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Introduction to Machine Learning in R
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R – Lists

A list in R programming is a generic object consisting of an ordered collection of objects. Lists are one-dimensional , heterogeneous data structures.

The list can be a list of vectors , a list of matrices, a list of characters, a list of functions , and so on.

A list is a vector but with heterogeneous data elements. A list in R is created with the use of the list() function .

R allows accessing elements of an R list with the use of the index value. In R, the indexing of a list starts with 1 instead of 0.

Creating a List

To create a List in R you need to use the function called “ list() “.

In other words, a list is a generic vector containing other objects. To illustrate how a list looks, we take an example here. We want to build a list of employees with the details. So for this, we want attributes such as ID, employee name, and the number of employees.

Example:

Naming List Components

Naming list components make it easier to access them.

Accessing R List Components

We can access components of an R list in two ways.

1. Access components by names:

All the components of a list can be named and we can use those names to access the components of the R list using the dollar command.

Example:

2. Access components by indices:

We can also access the components of the R list using indices.

To access the top-level components of a R list we have to use a double slicing operator “ [[ ]] ” which is two square brackets and if we want to access the lower or inner-level components of a R list we have to use another square bracket “ [ ] ” along with the double slicing operator “ [[ ]] “.

Modifying Components of a List

A R list can also be modified by accessing the components and replacing them with the ones which you want.

Concatenation of lists

Two R lists can be concatenated using the concatenation function. So, when we want to concatenate two lists we have to use the concatenation operator.

Syntax:

list = c(list, list1) list = the original list list1 = the new list

Adding Item to List

To add an item to the end of list, we can use append() function.

Deleting Components of a List

To delete components of a R list, first of all, we need to access those components and then insert a negative sign before those components. It indicates that we had to delete that component.

Merging list

We can merge the R list by placing all the lists into a single list.

Converting List to Vector

Here we are going to convert the R list to vector, for this we will create a list first and then unlist the list into the vector.

R List to matrix

We will create matrices using matrix() function in R programming. Another function that will be used is unlist() function to convert the lists into a vector.

In this article we have covered Lists in R, we have covered list operations like creating, naming, merging and deleting a list in R language. R list is an important concept and should not be skipped.

Hope you learnt about R lists and it’s operations in this article.

Also Check:

R – Matrices

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6 Lists and data frames

An R list is an object consisting of an ordered collection of objects known as its components .

There is no particular need for the components to be of the same mode or type, and, for example, a list could consist of a numeric vector, a logical value, a matrix, a complex vector, a character array, a function, and so on. Here is a simple example of how to make a list:

Components are always numbered and may always be referred to as such. Thus if Lst is the name of a list with four components, these may be individually referred to as Lst[[1]] , Lst[[2]] , Lst[[3]] and Lst[[4]] . If, further, Lst[[4]] is a vector subscripted array then Lst[[4]][1] is its first entry.

If Lst is a list, then the function length(Lst) gives the number of (top level) components it has.

Components of lists may also be named , and in this case the component may be referred to either by giving the component name as a character string in place of the number in double square brackets, or, more conveniently, by giving an expression of the form

for the same thing.

This is a very useful convention as it makes it easier to get the right component if you forget the number.

So in the simple example given above:

Lst$name is the same as Lst[[1]] and is the string "Fred" ,

Lst$wife is the same as Lst[[2]] and is the string "Mary" ,

Lst$child.ages[1] is the same as Lst[[4]][1] and is the number 4 .

Additionally, one can also use the names of the list components in double square brackets, i.e., Lst[["name"]] is the same as Lst$name . This is especially useful, when the name of the component to be extracted is stored in another variable as in

It is very important to distinguish Lst[[1]] from Lst[1] . [[...]] is the operator used to select a single element, whereas [...] is a general subscripting operator. Thus the former is the first object in the list Lst , and if it is a named list the name is not included. The latter is a sublist of the list Lst consisting of the first entry only. If it is a named list, the names are transferred to the sublist.

The names of components may be abbreviated down to the minimum number of letters needed to identify them uniquely. Thus Lst$coefficients may be minimally specified as Lst$coe and Lst$covariance as Lst$cov .

The vector of names is in fact simply an attribute of the list like any other and may be handled as such. Other structures besides lists may, of course, similarly be given a names attribute also.

6.2 Constructing and modifying lists

New lists may be formed from existing objects by the function list() . An assignment of the form

sets up a list Lst of m components using object_1 , …, object_m for the components and giving them names as specified by the argument names, (which can be freely chosen). If these names are omitted, the components are numbered only. The components used to form the list are copied when forming the new list and the originals are not affected.

Lists, like any subscripted object, can be extended by specifying additional components. For example

6.2.1 Concatenating lists

When the concatenation function c() is given list arguments, the result is an object of mode list also, whose components are those of the argument lists joined together in sequence.

Recall that with vector objects as arguments the concatenation function similarly joined together all arguments into a single vector structure. In this case all other attributes, such as dim attributes, are discarded.

6.3 Data frames

A data frame is a list with class "data.frame" . There are restrictions on lists that may be made into data frames, namely

The components must be vectors (numeric, character, or logical), factors, numeric matrices, lists, or other data frames.
Matrices, lists, and data frames provide as many variables to the new data frame as they have columns, elements, or variables, respectively.
Vector structures appearing as variables of the data frame must all have the same length , and matrix structures must all have the same number of rows .

A data frame may for many purposes be regarded as a matrix with columns possibly of differing modes and attributes. It may be displayed in matrix form, and its rows and columns extracted using matrix indexing conventions.

6.3.1 Making data frames

Objects satisfying the restrictions placed on the columns (components) of a data frame may be used to form one using the function data.frame :

A list whose components conform to the restrictions of a data frame may be coerced into a data frame using the function as.data.frame()

The simplest way to construct a data frame from scratch is to use the read.table() function to read an entire data frame from an external file. This is discussed further in Reading data from files .

6.3.2 attach() and detach()

The $ notation, such as accountants$home , for list components is not always very convenient. A useful facility would be somehow to make the components of a list or data frame temporarily visible as variables under their component name, without the need to quote the list name explicitly each time.

