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# MAKE FRIENDS WITH R
# BASED ON MAKE FRIENDS WITH R BY JONATHAN TEMPLIN
# CREATED BY JIHONG ZHANG
# R comments begin with a # -- there are no multiline comments
# RStudio helps you build syntax
# GREEN: Comments and character values in single or double quotes
# You can use the tab key to complete object names, functions, and arugments
# R is case sensitive. That means R and r are two different things.# In R, every statement is a function
# The print function prints the contents of what is inside to the console
print(x = 10)
# The terms inside the function are called the arguments; here print takes x
# To find help with what the arguments are use:
?print
# Each function returns an object
print(x = 10)
# You can determine what type of object is returned by using the class function
class(print(x = 10))# Each objects can be saved into the R environment (the workspace here)
# You can save the results of a function call to a variable of any name
MyObject = print(x = 10)
class(MyObject)
# You can view the objects you have saved in the Environment tab in RStudio
# Or type their name
MyObject
# There are literally thousands of types of objects in R (you can create them),
# but for our course we will mostly be working with data frames (more later)
# The process of saving the results of a function to a variable is called
# assignment. There are several ways you can assign function results to
# variables:
# The equals sign takes the result from the right-hand side and assigns it to
# the variable name on the left-hand side:
MyObject = print(x = 10)
# The <- (Alt "-" in RStudio) functions like the equals (right to left)
MyObject2 <- print(x = 10)
identical(MyObject, MyObject2)
# The -> assigns from left to right:
print(x = 10) -> MyObject3
identical(MyObject, MyObject2, MyObject3)The data frame is an R object that is a rectangular array of data. The variables in the data frame can be any class (e.g., numeric, character) and go across the columns. The observations are across the rows.
We will start by importing data from a comma-separated values (csv) file.
We will use the read.csv() function. Here, the argument stringsAsFactors keeps R from creating data strings
We will use here::here() function to quickly point to the target data file.
# You can also set the directory using setwd(). Here, I set my directory to
# my root folder:
# setwd("~")
getwd()
dir()
# If I tried to re-load the data, I would get an error:
HeightsData = read.csv(file = "heights.csv", stringsAsFactors = FALSE)# Method 2: I can use the full path to the file:
# HeightsData =
# read.csv(
# file = "/Users/jihong/Documents/website-jihong/teaching/2024-07-21-applied-multivariate-statistics-esrm64503/Lecture01/data/heights.csv",
# stringsAsFactors = FALSE)
# Or, I can reset the current directory and use the previous syntax:
# setwd("/Users/jihong/Documents/website-jihong/teaching/2024-07-21-applied-multivariate-statistics-esrm64503/Lecture01/data/")
HeightsData = read.csv(file = "teaching/2025-01-13-Experiment-Design/Lecture01/heights.csv",
stringsAsFactors = FALSE)
HeightsData# Note: Windows users will have to either change the direction of the slash
# or put two slashes between folder levels.
# To show my data in RStudio, I can either double click it in the
# Environment tab or use the View() function
# View(HeightsData)
# You can see the variable names and contents by using the $:
HeightsData$ID
# To read in SPSS files, we will need the foreign library. The foreign
# library comes installed with R (so no need to use install.packages()).
library(foreign)
# The read.spss() function imports the SPSS file to an R data frame if the
# argument to.data.frame is TRUE
WideData = read.spss(file = "teaching/2025-01-13-Experiment-Design/Lecture01/wide.sav",
to.data.frame = TRUE)
WideData# The WideData and HeightsData have the same set of ID numbers. We can use the merge() function to merge them into a single data frame. Here, x is the name of the left-side data frame and y is the name of the right-side data frame. The arguments by.x and by.y are the name of the variable(s) by which we will merge:
AllData = merge(x = WideData, y = HeightsData, by.x = "ID", by.y = "ID")
AllData
## Method 2: Use dplyr method, |> can be typed using `command + shift + M`
library(dplyr)
WideData |>
left_join(HeightsData, by = "ID")# Sometimes, certain packages require repeated measures data to be in a long
# format.
library(dplyr) # contains variable selection
## Wrong Way
AllData |>
tidyr::pivot_longer(starts_with("DVTime"), names_to = "DV", values_to = "DV_Value") |>
tidyr::pivot_longer(starts_with("AgeTime"), names_to = "Age", values_to = "Age_Value")
## Correct Way
AllData |>
tidyr::pivot_longer(c(starts_with("DVTime"), starts_with("AgeTime"))) |>
tidyr::separate(name, into = c("Variable", "Time"), sep = "Time") |>
tidyr::pivot_wider(names_from = "Variable", values_from = "value") -> AllDataLong# The psych package makes getting descriptive statistics very easy.
## install.packages("psych")
library(psych)
# We can use describe() to get descriptive statistics across all cases:
DescriptivesWide = describe(AllData)
DescriptivesWide
DescriptivesLong = describe(AllDataLong)
DescriptivesLong
# We can use describeBy() to get descriptive statistics by groups:
DescriptivesLongID = describeBy(AllDataLong, group = AllDataLong$ID)
DescriptivesLongID# Transforming data is accomplished by the creation of new variables.
AllDataLong$AgeC = AllDataLong$Age - mean(AllDataLong$Age)
# You can also use functions to create new variables. Here we create new terms
# using the function for significant digits:
AllDataLong$AgeYear = signif(x = AllDataLong$Age, digits = 2)
AllDataLong$AgeDecade = signif(x = AllDataLong$Age, digits = 1)
head(AllDataLong)