# Chapter 1 Introduction to Data Science

This is an open source textbook aimed at introducing undergraduate students to data science. It was originally written for the University of British Columbia’s DSCI 100 - Introduction to Data Science course. In this book, we define data science as the study and development of reproducible, auditable processes to obtain value (i.e., insight) from data.

The book is structured so that learners spend the first four chapters learning how to use the R programming language and Jupyter notebooks to load, wrangle/clean, and visualize data, while answering descriptive and exploratory data analysis questions. The remaining chapters illustrate how to solve four common problems in data science, which are useful for answering predictive and inferential data analysis questions:

1. Predicting a class/category for a new observation/measurement (e.g., cancerous or benign tumour)
2. Predicting a value for a new observation/measurement (e.g., 10 km race time for 20 year old females with a BMI of 25).
3. Finding previously unknown/unlabelled subgroups in your data (e.g., products commonly bought together on Amazon)
4. Estimating an average or a proportion from a representative sample (group of people or units) and using that estimate to generalize to the broader population (e.g., the proportion of undergraduate students that own an iphone)

For each of these problems, we map them to the type of data analysis question being asked and discuss what kinds of data are needed to answer such questions. More advanced (e.g., causal or mechanistic) data analysis questions are beyond the scope of this text.

Types of data analysis questions

Question type Description Example
Descriptive A question which asks about summarized characteristics of a data set without interpretation (i.e., report a fact). How many people live in each US state?
Exploratory A question asks if there are patterns, trends, or relationships within a single data set. Often used to propose hypotheses for future study. Does politcal party voting change with indicators of wealth in a set of data collected from groups of individuals from several regions in the United States?
Inferential A question that looks for patterns, trends, or relationships in a single data set and also asks for quantification of how applicable these findings are to the wider population. Does politcal party voting change with indicators of wealth in the United States?
Predictive A question that asks about predicting measurements or labels for individuals (people or things). The focus is on what things predict some outcome, but not what causes the outcome. What political party will someone vote for in the next US election?
Causal A question that asks about whether changing one factor will lead to a change in another factor, on average, in the wider population. Does wealth lead to voting for a certain political party candidate in the US Presidential election?
Mechanistic A question that asks about the underlying mechanism of the observed patterns, trends, or relationship (i.e., how does it happen?) How does wealth lead to voting for a certain political party candidate in the US Presidential election?

Source: What is the question? by Jeffery T. Leek, Roger D. Peng & The Art of Data Science by Roger Peng & Elizabeth Matsui

## 1.1 Chapter learning objectives

By the end of the chapter, students will be able to:

• use a Jupyter notebook to execute provided R code
• edit code and markdown cells in a Jupyter notebook
• create new code and markdown cells in a Jupyter notebook
• load the tidyverse library into R
• create new variables and objects in R using the assignment symbol
• use the help and documentation tools in R
• match the names of the following functions from the tidyverse library to their documentation descriptions:
• read_csv
• select
• mutate
• filter
• ggplot
• aes

## 1.2 Jupyter notebooks

Jupyter notebooks are documents that contain a mix of computer code (and its output) and formattable text. Given that they are able to combine these two in a single document—code is not separate from the output or written report—notebooks are one of the leading tools to create reproducible data analyses. A reproducible data analysis is one where you can reliably and easily recreate the same results when analyzing the same data. Although this sounds like something that should always be true of any data analysis, in reality this is not often the case; one needs to make a conscious effort to perform data analysis in a reproducible manner.

The name Jupyter came from combining the names of the three programming language that it was initially targeted for (Julia, Python, and R), and now many other languages can be used with Jupyter notebooks.

A notebook looks like this:

We have included a short demo video here to help you get started and to introduce you to R and Jupyter. However, the best way to learn how to write and run code and formattable text in a Jupyter notebook is to do it yourself! Here is a worksheet that provides a step-by-step guide through the basics.

## 1.3 Loading a spreadsheet-like dataset

Often, the first thing we need to do in data analysis is to load a dataset into R. When we bring spreadsheet-like (think Microsoft Excel tables) data, generally shaped like a rectangle, into R it is represented as what we call a data frame object. It is very similar to a spreadsheet where the rows are the collected observations and the columns are the variables.

