R for data science pdf download free

And like most creative processes, the key to asking quality questions is to generate a large quantity of questions. It is difficult to ask revealing questions at the start of your analysis because you do not know what insights are contained in your dataset.

On the other hand, each new question that you ask will expose you to a new aspect of your data and increase your chance of making a discovery. You can quickly drill down into the most interesting parts of your data—and develop a set of thought-provoking questions—if you follow up each question with a new question based on what you find. There is no rule about which questions you should ask to guide your research. However, two types of questions will always be useful for making discoveries within your data.

You can loosely word these questions as: 1. What type of variation occurs within my variables? What type of covariation occurs between my variables? The value of a variable may change from measurement to measurement. An observation will contain several values, each associated with a different variable. Variation Variation is the tendency of the values of a variable to change from measurement to measurement.

You can see variation easily in real life; if you measure any continuous variable twice, you will get two different results. This is true even if you measure quantities that are constant, like the speed of light. Each of your measurements will include a small amount of error that varies from measurement to measurement. Categorical variables can also vary if you measure across different subjects e. Every variable has its own pattern of variation, which can reveal interesting information.

Numbers and date-times are two examples of continuous variables. In the preceding graph, the tallest bar shows that almost 30, observations have a carat value between 0. You can set the width of the intervals in a histogram with the bin width argument, which is measured in the units of the x variable. You should always explore a variety of binwidths when working with histograms, as different binwidths can reveal different patterns.

Places that do not have bars reveal values that were not seen in your data. What might explain them? Sometimes outliers are data entry errors; other times outliers suggest important new science.

When you have a lot of data, outliers are sometimes difficult to see in a histogram. Explore the distribution of each of the x, y, and z variables in diamonds. What do you learn? Think about a diamond and how you might decide which dimension is the length, width, and depth.

Explore the distribution of price. Do you discover anything unusual or surprising? Hint: carefully think about the bin width and make sure you try a wide range of values. How many diamonds are 0. How many are 1 carat? What do you think is the cause of the difference? What happens if you leave binwidth unset? What happens if you try and zoom so only half a bar shows?

The easiest way to do this is to use mutate to replace the variable with a modified copy. The first argument test should be a logical vector. Like R, ggplot2 subscribes to the philosophy that missing values should never silently go missing. To suppress that warning, set na.

So you might want to compare the scheduled departure times for cancelled and noncancelled times. You can do this by making a new variable with is. What happens to missing values in a histogram? What happens to missing values in a bar chart? Why is there a difference? What does na. Covariation If variation describes the behavior within a variable, covariation describes the behavior between variables.

How you do that should again depend on the type of variables involved. In the middle of the box is a line that displays the median, i. These three lines give you a sense of the spread of the distribution and whether or not the distribution is symmetric about the median or skewed to one side.

These outlying points are unusual, so they are plotted individually. One way to do that is with the reorder function. For example, take the class variable in the mpg dataset. What variable in the diamonds dataset is most important for predicting the price of a diamond? Install the ggstance package, and create a horizontal boxplot.

How do you interpret the plots? What are the pros and cons of each method? List them and briefly describe what each one does. Covariation will appear as a strong correlation between specific x values and specific y values.

For larger plots, you might want to try the d3heatmap or heatmaply packages, which create interactive plots. How could you rescale the count dataset to more clearly show the distribution of cut within color, or color within cut? What makes the plot difficult to read? You can see covariation as a pattern in the points. Another solution is to use bin. Another approach is to display approximately the same number of points in each bin. How does that impact a visualization of the 2D distribution of carat and price?

Visualize the distribution of carat, partitioned by price. Is it as you expect, or does it surprise you? Two-dimensional plots reveal outliers that are not visible in one-dimensional plots. Patterns and Models Patterns in your data provide clues about relationships. A scatterplot of Old Faithful eruption lengths versus the wait time between eruptions shows a pattern: longer wait times are associated with longer eruptions.

If two variables covary, you can use the values of one variable to make better predictions about the values of the second. Models are a tool for extracting patterns out of data. For example, consider the diamonds data. The following code fits a model that predicts price from carat and then computes the residuals the difference between the predicted value and the actual value. The first two arguments to ggplot are data and mapping, and the first two arguments to aes are x and y. One day you will be working on multiple analyses simultaneously that all use R and you want to keep them separate.

One day you will need to bring data from the outside world into R and send numerical results and figures from R back out into the world. To handle these real-life situations, you need to make two decisions: 1. What Is Real? But this short-term pain will save you long-term agony because it forces you to capture all important interactions in your code.

