FIREFEEDER REPORT
Sample report for firefeeders
This is just an illustration of the use of FIRE feeder event data from first 24 hours of use.
setwd("C:/Users/Admin/OneDrive/Documents/phenomics/FIRE")
library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.4.3## -- Attaching packages --------------------------------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 2.2.1 v purrr 0.2.4
## v tibble 1.4.2 v dplyr 0.7.4
## v tidyr 0.8.0 v stringr 1.2.0
## v readr 1.1.1 v forcats 0.3.0## Warning: package 'tibble' was built under R version 3.4.3## Warning: package 'tidyr' was built under R version 3.4.3## Warning: package 'readr' was built under R version 3.4.3## Warning: package 'purrr' was built under R version 3.4.3## Warning: package 'dplyr' was built under R version 3.4.3## Warning: package 'forcats' was built under R version 3.4.3## -- Conflicts ------------------------------------------------------------------------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()fire_file<-"FIRE_03_30_18.csv"
fire_data<-read_csv(fire_file)## Parsed with column specification:
## cols(
## Location = col_integer(),
## Tag = col_character(),
## Date = col_character(),
## Entry = col_time(format = ""),
## Exit = col_time(format = ""),
## `Ent Wt` = col_double(),
## `Ext Wt` = col_double(),
## Consumed = col_double(),
## Weight = col_double(),
## `Topup Amount` = col_double()
## )fire_data## # A tibble: 144 x 10
## Location Tag Date Entry Exit `Ent Wt` `Ext Wt` Consumed Weight
## <int> <chr> <chr> <tim> <tim> <dbl> <dbl> <dbl> <dbl>
## 1 1 00000000~ 3/28/~ 15:24 15:24 0. 0.00200 -0.00200 0.
## 2 1 00000000~ 3/28/~ 15:37 15:38 -0.00200 -0.00400 0.00200 0.
## 3 1 00000000~ 3/29/~ 08:25 08:26 -0.00300 -0.00300 0. 0.
## 4 1 00000000~ 3/29/~ 08:31 08:33 -0.00100 -0.00500 0.00400 0.
## 5 1 00000000~ 3/29/~ 08:37 08:37 -0.00600 -0.00800 0.00200 0.
## 6 1 00000000~ 3/29/~ 08:50 08:50 -0.00700 -0.00800 0.00100 0.
## 7 1 98515201~ 3/29/~ 08:51 08:55 0.433 -0.00800 0.441 0.
## 8 1 98515201~ 3/29/~ 10:59 10:59 0.815 0.810 0.00500 82.2
## 9 1 98515201~ 3/29/~ 11:01 11:07 0.810 0.779 0.0310 64.0
## 10 1 98515201~ 3/29/~ 11:09 11:09 0.779 0.774 0.00500 82.2
## # ... with 134 more rows, and 1 more variable: `Topup Amount` <dbl>key<-read_csv("FIRE_key.csv")## Parsed with column specification:
## cols(
## unit = col_integer(),
## pen = col_integer(),
## ear_notch = col_character(),
## RFID_tag = col_integer()
## )key## # A tibble: 24 x 4
## unit pen ear_notch RFID_tag
## <int> <int> <chr> <int>
## 1 1 2 Y67-14 693
## 2 1 2 Y69-12 703
## 3 1 2 Y66-12 704
## 4 1 2 Y70-13 700
## 5 1 2 Y71-12 699
## 6 1 2 Y64-16 697
## 7 1 2 Y64-17 706
## 8 1 2 Y77-11 701
## 9 1 2 Y77-10 705
## 10 1 2 Y68-11 708
## # ... with 14 more rows#zerotags
filter(fire_data,Tag=="000000000000000")## Warning: package 'bindrcpp' was built under R version 3.4.3## # A tibble: 7 x 10
