Chapter 2 Reading in data locally and from the web

2.1 Overview

In this chapter, you’ll learn to read tabular data of various formats into R from your local device (e.g., your laptop) and the web. “Reading” (or “loading”) is the process of converting data (stored as plain text, a database, HTML, etc.) into an object (e.g., a data frame) that R can easily access and manipulate. Thus reading data is the gateway to any data analysis; you won’t be able to analyze data unless you’ve loaded it first. And because there are many ways to store data, there are similarly many ways to read data into R. The more time you spend upfront matching the data reading method to the type of data you have, the less time you will have to devote to re-formatting, cleaning and wrangling your data (the second step to all data analyses). It’s like making sure your shoelaces are tied well before going for a run so that you don’t trip later on!

2.2 Chapter learning objectives

By the end of the chapter, readers will be able to do the following:

  • Define the following:
    • absolute file path
    • relative file path
    • Uniform Resource Locator (URL)
  • Read data into R using a relative path and a URL.
  • Compare and contrast the following functions:
    • read_csv
    • read_tsv
    • read_csv2
    • read_delim
    • read_excel
  • Match the following tidyverse read_* function arguments to their descriptions:
    • file
    • delim
    • col_names
    • skip
  • Choose the appropriate tidyverse read_* function and function arguments to load a given plain text tabular data set into R.
  • Use readxl package’s read_excel function and arguments to load a sheet from an excel file into R.
  • Connect to a database using the DBI package’s dbConnect function.
  • List the tables in a database using the DBI package’s dbListTables function.
  • Create a reference to a database table that is queriable using the tbl from the dbplyr package.
  • Retrieve data from a database query and bring it into R using the collect function from the dbplyr package.
  • Use write_csv to save a data frame to a .csv file.
  • (Optional) Obtain data using application programming interfaces (APIs) and web scraping.
    • Read HTML source code from a URL using the rvest package.
    • Read data from the Twitter API using the rtweet package.
    • Compare downloading tabular data from a plain text file (e.g., .csv), accessing data from an API, and scraping the HTML source code from a website.

2.3 Absolute and relative file paths

This chapter will discuss the different functions we can use to import data into R, but before we can talk about how we read the data into R with these functions, we first need to talk about where the data lives. When you load a data set into R, you first need to tell R where those files live. The file could live on your computer (local) or somewhere on the internet (remote).

The place where the file lives on your computer is called the “path.” You can think of the path as directions to the file. There are two kinds of paths: relative paths and absolute paths. A relative path is where the file is with respect to where you currently are on the computer (e.g., where the file you’re working in is). On the other hand, an absolute path is where the file is in respect to the computer’s filesystem base (or root) folder.

Suppose our computer’s filesystem looks like the picture in Figure 2.1, and we are working in a file titled worksheet_02.ipynb. If we want to read the .csv file named happiness_report.csv into R, we could do this using either a relative or an absolute path. We show both choices below.

Example file system.

Figure 2.1: Example file system.

Reading happiness_report.csv using a relative path:

happy_data <- read_csv("data/happiness_report.csv")

Reading happiness_report.csv using an absolute path:

happy_data <- read_csv("/home/dsci-100/worksheet_02/data/happiness_report.csv")

So which one should you use? Generally speaking, you should use relative paths. Using a relative path helps ensure that your code can be run on a different computer (and as an added bonus, relative paths are often shorter—easier to type!). This is because a file’s relative path is often the same across different computers, while a file’s absolute path (the names of all of the folders between the computer’s root, represented by /, and the file) isn’t usually the same across different computers. For example, suppose Fatima and Jayden are working on a project together on the happiness_report.csv data. Fatima’s file is stored at

/home/Fatima/project/data/happiness_report.csv,

while Jayden’s is stored at

/home/Jayden/project/data/happiness_report.csv.

Even though Fatima and Jayden stored their files in the same place on their computers (in their home folders), the absolute paths are different due to their different usernames. If Jayden has code that loads the happiness_report.csv data using an absolute path, the code won’t work on Fatima’s computer. But the relative path from inside the project folder (data/happiness_report.csv) is the same on both computers; any code that uses relative paths will work on both! In the additional resources section, we include a link to a short video on the difference between absolute and relative paths. You can also check out the here package, which provides methods for finding and constructing file paths in R.

Beyond files stored on your computer (i.e., locally), we also need a way to locate resources stored elsewhere on the internet (i.e., remotely). For this purpose we use a Uniform Resource Locator (URL), i.e., a web address that looks something like https://datasciencebook.ca/. URLs indicate the location of a resource on the internet and help us retrieve that resource.

2.4 Reading tabular data from a plain text file into R

2.4.1 read_csv to read in comma-separated files

Now that we have learned about where data could be, we will learn about how to import data into R using various functions. Specifically, we will learn how to read tabular data from a plain text file (a document containing only text) into R and write tabular data to a file out of R. The function we use to do this depends on the file’s format. For example, in the last chapter, we learned about using the tidyverse read_csv function when reading .csv (comma-separated values) files. In that case, the separator or delimiter that divided our columns was a comma (,). We only learned the case where the data matched the expected defaults of the read_csv function (column names are present, and commas are used as the delimiter between columns). In this section, we will learn how to read files that do not satisfy the default expectations of read_csv.

Before we jump into the cases where the data aren’t in the expected default format for tidyverse and read_csv, let’s revisit the more straightforward case where the defaults hold, and the only argument we need to give to the function is the path to the file, data/can_lang.csv. The can_lang data set contains language data from the 2016 Canadian census. We put data/ before the file’s name when we are loading the data set because this data set is located in a sub-folder, named data, relative to where we are running our R code. Here is what the text in the file data/can_lang.csv looks like.

category,language,mother_tongue,most_at_home,most_at_work,lang_known
Aboriginal languages,"Aboriginal languages, n.o.s.",590,235,30,665
Non-Official & Non-Aboriginal languages,Afrikaans,10260,4785,85,23415
Non-Official & Non-Aboriginal languages,"Afro-Asiatic languages, n.i.e.",1150,44
Non-Official & Non-Aboriginal languages,Akan (Twi),13460,5985,25,22150
Non-Official & Non-Aboriginal languages,Albanian,26895,13135,345,31930
Aboriginal languages,"Algonquian languages, n.i.e.",45,10,0,120
Aboriginal languages,Algonquin,1260,370,40,2480
Non-Official & Non-Aboriginal languages,American Sign Language,2685,3020,1145,21
Non-Official & Non-Aboriginal languages,Amharic,22465,12785,200,33670

And here is a review of how we can use read_csv to load it into R. First we load the tidyverse package to gain access to useful functions for reading the data.

library(tidyverse)

Next we use read_csv to load the data into R, and in that call we specify the relative path to the file.

canlang_data <- read_csv("data/can_lang.csv")
## Rows: 214 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): category, language
## dbl (4): mother_tongue, most_at_home, most_at_work, lang_known
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Note: It is also normal and expected that a message is printed out after using the read_csv and related functions. This message lets you know the data types of each of the columns that R inferred while reading the data into R. In the future when we use this and related functions to load data in this book, we will silence these messages to help with the readability of the book.