I use this pattern hundreds of times a week. R has a powerful notion of the working directory. This is where R looks for files that you ask it to load, and where it will put any files that you ask it to save. Mac and Linux use slashes e. In Windows they start with a drive letter e. You should never use absolute paths in your scripts, because they hinder sharing: no one else will have exactly the same directory configuration as you. RStudio Projects R experts keep all the files associated with a project together—input data, R scripts, analytical results, figures.

This is such a wise and common practice that RStudio has built-in support for this via projects. Now enter the following commands in the script editor, and save the file, calling it diamonds. Inspect the folder associated with your project— notice the. Rproj file. Double-click that file to reopen the project. This is huge win! One day you will want to remake a figure or just understand where it came from. Everything you need is in one place, and cleanly separated from all the other projects that you are working on.

They are used when a variable has a fixed set of possible values, or when you want to use a nonalphabetical ordering of a string. Tibbles are data frames, but they tweak some older behaviors to make life a little easier.

R is an old language, and some things that were useful 10 or 20 years ago now get in your way. If this chapter leaves you wanting to learn more about tibbles, you might enjoy vignette "tibble". Length Sepal. Width Petal. Length Petal. For example, they might not start with a letter, or they might contain unusual characters like a space. Tibbles Versus data.

Printing Tibbles have a refined print method that shows only the first 10 rows, and all the columns that fit on screen. But sometimes you need more output than the default display. There are a few options that can help. First, you can explicitly print the data frame and control the number of rows n and the width of the display. Use options dplyr. You can see a complete list of options by looking at the package help with package?

If you encounter one of these functions, use as. With base R data frames, [ sometimes returns a data frame, and sometimes returns a vector. With tibbles, [ always returns another tibble. How can you tell if an object is a tibble? Hint: try printing mtcars, which is a regular data frame. Compare and contrast the following operations on a data.

What is different? Why might the default data frame behaviors cause you frustration? If you have the name of a variable stored in an object, e. Practice referring to nonsyntactic names in the following data frame by: a. Extracting the variable called 1.

Plotting a scatterplot of 1 versus 2. Creating a new column called 3, which is 2 divided by 1. What does tibble::enframe do?

What option controls how many additional column names are printed at the footer of a tibble? You can also supply an inline CSV file. These are common sources of frustration with the base R functions. Sometimes strings in a CSV file contain commas. To prevent them from causing problems they need to be surrounded by a quoting character, like " or '. Identify what is wrong with each of the following inline CSV files. What happens when you run the code?

These are more complicated than you might expect because different parts of the world write numbers in different ways. But one complication makes it quite important: character encodings. These are the most complicated because there are so many different ways of writing dates.

The following sections describe these parsers in more detail. For example, some countries use. When parsing numbers, the most important option is the character you use for the decimal mark. You can override the default value of. An alternative approach would be to try and guess the defaults from your operating system.

Things get more complicated for languages other than English. In the early days of computing there were many competing standards for encoding non-English characters, and to correctly interpret a string you needed to know both the values and the encoding. If this happens to you, your strings will look weird when you print them.

Factors R uses factors to represent categorical variables that have a known set of possible values. Note: beware of abbreviations. It is Eastern Standard Time! The best way to figure out the correct format is to create a few examples in a character vector, and test with one of the parsing functions.

What are the most important arguments to locale? What do they do? Construct an example that shows when they might be useful. What are the most common encodings used in Europe? What are the most common encodings used in Asia? If none of these rules apply, then the column will stay as a vector of strings. For example, you might have a column of doubles that only contains integers in the first rows.

There are two printed outputs: the column specification generated by looking at the first rows, and the first five parsing failures. Here we can see that there are a lot of parsing problems with the x column—there are trailing characters after the integer value. That suggests we need to use a double parser instead.

If you rely on the default guesses and your data changes, readr will continue to read it in. That will accelerate your iterations while you eliminate common problems. The most important arguments are x the data frame to save and path the location to save it.

Other Types of Data To get other types of data into R, we recommend starting with the tidyverse packages listed next. Getting your data into this format requires some up-front work, but that work pays off in the long term.

Once you have tidy data and the tidy tools provided by packages in the tidyverse, you will spend much less time munging data from one representation to another, allowing you to spend more time on the analytic questions at hand. This chapter will give you a practical introduction to tidy data and the accompanying tools in the tidyr package. The following example shows the same data organized in four different ways. One dataset, the tidy dataset, will be much easier to work with inside the tidyverse.

