Lineplot

#Libraries

Line plots are charts that are designed to show us the change of a variable over a set interval across multiple experiments and create a line of best fit that attempts to show us how the average across each time interval changes.

mean_df_big <- df_big %>% 
  filter(rep %in% c("rep1", "rep2")) %>% 
  group_by(ver_pos, conditions, hour) %>% 
  mutate(
    mean = mean(abs, na.rm =TRUE),
    sd = sd(abs, na.rm = TRUE)
  )

mean_df_big %>% 
  filter(ver_pos == "2401A10") %>%
  filter(conditions == "LS37") %>% 
  ggplot(aes(x=hour,y=mean))+
  geom_point(aes(color = conditions), shape = 15, fill = "#F8766D")+
   geom_errorbar(aes(x = as.numeric(hour),
                    ymin = as.numeric(mean) - as.numeric(sd),
                    ymax = as.numeric(mean) + as.numeric(sd)),
                 alpha = 0.5)+
  geom_smooth(method = "lm", se = FALSE, aes(color = conditions))+
  theme_bw()

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

  # scale_y_continuous(transform = "log10")

mean_df_big %>% 
  filter(ver_pos == "2401A10") %>%
  ggplot(aes(x=hour,y=mean))+
  geom_point(aes(color = conditions), shape = 15)+
   geom_errorbar(aes(x = as.numeric(hour),
                    ymin = as.numeric(mean) - as.numeric(sd),
                    ymax = as.numeric(mean) + as.numeric(sd)),
                 alpha = 0.5)+
  geom_smooth(method = "lm", se = FALSE, aes(color = conditions))+
  theme_bw()

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