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# frame.match ####

  frame.match <- function(data.1, data.2, id.col, ...) {

    # User defined for comparing dataframe cells

    require(dplyr)

    results <- list()

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# Most of R, from an applied point of view anyway, is the process of creating objects and feeding them into functions to make amazing,  
# new objects. 

# amazing_new_object <- f(object)

  x <- c(3, 4, 5)
  y <- mean(x)

# This is true in the big picture sense as well
# information

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# Table 1

# A basic, descriptive table that you would usually see as Table 1 in a
# publication

# Tests if multiple groups (data$arm)
  tests.1 <- function(data, ...) {

    tests.list <- list()

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  install.packages("tidyverse")
  library(tidyverse)

# Objects ####

# Most of R, from an applied point of view anyway, is the process of creating
# objects and feeding them into functions to make amazing, new objects.

# amazing_new_object <- f(object)

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  urlfile <-"https://raw.githubusercontent.com/dantalus/intro_workshop/master/plot.csv"
  plotDf <-read.csv(urlfile)
  
# install.packages(c("ggthemes", "ggbeeswarm"))
  library(ggthemes)
  library(ggbeeswarm)
  library(ggplot2)
  library(dplyr)

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# Apply functions ####

# There are cases where you want to "do something" to each element in a given
# data structure. For example, we might want to calcuate the
# mean for each variable (column) in a dataframe.

# Looping is a common way to do this.

# Data

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# Table 1
  
# A basic, descriptive table that you would usually see as Table 1 in a
# publication

  plotDf <-read_csv(urlfile)

# Tests if multiple groups (data$arm)
  tests.1 <- function(data, ...) {