Encounter Report Code
LGC
March 18, 2019
Encounter Report Code
updated 3/18/2019 by Lindsay Carlson lgcarlson@aggiemail.usu.edu
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osencounter<-read.csv("osborne_encounterrecords.csv",header=T)
#head(osencounter)
#str(osencounter)
osencounter<-as.data.frame(osencounter)Summary Statistics
Create a table of number of encountered birds by year. These correspond to “years” not “hunting seasons.”
nbyyear<-osencounter %>%
group_by(enc_year) %>%
summarise(n=n())
nbyyear## # A tibble: 6 x 2
## enc_year n
## <int> <int>
## 1 2014 3
## 2 2015 30
## 3 2016 93
## 4 2017 206
## 5 2018 379
## 6 2019 103
Create a table of number of encountered birds by species
#this command is creating a tibble (table) called nbyspecies from data from "osencounter"
#it is grouping the data by species, then using summarize to count the n (number) of cases in each group
#finally, it arranges them in descending order
nbyspecies<-osencounter %>%
group_by(species) %>%
summarise(n=n()) %>%
arrange(desc(n))
nbyspecies## # A tibble: 8 x 2
## species n
## <fct> <int>
## 1 MALL 716
## 2 AGWT 36
## 3 WODU 36
## 4 GADW 20
## 5 RNDU 3
## 6 AMCO 1
## 7 NOPI 1
## 8 NSHO 1As of 3/36/19, 88% of the recoveries were mallards.
Create a table of number of encountered birds by location.
nbylocation<-osencounter %>%
group_by(band_location) %>%
summarise(n=n()) %>%
arrange(desc(n))
nbylocation## # A tibble: 8 x 2
## band_location n
## <fct> <int>
## 1 NGLORY 300
## 2 CARMICAL 180
## 3 SIL_CAMP 110
## 4 WHITEOAKS 105
## 5 MAX_BB 67
## 6 7DEVILS 30
## 7 DDF 13
## 8 FISHPOND 9
Create a table of number of encountered birds by state.
nbystate<-osencounter %>%
group_by(enc_state) %>%
summarise(n=n())
nbystate## # A tibble: 30 x 2
## enc_state n
## <fct> <int>
## 1 AL 1
## 2 ALB 5
## 3 AR 307
## 4 CA 1
## 5 GA 2
## 6 IA 12
## 7 IL 33
## 8 IN 4
## 9 KS 10
## 10 KY 16
## # ... with 20 more rows
Create a table of number of encountered birds by month
nbymonth<-osencounter %>%
group_by(enc_mo) %>%
summarise(n=n())
nbymonth## # A tibble: 10 x 2
## enc_mo n
## <int> <int>
## 1 1 309
## 2 2 9
## 3 3 2
## 4 4 5
## 5 5 1
## 6 8 6
## 7 9 24
## 8 10 102
## 9 11 143
## 10 12 213Note that these plots are not calling on the original dataset, they are calling on the tibble created above.
Distance from banding location
Calculate distance from banding location to encounter location.
## # A tibble: 10 x 5
## enc_mo mean_distkm n sd se
## <int> <dbl> <int> <dbl> <dbl>
## 1 1 212. 309 197. 11.2
## 2 2 159. 9 170. 56.7
## 3 3 42.3 2 37.5 26.5
## 4 4 2315. 5 249. 111.
## 5 5 1162. 1 NaN NaN
## 6 8 1965. 6 428. 175.
## 7 9 1811. 24 612. 125.
## 8 10 1789. 102 530. 52.5
## 9 11 541. 143 495. 41.4
## 10 12 326. 213 333. 22.8
Create summary tables
Create a table containing annual encounter totals by banding location. Here, we are grouping by two variables. It will group by the first listed variable first (location) in this case.