There are three interrelated rules which make a dataset tidy: 1. Each variable must have its own column. Each observation must have its own row. Each value must have its own cell. Figure shows the rules visually. Figure That interrelationship leads to an even simpler set of practical instructions: 1. Put each dataset in a tibble. Put each variable in a column. In this example, only table1 is tidy.

Why ensure that your data is tidy? That makes transforming tidy data feel particularly natural. Using prose, describe how the variables and observations are organized in each of the sample tables. You will need to perform four operations: a. Extract the number of TB cases per country per year. Extract the matching population per country per year.

Divide cases by population, and multiply by 10, Store back in the appropriate place. Which representation is easiest to work with? Which is hardest? Re-create the plot showing change in cases over time using table2 instead of table1.

What do you need to do first? For example, data is often organized to make entry as easy as possible. Gathering A common problem is a dataset where some of the column names are not names of variables, but values of a variable. In this example, those are the columns and I call that the key, and here it is year. In the final result, the gathered columns are dropped, and we get new key and value columns. Otherwise, the relationships between the original variables are preserved.

Visually, this is shown in Figure Why are gather and spread not perfectly symmetrical? Both spread and gather have a convert argument.

Why does this code fail? Why does spreading this tibble fail? How could you add a new column to fix the problem? Tidy this simple tibble. Do you need to spread or gather it? What are the variables? Separating table3 makes it tidy By default, separate will split values wherever it sees a non- alphanumeric character i. For example, in the preceding code, separate split the values of rate at the forward slash characters. If you wish to use a specific character to separate a column, you can pass the character to the sep argument of separate.

This is the default behavior in sepa rate : it leaves the type of the column as is. Positive values start at 1 on the far left of the strings; negative values start at —1 on the far right of the strings.

When using integers to separate strings, the length of sep should be one less than the number of names in into. You can use this arrangement to separate the last two digits of each year.

We can use unite to rejoin the century and year columns that we created in the last example. That data is saved as tidyr::table5. Uniting table5 makes it tidy In this case we also need to use the sep argument. What do the extra and fill arguments do in separate? Both unite and separate have a remove argument. Compare and contrast separate and extract.

Why are there three variations of separation by position, by separator, and with groups , but only one unite? Missing Values Changing the representation of a dataset brings up an important subtlety of missing values. One way to think about the difference is with this Zen-like koan: an explicit missing value is the presence of an absence; an implicit missing value is the absence of a presence.

The way that a dataset is represented can make implicit values explicit. It then ensures the original dataset contains all those values, filling in explicit NAs where necessary. Compare and contrast the fill arguments to spread and com plete. What does the direction argument to fill do?

The tidyr::who dataset contains tuberculosis TB cases broken down by year, country, age, gender, and diagnosis method. Like dplyr, tidyr is designed so that each function does one thing well. It tells us: 1. The sixth letter gives the sex of TB patients. The dataset groups cases by males m and females f. The remaining numbers give the age group. In this case study I set na. Is this reasonable?

Think about how missing values are represented in this dataset. Are there implicit missing values? What happens if you neglect the mutate step? I claimed that iso2 and iso3 were redundant with country. Confirm this claim. For each country, year, and sex compute the total number of cases of TB. Make an informative visualization of the data. But there are good reasons to use other structures; tidy data is not the only way. Collectively, multiple tables of data are called relational data because it is the relations, not just the individual datasets, that are important.

Relations are always defined between a pair of tables. Sometimes both elements of a pair can be the same table! This is needed if, for example, you have a table of people, and each person has a reference to their parents.

To work with relational data you need verbs that work with pairs of tables. Prerequisites We will explore relational data from nycflights13 using the two- table verbs from dplyr. The key to understanding diagrams like this is to remember each relation always concerns a pair of tables. Imagine you wanted to draw approximately the route each plane flies from its origin to its destination.

What variables would you need? What tables would you need to combine? I forgot to draw the relationship between weather and air ports. What is the relationship and how should it appear in the diagram? If it contained weather records for all airports in the USA, what additional relation would it define with flights? How might you represent that data as a data frame? What would be the primary keys of that table? How would it connect to the existing tables? Keys The variables used to connect each pair of tables are called keys.

A key is a variable or set of variables that uniquely identifies an observation. In simple cases, a single variable is sufficient to identify an observation. For example, each plane is uniquely identified by its tailnum.