Finally, to view the first 10 rows of the data frame, we must call it:

canlang_data
## # A tibble: 214 × 6
##    category       language    mother_tongue most_at_home most_at_work lang_known
##    <chr>          <chr>               <dbl>        <dbl>        <dbl>      <dbl>
##  1 Aboriginal la… Aboriginal…           590          235           30        665
##  2 Non-Official … Afrikaans           10260         4785           85      23415
##  3 Non-Official … Afro-Asiat…          1150          445           10       2775
##  4 Non-Official … Akan (Twi)          13460         5985           25      22150
##  5 Non-Official … Albanian            26895        13135          345      31930
##  6 Aboriginal la… Algonquian…            45           10            0        120
##  7 Aboriginal la… Algonquin            1260          370           40       2480
##  8 Non-Official … American S…          2685         3020         1145      21930
##  9 Non-Official … Amharic             22465        12785          200      33670
## 10 Non-Official … Arabic             419890       223535         5585     629055
## # … with 204 more rows

2.4.2 Skipping rows when reading in data

Oftentimes, information about how data was collected, or other relevant information, is included at the top of the data file. This information is usually written in sentence and paragraph form, with no delimiter because it is not organized into columns. An example of this is shown below. This information gives the data scientist useful context and information about the data, however, it is not well formatted or intended to be read into a data frame cell along with the tabular data that follows later in the file.

Data source: https://ttimbers.github.io/canlang/
Data originally published in: Statistics Canada Census of Population 2016.
Reproduced and distributed on an as-is basis with their permission.
category,language,mother_tongue,most_at_home,most_at_work,lang_known
Aboriginal languages,"Aboriginal languages, n.o.s.",590,235,30,665
Non-Official & Non-Aboriginal languages,Afrikaans,10260,4785,85,23415
Non-Official & Non-Aboriginal languages,"Afro-Asiatic languages, n.i.e.",1150,44
Non-Official & Non-Aboriginal languages,Akan (Twi),13460,5985,25,22150
Non-Official & Non-Aboriginal languages,Albanian,26895,13135,345,31930
Aboriginal languages,"Algonquian languages, n.i.e.",45,10,0,120
Aboriginal languages,Algonquin,1260,370,40,2480
Non-Official & Non-Aboriginal languages,American Sign Language,2685,3020,1145,21
Non-Official & Non-Aboriginal languages,Amharic,22465,12785,200,33670

With this extra information being present at the top of the file, using read_csv as we did previously does not allow us to correctly load the data into R. In the case of this file we end up only reading in one column of the data set:

canlang_data <- read_csv("data/can_lang_meta-data.csv")

Note: In contrast to the normal and expected messages above, this time R printed out a warning for us indicating that there might be a problem with how our data is being read in.

canlang_data
## Warning: One or more parsing issues, see `problems()` for details
## # A tibble: 217 × 1
##    `Data source: https://ttimbers.github.io/canlang/`                           
##    <chr>                                                                        
##  1 "Data originally published in: Statistics Canada Census of Population 2016." 
##  2 "Reproduced and distributed on an as-is basis with their permission."        
##  3 "category,language,mother_tongue,most_at_home,most_at_work,lang_known"       
##  4 "Aboriginal languages,\"Aboriginal languages, n.o.s.\",590,235,30,665"       
##  5 "Non-Official & Non-Aboriginal languages,Afrikaans,10260,4785,85,23415"      
##  6 "Non-Official & Non-Aboriginal languages,\"Afro-Asiatic languages, n.i.e.\",…
##  7 "Non-Official & Non-Aboriginal languages,Akan (Twi),13460,5985,25,22150"     
##  8 "Non-Official & Non-Aboriginal languages,Albanian,26895,13135,345,31930"     
##  9 "Aboriginal languages,\"Algonquian languages, n.i.e.\",45,10,0,120"          
## 10 "Aboriginal languages,Algonquin,1260,370,40,2480"                            
## # … with 207 more rows

To successfully read data like this into R, the skip argument can be useful to tell R how many lines to skip before it should start reading in the data. In the example above, we would set this value to 3.

canlang_data <- read_csv("data/can_lang_meta-data.csv", 
                         skip = 3)
canlang_data
## # A tibble: 214 × 6
##    category       language    mother_tongue most_at_home most_at_work lang_known
##    <chr>          <chr>               <dbl>        <dbl>        <dbl>      <dbl>
##  1 Aboriginal la… Aboriginal…           590          235           30        665
##  2 Non-Official … Afrikaans           10260         4785           85      23415
##  3 Non-Official … Afro-Asiat…          1150          445           10       2775
##  4 Non-Official … Akan (Twi)          13460         5985           25      22150
##  5 Non-Official … Albanian            26895        13135          345      31930
##  6 Aboriginal la… Algonquian…            45           10            0        120
##  7 Aboriginal la… Algonquin            1260          370           40       2480
##  8 Non-Official … American S…          2685         3020         1145      21930
##  9 Non-Official … Amharic             22465        12785          200      33670
## 10 Non-Official … Arabic             419890       223535         5585     629055
## # … with 204 more rows

How did we know to skip three lines? We looked at the data! The first three lines of the data had information we didn’t need to import:

Data source: https://ttimbers.github.io/canlang/
Data originally published in: Statistics Canada Census of Population 2016.
Reproduced and distributed on an as-is basis with their permission.

The column names began at line 4, so we skipped the first three lines.

2.4.3 read_tsv to read in tab-separated files

Another common way data is stored is with tabs as the delimiter. Notice the data file, can_lang_tab.tsv, has tabs in between the columns instead of commas.

category    language    mother_tongue   most_at_home    most_at_work    lang_kno
Aboriginal languages    Aboriginal languages, n.o.s.    590 235 30  665
Non-Official & Non-Aboriginal languages Afrikaans   10260   4785    85  23415
Non-Official & Non-Aboriginal languages Afro-Asiatic languages, n.i.e.  1150    
Non-Official & Non-Aboriginal languages Akan (Twi)  13460   5985    25  22150
Non-Official & Non-Aboriginal languages Albanian    26895   13135   345 31930
Aboriginal languages    Algonquian languages, n.i.e.    45  10  0   120
Aboriginal languages    Algonquin   1260    370 40  2480
Non-Official & Non-Aboriginal languages American Sign Language  2685    3020    
Non-Official & Non-Aboriginal languages Amharic 22465   12785   200 33670

To read in this type of data, we can use the read_tsv to read in .tsv (tab separated values) files.

canlang_data <- read_tsv("data/can_lang_tab.tsv")
canlang_data
## # A tibble: 214 × 6
##    category       language    mother_tongue most_at_home most_at_work lang_known
##    <chr>          <chr>               <dbl>        <dbl>        <dbl>      <dbl>
##  1 Aboriginal la… Aboriginal…           590          235           30        665
##  2 Non-Official … Afrikaans           10260         4785           85      23415
##  3 Non-Official … Afro-Asiat…          1150          445           10       2775
##  4 Non-Official … Akan (Twi)          13460         5985           25      22150
##  5 Non-Official … Albanian            26895        13135          345      31930
##  6 Aboriginal la… Algonquian…            45           10            0        120
##  7 Aboriginal la… Algonquin            1260          370           40       2480
##  8 Non-Official … American S…          2685         3020         1145      21930
##  9 Non-Official … Amharic             22465        12785          200      33670
## 10 Non-Official … Arabic             419890       223535         5585     629055
## # … with 204 more rows

Let’s compare the data frame here to the resulting data frame in Section 2.4.1 after using read_csv. Notice anything? They look the same! The same number of columns/rows and column names! So we needed to use different tools for the job depending on the file format and our resulting table (canlang_data) in both cases was the same!