The attach() function takes a ‘database’ such as a list or data frame as its argument. Thus suppose lentils is a data frame with three variables lentils$u , lentils$v , lentils$w . The attach

places the data frame in the search path at position 2, and provided there are no variables u , v or w in position 1, u , v and w are available as variables from the data frame in their own right. At this point an assignment such as

does not replace the component u of the data frame, but rather masks it with another variable u in the workspace at position 1 on the search path. To make a permanent change to the data frame itself, the simplest way is to resort once again to the $ notation:

However the new value of component u is not visible until the data frame is detached and attached again.

To detach a data frame, use the function

More precisely, this statement detaches from the search path the entity currently at position 2. Thus in the present context the variables u , v and w would be no longer visible, except under the list notation as lentils$u and so on. Entities at positions greater than 2 on the search path can be detached by giving their number to detach , but it is much safer to always use a name, for example by detach(lentils) or detach("lentils")

Note: In R lists and data frames can only be attached at position 2 or above, and what is attached is a copy of the original object. You can alter the attached values via assign , but the original list or data frame is unchanged.

6.3.3 Working with data frames

A useful convention that allows you to work with many different problems comfortably together in the same workspace is

gather together all variables for any well defined and separate problem in a data frame under a suitably informative name;
when working with a problem attach the appropriate data frame at position 2, and use the workspace at level 1 for operational quantities and temporary variables;
before leaving a problem, add any variables you wish to keep for future reference to the data frame using the $ form of assignment, and then detach() ;
finally remove all unwanted variables from the workspace and keep it as clean of left-over temporary variables as possible.

In this way it is quite simple to work with many problems in the same directory, all of which have variables named x , y and z , for example.

6.3.4 Attaching arbitrary lists

attach() is a generic function that allows not only directories and data frames to be attached to the search path, but other classes of object as well. In particular any object of mode "list" may be attached in the same way:

Anything that has been attached can be detached by detach , by position number or, preferably, by name.

6.3.5 Managing the search path

The function search shows the current search path and so is a very useful way to keep track of which data frames and lists (and packages) have been attached and detached. Initially it gives

where .GlobalEnv is the workspace. 1

1 See the on-line help for autoload for the meaning of the second term.

After lentils is attached we have

and as we see ls (or objects ) can be used to examine the contents of any position on the search path.

Finally, we detach the data frame and confirm it has been removed from the search path.

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R Tutorials

R Return Value from Function

A List is a collection of similar or different types of data.

In R, we use the list() function to create a list. For example,

list1 - list of integers
list2 - list containing string, integer, and boolean value
Access List Elements in R

In R, each element in a list is associated with a number. The number is known as a list index.

We can access elements of a list using the index number (1, 2, 3 …) . For example,

In the above example, we have created a list named list1 .

Here, we have used the vector index to access the vector elements

list1[1] - access the first element 24
list1[4] - accesses the third element "Nepal"

Note : In R, the list index always starts with 1 . Hence, the first element of a list is present at index 1 , second element at index 2 and so on.

Modify a List Element in R

To change a list element, we can simply reassign a new value to the specific index. For example,

Here, we have reassigned a new value to index 2 to change the list element from "Sabby" to "Cathy" .

Add Items to R List

We use the append() function to add an item at the end of the list. For example,

In the above example, we have created a list named list1 . Notice the line,

Here, append() adds 3.14 at the end of the list.

Remove Items From a List in R

R allows us to remove items for a list. We first access elements using a list index and add negative sign - to indicate we want to delete the item. For example,

[-1] - removes 1st item
[-2] - removes 2nd item and so on.

Here, list[-4] removes the 4th item of list1 .

Length of R List

In R, we can use the length() function to find the number of elements present inside the list. For example,

Here, we have used the length() function to find the length of list1 . Since there are 4 elements in list1 so length() returns 4 .

Loop Over a List

In R, we can also loop through each element of the list using the for loop . For example,

Check if Element Exists in R List

In R, we use the %in% operator to check if the specified element is present in the list or not and returns a boolean value.

TRUE - if specified element is present in the list
FALSE - if specified element is not present in the list

For example,

"Larry" is present in list1 , so the method returns TRUE
"Kinsley" is not present in list1 , so the method returns FALSE

Introduction

Sorry about that.

Create a list in R

If you don’t know how to create a list in R , you just need to use the list function as follows, specifying the objects you want to join inside your list.

Naming lists in R

As other R data structures, you can name your list element objects in order to identify or having an easier access to the elements. The following block of code shows how to create a named list in R.

Extract elements from list in R

Now, we are going to index the list elements in order to access the data we want. For that purpose, you can extract the list elements with single or double brackets .

However, in case you have named your list elements you can use the previous way to get values, specify the names of the elements of the list you want to access inside brackets, or use the $ operator as in the following examples:

If you use double square brackets instead of single square brackets for subsetting a list, the output class will be simplified as much as possible.

Length of a list in R

The length of a list is the number of R objects inside the list . With this in mind, you can obtain the number of list elements with the length function. However, if you want to know the length of any object inside the list, first you will need to extract the corresponding element.

Create empty list in R

Sometimes you need to fill a list inside a for loop . For that purpose, it is interesting to create an empty list in R . Although you can create an empty list with the list function, if you want a prespecified length you need to use the vector function and specify the final length in the length argument to create a list of length n .

So if you want to fill this list within a for loop, you could do as follows:

Append to list in R

If you need to add an element to a list , you can specify the object you want to add at the next index of the length of the list. If the list length is 3, you can add the new object to the fourth slot.

Now, print your new list and you will have the following output:

Remove element from list in R

In order to delete some elements from a list you can set the index of the element of the list you want to remove as NULL , or indicate inside brackets the index with the - operator. If you want to remove several elements at once, combine them with the c function.

Creating a list of lists

You can also nest lists in R . This means that in all or some elements of your root list you will have a list. That list can also store other lists or other types of objects. You can achieve this with the list function, adding new lists as list elements. In this example we are going to add our two previously created lists inside a new list.

