figure4_visuals_new.qmd

---
title: "figure_4"
author: "vb"
format: html
editor: source
---

## nuclei vs estimated cell number

### functions and library

```{r}

#libraries
library(tidyverse)


```

### set parameters and input

```{r}

#summarized_thp1
#summarized_c2c12
#summarized_a172
#summarized_a172_13
#summarized_a549
#summarized_a549_13


```

### THP1

```{r}
#| fig-width: 12
#| fig-height: 12

rho_thp1 <- cor.test(x=summarized_thp1$nuclei, 
         y=summarized_thp1$n, method = 'spearman') %>%  
  pluck("estimate") %>% unname()

label_rho <- paste0("R = ", round(rho_thp1, 3), ", p < 0.001")

summarized_thp1 %>% 
  filter(!str_detect(file, "E9")) %>% #E9 image is blurry
    ggplot(aes(x = nuclei, y = n))+
      geom_point(size = 10, alpha = 0.8, color = "darkgreen")+
      geom_abline(slope=1, intercept = 0, linetype = "dotted")+
      annotate(geom = "text",
               x = 100,
               y = 5000,
               label = label_rho,
               size = 12,
               hjust = 0)+
      annotate(geom = "text",
               x = 500,
               y = 100,
               label = "... = y ~ x",
               size = 12,
               hjust = 0,
               )+
      labs(subtitle = "THP1",
        x = "Number of nuclei",
           y = "Estimated number of cells")+
      lims(x = c(0, 5200),
            y = c(0, 5200))+
      scale_y_log10()+
      scale_x_log10()+
      theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)




```
#### THP1 relative difference (rebuttal)

```{r}
#| fig-width: 12
#| fig-height: 12



summarized_thp1 %>% 
  select(file, n, nuclei) %>% 
  #filter(nuclei>500) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  #summarize(mean = mean(rel_diff))
  ggplot(aes( x = nuclei, y = rel_diff))+
    geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.1, 
             linetype = "dashed",
             linewidth = 2)+
    geom_hline(yintercept = -0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "black")+
  scale_y_continuous(label = scales::percent,
                     limits = c(-0.1, 0.6))+
    labs(subtitle = "THP1",
            x = "Number of nuclei",
       y = "relative error")+ 
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

summarized_thp1 %>% 
  select(file, n, nuclei) %>% 
  filter(nuclei>500) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  #summarize(mean = mean(rel_diff))
  ggplot(aes( x = nuclei, y = rel_diff))+
    geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.05, 
             linetype = "dashed",
             linewidth = 2)+
    geom_hline(yintercept = -0.05, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "black")+
  scale_y_continuous(label = scales::percent)+
   labs(subtitle = "THP1",
            x = "Number of nuclei",
       y = "relative error")+ 
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)
  


```
### C2C12

```{r}
#| fig-width: 12
#| fig-height: 12

rho_c2c12 <- cor.test(x=summarized_c2c12$nuclei, 
         y=summarized_c2c12$n, method = 'spearman') %>%  
  pluck("estimate") %>% unname()

label_rho <- paste0("R = ", round(rho_c2c12, 3), ", p < 0.001")

summarized_c2c12 %>% 
    ggplot(aes(x = nuclei, y = n))+
      geom_point(size = 10, alpha = 0.8, color = "darkgreen")+
      geom_abline(slope=1, intercept = 0, linetype = "dotted")+
      annotate(geom = "text",
               x = 100,
               y = 2000,
               label = label_rho,
               size = 12,
               hjust = 0)+
      annotate(geom = "text",
               x = 200,
               y = 100,
               label = "... = y ~ x",
               size = 12,
               hjust = 0,
               )+
      labs(subtitle = "C2C12",
            x = "Number of nuclei",
           y = "Estimated number of cells")+
      lims(x = c(0, 2100),
           y = c(0, 2100))+
      #scale_y_log10()+
      #scale_x_log10()+
      theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)




