Scatter

#Libraries

Basic Scatter

##Small Scatter

df_box %>% 
  ggplot(aes(x=SD30, y=SD37))+
  geom_point()+
  geom_smooth(method = "lm", se = TRUE)

## `geom_smooth()` using formula = 'y ~ x'

Large Scatter

df_scatter %>% 
  ggplot(aes(x=SD_average,y=LN_average))+
  geom_point()+
  geom_smooth(method="lm",se=TRUE)+
   scale_x_continuous(breaks=c(0, 0.25, 0.5, 0.75, 1), limits = c(0,.95))+
   scale_y_continuous(breaks=c(0,0.25,0.5,0.75, 1), limits=c(0,.95))

## `geom_smooth()` using formula = 'y ~ x'

Customized Scatter

Colorization

Standard Error Band

df_box %>% 
  ggplot(aes(x = SD30, y=LN30))+
  geom_point()+
  geom_smooth(method = "lm", se = TRUE, fill = "blue")

## `geom_smooth()` using formula = 'y ~ x'

###Line Color

df_box %>% 
  ggplot(aes(x=SD30, y=LN30))+
  geom_point()+
  geom_smooth(method = "lm", se = TRUE, color = "black")

## `geom_smooth()` using formula = 'y ~ x'

###Points Outside SE Band

df_scatter_small <- df_box

p <- df_scatter_small %>% 
  ggplot(aes(x = SD30, y = LN30)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE)
              
built_data <- ggplot_build(p)

## `geom_smooth()` using formula = 'y ~ x'

smooth_data <- built_data$data[[2]]

df_scatter_small$ymin_smooth <- approx(smooth_data$x, smooth_data$ymin, xout = df_scatter_small$SD30)$y
df_scatter_small$ymax_smooth <- approx(smooth_data$x, smooth_data$ymax, xout = df_scatter_small$SD30)$y


df_scatter_small <- df_scatter_small %>%
  mutate(
    position_se = case_when(
      LN30 > ymax_smooth ~ "above",
      LN30 < ymin_smooth ~ "below",
      TRUE ~ "inside"))

df_scatter_small %>% 
ggplot(aes(x = SD30, y = LN30)) +
  geom_point(aes(color = position_se), size = 3)+
  geom_smooth(method = "lm", se = TRUE, level = 0.95, color = "orange")+
  scale_color_manual(values=c("above" = "#00BA38", "below" = "#F8766D", "inside" = "black"))+
  scale_x_continuous(limits = c(0.1, 0.4), breaks = c(0.1, 0.2,0.3, 0.4))+
  coord_equal(ratio = 1)

## `geom_smooth()` using formula = 'y ~ x'

Labeling Outliers

df_scatter_small_lab<- df_scatter_small %>%
  filter(position_se == "above" |position_se == "below") %>%
  mutate(outlier_label = paste0(ver_pos))

df_scatter_small %>% 
ggplot(aes(x = SD30, y = LN30)) +
  geom_point(aes(color = position_se), size = 3)+
  geom_smooth(method = "lm", se = TRUE, level = 0.95, color = "orange")+
  scale_color_manual(values=c("above" = "#00BA38", "below" = "#F8766D", "inside" = "black"))+
  scale_x_continuous(limits = c(0.1, 0.4), breaks = c(0.1, 0.2,0.3, 0.4))+
  coord_equal(ratio = 1)+
geom_text(data = df_scatter_small_lab,
            aes(x = SD30, y = LN30, label = outlier_label),
            color = "black",
            vjust = -1,
            size = 3,
            stroke = 4)

