1 Introduction to R and RStudio
This walkthrough is presented by the IMMERSE team and will go through some common tasks carried out in R. There are many free resources available to get started with R and RStudio. One of our favorites is R for Data Science.
R3 is a free, open-source programming language and environment widely used for statistical computing, data analysis, and data visualization.
RStudio4 is an integrated development environment (IDE) for R, providing an intuitive interface that makes coding, visualization, and project management more accessible.
Mplus5 is a statistical modeling program used for analyzing complex data, such as latent variable models, structural equation modeling, and growth modeling. This book uses an R package called
MplusAutomationto automate the process of running models, extracting results, and generating data visualizations.
1.1 Installation
1.1.1 Step 0: Install R, RStudio, and Mplus
Here you will find a guide to installing both R and R Studio. You can also install Mplus here.
Note: The installation of Mplus requires a paid license with the mixture add-on. IMMERSE fellows will be given their own copy of Mplus for use during the one year training.
1.2 Set-up
1.2.1 Step 1: Create a new R-project in RStudio
R-projects help us organize our folders , filepaths, and scripts. To create a new R project:
- File –> New Project…
Click “New Directory” –> New Project –> Name your project
1.2.2 Step 2: Create an R-markdown document
An R-markdown file provides an authoring framework for data science that allows us to organize our reports using texts and code chunks. This document you are reading was made using R-markdown!
To create an R-markdown:
- File –> New File –> R Markdown…
In the window that pops up, give the R-markdown a title such as “Introduction to R and RStudio” Click “OK.” You should see a new markdown with some example text and code chunks. We want a clean document to start off with so delete everything from line 10 down. Go ahead and save this document in your R Project folder.
1.2.3 Step 3: Load packages
Your first code chunk in any given markdown should be the packages you will be using. To insert a code chunk, etiher use the keyboard shortcut ctrl + alt + i or Code –> Insert Chunk or click the green box with the letter C on it. There are a few packages we want our markdown to read in:
library(psych) # describe()
library(here) #helps with filepaths
library(gt) # create tables
library(tidyverse) #collection of R packages designed for data scienceAs a reminder, if a function does not work and you receive an error like this: could not find function "random_function"; or if you try to load a package and you receive an error like this: there is no package called `random_package` , then you will need to install the package using install.packages("random_package") in the console (the bottom-left window in R studio).
Once you have installed the package you will never need to install it again, however you must always load in the packages at the beginning of your R markdown using library(random_package), as shown in this document.
The style of code and package we will be using is called tidyverse6 .
Most functions are within the tidyverse package and if not, I’ve indicated the packages used in the code chunk above.
1.3 Explore the data
1.3.1 Step 4: Read in data
To demonstrate mixture modeling in the training program and online resource components of the IES grant we utilize the Civil Rights Data Collection (CRDC) (CRDC) data repository. The CRDC is a federally mandated school-level data collection effort that occurs every other year. This public data is currently available for selected latent class indicators across 4 years (2011, 2013, 2015, 2017) and all US states. In this example, we use the Arizona state sample. We utilize six focal indicators which constitute the latent class model in our example; three variables which report on harassment/bullying in schools based on disability, race, or sex, and three variables on full-time equivalent school staff hires (counselor, psychologist, law enforcement). This data source also includes covariates on a variety of subjects and distal outcomes reported in 2018 such as math/reading assessments and graduation rates.
| LCA indicators1 | ||
| Name | Label | Values |
|---|---|---|
| leaid | District Identification Code | |
| ncessch | School Identification Code | |
| report_dis | Number of students harassed or bullied on the basis of disability | 0 = No reported incidents, 1 = At least one reported incident |
| report_race | Number of students harassed or bullied on the basis of race, color, or national origin | 0 = No reported incidents, 1 = At least one reported incident |
| report_sex | Number of students harassed or bullied on the basis of sex | 0 = No reported incidents, 1 = At least one reported incident |
| counselors_fte | Number of full time equivalent counselors hired as school staff | 0 = No staff present, 1 = At least one staff present |
| report_sex | Number of full time equivalent psychologists hired as school staff | 0 = No staff present, 1 = At least one staff present |
| counselors_fte | Number of full time equivalent law enforcement officers hired as school staff | 0 = No staff present, 1 = At least one staff present |
| 1 Civil Rights Data Collection (CRDC) | ||
To read in data in R:
Ways to view data in R:
- click on the data in your Global Environment (upper right pane) or use…
View(data)-
summary()gives basic summary statistics & shows number of NA values (great for checking that data has been read in correctly)
summary(data)
#> leaid ncessch report_dis
#> Length:2027 Length:2027 Min. :0.0000
#> Class :character Class :character 1st Qu.:0.0000
#> Mode :character Mode :character Median :0.0000
#> Mean :0.0425
#> 3rd Qu.:0.0000
#> Max. :1.0000
#> NA's :27
#> report_race report_sex counselors_fte
#> Min. :0.000 Min. :0.00 Min. :0.0000
#> 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.0000
#> Median :0.000 Median :0.00 Median :0.0000
#> Mean :0.103 Mean :0.17 Mean :0.4595
#> 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:1.0000
#> Max. :1.000 Max. :1.00 Max. :1.0000
#> NA's :27 NA's :27 NA's :27
#> psych_fte law_fte
#> Min. :0.0000 Min. :0.0000
#> 1st Qu.:0.0000 1st Qu.:0.0000
#> Median :0.0000 Median :0.0000
#> Mean :0.4742 Mean :0.1255
#> 3rd Qu.:1.0000 3rd Qu.:0.0000
#> Max. :1.0000 Max. :1.0000
#> NA's :30 NA's :27-
names()provides a list of column names. Very useful if you don’t have them memorized!
