Programming with R for Official Statistics: All in One View

Content from Introduction to Programming

Last updated on 2024-07-11 | Edit this page

Estimated time: 15 minutes

Overview

Questions

What is programming?
What is object oriented programming?
How to document code?
What is a directory?

Objectives

Learn basic concepts of programming

What is programming?

Programmers use programming languages to give instructions to their computers. In this course, we will learn how to use the open source language R to complete common tasks required in the field of official statistics. This includes the basics of R, data manipulation, and best practices.

There are a few reasons why programming with R is useful for official statistics. Data manipulation and analysis with R is:

Time-saving: R can complete many computations on a large amount of data that would take a person a long time manually
Reproducible: This code can be re-run with other data with small modifications and shared with others to be applied to other new purposes
Transparent: When you’ve completed a script using best practices, you should be left with a clear list of instructions to complete the data analysis in the form of code. This avoids “black boxes” where an analyst is unsure what they’ve done to the data to get it to it’s final form

R is an object oriented programming language

Object oriented programming languages use objects as their main tools. These objects have classes, which describe their general properties. For example, in R you might work with numeric objects, which would contain numbers. You could also work with characters, which would be composed of text. We’ll explore classes and data types thoroughly in Episode 3 (Data Types and Structures). We can assign “labels” to these objects, creating a variable and use them interchangeably. We assign objects with an assignment operator. In R, the most commonly used assignment operator is <-. Try reproducing the example below on your machine by entering the code into the console and hitting the “run” button.

R

# Assign a number to a variable
number_flowers <- 8

# Print the variable's contents
print(number_flowers)

We can get the value stored within the variable by printing it.

[1] 8

Assigning a new value to a variable breaks the connection with the old value; R forgets that number and applies the variable name to the new value.

When you assign a value to a variable, R only stores the value, not the calculation you used to create it. This is an important point if you’re used to the way a spreadsheet program automatically updates linked cells. Let’s look at an example.

# Reassign the variable
number_flowers <- 7

{: .language-r)

OUTPUT

[1] 7

Variable Naming Conventions

Historically, R programmers have used a variety of conventions for naming variables. The . > character in R can be a valid part of a variable name; thus the above assignment could have easily been weight.kg <- 57.5. This is often confusing to R newcomers who have programmed in languages where . has a more significant meaning. Today, most R programmers 1) start variable names with lower case letters, 2) separate words > in variable names with underscores, and 3) use only lowercase letters, underscores, and numbers in variable names. The Tidyverse Style Guide includes a section on this and other style considerations.

Documenting Code

Notice that in the above examples, hashtags (#) are used before giving instructions that are intended for you rather than R. Hashtags produce comments, which are handy for leaving information about the code that will follow. Commenting as much code as possible is part of best practices. Always comment your code! You owe it to your colleagues who may see your code (not to mention your future coding self).

# Hashtags go before commented code, which is not run

# print("This code will not be run")
print("Always comment your code!")

OUTPUT

[1] "Always comment your code!"

Directories

A directory is a location on your machine. Say you’d like to open a file that’s located in a folder on your computer. We need to tell R where to look for the file if we expect to find it. Directories are usually listed by referencing nested folders separated by slashes. There are small differences due to operating system (OS), so refer to documentation specific to your OS when learning to work with folder structures.

For example: /Users/Documents/Learning-R points to a folder called “Learning-R” in a user’s documents folder. Depending on your IDE (Integrated Development Environment) and setup, you can print your current directory, known as the working directory. R automatically reads and writes files from and to your current working directory.

R

# print current working directory 
getwd()

OUTPUT

[1] "/Users/Documents/

Before beginning our lessons, please set your working directory to the folder that we created in the setup section with setwd(). For example, if your folder is named Learning-R:

R

# change current working directory 
setwd("~/Documents/Learning-R")

Key Points

Programming makes our work faster, more reproducible, and more transparent.
R is an object oriented programming language
Document your code with comments
A working directory is the active location on your computer where R can read and write files

Content from R Fundamentals

Last updated on 2024-07-11 | Edit this page

Estimated time: 60 minutes

Overview

Questions

How do I use the Rstudio IDE?
How do I read data into R?
What is a data frame?
How do I access subsets of a data frame?
How do I calculate simple statistics like mean and median?
What is plotting?
How do I install and use packages?

Objectives

read data into R
perform basic data operations

The Rstudio IDE

Rstudio (which will soon be known as Posit) is an IDE (Integrated Development Environment). Just as certain word processing software provide a handy squiggly line under a misspelled word, your IDE provides tools for helping you write good code. Rstudio has 4 panels by default. Utilize them and you’re on your way to becoming the programming data scientist you always dreamed you’d be. Check out this link from r-bloggers.com for an in depth tour.