Printing the nested list will result into the following:

Accessing list of list elements

Accessing elements of lists inside lists is similar to access the elements to one list . In fact, if you access one list and store it inside an object, the process is the same as presented in the single-list element extraction section. If not, you will have to know and follow the level of hierarchy of the list . You have some examples in the following code block:

Combine lists in R

Two or more R lists can be joined together. For that purpose, you can use the append , the c or the do.call functions. When combining the lists this way, the second list elements will be appended at the end of the first list .

Merge list with same names

When you have two or more lists with the same element names , you can merge the list elements withing a list of the same length as the previous ones, where the elements with the same name will be concatenated. First, we are going to copy our named list in other variable:

Now, you can use the Map or the mapply functions with those lists the following way.

You can see the output of the list with concatenated elements:

Note that the element C is no longer a matrix .

Compare two lists in R

You can compare two lists in different ways. This include obtaining the common elements, the different elements or comparing the equal ones. Before each explanation we are going to copy our first list and change its first element.

Common elements in two R lists

First, if you want to obtain the same elements in two lists you can use the intersect function, that will return a list with the common elements of the lists.

Different elements in two R lists

Second, with the setdiff function you will obtain the different items between your lists . Note that the order you pass the lists is important .

Compare equal elements in two R lists

Third, you can also compare the equal items of two lists with %in% operator or with the compare.list function of the useful library. The result will be a logical vector.

List to vector

Sometimes you need to convert your list to other object types. The most common conversion is converting a list to vector . For that purpose, you can use the unlist function.

If you prefer to convert the full list to vector, set the use.names to FALSE .

Note that in this case all the elements are now a characters.

In addition, the purrr library have some functions similar to the unlist function but in this case you can specify the output class . In this example we are converting our new list to double and then to character.

List to dataframe

Suppose you have a list containing data of some variables. In that case, you may want to convert your list to a data frame. In order to achieve it, you can use the unlist , matrix and data.frame functions or the do.call function as follows.

If you are working with characters, set stringsAsFactors = FALSE to avoid character columns converted to factor.

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Introduction to Programming with R

Chapter 9 lists.

In terms of ‘data types’ (objects containing data) we have only been working with atomic vectors and matrices which are both homogenous objects – they can always only contain data of one specific type.

Figure 9.1: As we will learn, data frames are typically lists of atomic vectors of the same length.

In this chapter we will learn about lists and data frames which allow to store heterogenous data, e.g., integers and characters both in the same object. This chapter does not cover all details of lists, but we will learn all the important aspects we need in the next chapter when learning about data frames as data frames are based on lists (similar to matrices being based on vectors).

Quick reminder: Frequently used data types and how they can be distinguished by their dimensionality and whether they are homogeneous (all elements of the same type) vs. heterogeneous (elements can be of different types).

Dimension	Homogenous	Heterogenous
1	Atomic vectors	Lists
2	Matrix	Data frame
$\ge 1$	Array

9.1 List introduction

As ( atomic ) vectors , lists are also sequences of elements . However, in contrast to (atomic) vectors, lists allow for heterogenity. Technically a list is a generic vector but often only called ‘ list ’. We will use the same term in this book (atomic vectors: vectors; generic vectors: lists).

Basis : Lists serve as the basis for most complex objects in R .

Data frames : Lists of variables of the same length, often (but not necessarily) atomic vectors.
Fitted regression models : Lists of different elements such as the model parameters, covariance matrix, residuals, but also more technical information such as the regression terms or certain matrix decompositions.

Difference : The difference between vectors and lists.

Vector : All elements must have the same basic type.
List : Different elements can have different types, including vectors, matrices, lists or more complex objects.

As lists are ‘generic vectors’, empty lists can also be created using the vector() function (compare Creating vectors ).

Elements of (unnamed) lists are indexed by [[...]] , while we had [...] for vectors. The empty list above has two elements where both (first [[1]] and second [[2]] ) both contain NULL .

9.2 Creating lists

We start right away with constructing a few simple lists for illustration. While vectors can be created using c() , lists are most often constructed using the function list() .

A vector : Vector of length 2.

A list : List containing values of the same types/classes.

This list with two elements ( [[1]] , [[2]] ) contains two vectors, each of length one, indicated by [1] (first vector element).

Another list : Store objects of different types/classes into a list.

The last example shows a list of length 3 which contains a numeric vector of length $1$ (first element; [[1]] ), a character vector of length $2$ (second element; [[2]] ), and a matrix of dimension $2 \times 3$ (third element; [[3]] ).

The two functions c() and list() work very similarly, except that c(a, b) where a and b are vectors performs coercion to convert all values into one specific type (see Vectors: Coercion ).

9.3 Recursive structure

A more practical example : We have some information about Peter Falk , a famous actor who played “Lieutenant Columbo” in the long-running TV series Columbo between 1968–2003.

We would like to store all the information in one single object. As we have to deal with both characters (name; month of birth) and numeric values (year and day of birth), we need an object which allows for heterogenity – a list.

For named lists, the representation (print) changes again compared to unnamed lists. The elements are now indicated by e.g., $name or $date_of_birth$year which we can use to access specific elements. We will come back later when Subsetting lists .

Our new object person is a list which contains a named vector ( $name ) and a second element ( $date_of_birth ) which itself contains another named list. This is called a recursive structure , a list can contain lists (can contain lists (can …)). The figure below shows the structure of the object person with the two lists in green, and the different (integer/character) vectors in blue.

Figure 9.2: Graphical representation of the recursive list object person .

A nice way to get an overview of potentially complex (list-)objects is by using the function str() (structure) which we have already seen in the vectors chapter. str() returns us a text-representation similar to the image shown above.

How to read : the (first-level) list has two elements, one called name , one date_of_birth . The name element itself contains a named character of length $2$ , the second element date_of_birth is again a list with $3$ named elements ( year , month , day ) containing unnamed (plain) vectors of length $1$ (numeric, character, numeric). The indent shows the recursive structure, the more to the right of the $ , the deeper a specific entry in the list.