The first kind of data we will learn how to load into R (as a data frame) is the spreadsheet-like comma-separated values format (.csv for short). These files have names ending in .csv, and can be opened open and saved from common spreadsheet programs like Microsoft Excel and Google Sheets. For example, a .csv file named state_property_vote.csv is included with the code for this book. This file— originally from Data USA—has US state-level property, income, population and voting data from 2015 and 2016. If we were to open this data in a plain text editor, we would see each row on its own line, and each entry in the table separated by a comma:

state,med_income,med_prop_val,population,mean_commute_minutes,party
AK,64222,197300,733375,10.46830207,Republican
AL,36924,94800,4830620,25.30990746,Republican
AR,35833,83300,2958208,22.40108933,Republican
AZ,44748,128700,6641928,20.58786,Republican
CA,53075,252100,38421464,23.38085172,Democrat
CO,48098,198900,5278906,19.50792188,Democrat
CT,69228,246450,3593222,24.349675,Democrat
DC,70848,475800,647484,28.2534,Democrat
DE,54976,228500,926454,24.45553333,Democrat

To load this data into R, and then to do anything else with it afterwards, we will need to use something called a function. A function is a special word in R that takes in instructions (we call these arguments) and does something. The function we will use to read a .csv file into R is called read_csv.

In its most basic use-case, read_csv expects that the data file:

• has column names (or headers),
• uses a comma (,) to separate the columns, and
• does not have row names.

Below you’ll see the code used to load the data into R using the read_csv function. But there is one extra step we need to do first. Since read_csv is not included in the base installation of R, to be able to use it we have to load it from somewhere else: a collection of useful functions known as a library. The read_csv function in particular is in the tidyverse library (more on this later), which we load using the library function.

Next, we call the read_csv function and pass it a single argument: the name of the file, "state_property_vote.csv". We have to put quotes around filenames and other letters and words that we use in our code to distinguish it from the special words that make up R programming language. This is the only argument we need to provide for this file, because our file satifies everthing else the read_csv function expects in the default use-case (which we just discussed). Later in the course, we’ll learn more about how to deal with more complicated files where the default arguments are not appropriate. For example, files that use spaces or tabs to separate the columns, or with no column names.

library(tidyverse)
read_csv("state_property_vote.csv")
## # A tibble: 52 x 6
##    state med_income med_prop_val population mean_commute_minutes party
##    <chr>      <dbl>        <dbl>      <dbl>                <dbl> <chr>
##  1 AK         64222       197300     733375                 10.5 Republican
##  2 AL         36924        94800    4830620                 25.3 Republican
##  3 AR         35833        83300    2958208                 22.4 Republican
##  4 AZ         44748       128700    6641928                 20.6 Republican
##  5 CA         53075       252100   38421464                 23.4 Democrat
##  6 CO         48098       198900    5278906                 19.5 Democrat
##  7 CT         69228       246450    3593222                 24.3 Democrat
##  8 DC         70848       475800     647484                 28.3 Democrat
##  9 DE         54976       228500     926454                 24.5 Democrat
## 10 FL         43355       125600   19645772                 24.8 Republican
## # … with 42 more rows

Above you can also see something neat that Jupyter does to help us understand our code: it colours text depending on its meaning in R. For example, you’ll note that functions get bold green text, while letters and words surrounded by quotations like filenames get blue text.

In case you want to know more (optional): We use the read_csv function from the tidyverse instead of the base R function read.csv because it’s faster and it creates a nicer variant of the base R data frame called a tibble. This has several benefits that we’ll discuss in further detail later in the course.

## 1.4 Assigning value to a data frame

When we loaded the US state-level property, income, population, and voting data in R above using read_csv, we did not give this data frame a name, so it was just printed to the screen and we cannot do anything else with it. That isn’t very useful; what we would like to do is give a name to the data frame that read_csv outputs so that we can use it later for analysis and visualization.