The Four Corners of Rstudio

Top Left: Your script editor. From the top left you can select the type of file within RStudio you wish to run. In the case of this tutorial, you can use the plus sign to select for a R script. This section contains files that you can edit and save for later.

Top Right: This is your environment. It tells you all of the objects and datafiles that are active within your working directory. It also tells you useful information such as the the type of file, for example numerical or character based.

Bottom Left: This is your console. Think of this as the interactive pane, where you can write practice lines without saving them.

Bottom Right: This is the management section. From here you can browse the files within your computer and manually select a working directory. Here is where the help finder will pop up when we use it later in this tutorial. Any plots that are produced will be created here.

The 4 Corners of Rstudio. Photo courtsey of u/randy3k on the r/rstats subreddit

Reading Data into R

The files you were asked to download in the setup section are in comma-separated values (CSV) format. Each row holds the observations and each column holds information per that observation. This is what the first few rows look like.

R

tmp <- read.csv("data/inflammation-01.csv", header = FALSE, nrows = 5)
write.table(tmp, quote = FALSE, sep = ",", row.names = FALSE, col.names = FALSE)
rm(tmp)

We want to:

Load data into memory,
Calculate the average value of inflammation per day across all patients, and
Plot the results.

To do all that, we’ll have to learn a little bit about programming.

Getting started

Since we want to import the file called inflammation-01.csv into our R environment, we need to be able to tell our computer where the file is. To do this, we will create a “Project” with RStudio that contains the data we want to work with. The “Projects” interface in RStudio not only creates a working directory for you, but also remembers its location (allowing you to quickly navigate to it). The interface also (optionally) preserves custom settings and open files to make it easier to resume work after a break.

Create a new project

Under the File menu in RStudio, click on New project, choose New directory, then New project
Enter a name for this new folder (or “directory”) and choose a convenient location for it. This will be your working directory for the rest of the day (e.g., ~/Desktop/r-novice-inflammation).
Click on Create project
Create a new file where we will type our scripts. Go to File > New File > R script. Click the save icon on your toolbar and save your script as “script.R”.
Make sure you copy the data for the lesson into this folder, if they’re not there already.

Loading Data

Now that we are set up with an Rstudio project, we are sure that the data and scripts we are using are all in our working directory. The data files should be located in the directory data, inside the working directory. Now we can load the data into R using read.csv:

R

read.csv(file = "data/inflammation-01.csv", header = FALSE)

The expression read.csv(...) is a function call that asks R to run the function read.csv.

read.csv has two arguments: the name of the file we want to read, and whether the first line of the file contains names for the columns of data. The filename needs to be a character string (or string for short), so we put it in quotes. Assigning the second argument, header, to be FALSE indicates that the data file does not have column headers. We’ll talk more about the value FALSE, and its converse TRUE, in lesson 04. In case of our inflammation-01.csv example, R auto-generates column names in the sequence V1 (for “variable 1”), V2, and so on, until V40.

Loading Data with Headers

What happens if you forget to put header = FALSE? The default value is header = TRUE, which you can check with ?read.csv or help(read.csv). What do you expect will happen if you leave the default value of header? Before you run any code, think about what will happen to the first few rows of your data frame, and its overall size. Then run the following code and see if your expectations agree:

R

read.csv(file = "data/inflammation-01.csv")

Show me the solution

R will construct column headers from values in your first row of data, resulting in X0 X0.1 X1 X3 X1.1 X2 ....

Note that the character X is prepended: a standalone number would not be a valid variable name. Because column headers are variables, the same naming rules apply. Appending .1, .2 etc. is necessary to avoid duplicate column headers.

Reading Different Decimal Point Formats

Depending on the country you live in, your standard can use the “dot” or the “comma” as decimal mark. Also, different devices or software can generate data with different kinds of decimal marks. Take a look at ?read.csv and write the code to load a file called commadec.txt that has numeric values with commas as decimal mark, separated by semicolons.

Show me the solution

OUTPUT

read.csv(file = "data/commadec.txt", sep = ";", dec = ",")

or the built-in shortcut:

OUTPUT

read.csv2(file = "data/commadec.txt")

Data Frames

Now that we know how to assign things to variables, let’s re-run read.csv and save its result into a variable called ‘dat’:

R

dat <- read.csv(file = "data/inflammation-01.csv", header = FALSE)

This statement doesn’t produce any output because the assignment doesn’t display anything. If we want to check if our data has been loaded, we can print the variable’s value by typing the name of the variable dat. However, for large data sets it is convenient to use the function head to display only the first few rows of data.