2.4.4 read_delim as a more flexible method to get tabular data into R

read_csv and read_tsv are actually just special cases of the more general read_delim function. We can use read_delim to import both comma and tab-separated files (and more), we just have to specify the delimiter. The can_lang.tsv is a different version of this same data set with no column names and uses tabs as the delimiter instead of commas.

Here is how the file would look in a plain text editor:

Aboriginal languages    Aboriginal languages, n.o.s.    590 235 30  665
Non-Official & Non-Aboriginal languages Afrikaans   10260   4785    85  23415
Non-Official & Non-Aboriginal languages Afro-Asiatic languages, n.i.e.  1150    
Non-Official & Non-Aboriginal languages Akan (Twi)  13460   5985    25  22150
Non-Official & Non-Aboriginal languages Albanian    26895   13135   345 31930
Aboriginal languages    Algonquian languages, n.i.e.    45  10  0   120
Aboriginal languages    Algonquin   1260    370 40  2480
Non-Official & Non-Aboriginal languages American Sign Language  2685    3020    
Non-Official & Non-Aboriginal languages Amharic 22465   12785   200 33670
Non-Official & Non-Aboriginal languages Arabic  419890  223535  5585    629055

To get this into R using the read_delim function, we specify the first argument as the path to the file (as done with read_csv), and then provide values to the delim argument (here a tab, which we represent by "\t") and the col_names argument (here we specify that there are no column names to assign, and give it the value of FALSE). read_csv, read_tsv and read_delim have a col_names argument and the default is TRUE.

Note: \t is an example of an escaped character, which always starts with a backslash (\). Escaped characters are used to represent non-printing characters (like the tab) or those with special meanings (such as quotation marks).

canlang_data <- read_delim("data/can_lang.tsv", 
                           delim = "\t", 
                           col_names = FALSE)
canlang_data
## # A tibble: 214 × 6
##    X1                                      X2             X3     X4    X5     X6
##    <chr>                                   <chr>       <dbl>  <dbl> <dbl>  <dbl>
##  1 Aboriginal languages                    Aborigina…    590    235    30    665
##  2 Non-Official & Non-Aboriginal languages Afrikaans   10260   4785    85  23415
##  3 Non-Official & Non-Aboriginal languages Afro-Asia…   1150    445    10   2775
##  4 Non-Official & Non-Aboriginal languages Akan (Twi)  13460   5985    25  22150
##  5 Non-Official & Non-Aboriginal languages Albanian    26895  13135   345  31930
##  6 Aboriginal languages                    Algonquia…     45     10     0    120
##  7 Aboriginal languages                    Algonquin    1260    370    40   2480
##  8 Non-Official & Non-Aboriginal languages American …   2685   3020  1145  21930
##  9 Non-Official & Non-Aboriginal languages Amharic     22465  12785   200  33670
## 10 Non-Official & Non-Aboriginal languages Arabic     419890 223535  5585 629055
## # … with 204 more rows

Data frames in R need to have column names. Thus if you read in data that don’t have column names, R will assign names automatically. In the example above, R assigns each column a name of X1, X2, X3, X4, X5, X6.

It is best to rename your columns to help differentiate between them (e.g., X1, X2, etc., are not very descriptive names and will make it more confusing as you code). To rename your columns, you can use the rename function from the dplyr R package (Wickham, François, et al. 2021) (one of the packages loaded with tidyverse, so we don’t need to load it separately). The first argument is the data set, and in the subsequent arguments you write new_name = old_name for the selected variables to rename. We rename the X1, X2, ..., X6 columns in the canlang_data data frame to more descriptive names below.

canlang_data <- rename(canlang_data,
       category = X1, 
       language = X2, 
       mother_tongue = X3,
       most_at_home = X4, 
       most_at_work = X5, 
       lang_known = X6)
canlang_data
## # A tibble: 214 × 6
##    category       language    mother_tongue most_at_home most_at_work lang_known
##    <chr>          <chr>               <dbl>        <dbl>        <dbl>      <dbl>
##  1 Aboriginal la… Aboriginal…           590          235           30        665
##  2 Non-Official … Afrikaans           10260         4785           85      23415
##  3 Non-Official … Afro-Asiat…          1150          445           10       2775
##  4 Non-Official … Akan (Twi)          13460         5985           25      22150
##  5 Non-Official … Albanian            26895        13135          345      31930
##  6 Aboriginal la… Algonquian…            45           10            0        120
##  7 Aboriginal la… Algonquin            1260          370           40       2480
##  8 Non-Official … American S…          2685         3020         1145      21930
##  9 Non-Official … Amharic             22465        12785          200      33670
## 10 Non-Official … Arabic             419890       223535         5585     629055
## # … with 204 more rows

2.4.5 Reading tabular data directly from a URL

We can also use read_csv, read_tsv or read_delim (and related functions) to read in data directly from a Uniform Resource Locator (URL) that contains tabular data. Here, we provide the URL to read_* as the path to the file instead of a path to a local file on our computer. We need to surround the URL with quotes similar to when we specify a path on our local computer. All other arguments that we use are the same as when using these functions with a local file on our computer.

url <- "https://raw.githubusercontent.com/UBC-DSCI/data/main/can_lang.csv"
canlang_data <- read_csv(url)

canlang_data
## # A tibble: 214 × 6
##    category       language    mother_tongue most_at_home most_at_work lang_known
##    <chr>          <chr>               <dbl>        <dbl>        <dbl>      <dbl>
##  1 Aboriginal la… Aboriginal…           590          235           30        665
##  2 Non-Official … Afrikaans           10260         4785           85      23415
##  3 Non-Official … Afro-Asiat…          1150          445           10       2775
##  4 Non-Official … Akan (Twi)          13460         5985           25      22150
##  5 Non-Official … Albanian            26895        13135          345      31930
##  6 Aboriginal la… Algonquian…            45           10            0        120
##  7 Aboriginal la… Algonquin            1260          370           40       2480
##  8 Non-Official … American S…          2685         3020         1145      21930
##  9 Non-Official … Amharic             22465        12785          200      33670
## 10 Non-Official … Arabic             419890       223535         5585     629055
## # … with 204 more rows

2.4.6 Previewing a data file before reading it into R

In all the examples above, we gave you previews of the data file before we read it into R. Previewing data is essential to see whether or not there are column names, what the delimiters are, and if there are lines you need to skip. You should do this yourself when trying to read in data files. You can preview files in a plain text editor by right-clicking on the file, selecting “Open With,” and choosing a plain text editor (e.g., Notepad).

2.5 Reading tabular data from a Microsoft Excel file

There are many other ways to store tabular data sets beyond plain text files, and similarly, many ways to load those data sets into R. For example, it is very common to encounter, and need to load into R, data stored as a Microsoft Excel spreadsheet (with the file name extension .xlsx). To be able to do this, a key thing to know is that even though .csv and .xlsx files look almost identical when loaded into Excel, the data themselves are stored completely differently. While .csv files are plain text files, where the characters you see when you open the file in a text editor are exactly the data they represent, this is not the case for .xlsx files. Take a look at a snippet of what a .xlsx file would look like in a text editor:

,?'O
    _rels/.rels???J1??>E?{7?
<?V????w8?'J???'QrJ???Tf?d??d?o?wZ'???@>?4'?|??hlIo??F
t                                                       8f??3wn
????t??u"/
          %~Ed2??<?w??
                       ?Pd(??J-?E???7?'t(?-GZ?????y???c~N?g[^_r?4
                                                                  yG?O
                                                                      ?K??G?
                                                                      
                                                                                                                    
     ]TUEe??O??c[???????6q??s??d?m???\???H?^????3} ?rZY? ?:L60?^?????XTP+?|?
X?a??4VT?,D?Jq