To assign name to something in R, there are two possible ways—using either the assignment symbol (<-) or the equals symbol (=). From a style perspective, the assignment symbol is preferred and is what we will use in this course. When we name something in R using the assignment symbol, <-, we do not need to surround it with quotes like the filename. This is because we are formally telling R about this word and giving it a value. Only characters and words that act as values need to be surrounded by quotes.

Let’s now use the assignment symbol to give the name us_data to the US state-level property, income, population, and voting data frame that we get from read_csv.

us_data <- read_csv("state_property_vote.csv")

Wait a minute! Nothing happened this time! Or at least it looks like that. But actually something did happen: the data was read in and now has the name us_data associated with it. And we can use that name to access the data frame and do things with it. First we will type the name of the data frame to print it to the screen.

us_data
## # A tibble: 52 x 6
##    state med_income med_prop_val population mean_commute_minutes party
##    <chr>      <dbl>        <dbl>      <dbl>                <dbl> <chr>
##  1 AK         64222       197300     733375                 10.5 Republican
##  2 AL         36924        94800    4830620                 25.3 Republican
##  3 AR         35833        83300    2958208                 22.4 Republican
##  4 AZ         44748       128700    6641928                 20.6 Republican
##  5 CA         53075       252100   38421464                 23.4 Democrat
##  6 CO         48098       198900    5278906                 19.5 Democrat
##  7 CT         69228       246450    3593222                 24.3 Democrat
##  8 DC         70848       475800     647484                 28.3 Democrat
##  9 DE         54976       228500     926454                 24.5 Democrat
## 10 FL         43355       125600   19645772                 24.8 Republican
## # … with 42 more rows

## 1.5 Creating subsets of data frames with select & filter

Now, we are going to learn how to obtain subsets of data from a data frame in R using two other tidyverse functions: select and filter. The select function allows you to create a subset of the columns of a data frame, while the filter function allows you to obtain a subset of the rows with specific values.

Before we start using select and filter, let’s take a look at the US state-level property, income, and population data again to familiarize ourselves with it. We will do this by printing the data we loaded earlier in the chapter to the screen.

us_data
## # A tibble: 52 x 6
##    state med_income med_prop_val population mean_commute_minutes party
##    <chr>      <dbl>        <dbl>      <dbl>                <dbl> <chr>
##  1 AK         64222       197300     733375                 10.5 Republican
##  2 AL         36924        94800    4830620                 25.3 Republican
##  3 AR         35833        83300    2958208                 22.4 Republican
##  4 AZ         44748       128700    6641928                 20.6 Republican
##  5 CA         53075       252100   38421464                 23.4 Democrat
##  6 CO         48098       198900    5278906                 19.5 Democrat
##  7 CT         69228       246450    3593222                 24.3 Democrat
##  8 DC         70848       475800     647484                 28.3 Democrat
##  9 DE         54976       228500     926454                 24.5 Democrat
## 10 FL         43355       125600   19645772                 24.8 Republican
## # … with 42 more rows

In this data frame there are 52 rows (corresponding to the 50 US states, the District of Columbia and the US territory, Puerto Rico) and 6 columns:

1. US state abbreviation
2. Median household income
3. Median property value
4. US state population
5. Mean commute time in minutes
6. The party each state voted for in the 2016 US presidential election

Now let’s use select to extract the state column from this data frame. To do this, we need to provide the select function with two arguments. The first argument is the name of the data frame object, which in this example is us_data. The second argument is the column name that we want to select, here state. After passing these two arguments, the select function returns a single column (the state column that we asked for) as a data frame.

state_column <- select(us_data, state)
state_column
## # A tibble: 52 x 1
##    state
##    <chr>
##  1 AK
##  2 AL
##  3 AR
##  4 AZ
##  5 CA
##  6 CO
##  7 CT
##  8 DC
##  9 DE
## 10 FL
## # … with 42 more rows

### 1.5.1 Using select to extract multiple columns

We can also use select to obtain a subset of the data frame with multiple columns. Again, the first argument is the name of the data frame. Then we list all the columns we want as arguments separated by commas. Here we create a subset of three columns: state, median property value, and mean commute time in minutes.

