OPmeans <- amine_data %>%
mutate(response.time = case_when(
response.time < 1 ~ 1,
TRUE ~ response.time)) %>%
group_by(genotype, food, date) %>%
summarise(meanOP = mean(log(response.time))) %>%
filter(food == "OP50") %>%
ungroup() %>%
select(genotype, date, meanOP)
plot <- full_join(amine_data, OPmeans) %>%
mutate(
rel_log = log(response.time) - meanOP,
food = fct_relevel(food, "OP50")) %>%
ggplot(aes(x = data_type, y = rel_log)) +
stat_summary(geom = "bar", fun.y = mean, aes(fill = food), width = 0.75, alpha = 0.5) +
ggbeeswarm::geom_quasirandom(aes(colour = food), width = 0.2, alpha = 0.75) +
scale_color_plot("grey-blue", drop = TRUE) +
scale_fill_plot("grey-blue", drop = TRUE) +
facet_grid(.~genotype + food,
labeller = labeller(genotype = label_wrap_gen(10),
food = as_labeller(c("OP50" = "",
"JUb39" = "")))) +
stat_summary(geom = "errorbar", fun.data = mean_se, width = 0.2) +
add.n('data_type', y.pos = -1) +
# stat_pointinterval(aes(y=response.time, x = 1.5),
# data = fitted, fatten_point = 0,
# size_range = c(0.3, 1), colour = "grey") +
# stat_summary(data = fitted,
# aes(y=response.time, x = 1.5),
# fun.y = median,
# fun.ymin = median,
# fun.ymax = median,
# geom = "crossbar",
# width = 0.05,
# lwd = 0.35,
# colour = "grey") +
labs(x = "genotype",
y = "time to reversal (s)") +
guides(colour = FALSE) +
theme(axis.text.x = element_blank())
#emmeans(stan_mod, pairwise ~ (food | genotype))$contrasts %>% c
#oda::as.mcmc() %>% bayesplot::mcmc_intervals(prob = 0.66, prob_outer = 0.95)
plot <- full_join(amine_data, OPmeans) %>%
mutate(
rel_log = log(response.time) - meanOP,
food = fct_relevel(food, "OP50"),
genotype = fct_relevel(genotype, c("N2", "tdc-1", "tbh-1"))) %>%
ggplot(aes(x = data_type)) +
geom_relLatency(fitted = fitted1,
fillvar = food,
dotvar = food,
yvar = rel_log) +
scale_color_plot("grey-blue", drop = TRUE) +
scale_fill_plot("grey-blue", drop = TRUE) +
facet_grid(.~genotype+food) +
add.n('data_type', y.pos = -1.6) +
labs(x = "genotype",
y = "relative reversal latency [log(s)]") +
guides(colour = FALSE) +
theme(axis.text.x = element_blank())
gt <- ggplot_gtable(ggplot_build(plot))
gt$widths[c(8,12,16,20)] = 5*gt$widths[c(8,12,16,20)]
gt$widths[c(6,10,14,18,18)] = .3*gt$widths[c(6,10,14,16,18)]
grid::grid.draw(gt)
#tyrosine data
OPmeans <- tyrosine_data %>%
mutate(response.time = case_when(
response.time < 1 ~ 1,
TRUE ~ response.time)) %>%
group_by(food, date, cond) %>%
summarise(meanOP = mean(log(response.time))) %>%
filter(food == "OP50") %>%
ungroup() %>%
select(cond, date, meanOP)
plot <- mutate(tyrosine_data, response.time = case_when(
response.time < 1 ~ 1,
TRUE ~ response.time)) %>%
full_join(., OPmeans) %>%
mutate(
rel_log = log(response.time) - meanOP,
food = fct_relevel(food, "OP50")) %>%
ggplot(aes(x = data_type, y = rel_log)) +
ggbeeswarm::geom_quasirandom(aes(colour = food), width = 0.2, alpha = 0.75) +
geom_boxplot(aes(fill = food), alpha = 0.5)+
scale_color_plot("grey-blue", drop = TRUE) +
scale_fill_plot("grey-blue", drop = TRUE) +
facet_grid(.~cond + food,
labeller = labeller(genotype = label_wrap_gen(10),
food = as_labeller(c("OP50" = "",
"JUb39" = "")))) +
add.n('data_type', y.pos = -1) +
labs(x = "genotype",
y = "time to reversal (s)") +
guides(colour = FALSE) +
add.mean(rel_log, colour = "red") +
theme(axis.text.x = element_blank())
gt <- ggplot_gtable(ggplot_build(plot))
gt$widths[c(8,12)] = 2.5*gt$widths[c(8,12)]
gt$widths[c(6,10,14)] = .3*gt$widths[c(6,10,14)]
grid::grid.draw(gt)
#tyrosine data
OPmeans <- receptors %>%
mutate(response.time = case_when(
response.time < 1 ~ 1,
TRUE ~ response.time)) %>%
group_by(food, date, genotype) %>%
summarise(meanOP = mean(log(response.time))) %>%
filter(food == "OP50") %>%
ungroup() %>%
select(genotype, date, meanOP)
plot <- mutate(receptors, response.time = case_when(
response.time < 1 ~ 1,
TRUE ~ response.time)) %>%
full_join(., OPmeans) %>%
mutate(
rel_log = log(response.time) - meanOP,
food = fct_relevel(food, "OP50")) %>%
ggplot(aes(x = data_type, y = rel_log)) +
ggbeeswarm::geom_quasirandom(aes(colour = food), width = 0.2, alpha = 0.75) +
geom_boxplot(aes(fill = food), alpha = 0.5)+
scale_color_plot("grey-blue", drop = TRUE) +
scale_fill_plot("grey-blue", drop = TRUE) +
facet_grid(.~genotype + food,
labeller = labeller(genotype = label_wrap_gen(10),
food = as_labeller(c("OP50" = "",
"JUb39" = "")))) +
add.n('data_type', y.pos = -1) +
labs(x = "genotype",
y = "time to reversal (s)") +
guides(colour = FALSE) +
add.mean(rel_log, colour = "red") +
theme(axis.text.x = element_blank())
gt <- ggplot_gtable(ggplot_build(plot))
gt$widths[c(8,12)] = 2.5*gt$widths[c(8,12)]
gt$widths[c(6,10,14)] = .3*gt$widths[c(6,10,14)]
grid::grid.draw(gt)
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