Data science with {hyenaR}:
LESSON 3
Prepare our workspace
STEP 1: Load required packages
Recap: What did we cover last week?
EXAMPLE 1: Find 10 most successful males
create_id_starting.table(
sex = "male") %>%
mutate(RS = fetch_id_number.offspring(ID = ID),
#Determine the rank of males based on reproductive success
#Largest RS is ranked 1
rank = dense_rank(-RS)) %>%
#Filter top 10 ranked males (allows for ties)
filter(rank %in% 1:10) %>%
arrange(rank)# A tibble: 27 × 3
ID RS rank
<chr> <int> <int>
1 F-146 29 1
2 A-150 26 2
3 M-046 22 3
4 X-024 20 4
5 L-047 19 5
6 M-240 19 5
7 X-017 19 5
8 A-122 18 6
9 A-128 18 6
10 E-094 17 7
# … with 17 more rowsEXAMPLE 2: Find clan that produces most successful males on average
create_id_starting.table(
sex = "male") %>%
mutate(RS = fetch_id_number.offspring(ID = ID),
birth.clan = fetch_id_clan.birth(ID = ID)) %>%
filter(birth.clan %in% find_clan_name.all(main.clans = TRUE)) %>%
group_by(birth.clan) %>%
summarise(meanRS = mean(RS),
maxRS = max(RS)) %>%
arrange(desc(meanRS))# A tibble: 8 × 3
birth.clan meanRS maxRS
<chr> <dbl> <int>
1 N 1.46 14
2 M 1.42 19
3 A 1.27 26
4 L 1.13 14
5 F 1.13 29
6 E 1.01 17
7 T 0.462 8
8 S 0.357 7EXAMPLE 3: Reproductive success of uncensored males
## Do the same thing with `create_id_starting table()`
create_id_starting.table(
## Select all males..
sex = "male",
## Only during the observation period
## (rather than from first estimated birth or conception)
from = find_pop_date.observation.first(),
to = find_pop_date.observation.last(),
## Males that 'started being cubs' during our focal period
## i.e. they were born during this time
lifestage = "cub", lifestage.overlap = "start",
## Only individuals born into main clans
clan = find_clan_name.all(main.clans = TRUE)
)EXAMPLE 3: Reproductive success of uncensored males
# Extract data 'manually' without `create_id_starting.table()` arguments
create_id_starting.table(
sex = "male"
) %>%
#Extract birth date and birth clan
mutate(birth.date = fetch_id_date.birth(ID = ID),
birth.clan = fetch_id_clan.birth(ID = ID)) %>%
filter(
#Filter individuals born after study started (i.e. non left censored)
birth.date > find_pop_date.observation.first(),
#Filter individuals born in main clans
birth.clan %in% find_clan_name.all(main.clans = TRUE))EXAMPLE 3: Reproductive success of uncensored males
create_id_starting.table(sex = "male", lifestage = "cub", clan = find_clan_name.all(main.clans = TRUE), lifestage.overlap = "start", from = find_pop_date.observation.first(), to = find_pop_date.observation.last()) %>%