three_columns <- select(us_data, state, med_prop_val, mean_commute_minutes)
three_columns
## # A tibble: 52 x 3
##    state med_prop_val mean_commute_minutes
##    <chr>        <dbl>                <dbl>
##  1 AK          197300                 10.5
##  2 AL           94800                 25.3
##  3 AR           83300                 22.4
##  4 AZ          128700                 20.6
##  5 CA          252100                 23.4
##  6 CO          198900                 19.5
##  7 CT          246450                 24.3
##  8 DC          475800                 28.3
##  9 DE          228500                 24.5
## 10 FL          125600                 24.8
## # … with 42 more rows

### 1.5.2 Using select to extract a range of columns

We can also use select to obtain a subset of the data frame constructed from a range of columns. To do this we use the colon (:) operator to denote the range. For example, to get all the columns in the data frame from state to med_prop_val we pass state:med_prop_val as the second argument to the select function.

column_range <- select(us_data, state:med_prop_val)
column_range
## # A tibble: 52 x 3
##    state med_income med_prop_val
##    <chr>      <dbl>        <dbl>
##  1 AK         64222       197300
##  2 AL         36924        94800
##  3 AR         35833        83300
##  4 AZ         44748       128700
##  5 CA         53075       252100
##  6 CO         48098       198900
##  7 CT         69228       246450
##  8 DC         70848       475800
##  9 DE         54976       228500
## 10 FL         43355       125600
## # … with 42 more rows

### 1.5.3 Using filter to extract a single row

We can use the filter function to obtain the subset of rows with desired values from a data frame. Again, our first argument is the name of the data frame object, us_data. The second argument is a logical statement to use when filtering the rows. Here, for example, we’ll say that we are interested in rows where state equals NY (for New York). To make this comparison, we use the equivalency operator == to compare the values of the state column with the value "NY". Similar to when we loaded the data file and put quotes around the filename, here we need to put quotes around "NY" to tell R that this is a character value and not one of the special words that make up R programming language, nor one of the names we have given to data frames in the code we have already written.

With these arguments, filter returns a data frame that has all the columns of the input data frame but only the rows we asked for in our logical filter statement.

new_york <- filter(us_data, state == "NY")
new_york
## # A tibble: 1 x 6
##   state med_income med_prop_val population mean_commute_minutes party
##   <chr>      <dbl>        <dbl>      <dbl>                <dbl> <chr>
## 1 NY         50839       134150   19673174                 24.4 Democrat

### 1.5.4 Using filter to extract rows with values above a threshold

If we are interested in finding information about the states who have a longer mean commute time than New York—whose mean commute time is 21.5 minutes—then we can create a filter to obtain rows where the value of mean_commute_minutes is greater than 21.5. In this case, we see that filter returns a data frame with 33 rows; this indicates that there are 33 states with longer commute times on average than New York.

long_commutes <- filter(us_data, mean_commute_minutes > 21.5)
long_commutes
## # A tibble: 33 x 6
##    state med_income med_prop_val population mean_commute_minutes party
##    <chr>      <dbl>        <dbl>      <dbl>                <dbl> <chr>
##  1 AL         36924        94800    4830620                 25.3 Republican
##  2 AR         35833        83300    2958208                 22.4 Republican
##  3 CA         53075       252100   38421464                 23.4 Democrat
##  4 CT         69228       246450    3593222                 24.3 Democrat
##  5 DC         70848       475800     647484                 28.3 Democrat
##  6 DE         54976       228500     926454                 24.5 Democrat
##  7 FL         43355       125600   19645772                 24.8 Republican
##  8 GA         37865       101700   10006693                 24.5 Republican
##  9 IL         47898        97350   12873761                 22.6 Democrat
## 10 IN         47194       111800    6568645                 23.5 Republican
## # … with 23 more rows

## 1.6 Exploring data with visualizations

Creating effective data visualizations is an essential piece to any data analysis. For the remainder of Chapter 1, we will learn how to use functions from the tidyverse to make visualizations that let us explore relationships in data. In particular, we’ll develop a visualization of the US property, income, population, and voting data we’ve been working with that will help us understand two potential relationships in the data: first, the relationship between median household income and median propery value across the US, and second, whether there is a pattern in which party each state voted for in the 2016 US election. This is an example of an exploratory data analysis question: we are looking for relationships and patterns within the data set we have, but are not trying to generalize what we find beyond this data set.

