── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0 ✔ purrr 1.0.1
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
behavior_23Feb23 <- read.csv("behavior_23Feb23.csv")
behaviorprop <- behavior_23Feb23
behaviorprop$stationaryprop = (behaviorprop$stationary/behaviorprop$total_alive)
behaviorprop$movingprop = (behaviorprop$moving/behaviorprop$total_alive)
behaviorprop$incubatingprop = (behaviorprop$incubating/behaviorprop$total_alive)
behaviorprop$feedingprop = (behaviorprop$feeding/behaviorprop$total_alive)
behaviorprop$fanningprop = (behaviorprop$fanning/behaviorprop$total_alive)
behaviorprop <- behaviorprop %>%
filter(fanningprop<=1)
behavior2 <- behaviorprop[,c(6,14,19,20,21,22,23)]
do i have to pivot the goddamn behaviors to get the goddamn average (i think so)
behavior3<- behavior2 %>%
pivot_longer(stationaryprop:fanningprop,names_to='behaviors',values_to='prop')
head(behavior3)
# A tibble: 6 × 4
temp infected behaviors prop
<int> <int> <chr> <dbl>
1 37 0 stationaryprop 0
2 37 0 movingprop 0.333
3 37 0 incubatingprop 0.222
4 37 0 feedingprop 0.444
5 37 0 fanningprop 0.111
6 37 0 stationaryprop 0.143
behavior3$behaviors <- recode_factor(behavior3$behaviors, fanningprop="Fanning",stationaryprop="Stationary",movingprop="Moving",incubatingprop="Incubating",feedingprop="Feeding")
head(behavior3)
# A tibble: 6 × 4
temp infected behaviors prop
<int> <int> <fct> <dbl>
1 37 0 Stationary 0
2 37 0 Moving 0.333
3 37 0 Incubating 0.222
4 37 0 Feeding 0.444
5 37 0 Fanning 0.111
6 37 0 Stationary 0.143
beemean<-behavior3 %>%
group_by(behaviors, temp, infected) %>%
summarize(mean = mean(prop), sd=sd(prop),n=n(),se=sd/sqrt(n))
`summarise()` has grouped output by 'behaviors', 'temp'. You can override using
the `.groups` argument.
# A tibble: 50 × 7
# Groups: behaviors, temp [25]
behaviors temp infected mean sd n se
<fct> <int> <int> <dbl> <dbl> <int> <dbl>
1 Fanning 23 0 0 0 203 0
2 Fanning 23 1 0.0136 0.0563 132 0.00490
3 Fanning 30 0 0.00415 0.0204 174 0.00154
4 Fanning 30 1 0.00501 0.0223 82 0.00246
5 Fanning 34 0 0.154 0.165 63 0.0208
6 Fanning 34 1 0.117 0.135 126 0.0120
7 Fanning 37 0 0.375 0.228 191 0.0165
8 Fanning 37 1 0.399 0.205 106 0.0199
9 Fanning 40 0 0.251 0.214 64 0.0267
10 Fanning 40 1 0.176 0.152 136 0.0130
# … with 40 more rows
beemean$temp=as.factor(beemean$temp)
beemean$infected=as.factor(beemean$infected)
ggplot(data=beemean,aes(x=behaviors, y=mean,color=temp,shape=infected))+
geom_point()+
geom_errorbarh(aes(xmax=mean+se, xmin=mean-se),height=0.2)+
scale_shape_discrete(solid=T)+
theme_classic()
This code is not working right now, package ggalt is saying it’s installing but then it’s actually not?? Therefore geom_dumbbell can’t work…
#install.packages('ggalt')
#library(ggalt)
beegraph <- ggplot(beemean, aes(x=mean, y=behaviors, group=behaviors)) + geom_dumbbell(color=“#a3c4dc”, size=0.75, point.colour.l=“#0e668b”) + scale_x_continuous(label=percent) + labs(x=NULL, y=NULL, title=“Average Behavior Props”, subtitle=“yas”, caption=“bees”) + theme(plot.title = element_text(hjust=0.5, face=“bold”), plot.background=element_rect(fill=“#f7f7f7”), panel.background=element_rect(fill=“#f7f7f7”), panel.grid.minor=element_blank(), panel.grid.major.y=element_blank(), panel.grid.major.x=element_line(), axis.ticks=element_blank(), legend.position=“top”, panel.border=element_blank())