9.4 List attributes

As all other objects lists always have a specific type and length (object properties). Besides these mandatory properties the default attributes for lists are:

Class : Objects of class "list" .
Names : Can have names (optional; just like vectors).

Let us investigate the person object from above:

The is.*() function family can be used to check the object.

is.list() : Always returns TRUE for lists.
is.vector() : As lists are generic vectors they also count as vectors, as long as they only have class, length, type and names (optional).
A list is never numeric, integer, character, or logical, no matter what the list contains.

9.5 Subsetting lists

Subsetting on lists works slightly different than on vectors and matrices. The basic concepts stay the same, however, due to the more complex structure of the object, additional operators for subsetting become available.

Operator	Return	Description
		Select sub-list containing one or more elements. The index vector can be integer (possibly negative), character, or logical.
		Select the of a single list element if is a single (positive) integer or a single character.
		Select the of a single list element using the name of the element (without quotes).
		If is a vector, nested/recursive subsetting takes place.

Note that there is a distinct difference between single brackets ( [...] ) and double brackets ( [[...]] ). The first always returns a list which is a subset of the original object to be subsetted, while the latter returns the content of these elements.

Using [i] : The method is similar to vector subsetting except that the result will not be the content of the elements specified, but a sub-list. Let us call persons[1] and see what we get:

The result of person[1] is again a list, but only contains the first entry of the original object person . In the same way we can use person["name"] or person[c(TRUE, FALSE)] . This also works with vectors, e.g., extracting elements 2:1 (both but reverse order):

A negative index ( person[-1] ) can be used to get all but the first element (does not work with characters). The result is again a sub-list (like for positive indices).

Using [[i]] , single value : This subsetting type is most comparable to vector subsetting. Instead of a sub-list, we will get the content of the element, in this case a named character vector of length $2$ .

The same can be achieved using person[["name"]] on named lists. Note that subsetting with negative indices ( person[[-1]] ) does not work in combination with double brackets.

Using the $ operator : Most commonly used when working with named lists is the $ operator. This operator is called the dollar operator . Instead of calling person[["name"]] we can also call person$name as long as the name does not contain blanks or special characters. Note that there is no blank before/after the $ operator (not as shown in the output of str() ).

Multiple $ operators can also be combined. If we are interested in the month of birth ( month ) which is stored within date_of_birth we can use:

How to read :

Right to left : Return month from date_of_birth of the object person .
Left to right : Inside object person access the element date_of_birth , inside date_of_birth access element month .

Using [[j]] with vectors : Take care, something unexpected happens. As an example, let us call person[[c(1, 2)]] . We could think this returns us person[[1]] and person[[2]] , but that’s not the case. Instead, nested or recursive subsetting is performed. The two indices ( c(1, 2) ) are used as indices for different depths of the recursive list.

What happened: The first element of the vector (here 1 ) is used for the top-level list. Our first entry the vector name . The second element of the vector ( 2 ) is then used to extract the second element of whatever name contains. It is the same as:

Note : We will not use this often, but keep it in mind if you run into interesting results when subsetting lists or data frames. The same happens if you use a vector, e.g., person[[c("name", "last_name")]] or person[[c("date_of_birth", "month")]] .

Exercise 9.1 Practicing subsetting on lists : The following object demo is a list with information about two persons, Frank and Petra (simply copy&paste it into your R session).

How do we get Franks location?
Try demo["Frank"]$location (will return NULL ). Why doesn’t this work?
How many kids does Frank have (use code to answer)?
Our friend Petra moves from Birmingham to Vienna. Change her location (inside demo ) to Vienna.

Solution . Franks location : demo is a list with two elements, one called "Frank" . Thus, we can access the first list element using demo$Frank . This returns the content of this list element which itself is, again, a list. In there, we have the location we are looking for.

Thus, one option to get Franks location is to use:

Alternatively, we could use brackets and subsetting by name. Warning: we need double brackets to access the content .

Try demo["Frank"]$location : This will not work. The reason is that we only use single brackets!

demo["Frank"] returns a sub-list of demo which now only contains "Frank" (no longer "Petra" ). However, we do not get the content or information for Frank. Let see:

The last line looks werid but always only just extract "Frank" from itself but does not access the list element for "Frank" . Thus, when we try to access location (which does not exist on this level) we get a NULL in return.

How many kids does Frank have? To answer this question, we need to find out how long the kids vector is. Again, we can access this specific element in different ways, the most easy one:

Petra moves to Vienna : You can use any subsetting technique and assign a new value. I will stick to the $ operator and do the following:

9.6 Replacing/deleting elements

Replacement functions are available for all subset types above which can be used to overwrite elements in an existing list or add new elements to a list.

As an example, let us replace the element $name in the person object. We use subsetting with the $ operator and assign (store) a new object. As person$name exists, it will be replaced.

Or replace the month in the date of birth with an integer 9L instead of "September" :

The same way, new elements can be added. If the element we assign an object to does not yet exist, it will be added to the original list object. Let us add a job element containing "Actor" :

Delete elements : To delete an element, we simply have to replace it with a NULL object. An example using a very simple list:

As you can see it is possible that a list element can contain NULL (see element c ) but if assigned ( x$a <- NULL ) R will remove the element completely (not storing NULL on it).

Exercise 9.2 Practicing replacement : As in the previous exercise we will use the following list to work with. The list contains information about Petra and Frank.

We need to update this list and add or change some of the elements.

Petra moves from Birmingham to Vienna. Update her location.
Frank just got a newborn baby called "Malena" . Add her name to the kids vector.
Add a third person called ‘Regina’, located in ‘Sydney’. She has one child called ‘Lea’ and works as a ‘Teacher’.

Solution . Petra moves to Vienna : You can use any subsetting methods we have just seen. In this solution we will stick to the $ operator. All we have to do is to access Petras location, and assign a new value.

Frank got a third child : Here, we could do the same as for Petra and simply assign a new vector with all three kids to Frank ( demo$Frank$kids <- c("Peter", "Paul", "Malena") ). However, this is not super nice (hard-coded).