```

#### C2C12 relative difference (rebuttal)

```{r}
#| fig-width: 12
#| fig-height: 12
#| 
summarized_c2c12 %>% 
  select(file, n, nuclei) %>% 
  filter(n>300) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  #summarize(mean = median(rel_diff),
  #          lower_bound = quantile(rel_diff, prob = 0.025),
  #          upper_bound = quantile(rel_diff, prob = 0.975))
  ggplot(aes( x = nuclei, y = rel_diff))+
  geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.05, 
             linetype = "dashed",
             linewidth = 2)+
    geom_hline(yintercept = -0.05, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "black")+
  scale_y_continuous(label = scales::percent)+
    labs(subtitle = "C2C12",
            x = "Number of nuclei",
       y = "relative error")+ 
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

summarized_c2c12 %>% 
  select(file, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  #summarize(mean = median(rel_diff),
  #          lower_bound = quantile(rel_diff, prob = 0.025),
  #          upper_bound = quantile(rel_diff, prob = 0.975))
  ggplot(aes( x = nuclei, y = rel_diff))+
  geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = -0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "grey30")+
  scale_y_continuous(label = scales::percent,
                     limits = c(-0.1, 0.6))+
  labs(subtitle = "C2C12",
            x = "Number of nuclei",
       y = "relative error")+    

  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)
  

```

### A172

```{r}
#| fig-width: 12
#| fig-height: 12

my_rho <- cor.test(x=summarized_a172 %>% 
                       filter(timepoint == 7) %>%  pluck("nuclei"), 
         y=summarized_a172 %>% filter(timepoint == 7) %>%  pluck("n"), 
         method = 'spearman') %>%  
  pluck("estimate") %>% unname()

label_rho <- paste0("R = ", round(my_rho, 3), ", p < 0.001")

summarized_a172 %>% 
  filter(timepoint == 7) %>% 
    ggplot(aes(x = nuclei, y = n))+
      geom_point(size = 10, alpha = 0.8, color = "darkgreen")+
      geom_abline(slope=1, intercept = 0, linetype = "dotted")+
      annotate(geom = "text",
               x = 20,
               y = 1100,
               label = label_rho,
               size = 12,
               hjust = 0)+
      annotate(geom = "text",
               x = 200,
               y = 100,
               label = "... = y ~ x",
               size = 12,
               hjust = 0,
               )+
      labs(subtitle = "A172 - 24h",
            x = "Number of nuclei",
           y = "Estimated number of cells")+
      lims(x = c(0, 1100),
             y = c(0, 1100))+
      theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)



my_rho <- cor.test(x=summarized_a172_13 %>% 
                       filter(timepoint == 13) %>%  pluck("nuclei"), 
         y=summarized_a172_13 %>% filter(timepoint == 13) %>%  pluck("n"), 
         method = 'spearman') %>%  
  pluck("estimate") %>% unname()

label_rho <- paste0("R = ", round(my_rho, 3), ", p < 0.001")

summarized_a172_13 %>% 
  filter(timepoint == 13) %>% 
      ggplot(aes(x = nuclei, y = n))+
      geom_point(size = 10, alpha = 0.8, color = "darkgreen")+
      geom_abline(slope=1, intercept = 0, linetype = "dotted")+
      annotate(geom = "text",
               x = 20,
               y = 2100,
               label = label_rho,
               size = 12,
               hjust = 0)+
      annotate(geom = "text",
               x = 200,
               y = 100,
               label = "... = y ~ x",
               size = 12,
               hjust = 0,
               )+
      labs(subtitle = "A172 - 48h",
            x = "Number of nuclei",
           y = "Estimated number of cells")+
      lims(x = c(0, 2200),
             y = c(0, 2200))+
      theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