## Warning in geom_text(data = df_scatter_small_lab, aes(x = SD30, y = LN30, :
## Ignoring unknown parameters: `stroke`

## `geom_smooth()` using formula = 'y ~ x'

##Expanding a Scatter Line

df_scatter_small %>% 
ggplot(aes(x = SD30, y = LN30)) +
  geom_point(aes(color = position_se), size = 3)+
  geom_smooth(method = "lm", se = TRUE, level = 0.95, color = "orange", fullrange = TRUE)+
  scale_color_manual(values=c("above" = "#00BA38", "below" = "#F8766D", "inside" = "black"))+
  scale_x_continuous(limits = c(0.0, 0.4), breaks = c(0.0,0.1, 0.2,0.3, 0.4))+
  scale_y_continuous(limits = c(0.0, 0.4), breaks = c(0.0,0.1, 0.2,0.3, 0.4))+
  labs(title = "Full Range Scatterplot")+
  coord_equal(ratio = 1)

## `geom_smooth()` using formula = 'y ~ x'

df_scatter_outlier_removal <- df_scatter %>%
  pivot_longer(cols = 4:15,
               names_to = "conditions",
               values_to = "abs")

df_scatter_outlier_removal <- df_scatter_outlier_removal %>% 
  group_by(conditions) %>% 
  mutate(is_outlier = abs <0.0001) %>% 
  ungroup()
  
df_scatter_outlier_removal <- df_scatter_outlier_removal %>%
  group_by(conditions) %>% 
  group_by(is_outlier) %>% 
  filter(!is_outlier == TRUE) %>% 
  ungroup()

df_scatter_outlier_removal <- df_scatter_outlier_removal %>% 
  pivot_wider(names_from = "conditions",
              values_from = "abs")

Error Bars

df_scatter_long <- df_scatter %>% 
  pivot_longer(cols = 4:15,
               names_to = "condition_type",
               values_to = "absorb")
df_scatter_long <- df_scatter_long %>% 
  separate(condition_type, into = c("condition_type", "replicate"), sep = "_")
  
summary_error <- df_scatter_long %>% 
  group_by(position, condition_type) %>% 
  summarise(mean_val = mean(absorb))

## `summarise()` has grouped output by 'position'. You can override using the
## `.groups` argument.

summary_sd_error <- df_scatter_long %>% 
  group_by(position, condition_type) %>% 
  summarise(sd_val = sd(absorb))

## `summarise()` has grouped output by 'position'. You can override using the
## `.groups` argument.

summary_error <- summary_error %>% 
  pivot_wider(names_from = "condition_type",
              values_from = "mean_val")

colnames(summary_error)[2] <- "LN_avg"
colnames(summary_error)[3] <- "LS_avg"
colnames(summary_error)[4] <- "SD_avg"

summary_sd_error <- summary_sd_error %>% 
  pivot_wider(names_from = "condition_type",
              values_from = "sd_val")
colnames(summary_sd_error)[1] <- "position"
colnames(summary_sd_error)[2] <- "LN_sd"
colnames(summary_sd_error)[3] <- "LS_sd"
colnames(summary_sd_error)[4] <- "SD_sd"

summary_sd_error <- summary_sd_error %>% 
  head(10)

summary_error <- summary_error %>% 
  head(10)

summary_error<- left_join(summary_error, summary_sd_error, by = "position")

summary_error <- summary_error %>% 
  head(5)

summary_error %>%
  ggplot(aes(x = SD_avg, y = LN_avg)) +
  geom_point(size = 3)+
  geom_smooth(
    method = "lm",
    se = FALSE,
    level = 0.95,
    color = "orange",
    fullrange = TRUE)+
  geom_errorbar(
    aes(ymin = LN_avg - LN_sd, ymax = LN_avg + LN_sd, x = SD_avg),
                width = 0.03,
                color = "#F8766D") +
   geom_errorbarh(aes(xmin = SD_avg - SD_sd, xmax = SD_avg + SD_sd, y = LN_avg),
                  height = 0.03,
                 color = "#00BA38") +
  scale_y_continuous(limits = c(0.45, .8), breaks = c(0.4, 0.5, 0.6, 0.7, .8))+
  scale_x_continuous(limits = c(0.45, .8), breaks = c(0.4, 0.5, 0.6, 0.7, .8))+
  coord_equal(ratio = 1)

## Warning: `geom_errorbarh()` was deprecated in ggplot2 4.0.0.
## ℹ Please use the `orientation` argument of `geom_errorbar()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## `geom_smooth()` using formula = 'y ~ x'
## `height` was translated to `width`.

