This code is for the graphical analysis of percent observations of roosting and being in water at different time points.
captive.data = read.csv("2023 Chick Yards.csv", header=T)
names(captive.data)
## [1] "YARD" "Chick" "Date"
## [4] "Image.." "Time" "Hour"
## [7] "In.H2O" "Comfort.Behavior" "Proxim.to.edge..m."
## [10] "Light.Dark" "X"
# get rid of NAs in Hour
captive.data = captive.data %>%
filter(!is.na(Hour))
# convert hour to factor with specific hours
captive.data = filter(captive.data, Hour %in% c("100", "200", "300", "400", "500", "600", "1800", "1900", "2000", "2100", "2200", "2300", "2400"))
captive.data$time.factor <- factor(captive.data$Hour)
levels(captive.data$time.factor)
## [1] "100" "1800" "1900" "200" "2000" "2100" "2200" "2300" "2400" "300"
## [11] "400" "500" "600"
While you can’t say anything about water usage at this point (talk about that), you can talk about how often animals that were in water were roosting.
# get rid of NAs in In.H20
water.data = captive.data %>%
filter(!is.na(In.H2O))
# convert In.H2O to factors
water.data$water.factor = factor(water.data$In.H2O)
#levels(water.data$water.factor)
ggplot(water.data, aes(factor(time.factor, level=c('1800', '1900', '2000', '2100', '2200', '2300', '2400', '100', '200', '300', '400', '500', '600')), after_stat(count))) +
geom_bar(aes(fill = water.factor), position = "dodge") +
labs(x="Hour of day",y="Number of observations roosting", fill = "In (1) or out (0) of water")
# create a table with number of roosting observations per hour of day
water.table <- water.data %>% group_by(time.factor) %>%
summarise(
count.roost = n(),
total.in.roost=sum(In.H2O),
total.out.roost = (length(In.H2O) - sum(In.H2O)),
)
# create table with proportion roosting in and out of water per hour
water.table = water.table %>%
mutate(prop.in.roost = (total.in.roost / count.roost))
water.table = water.table %>%
mutate(prop.out.roost = (total.out.roost / count.roost))
# create table with one column for roosting prop in and out}
water.table = gather(water.table, key="in.out.roost", value="proportion.roost", 5:6)
water.table
## # A tibble: 26 × 6
## time.factor count.roost total.in.roost total.out.roost in.out.roost
## <fct> <int> <int> <int> <chr>
## 1 100 194 160 34 prop.in.roost
## 2 1800 173 64 109 prop.in.roost
## 3 1900 177 101 76 prop.in.roost
## 4 200 190 151 39 prop.in.roost
## 5 2000 206 153 53 prop.in.roost
## 6 2100 202 163 39 prop.in.roost
## 7 2200 212 183 29 prop.in.roost
## 8 2300 197 172 25 prop.in.roost
## 9 2400 190 160 30 prop.in.roost
## 10 300 188 153 35 prop.in.roost
## # ℹ 16 more rows
## # ℹ 1 more variable: proportion.roost <dbl>
ggplot(water.table, aes(x = factor(time.factor, level=c('1800', '1900', '2000', '2100', '2200', '2300', '2400', '100', '200', '300', '400', '500', '600')), y = proportion.roost, fill = in.out.roost)) +
geom_bar(stat = "identity", position = "dodge") +
labs(x="Hour of day",y="Proportion of observations roosting") +
scale_fill_manual(name="In or out of water",labels=c("in", "out"), values=c("#F8766D", "#00BFC4"))
###2023 Chick Yard Data
Davin Lopez
Stewart,S. 2020. INVESTIGATING CAUSE-SPECIFIC MORTALITY OF WHOOPING CRANE (GRUS AMERICANA) CHICKS AT NECEDAH NATIONAL WILDLIFE REFUGE. Thesis, Biology Dept, Univ WI Oshkosh