Descriptive analysis of hunting data from J Dulat’s internship

1 Introduction

We have information about hunting bags, hunting effort and hunting areas of 25 hunting teams.

1.1 Hunting areas

Table 1.1: Data summary
Name h_areas
Number of rows 25
Number of columns 11
_______________________
Column type frequency:
character 4
numeric 7
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
id_team 0 1.0 5 5 0 25 0
microregion 0 1.0 6 13 0 10 0
municipality 0 1.0 6 22 0 24 0
vegetation_cover 5 0.8 6 90 0 18 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
area_km2 0 1.00 18.79 16.26 2.20 5.80 12.4 25.60 66.20 ▇▃▂▁▁
mean_h_drives 2 0.92 16.09 13.07 2.00 10.00 12.0 19.00 60.00 ▇▃▁▁▁
mean_h_drive_size 2 0.92 1.22 0.60 0.43 0.77 1.1 1.66 2.67 ▇▆▃▃▁
perc_plain 6 0.76 16.05 33.44 0.00 0.00 0.0 2.50 100.00 ▇▁▁▁▁
perc_piedmont 6 0.76 42.37 41.31 0.00 0.00 50.0 77.50 100.00 ▇▁▅▁▅
perc_midmountain 6 0.76 41.58 44.66 0.00 0.00 35.0 100.00 100.00 ▇▁▂▁▅
h_days_week 7 0.72 2.06 0.87 1.00 1.25 2.0 2.75 4.00 ▅▇▁▃▁

Missing values are concentrated on 8 interviewees out of 25.

Distribution of the missing values on the hunting areas.

Figure 1.1: Distribution of the missing values on the hunting areas.

Unfortunately, the hunting areas were drawn by hand during the interview and we don’t have the geographically referenced boundaries. As a result, we can not gather environmental or geographical data from official sources and need to rely on the perceptual information given by the interviewees.

For instance, concerning the vegetation cover, the data is given qualitatively as follows:

##  [1] "fruitiers emmaquisés"                                                                      
##  [2] "fruitiers"                                                                                 
##  [3] "50/50 ciste, arbouses, bruyères et forêt chataigniers et chênes"                           
##  [4] "gros maquis"                                                                               
##  [5] "fruitiers, arbousiers"                                                                     
##  [6] "prairies, maquis dense, cultures"                                                          
##  [7] "prairies, maquis bas"                                                                      
##  [8] "cultures, fruitiers, maquis"                                                               
##  [9] "fruitiers, maquis"                                                                         
## [10] "fruitiers, prairie, maquis"                                                                
## [11] "chêne, châtaignier, bruyère"                                                               
## [12] "amandiers, figues de barbarie, figues, maquis dense, ciste, chêne vert, olivier, lantisque"
## [13] NA                                                                                          
## [14] "maquis"                                                                                    
## [15] "maquis, chênes, châtaigniers"                                                              
## [16] "chênes, châtagniers, fruitiers, luzerne, maquis dense"                                     
## [17] "figues,arbouses, ronces, cistes, genets"                                                   
## [18] "chêne, châtaignier, pins"                                                                  
## [19] "maquis chêne pin"

We have some approximate geographical references from the municipalities and micro-regions so we can locate the hunting teams on a map.

1.2 Hunting bags and effort

Table 1.2: Data summary
Name hunting_season_2019_jd
Number of rows 24
Number of columns 14
_______________________
Column type frequency:
character 1
logical 2
numeric 9
POSIXct 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
id_team 0 1 5 5 0 24 0

Variable type: logical

skim_variable n_missing complete_rate mean count
dog_blood 5 0.79 0.11 FAL: 17, TRU: 2
dog_feet 5 0.79 0.11 FAL: 17, TRU: 2

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
hb_2019 0 1.00 101.58 93.15 20.00 40.00 56.5 116.75 330 ▇▂▁▁▂
prior_hunt_pop 7 0.71 35.76 18.06 10.00 20.00 33.0 50.00 70 ▇▆▃▆▃
mean_age 0 1.00 50.11 9.73 30.00 43.75 50.0 60.00 63 ▃▃▃▇▇
n_hunters_total 0 1.00 18.71 7.71 5.00 12.75 19.0 20.00 35 ▂▇▇▁▃
n_hunters_day 4 0.83 9.90 3.40 5.00 8.00 10.0 10.50 17 ▅▆▇▁▅
n_dogs_day 5 0.79 11.26 6.39 2.00 8.00 10.0 12.50 30 ▂▇▁▁▁
h_days_week 2 0.92 2.14 0.77 1.00 2.00 2.0 2.75 4 ▂▇▁▃▁
season_days 4 0.83 171.15 32.39 121.00 155.75 168.0 168.00 271 ▂▇▁▁▁
h_days_total 4 0.83 49.92 19.33 17.29 42.43 48.0 58.07 96 ▃▇▁▃▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
season_starts 4 0.83 2020-06-01 2020-10-01 00:00:00 2020-08-15 00:00:00 4
season_ends 4 0.83 2020-12-31 2021-02-27 06:00:00 2021-01-30 06:00:00 8

Hunting team Cas_6 did not provide information on its hunting bag for 2019 and thus was removed here.

Missing values are concentrated on 9 interviewees out of 24.

Distribution of the missing values on the hunting season.

Figure 1.2: Distribution of the missing values on the hunting season.

2 Relationships

Pairwise matrix plot of hunting season variables.

Figure 2.1: Pairwise matrix plot of hunting season variables.

Pairwise matrix plot of hunting bag and hunting areas variables.

Figure 2.2: Pairwise matrix plot of hunting bag and hunting areas variables.