filter(!fetch_id_is.censored.right(ID = ID)) %>%
mutate(RS = fetch_id_number.offspring(ID = ID, age.mature = 6, unit = "months"),
birth.clan = fetch_id_clan.birth(ID = ID)) %>%
group_by(birth.clan) %>%
summarise(meanRS = mean(RS)) %>%
arrange(desc(meanRS))# A tibble: 8 × 2
birth.clan meanRS
<chr> <dbl>
1 M 1.34
2 A 1.24
3 N 1.13
4 L 1.03
5 F 0.927
6 E 0.908
7 T 0.388
8 S 0.370Recap: Any questions after last week?
Today’s goals
GOAL 1: create_id_starting.table()
GOAL 2: fetch_id_rank()
GOAL 3: reshape_row_date.seq()
TASK: Rank of females
EXAMPLE 1: Hierarchy of Airstrip females
We want all females in Airstrip on a single date
# Extract all within `create_id_starting.table()`
create_id_starting.table(
## Select all females..
sex = "female",
## In Airstrip...
clan = "A",
## On this date...
at = "2007-01-01"
)# A tibble: 31 × 1
ID
<chr>
1 A-001
2 A-006
3 A-013
4 A-015
5 A-016
6 A-081
7 A-102
8 A-106
9 A-107
10 A-115
# … with 21 more rowsEXAMPLE 1: Hierarchy of Airstrip females
# Extract data 'manually' without using `create_id_starting.table()` arguments
#Return all individuals (no filtering)
create_id_starting.table() %>%
#Extract variables for filtering...
mutate(sex = fetch_id_sex(ID = ID),
alive = fetch_id_is.alive(ID = ID, at = "2007-01-01"),
clan = fetch_id_clan.current(ID = ID, at = "2007-01-01")) %>%
#Filter to just return Airstrip females alive on 2007-01-01
filter(sex == "female" & alive & clan == "A") %>%
#Remove filtering columns
select(ID)# A tibble: 31 × 1
ID
<chr>
1 A-001
2 A-006
3 A-013
4 A-015
5 A-016
6 A-081
7 A-102
8 A-106
9 A-107
10 A-115
# … with 21 more rowsEXAMPLE 1: Hierarchy of Airstrip females
create_id_starting.table(
sex = "female",
clan = "A",
at = "2007-01-01"
) %>%
#Find rank of each female (only among females)
mutate(rank = fetch_id_rank.sex(ID = ID, at = "2007-01-01"))# A tibble: 31 × 2
ID rank
<chr> <int>
1 A-001 10
2 A-006 13
3 A-013 1
4 A-015 15
5 A-016 7
6 A-081 20
7 A-102 8
8 A-106 11
9 A-107 21
10 A-115 19
# … with 21 more rowsEXAMPLE 1: Hierarchy of Airstrip females
create_id_starting.table(
sex = "female",
clan = "A",
at = "2007-01-01"
) %>%
mutate(rank = fetch_id_rank.sex(ID = ID, at = "2007-01-01")) %>%
#Arrange to show alpha on top
arrange(rank)# A tibble: 31 × 2
ID rank
<chr> <int>
1 A-013 1
2 A-159 2
3 A-119 3
4 A-186 4
5 A-178 5
6 A-145 6
7 A-016 7
8 A-102 8
9 A-164 9
10 A-001 10
# … with 21 more rowsTASK: Rank of females
EXAMPLE 2: Change in female rank
We want all females alive in Airstrip over a year
create_id_starting.table(
## Select all females..
sex = "female",
## In Airstrip...
clan = "A",
## During all of 2007...
from = "2007-01-01", to = "2008-01-01",
#Individuals were alive this whole period (alive = !dead)
lifestage = "!dead", lifestage.overlap = "always"
)# A tibble: 16 × 1
ID
<chr>
1 A-006
2 A-013
3 A-016
4 A-081
5 A-102
6 A-106
7 A-107
8 A-116
9 A-119
10 A-129
11 A-132
12 A-139
13 A-140
14 A-145
15 A-164
16 A-178EXAMPLE 2: Change in female rank
WARNING
This has a bug in the {drat} version, but I am fixing it!!
EXAMPLE 2: Change in female rank
# Extract data 'manually' without using `create_id_starting.table()` arguments
#Return all individuals (no filtering)
create_id_starting.table() %>%
#Extract variables for filtering...