In other cases, multiple variables may be needed. A variable can be both a primary key and a foreign key. Unfortunately that is not the case! This is called a surrogate key. A primary key and the corresponding foreign key in another table form a relation.

Relations are typically one-to-many. For example, each flight has one plane, but each plane has many flights. You can think of this as a special case of 1-to-many. You can model many-to-many relations with a many-to-1 relation plus a 1-to-many relation.

Add a surrogate key to flights. Identify the keys in the following datasets: a. Lahman::Batting b. Draw a diagram illustrating the connections between the Bat ting, Master, and Salaries tables in the Lahman package.

Draw another diagram that shows the relationship between Mas ter, Managers, and AwardsManagers. How would you characterize the relationship between the Bat ting, Pitching, and Fielding tables? A mutating join allows you to combine variables from two tables.

It first matches observations by their keys, then copies across variables from one table to the other. Imagine you want to add the full airline name to the flights2 data. This is why I call this type of join a mutating join. The following sections explain, in detail, how mutating joins work.

A join is a way of connecting each row in x to zero, one, or more rows in y. This is to emphasize that joins match based on the key; the value is just carried along for the ride. In an actual join, matches will be indicated with dots.

Inner Join The simplest type of join is the inner join. The output of an inner join is a new data frame that contains the key, the x values, and the y values. Outer Joins An inner join keeps observations that appear in both tables. An outer join keeps observations that appear in at least one of the tables. This observation has a key that always matches if no other key matches , and a value filled with NA. The left join should be your default join: use it unless you have a strong reason to prefer one of the others.

This section explains what happens when the keys are not unique. This is usually an error because in neither table do the keys uniquely identify an observation. This is like a natural join, but uses only some of the common variables. This will match variable a in table x to variable b in table y. The variables from x will be used in the output. Compute the average delay by destination, then join on the air ports data frame so you can show the spatial distribution of delays.

You might want to use the size or color of the points to display the average delay for each airport. Add the location of the origin and destination i. Is there a relationship between the age of a plane and its delays? What weather conditions make it more likely to see a delay? What happened on June 13, ? Display the spatial pattern of delays, and then use Google to cross-reference with the weather. Joining different variables between the tables, e.

As this syntax suggests, SQL supports a wider range of join types than dplyr because you can connect the tables using constraints other than equality sometimes called non-equijoins. Filtering Joins Filtering joins match observations in the same way as mutating joins, but affect the observations, not the variables. Semi-joins are useful for matching filtered summary tables back to the original rows.

How would you construct the filter statement that used year, month, and day to match it back to flights? What does it mean for a flight to have a missing tailnum? Filter flights to only show flights with planes that have flown at least flights.

Combine fueleconomy::vehicles and fueleconomy::common to find only the records for the most common models. Find the 48 hours over the course of the whole year that have the worst delays.

Cross-reference it with the weather data. Can you see any patterns? Your own data is unlikely to be so nice, so there are a few things that you should do with your own data to make your joins go smoothly: 1. Start by identifying the variables that form the primary key in each table. Check that none of the variables in the primary key are missing. Check that your foreign keys match primary keys in another table. Fixing these is often a lot of work. Be aware that simply checking the number of rows before and after the join is not sufficient to ensure that your join has gone smoothly.

If you have an inner join with duplicate keys in both tables, you might get unlucky as the number of dropped rows might exactly equal the number of duplicated rows! Set Operations The final type of two-table verb are the set operations. Generally, I use these the least frequently, but they are occasionally useful when you want to break a single complex filter into simpler pieces. All these operations work with a complete row, comparing the values of every variable.

These expect the x and y inputs to have the same variables, and treat the observations like sets: intersect x, y Return only observations in both x and y. Unlike other languages, there is no difference in behavior. Beware that the printed representation of a string is not the same as string itself, because the printed representation shows the escapes.

If you leave the locale blank, it will use the current locale, as provided by your operating system. What stringr function are they equivalent to? What will you do if the string has an even number of characters? When might you want to use it? Write a function that turns e. Think carefully about what it should do if given a vector of length 0, 1, or 2. Matching Patterns with Regular Expressions Regexps are a very terse language that allow you to describe patterns in strings.

So to match an. Unfortunately this creates a problem. And this tells R to look for an explicit. How would you represent it as a string? Anchors By default, regular expressions will match any part of a string. Given the corpus of common words in stringr::words, create regular expressions that find all words that: a. Are exactly three letters long. Have seven letters or more. Character Classes and Alternatives There are a number of special patterns that match more than one character. For example, abc d..

