In brouwern/shroom: Example Analyses for Cell and Molecular Biology

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
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Introduction

This tutorial will demonstrate how to use dplyr's summarize() command to calculate summary statistics such as the mean, standard deviation (SD) and sample size (n). First we'll process a dataset of female fruit flies to determine summary stats on their wing phenotype (mean, SD etc.). We'll use the group_by() command to split the data up by the experimental groups the flies were in. Then we'll look at a larger dataset with both male and female flies in the two experimental groups and extend the use of the group_by() function to split larger datasets up with multiple grouping variables: sex (male vs. female) and experimental group (control vs. experimental.

Preliminaries

Load libraries

Any libraries not already downloaded can be loaded using the code "install.packages(...)", such as "install.packages('plotrix)".

library(shroom)  #data
library(dplyr)   #summarize() and groub_by()
library(plotrix) #function for SE

Load data

The wingscores_s1F dataset has data for the 24 female flies examined by the 1st student in the dataset ("_s1F"). The "wingscores_s1" dataset has all of the 1st students data. We'll look at both over the course of this tutorial.

data("wingscores_s1F")
data("wingscores_s1")

Numeric summaries with summarize()

We can calculate the mean of a column using summarize with the mean() command within it. Note that the format is "summary.name = function(data.column)". "mean =" generates the label for the summary and "mean(wing.score)" is the summary command.

wingscores_s1F %>% 
  summarize(mean = mean(wing.score))

summarize() is most powerful when you want multiple summary statistics worked up for the same set of data. We can ask for both the mean and the standard deviation (SD) like this:

wingscores_s1F %>% 
  summarize(mean = mean(wing.score),
            SD = sd(wing.score))

A handy function is n() which counts up the sample size. Note that nothing goes in n()

wingscores_s1F %>% 
  summarize(mean = mean(wing.score),
            SD = sd(wing.score),
            n = n())

Numeric summaries by group

group_by() splits the dataframe up by a categorical (aka factor) variable. Here, I split by the 2 experimental conditions: E = "experimental" flies and C = "control" flies and the calculate the summary stats.

wingscores_s1F %>%                   # data
  group_by(E.or.C) %>%               # group_by()
  summarize(mean = mean(wing.score), # summarize()
            n = n(),
            sd = sd(wing.score))

Cool summarize() tricks

Here are some demonstrations of the functionality of summarize().

Using summary functions from other packages

I can also use functions from other packages. The plotrix package has a standard error function: std.error(). I'll call it using plotrix::std.error() so its obvious that its from that package.

I can use this std.error() function within summarize()

wingscores_s1F %>% 
  group_by(E.or.C) %>% 
  summarize(mean = mean(wing.score),
            n = n(),
            sd = sd(wing.score),
            se = plotrix::std.error(wing.score))

Doing math within summarize()

I can even do math within summarize. Here, I calculate an approximate 95% confidence interval using std.error() by multiplying it within summarize() on the fly.

wingscores_s1F %>% 
  group_by(E.or.C) %>% 
  summarize(mean = mean(wing.score),
            n = n(),
            sd = sd(wing.score),
            se = plotrix::std.error(wing.score),
            CI95 = 1.96*plotrix::std.error(wing.score))

Saving summarize() output to an R object.

I can save this summary table to an object if I want

s1_females_summary <- wingscores_s1F %>% 
  group_by(E.or.C) %>% 
  summarize(mean = mean(wing.score),
            n = n(),
            sd = sd(wing.score),
            se = plotrix::std.error(wing.score),
            CI95 = 1.96*plotrix::std.error(wing.score))

And call it up later

s1_females_summary

I can even save this to a .csv file

write.csv(s1_females_summary, file = "s1_summary.csv")

Numeric summaries by multiple groups

You can give group_by() multiple categories. The "wingscores_s1" data has both males and females from both experimental conditions. TO make the cod cleaner I'll make a vector called "columns." that will store the 3 names of the columns I want to work with.

#names of columns to examine
columns. <- c("E.or.C", "sex","wing.score")

# check that everything works
summary(wingscores_s1[,columns.])

Calculate the mean for both sexes and for both experimental conditions.

wingscores_s1 %>% 
  group_by(E.or.C,
           sex) %>% 
  summarize(mean = mean(wing.score))

brouwern/shroom documentation built on May 24, 2019, 7:54 a.m.