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#R Orientation
#Author: Jonathan H. Morgan
#Based in Part on Jake Fisher's introduction to R: https://dnac.ssri.duke.edu/r-labs/2017/01_data_management.php
#9 May 2018
###################################
# STARTING FROM A CLEAR SLATE #
###################################
#Remove: Removes objects from memory
rm(list = ls())
#Garbage Collection: Frees up memory, but preserves variables created in previous steps
gc()
#######################################
# INSTALLING AND LOADING PACKAGES #
#######################################
#R is a modular language.
#The intuition is that you have in memory only what you need to perform the analysis tasks specified in the script.
#Consequently, we need to load the packages we will be using during our analyses each time we run a new instance of R.
#Installing a Package
#Installing a package multiple times can result in R being unable to read the package files.
#Consequently, if you are uncertain whether a particular package is installed on you machine,
#use the search window in the Packages tab to check.
install.packages("readr")
#Reading a Package
library(readr) #Import csv and other delimited files
library(haven) #Import SPSS, SAS, or Stata files
library(magrittr) #Supports pipe (%>%) commands that allow you to perform multiple operations with one statement
library(dplyr) #Manipulate data
library(tidyr) #Additional functions for manipulating data
library(ggplot2) #Visualizing data
library(statnet) #Network Analysis Software
library(ggnetwork) #Network Visualization
##################################
# DATA AND OBJECT TYPES IN R #
##################################
# R is an object-oriented language, which puts it somewhere between statistical
# computing languages such as SAS, Stata, or SPSS, and object-oriented programming
# languages such as Python or Java.
# We will focus mainly on using R for statistical computing, but
# I will demonstrate one instance where writing a simple function is quite useful for importing large data sets.
#DATA TYPES
#Logical: TRUE or FALSE
#Integer: 1, 2, 3, 4, ...
#Numeric: 1.2, 3.5, 5.5, 0, -1
#Complex: 1 + 2i (imaginary numbers)
#Character: "Jon", "1.2"
#Raw: A mixture of types in the same cell: "Jon" 1 2 2.5 -1
#Function: Essentially, conditional statements or transformations you want to apply to multiple cases
#that utilize operations from Base R or packages that you load.
#When reading in data, R will, by default, treat columns with different data types as different types of objects.
#There are a few instances where this can be problematic.
#For example, R tends to treat a column consisting of character variables as a factor,
#essentially treating it as a categorical varaible when you may simply want a list of names.
#We can avoid these problems if we are mindful of the data types in our data,
#and specify the data type when importing our data in R.
#We can also "coerce" or transform a variable from one type to another.
#We discuss both methods in this orientation.
#OBJECT TYPES: Vectors, Lists, and Factors Oh My!
#Vectors
#Lists
#Factors
#Arrays
#Matrices
#Data Frames
#Functions
#Vectors: A vector is a sequence of data elements of the same basic type.
c(2, 3, 5)
c(TRUE, FALSE, TRUE, FALSE, FALSE)
c("aa", "bb", "cc", "dd", "ee")
#Lists: A list is a generic vector containing other objects.
#For example, the following variable x is a list containing copies of three members n, c, l, and a numeric value 3.
n = c(2, 3, 5)
c = c("aa", "bb", "cc", "dd", "ee")
l = c(TRUE, FALSE, TRUE, FALSE, FALSE)
x = list(n, c, l, 3) # x contains copies of n, s, b and the number 3
#Slicing a list: We retrieve a list slice with the single square bracket "[]" operator.
#The following is a slice containing the second member of x, which is a copy of c.
x[2]
#Modifying a list
x[[2]][1] = "ta" #We are manipulating the list directly, indicated by the double brackets around the 2,
#the 1 first element of the second member of the list.
x[2]
#Factors: A vector of integer values with a corresponding set of character values to use when the factor is displayed.
#Factors are R's way of representing categorical variables.
#Creating an example factor
data = c(1,2,2,3,1,2,3,3,1,2,3,3,1)
factor = factor(data) #We are specifying that 1, 2, and 3 correspond to levels, similar to SAS's class statement.
factor
#Creating Labels for Levels: 1 2 3
factor = factor(factor,labels=c("I","II","III"))
factor
#When importing data, R will specify varibles that it thinks have levels as factors.