Log 10 Scatter Small

df_box %>% 
  ggplot(aes(x=SD30, y=SD37))+
  geom_point()+
  geom_smooth(method = "lm", se = TRUE)+
  scale_x_continuous(trans = "log10", breaks = c(0, 0.1, 0.2, 0.3, 0.4))+
  scale_y_continuous(trans = "log10", breaks = c(0, 0.1, 0.2, 0.3, 0.4))+
  coord_fixed(ratio = 1/1.35)

## `geom_smooth()` using formula = 'y ~ x'

Log 10 Scatter Big

df_scatter %>% 
  ggplot(aes(x=SD_average,y=LN_average))+
  geom_point()+
  geom_smooth(method="lm",se=TRUE)+
   scale_x_continuous(breaks=c(0, 0.25, 0.5, 0.75, 1), limits = c(0,.95))+
   scale_y_continuous(breaks=c(0,0.25,0.5,0.75, 1), limits=c(0,.95))

## `geom_smooth()` using formula = 'y ~ x'

log_minor_breaks <- function(limits) {
  min_log <- floor(log10(limits[1]))
  max_log <- ceiling(log10(limits[2]))
  
  # Generate all multiples of 10^e from 2 to 9 for each decade
  breaks_list <- lapply(min_log:(max_log -1), function(e) {
    seq(2 * 10^e, 9 * 10^e, by = 1 * 10^e)
  })
  
  # Flatten the list and filter to only include breaks within the actual limits
  unique(unlist(breaks_list)[
    unlist(breaks_list) >= limits[1] & unlist(breaks_list) <= limits[2]
  ])
}

df_scatter_outlier_removal %>% 
  ggplot(aes(x=SD_average,y=LN_average))+
  geom_point(size = 0.5)+
  geom_smooth(method="lm",se=TRUE)+
 scale_x_log10(
    limits = c(10^-4, 10^0),
     breaks = trans_breaks("log10", function(x) 10^x),
     minor_breaks = log_minor_breaks,
     labels = trans_format("log10", math_format(10^.x))
  ) +
  annotation_logticks(sides = "b") +
  scale_y_log10(
    limits = c(10^-4, 10^0),
     breaks = trans_breaks("log10", function(x) 10^x),
     minor_breaks = log_minor_breaks,
     labels = trans_format("log10", math_format(10^.x))
  )+
annotation_logticks(sides = "l") +
  coord_equal(ratio = 1)+
  theme_minimal()+
  theme(
    # --- Enable and customize major grid lines ---
    panel.grid.major = element_line(color = "grey0", linetype = "solid", linewidth = 0.4),
    # --- Enable and customize minor grid lines ---
    panel.grid.minor = element_line(color = "grey10", linetype = "dotted", linewidth = 0.2))+
  labs(x = "log10 SD average",
       y = "log10 LN average")

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 11 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).

##Line through center

df_scatter_outlier_removal %>% 
  ggplot(aes(x=SD_average, y=LN_average))+
  geom_point()+
  geom_smooth(method="loess", se = TRUE, color = "orange", full_range = TRUE)+
  geom_abline(intercept = 0, slope = 1, color = "grey60", linetype = "solid")+
scale_x_continuous(limits = c(0, 1), breaks =c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))+
  scale_y_continuous(limits = c(0, 1), breaks =c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))+
  coord_equal(ratio = 1)+
  theme_bw()

## Warning in geom_smooth(method = "loess", se = TRUE, color = "orange",
## full_range = TRUE): Ignoring unknown parameters: `full_range`

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 11 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).

