class(terns)[1] "data.frame"16 Aggregation & Grouping
NoteLearning Goals
After this lesson, you should be able to:
- Call a function repeatedly with
sapplyorlapply - Split data into groups and apply a function to each
16.1 Apply Functions
Section 13.1.3 introduced vectorization, a convenient and efficient way to compute multiple results. That section also mentioned that some of R’s functions—the ones that summarize or aggregate data—are not vectorized.
The class function is an example of a function that’s not vectorized. If we call the class function on the least terns data set, we get just one result for the data set as a whole:
What if we want to get the class of each column? We can get the class for a single column by selecting the column with $, the dollar sign operator:
class(terns$year)[1] "integer"But what if we want the classes for all the columns? We could write a call to class for each column, but that would be tedious. When you’re working with a programming language, you should try to avoid tedium; there’s usually a better, more automated way.
Section 13.2.1 pointed out that data frames are technically lists, where each column is one element. With that in mind, what we need here is a line of code that calls class on each element of the data frame. The idea is similar to vectorization, but since we have a list and a non-vectorized function, we have to do a bit more than just call class(terns).
The lapply function calls, or applies, a function on each element of a list or vector. The syntax is:
lapply(X, FUN, ...)The function FUN is called once for each element of X, with the element as the first argument. The ... is for additional arguments to FUN, which are held constant across all the elements.
Let’s try this out with the least terns data and the class function:
lapply(terns, class)$year
[1] "integer"
$site_name
[1] "character"
$site_name_2013_2018
[1] "character"
$site_name_1988_2001
[1] "character"
$site_abbr
[1] "character"
$region_3
[1] "character"
$region_4
[1] "character"
$event
[1] "character"
$bp_min
[1] "numeric"
$bp_max
[1] "numeric"
$fl_min
[1] "integer"
$fl_max
[1] "integer"
$total_nests
[1] "integer"
$nonpred_eggs
[1] "integer"
$nonpred_chicks
[1] "integer"
$nonpred_fl
[1] "integer"
$nonpred_ad
[1] "integer"
$pred_control
[1] "character"
$pred_eggs
[1] "integer"
$pred_chicks
[1] "integer"
$pred_fl
[1] "integer"
$pred_ad
[1] "integer"
$pred_pefa
[1] "character"
$pred_coy_fox
[1] "character"
$pred_meso
[1] "character"
$pred_owlspp
[1] "character"
$pred_corvid
[1] "character"
$pred_other_raptor
[1] "character"
$pred_other_avian
[1] "character"
$pred_misc
[1] "character"
$total_pefa
[1] "integer"
$total_coy_fox
[1] "integer"
$total_meso
[1] "integer"
$total_owlspp
[1] "integer"
$total_corvid
[1] "integer"
$total_other_raptor
[1] "integer"
$total_other_avian
[1] "integer"
$total_misc
[1] "integer"
$first_observed
[1] "character"
$last_observed
[1] "character"
$first_nest
[1] "character"
$first_chick
[1] "character"
$first_fledge
[1] "character"The result is similar to if the class function was vectorized. In fact, if we use a vector and a vectorized function with lapply, the result is nearly identical to the result from vectorization:
x = c(1, 2, pi)
sin(x)[1] 8.414710e-01 9.092974e-01 1.224647e-16lapply(x, sin)[[1]]
[1] 0.841471
[[2]]
[1] 0.9092974
[[3]]
[1] 1.224647e-16The only difference is that the result from lapply is a list. In fact, the lapply function always returns a list with one element for each element of the input data. The “l” in lapply stands for “list”.
The lapply function is one member of a family of functions called apply functions. All of the apply functions provide ways to apply a function repeatedly to different parts of a data structure. We’ll meet a few more apply functions soon.
When you have a choice between using vectorization or an apply function, you should always choose vectorization. Vectorization is clearer—compare the two lines of code above—and it’s also significantly more efficient. In fact, vectorization is the most efficient way to call a function repeatedly in R.
As we saw with the class function, there are some situations where vectorization is not possible. That’s when you should think about using an apply function.
16.1.1 The sapply Function
The related sapply function calls a function on each element of a list or vector, and simplifies the result. That last part is the crucial difference compared to lapply. When results from the calls all have the same type and length, sapply returns a vector or matrix instead of a list. When the results have different types or lengths, the result is the same as for lapply. The “s” in sapply stands for “simplify”.
For instance, if we use sapply to find the classes of the columns in the least terns data, we get a character vector:
sapply(terns, class) year site_name site_name_2013_2018 site_name_1988_2001
"integer" "character" "character" "character"
site_abbr region_3 region_4 event
"character" "character" "character" "character"
bp_min bp_max fl_min fl_max
"numeric" "numeric" "integer" "integer"
total_nests nonpred_eggs nonpred_chicks nonpred_fl
"integer" "integer" "integer" "integer"
nonpred_ad pred_control pred_eggs pred_chicks
"integer" "character" "integer" "integer"
pred_fl pred_ad pred_pefa pred_coy_fox
"integer" "integer" "character" "character"
pred_meso pred_owlspp pred_corvid pred_other_raptor
"character" "character" "character" "character"
pred_other_avian pred_misc total_pefa total_coy_fox
"character" "character" "integer" "integer"
total_meso total_owlspp total_corvid total_other_raptor
"integer" "integer" "integer" "integer"
total_other_avian total_misc first_observed last_observed
"integer" "integer" "character" "character"
first_nest first_chick first_fledge
"character" "character" "character" Likewise, if we use sapply to compute the sin values, we get a numeric vector, the same as from vectorization:
sapply(x, sin)[1] 8.414710e-01 9.092974e-01 1.224647e-16In spite of that, vectorization is still more efficient than sapply, so use vectorization instead when possible.
Apply functions are incredibly useful for summarizing data. For example, suppose we want to compute the frequencies for all of the columns in the least terns data set that aren’t numeric.
First, we need to identify the columns. One way to do this is with the is.numeric function. Despite the name, this function actually tests whether its argument is a real number, not whether it its argument is a numeric vector. In other words, it also returns true for integer values. We can use sapply to apply this function to all of the columns in the least terns data set:
is_not_number = !sapply(terns, is.numeric)
is_not_number year site_name site_name_2013_2018 site_name_1988_2001
FALSE TRUE TRUE TRUE
site_abbr region_3 region_4 event
TRUE TRUE TRUE TRUE
bp_min bp_max fl_min fl_max
FALSE FALSE FALSE FALSE
total_nests nonpred_eggs nonpred_chicks nonpred_fl
FALSE FALSE FALSE FALSE
nonpred_ad pred_control pred_eggs pred_chicks
FALSE TRUE FALSE FALSE
pred_fl pred_ad pred_pefa pred_coy_fox
FALSE FALSE TRUE TRUE
pred_meso pred_owlspp pred_corvid pred_other_raptor
TRUE TRUE TRUE TRUE
pred_other_avian pred_misc total_pefa total_coy_fox
TRUE TRUE FALSE FALSE
total_meso total_owlspp total_corvid total_other_raptor
FALSE FALSE FALSE FALSE
total_other_avian total_misc first_observed last_observed
FALSE FALSE TRUE TRUE
first_nest first_chick first_fledge
TRUE TRUE TRUE Is it worth using R code to identify the non-numeric columns? Since there are only 43 columns in the least terns data set, maybe not. But if the data set was larger, with say 100 columns, it definitely would be.
In general, it’s a good habit to use R to do things rather than do them manually. You’ll get more practice programming, and your code will be more flexible if you want to adapt it to other data sets.