#This is problematic because R is now treating the variable as a catgorical variable,
#and thus will not perform many basic operations.
#Arrays: A multidimensional rectangular data object.
#"Rectangular" refers to the fact that each row is the same length, and likewise for each column.
Three_D_Array <- array(
1:24, #24 rows for each dimension
dim = c(4, 3, 2), #3 dimensions consisting of 4, 3, and 2 objects
dimnames = list(
c("one", "two", "three", "four"),
c("ein", "zwei", "drei"),
c("un", "deux")
)
)
Three_D_Array #Enlish Numbers by German Numbers by French Numbers
#Matrix: A collection of data elements arranged in a two-dimensional rectangular layout.
#A matrix is a special case of an array, the 2D version.
Matrix <- matrix(
1:12, #Creating cell values
nrow = 4, #Specifying the number of rows, ncol = 3 works the same
dimnames = list(
c("one", "two", "three", "four"), #Specifying the rows and columns
c("ein", "zwei", "drei")
)
)
Matrix
#Data Frame: A list of vectors of equal length.
#A data frame is a special case of a matrix,
#one where we have specified that the data elments in each column are the same type.
#Data frames are R's counterpart to a classic statistical package's data set.
#The top line of the table is a header, and contains the column names.
#Each horizontal line after the header denotes a data row, which begins with the name of the row,
#and then followed by the actual data.
#Each data member of a row is called a cell.
n = c(2, 3, 5)
c = c("aa", "bb", "cc")
l = c(TRUE, FALSE, TRUE)
data_frame = data.frame(n, c, l) #In R forums, df is often used to refer to a data frame.
data_frame
######################
# IMPORTING DATA #
######################
#Getting and Setting Your Work Directory
#It's important to know where you are saving the data.
#By default, R will save your data to the highest level of your user directory.
#You can determine where R is saving your data by using the following command:
getwd()
#We can set a working directory which is quite useful because we, then, do not have specify
#the file location of eah our data sets when we import them.
#You can even synchronize your work directory with an online directory.
#We can set our work directory by using the following command:
setwd("C:/Users/Jonathan H Morgan/Desktop/SN&H 2018") # Note: forward slashes
#Importing data into R
#There are numerous functions and packages for importing data into R. I am going to priamrily discuss "readr"
#because this package is capable of importing multiple data types, and is capable of importing large data
#sets (e.g., 87 GB).
#For importing SPSS, SAS, and Stata files directly, we recommend using the "haven" package.
#Documentaion for Haven: https://cran.r-project.org/web/packages/haven/haven.pdf
#R does provide a GUI based option, but this is not optimal for large data sets
AHS_Base=read.csv(file.choose(),header=TRUE)
#Reading the CSV where readr is inferring the data type based on the first 1000 rows of data
AHS_Base <- read_csv ('C:/Users/Jonathan H Morgan/Desktop/SN&H 2018/ahs_wpvar.csv',
col_names = TRUE)
#Useful functionality when importing very large data sets by subsets
f <- function(x, pos) subset(x, x[[27]] == 2) #Subsetting by gender to isolate female respondents
#I am using column's index number because this notation
#works whether the file has a header or not.
AHS_Base <- read_csv_chunked("C:/Users/Jonathan H Morgan/Desktop/SN&H 2018/ahs_wpvar.csv",
col_names = TRUE,
DataFrameCallback$new(f),
chunk_size = 10000,
progress=TRUE
)
#Transforming Grade and Sex into factors
#Specifying a vector that specifies which variables I want to transform
cols <- c("sex", "grade")
AHS_Base %<>%
mutate_each_(funs(factor(.)),cols)
#Confirming that sex and grade now have levels
str(AHS_Base)
##############################
# DATA MANAGEMENT BASICS #
##############################
#The Basic Grammar of Data Management in R
#Selecting
#Arranging
#Mutating
#Filtering
#Renaming
#Gathering
#Summarizing
#Separating
#Making Distinct
#Joining
#Selecting: "Selecting" always refers to selecting the columns you want.