This type of file representation allows Excel files to store additional things that you cannot store in a .csv file, such as fonts, text formatting, graphics, multiple sheets and more. And despite looking odd in a plain text editor, we can read Excel spreadsheets into R using the readxl package developed specifically for this purpose.

library(readxl)

canlang_data <- read_excel("data/can_lang.xlsx")
canlang_data
## # A tibble: 214 × 6
##    category       language    mother_tongue most_at_home most_at_work lang_known
##    <chr>          <chr>               <dbl>        <dbl>        <dbl>      <dbl>
##  1 Aboriginal la… Aboriginal…           590          235           30        665
##  2 Non-Official … Afrikaans           10260         4785           85      23415
##  3 Non-Official … Afro-Asiat…          1150          445           10       2775
##  4 Non-Official … Akan (Twi)          13460         5985           25      22150
##  5 Non-Official … Albanian            26895        13135          345      31930
##  6 Aboriginal la… Algonquian…            45           10            0        120
##  7 Aboriginal la… Algonquin            1260          370           40       2480
##  8 Non-Official … American S…          2685         3020         1145      21930
##  9 Non-Official … Amharic             22465        12785          200      33670
## 10 Non-Official … Arabic             419890       223535         5585     629055
## # … with 204 more rows

If the .xlsx file has multiple sheets, you have to use the sheet argument to specify the sheet number or name. You can also specify cell ranges using the range argument. This functionality is useful when a single sheet contains multiple tables (a sad thing that happens to many Excel spreadsheets since this makes reading in data more difficult).

As with plain text files, you should always explore the data file before importing it into R. Exploring the data beforehand helps you decide which arguments you need to load the data into R successfully. If you do not have the Excel program on your computer, you can use other programs to preview the file. Examples include Google Sheets and Libre Office.

In Table 2.1 we summarize the read_* functions we covered in this chapter. We also include the read_csv2 function for data separated by semicolons ;, which you may run into with data sets where the decimal is represented by a comma instead of a period (as with some data sets from European countries).

Table 2.1: Summary of read_* functions
Data File Type R Function R Package
Comma (,) separated files read_csv readr
Tab (\t) separated files read_tsv readr
Semicolon (;) separated files read_csv2 readr
Various formats (.csv, .tsv) read_delim readr
Excel files (.xlsx) read_excel readxl

Note: readr is a part of the tidyverse package so we did not need to load this package separately since we loaded tidyverse.

2.6 Reading data from a database

Another very common form of data storage is the relational database. Databases are great when you have large data sets or multiple users working on a project. There are many relational database management systems, such as SQLite, MySQL, PostgreSQL, Oracle, and many more. These different relational database management systems each have their own advantages and limitations. Almost all employ SQL (structured query language) to obtain data from the database. But you don’t need to know SQL to analyze data from a database; several packages have been written that allow you to connect to relational databases and use the R programming language to obtain data. In this book, we will give examples of how to do this using R with SQLite and PostgreSQL databases.

2.6.1 Reading data from a SQLite database

SQLite is probably the simplest relational database system that one can use in combination with R. SQLite databases are self-contained and usually stored and accessed locally on one computer. Data is usually stored in a file with a .db extension. Similar to Excel files, these are not plain text files and cannot be read in a plain text editor.

The first thing you need to do to read data into R from a database is to connect to the database. We do that using the dbConnect function from the DBI (database interface) package. This does not read in the data, but simply tells R where the database is and opens up a communication channel that R can use to send SQL commands to the database.

library(DBI)

conn_lang_data <- dbConnect(RSQLite::SQLite(), "data/can_lang.db")

Often relational databases have many tables; thus, in order to retrieve data from a database, you need to know the name of the table in which the data is stored. You can get the names of all the tables in the database using the dbListTables function:

tables <- dbListTables(conn_lang_data)
tables
## [1] "lang"

The dbListTables function returned only one name, which tells us that there is only one table in this database. To reference a table in the database (so that we can perform operations like selecting columns and filtering rows), we use the tbl function from the dbplyr package. The object returned by the tbl function allows us to work with data stored in databases as if they were just regular data frames; but secretly, behind the scenes, dbplyr is turning your function calls (e.g., select and filter) into SQL queries!

library(dbplyr)

lang_db <- tbl(conn_lang_data, "lang")
lang_db 
## # Source:   table<lang> [?? x 6]
## # Database: sqlite 3.36.0
## #   [/home/rstudio/introduction-to-datascience/data/can_lang.db]
##    category       language    mother_tongue most_at_home most_at_work lang_known
##    <chr>          <chr>               <dbl>        <dbl>        <dbl>      <dbl>
##  1 Aboriginal la… Aboriginal…           590          235           30        665
##  2 Non-Official … Afrikaans           10260         4785           85      23415
##  3 Non-Official … Afro-Asiat…          1150          445           10       2775
##  4 Non-Official … Akan (Twi)          13460         5985           25      22150
##  5 Non-Official … Albanian            26895        13135          345      31930
##  6 Aboriginal la… Algonquian…            45           10            0        120
##  7 Aboriginal la… Algonquin            1260          370           40       2480
##  8 Non-Official … American S…          2685         3020         1145      21930
##  9 Non-Official … Amharic             22465        12785          200      33670
## 10 Non-Official … Arabic             419890       223535         5585     629055
## # … with more rows

Although it looks like we just got a data frame from the database, we didn’t! It’s a reference; the data is still stored only in the SQLite database. The dbplyr package works this way because databases are often more efficient at selecting, filtering and joining large data sets than R. And typically the database will not even be stored on your computer, but rather a more powerful machine somewhere on the web. So R is lazy and waits to bring this data into memory until you explicitly tell it to using the collect function. Figure 2.2 highlights the difference between a tibble object in R and the output we just created. Notice in the table on the right, the first two lines of the output indicate the source is SQL. The last line doesn’t show how many rows there are (R is trying to avoid performing expensive query operations), whereas the output for the tibble object does.

Comparison of a reference to data in a database and a tibble in R.

Figure 2.2: Comparison of a reference to data in a database and a tibble in R.

We can look at the SQL commands that are sent to the database when we write tbl(conn_lang_data, "lang") in R with the show_query function from the dbplyr package.

show_query(tbl(conn_lang_data, "lang"))
## <SQL>
## SELECT *
## FROM `lang`

The output above shows the SQL code that is sent to the database. When we write tbl(conn_lang_data, "lang") in R, in the background, the function is translating the R code into SQL, sending that SQL to the database, and then translating the response for us. So dbplyr does all the hard work of translating from R to SQL and back for us; we can just stick with R!