Now that we know which columns are non-numeric, we can use the table function to compute frequencies. We only want to compute frequencies for those columns, so we need to subset the data:
lapply(terns[, is_not_number], table)$site_name
ALAMEDA POINT
21
ALBANY CENTRAL AVE
2
ANAHEIM LAKE
3
ARIZONA GLENDALE
1
BATIQUITOS LAGOON ECOLOGICAL RESERVE
21
BOLSA CHICA ECOLOGICAL RESERVE
21
BURRIS ISLAND
18
CHULA VISTA WILDLIFE RESERVE
21
COAL OIL POINT RESERVE
14
DSTREET FILL SWEETWATER MARSH NWR
21
EDEN LANDING ECOLOGICAL RESERVE
17
FAIRBANKS RANCH
9
GUADALUPE NIPOMO DUNES NWR
1
HAYWARD REGIONAL SHORELINE
19
HOLLYWOOD BEACH
18
HUNTINGTON STATE BEACH
21
KETTLEMAN CITY
15
LA HARBOR
21
MALIBU LAGOON
7
MCB CAMP PENDLETON
21
MISSION BAY FAA ISLAND
21
MISSION BAY MARINERS POINT
21
MISSION BAY NORTH FIESTA ISLAND
21
MISSION BAY SAN DIEGO RIVER MOUTH
14
MISSION BAY STONY POINT
18
MONTEZUMA WETLANDS
18
NAPA SONOMA MARSH WILDLIFE AREA
17
NAS NORTH ISLAND
21
NAVAL AMPHIBIOUS BASE CORONADO
21
NBVC POINT MUGU
21
OCEANO DUNES STATE VEHICULAR RECREATION AREA
21
ORMOND BEACH
21
PITTSBURG POWER PLANT
15
RANCHO GUADALUPE DUNES PRESERVE
20
SACRAMENTO BUFFERLANDS
10
SALTON SEA
7
SAN DIEGUITO LAGOON ECOLOGICAL RESERVE
10
SAN ELIJO LAGOON ECOLOGICAL RESERVE
21
SAN PABLO BAY NWR
3
SANTA CLARA RIVER MCGRATH STATE BEACH
21
SATICOY UNITED WATER CONSERVATION DISTRICT
9
SDIA LINDBERGH FIELD
21
SEAL BEACH NWR ANAHEIM BAY
21
SILVER STRAND STATE BEACH
1
SOUTH SAN DIEGO BAY UNIT SDNWR SALTWORKS
21
TIJUANA ESTUARY NERR
21
UPPER NEWPORT BAY ECOLOGICAL RESERVE
21
VANDENBERG SFB
21
VENICE BEACH
21
$site_name_2013_2018
3
Alameda Point
21
Anaheim Lake
3
Batiquitos Lagoon
21
Bolsa Chica
21
Bufferlands
10
Burris Basin
18
Camp Pendleton
21
Chula Vista Wildlife Refuge
21
Coal Oil Point Reserve
14
D Street Fill
21
Eden Landing Ecological Reserve
17
FAA Island
21
Fairbanks Ranch
9
Hayward Regional Shoreline
19
Hollywood Beach
18
Huntington Beach State Park
21
Kettleman
15
Lindbergh Field/Former Naval Training Center
21
Malibu Lagoon
7
Mariner's Point
21
Montezuma
18
NA_NO POLYGON
5
Napa Sonoma Marsh Wildlife Area Huichica Unit (Pond 7/7A)
17
Naval Base Coronado
42
NBVC Point Mugu
21
North Fiesta Island
21
Oceano Dunes State Vehicular Recreation Area
21
Ormond Beach
21
Pittsburg Power Plant
15
Port of LA
21
Rancho Guadalupe Dunes Preserve
20
Salton Sea
7
Saltworks
21
San Diego River Mouth
14
San Diequito Lagoon
10
San Elijo Lagoon
21
Santa Clara River
21
Saticoy United Water Conservation District
9
Seal Beach National Wildlife Refuge
21
Stony Point
18
Tijuana Estuary
21
Upper Newport Bay
21
Vandenberg AFB
21
Venice Beach
21
$site_name_1988_2001
Albany Central Avenue NA_2013_2018 POLYGON
3 2 783
NA_NO POLYGON
3
$site_abbr
AL_CENTAVE ALAM_PT ANA_LK ARZ_GLEN BCER BLER
2 21 3 1 21 21
BUR_ISL COAL_OIL CV D_ST ELER FAIR_RAN
18 14 21 21 17 9
GND_NWR HAY_REG_SHOR HB HSB KET_CTY LA_HARB
1 19 18 21 15 21
MAL_LAG MB_FAA MB_MAR_PT MB_NFI MB_SDRIV_S MB_STONY
7 21 21 21 14 18
MCBCP MONT NAB NASNI NSMWA OCEANO_DUNES
21 18 21 21 17 21
ORMOND PITT_POWER PT_MUGU RGDP S_CLAR_MCG SAC_BUF
21 15 21 20 21 10
SALT SALT_SEA SAN_ELIJO SANDIEGU_LAG SAT_WCD SDIA_LF
21 7 21 10 9 21
SEAL_BCH_NWR SLVRSTRD SPBNWR TJ_RIV UNBER VAN_SFB
21 1 3 21 21 21
VEN_BCH
21
$region_3
ARIZONA CENTRAL KINGS S.F._BAY SACRAMENTO SOUTHERN
1 77 15 112 10 576
$region_4
ARIZONA CENTRAL KINGS S.F._BAY SACRAMENTO SOUTHERN VENTURA
1 77 15 112 10 486 90
$event
EL_NINO LA_NINA NEUTRAL
120 258 413
$pred_control
N Y
342 134 315
$pred_pefa
N Y
143 423 225
$pred_coy_fox
N Y
142 535 114
$pred_meso
N Y
142 552 97
$pred_owlspp
N Y
142 531 118
$pred_corvid
N Y
142 423 226
$pred_other_raptor
N NN Y
145 437 1 208
$pred_other_avian
N Y
143 395 253
$pred_misc
N Y
159 415 217
$first_observed
2000-04-16 2000-04-19 2000-04-21 2000-04-22 2000-04-23 2000-04-26
141 1 1 1 1 1 1
2000-04-28 2000-05-01 2000-05-04 2000-05-06 2000-05-07 2000-05-09 2000-05-11
5 1 3 1 2 1 1
2000-05-21 2000-06-06 2000-06-10 2004-04-08 2004-04-11 2004-04-12 2004-04-21
1 1 1 1 1 2 1
2004-04-22 2004-04-23 2004-04-27 2004-04-30 2004-05-01 2004-05-03 2004-05-07
2 1 1 1 1 2 1
2004-05-09 2004-05-10 2004-05-14 2004-05-17 2004-05-31 2004-06-03 2005-04-14
1 1 1 1 1 1 1
2005-04-15 2005-04-18 2005-04-19 2005-04-20 2005-04-21 2005-04-22 2005-04-23
1 2 1 2 2 1 2
2005-04-24 2005-04-28 2005-04-29 2005-04-30 2005-05-01 2005-05-05 2005-05-07
1 2 1 1 1 1 1
2005-05-08 2005-05-10 2005-05-13 2005-05-27 2005-05-28 2006-04-08 2006-04-10
1 1 1 1 1 2 1
2006-04-13 2006-04-15 2006-04-16 2006-04-20 2006-04-22 2006-04-27 2006-04-28
1 1 1 1 3 1 1
2006-05-01 2006-05-03 2006-05-08 2006-05-10 2006-05-14 2006-05-15 2006-05-20
1 2 1 1 2 1 1
2006-06-23 2006-06-24 2007-04-16 2007-04-18 2007-04-22 2007-04-23 2007-04-24
1 1 1 1 1 1 2
2007-04-25 2007-04-26 2007-04-28 2007-05-01 2007-05-02 2007-05-03 2007-05-11
4 2 1 3 3 1 1
2007-05-13 2007-05-14 2007-05-18 2007-05-24 2007-06-02 2007-06-06 2007-06-08
2 1 1 1 1 1 1
2008-04-11 2008-04-16 2008-04-23 2008-04-24 2008-04-25 2008-04-27 2008-04-28
1 2 1 4 1 3 3
2008-05-01 2008-05-04 2008-05-05 2008-05-07 2008-05-08 2008-05-09 2008-05-12
1 1 1 1 1 2 1
2008-05-14 2008-05-15 2008-05-21 2008-05-22 2008-05-24 2008-05-28 2008-05-31