1.5 M bubble

M_val <- log2(df_scatter_outlier_removal$SD_average)-log2( df_scatter_outlier_removal$LN_average)

model <- lm(df_scatter_outlier_removal$SD_average~ df_scatter_outlier_removal$LN_average)

b0 <- coef(model)[1] #intercept
b1 <- coef(model)[2] #slope

m_value <- summary(model)$sigma

offset_multiplier <- 1.5
upper_offset <- offset_multiplier * m_value
lower_offset <- -offset_multiplier * m_value

df_M_scatter<- df_scatter_outlier_removal

df_M_scatter$Predicted_Y <- predict(model, newdata = df_scatter_outlier_removal)
df_M_scatter$Upper_Line_Y <- df_M_scatter$Predicted_Y + upper_offset
df_M_scatter$Lower_Line_Y <- df_M_scatter$Predicted_Y + lower_offset

offset <- 1.5
posM <- offset * M_val
negM <- -offset * M_val

df_M_scatter$SD_M_avg <- df_M_scatter$SD_average + posM
df_M_scatter$LN_M_avg <- df_M_scatter$LN_average + posM
df_M_scatter$SD_m_avg <- df_M_scatter$SD_average + negM
df_M_scatter$LN_m_avg <- df_M_scatter$LN_average + negM

df_M_scatter %>% 
  ggplot(aes(x=SD_average, y=LN_average))+
  geom_point()+
  geom_smooth(method="loess", se = FALSE, color = "orange")+
  geom_abline(intercept = 0, slope = 1, color = "grey60", linetype = "solid", linewidth = 1)+
  geom_abline(intercept = m_value, slope = 1, color = "#F8766D", linewidth = 1, linetype = "solid")+
  geom_abline(intercept = -m_value, slope = 1, color = "#00BA38", linewidth = 1, linetype = "solid")+
  # geom_smooth(aes(x = SD_M_avg, y=LN_M_avg),color = "#F8766D", se = FALSE, method = "lm")+
  # geom_smooth(aes(x = SD_m_avg, y = LN_m_avg), color = "#00BA38", se = FALSE, method = "lm")+
scale_x_continuous( limits = c(0, 1), breaks =c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))+
  scale_y_continuous(limits = c(0, 1), breaks =c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))+
  coord_equal(ratio = 1)

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 11 rows containing non-finite outside the scale range
## (`stat_smooth()`).

## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).

###Coloring outside bubble

df_M_val <- df_M_scatter %>% 
select(c(1:18))

slope <-1
up_int <- m_value
low_int <- -m_value

df_M_val$point_pos_up <- slope * df_M_val$SD_average + up_int
df_M_val$point_pos_low <- slope * df_M_val$SD_average + low_int

df_M_val$point_pos <- with(df_M_val,
                           ifelse(
                             LN_average > point_pos_up, "above",
                             ifelse(
                               LN_average < point_pos_low, "below", "inside"
                             )
                           ))

df_M_val <- df_M_val[!is.na(df_M_val$point_pos), ]
df_M_val$point_pos <- factor(df_M_val$point_pos, levels = c("above", "inside", "below"))

df_M_val %>% 
  ggplot(aes(x=SD_average, y=LN_average, color = point_pos))+
  geom_point(size = 0.5)+
  geom_smooth(method="loess", se = FALSE, color = "orange")+
  geom_abline(intercept = 0, slope = 1, color = "grey60", linetype = "solid", linewidth = 1)+
  geom_abline(intercept = m_value, slope = 1, color = "#F8766D", linewidth = 1, linetype = "solid")+
  geom_abline(intercept = -m_value, slope = 1, color = "#00BA38", linewidth = 1, linetype = "solid")+
  scale_color_manual(name = "Position",
                     values = c("above" = "#F8766D", "inside" = "black", "below" = "#00BA38"))+
  # geom_smooth(aes(x = SD_M_avg, y=LN_M_avg),color = "#F8766D", se = FALSE, method = "lm")+
  # geom_smooth(aes(x = SD_m_avg, y = LN_m_avg), color = "#00BA38", se = FALSE, method = "lm")+
scale_x_continuous( limits = c(0, 1), breaks =c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))+
  scale_y_continuous(limits = c(0, 1), breaks =c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))+
  coord_equal(ratio = 1)+
  theme_bw()

## `geom_smooth()` using formula = 'y ~ x'