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_noon.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: 181 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 171 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
## 9 1 2 Y77-10 705 74.8 0.906 3 25.51667
## 7 1 2 Y64-17 706 74.0 0.935 2 28.15000
## 8 1 2 Y77-11 701 68.7 0.967 2 34.10000
## 18 2 5 Y70-11 695 71.2 0.980 1 39.25000
## 1 1 2 Y67-14 693 75.6 1.879 10 71.40000
## 6 1 2 Y64-16 697 77.2 3.004 9 75.41667
## 2 1 2 Y69-12 703 80.6 2.014 10 88.53333
## 14 2 5 Y65-10 694 71.6 1.895 5 90.78333
## 13 2 5 Y64-14 686 71.0 3.002 7 92.55000
## 11 1 2 Y66-14 702 76.1 2.309 12 95.91667
## 20 2 5 Y68-14 687 72.5 2.595 12 96.80000
## 19 2 5 Y74-14 689 68.6 2.134 5 98.30000
## 24 2 5 Y76-15 696 62.2 2.445 7 100.83333
## 4 1 2 Y70-13 700 74.4 2.763 13 102.45000
## 15 2 5 Y66-10 688 77.7 2.936 14 106.28333
## 22 2 5 Y76-13 685 72.4 2.813 17 141.75000
## 5 1 2 Y71-12 699 63.7 2.560 18 143.73333
## 10 1 2 Y68-11 708 72.8 2.736 11 144.05000
## 17 2 5 Y77-12 692 78.6 3.349 15 154.08333
## 3 1 2 Y66-12 704 NA NA NA NA
## 12 1 2 Y68-12 707 NA NA NA NA
## 16 2 5 Y70-12 690 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)