Instead we use c() and combine the vector containing the first two kids with the new born, and store the vector (combination of subsetting and replacement) as follows:

Adding Regina : Regina is not yet in our list, however, we can assign new elements the same way we replace elements. If the element exists it will be overwritten. If it does not exist, it will be added. In this case we want to add a new list to the existing object demo called $Regina :

9.7 Combining lists

Multiple lists can be combined using either c() or list() .

c(<list 1>, <list 2>) : Creates a new list by combining all elements from the two lists. The result is a list with a length of the number of elements from <list 1> and <list 2> combined.
list(<list 1>, <list 2>) : Creates a new list of length 2 , where the first element contains <list 1> , the second <list 2> .

Example: Using two lists list1 and list2 (both of length 2).

The latter results in a recursive list where each element from the list res2 itself contains a list with two elements. As shown, we can also name the elements (similar to cbind() / rbind() when creating matrices; Matrices: Combining objects ). Note that the naming of the list-elements has a different effect when using c() (try c(list_one = list1, list_two = list2) ).

9.8 Summary

Just as a brief summary to recap the new content:

Creating lists : Using the function list() .
Name attribute : Lists can be named or unnamed.
Heterogenity : Allows to store objects of different types.
Replacement : Subsetting can be used to replace existing elements, add new elements, or delete elemets (assigning NULL ).
Recursive lists : Lists can be recursive (lists containing lists containing lists …).

An overview of the different subsetting methods we have learned for different objects (vectors, matrices, and lists).

Subset	By index	By name	Logical
Element			[possible]
Element	or		[possible]
Row			[possible]
Column			[possible]
List			[possible]
Element		or	[not possible]
Element (recursive)			[not possible]

Most subsetting methods also work with vectors (vectors of indices or names). You will see that we can re-use most of this when working with data frames, our next topic.

What is a List?

Vectors and matrices are incredibly useful data structure in R, but they have one distinct limitation: they can store only one type of data.

Lists, however, can store multiple types of values at once. A list can contain a numeric matrix, a logical vector, a character string, a factor object and even another list.

Create a List

Creating a list is much like creating a vector; just pass a comma-separated sequence of elements to the list() function.

The best way to understand the contents of a list is to use the structure function str() . It provides a compact display of the internal structure of a list.

Nested List

A list can contain sublists, which in turn can contain sublists themselves, and so on. This is known as nested list or recursive vectors.

Subsetting List by Position

There are two ways to extract elements from a list:

Using [[]] gives you the element itself.
Using [] gives you a list with the selected elements.

You can use [] to extract either a single element or multiple elements from a list. However, the result will always be a list.

You can use [[]] to extract only a single element from a list. Unlike [] , [[]] gives you the element itself.

You can’t use logical vectors or negative numbers as indices when using [[]]

Difference Between Single Bracket [] and Double Bracket [[]]

The difference between [] and [[]] is really important for lists, because [[]] returns the element itself while [] returns a list with the selected elements.

The difference becomes clear when we inspect the structure of the output – one is a character and the other one is a list.

The difference becomes annoyingly obvious when we cat the value. As you know cat() can print any value except the structured object.

Subsetting List by Names

Each list element can have a name. You can access individual element by specifying its name in double square brackets [[]] or use $ operator.

$ works similarly to [[]] except that you don’t need to use quotes.

Subsetting Nested List

You can access individual items in a nested list by using the combination of [[]] or $ operator and the [] operator.

Modify List Elements

Modifying a list element is pretty straightforward. You use either the [[]] or the $ to access that element, and simply assign a new value.

You can modify components using [] as well, but you have to assign a list of components.

Using [] allows you to modify more than one component at once.

Add Elements to a List

You can use same method for modifying elements and adding new one. If the element is already present in the list, it is updated else, a new element is added to the list.

By using append() method you can append one or more elements to the list.

Remove an Element from a List

To remove a list element, select it by position or by name, and then assign NULL to it.

Using [] , you can delete more than one component at once.

By using a logical vector, you can remove list elements based on the condition.

Combine Lists

The c() does a lot more than just creating vectors. It can be used to combine lists into a new list as well.

Flatten a List into a Vector

Basic statistical functions work on vectors but not on lists.

For example, you cannot directly compute the mean of list of numbers. In that case, you have to flatten the list into a vector using unlist() first and then compute the mean of the result.

Find List Length

To find the length of a list, use length() function.

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Understanding lists in r programming.

Posted on December 18, 2023 by M. Fatih Tüzen in R bloggers | 0 Comments

Introduction

R, a powerful statistical programming language, offers various data structures, and among them, lists stand out for their versatility and flexibility. Lists are collections of elements that can store different data types, making them highly useful for managing complex data. Thinking of lists in R as a shopping basket, imagine you’re at a store with a basket in hand. In this case:

Items in the Basket : Each item you put in the basket represents an element in the list. These items can vary in size, shape, or type, just like elements in a list can be different data structures.

Versatility in Choices : Just as you can put fruits, vegetables, and other products in your basket, a list in R can contain various data types like numbers, strings, vectors, matrices, or even other lists. This versatility allows you to gather different types of information or data together in one container.

Organizing Assortments : Similar to how you organize items in a basket to keep them together, a list helps in organizing different pieces of information or data structures within a single entity. This organization simplifies handling and retrieval, just like a well-organized basket makes it easier for you to find what you need.

Handling Multiple Items : In a market basket, you might have fruits, vegetables, and other goods separately. Likewise, in R, lists can store outputs from functions that generate multiple results. For instance, a list can hold statistical summaries, model outputs, or simulation results together, allowing for easy access and analysis.

Hierarchy and Nesting : Sometimes, within a basket, you might have smaller bags or containers holding different items. Similarly, lists in R can be hierarchical or nested, containing sub-lists or various data structures within them. This nested structure is handy for representing complex data relationships.

In essence, just as a shopping basket helps you organize and carry diverse items conveniently while shopping, lists in R serve as flexible containers to organize and manage various types of data efficiently within a single entity. This flexibility enables the creation of hierarchical and heterogeneous structures, making lists one of the most powerful data structures in R.