```

### A172 combined

```{r}

  my_rho <- cor.test(x= summarized_a172 %>% 
                          filter(timepoint == 7) %>% 
                          bind_rows(summarized_a172_13 %>% 
                          filter(timepoint == 13)) %>% pluck("nuclei"), 
         y= summarized_a172 %>% 
                          filter(timepoint == 7) %>% 
                          bind_rows(summarized_a172_13 %>% 
                          filter(timepoint == 13)) %>%  pluck("n"), 
         method = 'spearman') %>%  
  pluck("estimate") %>% unname()

label_rho <- paste0("R = ", round(my_rho, 3), ", p < 0.001")

  
summarized_a172 %>% 
  filter(timepoint == 7) %>% 
  bind_rows(summarized_a172_13 %>% 
              filter(timepoint == 13)) %>% 
      ggplot(aes(x = nuclei, y = n))+
      geom_point(size = 10, alpha = 0.8, color = "darkgreen")+
      geom_abline(slope=1, intercept = 0, linetype = "dotted")+
      annotate(geom = "text",
               x = 20,
               y = 2100,
               label = label_rho,
               size = 12,
               hjust = 0)+
      annotate(geom = "text",
               x = 200,
               y = 100,
               label = "... = y ~ x",
               size = 12,
               hjust = 0,
               )+
      labs(subtitle = "A172",
            x = "Number of nuclei",
           y = "Estimated number of cells")+
      lims(x = c(0, 2200),
             y = c(0, 2200))+
      theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

```

#### A172 relative difference (rebuttal)

```{r}
#| fig-width: 12
#| fig-height: 12

summarized_a172 %>% 
    filter(timepoint == 7) %>% 
  select(file_name, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  #summarize(mean = mean(rel_diff))
  ggplot(aes( x = nuclei, y = rel_diff))+
    geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = -0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "grey30")+
  scale_y_continuous(label = scales::percent,
                     limits = c(-0.16, 0.8))+
  labs(subtitle = "A172 - 0h",
            x = "Number of nuclei",
       y = "relative error")+
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)
  

summarized_a172_13 %>% 
    filter(timepoint == 13) %>% 

  select(file_name, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  #summarize(mean = mean(rel_diff))
  ggplot(aes( x = nuclei, y = rel_diff))+
    geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = -0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "grey30")+
  scale_y_continuous(label = scales::percent,
                     limits = c(-0.16, 0.8))+
  labs(subtitle = "A172 - 48h",
            x = "Number of nuclei",
       y = "relative error")+
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

#a172 conmbined
summarized_a172 %>% 
  filter(timepoint == 7) %>% 
  bind_rows(summarized_a172_13 %>% 
              filter(timepoint == 13)) %>% 
    select(file_name, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  #summarize(mean = mean(rel_diff))
  ggplot(aes( x = nuclei, y = rel_diff))+
    geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = -0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "grey30")+
  scale_y_continuous(label = scales::percent,
                     limits = c(-0.1, 0.6))+
  labs(subtitle = "A172",
            x = "Number of nuclei",
       y = "relative error")+
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)


```
### A549

```{r}
#| fig-width: 12
#| fig-height: 12

my_rho <- cor.test(x=summarized_a549 %>% 
                       filter(timepoint == 7) %>%  pluck("nuclei"), 
         y=summarized_a549 %>% filter(timepoint == 7) %>%  pluck("n"), 
         method = 'spearman') %>%  
  pluck("estimate") %>% unname()

label_rho <- paste0("R = ", round(my_rho, 3), ", p < 0.001")

summarized_a549 %>% 
  filter(timepoint == 7) %>% 
  ggplot(aes(x = nuclei, y = n))+
      geom_point(size = 10, alpha = 0.8, color = "darkgreen")+
      geom_abline(slope=1, intercept = 0, linetype = "dotted")+
      annotate(geom = "text",
               x = 20,
               y = 800,
               label = label_rho,
               size = 12,
               hjust = 0)+
      annotate(geom = "text",
               x = 200,
               y = 100,
               label = "... = y ~ x",
               size = 12,
               hjust = 0,
               )+
      labs(subtitle = "A549 - 24h",
            x = "Number of nuclei",
           y = "Estimated number of cells")+
      # lims(x = c(0, 2200),
      #        y = c(0, 2200))+
      theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

my_rho <- cor.test(x=summarized_a549_13 %>% 
                       filter(timepoint == 13) %>%  pluck("nuclei"), 
         y=summarized_a549_13 %>% filter(timepoint == 13) %>%  pluck("n"), 
         method = 'spearman') %>%  
  pluck("estimate") %>% unname()

label_rho <- paste0("R = ", round(my_rho, 3), ", p < 0.001")


summarized_a549_13 %>% 
  filter(timepoint == 13) %>% 
  ggplot(aes(x = nuclei, y = n))+
      geom_point(size = 10, alpha = 0.8, color = "darkgreen")+
      geom_abline(slope=1, intercept = 0, linetype = "dotted")+
      annotate(geom = "text",
               x = 20,
               y = 2100,
               label = label_rho,
               size = 12,
               hjust = 0)+
      annotate(geom = "text",
               x = 200,
               y = 100,
               label = "... = y ~ x",
               size = 12,
               hjust = 0,
               )+
      labs(subtitle = "A549 - 48h",
            x = "Number of nuclei",
           y = "Estimated number of cells")+
      # lims(x = c(0, 2200),
      #        y = c(0, 2200))+
      theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