mutate(sex = fetch_id_sex(ID = ID),
alive_07 = fetch_id_is.alive(ID = ID, at = "2007-01-01"),
alive_08 = fetch_id_is.alive(ID = ID, at = "2008-01-01"),
clan = fetch_id_clan.current(ID = ID, at = "2007-01-01")) %>%
#Filter to just return Airstrip females alive on 2007-01-01
filter(sex == "female" & alive_07 & alive_08 & clan == "A") %>%
#Remove filtering columns
select(ID)# A tibble: 25 × 1
ID
<chr>
1 A-006
2 A-013
3 A-016
4 A-081
5 A-102
6 A-106
7 A-107
8 A-116
9 A-119
10 A-129
# … with 15 more rowsEXAMPLE 2: Change in female rank
create_id_starting.table(
sex = "female",
clan = "A",
from = "2007-01-01", to = "2008-01-01",
lifestage = "!dead", lifestage.overlap = "always"
) %>%
#Expand our data to include multiple dates for each female
reshape_row_date.seq(ID, from = "2007-01-01", to = "2008-01-01",
by = "year")# A tibble: 32 × 2
ID date
<chr> <date>
1 A-006 2007-01-01
2 A-006 2008-01-01
3 A-013 2007-01-01
4 A-013 2008-01-01
5 A-016 2007-01-01
6 A-016 2008-01-01
7 A-081 2007-01-01
8 A-081 2008-01-01
9 A-102 2007-01-01
10 A-102 2008-01-01
# … with 22 more rowsEXAMPLE 2: Change in female rank
create_id_starting.table(
sex = "female",
clan = "A",
from = "2007-01-01", to = "2008-01-01",
lifestage = "!dead", lifestage.overlap = "always"
) %>%
reshape_row_date.seq(ID, from = "2007-01-01", to = "2008-01-01",
by = "year") %>%
#Extract (standardised) rank information for each female on each date
mutate(rank = fetch_id_rank.sex.std(ID = ID, at = date))# A tibble: 32 × 3
ID date rank
<chr> <date> <dbl>
1 A-006 2007-01-01 -0.2
2 A-006 2008-01-01 -0.143
3 A-013 2007-01-01 1
4 A-013 2008-01-01 1
5 A-016 2007-01-01 0.4
6 A-016 2008-01-01 0.429
7 A-081 2007-01-01 -0.9
8 A-081 2008-01-01 -0.905
9 A-102 2007-01-01 0.3
10 A-102 2008-01-01 0.333
# … with 22 more rowsEXAMPLE 2: Change in female rank
create_id_starting.table(
sex = "female",
clan = "A",
from = "2007-01-01", to = "2008-01-01",
lifestage = "!dead", lifestage.overlap = "always"
) %>%
reshape_row_date.seq(ID, from = "2007-01-01", to = "2008-01-01",
by = "year") %>%
mutate(rank = fetch_id_rank.sex.std(ID = ID, at = date)) %>%
#Group by/summarise to see how individual ranks changed over the year
group_by(ID) %>%
## first() and last() are dplyr functions. Useful with group_by()
summarise(rank_change = last(rank) - first(rank)) %>%
arrange(rank_change)# A tibble: 16 × 2
ID rank_change
<chr> <dbl>
1 A-139 -0.114
2 A-140 -0.110
3 A-119 -0.0857
4 A-132 -0.0333
5 A-116 -0.0238
6 A-081 -0.00476
7 A-013 0
8 A-107 0
9 A-178 0.0190
10 A-145 0.0238
11 A-016 0.0286
12 A-102 0.0333
13 A-164 0.0381
14 A-106 0.0476
15 A-129 0.0524
16 A-006 0.0571 EXAMPLE 2: Change in female rank
## TIP: DEFINE REPEATED VALUES ONCE
start_date <- "2007-01-01"
end_date <- "2008-01-01"
create_id_starting.table(
sex = "female",
clan = "A",
from = start_date, to = end_date,
lifestage = "!dead", lifestage.overlap = "always"
) %>%
#Quicker way!