With our lang_db table reference for the 2016 Canadian Census data in hand, we can mostly continue onward as if it were a regular data frame. For example, we can use the filter function to obtain only certain rows. Below we filter the data to include only Aboriginal languages.

aboriginal_lang_db <- filter(lang_db, category == "Aboriginal languages")
aboriginal_lang_db 
## # Source:   lazy query [?? x 6]
## # Database: sqlite 3.36.0
## #   [/home/rstudio/introduction-to-datascience/data/can_lang.db]
##    category     language      mother_tongue most_at_home most_at_work lang_known
##    <chr>        <chr>                 <dbl>        <dbl>        <dbl>      <dbl>
##  1 Aboriginal … Aboriginal l…           590          235           30        665
##  2 Aboriginal … Algonquian l…            45           10            0        120
##  3 Aboriginal … Algonquin              1260          370           40       2480
##  4 Aboriginal … Athabaskan l…            50           10            0         85
##  5 Aboriginal … Atikamekw              6150         5465         1100       6645
##  6 Aboriginal … Babine (Wets…           110           20           10        210
##  7 Aboriginal … Beaver                  190           50            0        340
##  8 Aboriginal … Blackfoot              2815         1110           85       5645
##  9 Aboriginal … Carrier                1025          250           15       2100
## 10 Aboriginal … Cayuga                   45           10           10        125
## # … with more rows

Above you can again see the hints that this data is not actually stored in R yet: the source is a lazy query [?? x 6] and the output says ... with more rows at the end (both indicating that R does not know how many rows there are in total!), and a database type sqlite 3.36.0 is listed. In order to actually retrieve this data in R as a data frame, we use the collect function. Below you will see that after running collect, R knows that the retrieved data has 67 rows, and there is no database listed any more.

aboriginal_lang_data <- collect(aboriginal_lang_db)
aboriginal_lang_data
## # A tibble: 67 × 6
##    category     language      mother_tongue most_at_home most_at_work lang_known
##    <chr>        <chr>                 <dbl>        <dbl>        <dbl>      <dbl>
##  1 Aboriginal … Aboriginal l…           590          235           30        665
##  2 Aboriginal … Algonquian l…            45           10            0        120
##  3 Aboriginal … Algonquin              1260          370           40       2480
##  4 Aboriginal … Athabaskan l…            50           10            0         85
##  5 Aboriginal … Atikamekw              6150         5465         1100       6645
##  6 Aboriginal … Babine (Wets…           110           20           10        210
##  7 Aboriginal … Beaver                  190           50            0        340
##  8 Aboriginal … Blackfoot              2815         1110           85       5645
##  9 Aboriginal … Carrier                1025          250           15       2100
## 10 Aboriginal … Cayuga                   45           10           10        125
## # … with 57 more rows

Aside from knowing the number of rows, the data looks pretty similar in both outputs shown above. And dbplyr provides many more functions (not just filter) that you can use to directly feed the database reference (lang_db) into downstream analysis functions (e.g., ggplot2 for data visualization). But dbplyr does not provide every function that we need for analysis; we do eventually need to call collect. For example, look what happens when we try to use nrow to count rows in a data frame:

nrow(aboriginal_lang_db)
## [1] NA

or tail to preview the last six rows of a data frame:

tail(aboriginal_lang_db)
## Error: tail() is not supported by sql sources

Additionally, some operations will not work to extract columns or single values from the reference given by the tbl function. Thus, once you have finished your data wrangling of the tbl database reference object, it is advisable to bring it into R as a data frame using collect. But be very careful using collect: databases are often very big, and reading an entire table into R might take a long time to run or even possibly crash your machine. So make sure you use filter and select on the database table to reduce the data to a reasonable size before using collect to read it into R!

2.6.2 Reading data from a PostgreSQL database

PostgreSQL (also called Postgres) is a very popular and open-source option for relational database software. Unlike SQLite, PostgreSQL uses a client–server database engine, as it was designed to be used and accessed on a network. This means that you have to provide more information to R when connecting to Postgres databases. The additional information that you need to include when you call the dbConnect function is listed below:

  • dbname: the name of the database (a single PostgreSQL instance can host more than one database)
  • host: the URL pointing to where the database is located
  • port: the communication endpoint between R and the PostgreSQL database (usually 5432)
  • user: the username for accessing the database
  • password: the password for accessing the database

Additionally, we must use the RPostgres package instead of RSQLite in the dbConnect function call. Below we demonstrate how to connect to a version of the can_mov_db database, which contains information about Canadian movies. Note that the host (fakeserver.stat.ubc.ca), user (user0001), and password (abc123) below are not real; you will not actually be able to connect to a database using this information.

library(RPostgres)
conn_mov_data <- dbConnect(RPostgres::Postgres(), dbname = "can_mov_db",
                        host = "fakeserver.stat.ubc.ca", port = 5432,
                        user = "user0001", password = "abc123")

After opening the connection, everything looks and behaves almost identically to when we were using an SQLite database in R. For example, we can again use dbListTables to find out what tables are in the can_mov_db database:

dbListTables(conn_mov_data)
 [1] "themes"            "medium"           "titles"     "title_aliases"       "forms"            
 [6] "episodes"          "names"      "names_occupations" "occupation"       "ratings" 

We see that there are 10 tables in this database. Let’s first look at the "ratings" table to find the lowest rating that exists in the can_mov_db database:

ratings_db <- tbl(conn_mov_data, "ratings")
ratings_db
# Source:   table<ratings> [?? x 3]
# Database: postgres [user0001@fakeserver.stat.ubc.ca:5432/can_mov_db]
   title              average_rating num_votes
   <chr>                    <dbl>     <int>
 1 The Grand Seduction       6.6       150
 2 Rhymes for Young Ghouls   6.3      1685
 3 Mommy                     7.5      1060
 4 Incendies                 6.1      1101
 5 Bon Cop, Bad Cop          7.0       894
 6 Goon                      5.5      1111
 7 Monsieur Lazhar           5.6       610
 8 What if                   5.3      1401
 9 The Barbarian Invations   5.8        99
10 Away from Her             6.9      2311
# … with more rows

To find the lowest rating that exists in the data base, we first need to extract the average_rating column using select:

avg_rating_db <- select(ratings_db, average_rating)
avg_rating_db
# Source:   lazy query [?? x 1]
# Database: postgres [user0001@fakeserver.stat.ubc.ca:5432/can_mov_db]
   average_rating
            <dbl>
 1            6.6
 2            6.3
 3            7.5
 4            6.1
 5            7.0
 6            5.5
 7            5.6
 8            5.3
 9            5.8
10            6.9
# … with more rows

Next we use min to find the minimum rating in that column:

min(avg_rating_db)
Error in min(avg_rating_db) : invalid 'type' (list) of argument

Instead of the minimum, we get an error! This is another example of when we need to use the collect function to bring the data into R for further computation:

avg_rating_data <- collect(avg_rating_db)
min(avg_rating_data)
[1] 1

We see the lowest rating given to a movie is 1, indicating that it must have been a really bad movie…

2.6.3 Why should we bother with databases at all?

Opening a database stored in a .db file involved a lot more effort than just opening a .csv, .tsv, or any of the other plain text or Excel formats. It was a bit of a pain to use a database in that setting since we had to use dbplyr to translate tidyverse-like commands (filter, select etc.) into SQL commands that the database understands. Not all tidyverse commands can currently be translated with SQLite databases. For example, we can compute a mean with an SQLite database but can’t easily compute a median. So you might be wondering: why should we use databases at all?

Databases are beneficial in a large-scale setting:

  • They enable storing large data sets across multiple computers with backups.
  • They provide mechanisms for ensuring data integrity and validating input.
  • They provide security and data access control.
  • They allow multiple users to access data simultaneously and remotely without conflicts and errors. For example, there are billions of Google searches conducted daily in 2021 (Real Time Statistics Project 2021). Can you imagine if Google stored all of the data from those searches in a single .csv file!? Chaos would ensue!