1 1 1 1 1 1 1
2009-04-16 2009-04-19 2009-04-22 2009-04-23 2009-04-24 2009-04-25 2009-04-26
2 1 1 4 2 1 3
2009-04-29 2009-04-30 2009-05-02 2009-05-03 2009-05-04 2009-05-05 2009-05-06
1 1 1 2 1 1 1
2009-05-08 2009-05-11 2009-05-13 2009-05-28 2010-04-14 2010-04-16 2010-04-17
3 1 2 1 1 2 1
2010-04-18 2010-04-19 2010-04-21 2010-04-22 2010-04-25 2010-04-27 2010-04-28
2 1 1 2 2 1 1
2010-04-29 2010-05-01 2010-05-02 2010-05-04 2010-05-06 2010-05-07 2010-05-13
1 1 1 1 1 1 2
2010-05-14 2010-05-15 2010-05-16 2010-05-26 2010-05-29 2010-05-30 2010-06-09
2 4 1 1 1 1 1
2011-04-09 2011-04-15 2011-04-18 2011-04-20 2011-04-21 2011-04-22 2011-04-24
1 1 1 1 5 2 1
2011-04-25 2011-04-27 2011-04-28 2011-04-29 2011-05-01 2011-05-04 2011-05-05
1 1 1 1 1 1 2
2011-05-07 2011-05-08 2011-05-11 2011-05-12 2011-05-14 2011-05-28 2011-06-11
1 1 1 1 3 1 1
2011-06-14 2011-06-25 2012-04-14 2012-04-15 2012-04-17 2012-04-18 2012-04-19
1 1 2 1 2 3 5
2012-04-20 2012-04-21 2012-04-22 2012-04-25 2012-04-29 2012-05-01 2012-05-03
1 2 2 1 2 1 1
2012-05-06 2012-05-08 2012-05-10 2012-05-11 2012-05-13 2012-05-14 2012-05-23
1 1 3 1 1 1 1
2012-05-30 2012-06-09 2013-04-14 2013-04-15 2013-04-18 2013-04-21 2013-04-24
1 1 1 1 1 1 2
2013-04-25 2013-04-29 2013-04-30 2013-05-01 2013-05-02 2013-05-03 2013-05-07
1 1 1 2 2 3 1
2013-05-10 2013-05-11 2013-05-12 2013-05-13 2013-05-15 2013-05-17 2013-05-19
1 1 1 1 1 1 1
2013-05-27 2013-06-01 2013-06-09 2013-06-30 2013-07-19 2014-04-15 2014-04-17
1 2 1 1 1 2 5
2014-04-19 2014-04-21 2014-04-24 2014-04-25 2014-04-26 2014-04-30 2014-05-01
1 1 1 2 1 1 1
2014-05-02 2014-05-04 2014-05-08 2014-05-09 2014-05-10 2014-05-11 2014-05-16
1 2 1 2 2 1 1
2014-05-22 2014-05-24 2014-05-25 2014-05-31 2015-04-06 2015-04-15 2015-04-17
1 2 1 1 1 2 2
2015-04-18 2015-04-19 2015-04-22 2015-04-23 2015-04-27 2015-04-29 2015-04-30
1 3 3 1 1 2 3
2015-05-01 2015-05-07 2015-05-08 2015-05-09 2015-05-14 2015-05-17 2015-05-20
3 1 2 1 2 1 1
2015-05-30 2015-06-05 2015-07-05 2015-07-07 2015-07-22 2016-04-10 2016-04-11
1 1 1 1 1 2 1
2016-04-12 2016-04-13 2016-04-14 2016-04-15 2016-04-18 2016-04-20 2016-04-21
1 1 2 1 2 1 1
2016-04-23 2016-04-26 2016-04-30 2016-05-01 2016-05-02 2016-05-03 2016-05-04
1 1 2 1 1 2 1
2016-05-06 2016-05-07 2016-05-11 2016-05-12 2016-05-14 2016-05-18 2016-05-25
2 2 2 1 1 2 1
2016-05-31 2016-06-12 2016-06-16 2016-06-18 2016-07-15 2017-04-13 2017-04-14
1 1 1 2 1 2 1
2017-04-15 2017-04-16 2017-04-17 2017-04-18 2017-04-19 2017-04-20 2017-04-21
2 1 2 2 1 1 1
2017-04-22 2017-04-24 2017-04-26 2017-04-29 2017-05-04 2017-05-06 2017-05-07
1 1 1 1 2 3 2
2017-05-10 2017-05-11 2017-05-12 2017-05-19 2017-05-25 2017-06-03 2017-07-02
3 2 1 1 2 1 1
2017-07-04 2018-03-25 2018-03-30 2018-04-13 2018-04-18 2018-04-19 2018-04-20
1 1 1 2 1 2 3
2018-04-21 2018-04-25 2018-04-26 2018-04-27 2018-04-28 2018-04-29 2018-05-04
2 2 1 1 4 2 1
2018-05-05 2018-05-10 2018-05-11 2018-05-13 2018-05-16 2018-05-20 2018-05-21
1 2 1 1 1 1 1
2019-04-06 2019-04-12 2019-04-14 2019-04-17 2019-04-18 2019-04-19 2019-04-20
1 3 1 1 1 3 2
2019-04-22 2019-04-24 2019-04-26 2019-04-27 2019-05-01 2019-05-02 2019-05-03
1 1 1 4 1 1 2
2019-05-06 2019-05-08 2019-05-09 2019-05-10 2019-05-11 2019-05-12 2020-04-06
2 1 2 1 3 1 1
2020-04-10 2020-04-11 2020-04-17 2020-04-18 2020-04-20 2020-04-21 2020-04-23
1 1 2 1 2 1 1
2020-04-24 2020-04-25 2020-04-26 2020-04-29 2020-04-30 2020-05-01 2020-05-02
2 2 2 2 2 1 1
2020-05-06 2020-05-08 2020-05-09 2020-05-10 2020-05-14 2020-05-17 2020-06-24
2 3 3 1 1 1 1
2021-04-10 2021-04-12 2021-04-13 2021-04-17 2021-04-18 2021-04-19 2021-04-21
1 1 1 1 1 2 4
2021-04-22 2021-04-23 2021-04-24 2021-04-25 2021-04-27 2021-04-28 2021-04-29
3 1 2 1 1 2 2
2021-05-01 2021-05-06 2021-05-11 2021-05-13 2021-05-16 2021-05-21 2021-05-26
1 2 1 2 1 1 1
2021-05-27 2021-06-04 2021-06-16 2022-04-10 2022-04-13 2022-04-15 2022-04-17
1 1 1 1 1 1 2
2022-04-18 2022-04-21 2022-04-22 2022-04-23 2022-04-24 2022-04-26 2022-04-28
2 2 2 1 1 2 2
2022-04-30 2022-05-05 2022-05-06 2022-05-08 2022-05-11 2022-05-14 2022-05-15
3 1 2 2 1 1 1
2022-05-17 2022-05-19 2022-05-25 2022-05-29 2023-04-15 2023-04-18 2023-04-20
2 2 1 1 1 1 2
2023-04-22 2023-04-23 2023-04-24 2023-04-26 2023-04-27 2023-04-28 2023-04-29
2 4 1 1 1 2 1
2023-04-30 2023-05-01 2023-05-03 2023-05-04 2023-05-06 2023-05-07 2023-05-10
2 1 1 1 1 2 1
2023-05-11 2023-05-17 2023-05-19 2023-05-24 2023-05-25 2023-05-27 2023-05-28
2 1 1 1 1 1 2
2023-07-23 2023-08-04 2028-04-19
1 1 1
$last_observed
2000-07-21 2000-08-05 2000-08-12 2000-08-13 2000-08-14 2000-08-17
149 1 2 1 2 1 1
2000-08-18 2000-08-19 2000-08-20 2000-08-24 2000-08-26 2000-08-30 2000-08-31
1 1 3 1 1 1 1
2000-09-01 2000-09-05 2000-09-06 2000-09-07 2000-09-15 2000-09-24 2004-06-14
1 1 1 1 1 1 1
2004-07-01 2004-07-18 2004-07-21 2004-07-22 2004-07-23 2004-08-02 2004-08-06
2 1 1 1 1 1 1
2004-08-09 2004-08-10 2004-08-13 2004-08-16 2004-08-19 2004-08-22 2004-08-23
1 1 1 1 1 1 1
2004-08-24 2004-09-03 2004-09-09 2004-09-10 2005-06-10 2005-06-17 2005-06-24
1 1 1 1 1 1 1
2005-06-29 2005-07-07 2005-07-23 2005-07-26 2005-07-28 2005-07-29 2005-07-31
1 1 1 1 1 1 1
2005-08-03 2005-08-05 2005-08-13 2005-08-15 2005-08-17 2005-08-18 2005-08-24
