library(readxl) #allows you to read in excel files
library(data.table) #for data organizing
library(tidyverse) #for this script, uses for dplyr and ggplot2
library(lubridate) #Deals with date formats
library(dplyr) #for data organizing
library(openxlsx) #read, write, and edits xlsx files
n2023 <- read_excel("C:/Users/rfriendly/Desktop/kgun/data/kgun_NOPI_2023.xlsx") #read in nopi file
## New names:
## • `` -> `...13`
n2023 <- n2023[-13] #delete column 13
a2023 <- read_excel("C:/Users/rfriendly/Desktop/kgun/data/kgun_AGWT_2023.xlsx") #read in agwt file
m2023 <- read_excel("C:/Users/rfriendly/Desktop/kgun/data/kgun_MALL_2023.xlsx") #read in mall file
all_species <- rbind(n2023,m2023,a2023) #combines data
summary(all_species)
##      PREFIX         SUFFIX         BANDER            SPECIES         
##  Min.   :1144   Min.   :12032   Length:485         Length:485        
##  1st Qu.:1226   1st Qu.:74520   Class :character   Class :character  
##  Median :1226   Median :74641   Mode  :character   Mode  :character  
##  Mean   :1264   Mean   :71813                                        
##  3rd Qu.:1226   3rd Qu.:74762                                        
##  Max.   :2227   Max.   :74883                                        
##      SEX               HOWSEX              AGE           
##  Length:485         Length:485         Length:485        
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##                                                          
##                                                          
##                                                          
##       DATE                          START TIME                    
##  Min.   :2023-08-02 00:00:00.00   Min.   :1899-12-31 09:25:00.00  
##  1st Qu.:2023-08-11 00:00:00.00   1st Qu.:1899-12-31 09:48:00.00  
##  Median :2023-08-14 00:00:00.00   Median :1899-12-31 10:19:00.00  
##  Mean   :2023-08-13 09:00:22.26   Mean   :1899-12-31 13:00:19.67  
##  3rd Qu.:2023-08-17 00:00:00.00   3rd Qu.:1899-12-31 18:48:00.00  
##  Max.   :2023-08-22 00:00:00.00   Max.   :1899-12-31 20:36:00.00  
##     END TIME                        TRAP_ID             NOTES          
##  Min.   :1899-12-31 09:36:00.00   Length:485         Length:485        
##  1st Qu.:1899-12-31 10:21:00.00   Class :character   Class :character  
##  Median :1899-12-31 10:53:00.00   Mode  :character   Mode  :character  
##  Mean   :1899-12-31 13:31:58.03                                        
##  3rd Qu.:1899-12-31 19:04:00.00                                        
##  Max.   :1899-12-31 21:36:00.00

R Markdown Simple Kgun Report, 2023

This is just practice to learn. We are going to summarize the data to get simple statistics for Northern Pintail, Mallard, and American green-winged teals.You can see some information from the imported data above. First, I am going to clean up the data before starting, these codes will not show.

From the data above let’s get a table of species with the number of age and sex classes. And other tables.

table_sas <- all_species %>% count(SPECIES, AGE, SEX) #count by species and age and sex
table_sas
## # A tibble: 11 × 4
##    SPECIES AGE   SEX       n
##    <fct>   <fct> <fct> <int>
##  1 AGWT    AHY   M         4
##  2 AGWT    HY    F         2
##  3 MALL    AHY   M        13
##  4 MALL    HY    F         3
##  5 MALL    HY    M         3
##  6 NOPI    AHY   F       136
##  7 NOPI    AHY   M        98
##  8 NOPI    HY    F       109
##  9 NOPI    HY    M       110
## 10 NOPI    L     F         5
## 11 NOPI    L     M         2
species_number <- all_species %>% count(SPECIES) #count by species
species_number
## # A tibble: 3 × 2
##   SPECIES     n
##   <fct>   <int>
## 1 AGWT        6
## 2 MALL       19
## 3 NOPI      460
trap_sp <- all_species %>% count(TRAP_ID, SPECIES) #count by trap location and species
trap_sp
## # A tibble: 8 × 3
##   TRAP_ID SPECIES     n
##   <fct>   <fct>   <int>
## 1 N1      AGWT        1
## 2 N1      MALL       12
## 3 N1      NOPI      148
## 4 N2      MALL        7
## 5 N2      NOPI      223
## 6 N3      AGWT        5
## 7 N3      NOPI       68
## 8 NOPI2   NOPI       21
trap <- all_species %>% count(TRAP_ID) #count by trap location
trap
## # A tibble: 4 × 2
##   TRAP_ID     n
##   <fct>   <int>
## 1 N1        161
## 2 N2        230
## 3 N3         73
## 4 NOPI2      21

From the information above we are going to create 2 tables

Trap by Species

bird_table <- data.frame(Trap = c('N1','N2','N3','NOPI2'),
                         AGWT = c('1','0','5','0'),
                         NOPI = c('148','223','68','21'),
                         MALL = c('12','7','0','0'),
                         Total = c('161','230','73','21'))
bird_table
##    Trap AGWT NOPI MALL Total
## 1    N1    1  148   12   161
## 2    N2    0  223    7   230
## 3    N3    5   68    0    73
## 4 NOPI2    0   21    0    21

Age by species

bird_age_table <- data.frame(Age = c('L','HY','AHY'),
                             AGWT = c('0','2','4'),
                             NOPI = c('7','219','234'),
                             MALL = c('0','6','13'))
bird_age_table
##   Age AGWT NOPI MALL
## 1   L    0    7    0
## 2  HY    2  219    6
## 3 AHY    4  234   13

Figures

library(ggplot2)
nopi_years <- read_excel("C:/Users/rfriendly/Desktop/kgun/data/nopi_numbers_2023.xlsx") #read in nopi file
mall_years <- read_excel("C:/Users/rfriendly/Desktop/kgun/data/mall_numbers_2023.xlsx") #read in mall file
agwt_years <- read_excel("C:/Users/rfriendly/Desktop/kgun/data/agwt_numbers_2023.xlsx") #read in agwt file

NOPI banded per year

nopi_graph

MALL banded per year

mall_graph

AGW banded per year

agwt_graph