Note that the precedence for is low, so that abc xyz matches abc or xyz not abcyz or abxyz. Create regular expressions to find all words that: a. Start with a vowel. Only contain consonants. End with ed, but not with eed. End with ing or ize. Create a regular expression that will match telephone numbers as commonly written in your country. Describe the equivalents of? Start with three consonants. Have three or more vowels in a row. Have two or more vowel-consonant pairs in a row.

Grouping and Backreferences Earlier, you learned about parentheses as a way to disambiguate complex expressions. Describe, in words, what these expressions will match: a. Construct regular expressions to match words that: a. Start and end with the same character. Contain a repeated pair of letters e.

Contain one letter repeated in at least three places e. See the stackoverflow discussion for more details. That makes sum and mean useful if you want to answer questions about matches across a larger vector: How many common words start with t?

If your regular expression gets overly complicated, try breaking it up into smaller pieces, giving each piece a name, and then combining the pieces with logical operations.

For example, in "abababa", how many times will the pattern "aba" match? Find all words that start or end with x. What word has the highest number of vowels? What word has the highest proportion of vowels? Hint: what is the denominator? Modify the regex to fix the problem. From the Harvard sentences data, extract: a. The first word from each sentence. All words ending in ing. All plurals. Grouped Matches Earlier in this chapter we talked about the use of parentheses for clarifying precedence and for backreferences when matching.

You can also use parentheses to extract parts of a complex match. For example, imagine we want to extract nouns from the sentences. Pull out both the number and the word. Find all contractions. Separate out the pieces before and after the apostrophe.

Replace all forward slashes in a string with backslashes. Switch the first and last letters in words. Which of those strings are still words? This is another sentence. Why is it better to split up by boundary "word" than " "? What does splitting with an empty string "" do? Experiment, and then read the documentation.

These are particularly useful when none of the other functions does exactly what you want. Spaces are ignored, as is everything after. It ignores all special regular expressions and operates at a very low level. This allows you to avoid complex escaping and can be much faster than regular expressions. It is problematic because there are often multiple ways of representing the same character. This is useful for doing case-insensitive matching.

Note that coll takes a locale parameter that controls which rules are used for comparing characters. Unfortunately different parts of the world use different rules! What are the five most common words in sentences? The pattern argument takes a regular expression and only returns filenames that match the pattern. Rmd" "communicate. Rmd" "EDA. Rmd" "factors. Rmd, you can convert them to regular expressions with glob2rx. The packages work very similarly, so you should be able to translate your stringr knowledge in a natural way.

Find the stringi functions that: a. Count the number of words. Find duplicated strings. Generate random text. They are also useful when you want to display character vectors in a non- alphabetical order. Historically, factors were much easier to work with than characters. As a result, many of the functions in base R automatically convert characters to factors. Those operations are described in the following sections.

Explore the distribution of rincome reported income. What makes the default bar chart hard to understand? How could you improve the plot? What is the most common relig in this survey? Which relig does denom denomination apply to? How can you find out with a table? The default value is median. There are some suspiciously high numbers in tvhours. Is the mean a good summary?

Modifying Factor Levels More powerful than changing the orders of the levels is changing their values. This allows you to clarify labels for publication, and collapse levels for high-level displays.

It allows you to recode, or change, the value of each level. How have the proportions of people identifying as Democrat, Republican, and Independent changed over time? How could you collapse rincome into a small set of categories?

At first glance, dates and times seem simple. However, the more you learn about dates and times, the more complicated they seem to get. It has three parts. You might have remembered that many parts of the world use daylight saving time DST , so that some days have 23 hours, and others have Dates and times are hard because they have to reconcile two physical phenomena the rotation of the Earth and its orbit around the sun with a whole raft of geopolitical phenomena including months, time zones, and DST.

We will also need nycflights13 for practice data. If you need one, you can use the hms package. You should always use the simplest possible data type that works for your needs. That means if you can use a date instead of a date-time, you should. They work as follows. Another approach is to use the helpers provided by lubridate. They automatically work out the format once you specify the order of the component. For dates, 1 means 1 day.

From Other Types You may want to switch between a date-time and a date. What happens if you parse a string that contains invalid dates? What does the tzone argument to today do? The next section will look at how arithmetic works with date-times.

It looks like flights leaving in minutes 20—30 and 50—60 have much lower delays than the rest of the hour! How does the distribution of flight times within a day change over the course of the year? Are they consistent? Explain your findings. Hint: consider the location of the airport.