1 1 1 1 1 1 1
2005-08-26 2005-08-27 2005-08-29 2005-09-08 2005-09-12 2005-09-15 2006-06-18
2 1 1 2 1 1 1
2006-06-27 2006-07-13 2006-07-23 2006-07-29 2006-08-05 2006-08-07 2006-08-09
1 1 1 1 1 2 1
2006-08-11 2006-08-16 2006-08-17 2006-08-18 2006-08-31 2006-09-02 2006-09-05
1 2 1 1 1 1 1
2006-09-10 2006-09-11 2006-09-13 2006-09-17 2006-09-18 2007-06-24 2007-07-18
1 1 1 1 1 1 1
2007-08-01 2007-08-06 2007-08-08 2007-08-10 2007-08-11 2007-08-15 2007-08-17
1 1 1 1 1 2 1
2007-08-19 2007-08-20 2007-08-22 2007-08-24 2007-08-25 2007-09-02 2007-09-06
3 3 1 2 3 1 1
2007-09-08 2007-09-09 2007-09-12 2007-09-15 2008-06-09 2008-06-10 2008-06-14
1 1 1 1 1 1 1
2008-06-27 2008-07-18 2008-07-31 2008-08-05 2008-08-07 2008-08-10 2008-08-13
1 2 1 1 1 1 1
2008-08-15 2008-08-17 2008-08-19 2008-08-21 2008-08-24 2008-08-26 2008-08-28
1 3 1 1 1 1 1
2008-08-29 2008-08-31 2008-09-01 2008-09-04 2008-09-19 2008-09-20 2009-06-10
3 2 1 1 1 1 1
2009-07-13 2009-07-17 2009-07-18 2009-07-24 2009-07-26 2009-07-30 2009-08-01
1 2 1 1 1 1 1
2009-08-05 2009-08-06 2009-08-07 2009-08-10 2009-08-11 2009-08-12 2009-08-13
1 1 1 1 1 1 2
2009-08-14 2009-08-15 2009-08-19 2009-08-20 2009-08-21 2009-08-22 2009-08-24
1 1 1 1 1 1 1
2009-08-25 2009-08-27 2009-09-05 2009-09-26 2009-09-27 2010-07-01 2010-07-17
1 1 1 1 1 2 1
2010-07-21 2010-07-22 2010-07-27 2010-07-29 2010-07-30 2010-08-01 2010-08-04
1 1 1 3 2 1 1
2010-08-05 2010-08-07 2010-08-09 2010-08-11 2010-08-12 2010-08-13 2010-08-14
1 1 1 2 4 1 1
2010-08-15 2010-08-17 2010-08-18 2010-08-21 2010-08-23 2010-08-26 2010-09-01
1 1 1 1 1 1 1
2010-09-03 2011-06-25 2011-07-06 2011-07-19 2011-07-21 2011-07-26 2011-07-27
1 1 1 1 1 1 1
2011-07-29 2011-07-31 2011-08-01 2011-08-02 2011-08-03 2011-08-04 2011-08-05
3 1 1 1 1 1 1
2011-08-06 2011-08-10 2011-08-11 2011-08-13 2011-08-15 2011-08-17 2011-08-18
2 1 1 1 1 1 1
2011-08-20 2011-08-22 2011-08-23 2011-08-24 2011-08-26 2011-08-27 2012-06-13
1 1 1 1 1 3 1
2012-06-16 2012-06-19 2012-06-22 2012-07-01 2012-07-10 2012-07-12 2012-07-13
1 1 1 1 1 1 2
2012-07-17 2012-07-19 2012-07-22 2012-07-24 2012-07-25 2012-07-27 2012-08-01
1 1 1 1 1 1 1
2012-08-05 2012-08-06 2012-08-08 2012-08-09 2012-08-10 2012-08-11 2012-08-12
1 1 1 1 2 1 1
2012-08-16 2012-08-25 2012-08-26 2012-08-31 2012-09-01 2012-09-15 2013-05-09
1 1 2 1 1 2 1
2013-06-07 2013-06-09 2013-06-10 2013-06-23 2013-06-26 2013-06-28 2013-07-01
1 1 1 1 1 1 1
2013-07-10 2013-07-11 2013-07-21 2013-07-26 2013-07-28 2013-08-01 2013-08-02
1 1 1 1 1 1 1
2013-08-03 2013-08-08 2013-08-11 2013-08-12 2013-08-16 2013-08-17 2013-08-21
1 1 2 1 1 1 3
2013-08-26 2013-08-30 2013-09-02 2014-04-21 2014-05-03 2014-06-13 2014-06-29
2 1 1 1 1 1 1
2014-07-15 2014-07-16 2014-07-24 2014-07-25 2014-07-26 2014-07-27 2014-08-02
1 3 1 1 1 1 1
2014-08-03 2014-08-06 2014-08-07 2014-08-09 2014-08-10 2014-08-13 2014-08-14
2 2 1 1 1 1 2
2014-08-15 2014-08-16 2014-08-24 2014-08-30 2014-09-06 2014-09-11 2014-09-16
1 1 1 1 1 1 1
2015-05-12 2015-06-19 2015-06-22 2015-07-05 2015-07-09 2015-07-16 2015-07-17
1 1 1 1 1 2 1
2015-07-23 2015-07-24 2015-07-27 2015-07-28 2015-07-29 2015-07-30 2015-07-31
1 2 1 1 3 1 1
2015-08-03 2015-08-04 2015-08-06 2015-08-07 2015-08-09 2015-08-14 2015-08-15
1 1 1 1 1 1 1
2015-08-16 2015-08-19 2015-08-20 2015-08-21 2015-08-23 2015-08-30 2015-09-02
1 1 2 1 1 1 1
2015-09-03 2015-09-06 2016-06-02 2016-07-07 2016-07-10 2016-07-13 2016-07-14
1 1 1 1 1 2 2
2016-07-15 2016-07-17 2016-07-20 2016-07-27 2016-07-28 2016-07-30 2016-07-31
3 1 2 1 2 1 1
2016-08-02 2016-08-03 2016-08-04 2016-08-06 2016-08-11 2016-08-12 2016-08-14
1 2 1 3 1 1 1
2016-08-15 2016-08-18 2016-08-24 2016-08-25 2016-08-26 2016-08-28 2016-09-02
1 3 2 1 1 1 1
2016-09-03 2017-05-17 2017-05-21 2017-06-07 2017-06-24 2017-07-12 2017-07-17
1 1 1 1 1 2 1
2017-07-21 2017-07-22 2017-07-23 2017-07-24 2017-07-28 2017-07-29 2017-07-30
1 1 1 1 1 1 1
2017-07-31 2017-08-03 2017-08-04 2017-08-05 2017-08-10 2017-08-12 2017-08-13
1 2 1 1 2 1 3
2017-08-18 2017-08-19 2017-08-21 2017-08-23 2017-08-24 2017-08-26 2017-08-30
1 1 1 1 1 2 1
2017-09-11 2018-06-21 2018-06-23 2018-06-25 2018-07-05 2018-07-13 2018-07-16
2 1 1 1 1 1 1
2018-07-21 2018-07-22 2018-07-23 2018-07-25 2018-07-26 2018-07-28 2018-07-29
1 1 1 2 1 1 1
2018-07-30 2018-08-01 2018-08-02 2018-08-04 2018-08-08 2018-08-12 2018-08-15
1 1 2 2 1 1 1
2018-08-23 2018-08-26 2018-08-29 2018-09-03 2018-09-06 2018-09-10 2019-05-20
1 1 3 1 1 1 1
2019-06-12 2019-07-03 2019-07-18 2019-07-21 2019-07-24 2019-07-25 2019-07-26
2 1 1 1 1 1 1
2019-07-30 2019-07-31 2019-08-01 2019-08-08 2019-08-09 2019-08-10 2019-08-12
1 1 1 1 2 1 1
2019-08-14 2019-08-17 2019-08-18 2019-08-19 2019-08-21 2019-08-24 2019-08-26
2 1 1 2 2 2 1
2019-08-27 2019-08-31 2019-09-06 2019-09-08 2019-09-16 2020-06-21 2020-07-02
1 1 1 1 1 1 1
2020-07-15 2020-07-23 2020-07-26 2020-08-03 2020-08-05 2020-08-09 2020-08-11
1 3 1 1 1 1 2
2020-08-12 2020-08-13 2020-08-14 2020-08-16 2020-08-19 2020-08-20 2020-08-22
2 3 1 1 2 1 1
2020-08-26 2020-08-27 2020-08-28 2020-08-30 2020-09-04 2020-09-09 2020-09-12
2 2 1 1 1 1 2
2020-09-17 2021-05-12 2021-06-03 2021-06-04 2021-06-06 2021-06-08 2021-07-04
1 1 1 1 1 1 1