```

### A549 combined

```{r}
#| fig-width: 12
#| fig-height: 12
#| 
  my_rho <- cor.test(x= summarized_a549 %>% 
                          filter(timepoint == 7) %>% 
                          bind_rows(summarized_a549_13 %>% 
                          filter(timepoint == 13)) %>% pluck("nuclei"), 
         y= summarized_a549 %>% 
                          filter(timepoint == 7) %>% 
                          bind_rows(summarized_a549_13 %>% 
                          filter(timepoint == 13)) %>%  pluck("n"), 
         method = 'spearman') %>%  
  pluck("estimate") %>% unname()

label_rho <- paste0("R = ", round(my_rho, 3), ", p < 0.001")

  
summarized_a549 %>% 
  filter(timepoint == 7) %>% 
  bind_rows(summarized_a549_13 %>% 
              filter(timepoint == 13)) %>% 
      ggplot(aes(x = nuclei, y = n))+
      geom_point(size = 10, alpha = 0.8, color = "darkgreen")+
      geom_abline(slope=1, intercept = 0, linetype = "dotted")+
      annotate(geom = "text",
               x = 20,
               y = 2100,
               label = label_rho,
               size = 12,
               hjust = 0)+
      annotate(geom = "text",
               x = 200,
               y = 100,
               label = "... = y ~ x",
               size = 12,
               hjust = 0,
               )+
      labs(subtitle = "A549",
            x = "Number of nuclei",
           y = "Estimated number of cells")+
      lims(x = c(0, 2200),
             y = c(0, 2200))+
      theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

```

#### A549 relative difference (rebuttal)

```{r}
#| fig-width: 12
#| fig-height: 12
#| 
  

summarized_a549_13 %>% 
  filter(timepoint == 13) %>% 
  select(file_name, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = (log10(n) - log10(nuclei))/log10(nuclei)) %>% 
  #summarize(mean = mean(rel_diff))
  ggplot(aes(x = nuclei, y = rel_diff))+
  geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.1, 
             linetype = "dashed",
             linewidth = 2)+
    geom_hline(yintercept = -0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "black")+
 scale_y_continuous(label = scales::percent,
                     limits = c(-0.16, 0.8))+
    labs(subtitle = "A549 - 48h",
            x = "Number of nuclei",
       y = "relative error")+
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

summarized_a549 %>% 
  filter(timepoint == 7) %>% 
  select(file_name, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = (log10(n) - log10(nuclei))/log10(nuclei)) %>% 
  #summarize(mean = mean(rel_diff))
  ggplot(aes(x = nuclei, y = rel_diff))+
  geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.1, 
             linetype = "dashed",
             linewidth = 2)+
    geom_hline(yintercept = -0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "black")+
  scale_y_continuous(label = scales::percent,
                     limits = c(-0.16, 0.8))+
  labs(subtitle = "A549 - 24h",
            x = "Number of nuclei",
       y = "relative error")+
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

#combined
summarized_a549 %>% 
  filter(timepoint == 7) %>% 
  bind_rows(summarized_a549_13 %>% 
              filter(timepoint == 13)) %>% 
    select(file_name, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  #summarize(mean = mean(rel_diff))
  ggplot(aes( x = nuclei, y = rel_diff))+
    geom_point(size = 10, alpha = 0.8, color = "black")+ 
  geom_hline(yintercept = 0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = -0.1, 
             linetype = "dashed",
             linewidth = 2)+
  geom_hline(yintercept = 0, 
             linetype = "solid",
             linewidth = 2,
             color = "grey30")+
  scale_y_continuous(label = scales::percent,
                     limits = c(-0.1, 0.6))+
  labs(subtitle = "A549",
            x = "Number of nuclei",
       y = "relative error")+
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)