How does the average delay time change over the course of a day? Confirm my hypothesis that the early departures of flights in minutes 20—30 and 50—60 are caused by scheduled flights that leave early.

Hint: create a binary variable that tells you whether or not a flight was delayed. Durations In R, when you subtract two dates, you get a difftime object: How old is Hadley? This ambiguity can make difftimes a little painful to work with, so lubridate provides an alternative that always uses seconds—the duration: as. If you look carefully at the date you might also notice that the time zones have changed. Periods To solve this problem, lubridate provides periods. We used the same date information for both the departure and the arrival times, but these flights arrived on the following day.

Well, if the year was it should return , but if it was , it should return ! As always, pick the simplest data structure that solves your problem. If you only care about physical time, use a duration; if you need to add human times, use a period; if you need to figure out how long a span is in human units, use an interval.

Figure summarizes permitted arithmetic operations between the different data types. Why is there months but no dmonths? How does it work? Create a vector of dates giving the first day of every month in Create a vector of dates giving the first day of every month in the current year. Write a function that, given your birthday as a date , returns how old you are in years.

Time Zones Time zones are an enormously complicated topic because of their interaction with geopolitical entities. The first challenge is that everyday names of time zones tend to be ambiguous. In the course of decades, countries change names or break apart fairly frequently, but city names tend to stay the same.

Another problem is that name needs to reflect not only to the current behavior, but also the complete history. You can find out what R thinks your current time zone is with Sys. It does not have DST, which makes it a convenient representation for computation.

Programming is a cross-cutting skill needed for all data science work: you must use a computer to do data science; you cannot do it in your head, or with pencil and paper. Programming produces code, and code is a tool of communication. Obviously code tells the computer what you want it to do.

But it also communicates meaning to other humans. Thinking about code as a vehicle for communication is important because every project you do is fundamentally collaborative.

Writing code is similar in many ways to writing prose. One parallel that I find particularly useful is that in both cases rewriting is the key to clarity. If you spend a little time rewriting your code while the ideas are fresh, you can save a lot of time later trying to re-create what your code did.

But the more you rewrite your functions the more likely your first attempt will be clear. Repeating yourself in code is dangerous because it can easily lead to errors and inconsistencies. You must master the four common atomic vectors and the three important S3 classes built on top of them, and understand the mysteries of the list and data frame.

You need tools for iteration that let you do similar things again and again. To learn more you need to study R as a programming language, not just an interactive environment for data science. This is an introduction to R as a programming language and is a great place to start if R is your first programming language. It covers similar material to these chapters, but with a different style and different motivation examples based in the casino. This dives into the details of R the programming language.

This is a great place to start if you have existing programming experience. If there are natural names, this is a good idea, and you should do it. First, note that proactively worrying about memory is not a useful way to spend your time: worry about it when it becomes a problem i. How can that work? These variables will only get copied if you modify one of them. That means the carat variable can no longer be shared between the two data frames, and a copy must be made. In short, this code is hard for a human to consume.

Foo Foo hops, then scoops, then bops. The downside, of course, is that you need to be familiar with the pipe. One place that this is a problem is tryCatch , which lets you capture and handle errors: tryCatch stop "! There are a relatively wide class of functions with this behavior, including try , suppressMessages , and suppressWarn ings in base R. In that case, create intermediate objects with meaningful names.

Pipes are fundamentally linear and expressing complex relationships with them will typically yield confusing code. Maybe you want to print out the current object, or plot it, or save it to disk. In my opinion, a little bit of duplication i. Functions allow you to automate common tasks in a more powerful and general way than copying and pasting. Writing good functions is a lifetime journey. As well as practical advice for writing functions, this chapter also gives you some suggestions for how to style your code.

You can read R for Data Science book online for free. Read online. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.

Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details.

Each section of the book is paired with exercises to help you practice what you've learned along the way.

You'll learn how to: - Wrangle: transform your datasets into a form convenient for analysis; - Program: learn powerful R tools for solving data problems with greater clarity and ease; - Explore: examine your data, generate hypotheses, and quickly test them; - Model: provide a low-dimensional summary that captures true "signals" in your dataset; - Communicate: learn R Markdown for integrating prose, code, and results.

Regression Models for Data Science in R. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

The student should have a basic understanding of statistical inference such as contained in "Statistical inference for data science". The book gives a rigorous treatment of the elementary concepts of regr The Data Science Design Manual. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.