2021-07-11 2021-07-14 2021-07-24 2021-07-29 2021-08-01 2021-08-02 2021-08-03
1 1 1 1 1 1 1
2021-08-04 2021-08-05 2021-08-11 2021-08-12 2021-08-13 2021-08-15 2021-08-18
1 1 2 1 2 1 2
2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-26 2021-08-29 2021-09-01
1 2 1 1 1 1 1
2021-09-10 2021-09-18 2021-09-23 2022-05-15 2022-05-27 2022-06-15 2022-06-29
1 1 2 1 1 1 1
2022-07-01 2022-07-09 2022-07-18 2022-07-19 2022-07-21 2022-07-22 2022-07-25
1 1 1 1 1 1 1
2022-07-26 2022-07-27 2022-07-31 2022-08-01 2022-08-02 2022-08-05 2022-08-06
1 1 2 1 1 1 2
2022-08-09 2022-08-10 2022-08-11 2022-08-12 2022-08-17 2022-08-18 2022-08-19
1 1 1 1 1 1 1
2022-08-24 2022-08-25 2022-08-28 2022-09-01 2022-09-02 2022-09-13 2023-05-28
1 1 2 1 1 1 1
2023-06-14 2023-07-15 2023-07-26 2023-07-28 2023-07-29 2023-07-30 2023-08-02
1 1 2 1 1 1 1
2023-08-03 2023-08-04 2023-08-09 2023-08-16 2023-08-17 2023-08-19 2023-08-20
1 2 2 2 1 4 1
2023-08-24 2023-08-25 2023-08-26 2023-08-27 2023-08-28 2023-08-31 2023-09-02
1 1 1 1 1 2 2
2023-09-09 2023-09-11 2023-09-22
2 1 2
$first_nest
2000-05-05 2000-05-09 2000-05-10 2000-05-11 2000-05-13 2000-05-16
189 1 2 3 1 3 1
2000-05-18 2000-05-19 2000-05-26 2000-05-28 2000-05-29 2000-05-31 2000-06-01
2 1 1 2 1 1 1
2000-06-06 2000-06-08 2000-06-17 2004-05-05 2004-05-08 2004-05-09 2004-05-11
1 1 1 1 1 1 1
2004-05-13 2004-05-14 2004-05-15 2004-05-16 2004-05-19 2004-05-20 2004-05-21
2 1 1 2 2 1 1
2004-05-22 2004-05-26 2004-05-27 2004-05-28 2004-06-03 2004-06-06 2004-06-14
1 1 1 1 1 1 1
2004-06-17 2005-05-04 2005-05-06 2005-05-07 2005-05-08 2005-05-11 2005-05-12
1 1 3 1 1 1 3
2005-05-13 2005-05-14 2005-05-17 2005-05-18 2005-05-19 2005-05-20 2005-05-21
1 2 1 1 1 1 1
2005-05-26 2005-05-28 2005-06-03 2005-06-10 2005-06-15 2005-06-16 2006-05-10
2 1 1 1 1 1 1
2006-05-12 2006-05-14 2006-05-15 2006-05-17 2006-05-18 2006-05-20 2006-05-22
3 1 1 2 1 3 3
2006-05-23 2006-05-25 2006-05-27 2006-06-01 2006-06-09 2006-06-10 2006-06-21
1 1 2 1 1 1 1
2007-05-12 2007-05-14 2007-05-16 2007-05-17 2007-05-18 2007-05-19 2007-05-22
1 1 3 4 1 2 1
2007-05-23 2007-05-25 2007-05-26 2007-05-31 2007-06-02 2007-06-04 2007-06-05
1 1 2 1 1 1 1
2007-06-06 2007-06-08 2007-06-17 2007-06-21 2007-06-27 2008-05-06 2008-05-07
1 2 1 1 1 1 1
2008-05-09 2008-05-10 2008-05-12 2008-05-15 2008-05-16 2008-05-17 2008-05-18
1 1 2 2 3 2 3
2008-05-21 2008-05-23 2008-05-26 2008-05-28 2008-05-31 2008-06-05 2008-06-11
2 1 1 1 1 3 1
2008-06-19 2009-05-03 2009-05-04 2009-05-06 2009-05-07 2009-05-09 2009-05-10
1 1 1 1 1 2 1
2009-05-12 2009-05-13 2009-05-14 2009-05-18 2009-05-20 2009-05-21 2009-05-24
2 2 4 1 2 1 2
2009-06-03 2009-06-06 2009-06-08 2009-06-14 2009-07-29 2010-05-01 2010-05-05
1 2 1 1 1 1 2
2010-05-06 2010-05-07 2010-05-08 2010-05-09 2010-05-13 2010-05-14 2010-05-16
3 1 1 1 3 4 2
2010-05-21 2010-05-22 2010-05-25 2010-05-27 2010-05-28 2010-06-04 2010-06-05
1 2 1 2 1 2 1
2010-06-06 2010-06-09 2010-06-12 2010-06-17 2010-06-18 2011-04-30 2011-05-02
1 1 1 1 1 1 1
2011-05-04 2011-05-06 2011-05-08 2011-05-09 2011-05-12 2011-05-14 2011-05-15
1 2 1 2 2 2 3
2011-05-17 2011-05-18 2011-05-20 2011-05-21 2011-05-28 2011-05-29 2011-06-02
1 2 1 1 2 3 1
2011-06-07 2011-06-08 2011-06-11 2012-04-30 2012-05-02 2012-05-04 2012-05-07
1 1 1 1 1 3 1
2012-05-08 2012-05-09 2012-05-10 2012-05-11 2012-05-12 2012-05-13 2012-05-14
1 1 4 4 1 1 2
2012-05-15 2012-05-16 2012-05-20 2012-05-24 2012-05-26 2012-05-29 2012-05-30
1 1 2 1 1 1 1
2012-06-01 2012-06-11 2013-05-02 2013-05-03 2013-05-06 2013-05-09 2013-05-10
1 1 1 2 1 1 2
2013-05-11 2013-05-12 2013-05-13 2013-05-14 2013-05-15 2013-05-16 2013-05-17
2 1 1 2 1 3 1
2013-05-26 2013-06-01 2013-06-02 2013-06-06 2013-06-07 2013-06-29 2013-08-15
3 2 1 1 2 1 1
2014-04-29 2014-05-01 2014-05-03 2014-05-08 2014-05-09 2014-05-10 2014-05-11
1 1 1 3 4 2 1
2014-05-12 2014-05-14 2014-05-16 2014-05-17 2014-05-21 2014-05-22 2014-05-24
2 2 2 1 1 2 2
2014-05-28 2014-05-29 2014-05-31 2014-06-01 2014-07-05 2015-04-27 2015-05-03
1 2 1 1 1 1 1
2015-05-06 2015-05-07 2015-05-08 2015-05-09 2015-05-11 2015-05-12 2015-05-14
3 2 1 3 4 1 2
2015-05-15 2015-05-16 2015-05-19 2015-05-20 2015-05-21 2015-05-24 2015-05-28
2 1 1 2 1 1 1
2015-05-30 2015-06-05 2015-06-13 2015-07-11 2016-04-27 2016-04-29 2016-05-01
1 1 1 1 1 1 1
2016-05-04 2016-05-05 2016-05-06 2016-05-08 2016-05-11 2016-05-12 2016-05-13
1 2 1 1 2 3 2
2016-05-14 2016-05-16 2016-05-18 2016-05-19 2016-05-22 2016-05-25 2016-05-26
2 1 1 1 1 3 1
2016-05-28 2016-05-29 2016-06-02 2016-06-04 2016-06-16 2016-06-18 2017-04-28
1 1 1 1 1 2 1
2017-04-30 2017-05-03 2017-05-04 2017-05-06 2017-05-07 2017-05-08 2017-05-10
2 1 1 1 2 1 2
2017-05-11 2017-05-12 2017-05-13 2017-05-19 2017-05-21 2017-05-24 2017-05-26
2 3 1 3 1 1 1
2017-05-28 2017-05-31 2017-06-02 2017-06-03 2017-06-04 2017-06-14 2018-05-03
1 1 2 1 1 1 1
2018-05-04 2018-05-06 2018-05-10 2018-05-11 2018-05-12 2018-05-14 2018-05-16
1 2 2 2 2 2 3
2018-05-17 2018-05-18 2018-05-19 2018-05-20 2018-05-23 2018-05-29 2018-05-30
2 1 1 2 1 1 2
2018-05-31 2018-06-01 2018-06-02 2018-06-15 2019-05-03 2019-05-04 2019-05-05
3 1 1 1 1 1 1
2019-05-09 2019-05-10 2019-05-11 2019-05-14 2019-05-16 2019-05-17 2019-05-22
4 3 5 1 1 1 1
