = "%m/%d/%Y",
aggregate = function(x) tail(x, 1))
z## views number
## 2009-06-28 910401 1246192687
## 2009-06-29 921537 1246278917
## 2009-06-30 934280 1246365403
## 2009-07-06 986463 1246888699
## 2009-07-07 995002 1246970243
## 2009-07-08 1005211 1247079398
## 2009-07-09 1011144 1247135553
## 2009-07-11 1026765 1247308591
## 2009-07-12 1036856 1247436951
## 2009-07-13 1040909 1247481564
## 2009-07-14 1057337 1247568387
## 2009-07-15 1066999 1247665787
## 2009-07-16 1077726 1247778752
## 2009-07-17 1083059 1247845824
## 2009-07-18 1089529 1247914194Extract all Thursdays and Fridays.
## views number
## 2009-07-09 1011144 1247135553
## 2009-07-16 1077726 1247778752
## 2009-07-17 1083059 1247845824Keep last entry in each week.
## views number
## 2009-07-09 1011144 1247135553
## 2009-07-17 1083059 1247845824Alternative approach: Above approach labels each point as it was originally labeled, i.e., if Thursday is used it gets the date of that Thursday. Another approach is to always label the resulting point as Friday and also use the last available value even if its not Thursday.
Create daily grid and fill in so Friday is filled in with prior value
if Friday is NA.
Extract Fridays, including those filled in from previous day.
## views number
## 2009-07-03 934280 1246365403
## 2009-07-10 1011144 1247135553
## 2009-07-17 1083059 1247845824Input class: Text file/connection (comma-separated with header).
Input index: factors with labels
indicating dates (column 1) and times (column 2).
Output class: Multivariate zoo
series.
Output index: chron (from
chron) or POSIXct.
Strategy: Three versions, all using vector
index = 1:2.
Lines <- "
Date,Time,Open,High,Low,Close,Up,Down
05.02.2001,00:30,421.20,421.20,421.20,421.20,11,0
05.02.2001,01:30,421.20,421.40,421.20,421.40,7,0
05.02.2001,02:00,421.30,421.30,421.30,421.30,0,5"With custom FUN using chron() after
appending seconds.
f <- function(d, t) chron(d, paste(t, "00", sep = ":"),
format = c("m.d.y", "h:m:s"))
z <- read.zoo(text = Lines, sep = ",", header = TRUE,
index = 1:2, FUN = f)
z## Open High Low Close Up Down
## (05.02.01 00:30:00) 421.2 421.2 421.2 421.2 11 0
## (05.02.01 01:30:00) 421.2 421.4 421.2 421.4 7 0
## (05.02.01 02:00:00) 421.3 421.3 421.3 421.3 0 5With custom FUN using as.chron() with
suitable format.
f2 <- function(d, t) as.chron(paste(d, t), format = "%d.%m.%Y %H:%M")
z2 <- read.zoo(text = Lines, sep = ",", header = TRUE,
index = 1:2, FUN = f2)
z2## Open High Low Close Up Down
## (02/05/01 00:30:00) 421.2 421.2 421.2 421.2 11 0
## (02/05/01 01:30:00) 421.2 421.4 421.2 421.4 7 0
## (02/05/01 02:00:00) 421.3 421.3 421.3 421.3 0 5Without FUN, hence the index columns are
pasted together and then passt do as.POSIXct() because
tz and format are specified.
z3 <- read.zoo(text = Lines, sep = ",", header = TRUE,
index = 1:2, tz = "", format = "%d.%m.%Y %H:%M")
z3## Open High Low Close Up Down
## 2001-02-05 00:30:00 421.2 421.2 421.2 421.2 11 0
## 2001-02-05 01:30:00 421.2 421.4 421.2 421.4 7 0
## 2001-02-05 02:00:00 421.3 421.3 421.3 421.3 0 5Input class: Text file/connection (space-separated with header).
Input index: factors with labels
indicating dates (column 1) and times (column 2).
Output class: Multivariate zoo
series.
Output index: POSIXct.
Strategy: Due to standard date/time formats, only
index = 1:2 and tz = "" need to be specified
to produce POSIXct index.
Lines <- "Date Time V2 V3 V4 V5
2010-10-15 13:43:54 73.8 73.8 73.8 73.8
2010-10-15 13:44:15 73.8 73.8 73.8 73.8
2010-10-15 13:45:51 73.8 73.8 73.8 73.8
2010-10-15 13:46:21 73.8 73.8 73.8 73.8
2010-10-15 13:47:27 73.8 73.8 73.8 73.8
2010-10-15 13:47:54 73.8 73.8 73.8 73.8
2010-10-15 13:49:51 73.7 73.7 73.7 73.7
"
z <- read.zoo(text = Lines, header = TRUE, index = 1:2, tz = "")
z## V2 V3 V4 V5
## 2010-10-15 13:43:54 73.8 73.8 73.8 73.8
## 2010-10-15 13:44:15 73.8 73.8 73.8 73.8
## 2010-10-15 13:45:51 73.8 73.8 73.8 73.8
## 2010-10-15 13:46:21 73.8 73.8 73.8 73.8
## 2010-10-15 13:47:27 73.8 73.8 73.8 73.8
## 2010-10-15 13:47:54 73.8 73.8 73.8 73.8
## 2010-10-15 13:49:51 73.7 73.7 73.7 73.7Input class: Text file/connection (space-separated without header).
Input index: factor with labels
indicating dates.
