In brouwern/sparrowtelomeres: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(echo = TRUE)

knitr::opts_chunk$set(echo = FALSE)
library(tufte)
library(formatR)

library(readxl)

library(ggpubr)
library(cowplot)

library(lme4)

file. <- "Meillere_et_al_2015_telomere_data.xlsx"


#load data
dat.full <- readxl::read_xlsx(file.,sheet = "orig")

# clean up data
dat.full$cort <- as.numeric(dat.full$cort)
dat.full$Group <- with(dat.full,
                       paste(treatment,sex))

dat.full$Group <- gsub("control","Control",dat.full$Group)
dat.full$Group <- gsub("disturbed","Noise exposure",dat.full$Group)

dat.full$Group <- gsub(" F","\nFemales",dat.full$Group)
dat.full$Group <- gsub(" M","\nMales",dat.full$Group)
dat.full$cort.log <- log(dat.full$cort)
dat.full$telomere.length.log <- log(dat.full$telomere.length)


names(dat.full) <- gsub("treatment",
                        "Treatment",
                        names(dat.full))

names(dat.full) <- gsub("sex",
                        "Sex",
                        names(dat.full))

medians <- dat.full %>%
 group_by(Treatment) %>%
  summarize(median = median(telomere.length),
            max = max(telomere.length),
            min = min(telomere.length),
            upper = fivenum(dat.full$telomere.length)[4],
            lower = fivenum(dat.full$telomere.length)[2])

ggplot(data = dat.full,
       aes(y = telomere.length,
           x = Treatment,
           fill = Treatment)) +
  geom_boxplot() +
  scale_x_discrete(limits = c("","control","disturbed","")) +
  theme(legend.position="none") +
   ggtitle("Box & Whisker plot: median & data distribution\n") +
   xlab("Predictor: Experimental group\n") +
   ylab("Response: Telomere length (kb)\n") +

  #Full CI
   geom_segment(x = 2.15, 
                xend = 2.05,
                y = means[1,"CI.top"][[1]],
                yend = means[1,"CI.top"][[1]],
               color = "black",
               arrow = arrow(length = unit(0.03, "npc"))) +
   geom_segment(x = 2.15, 
                xend = 2.05,
                y = means[1,"CI.bottom"][[1]],
                yend = means[1,"CI.bottom"][[1]],
               color = "black",
               arrow = arrow(length = unit(0.03, "npc"))) +
  geom_segment(x = 2.15, 
                xend = 2.15,
                y = means[1,"CI.top"][[1]],
                yend = means[1,"CI.bottom"][[1]],
               color = "black") +
  annotate(geom = "text",
           x = 2.5,
           y = means[1,"mean"][[1]],
           label = "Full confidence\ninterval\n(+/- 1.96*SE)") +
  # Upper CI
   geom_segment(x = 1.75, 
                xend = 1.95,
                y = means[1,"CI.top"][[1]],
                yend = means[1,"CI.top"][[1]],
               color = "black",
               arrow = arrow(length = unit(0.03, "npc"))) +
  annotate(geom = "text",
           x = 1.25,
           y = means[1,"CI.top"][[1]],
           label = "Top of CI\n(Y = mean+1.96*SE)")+

  #Lower CI
   geom_segment(x = 1.75, 
                xend = 1.95,
                y = means[1,"CI.bottom"][[1]],
                yend = means[1,"CI.bottom"][[1]],
               color = "black",
               arrow = arrow(length = unit(0.03, "npc"))) +
  annotate(geom = "text",
           x = 1.25,
           y = means[1,"CI.bottom"][[1]],
           label = "Bottom of CI\n(Y = mean - 1.96*SE)") +

  # Mean
  geom_segment(x = 1.75, 
                xend = 1.9,
                y = means[1,"mean"][[1]],
                yend = means[1,"mean"][[1]],
               color = "black",
               arrow = arrow(length = unit(0.03, "npc")))  +
  annotate(geom = "text",
           x = 1.5,
           y = means[1,"mean"][[1]],
           label = "Mean") +
  #Half CI
   geom_segment(x = 3.25, 
                xend = 3.15,
                y = means[2,"CI.top"][[1]],
                yend = means[2,"CI.top"][[1]],
               color = "black",
               arrow = arrow(length = unit(0.03, "npc"))) +
   geom_segment(x = 3.25, 
                xend = 3.15,
                y = means[2,"mean"][[1]],
                yend = means[2,"mean"][[1]],
               color = "black",
               arrow = arrow(length = unit(0.03, "npc"))) +
  geom_segment(x = 3.25, 
                xend = 3.25,
                y = means[2,"CI.top"][[1]],
                yend = means[2,"mean"][[1]],
               color = "black") +
  annotate(geom = "text",
           x = 3.75,
           y = 0.98*means[2,"CI.top"][[1]],
           label = "Upper error bar of\nconfidence interval\n(mean to \nmean + 1.96*SE)")  +

  #text
  annotate(geom = "text",
           x = 3,
           y = 1.25,
           label = "An error bar extends from the
mean up nor down to a certain
value.

A confidence interval refers to
the whole span from the top
of the top bar to the bottom
of the lower bar.",hjust =0)  +
  ylim(0.95,1.35)

An error bar represents variation around a mean value or uncertainty we may have in our estimate of the mean. Variation is represented by the standard deviation (SD) and characterizes just variation in the data. Uncertainty in the estimate is represented by the standard error (SE) or a 95% confidence interval (95% CI).

When we refer to "an error bar" we usually are referring to a single bar extending either above or below the mean. When we refer to a confidence interval we are referring to both bars. A confidence interval is the full span from the top of the upper error bar to the bottom of the lower bar. There is therefore a subtle difference between referring to error bars from standard deviations or standard error and the 95% confidence.

One thing to remember is that the bars shown in an error plot or barplot are completely different than those in a boxplot. Its useful to remember that the full name of a boxplot is a "box and whisker plot". The bars extending above the boxes in a boxplot are not error bars. The top of upper "whisker" goes up to the maximum value of the data (except when there are outliers); the bottom of the lower whisker goes down to the minimum value (except when there are outliers). These whiskers span upper and lower quartiles and are mean to capture aspects of the distribution of the data.

brouwern/sparrowtelomeres documentation built on May 23, 2020, 12:34 a.m.