2019-05-23 2019-05-24 2019-05-25 2019-06-01 2019-06-02 2019-06-05 2019-06-06
1 1 2 1 1 1 2
2019-06-12 2019-07-06 2020-05-07 2020-05-10 2020-05-11 2020-05-13 2020-05-14
1 1 1 1 1 2 2
2020-05-15 2020-05-16 2020-05-17 2020-05-19 2020-05-20 2020-05-21 2020-05-22
3 1 1 1 1 2 3
2020-05-23 2020-05-24 2020-05-28 2020-05-29 2020-05-30 2020-06-03 2020-06-11
1 1 1 2 2 1 1
2020-06-12 2020-06-27 2020-07-03 2021-05-05 2021-05-06 2021-05-08 2021-05-09
1 1 1 2 1 1 2
2021-05-10 2021-05-12 2021-05-13 2021-05-14 2021-05-17 2021-05-19 2021-05-20
1 2 1 3 1 4 2
2021-05-21 2021-05-25 2021-05-27 2021-05-28 2021-05-30 2021-06-02 2021-06-04
1 1 1 1 1 2 2
2021-06-17 2022-05-06 2022-05-07 2022-05-08 2022-05-09 2022-05-11 2022-05-14
1 1 1 3 1 1 1
2022-05-15 2022-05-16 2022-05-17 2022-05-18 2022-05-19 2022-05-20 2022-05-21
1 1 1 1 2 2 3
2022-05-25 2022-05-26 2022-05-27 2022-05-29 2022-06-03 2022-06-08 2022-06-11
1 1 1 2 2 1 1
2022-06-25 2022-06-27 2022-07-01 2023-05-07 2023-05-10 2023-05-11 2023-05-12
1 1 1 2 1 1 2
2023-05-13 2023-05-14 2023-05-16 2023-05-19 2023-05-21 2023-05-24 2023-05-26
1 3 2 1 1 2 1
2023-05-28 2023-05-30 2023-06-01 2023-06-02 2023-06-03 2023-06-07 2023-06-09
1 1 2 2 1 2 1
2023-06-10 2023-06-17 2023-07-23
1 1 1
$first_chick
2000-05-28 2000-05-31 2000-06-01 2000-06-02 2000-06-03 2000-06-04
262 1 1 1 2 3 1
2000-06-06 2000-06-07 2000-06-08 2000-06-09 2000-06-18 2000-06-20 2000-06-22
1 1 1 1 1 2 1
2000-06-24 2000-06-26 2000-06-28 2000-07-22 2004-05-31 2004-06-04 2004-06-05
1 1 1 1 2 2 1
2004-06-06 2004-06-09 2004-06-11 2004-06-14 2004-06-16 2004-06-18 2004-06-20
1 1 2 1 1 1 1
2004-06-21 2004-06-22 2004-06-27 2004-07-04 2004-07-18 2005-05-27 2005-05-28
1 2 1 1 1 1 1
2005-06-01 2005-06-02 2005-06-03 2005-06-05 2005-06-06 2005-06-09 2005-06-10
2 3 1 1 1 1 4
2005-06-11 2005-06-12 2005-06-15 2005-06-26 2005-07-01 2005-07-21 2006-06-02
1 1 1 2 1 1 1
2006-06-03 2006-06-05 2006-06-07 2006-06-09 2006-06-10 2006-06-11 2006-06-13
1 1 1 1 1 1 1
2006-06-14 2006-06-16 2006-06-17 2006-06-20 2006-06-24 2006-06-30 2006-07-03
2 2 2 1 2 1 1
2006-07-14 2007-06-06 2007-06-07 2007-06-09 2007-06-10 2007-06-11 2007-06-12
1 2 2 1 3 1 2
2007-06-13 2007-06-14 2007-06-15 2007-06-20 2007-06-27 2007-06-28 2007-06-29
1 2 3 3 1 1 1
2007-07-02 2007-07-15 2007-07-19 2008-05-30 2008-06-02 2008-06-04 2008-06-05
1 1 1 1 1 2 4
2008-06-07 2008-06-08 2008-06-09 2008-06-12 2008-06-17 2008-06-18 2008-06-25
1 2 1 2 1 1 1
2008-06-26 2008-06-27 2008-06-29 2008-07-06 2008-07-10 2009-05-27 2009-05-28
2 1 1 1 1 1 2
2009-05-29 2009-05-30 2009-05-31 2009-06-03 2009-06-04 2009-06-05 2009-06-06
1 1 2 1 4 1 3
2009-06-10 2009-06-11 2009-06-17 2009-06-20 2009-06-24 2009-06-25 2009-06-26
1 1 1 1 1 1 1
2009-06-29 2009-07-15 2010-05-25 2010-05-26 2010-05-27 2010-05-30 2010-06-01
1 1 1 2 3 3 1
2010-06-03 2010-06-04 2010-06-09 2010-06-10 2010-06-11 2010-06-16 2010-06-17
3 1 1 2 1 2 1
2010-06-18 2010-06-25 2010-06-30 2010-07-01 2010-07-02 2010-07-07 2010-07-09
1 1 2 1 1 1 1
2011-05-25 2011-05-26 2011-05-27 2011-05-29 2011-05-31 2011-06-02 2011-06-03
1 1 1 1 1 2 1
2011-06-04 2011-06-05 2011-06-06 2011-06-08 2011-06-09 2011-06-10 2011-06-11
1 1 1 2 1 1 1
2011-06-12 2011-06-14 2011-06-16 2011-06-17 2011-06-18 2011-06-30 2011-07-01
1 1 1 1 1 1 1
2011-07-10 2011-07-13 2011-07-21 2012-05-20 2012-05-23 2012-05-24 2012-05-26
1 1 1 1 1 1 1
2012-05-27 2012-05-28 2012-05-31 2012-06-01 2012-06-02 2012-06-04 2012-06-06
1 2 1 2 1 1 3
2012-06-08 2012-06-10 2012-06-15 2012-06-20 2012-06-23 2012-06-24 2012-07-01
1 1 2 1 3 1 1
2013-05-11 2013-05-26 2013-05-27 2013-05-29 2013-05-31 2013-06-01 2013-06-03
1 3 1 1 1 1 1
2013-06-06 2013-06-07 2013-06-08 2013-06-09 2013-06-12 2013-06-13 2013-06-19
2 3 1 2 1 1 1
2013-06-21 2013-06-22 2013-06-23 2013-06-26 2013-06-29 2014-05-22 2014-05-24
1 1 1 1 1 1 1
2014-05-25 2014-05-29 2014-05-31 2014-06-01 2014-06-02 2014-06-03 2014-06-05
1 2 2 2 1 1 3
2014-06-06 2014-06-09 2014-06-11 2014-06-13 2014-06-14 2014-06-15 2014-06-19
2 1 1 1 2 1 1
2014-06-20 2014-06-21 2014-06-29 2014-07-02 2014-07-26 2014-08-01 2015-05-22
1 1 1 1 1 1 1
2015-05-25 2015-05-26 2015-05-28 2015-05-29 2015-05-30 2015-05-31 2015-06-04
1 1 2 1 1 2 6
2015-06-05 2015-06-06 2015-06-08 2015-06-10 2015-06-11 2015-06-12 2015-06-19
1 1 1 1 1 1 1
2015-06-26 2015-06-27 2015-08-02 2016-05-22 2016-05-25 2016-05-26 2016-05-27
1 1 1 1 1 2 1
2016-05-28 2016-05-29 2016-06-02 2016-06-04 2016-06-05 2016-06-06 2016-06-08
2 3 1 1 1 1 5
2016-06-09 2016-06-10 2016-06-12 2016-06-16 2016-06-17 2016-06-18 2016-07-13
1 1 2 2 1 1 1
2016-07-14 2016-07-15 2016-07-16 2017-05-22 2017-05-25 2017-05-26 2017-05-28
1 1 1 1 3 1 3
2017-05-30 2017-06-01 2017-06-02 2017-06-03 2017-06-08 2017-06-11 2017-06-12
1 3 3 1 1 1 1
2017-06-14 2017-06-16 2017-06-17 2017-06-21 2017-06-22 2017-06-24 2018-05-27
2 1 1 2 1 1 1
2018-05-30 2018-05-31 2018-06-02 2018-06-03 2018-06-04 2018-06-05 2018-06-07
1 3 1 1 1 1 4
2018-06-08 2018-06-10 2018-06-13 2018-06-14 2018-06-18 2018-06-20 2018-06-23
2 2 1 1 1 1 1
2018-06-25 2018-06-28 2018-07-13 2019-05-30 2019-05-31 2019-06-02 2019-06-03
1 1 1 2 2 1 2
2019-06-04 2019-06-05 2019-06-06 2019-06-07 2019-06-09 2019-06-16 2019-06-18
1 1 1 1 1 3 1
