I've spent the past few days looking through so many forums and sites, so I hope you can help.
You can find the data I've been using here , as well as the three model predictions.
I'm predicting subjective well-being (ie positive affect, negative affect, and life satisfaction) from last night's person-centered sleep satisfaction. I came up with three models that I now want to plot next to each other. The problem is that facet_wrap puts the models next to each other alphabetically and not how I want them (positive affect, negative affect, and life satisfaction).
You can view my current graph here
This is my code to get the graph going:
library("afex")
library("tidyverse")
library("tidylog")
theme_set(theme_bw(base_size = 15))
library("sjPlot")
d3 <- read.csv("d3.csv")
d3 <- d3 %>%
group_by(ID) %>%
mutate(SD_person_centred = sleepDur - mean(sleepDur, na.rm = TRUE)) %>%
mutate(sleep_satisfaction_person_centred = Sleep_quality_open - mean(Sleep_quality_open, na.rm = TRUE)) %>%
mutate(MS_person_centred = mid_sleep_modified - mean(mid_sleep_modified, na.rm = TRUE)) %>%
mutate(MS_person_freeday_centred = abs(mid_sleep_modified -
mean(mid_sleep_modified[Routine_work_day_open == "No"], na.rm = TRUE))) %>%
mutate(MS_person_mctq_centred = abs(mid_sleep_modified - MCTQ_MSF_number)) %>%
mutate(sleep_onset_person_centred = Sleep_Onset_open - mean(Sleep_Onset_open, na.rm = TRUE)) %>%
mutate(sleep_efficiency_person_centred = SleepEfficiency_act - mean(SleepEfficiency_act, na.rm = TRUE)) %>%
ungroup
m_p_sls_1 <- readRDS("m_p_sls_1.rds")
m_n_sls_1 <- readRDS("m_n_sls_1.rds")
m_s_sls_1 <- readRDS("m_s_sls_1.rds")
tmp <- get_model_data(m_p_sls_1$full_model, type = "pred", terms = "sleep_satisfaction_person_centred")
tmp$DV <- "positive_affect"
tmp2 <- get_model_data(m_n_sls_1$full_model, type = "pred", terms = "sleep_satisfaction_person_centred")
tmp2$DV <- "negative_affect"
tmp3 <- get_model_data(m_s_sls_1$full_model, type = "pred", terms = "sleep_satisfaction_person_centred")
tmp3$DV <- "life_satisfaction"
tmp <- bind_rows(tmp, tmp2, tmp3)
tmp
tmp$DV
Here I change tmp$DV into a factor as this was the solution I found online. However, this did not change anything:
tmp$DV <- factor(tmp$DV, levels=c("positive_affect","negative_affect","life_satisfaction"))
levels(tmp$DV)
This is my code for the graph:
variable_names <- list(
"positive_affect" = "positive affect" ,
"negative_affect" = "negative affect",
"life_satisfaction" = "life satisfaction"
)
variable_labeller <- function(variable,value){
return(variable_names[value])
}
d3 %>%
pivot_longer(cols="positive_affect":"life_satisfaction", names_to = "DV", values_to = "Score") %>%
ggplot(aes(x = sleep_satisfaction_person_centred, y = Score)) +
geom_ribbon(data = tmp, aes(x = x, ymin = conf.low, ymax = conf.high, y = predicted),
fill = "lightgrey") +
geom_line(data = tmp, aes(x = x, y = predicted, group = 1)) +
geom_point(alpha = 0.2) +
facet_wrap(~DV, scales = "free_y",labeller=variable_labeller) +
labs(y = "Score",
x = "Sleep satisfaction person centered")
When I give the factor of tmp$DV a different name, ie tmp$facet and add this to my code, I do get the right order, but the scales are not free on the y-axis anymore. Please have a look here.
tmp$facet <- factor(tmp$DV, levels=c("positive_affect", "negative_affect", "life_satisfaction"))
d3 %>%
pivot_longer(cols="positive_affect":"life_satisfaction", names_to = "DV", values_to = "Score") %>%
ggplot(aes(x = sleep_satisfaction_person_centred, y = Score)) +
geom_ribbon(data = tmp, aes(x = x, ymin = conf.low, ymax = conf.high, y = predicted),
fill = "lightgrey") +
geom_line(data = tmp, aes(x = x, y = predicted, group = 1)) +
geom_point(alpha = 0.2) +
facet_wrap(~facet, scales = "free_y",labeller=variable_labeller) +
labs(y = "Score",
x = "Sleep satisfaction person centered")
When I change pivot_longer to facet in the first row, I get the same graph as the one before.
Sorry for the long post, but I tried to be as clear as possible. Please let me know if I wasn't.
I'd appreciate any kind of hints. Thanks a lot for your time.
All the best, Anita
Just got the answer from my colleague Henrik Singmann, in case anybody was wondering:
d3 %>%
pivot_longer(cols="positive_affect":"life_satisfaction", names_to = "DV", values_to = "Score") %>%
mutate(DV = factor(DV, levels=c("positive_affect","negative_affect","life_satisfaction"))) %>%
ggplot(aes(x = sleep_satisfaction_person_centred, y = Score)) +
geom_ribbon(data = tmp, aes(x = x, ymin = conf.low, ymax = conf.high, y = predicted),
fill = "lightgrey") +
geom_line(data = tmp, aes(x = x, y = predicted, group = 1)) +
geom_point(alpha = 0.2) +
facet_wrap(~DV, scales = "free_y",labeller=variable_labeller) +
labs(y = "Score",
x = "Sleep satisfaction person centered")
So the factor needs to be defined in d3 before being handed over to ggplot.
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