Creating Lists

Creating a list in R is straightforward. Use the list() function, passing the elements you want to include:

Accessing Elements in Lists

Accessing elements within a list involves using double brackets [[ ]] or the $ operator. Double brackets extract individual elements based on their positions, while $ accesses elements by their names (if named).

Manipulating Lists

Adding elements.

Elements can easily be added to a list using indexing or appending functions like append() or c() .

Removing Elements

Removing elements from a list can be done using indexing or specific functions like NULL assignment or list subsetting.

Use Cases for Lists

Storing diverse data.

Lists are ideal for storing diverse data structures within a single container. For instance, in a statistical analysis, a list can hold vectors of different lengths, matrices, and even data frames, simplifying data management and analysis.

Example 1: Dataset Description

Suppose you’re working with a dataset that contains information about individuals. Using a list can help organize different aspects of this data.

In this example:

Each individual is represented as a list containing various attributes like name , age , gender , contact , and interests .

The contact attribute further contains a sub-list for email and phone details.

Finally, a individuals_list is a list that holds multiple individuals’ data.

Example 2: Experimental Results

Consider conducting experiments where each experiment yields different types of data. Lists can efficiently organize this diverse output.

Each experiment is represented as a list containing parameters and results .

parameters include details like temperature, duration, and method used in the experiment.

results can vary in structure - it could be a matrix, data frame, or any other data type.

Example 3: Survey Responses

Imagine collecting survey responses where each respondent provides different types of answers. Lists can organize this diverse set of responses.

Each respondent is represented as a list containing attributes like name , age , and answers .

answers contain responses to various questions where responses can be strings, vectors, data frames, or matrices.

Function Outputs

Lists are commonly used to store outputs from functions that produce multiple results. This approach keeps the results organized and accessible, enabling easy retrieval and further processing. Here are a few examples of how lists can be used to store outputs from functions that produce multiple results.

Example 1: Statistical Summary

Suppose you have a dataset and want to compute various statistical measures using a custom function:

Here, statistics is a list containing various statistical measures such as mean, median, standard deviation, and summary statistics of the input data.

Example 2: Model Fitting Outputs

Consider a scenario where you fit a machine learning model and want to store various outputs:

In this example, model_results is a list containing the fitted model object, predictions on the test data, and coefficients of the linear regression model.

Example 3: Simulation Outputs

Suppose you are running a simulation and want to store various outputs for analysis:

Here, simulations is a list containing the results of five separate simulations, each stored as a vector of simulated data.

These examples illustrate how lists can efficiently store multiple outputs from functions, making it easier to manage and analyze diverse results within R.

In conclusion, lists in R are a fundamental data structure, offering flexibility and versatility for managing and manipulating complex data. Mastering their use empowers R programmers to efficiently handle various types of data structures and hierarchies, facilitating seamless data analysis and manipulation.

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list: Lists -- Generic and Dotted Pairs

Description.

Functions to construct, coerce and check for both kinds of R lists.

as.list(x, …) # S3 method for environment as.list(x, all.names = FALSE, sorted = FALSE, …) as.pairlist(x)

is.list(x) is.pairlist(x)

alist(…)

objects, possibly named.

object to be coerced or tested.

a logical indicating whether to copy all values or (default) only those whose names do not begin with a dot.

a logical indicating whether the names of the resulting list should be sorted (increasingly). Note that this is somewhat costly, but may be useful for comparison of environments.

Almost all lists in R internally are Generic Vectors , whereas traditional dotted pair lists (as in LISP) remain available but rarely seen by users (except as formals of functions).

The arguments to list or pairlist are of the form value or tag = value . The functions return a list or dotted pair list composed of its arguments with each value either tagged or untagged, depending on how the argument was specified.

alist handles its arguments as if they described function arguments. So the values are not evaluated, and tagged arguments with no value are allowed whereas list simply ignores them. alist is most often used in conjunction with formals .

as.list attempts to coerce its argument to a list. For functions, this returns the concatenation of the list of formal arguments and the function body. For expressions, the list of constituent elements is returned. as.list is generic, and as the default method calls as.vector (mode = "list") for a non-list, methods for as.vector may be invoked. as.list turns a factor into a list of one-element factors. Attributes may be dropped unless the argument already is a list or expression. (This is inconsistent with functions such as as.character which always drop attributes, and is for efficiency since lists can be expensive to copy.)

is.list returns TRUE if and only if its argument is a list or a pairlist of length $> 0$. is.pairlist returns TRUE if and only if the argument is a pairlist or NULL (see below).

The " environment " method for as.list copies the name-value pairs (for names not beginning with a dot) from an environment to a named list. The user can request that all named objects are copied. Unless sorted = TRUE , the list is in no particular order (the order depends on the order of creation of objects and whether the environment is hashed). No enclosing environments are searched. (Objects copied are duplicated so this can be an expensive operation.) Note that there is an inverse operation, the as.environment () method for list objects.

An empty pairlist, pairlist() is the same as NULL . This is different from list() : some but not all operations will promote an empty pairlist to an empty list.

as.pairlist is implemented as as.vector (x, "pairlist") , and hence will dispatch methods for the generic function as.vector . Lists are copied element-by-element into a pairlist and the names of the list used as tags for the pairlist: the return value for other types of argument is undocumented.

list , is.list and is.pairlist are primitive functions.

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language . Wadsworth & Brooks/Cole.

vector ("list", length) for creation of a list with empty components; c , for concatenation; formals . unlist is an approximate inverse to as.list() .

‘ plotmath ’ for the use of list in plot annotation.

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R Data Structures

R statistics.

A list in R can contain many different data types inside it. A list is a collection of data which is ordered and changeable.

To create a list, use the list() function:

Access Lists

You can access the list items by referring to its index number, inside brackets. The first item has index 1, the second item has index 2, and so on:

Change Item Value

To change the value of a specific item, refer to the index number:

List Length

To find out how many items a list has, use the length() function:

Check if Item Exists

To find out if a specified item is present in a list, use the %in% operator:

Check if "apple" is present in the list:

Add List Items

To add an item to the end of the list, use the append() function:

Add "orange" to the list:

To add an item to the right of a specified index, add " after= index number " in the append() function:

Add "orange" to the list after "banana" (index 2):

Remove List Items

You can also remove list items. The following example creates a new, updated list without an "apple" item:

Remove "apple" from the list:

Range of Indexes

You can specify a range of indexes by specifying where to start and where to end the range, by using the : operator:

Note: When specifying a range, the return value will be a new list with the specified items.