2019-06-20 2019-06-23 2019-06-26 2019-06-27 2019-06-30 2019-07-02 2019-07-03
1 1 2 2 1 1 2
2019-07-04 2020-05-31 2020-06-01 2020-06-03 2020-06-04 2020-06-05 2020-06-07
1 1 1 1 3 1 1
2020-06-12 2020-06-13 2020-06-14 2020-06-19 2020-06-20 2020-06-21 2020-06-23
2 1 2 2 1 2 1
2020-06-27 2020-07-02 2020-07-03 2020-07-05 2020-07-09 2020-07-16 2020-07-20
1 1 2 2 1 1 1
2020-07-30 2021-05-28 2021-05-30 2021-05-31 2021-06-03 2021-06-04 2021-06-05
1 1 1 2 3 1 1
2021-06-06 2021-06-07 2021-06-11 2021-06-12 2021-06-14 2021-06-16 2021-06-18
1 1 2 2 1 1 1
2021-06-27 2021-06-30 2021-07-01 2021-07-02 2021-07-15 2022-05-27 2022-06-02
2 1 2 1 1 1 5
2022-06-03 2022-06-04 2022-06-06 2022-06-08 2022-06-11 2022-06-12 2022-06-13
1 1 1 1 1 2 1
2022-06-15 2022-06-16 2022-06-17 2022-06-19 2022-06-20 2022-06-22 2022-06-23
1 1 3 1 1 1 2
2022-06-25 2023-05-28 2023-05-31 2023-06-01 2023-06-04 2023-06-05 2023-06-09
1 1 1 1 1 4 1
2023-06-10 2023-06-14 2023-06-17 2023-06-18 2023-06-19 2023-06-23 2023-06-25
2 2 1 1 1 1 1
2023-06-29 2023-07-05 2023-07-09 2023-07-27 2023-08-02 2023-08-04 3000-06-02
1 1 1 1 1 1 1
$first_fledge
2000-06-20 2000-06-21 2000-06-22 2000-06-24 2000-06-25 2000-06-26
387 1 1 2 3 2 2
2000-06-30 2000-07-04 2000-07-08 2000-07-13 2000-07-15 2000-07-16 2000-07-17
2 1 1 1 1 1 1
2000-07-20 2000-07-24 2000-08-06 2004-06-23 2004-06-25 2004-06-26 2004-06-30
1 1 1 1 1 1 1
2004-07-02 2004-07-04 2004-07-10 2004-07-13 2004-07-14 2004-07-17 2004-07-18
1 2 1 1 2 1 1
2004-07-25 2005-06-22 2005-06-23 2005-06-24 2005-06-25 2005-06-30 2005-07-01
1 3 1 2 3 1 1
2005-07-02 2005-07-07 2005-07-08 2005-07-13 2005-07-18 2005-07-20 2005-07-22
1 1 1 1 1 1 1
2005-08-27 2006-06-23 2006-06-25 2006-06-29 2006-06-30 2006-07-01 2006-07-04
1 1 1 1 1 1 2
2006-07-07 2006-07-12 2006-07-13 2006-07-15 2006-07-17 2006-07-24 2006-07-28
3 1 1 2 1 1 1
2006-08-02 2006-08-11 2007-06-26 2007-06-27 2007-06-28 2007-06-29 2007-06-30
1 1 1 1 2 1 1
2007-07-02 2007-07-04 2007-07-05 2007-07-06 2007-07-10 2007-07-11 2007-07-12
2 3 1 1 1 2 2
2007-07-13 2007-07-18 2007-07-23 2007-08-08 2008-06-20 2008-06-22 2008-06-25
2 2 1 2 1 1 2
2008-06-26 2008-06-27 2008-06-28 2008-06-29 2008-07-02 2008-07-15 2008-07-17
3 1 2 1 1 1 1
2008-07-18 2008-07-20 2008-07-24 2008-07-30 2008-07-31 2008-08-17 2009-06-16
1 2 1 1 1 1 1
2009-06-20 2009-06-21 2009-06-24 2009-06-25 2009-06-26 2009-06-29 2009-06-30
2 1 3 3 1 1 1
2009-07-01 2009-07-03 2009-07-05 2009-07-10 2009-07-15 2009-07-16 2009-07-20
1 1 1 1 2 1 1
2009-08-07 2010-06-09 2010-06-16 2010-06-17 2010-06-18 2010-06-20 2010-06-22
1 1 1 2 3 2 1
2010-06-24 2010-06-25 2010-06-27 2010-06-30 2010-07-01 2010-07-09 2010-07-14
1 2 1 1 1 2 2
2010-07-21 2010-07-23 2010-07-27 2010-07-29 2010-07-30 2010-08-05 2011-06-14
1 1 1 1 1 1 1
2011-06-17 2011-06-19 2011-06-22 2011-06-23 2011-06-25 2011-06-27 2011-06-28
1 1 1 4 2 1 1
2011-07-01 2011-07-03 2011-07-06 2011-07-09 2011-07-11 2011-07-21 2011-07-22
3 1 1 1 1 1 1
2011-07-24 2012-06-15 2012-06-17 2012-06-19 2012-06-21 2012-06-22 2012-06-23
1 1 2 1 1 1 1
2012-06-24 2012-06-26 2012-06-27 2012-06-28 2012-06-29 2012-07-01 2012-07-03
1 1 1 1 1 1 1
2012-07-07 2012-07-14 2012-07-15 2012-07-19 2012-07-21 2012-08-17 2013-06-20
1 1 1 1 1 1 1
2013-06-21 2013-06-22 2013-06-23 2013-06-26 2013-06-27 2013-06-29 2013-06-30
1 2 1 1 2 2 1
2013-07-02 2013-07-04 2013-07-05 2013-07-10 2013-07-11 2013-07-17 2013-07-20
1 1 1 2 1 1 1
2013-07-31 2013-08-02 2014-06-15 2014-06-16 2014-06-18 2014-06-19 2014-06-21
1 1 1 2 1 3 1
2014-06-22 2014-06-23 2014-06-27 2014-06-28 2014-07-03 2014-07-04 2014-07-05
2 1 3 1 1 2 2
2014-07-09 2014-07-12 2014-07-16 2014-08-09 2014-08-15 2015-06-13 2015-06-14
1 1 1 1 1 1 3
2015-06-17 2015-06-18 2015-06-20 2015-06-22 2015-06-25 2015-06-26 2015-06-27
1 1 1 1 2 3 1
2015-06-30 2015-07-02 2015-07-03 2015-07-04 2015-07-17 2015-08-19 2016-06-09
1 1 2 1 1 1 1
2016-06-14 2016-06-15 2016-06-17 2016-06-19 2016-06-23 2016-06-24 2016-06-27
1 1 1 3 4 1 1
2016-06-29 2016-07-02 2016-07-03 2016-07-06 2016-07-07 2016-07-08 2016-07-09
2 3 3 2 2 2 1
2016-08-05 2017-06-11 2017-06-15 2017-06-17 2017-06-18 2017-06-20 2017-06-21
1 2 1 1 1 1 1
2017-06-22 2017-06-23 2017-06-24 2017-06-27 2017-06-29 2017-07-02 2017-07-05
3 1 1 2 1 1 1
2017-07-06 2017-07-07 2017-07-11 2017-07-12 2017-07-14 2017-07-25 2017-08-05
1 1 1 1 1 1 1
2018-06-21 2018-06-22 2018-06-23 2018-06-24 2018-06-26 2018-06-27 2018-06-28
2 1 2 2 1 2 3
2018-06-29 2018-07-04 2018-07-05 2018-07-06 2018-07-12 2018-07-14 2018-07-19
1 2 1 1 1 1 1
2018-08-03 2019-06-19 2019-06-20 2019-06-21 2019-06-23 2019-06-26 2019-06-27
1 1 1 1 1 1 2
2019-06-28 2019-06-29 2019-06-30 2019-07-01 2019-07-03 2019-07-04 2019-07-07
1 1 1 1 1 1 1
2019-07-08 2019-07-10 2019-07-12 2019-07-13 2019-07-18 2019-07-19 2019-07-20
1 1 2 1 1 1 1
2019-07-21 2019-08-01 2020-06-20 2020-06-24 2020-06-25 2020-07-02 2020-07-03
3 1 1 2 3 1 1
2020-07-04 2020-07-08 2020-07-09 2020-07-13 2020-07-14 2020-07-15 2020-07-17
1 1 3 1 1 2 2
2020-07-24 2020-07-26 2020-08-06 2020-08-13 2020-08-23 2022-06-25
1 2 1 1 1 1 We use lapply rather than sapply for this step because the table for each column will have a different length (but try sapply and see what happens!).
