13-figureS5.Rmd

---
title: "Figure S5 (Time fluctuation of disease severity)"
author: "Tu Hu"
date: "06/07/2022"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(tidyr)
library(ggplot2)
```

# Disease severity change
### Figure S6
```{r}
extensive_meta <- 
  readr::read_csv("data/metadata/RNAseq_sample_annotation(extensive).csv")

time_course_ad_subject <- 
  extensive_meta %>% filter(visit == "02", group == "AD") %>% pull(subject) %>% unique

disease_severity <- extensive_meta %>% 
  dplyr::filter(subject %in% time_course_ad_subject) %>% 
  dplyr::select(-skin_type) %>% 
  distinct() %>% 
  dplyr::select(visit, visit_quarter, subject, 
                easi_total_score, scorad_objective, scorad) %>% 
  tidyr::pivot_longer(cols = c("easi_total_score",
                               "scorad_objective",
                               "scorad"), 
                      names_to = "disease_severity") %>% 
  filter(subject != "AD_11") %>% 
  mutate(disease_severity = 
            case_when(disease_severity == "easi_total_score" ~ "EASI",
                      disease_severity == "scorad" ~ "SCORAD",
                      disease_severity == "scorad_objective" ~ "oSCORAD"),
         disease_severity = forcats::fct_relevel(disease_severity,
                                                 c("EASI", 
                                                   "SCORAD",
                                                   "oSCORAD")),
         visit = visit %>% as.character(),
         visit_quarter = visit_quarter %>% as.character())


```

```{r}
disease_severity_natural_year <- extensive_meta %>% 
  dplyr::filter(subject %in% time_course_ad_subject) %>% 
  dplyr::select(-skin_type) %>% 
  distinct() %>% 
  mutate(year = date_visit %>% format("%Y"),
         year_quarter = paste0(year, visit_quarter)) %>% 
  dplyr::select(visit, visit_quarter, year_quarter, subject, 
                easi_total_score, scorad_objective, scorad) %>% 
  tidyr::pivot_longer(cols = c("easi_total_score",
                               "scorad_objective",
                               "scorad"), 
                      names_to = "disease_severity") %>% 
  filter(subject != "AD_11") %>% 
  mutate(disease_severity = 
            case_when(disease_severity == "easi_total_score" ~ "EASI",
                      disease_severity == "scorad" ~ "SCORAD",
                      disease_severity == "scorad_objective" ~ "oSCORAD"),
         disease_severity = forcats::fct_relevel(disease_severity,
                                                 c("EASI", 
                                                   "SCORAD",
                                                   "oSCORAD")),
         visit = visit %>% as.character(),
         visit_quarter = visit_quarter %>% as.character())

disease_severity_natural_year_summary <- 
  disease_severity_natural_year %>% group_by(year_quarter, disease_severity) %>% summarise(value =mean(value)) %>% 
  mutate(subject = "mean")

disease_severity_natural_year_g <-
  disease_severity_natural_year %>% 
  ggplot(aes(year_quarter, value, group = subject, color = subject)) +
  geom_point(alpha = .3) + 
  geom_line(alpha = .3) + 
  # geom_line(data = disease_severity_natural_year_summary, size = 2) +
  facet_grid(disease_severity ~ ., scales = "free_y") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  xlab("Time") +
  theme_classic() +
  theme(axis.title.y =  element_blank())
```

```{r eval=FALSE}
ggsave("data/supplementary/figure_s7.png", 
       disease_severity_natural_year_g)
```