## Location Tag Date Entry Exit `Ent Wt` `Ext Wt` Consumed Weight
## <int> <chr> <chr> <tim> <tim> <dbl> <dbl> <dbl> <dbl>
## 1 1 00000000~ 3/28/2~ 15:24 15:24 0. 0.00200 -0.00200 0.
## 2 1 00000000~ 3/28/2~ 15:37 15:38 -0.00200 -0.00400 0.00200 0.
## 3 1 00000000~ 3/29/2~ 08:25 08:26 -0.00300 -0.00300 0. 0.
## 4 1 00000000~ 3/29/2~ 08:31 08:33 -0.00100 -0.00500 0.00400 0.
## 5 1 00000000~ 3/29/2~ 08:37 08:37 -0.00600 -0.00800 0.00200 0.
## 6 1 00000000~ 3/29/2~ 08:50 08:50 -0.00700 -0.00800 0.00100 0.
## 7 2 00000000~ 3/29/2~ 08:08 08:08 0.555 0.552 0.00300 0.
## # ... with 1 more variable: `Topup Amount` <dbl>fire_data<-filter(fire_data,Tag!="000000000000000")
ffd<-mutate(fire_data,visit_length=Exit-Entry,ID=round(as.numeric(Tag)-1000*985152014209))
idx<-match(ffd$ID,key$RFID_tag)
ffd<-(mutate(ffd,earnotch=key$ear_notch[idx]))
sum_per_animal<-ffd %>%group_by(earnotch)%>% summarize(med_weight=median(Weight),total_feed=sum(Consumed),visits=sum(Weight*0+1),total_time=as.numeric(sum(visit_length)/60))
idr<-match(key$ear_notch,sum_per_animal$earnotch)
animal_report<-cbind(key,sum_per_animal[idr,-1])
animal_report<-animal_report[order(animal_report$total_time),]
write.csv(animal_report[order(animal_report$total_time),],file=paste("animal_report",fire_file,sep="_"))
ggplot(ffd,aes(x=visit_length,y=Consumed,color=earnotch))+geom_point()## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.
print(animal_report)## unit pen ear_notch RFID_tag med_weight total_feed visits total_time
## 7 1 2 Y64-17 706 74.0 0.389 1 13.95000
## 9 1 2 Y77-10 705 37.4 0.665 2 19.33333
## 1 1 2 Y67-14 693 37.6 1.370 8 51.06667
## 6 1 2 Y64-16 697 76.2 2.050 7 51.86667
## 19 2 5 Y74-14 689 68.6 1.117 2 53.30000
## 24 2 5 Y76-15 696 61.7 1.615 4 63.90000
## 11 1 2 Y66-14 702 76.3 1.718 10 71.60000
## 2 1 2 Y69-12 703 80.9 1.603 8 71.83333
## 13 2 5 Y64-14 686 69.8 2.358 5 73.21667
## 14 2 5 Y65-10 694 71.7 1.637 4 76.81667
## 20 2 5 Y68-14 687 72.4 2.257 10 86.30000
## 4 1 2 Y70-13 700 74.0 2.338 11 88.05000
## 15 2 5 Y66-10 688 77.5 2.390 12 88.06667
## 10 1 2 Y68-11 708 72.8 1.889 8 96.23333
## 5 1 2 Y71-12 699 63.8 2.111 16 119.66667
## 22 2 5 Y76-13 685 72.7 2.440 14 124.26667
## 17 2 5 Y77-12 692 78.4 2.700 14 130.20000
## 3 1 2 Y66-12 704 NA NA NA NA
## 8 1 2 Y77-11 701 NA NA NA NA
## 12 1 2 Y68-12 707 NA NA NA NA
## 16 2 5 Y70-12 690 NA NA NA NA
## 18 2 5 Y70-11 695 NA NA NA NA
## 21 2 5 Y67-11 698 NA NA NA NA
## 23 2 5 Y77-13 691 NA NA NA NANotice: 1) ammount consumed and time in the feeder correlate very well, 2) some animals with NA in the summary have not gone through the feeder in the first 24 hrs in the pen, 3) scale seems to be off for some animals (too light)