AHS_Edges <- AHS_Base %>%
select(ego_nid, mfnid_1:mfnid_5, ffnid_1:ffnid_5, grade)
#Arranging: "Arranging" reorder rows with respect to columns.
AHS_Edges <- AHS_Base %>%
select(ego_nid, mfnid_1:mfnid_5, ffnid_1:ffnid_5, grade, sex) %>% #Using a second pipe to chain commands
arrange(ego_nid, sex) #Arraning the rows with respect to ego ID and gender
#Mutating: "Mutating" refers to creating a new variable based on operations peformed on another variable.
#Mutating is admittedly the strangest function name in the R Tidyverse, but it refers to the idea that
#a new variable is the result of a transformation of an old one.
AHS_Edges <- AHS_Base %>%
select(ego_nid, mfnid_1:mfnid_5, ffnid_1:ffnid_5, grade, sex) %>%
arrange(ego_nid, sex) %>%
mutate(Female = ifelse(sex == 2, 1, ifelse(sex != 2, 0, 0)))
#Filtering: "Filtering" refers to filtering by rows (e.g., choosing only 7th grade girls in this case).
AHS_Edges <- AHS_Base %>%
select(ego_nid, mfnid_1:mfnid_5, ffnid_1:ffnid_5, grade, sex) %>%
arrange(ego_nid, sex) %>%
mutate(Female = ifelse(sex == 2, 1, ifelse(sex != 2, 0, 0))) %>%
filter (grade == "7" & Female == 1) #Double == comes from set notation if and only if
#Renaming: "Renaming" refers to relabeling column names.
AHS_Edges <- AHS_Base %>%
select(ego_nid, mfnid_1:mfnid_5, ffnid_1:ffnid_5, grade, sex) %>%
arrange(ego_nid, sex) %>%
mutate(Female = ifelse(sex == 2, 1, ifelse(sex != 2, 0, 0))) %>%
filter (grade == "7" & Female == 1) %>%
rename( id = `ego_nid`,
gender = `sex`)
#Gathering: "Gathering" refers to gathering columns to transform a wide data set into a long one.
AHS_Edges <- AHS_Base %>%
select(ego_nid, mfnid_1:mfnid_5, ffnid_1:ffnid_5, grade, sex) %>%
arrange(ego_nid, sex) %>%
mutate(Female = ifelse(sex == 2, 1, ifelse(sex != 2, 0, 0))) %>%
filter (grade == "7" & Female == 1) %>%
rename( id = `ego_nid`,
gender = `sex`) %>%
gather(Alter_Label, Target, mfnid_1:mfnid_5, ffnid_1:ffnid_5, na.rm = TRUE)
#Summarizing: "Summarizing" refers to generating summary statitics for a given variable.
#In this case, we are going to calculate the average number of friends boys and girls have
gc()
#Reading in the data to calcualte separate gender means
AHS_Base <- read_csv ('C:/Users/Jonathan H Morgan/Desktop/SN&H 2018/ahs_wpvar.csv',
col_names = TRUE)
AHS_Edges <- AHS_Base %>%
select(ego_nid, mfnid_1:mfnid_5, ffnid_1:ffnid_5, commcnt, sex) %>%
gather(Alter_Label, Target, mfnid_1:mfnid_5, ffnid_1:ffnid_5, na.rm = TRUE) %>%
arrange(ego_nid, sex) %>%
filter (Target != 99999) #Eliminating 99999 values
#Generating Summary Statistics
Gender_Mean <- group_by(AHS_Edges, ego_nid, sex, commcnt) %>% #Group by ego ID to create a count of alters
filter (commcnt == 7 & sex != 0) %>% #Examining community 7's school network, and dropping 0s
summarise(count = n()) %>% #Creating a count of each students alters
group_by (sex) %>% #Grouping by gender to generate seaparate averages
summarise (Gender_Mean = mean(count)) #Generating male and female averages
gc()
#Separating: "Seperating" refers to splitting delimited values in one column into multiple columns
#Separating is very useful when dealing with delimited items in text data.