R

head(dat)

First, let’s ask what type of object dat is:

R

class(dat)

The output tells us that it’s a data frame. Think of this structure as a spreadsheet in MS Excel that many of us are familiar with. Data frames are very useful for storing data and you will use them frequently when programming in R. A typical data frame of experimental data contains individual observations in rows and variables in columns.

We can see the shape, or dimensions, of the data frame with the function dim:

R

dim(dat)

This tells us that our data frame, dat, has r nrow(dat) rows and r ncol(dat) columns.

If we want to get a single value from the data frame, we can provide an index in square brackets. The first number specifies the row and the second the column:

R

# first value in dat, row 1, column 1
dat[1, 1]
# middle value in dat, row 30, column 20
dat[30, 20]

The first value in a data frame index is the row, the second value is the column. If we want to select more than one row or column, we can use the function c, which combines the values you give it into one vector or list. For example, to pick columns 10 and 20 from rows 1, 3, and 5, we can do this:

R

dat[c(1, 3, 5), c(10, 20)]

We frequently want to select contiguous rows or columns, such as the first ten rows, or columns 3 through 7. You can use c for this, but it’s more convenient to use the : operator. This special function generates sequences of numbers:

R

1:5
3:12

For example, we can select the first ten columns of values for the first four rows like this:

R

dat[1:4, 1:10]

or the first ten columns of rows 5 to 10 like this:

R

dat[5:10, 1:10]

If you want to select all rows or all columns, leave that index value empty.

R

# All columns from row 5
dat[5, ]
# All rows from column 16-18
dat[, 16:18]

If you leave both index values empty (i.e., dat[,]), you get the entire data frame.

Addressing Columns by Name

Columns can also be addressed by name, with either the $ operator (ie. dat$V16) or square > brackets (ie. dat[, 'V16']). You can learn more about subsetting by column name in this supplementary lesson.

Now let’s perform some common mathematical operations to learn more about our inflammation data. When analyzing data we often want to look at partial statistics, such as the maximum value per patient or the average value per day. One way to do this is to select the data we want to create a new temporary data frame, and then perform the calculation on this subset:

R

# first row, all of the columns
patient_1 <- dat[1, ]
# max inflammation for patient 1
max(patient_1)

We don’t actually need to store the row in a variable of its own. Instead, we can combine the selection and the function call:

R

# max inflammation for patient 2
max(dat[2, ])

R also has functions for other common calculations, e.g. finding the minimum, mean, median, and standard deviation of the data:

R

# minimum inflammation on day 7
min(dat[, 7])
# mean inflammation on day 7
mean(dat[, 7])
# median inflammation on day 7
median(dat[, 7])
# standard deviation of inflammation on day 7
sd(dat[, 7])

Forcing Conversion

The code above may give you an error in some R installations, since R does not automatically convert a row from a data.frame to a vector. (Confusingly, subsetted columns are automatically converted.) If this happens, you can use the as.numeric command to convert the row of data to a numeric vector:

patient_1 <- as.numeric(dat[1, ])

max(patient_1)

You can also check the class of each object:

class(dat[1, ])

class(as.numeric(dat[1, ]))

R also has a function that summaries the previous common calculations:

R

# Summarize function
summary(dat[, 1:4])

For every column in the data frame, the function “summary” calculates: the minimun value, the first quartile, the median, the mean, the third quartile and the max value, giving helpful details about the sample distribution.

Plotting

The mathematician Richard Hamming once said, “The purpose of computing is insight, not numbers,” and the best way to develop insight is often to visualize data. Visualization deserves an entire lecture (or course) of its own, but we can explore a few of R’s plotting features.

Let’s take a look at the average inflammation over time. Recall that we already calculated these values above using apply(dat, 2, mean) and saved them in the variable avg_day_inflammation. Plotting the values is done with the function plot.

R

plot(avg_day_inflammation)

Above, we gave the function plot a vector of numbers corresponding to the average inflammation per day across all patients. plot created a scatter plot where the y-axis is the average inflammation level and the x-axis is the order, or index, of the values in the vector, which in this case correspond to the 40 days of treatment. The result is roughly a linear rise and fall, which is suspicious: based on other studies, we expect a sharper rise and slower fall. Let’s have a look at two other statistics: the maximum and minimum inflammation per day.

R

max_day_inflammation <- apply(dat, 2, max)
plot(max_day_inflammation)

R

min_day_inflammation <- apply(dat, 2, min)
plot(min_day_inflammation)

The maximum value rises and falls perfectly smoothly, while the minimum seems to be a step function. Neither result seems particularly likely, so either there’s a mistake in our calculations or something is wrong with our data.