mutate(start_rank = fetch_id_rank.sex.std(ID = ID, at = start_date),
end_rank = fetch_id_rank.sex.std(ID = ID, at = end_date),
rank_change = end_rank - start_rank) %>%
arrange(rank_change)# A tibble: 16 × 4
ID start_rank end_rank rank_change
<chr> <dbl> <dbl> <dbl>
1 A-139 -0.6 -0.714 -0.114
2 A-140 -0.7 -0.810 -0.110
3 A-119 0.8 0.714 -0.0857
4 A-132 -0.3 -0.333 -0.0333
5 A-116 -0.5 -0.524 -0.0238
6 A-081 -0.9 -0.905 -0.00476
7 A-013 1 1 0
8 A-107 -1 -1 0
9 A-178 0.6 0.619 0.0190
10 A-145 0.5 0.524 0.0238
11 A-016 0.4 0.429 0.0286
12 A-102 0.3 0.333 0.0333
13 A-164 0.2 0.238 0.0381
14 A-106 0 0.0476 0.0476
15 A-129 -0.100 -0.0476 0.0524
16 A-006 -0.2 -0.143 0.0571 TASK: Rank of females
BONUS: Plotting rank change over time
We want all females that were alive in Shamba for at least 1 day
create_id_starting.table(
sex = "female",
clan = "S",
## During this period...
from = "2000-01-01", to = "2002-01-01",
#Individuals could be alive at any point
lifestage = "!dead", lifestage.overlap = "any"
)# A tibble: 10 × 1
ID
<chr>
1 A-008
2 A-018
3 A-019
4 S-082
5 S-083
6 S-084
7 S-086
8 S-089
9 S-090
10 S-092BONUS: Plotting rank change over time
create_id_starting.table(
sex = "female",
clan = "S",
from = "2000-01-01", to = "2002-01-01",
lifestage = "!dead", lifestage.overlap = "any"
) %>%
#Expand our data to include multiple dates for each female
reshape_row_date.seq(ID, from = "2000-01-01", to = "2002-01-01",
by = "month")# A tibble: 250 × 2
ID date
<chr> <date>
1 A-008 2000-01-01
2 A-008 2000-02-01
3 A-008 2000-03-01
4 A-008 2000-04-01
5 A-008 2000-05-01
6 A-008 2000-06-01
7 A-008 2000-07-01
8 A-008 2000-08-01
9 A-008 2000-09-01
10 A-008 2000-10-01
# … with 240 more rowsBONUS: Plotting rank change over time
(create_id_starting.table(
sex = "female",
clan = "S",
from = "2000-01-01", to = "2002-01-01",
lifestage = "!dead", lifestage.overlap = "any"
) %>%
reshape_row_date.seq(ID, from = "2000-01-01", to = "2002-01-01",
by = "month") %>%
#Determine standardised rank at each date
mutate(rank = fetch_id_rank.sex.std(ID = ID, at = date)) %>%
#Only include cases where rank was returned
#(i.e. exclude when they were not alive)
filter(!is.na(rank)) -> plot_data)# A tibble: 130 × 3
ID date rank
<chr> <date> <dbl>
1 A-008 2000-01-01 0
2 A-008 2000-02-01 0
3 A-008 2000-03-01 -0.333
4 A-008 2000-04-01 -0.333
5 A-008 2000-05-01 -0.333
6 A-008 2000-06-01 -0.333
7 A-008 2000-07-01 -0.333
8 A-008 2000-08-01 -0.333
9 A-008 2000-09-01 -0.333
10 A-008 2000-10-01 -0.333
# … with 120 more rowsBONUS: Plotting rank change over time
Code
last_record <- plot_data %>%
group_by(ID) %>%
slice(n())
ggplot() +
geom_line(data = plot_data,
aes(x = date, y = rank, group = ID, colour = ID), size = 1) +
geom_text(data = last_record,
aes(x = date + 50, y = rank, label = ID, colour = ID), size = 3) +
labs(x = "", y = "Standardized rank of females (Shamba)") +
scale_x_date(date_labels = "%b-%y", date_breaks = "2 month") +
coord_cartesian(clip = "off", xlim = c(as.Date(NA), as.Date("2002-01-01"))) +
theme_classic() +
theme(legend.position = "none",
plot.margin = margin(r = 40, l = 20, t = 20))