#For example, Qulatrics output for questions where respondents can makes multiple responses
#has each response to the given question separated by commas in one column.
#The separate function combined with gather can be quite useful to splitting responses,
#and then grouping them by each responsdent.
#Simulating data where the output is a string
ID = c("Jim", "Molly", "Jaemin")
Male_Friends = c("Jon Jaemin Joe Jim", "Jim Mudit Marcus", "Jim Peter Chris Marcus")
Female_Friends = c("MC Molly Liann", "Crystal Molly Liann", "MC Molly Crystal")
data_frame = data.frame(ID, Male_Friends, Female_Friends) #In R forums, df is often used to refer to a data frame.
data_frame
#Converting varaibles into character variables to avoid potential problems with gathering
#and spearating data.
data_frame %<>%
mutate_if(is.factor,as.character)
#This data is a mess, lets fix it
Edges <- data_frame %>%
select (ID, Male_Friends, Female_Friends) %>%
separate(Male_Friends, c("MF_1", "MF_2", "MF_3", "MF_4"), " ") %>% #Separating each element separated by
#a space in the male friends into its
#own column
separate(Female_Friends, c("FF_1", "FF_2", "FF_3"), " ") %>% #Repeating this step for female friends
gather(Alter_Label, Target, MF_1:MF_4, FF_1:FF_3, na.rm = TRUE) %>% #Gathering all the variables to create
#an edgelist
select (ID, Target) #Dropping Alter_Label
#We have got the data into something we can use, but character IDs can be problematic
#Let make unique numeric IDs for all the nodes
#Distinct: Eliminates all duplicate values
Nodes <- Edges %>%
gather(Variable_Label, Sender, ID, Target, na.rm = TRUE)%>% #Gathering ID and Target into one list
mutate(ID = Sender) %>% #Creating Node Labels for later
select (ID) %>% #Dropping the other variables
distinct(ID) %>% #Isolating unique cases
(add_rownames) %>% #Getting the rownames to create sequential IDs
rename (Sender_ID = rowname)%>% #Renaming rowname to Sender
mutate(Sender_ID = as.numeric(Sender_ID)) #Converting rowname into a numeric variable
#Joing: "Joing" refers to merging data sets using key variable.
#There are several kinds of joins. We are going to do left and right joins in this case.
#To learn more about joins see: ttp://www.rpubs.com/williamsurles/293454
#We now want to merge our numeric IDs, Sender, with our edgelist with the ID variable
Edges <- Edges %>%
left_join(Nodes, by = c("ID"))
#Renaming to merge Nodes with Target to get Taret_ID
Nodes <- Nodes %>%
rename( Target_ID = `Sender_ID`,
Target = `ID`)
#Merging Numeric IDs for the alters or targets
Edges <- Edges %>%
right_join(Nodes, by = c("Target"))
#Final Formatting
Edges <- Edges %>%
select(Sender_ID, Target_ID) %>%
rename ( Target = `Target_ID`,
Sender = `Sender_ID`)
################################################################
# VISUALIZING OUR SIMULATED NETWORK: PREPARATION FOR DAY 2 #
################################################################
#Step 1: Formatting Sender and Target Variables to Construct a Statnet Network Object
Edges [,1]=as.character(Edges[,1])
Edges [,2]=as.character(Edges[,2])
#Step 2: Creating a Network Object
#Note, this is a directed graph. So, we specify that in the network object now.
#The specification of the graph as either directed or undirected is important because it impacts fundamentally how we interpret the relationships described by the graph.
AHS_Network=network(Edges,matrix.type="edgelist",directed=TRUE)
#Creating a label vertex to assign to the network
Label <- as.vector(Nodes$Target)
#Step 3: Assigning Attributes to Vertices from our nodelist
set.vertex.attribute(AHS_Network,"Label",Label)
#Step 5: Visualizing the Network
AHS_Network
summary(AHS_Network) #Get numerical summaries of the network
set.seed(12345)
ggnetwork(AHS_Network) %>%
ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(color = "lightgray") +
geom_nodelabel_repel (label = Label) + #For networks with fewer nodes, we might want to label
theme_blank() +
geom_density_2d()
