class(terns)[1] "data.frame"17 Aggregation & Grouping
NoteLearning Goals
After this lesson, you should be able to:
- Explain what a map (or apply) function does
- Call a function repeatedly with the purrr package’s map functions
- Split data into groups and apply a function to each
- Compute group statistics with the dplyr package’s
group_byandsummarizefunctions
ImportantRequired Packages
This chapter uses the following packages:
- dplyr
- purrr
Chapter 14 explains how to install and load packages.
17.1 The map Function
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 purrr package is a collection of functions to help you do things repeatedly or for each element of a data structure. Install and load the package in order to follow along:
# install.packages("purrr")
library("purrr")The package’s map function calls a function on each element of a vector or list. We sometimes also say it maps or applies a function over the elements. The syntax is:
map(DATA, F, ...)The map function calls the function F once for each element of DATA. It passes the element to F as the first argument. It also passes the ... arguments to F, which are constant across all of the calls.
Let’s try this out with the least terns data and the class function:
map(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 map, 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-16map(x, sin)[[1]]
[1] 0.841471
[[2]]
[1] 0.9092974
[[3]]
[1] 1.224647e-16The only difference is that the result from map is a list. In fact, the map function always returns a list with one element for each element of the input data.
17.2 Other Map Functions
The purrr package provides many different map functions, all of which have names that start with map. All of them call another function on each element of a data structure. They also all have the same syntax. Where they differ is in how they return results. A few of these are shown in Table 17.1.
| Function | Return Type |
|---|---|
map_lgl |
logical |
map_int |
integer |
map_dbl |
numeric (double) |
map_chr |
character |
map |
list |
NoteApply Functions
R’s apply functions are a built-in equivalent to map functions. The lapply, sapply, and tapply functions are the three most important functions in the family of apply functions, but there are many more. The lapply function is nearly identical to the map function.
We focus on and recommend the map functions rather than the apply functions because they are more consistent in their syntax and specific in their return types. You can learn more about R’s apply functions by reading this StackOverflow post.
When you have a choice between using vectorization or a map 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 a map function.
Let’s look at some examples of the other map functions. If we use map_chr to find the classes of the columns in the least terns data, we get a character vector:
map_chr(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 map_dbl to compute the sin values, we get a numeric vector, the same as from vectorization:
map_dbl(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.
Map 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 map_lgl to apply this function to all of the columns in the least terns data set:
is_not_number = !map_lgl(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:
map(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 map rather than one of the other map functions for this step because the table for each column will have a different length.
The purrr documentation provides more details about how to use the many functions in the package.
17.3 The Split-Apply Pattern
In a data set with categorical features, it’s often useful to compute something for each category. The map 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 filtering:
library("dplyr")
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionsouthern = filter(terns, region_3 == "SOUTHERN")
head(southern) year site_name
1 2000 SANTA CLARA RIVER MCGRATH STATE BEACH
2 2000 ORMOND BEACH
3 2000 NBVC POINT MUGU
4 2000 VENICE BEACH
5 2000 LA HARBOR
6 2000 SEAL BEACH NWR ANAHEIM BAY
site_name_2013_2018 site_name_1988_2001 site_abbr
1 Santa Clara River NA_2013_2018 POLYGON S_CLAR_MCG
2 Ormond Beach NA_2013_2018 POLYGON ORMOND
3 NBVC Point Mugu NA_2013_2018 POLYGON PT_MUGU
4 Venice Beach NA_2013_2018 POLYGON VEN_BCH
5 Port of LA NA_2013_2018 POLYGON LA_HARB
6 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
1 SOUTHERN VENTURA LA_NINA 21 21 9 9 22
2 SOUTHERN VENTURA LA_NINA 73 73 60 65 73
3 SOUTHERN VENTURA LA_NINA 166 167 64 64 252
4 SOUTHERN SOUTHERN LA_NINA 274 294 150 200 308
5 SOUTHERN SOUTHERN LA_NINA 437 437 570 570 565
6 SOUTHERN SOUTHERN LA_NINA 107 107 180 180 107
nonpred_eggs nonpred_chicks nonpred_fl nonpred_ad pred_control pred_eggs
1 4 3 NA NA NA
2 2 0 0 0 NA
3 NA NA NA NA NA
4 26 NA NA NA 32
5 77 NA NA NA 24
6 10 3 NA NA NA
pred_chicks pred_fl pred_ad pred_pefa pred_coy_fox pred_meso pred_owlspp
1 NA NA NA
2 NA NA NA N N Y N
3 NA NA NA
4 20 20 3 Y N Y N
5 NA 15 NA Y N N N
6 NA NA NA
pred_corvid pred_other_raptor pred_other_avian pred_misc total_pefa
1 NA
2 N Y N N NA
3 NA
4 Y N N N 2
5 N Y Y N 17
6 NA
total_coy_fox total_meso total_owlspp total_corvid total_other_raptor
1 NA NA NA NA NA
2 NA NA NA NA NA
3 NA NA NA NA NA
4 0 42 0 31 0
5 0 0 0 0 4
6 NA NA NA NA NA
total_other_avian total_misc first_observed last_observed first_nest
1 NA NA 2000-06-06 2000-09-05 2000-06-06
2 NA NA 2000-06-08
3 NA NA 2000-05-21 2000-08-12 2000-06-01
4 0 0 2000-04-19 2000-08-20 2000-05-29
5 24 0 2000-04-28 2000-08-20 2000-05-10
6 NA NA
first_chick first_fledge
1 2000-06-28 2000-07-24
2 2000-06-26 2000-07-17
3 2000-06-24 2000-07-16
4
5 3000-06-02 2000-06-22
6 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 a map 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 map_dbl the same way we did in previous examples:
map_dbl(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 a map (or apply) function to compute something on each category. Any time you want to compute results by category, you should think of this pattern.
17.4 Grouping
The dplyr package’s group_by and summarize functions are analogous to split and purrr’s map functions, but return the results in a data frame with one row for each category. In many cases, this is more convenient than a list or vector result.
As an example, here’s how to use group_by and summarize to compute the total_nests medians:
by_region = group_by(terns, region_3)
summarize(by_region, median(total_nests, na.rm = TRUE))# A tibble: 6 × 2
region_3 `median(total_nests, na.rm = TRUE)`
<chr> <dbl>
1 ARIZONA 3
2 CENTRAL 17
3 KINGS 1
4 S.F._BAY 35
5 SACRAMENTO 1
6 SOUTHERN 64The dplyr documentation provides more details about how to use the group_by and summarize functions.