```


### Densities of relative errors

```{r}

summarized_a549_13 %>% 
  select(file_name, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  ggplot(aes( x = rel_diff))+
  geom_density()+
  xlim(c(0,0.75))

summarized_a549_13 %>% 
  select(file_name, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = ((n) - (nuclei))/(nuclei)) %>% 
  ggplot(aes( x = rel_diff))+
  geom_density()
  xlim(c(0,0.75))

```

```{r}
#| fig-width: 12
#| fig-height: 12
#| 
#all connected
summarized_a549_13 %>% 
  filter(timepoint == 13) %>% 
  select(file_name, n, nuclei) %>% 
  #filter(n>300) %>% 
  mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
  mutate(cell_type = "A549") %>% 
  bind_rows(summarized_a549 %>% 
                filter(timepoint == 7) %>% 
    select(file_name, n, nuclei) %>% 
    #filter(n>300) %>% 
    mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
    mutate(cell_type = "A549")) %>% 
  bind_rows(summarized_a172_13 %>% 
                filter(timepoint == 13) %>% 
    select(file_name, n, nuclei) %>% 
    #filter(n>300) %>% 
    mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
    mutate(cell_type = "A172")) %>% 
  bind_rows(summarized_a172 %>% 
                filter(timepoint == 7) %>% 
    select(file_name, n, nuclei) %>% 
    #filter(n>300) %>% 
    mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
    mutate(cell_type = "A172")) %>% 
  bind_rows(summarized_c2c12 %>% 
    select(file, n, nuclei) %>% 
    #filter(n>300) %>% 
    mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
    mutate(cell_type = "C2C12")) %>% 
  bind_rows(summarized_thp1 %>% 
    select(file, n, nuclei) %>% 
    #filter(nuclei>500) %>% 
    mutate(rel_diff = (log(n) - log(nuclei))/log(nuclei)) %>% 
    mutate(cell_type = "THP1")) %>% 
  ggplot(aes(y = cell_type, x = rel_diff))+
  ggridges::geom_density_ridges()+
   scale_x_continuous(label = scales::percent,
                     limits = c(-0.12, 0.6))+
    geom_vline(xintercept = -0.1, 
             linetype = "dashed",
             linewidth = 2)+
      geom_vline(xintercept = 0.1, 
             linetype = "dashed",
             linewidth = 2)+

  labs(y = "",
       x = "relative error")+
  theme_BF_larger() +
      theme(legend.position = "none")+
      theme(aspect.ratio = 1)
  
```


#### my used function in this file

```{r}

#qmd code copied to R file
#NCmisc::list.functions.in.file(here::here("prepration.R"))

```

all functions and packages in this files:

\$.GlobalEnv \[1\] "theme_BF_larger"

\$`c("package:dplyr", "package:stats")` \[1\] "filter"

\$`package:base` \[1\] "c" "library" "paste0" "round" "unname"

\$`package:ggplot2` \[1\] "aes" "annotate" "geom_abline" "geom_point" "ggplot"\
\[6\] "labs" "lims" "theme"

\$`package:purrr` \[1\] "pluck"

\$`package:stats` \[1\] "cor.test"

\$`package:stringr` \[1\] "str_detect"