Output class: Multivariate zoo series,
with separate columns depending on column 2.
Output index: Date.
Strategy: Non-standard na.strings
format needs to be specified, series is split based on
second column, and date format (in column 1, default) needs
to be specified.
Lines <- "
13/10/2010 A 23
13/10/2010 B 12
13/10/2010 C 124
14/10/2010 A 43
14/10/2010 B 54
14/10/2010 C 65
15/10/2010 A 43
15/10/2010 B N.A.
15/10/2010 C 65
"
z <- read.zoo(text = Lines, na.strings = "N.A.",
format = "%d/%m/%Y", split = 2)
z## A B C
## 2010-10-13 23 12 124
## 2010-10-14 43 54 65
## 2010-10-15 43 NA 65Input class: Text file/connection (comma-separated with header).
Input index: factor with labels
indicating date/time.
Output class: Univariate zoo
series.
Output index: chron (from
chron) or POSIXct.
Strategy: Ignore first two columns by setting
colClasses to "NULL". Either produce
chron index via as.chron() or use all defaults
to produce POSIXct by setting tz.
Lines <- '
"","Fish_ID","Date","R2sqrt"
"1",1646,2006-08-18 08:48:59,0
"2",1646,2006-08-18 09:53:20,100
'
z <- read.zoo(text = Lines, header = TRUE, sep = ",",
colClasses = c("NULL", "NULL", "character", "numeric"),
FUN = as.chron)
z## (08/18/06 08:48:59) (08/18/06 09:53:20)
## 0 100z2 <- read.zoo(text = Lines, header = TRUE, sep = ",",
colClasses = c("NULL", "NULL", "character", "numeric"),
tz = "")
z2## 2006-08-18 08:48:59 2006-08-18 09:53:20
## 0 100Input class: Text file/connection (space-separated with non-matching header).
Input index: factor with labels
indicating date (column 3) and time (column 4).
Output class: Multivariate zoo
series.
Output index: chron (from
chron) or POSIXct.
Strategy: skip non-matching header and
extract date/time from two columns index = 3:4. Either
using sequence of two functions FUN and FUN2
or employ defaults yielding POSIXct.
Lines <-
" iteration Datetime VIC1 NSW1 SA1 QLD1
1 1 2011-01-01 00:30 5482.09 7670.81 2316.22 5465.13
2 1 2011-01-01 01:00 5178.33 7474.04 2130.30 5218.61
3 1 2011-01-01 01:30 4975.51 7163.73 2042.39 5058.19
4 1 2011-01-01 02:00 5295.36 6850.14 1940.19 4897.96
5 1 2011-01-01 02:30 5042.64 6587.94 1836.19 4749.05
6 1 2011-01-01 03:00 4799.89 6388.51 1786.32 4672.92
"
z <- read.zoo(text = Lines, skip = 1, index = 3:4,
FUN = paste, FUN2 = as.chron)
z## V1 V2 V5 V6 V7 V8
## (01/01/11 00:30:00) 1 1 5482.09 7670.81 2316.22 5465.13
## (01/01/11 01:00:00) 2 1 5178.33 7474.04 2130.30 5218.61
## (01/01/11 01:30:00) 3 1 4975.51 7163.73 2042.39 5058.19
## (01/01/11 02:00:00) 4 1 5295.36 6850.14 1940.19 4897.96
## (01/01/11 02:30:00) 5 1 5042.64 6587.94 1836.19 4749.05
## (01/01/11 03:00:00) 6 1 4799.89 6388.51 1786.32 4672.92## V1 V2 V5 V6 V7 V8
## 2011-01-01 00:30:00 1 1 5482.09 7670.81 2316.22 5465.13
## 2011-01-01 01:00:00 2 1 5178.33 7474.04 2130.30 5218.61
## 2011-01-01 01:30:00 3 1 4975.51 7163.73 2042.39 5058.19
## 2011-01-01 02:00:00 4 1 5295.36 6850.14 1940.19 4897.96
## 2011-01-01 02:30:00 5 1 5042.64 6587.94 1836.19 4749.05
## 2011-01-01 03:00:00 6 1 4799.89 6388.51 1786.32 4672.92Input class: data.frame.
Input index: Date.
Output class: Multivariate zoo
series.
Output index: Date.
Strategy: Given a data.frame only keep
last row in each month. Use read.zoo() to convert to
zoo and then na.locf() and
duplicated().