yearxbandlocation<-osencounter %>%
group_by(band_location,enc_year) %>%
summarise(n=n(),mean_distkm = mean(enc_distkm),sd = sd(enc_distkm),se = sd(enc_distkm)/sqrt(n))
yearxbandlocation## # A tibble: 31 x 6
## # Groups: band_location [?]
## band_location enc_year n mean_distkm sd se
## <fct> <int> <int> <dbl> <dbl> <dbl>
## 1 7DEVILS 2015 4 1067. 1160. 580.
## 2 7DEVILS 2016 9 620. 785. 262.
## 3 7DEVILS 2017 7 785. 594. 225.
## 4 7DEVILS 2018 9 449. 721. 240.
## 5 7DEVILS 2019 1 61.7 NaN NaN
## 6 CARMICAL 2016 16 625. 722. 180.
## 7 CARMICAL 2017 71 659. 758. 89.9
## 8 CARMICAL 2018 78 505. 650. 73.6
## 9 CARMICAL 2019 15 199. 165. 42.7
## 10 DDF 2017 5 380. 661. 295.
## # ... with 21 more rows#write.csv(yearxbandlocation, file = "yearxbandlocation.csv")Create a table containing annual encounter totals by species.
yearxspecies<-osencounter %>%
group_by(species,enc_year) %>%
summarise(n=n())
yearxspecies## # A tibble: 23 x 3
## # Groups: species [?]
## species enc_year n
## <fct> <int> <int>
## 1 AGWT 2016 6
## 2 AGWT 2017 13
## 3 AGWT 2018 14
## 4 AGWT 2019 3
## 5 AMCO 2017 1
## 6 GADW 2016 4
## 7 GADW 2017 5
## 8 GADW 2018 10
## 9 GADW 2019 1
## 10 MALL 2014 3
## # ... with 13 more rows#write.csv(yearxspecies, file = "yearxspecies.csv")Create a table containing state totals by species.
statexspecies<-osencounter %>%
group_by(enc_state,species) %>%
summarise(n=n())
statexspecies## # A tibble: 62 x 3
## # Groups: enc_state [?]
## enc_state species n
## <fct> <fct> <int>
## 1 AL MALL 1
## 2 ALB AGWT 1
## 3 ALB MALL 4
## 4 AR AGWT 16
## 5 AR GADW 11
## 6 AR MALL 271
## 7 AR RNDU 1
## 8 AR WODU 8
## 9 CA AGWT 1
## 10 GA MALL 2
## # ... with 52 more rows#write.csv(statexspecies, file = "statexspecies.csv")Instagram plots
Create a histogram of the number of years banded before being encountered.
Create a histogram of the distance between the banding location and the encounter location.
Create a histogram of the month of harvest.
Note, the correct month names need to be fixed in Adobe, but you can’t do a hist with a cat variable on x.
Animated Maps
Create an animated map where encounter locations are shown by date (since 2015).
## Source : http://tile.stamen.com/terrain-lines/4/2/5.png## Source : http://tile.stamen.com/terrain-lines/4/3/5.png## Source : http://tile.stamen.com/terrain-lines/4/4/5.png## Source : http://tile.stamen.com/terrain-lines/4/2/6.png## Source : http://tile.stamen.com/terrain-lines/4/3/6.png## Source : http://tile.stamen.com/terrain-lines/4/4/6.png
Create an animated map where encounter locations are shown by month of the hunting season.
## 0 1.September 2.October 3.November 4.December 5.January
## 23 24 102 143 213 309## nframes and fps adjusted to match transition
Create a density map for each “hunting season” as opposed to each year. This is for mallards only
## 2014-2015 2015-2016 2016-2017 2017-2018 2018-2019
## 4 48 98 330 311
Animate the mallard density map by monthly density.
## Source : http://tile.stamen.com/terrain-lines/4/2/4.png## Source : http://tile.stamen.com/terrain-lines/4/3/4.png## Source : http://tile.stamen.com/terrain-lines/4/4/4.png## nframes and fps adjusted to match transition