Plotting Data

Create a plot showing the standard deviation of the inflammation data for each day across all patients.

Show me the solution

This is the body of the solution.

R

sd_day_inflammation <- apply(dat, 2, sd)
plot(sd_day_inflammation)

Installing Packages

Although R has many built in tools for calculations, most programmers use packages (also known as libraries) to add to their toolbox. Packages are bundles of functions that can be installed and loaded from CRAN or other library repositories.

How you download packages might be specific to your statistical organization, so consult with your instructor. Let’s try installing tidyverse, a widely used package for data management.

R

install.packages("dplyr")

Then, we’ll need to load the package with library()

R

library(dplyr)

Key Points

The Rstudio IDE gives you tools for programming
Read in data with `read.csv()
Data frames are the most common data type used in R
Index with square bracket notation to access specific parts of a dataframe
R has built in functions for many common calculations and operations
Use plots to visualize data
Install packages from CRAN with `install.packages()

Content from Data Types and Structures

Last updated on 2024-07-11 | Edit this page

Estimated time: 40 minutes

Overview

Questions

What are the different data types in R?
What are the different data structures in R?
How do I access data within the various data structures?

Objectives

be able to indentify and create different data types and structures in R
manipulate and perform operations on different data types and structures
access and perform operations on data within different data structures

FIXME

Key Points

while tibbles are important, users may come accross data that needs to be stored in different data types

Content from Functions

Last updated on 2024-07-11 | Edit this page

Estimated time: 15 minutes

Overview

Questions

What is a function?
How do I make a function?
How can I test my functions?
How should I document my code?

Objectives

create functions to reuse code
learn best practices for documentation

FIXME

Key Points

functions allow us to reuse code and make it more readable
documenting functions using best practices helps us and others in the future

Content from Making Choices

Last updated on 2024-07-11 | Edit this page

Estimated time: 30 minutes

Overview

Questions

How do I make choices using if and else statements?
How do I compare values?

Objectives

use conditional logic
compare values to make choices
document code

FIXME

Key Points

we can compare stored values
we can automate data production by setting up our choices in our R programs

Content from Loops in R

Last updated on 2024-07-11 | Edit this page

Estimated time: 30 minutes

Overview

Questions

How can I do the same thing multiple times more efficiently in R?
What is vectorization?
Should I use a loop or an apply statement?

Objectives

understand loops types and create them
identify and perform vectorized operations
identify and understand when to use loops or vectorized operations

FIXME

Key Points

loops consume computer resources and should be used sparingly
R has vectorized operations to make classical looping operations faster

Content from Plotting

Last updated on 2024-07-11 | Edit this page

Estimated time: 15 minutes

Overview

Questions

How can I use ggplot2?
What is a geom?
How can I use ggplot2 to create a scatterplot?
What other types of plots can I make with ggplot2?

Objectives

Learn to use ggplot to create visualizations

FIXME

Content from Dealing with Messy Data

Last updated on 2024-07-11 | Edit this page

Estimated time: 30 minutes

Overview

Questions

What do I do if my data is the wrong class?
What is an NA?
What if my data has missing values?
How do I change decimal points and rounding in numeric data?
What if text strings have different cases?

Objectives

learn to address common issues when cleaning data
learn to address incorrect variable types. NA values, missing values, decimal points, lowercase vs uppercase in strings

FIXME

Key Points

there are common issues a user can look for when working with new data
making sure your data is clean before you start analysing will help make it easier

Content from Best Practices for Writing R Code

Last updated on 2024-07-11 | Edit this page

Estimated time: 15 minutes

Overview

Questions

How can I write R code that other people can understand and use?

Objectives

learn best practices to make your code useable for others

FIXME

Key Points

making your code readable allows for others to collaborate

Content from Understanding Factors

Last updated on 2024-07-11 | Edit this page

Estimated time: 30 minutes

Overview

Questions

How is categorical data represented in R?
How do I work with factors?

Objectives

learn how to perform operations on factors

FIXME

Key Points

factors are what allow us to work with categorical data

Content from Outputting Data

Last updated on 2024-07-11 | Edit this page

Estimated time: 20 minutes

Overview

Questions

How can I put my text, code, and results all in one document?
How do I use knitr?
How do I write in Markdown?

Objectives

Create basic dynamic reports in R

FIXME

Key Points

Markdown as a method to publish results
Basic dynamic visualizations with R can be combined with data notes in RMarkdown