2.7 Writing data from R to a .csv file

At the middle and end of a data analysis, we often want to write a data frame that has changed (either through filtering, selecting, mutating or summarizing) to a file to share it with others or use it for another step in the analysis. The most straightforward way to do this is to use the write_csv function from the tidyverse package. The default arguments for this file are to use a comma (,) as the delimiter and include column names. Below we demonstrate creating a new version of the Canadian languages data set without the official languages category according to the Canadian 2016 Census, and then writing this to a .csv file:

no_official_lang_data <- filter(can_lang, category != "Official languages")
write_csv(no_official_lang_data, "data/no_official_languages.csv")

2.8 Obtaining data from the web

Note: This section is not required reading for the remainder of the textbook. It is included for those readers interested in learning a little bit more about how to obtain different types of data from the web.

Data doesn’t just magically appear on your computer; you need to get it from somewhere. Earlier in the chapter we showed you how to access data stored in a plain text, spreadsheet-like format (e.g., comma- or tab-separated) from a web URL using one of the read_* functions from the tidyverse. But as time goes on, it is increasingly uncommon to find data (especially large amounts of data) in this format available for download from a URL. Instead, websites now often offer something known as an application programming interface (API), which provides a programmatic way to ask for subsets of a data set. This allows the website owner to control who has access to the data, what portion of the data they have access to, and how much data they can access. Typically, the website owner will give you a token (a secret string of characters somewhat like a password) that you have to provide when accessing the API.

Another interesting thought: websites themselves are data! When you type a URL into your browser window, your browser asks the web server (another computer on the internet whose job it is to respond to requests for the website) to give it the website’s data, and then your browser translates that data into something you can see. If the website shows you some information that you’re interested in, you could create a data set for yourself by copying and pasting that information into a file. This process of taking information directly from what a website displays is called web scraping (or sometimes screen scraping). Now, of course, copying and pasting information manually is a painstaking and error-prone process, especially when there is a lot of information to gather. So instead of asking your browser to translate the information that the web server provides into something you can see, you can collect that data programmatically—in the form of hypertext markup language (HTML) and cascading style sheet (CSS) code—and process it to extract useful information. HTML provides the basic structure of a site and tells the webpage how to display the content (e.g., titles, paragraphs, bullet lists etc.), whereas CSS helps style the content and tells the webpage how the HTML elements should be presented (e.g., colors, layouts, fonts etc.).

This subsection will show you the basics of both web scraping with the rvest R package (Wickham 2021a) and accessing the Twitter API using the rtweet R package (Kearney 2019).

2.8.1 Web scraping

HTML and CSS selectors

When you enter a URL into your browser, your browser connects to the web server at that URL and asks for the source code for the website. This is the data that the browser translates into something you can see; so if we are going to create our own data by scraping a website, we have to first understand what that data looks like! For example, let’s say we are interested in knowing the average rental price (per square foot) of the most recently available one-bedroom apartments in Vancouver on Craiglist. When we visit the Vancouver Craigslist website and search for one-bedroom apartments, we should see something similar to Figure 2.3.

Craigslist webpage of advertisements for one-bedroom apartments.

Figure 2.3: Craigslist webpage of advertisements for one-bedroom apartments.

Based on what our browser shows us, it’s pretty easy to find the size and price for each apartment listed. But we would like to be able to obtain that information using R, without any manual human effort or copying and pasting. We do this by examining the source code that the web server actually sent our browser to display for us. We show a snippet of it below; the entire source is included with the code for this book:

        <span class="result-meta">
                <span class="result-price">$800</span>

                <span class="housing">
                    1br -
                </span>

                <span class="result-hood"> (13768 108th Avenue)</span>

                <span class="result-tags">
                    <span class="maptag" data-pid="6786042973">map</span>
                </span>

                <span class="banish icon icon-trash" role="button">
                    <span class="screen-reader-text">hide this posting</span>
                </span>

            <span class="unbanish icon icon-trash red" role="button" aria-hidden
            <a href="#" class="restore-link">
                <span class="restore-narrow-text">restore</span>
                <span class="restore-wide-text">restore this posting</span>
            </a>

        </span>
    </p>
</li>
         <li class="result-row" data-pid="6788463837">
                  
        <a href="https://vancouver.craigslist.org/nvn/apa/d/north-vancouver-luxu
                <span class="result-price">$2285</span>
        </a>

Oof…you can tell that the source code for a web page is not really designed for humans to understand easily. However, if you look through it closely, you will find that the information we’re interested in is hidden among the muck. For example, near the top of the snippet above you can see a line that looks like

<span class="result-price">$800</span>

That is definitely storing the price of a particular apartment. With some more investigation, you should be able to find things like the date and time of the listing, the address of the listing, and more. So this source code most likely contains all the information we are interested in!

Let’s dig into that line above a bit more. You can see that that bit of code has an opening tag (words between < and >, like <span>) and a closing tag (the same with a slash, like </span>). HTML source code generally stores its data between opening and closing tags like these. Tags are keywords that tell the web browser how to display or format the content. Above you can see that the information we want ($800) is stored between an opening and closing tag (<span> and </span>). In the opening tag, you can also see a very useful “class” (a special word that is sometimes included with opening tags): class="result-price". Since we want R to programmatically sort through all of the source code for the website to find apartment prices, maybe we can look for all the tags with the "result-price" class, and grab the information between the opening and closing tag. Indeed, take a look at another line of the source snippet above:

<span class="result-price">$2285</span>

It’s yet another price for an apartment listing, and the tags surrounding it have the "result-price" class. Wonderful! Now that we know what pattern we are looking for—a dollar amount between opening and closing tags that have the "result-price" class—we should be able to use code to pull out all of the matching patterns from the source code to obtain our data. This sort of “pattern” is known as a CSS selector (where CSS stands for cascading style sheet).

The above was a simple example of “finding the pattern to look for”; many websites are quite a bit larger and more complex, and so is their website source code. Fortunately, there are tools available to make this process easier. For example, SelectorGadget is an open-source tool that simplifies identifying the generating and finding of CSS selectors. At the end of the chapter in the additional resources section, we include a link to a short video on how to install and use the SelectorGadget tool to obtain CSS selectors for use in web scraping. After installing and enabling the tool, you can click the website element for which you want an appropriate selector. For example, if we click the price of an apartment listing, we find that SelectorGadget shows us the selector .result-price in its toolbar, and highlights all the other apartment prices that would be obtained using that selector (Figure 2.4).

Using the SelectorGadget on a Craigslist webpage to obtain the CCS selector useful for obtaining apartment prices.

Figure 2.4: Using the SelectorGadget on a Craigslist webpage to obtain the CCS selector useful for obtaining apartment prices.

If we then click the size of an apartment listing, SelectorGadget shows us the span selector, and highlights many of the lines on the page; this indicates that the span selector is not specific enough to capture only apartment sizes (Figure 2.5).

Using the SelectorGadget on a Craigslist webpage to obtain a CCS selector useful for obtaining apartment sizes.

Figure 2.5: Using the SelectorGadget on a Craigslist webpage to obtain a CCS selector useful for obtaining apartment sizes.

To narrow the selector, we can click one of the highlighted elements that we do not want. For example, we can deselect the “pic/map” links, resulting in only the data we want highlighted using the .housing selector (Figure 2.6).

Using the SelectorGadget on a Craigslist webpage to refine the CCS selector to one that is most useful for obtaining apartment sizes.

Figure 2.6: Using the SelectorGadget on a Craigslist webpage to refine the CCS selector to one that is most useful for obtaining apartment sizes.

So to scrape information about the square footage and rental price of apartment listings, we need to use the two CSS selectors .housing and .result-price, respectively. The selector gadget returns them to us as a comma-separated list (here .housing , .result-price), which is exactly the format we need to provide to R if we are using more than one CSS selector.