Return the second, third, fourth and fifth item:

Note: The search will start at index 2 (included) and end at index 5 (included).

Remember that the first item has index 1.

Loop Through a List

You can loop through the list items by using a for loop:

Print all items in the list, one by one:

Join Two Lists

There are several ways to join, or concatenate, two or more lists in R.

The most common way is to use the c() function, which combines two elements together:

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How to Create a List of Lists in R (With Example)

You can use the following basic syntax to create a list of lists in R:

The following example shows how to use this syntax in practice.

Example: Create List of Lists in R

The following code shows how to create a list that contains 3 lists in R:

We can then use single brackets [ ] to access a specific list.

For example, we can use the following syntax to access the second list:

We can also use double brackets [[ ]] and the dollar sign operator $ to access a specific element within a specific list.

For example, we can use the following syntax to access element d within the second list:

You can use similar syntax to access any element within any list.

Additional Resources

The following tutorials explain how to perform other common tasks with lists in R:

How to Convert a List to a Data Frame in R How to Append Values to List in R

Featured Posts

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Assignment Operators in R (3 Examples) | Comparing = vs. ＜- vs. ＜＜-

On this page you’ll learn how to apply the different assignment operators in the R programming language .

The content of the article is structured as follows:

Let’s dive right into the exemplifying R syntax!

Example 1: Why You Should Use ＜- Instead of = in R

Generally speaking, there is a preference in the R programming community to use an arrow (i.e. ＜-) instead of an equal sign (i.e. =) for assignment.

In my opinion, it makes a lot of sense to stick to this convention to produce scripts that are easy to read for other R programmers.

However, you should also take care about the spacing when assigning in R. False spacing can even lead to error messages .

For instance, the following R code checks whether x is smaller than minus five due to the false blank between ＜ and -:

A properly working assignment could look as follows:

However, this code is hard to read, since the missing space makes it difficult to differentiate between the different symbols and numbers.

In my opinion, the best way to assign in R is to put a blank before and after the assignment arrow:

As mentioned before, the difference between ＜- and = is mainly due to programming style . However, the following R code using an equal sign would also work:

In the following example, I’ll show a situation where ＜- and = do not lead to the same result. So keep on reading!

Example 2: When ＜- is Really Different Compared to =

In this Example, I’ll illustrate some substantial differences between assignment arrows and equal signs.

Let’s assume that we want to compute the mean of a vector ranging from 1 to 5. Then, we could use the following R code:

However, if we want to have a look at the vector x that we have used within the mean function, we get an error message:

Let’s compare this to exactly the same R code but with assignment arrow instead of an equal sign:

The output of the mean function is the same. However, the assignment arrow also stored the values in a new data object x:

This example shows a meaningful difference between = and ＜-. While the equal sign doesn’t store the used values outside of a function, the assignment arrow saves them in a new data object that can be used outside the function.

Example 3: The Difference Between ＜- and ＜＜-

So far, we have only compared ＜- and =. However, there is another assignment method we have to discuss: The double assignment arrow ＜＜- (also called scoping assignment).

The following code illustrates the difference between ＜- and ＜＜- in R. This difference mainly gets visible when applying user-defined functions .

Let’s manually create a function that contains a single assignment arrow:

Now, let’s apply this function in R:

The data object x_fun1, to which we have assigned the value 5 within the function, does not exist:

Let’s do the same with a double assignment arrow:

Let’s apply the function:

And now let’s return the data object x_fun2:

As you can see based on the previous output of the RStudio console, the assignment via ＜＜- saved the data object in the global environment outside of the user-defined function.

Video & Further Resources

I have recently released a video on my YouTube channel , which explains the R syntax of this tutorial. You can find the video below:

The YouTube video will be added soon.

In addition to the video, I can recommend to have a look at the other articles on this website.

R Programming Examples

In summary: You learned on this page how to use assignment operators in the R programming language. If you have further questions, please let me know in the comments.

assignment-operators-in-r How to use different assignment operators in R – 3 R programming examples – R programming language tutorial – Actionable R programming syntax in RStudio

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comment Function in R (2 Examples)

Rank Functions of dplyr Package in R (row_number, ntile, min_rank, dense_rank, percent_rank & cume_dist)

%<<-% {dub}

R Documentation

Assign nested components of a list to names

Description.

The %<<-% operator assigns multiple (nested) components of a list or vector to names via pattern matching (“unpacking assignment”). Think of the “dub(ble) arrow” <<- as a pictograph representing multiple <- 's.

%<<-% is especially convenient for:

assigning individual names to the multiple values that a function may return in the form of a list;

extracting deeply nested list components.

	Pattern of names that the components of are assigned to (see below).
	List or vector.

Returns value invisibly.

Pattern-matching names

Names are matched to the (nested) components of a list using a concise pattern matching syntax that mirrors the structure of the list. Apart from names, the syntax consists of two classes of symbols:

List constructors — Use a pair of parentheses to indicate a list, and a colon, rather than a comma, to indicate successive names.

Wildcards — Use a dot ( . ) to skip assignment of a specific component, or dots ( ... ) to skip assignment of a range of components.

See the examples for an illustration of common use cases.

Unpacking/multiple assignment appears in other languages (e.g., Python , JavaScript , Clojure ). While R has no such feature, using a custom operator to do this has long been a folklore method. An early implementation is due to Gabor Grothendieck (2004), cf. list in the gsubfn package.