### 1.6.1 Using ggplot to create a scatter plot

Taking another look at our dataset below, we can immediately see that the three columns (or variables) we are interested in visualizing—median household income, median property value, and election result—are all in separate columns. In addition, there is a single row (or observation) for each state. The data are therefore in what we call a tidy data format. This is particularly important and will be a major focus in the remainder of this course: many of the functions from tidyverse require tidy data, including the ggplot function that we will use shortly for our visualization. Note below that we use the print function to display the us_data rather than just typing us_data; for data frames, these do the same thing.

print(us_data)
## # A tibble: 52 x 6
##    state med_income med_prop_val population mean_commute_minutes party
##    <chr>      <dbl>        <dbl>      <dbl>                <dbl> <chr>
##  1 AK         64222       197300     733375                 10.5 Republican
##  2 AL         36924        94800    4830620                 25.3 Republican
##  3 AR         35833        83300    2958208                 22.4 Republican
##  4 AZ         44748       128700    6641928                 20.6 Republican
##  5 CA         53075       252100   38421464                 23.4 Democrat
##  6 CO         48098       198900    5278906                 19.5 Democrat
##  7 CT         69228       246450    3593222                 24.3 Democrat
##  8 DC         70848       475800     647484                 28.3 Democrat
##  9 DE         54976       228500     926454                 24.5 Democrat
## 10 FL         43355       125600   19645772                 24.8 Republican
## # … with 42 more rows

### 1.6.2 Using ggplot to create a scatter plot

We will begin with a scatter plot of the income and property value columns from our data frame. To create a scatter plot of these two variables using the ggplot function, we do the following:

1. call the ggplot function
2. provide the name of the data frame as the first argument
3. call the aesthetic function, aes, to specify which column will correspond to the x-axis and which will correspond to the y-axis
4. add a + symbol at the end of the ggplot call to add a layer to the plot
5. call the geom_point function to tell R that we want to represent the data points as dots/points to create a scatter plot.
ggplot(us_data, aes(x = med_income, y = med_prop_val)) +
geom_point()

In case you have used R before and are curious: There are a small number of situations in which you can have a single R expression span multiple lines. Here, the + symbol at the end of the first line tells R that the expression isn’t done yet and to continue reading on the next line. While not strictly necessary, this sort of pattern will appear a lot when using ggplot as it keeps things more readable.

### 1.6.3 Formatting ggplot objects

One common and easy way to format your ggplot visualization is to add additional layers to the plot object using the + symbol. For example, we can use the xlab and ylab functions to add layers where we specify human readable labels for the x and y axes. Again, since we are specifying words (e.g. "Income (USD)") as arguments to xlab and ylab, we surround them with double quotes. There are many more layers we can add to format the plot further, and we will explore these in later chapters.

ggplot(us_data, aes(x = med_income, y = med_prop_val)) +
geom_point() +
xlab("Income (USD)") +
ylab("Median property value (USD)")    

From this visualization we see that for the 52 US regions in this data set, as median household income increases so does median property value. When we see two variables do this, we call this a positive relationship. Because the increasing pattern is fairly clear (not fuzzy) we can say that the relationship is strong. Because of the data point in the lower left-hand corner, drawing a straight line through these points wouldn’t fit very well. When a straight-line doesn’t fit the data well we say that it’s non-linear. However, we should have caution when using one point to claim non-linearity. As we will see later, this might be due to a single point not really belonging in the data set (this is often called an outlier).

Learning how to describe data visualizations is a very useful skill. We will provide descriptions for you in this course (as we did above) until we get to Chapter 4, which focuses on data visualization. Then, we will explicitly teach you how to do this yourself, and how to not over-state or over-interpret the results from a visualization.

### 1.6.4 Coloring points by group

Now we’ll move onto the second part of our exploratory data analysis question: when considering the relationship between median household income and median property value, is there a pattern in which party each state voted for in the 2016 US election? One common way to explore this is to colour the data points on the scatter plot we have already created by group/category. For example, given that we have the party each state voted for in the 2016 US Presidential election in the column named party, we can colour the points in our previous scatter plot to represent who each stated voted for.