Stop! Are you allowed to scrape that website? Before scraping data from the web, you should always check whether or not you are allowed to scrape it! There are two documents that are important for this: the robots.txt file and the Terms of Service document. If we take a look at Craigslist’s Terms of Service document, we find the following text: “You agree not to copy/collect CL content via robots, spiders, scripts, scrapers, crawlers, or any automated or manual equivalent (e.g., by hand).” So unfortunately, without explicit permission, we are not allowed to scrape the website.

What to do now? Well, we could ask the owner of Craigslist for permission to scrape. However, we are not likely to get a response, and even if we did they would not likely give us permission. The more realistic answer is that we simply cannot scrape Craigslist. If we still want to find data about rental prices in Vancouver, we must go elsewhere. To continue learning how to scrape data from the web, let’s instead scrape data on the population of Canadian cities from Wikipedia. We have checked the Terms of Service document, and it does not mention that web scraping is disallowed. We will use the SelectorGadget tool to pick elements that we are interested in (city names and population counts) and deselect others to indicate that we are not interested in them (province names), as shown in Figure 2.7.

Using the SelectorGadget on a Wikipedia webpage.

Figure 2.7: Using the SelectorGadget on a Wikipedia webpage.

We include a link to a short video tutorial on this process at the end of the chapter in the additional resources section. SelectorGadget provides in its toolbar the following list of CSS selectors to use:

td:nth-child(5), 
td:nth-child(7), 
.infobox:nth-child(122) td:nth-child(1), 
.infobox td:nth-child(3)

Now that we have the CSS selectors that describe the properties of the elements that we want to target, we can use them to find certain elements in web pages and extract data.

Using rvest

Now that we have our CSS selectors we can use the rvest R package to scrape our desired data from the website. We start by loading the rvest package:

library(rvest)

Next, we tell R what page we want to scrape by providing the webpage’s URL in quotations to the function read_html:

page <- read_html("https://en.wikipedia.org/wiki/Canada")

The read_html function directly downloads the source code for the page at the URL you specify, just like your browser would if you navigated to that site. But instead of displaying the website to you, the read_html function just returns the HTML source code itself, which we have stored in the page variable. Next, we send the page object to the html_nodes function, along with the CSS selectors we obtained from the SelectorGadget tool. Make sure to surround the selectors with quotation marks; the function, html_nodes, expects that argument is a string. The html_nodes function then selects nodes from the HTML document that match the CSS selectors you specified. A node is an HTML tag pair (e.g., <td> and </td> which defines the cell of a table) combined with the content stored between the tags. For our CSS selector td:nth-child(5), an example node that would be selected would be:

<td style="text-align:left;background:#f0f0f0;">
<a href="/wiki/London,_Ontario" title="London, Ontario">London</a>
</td>

We store the result of the html_nodes function in the population_nodes variable. Note that below we use the paste function with a comma separator (sep=",") to build the list of selectors. The paste function converts elements to characters and combines the values into a list. We use this function to build the list of selectors to maintain code readability; this avoids having one very long line of code with the string "td:nth-child(5),td:nth-child(7),.infobox:nth-child(122) td:nth-child(1),.infobox td:nth-child(3)" as the second argument of html_nodes:

selectors <- paste("td:nth-child(5)",
             "td:nth-child(7)",
             ".infobox:nth-child(122) td:nth-child(1)",
             ".infobox td:nth-child(3)", sep = ",")

population_nodes <- html_nodes(page, selectors)
head(population_nodes)
## {xml_nodeset (6)}
## [1] <td style="text-align:left;background:#f0f0f0;"><a href="/wiki/London,_On ...
## [2] <td style="text-align:right;">543,551\n</td>
## [3] <td style="text-align:left;background:#f0f0f0;"><a href="/wiki/Halifax,_N ...
## [4] <td style="text-align:right;">465,703\n</td>
## [5] <td style="text-align:left;background:#f0f0f0;">\n<a href="/wiki/St._Cath ...
## [6] <td style="text-align:right;">433,604\n</td>

Note: head is a function that is often useful for viewing only a short summary of an R object, rather than the whole thing (which may be quite a lot to look at). For example, here head shows us only the first 6 items in the population_nodes object. Note that some R objects by default print only a small summary. For example, tibble data frames only show you the first 10 rows. But not all R objects do this, and that’s where the head function helps summarize things for you.

Next we extract the meaningful data—in other words, we get rid of the HTML code syntax and tags—from the nodes using the html_text function. In the case of the example node above, html_text function returns "London".

population_text <- html_text(population_nodes)
head(population_text)
## [1] "London"                 "543,551\n"              "Halifax"               
## [4] "465,703\n"              "St. Catharines–Niagara" "433,604\n"

Fantastic! We seem to have extracted the data of interest from the raw HTML source code. But we are not quite done; the data is not yet in an optimal format for data analysis. Both the city names and population are encoded as characters in a single vector, instead of being in a data frame with one character column for city and one numeric column for population (like a spreadsheet). Additionally, the populations contain commas (not useful for programmatically dealing with numbers), and some even contain a line break character at the end (\n). In Chapter 3, we will learn more about how to wrangle data such as this into a more useful format for data analysis using R.

2.8.2 Using an API

Rather than posting a data file at a URL for you to download, many websites these days provide an API that must be accessed through a programming language like R. The benefit of this is that data owners have much more control over the data they provide to users. However, unlike web scraping, there is no consistent way to access an API across websites. Every website typically has its own API designed especially for its own use case. Therefore we will just provide one example of accessing data through an API in this book, with the hope that it gives you enough of a basic idea that you can learn how to use another API if needed.

In particular, in this book we will show you the basics of how to use the rtweet package in R to access data from the Twitter API. One nice feature of this particular API is that you don’t need a special token to access it; you simply need to make an account with them. Your access to the data will then be authenticated and controlled through your account username and password. If you have a Twitter account already (or are willing to make one), you can follow along with the examples that we show here. To get started, load the rtweet package:

library(rtweet)

This package provides an extensive set of functions to search Twitter for tweets, users, their followers, and more. Let’s construct a small data set of the last 400 tweets and retweets from the @tidyverse account. A few of the most recent tweets are shown in Figure 2.8.

The tidyverse account Twitter feed.

Figure 2.8: The tidyverse account Twitter feed.

Stop! Think about your API usage carefully!

When you access an API, you are initiating a transfer of data from a web server to your computer. Web servers are expensive to run and do not have infinite resources. If you try to ask for too much data at once, you can use up a huge amount of the server’s bandwidth. If you try to ask for data too frequently—e.g., if you make many requests to the server in quick succession—you can also bog the server down and make it unable to talk to anyone else. Most servers have mechanisms to revoke your access if you are not careful, but you should try to prevent issues from happening in the first place by being extra careful with how you write and run your code. You should also keep in mind that when a website owner grants you API access, they also usually specify a limit (or quota) of how much data you can ask for. Be careful not to overrun your quota! In this example, we should take a look at the Twitter website to see what limits we should abide by when using the API.

Using rtweet

After checking the Twitter website, it seems like asking for 400 tweets one time is acceptable. So we can use the get_timelines function to ask for the last 400 tweets from the @tidyverse account.

tidyverse_tweets <- get_timelines('tidyverse', n=400)

When you call the get_timelines for the first time (or any other rtweet function that accesses the API), you will see a browser pop-up that looks something like Figure 2.9.

The rtweet authorization prompt.

Figure 2.9: The rtweet authorization prompt.