names(data)
#> [1] "leaid" "ncessch" "report_dis"
#> [4] "report_race" "report_sex" "counselors_fte"
#> [7] "psych_fte" "law_fte"- head() prints the top 6 rows of the dataframe
head(data)
#> # A tibble: 6 × 8
#> leaid ncessch report_dis report_race report_sex
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 0400001 040000100120 0 0 0
#> 2 0400001 040000100616 0 0 1
#> 3 0400001 040000101204 0 0 1
#> 4 0400001 040000101871 0 1 1
#> 5 0400001 040000101872 0 0 0
#> 6 0400001 040000102344 0 0 0
#> # ℹ 3 more variables: counselors_fte <dbl>,
#> # psych_fte <dbl>, law_fte <dbl>1.3.2 Step 5: Descriptive Statistics
Let’s look at descriptive statistics for each variable.
Because looking at the ID variables’ (leaid) and (necessch) descriptives is unnecessary, we use select() to remove the variable by using the minus (-) sign:
data %>%
select(-leaid, -ncessch) %>%
summary()
#> report_dis report_race report_sex
#> Min. :0.0000 Min. :0.000 Min. :0.00
#> 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.00
#> Median :0.0000 Median :0.000 Median :0.00
#> Mean :0.0425 Mean :0.103 Mean :0.17
#> 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.00
#> Max. :1.0000 Max. :1.000 Max. :1.00
#> NA's :27 NA's :27 NA's :27
#> counselors_fte psych_fte law_fte
#> Min. :0.0000 Min. :0.0000 Min. :0.0000
#> 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
#> Median :0.0000 Median :0.0000 Median :0.0000
#> Mean :0.4595 Mean :0.4742 Mean :0.1255
#> 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
#> Max. :1.0000 Max. :1.0000 Max. :1.0000
#> NA's :27 NA's :30 NA's :27Alternatively, we can use the psych::describe() function to give more information:
data %>%
select(-leaid, -ncessch) %>%
describe()
#> vars n mean sd median trimmed mad min
#> report_dis 1 2000 0.04 0.20 0 0.00 0 0
#> report_race 2 2000 0.10 0.30 0 0.00 0 0
#> report_sex 3 2000 0.17 0.38 0 0.09 0 0
#> counselors_fte 4 2000 0.46 0.50 0 0.45 0 0
#> psych_fte 5 1997 0.47 0.50 0 0.47 0 0
#> law_fte 6 2000 0.13 0.33 0 0.03 0 0
#> max range skew kurtosis se
#> report_dis 1 1 4.53 18.55 0.00
#> report_race 1 1 2.61 4.82 0.01
#> report_sex 1 1 1.76 1.08 0.01
#> counselors_fte 1 1 0.16 -1.97 0.01
#> psych_fte 1 1 0.10 -1.99 0.01
#> law_fte 1 1 2.26 3.11 0.01What if we want to look at a subset of the data?
For example, what if we want to subset the data to observe a specific school district?
(leaid) We can use tidyverse::filter() to subset the data using certain criteria.
data %>%
filter(leaid == "0408800") %>%
describe()
#> vars n mean sd median trimmed mad min
#> leaid* 1 86 1.00 0.00 1.0 1.00 0.00 1
#> ncessch* 2 86 43.50 24.97 43.5 43.50 31.88 1
#> report_dis 3 86 0.05 0.21 0.0 0.00 0.00 0
#> report_race 4 86 0.15 0.36 0.0 0.07 0.00 0
#> report_sex 5 86 0.19 0.39 0.0 0.11 0.00 0
#> counselors_fte 6 86 0.95 0.21 1.0 1.00 0.00 0
#> psych_fte 7 86 0.19 0.39 0.0 0.11 0.00 0
#> law_fte 8 86 0.14 0.35 0.0 0.06 0.00 0
#> max range skew kurtosis se
#> leaid* 1 0 NaN NaN 0.00
#> ncessch* 86 85 0.00 -1.24 2.69
#> report_dis 1 1 4.23 16.10 0.02
#> report_race 1 1 1.91 1.68 0.04
#> report_sex 1 1 1.59 0.52 0.04
#> counselors_fte 1 1 -4.23 16.10 0.02
#> psych_fte 1 1 1.59 0.52 0.04
#> law_fte 1 1 2.04 2.21 0.04
#You can use any operator to filter: >, <, ==, >=, etc.Since we have binary data (0,1), it would be helpful to look at the proportions:
data %>%
drop_na() %>%
pivot_longer(report_dis:law_fte, names_to = "variable") %>%
group_by(variable) %>%
summarise(prop = sum(value)/n(),
n = n()) %>%
arrange(desc(prop))
#> # A tibble: 6 × 3
#> variable prop n
#> <chr> <dbl> <int>
#> 1 psych_fte 0.481 1970
#> 2 counselors_fte 0.459 1970
#> 3 report_sex 0.173 1970
#> 4 law_fte 0.127 1970
#> 5 report_race 0.105 1970
#> 6 report_dis 0.0431 1970