To do this we modify our scatter plot code above. Specifically, we will add an argument to the aes function, specifying that the points should be coloured by the party column:

ggplot(us_data, aes(x = med_income, y = med_prop_val, color = party)) +
geom_point() +
xlab("Income (USD)") +
ylab("Median property value (USD)")

This data visualization shows that the one data point we singled out earlier on the far left of the plot has the label of “not applicable” instead of “democrat” or “republican”. Let’s use filter to look at the row that contains the “not applicable” value in the party column:

missing_party <- filter(us_data, party == "Not Applicable")
missing_party
## # A tibble: 0 x 6
## # … with 6 variables: state <chr>, med_income <dbl>, med_prop_val <dbl>,
## #   population <dbl>, mean_commute_minutes <dbl>, party <fct>

That explains it! That row in the dataset is actually not a US state, but rather the US territory of Peurto Rico. Similar to other US territories, residents of Puerto Rico cannot vote in presidential elections. Hence the “not applicable” label. Let’s remove this row from the data frame and rename the data frame vote_data. To do this, we use the opposite of the equivalency operator (==) for our filter statement, the not equivalent operator (!=).

vote_data <- filter(us_data, party != "Not Applicable")
vote_data
## # A tibble: 51 x 6
##    state med_income med_prop_val population mean_commute_minutes party
##    <chr>      <dbl>        <dbl>      <dbl>                <dbl> <fct>
##  1 AK         64222       197300     733375                 10.5 Republican
##  2 AL         36924        94800    4830620                 25.3 Republican
##  3 AR         35833        83300    2958208                 22.4 Republican
##  4 AZ         44748       128700    6641928                 20.6 Republican
##  5 CA         53075       252100   38421464                 23.4 Democrat
##  6 CO         48098       198900    5278906                 19.5 Democrat
##  7 CT         69228       246450    3593222                 24.3 Democrat
##  8 DC         70848       475800     647484                 28.3 Democrat
##  9 DE         54976       228500     926454                 24.5 Democrat
## 10 FL         43355       125600   19645772                 24.8 Republican
## # … with 41 more rows

Now we see that the data frame has 51 rows corresponding to the 50 states and the District of Columbia - all regions where residents can vote in US presidential elections. Let’s now recreate the scatter plot we made above using this data frame subset:

ggplot(vote_data, aes(x = med_income, y = med_prop_val, color = party)) +
geom_point() +
xlab("Income (USD)") +
ylab("Median property value (USD)")

What do we see when considering the second part of our exploratory question? Do we see a pattern in how certain states voted in the 2016 Presidential election? We do! Most of the US States who voted for the Republican candidate in the 2016 US Presidential election had lower median household income and lower median property values (data points primarily fall in lower left-hand side of the scatter plot), whereas most of the US states who voted for the Democratic candidate in the 2016 US Presidential election had higher median household income and higher median property values (data points primarily fall in the upper right-hand side of the scatter plot).

Does this mean that rich states usually vote for Democrats and poorer states generally vote for Republicans? Or could we use this data visualization on its own to predict which party each state will vote for in the next presidential election? The answer to both these questions is “no.” What we can do with this exploratory data analysis is create new hypotheses, ideas, and questions (like the ones at the beginning of this paragraph). Answering those questions would likely involve gathering additional data and doing more complex analyses, which we will see more of later in this course.

### 1.6.5 Putting it all together

Below, we put everything from this chapter together in one code chunk. This demonstrates the power of R: in relatively few lines of code, we are able to create an entire data science workflow.

library(tidyverse)

us_data <- read_csv("state_property_vote.csv")

vote_data <- filter(us_data, party != "Not Applicable")

ggplot(vote_data, aes(x = med_income, y = med_prop_val, color = party)) +
geom_point() +
xlab("Income (USD)") +
ylab("Median property value (USD)")

### 1.6.6 What’s next?

In the next chapter, we will dig in and spend more time learning how to load spreadsheet-like datasets of various formats into R, as well as how to scrape data from the web!