16.1.2 The Split-Apply Pattern
In a data set with categorical features, it’s often useful to compute something for each category. The lapply and sapply functions can compute something for each element of a data structure, but categories are not necessarily elements.
For example, the least terns data set has 6 different categories in the region_3 column. If we want all of the rows for one region, one way to get them is by indexing:
southern = terns[terns$region_3 == "SOUTHERN", ]
head(southern) year site_name
8 2000 SANTA CLARA RIVER MCGRATH STATE BEACH
9 2000 ORMOND BEACH
10 2000 NBVC POINT MUGU
11 2000 VENICE BEACH
12 2000 LA HARBOR
13 2000 SEAL BEACH NWR ANAHEIM BAY
site_name_2013_2018 site_name_1988_2001 site_abbr
8 Santa Clara River NA_2013_2018 POLYGON S_CLAR_MCG
9 Ormond Beach NA_2013_2018 POLYGON ORMOND
10 NBVC Point Mugu NA_2013_2018 POLYGON PT_MUGU
11 Venice Beach NA_2013_2018 POLYGON VEN_BCH
12 Port of LA NA_2013_2018 POLYGON LA_HARB
13 Seal Beach National Wildlife Refuge NA_2013_2018 POLYGON SEAL_BCH_NWR
region_3 region_4 event bp_min bp_max fl_min fl_max total_nests
8 SOUTHERN VENTURA LA_NINA 21 21 9 9 22
9 SOUTHERN VENTURA LA_NINA 73 73 60 65 73
10 SOUTHERN VENTURA LA_NINA 166 167 64 64 252
11 SOUTHERN SOUTHERN LA_NINA 274 294 150 200 308
12 SOUTHERN SOUTHERN LA_NINA 437 437 570 570 565
13 SOUTHERN SOUTHERN LA_NINA 107 107 180 180 107
nonpred_eggs nonpred_chicks nonpred_fl nonpred_ad pred_control pred_eggs
8 4 3 NA NA NA
9 2 0 0 0 NA
10 NA NA NA NA NA
11 26 NA NA NA 32
12 77 NA NA NA 24
13 10 3 NA NA NA
pred_chicks pred_fl pred_ad pred_pefa pred_coy_fox pred_meso pred_owlspp
8 NA NA NA
9 NA NA NA N N Y N
10 NA NA NA
11 20 20 3 Y N Y N
12 NA 15 NA Y N N N
13 NA NA NA
pred_corvid pred_other_raptor pred_other_avian pred_misc total_pefa
8 NA
9 N Y N N NA
10 NA
11 Y N N N 2
12 N Y Y N 17
13 NA
total_coy_fox total_meso total_owlspp total_corvid total_other_raptor
8 NA NA NA NA NA
9 NA NA NA NA NA
10 NA NA NA NA NA
11 0 42 0 31 0
12 0 0 0 0 4
13 NA NA NA NA NA
total_other_avian total_misc first_observed last_observed first_nest
8 NA NA 2000-06-06 2000-09-05 2000-06-06
9 NA NA 2000-06-08
10 NA NA 2000-05-21 2000-08-12 2000-06-01
11 0 0 2000-04-19 2000-08-20 2000-05-29
12 24 0 2000-04-28 2000-08-20 2000-05-10
13 NA NA
first_chick first_fledge
8 2000-06-28 2000-07-24
9 2000-06-26 2000-07-17
10 2000-06-24 2000-07-16
11
12 3000-06-02 2000-06-22
13 To get all 6 regions separately, we’d have to do this 6 times. If we want to compute something for each region, say the median of the total_nests column, we also have to repeat that computation 6 times. Here’s what it would look like for just the SOUTHERN region:
median(southern$total_nests, na.rm = TRUE)[1] 64If the categories were elements, we could avoid writing code to index each category, and just use the sapply (or lapply) function to apply the median function to each.
The split function splits a vector or data frame into groups based on a vector of categories. The first argument to split is the data, and the second argument is a congruent vector of categories.
We can use split to elegantly compute medians of total_nests broken down by country. First, we split the data by region Since we only want to compute on the total_nests column, we only split that column:
by_region = split(terns$total_nests, terns$region_3)
class(by_region)[1] "list"names(by_region)[1] "ARIZONA" "CENTRAL" "KINGS" "S.F._BAY" "SACRAMENTO"
[6] "SOUTHERN" The result from split is a list with one element for each category. The individual elements contain pieces of the original total_nests column:
head(by_region$SOUTHERN)[1] 22 73 252 308 565 107Since the categories are elements in the split data, now we can use sapply the same way we did in previous examples:
sapply(by_region, median, na.rm = TRUE) ARIZONA CENTRAL KINGS S.F._BAY SACRAMENTO SOUTHERN
3 17 1 35 1 64 This two-step process is an R idiom called the split-apply pattern. First you use split to convert categories into list elements, then you use an apply function to compute something on each category. Any time you want to compute results by category, you should think of this pattern.
The split-apply pattern is so useful that R provides the tapply function as a shortcut. The tapply function is equivalent to calling split and then sapply. Like split, the first argument is the data and the second argument is a congruent vector of categories. The third argument is a function to apply, like the function argument in sapply.
We can use tapply to compute the total_nests medians by region_3 for the least terns data set:
tapply(terns$total_nests, terns$region_3, median, na.rm = TRUE) ARIZONA CENTRAL KINGS S.F._BAY SACRAMENTO SOUTHERN
3 17 1 35 1 64 Notice that the result is identical to the one we computed before.
The “t” in tapply stands for “table”, because the tapply function is a generalization of the table function. If you use length as the third argument to tapply, you get the same results as you would from using the table function on the category vector.
The aggregate function is closely related to tapply. It computes the same results, but organizes them into a data frame with one row for each category. In some cases, this format is more convenient. The arguments are the same, except that the second argument must be a list or data frame rather than a vector.
As an example, here’s the result of using aggregate to compute the total_nests medians:
aggregate(terns$total_nests, list(terns$region_3), mean) Group.1 x
1 ARIZONA 3.0000000
2 CENTRAL 21.5584416
3 KINGS 0.8666667
4 S.F._BAY NA
5 SACRAMENTO 0.9000000
6 SOUTHERN NAThe lapply, sapply, and tapply functions are the three most important functions in the family of apply functions, but there are many more. You can learn more about all of R’s apply functions by reading this StackOverflow post.