This is the rtweet package asking you to provide your own Twitter account’s login information. When rtweet talks to the Twitter API, it uses your account information to authenticate requests; Twitter then can keep track of how much data you’re asking for, and how frequently you’re asking. If you want to follow along with this example using your own Twitter account, you should read over the list of permissions you are granting rtweet very carefully and make sure you are comfortable with it. Note that rtweet can be used to manage most aspects of your account (make posts, follow others, etc.), which is why rtweet asks for such extensive permissions. If you decide to allow rtweet to talk to the Twitter API using your account information, then input your username and password and hit “Sign In.” Twitter will probably send you an email to say that there was an unusual login attempt on your account, and in that case you will have to take the one-time code they send you and provide that to the rtweet login page too.

Note: Every API has its own way to authenticate users when they try to access data. Many APIs require you to sign up to receive a token, which is a secret password that you input into the R package (like rtweet) that you are using to access the API.

With the authentication setup out of the way, let’s run the get_timelines function again to actually access the API and take a look at what was returned:

tidyverse_tweets <- get_timelines('tidyverse', n = 400)
tidyverse_tweets
## # A tibble: 293 × 71
##    created_at          reply_to_status_id quoted_created_at reply_to_user_id
##    <dttm>              <lgl>              <dttm>            <lgl>           
##  1 2021-04-29 11:59:04 NA                 NA                NA              
##  2 2021-04-26 17:05:47 NA                 NA                NA              
##  3 2021-04-24 09:13:12 NA                 NA                NA              
##  4 2021-04-18 06:06:21 NA                 NA                NA              
##  5 2021-04-12 05:48:33 NA                 NA                NA              
##  6 2021-04-08 17:45:34 NA                 NA                NA              
##  7 2021-04-01 05:01:38 NA                 NA                NA              
##  8 2021-03-25 06:05:49 NA                 NA                NA              
##  9 2021-03-18 17:16:21 NA                 NA                NA              
## 10 2021-03-12 19:12:49 NA                 NA                NA              
## # … with 283 more rows, and 67 more variables: reply_to_screen_name <lgl>,
## #   is_quote <lgl>, is_retweet <lgl>, quoted_verified <lgl>,
## #   retweet_verified <lgl>, protected <lgl>, verified <lgl>,
## #   account_lang <lgl>, profile_background_url <lgl>, user_id <dbl>,
## #   status_id <dbl>, screen_name <chr>, text <chr>, source <chr>,
## #   ext_media_type <lgl>, lang <chr>, quoted_status_id <dbl>,
## #   quoted_text <chr>, quoted_source <chr>, quoted_user_id <dbl>, …

The data has quite a few variables! (Notice that the output above shows that we have a data table with 293 rows and 71 columns). Let’s reduce this down to a few variables of interest: created_at, retweet_screen_name, is_retweet, and text.

tidyverse_tweets <- select(tidyverse_tweets, 
                           created_at, 
                           retweet_screen_name,
                           is_retweet, 
                           text)

tidyverse_tweets
## # A tibble: 293 × 4
##    created_at          retweet_screen_name is_retweet text                      
##    <dttm>              <chr>               <lgl>      <chr>                     
##  1 2021-04-29 11:59:04 yutannihilat_en     TRUE       "Just curious, after the …
##  2 2021-04-26 17:05:47 statwonk            TRUE       "List columns provide a t…
##  3 2021-04-24 09:13:12 topepos             TRUE       "A new release of the {{r…
##  4 2021-04-18 06:06:21 ninarbrooks         TRUE       "Always typing `? pivot_l…
##  5 2021-04-12 05:48:33 rfunctionaday       TRUE       "If you are fluent in {dp…
##  6 2021-04-08 17:45:34 RhesusMaCassidy     TRUE       "R-Ladies of Göttingen! T…
##  7 2021-04-01 05:01:38 dvaughan32          TRUE       "I am ridiculously excite…
##  8 2021-03-25 06:05:49 rdpeng              TRUE       "New book out on using ti…
##  9 2021-03-18 17:16:21 SolomonKurz         TRUE       "The 0.2.0 version of my …
## 10 2021-03-12 19:12:49 hadleywickham       TRUE       "rvest 1.0.0 out now! — h…
## # … with 283 more rows

If you look back up at the image of the @tidyverse Twitter page, you will recognize the text of the most recent few tweets in the above data frame. In other words, we have successfully created a small data set using the Twitter API—neat! This data is also quite different from what we obtained from web scraping; it is already well-organized into a tidyverse data frame (although not every API will provide data in such a nice format). From this point onward, the tidyverse_tweets data frame is stored on your machine, and you can play with it to your heart’s content. For example, you can use write_csv to save it to a file and read_csv to read it into R again later; and after reading the next few chapters you will have the skills to compute the percentage of retweets versus tweets, find the most oft-retweeted account, make visualizations of the data, and much more! If you decide that you want to ask the Twitter API for more data (see the rtweet page for more examples of what is possible), just be mindful as usual about how much data you are requesting and how frequently you are making requests.

2.9 Exercises

Practice exercises for the material covered in this chapter can be found in the accompanying worksheets repository in the “Reading in data locally and from the web” row. You can launch an interactive version of the worksheet in your browser by clicking the “launch binder” button. You can also preview a non-interactive version of the worksheet by clicking “view worksheet.” If you instead decide to download the worksheet and run it on your own machine, make sure to follow the instructions for computer setup found in Chapter 13. This will ensure that the automated feedback and guidance that the worksheets provide will function as intended.

2.10 Additional resources

  • The readr documentation provides the documentation for many of the reading functions we cover in this chapter. It is where you should look if you want to learn more about the functions in this chapter, the full set of arguments you can use, and other related functions. The site also provides a very nice cheat sheet that summarizes many of the data wrangling functions from this chapter.
  • Sometimes you might run into data in such poor shape that none of the reading functions we cover in this chapter work. In that case, you can consult the data import chapter from R for Data Science (Wickham and Grolemund 2016), which goes into a lot more detail about how R parses text from files into data frames.
  • The here R package (Müller 2020) provides a way for you to construct or find your files’ paths.
  • The readxl documentation provides more details on reading data from Excel, such as reading in data with multiple sheets, or specifying the cells to read in.
  • The rio R package (Leeper 2021) provides an alternative set of tools for reading and writing data in R. It aims to be a “Swiss army knife” for data reading/writing/converting, and supports a wide variety of data types (including data formats generated by other statistical software like SPSS and SAS).
  • A video from the Udacity course Linux Command Line Basics provides a good explanation of absolute versus relative paths.
  • If you read the subsection on obtaining data from the web via scraping and APIs, we provide two companion tutorial video links for how to use the SelectorGadget tool to obtain desired CSS selectors for:
  • The polite R package (Perepolkin 2021) provides a set of tools for responsibly scraping data from websites.

References

Kearney, Michael. 2019. rtweet R package. https://github.com/ropensci/rtweet.
Leeper, Thomas. 2021. rio R package. https://cloud.r-project.org/web/packages/rio/index.html.
Müller, Kirill. 2020. here R package. https://here.r-lib.org/.
Perepolkin, Dmytro. 2021. polite R package. https://dmi3kno.github.io/polite/.
Real Time Statistics Project. 2021. “Internet Live Stats: Google Search Statistics.” https://www.internetlivestats.com/google-search-statistics/.
———. 2021a. rvest R package. https://rvest.tidyverse.org/.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2021. dplyr R package. https://dplyr.tidyverse.org/.
Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly. https://r4ds.had.co.nz/.