DF <- structure(list(
Date = structure(c(14609, 14638, 14640, 14666, 14668, 14699,
14729, 14757, 14759, 14760), class = "Date"),
A = c(4.9, 5.1, 5, 4.8, 4.7, 5.3, 5.2, 5.4, NA, 4.6),
B = c(18.4, 17.7, NA, NA, 18.3, 19.4, 19.7, NA, NA, 18.1),
C = c(32.6, NA, 32.8, NA, 33.7, 32.4, 33.6, NA, 34.5, NA),
D = c(77, NA, 78.7, NA, 79, 77.8, 79, 81.7, NA, NA)),
names = c("Date", "A", "B", "C", "D"), row.names = c(NA, -10L),
class = "data.frame")
DF## Date A B C D
## 1 2009-12-31 4.9 18.4 32.6 77.0
## 2 2010-01-29 5.1 17.7 NA NA
## 3 2010-01-31 5.0 NA 32.8 78.7
## 4 2010-02-26 4.8 NA NA NA
## 5 2010-02-28 4.7 18.3 33.7 79.0
## 6 2010-03-31 5.3 19.4 32.4 77.8
## 7 2010-04-30 5.2 19.7 33.6 79.0
## 8 2010-05-28 5.4 NA NA 81.7
## 9 2010-05-30 NA NA 34.5 NA
## 10 2010-05-31 4.6 18.1 NA NA## A B C D
## 2009-12-31 4.9 18.4 32.6 77.0
## 2010-01-31 5.0 17.7 32.8 78.7
## 2010-02-28 4.7 18.3 33.7 79.0
## 2010-03-31 5.3 19.4 32.4 77.8
## 2010-04-30 5.2 19.7 33.6 79.0
## 2010-05-31 4.6 18.1 34.5 81.7Input class: Text file/connection (space-separated without header).
Input index: factor with labels
indicating dates.
Output class: Univariate zoo
series.
Output index: Date.
Strategy: Only keep last point in case of duplicate dates.
Lines <- "
2009-10-07 0.009378
2009-10-19 0.014790
2009-10-23 -0.005946
2009-10-23 0.009096
2009-11-08 0.004189
2009-11-10 -0.004592
2009-11-17 0.009397
2009-11-24 0.003411
2009-12-02 0.003300
2010-01-15 0.010873
2010-01-20 0.010712
2010-01-20 0.022237
"
z <- read.zoo(text = Lines, aggregate = function(x) tail(x, 1))
z## 2009-10-07 2009-10-19 2009-10-23 2009-11-08 2009-11-10 2009-11-17 2009-11-24
## 0.009378 0.014790 0.009096 0.004189 -0.004592 0.009397 0.003411
## 2009-12-02 2010-01-15 2010-01-20
## 0.003300 0.010873 0.022237Input class: Text file/connection (comma-separated with header).
Input index: factor with labels
indicating date/time.
Output class: Multivariate zoo
series.
Output index: POSIXct or
chron (from chron).
Strategy: Dates and times are in standard format,
hence the default POSIXct can be produced by setting
tz or, alternatively, chron can be produced by
setting as.chron() as FUN.
Lines <- "
timestamp,time-step-index,value
2009-11-23 15:58:21,23301,800
2009-11-23 15:58:29,23309,950
"
z <- read.zoo(text = Lines, header = TRUE, sep = ",", tz = "")
z## time.step.index value
## 2009-11-23 15:58:21 23301 800
## 2009-11-23 15:58:29 23309 950## time.step.index value
## (11/23/09 15:58:21) 23301 800
## (11/23/09 15:58:29) 23309 950Input class: Text file/connection (space-separated with header).
Input index: factors with labels
indicating dates (column 1) times (column 2).
Output class: Univariate zoo
series.
Output index: chron (from
chron).
Strategy: Indicate vector index = 1:2
and use chron() (which takes two separate arguments for
dates and times) to produce chron index.
Lines <- "
Date Time Value
01/23/2000 10:12:15 12.12
01/24/2000 11:10:00 15.00
"
z <- read.zoo(text = Lines, header = TRUE, index = 1:2, FUN = chron)
z## (01/23/00 10:12:15) (01/24/00 11:10:00)
## 12.12 15.00Input class: Text file/connection (space-separated with header).
Input index: numeric year with quarters
represented by separate columns.
Output class: Univariate zoo
series.
Output index: yearqtr.
Strategy: First, create a multivariate annual time
series using the year index. Then, create a regular univariate quarterly
series by collapsing the annual series to a vector and adding a new
yearqtr index from scratch.
Lines <- "
Year Qtr1 Qtr2 Qtr3 Qtr4
1992 566 443 329 341
1993 344 212 133 112
1994 252 252 199 207
"
za <- read.zoo(text = Lines, header = TRUE)
za## Qtr1 Qtr2 Qtr3 Qtr4
## 1992 566 443 329 341
## 1993 344 212 133 112
## 1994 252 252 199 207## 1992 Q1 1992 Q2 1992 Q3 1992 Q4 1993 Q1 1993 Q2 1993 Q3 1993 Q4 1994 Q1 1994 Q2
## 566 443 329 341 344 212 133 112 252 252
## 1994 Q3 1994 Q4
## 199 207