R Operators

R Operator Precedence and Associativitys

R Flow Control

R if…else Statement

R ifelse() Function

R while Loop
R break and next Statement
R repeat loop
R Functions
R Return Value from Function
R Environment and Scope
R Recursive Function

R Infix Operator

R switch() Function

R Data Structures

R Data Frame

R Object & Class

R Classes and Objects
R Reference Class

R Graphs & Charts

R Histograms
R Pie Chart
R Strip Chart

R Advanced Topics

R Plot Function
R Multiple Plots
Saving a Plot in R
R Plot Color

Operator	Description
+	Addition
-	Subtraction
*	Multiplication
/	Division
^	Exponent
%%	Modulus(Remainder from division)
%/%	Integer Division

Operator	Description
	Less than
>	Greater than
	Less than or equal to
>=	Greater than or equal to
==	Equal to
!=	Not equal to

Operator	Description
!	Logical NOT
&	Element-wise logical AND
&&	Logical AND
\|	Element-wise logical OR
\|\|	Logical OR

Operator	Description
	Leftwards assignment
->, ->>	Rightwards assignment

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15 R Projects for Beginners (with Source Code)

R programming projects are essential for gaining practical data science experience. They provide the hands-on practice that bridges the gap between learning the required skills and deomonstrating you meet real-world job requirements. This process is particularly valuable when applying for jobs, as it addresses the common challenge of not having any experience when you're applying for your first data job .

A properly diversified portfolio of R projects will demonstrate your proficiency in:

Data manipulation
Data visualization
Advanced statistical analysis

These skills are fundamental to making informed business decisions―so being able to demonstrate that you have them makes you a valuable asset to potential employers.

In this post, we'll explore 15 practical R project ideas. Each project is designed to highlight critical data science capabilities that will enhance your job prospects. Whether you're a student aiming to launch your career or a professional seeking advancement, these projects on R will show your ability to handle real-world data challenges effectively.

Two individuals collaborating over an R project, highlighting the importance of practical experience

But first, to ensure you're developing in-demand R skills , we'll explain how to build your portfolio of projects on R by selecting the right ones and go over some of the common challenges you might face along the way. After we look at the 15 R project ideas in detail, we'll discuss how you can prepare for an R programming job.

Choosing the right R projects for your portfolio

Looking to improve your chances of landing a data science job? The R project ideas you select for your portfolio can make a big difference. A well-chosen set of projects on R shows off your skills and proves you can tackle real-world problems. Here's how to select R projects that help you grow, match your interests, and impress potential employers.

Find the sweet spot: Your skills, interests, and market demand

The best projects combine what you enjoy, what you're good at, and what employers want. This balance keeps you motivated and makes you more appealing to hiring managers. For example, if you love sports, you might create a project that uses R to predict game outcomes. This type of project lets you practice working with data and creating visualizations—skills that are valuable in many industries.

How to pick your R projects: A step-by-step approach

Know your strengths (and weaknesses): Assess your R programming skills. What are you comfortable with? Where do you need practice? Knowing the answers these questions will help you choose projects that challenge you appropriately.
Explore different tools and techniques: Pick projects that use various R packages and data types. This shows your versatility as a data scientist.
Focus on solving problems: ChR project ideasoose projects with clear goals, like predicting customer behavior or analyzing social media trends. These projects are engaging and show employers you can deliver results.
Seek feedback: Ask others to review your code and approach. Their input can help you improve your skills and projects.

Common challenges (and how to overcome them)

Many learners struggle with choosing projects on R that are too complex or aren't able to manage their time effectively. To avoid these issues:

Start small : Begin with manageable projects that match your current skill level.
Use available resources : When you get stuck, look for help in online tutorials or community forums .

Keep improving: The power of iteration

Don't stop after your first attempt. Reworking and refining your R projects based on feedback is key. This process of continuous improvement enhances the quality of your work and shows potential employers your commitment to excellence. It also helps prepare you for the workplace where iterating on your work is common.

Wrapping up

Carefully selecting your R project ideas can significantly improve your skills and how you present them to potential employers. As you review the list of 15 R project ideas below, use these tips to choose projects that will strengthen your portfolio and align with your career goals.

Getting started with R programming projects

Hands-on projects are key to developing practical R programming skills. They'll boost your understanding of the language and prepare you for real-world data tasks. Here's how to get started:

Common tools and packages

First, familiarize yourself with these R tools and packages:

RStudio: An IDE that simplifies code writing, debugging, and visualization .
dplyr : Streamlines data manipulation tasks .
ggplot2 : Creates complex visualizations easily .
data.table : Processes large datasets efficiently .

These tools will streamline your project workflow. For more insights, explore this guide on impactful R packages .

Setting up your project on R

Follow these steps to start your R programming project:

Install R and RStudio: These are your foundational tools .
Create a new project in RStudio: This keeps your files organized.
Learn the RStudio environment: Understand each part of the IDE to get the most out of it .
Import necessary packages: Load libraries like tidyverse or shiny as needed.

Overcoming common challenges

As a beginner, you might face some hurdles. Here are some strategies to help:

Keep your code organized and use Git for version control.
Start small to build confidence before tackling complex projects.
Use community forums and official documentation when you need help.

15 R Project Ideas with Source Code

The beauty of the following R projects lies in their diverse range of scenarios. You'll start by investigating COVID-19 virus trends and soon find yourself analyzing forest fire data. This variety ensures that you can apply your R programming skills to uncover valuable insights in different contexts. Although most of these R projects are suitable for beginners, the more advanced ones towards the end of the list may require additional effort and expertise to complete.

Here's what we'll cover:

Beginner R Projects

Investigating COVID-19 Virus Trends
Creating An Efficient Data Analysis Workflow
Creating An Efficient Data Analysis Workflow, Part 2
Analyzing Forest Fire Data
NYC Schools Perceptions
Analyzing Movie Ratings

Intermediate R Projects

New York Solar Resource Data
Investigating Fandango Movie Ratings
Finding the Best Markets to Advertise In
Mobile App for Lottery Addiction
Building a Spam Filter with Naive Bayes
Winning Jeopardy

Advanced R Projects

Predicting Condominium Sale Prices
Predicting Car Prices
Creating a Project Portfolio

In the sections that follow, we'll provide detailed walkthroughs for each project. You'll find step-by-step instructions and expected outcomes to guide you through the process. Let's get started with building your portfolio of projects on R!

1. Investigating COVID-19 Virus Trends

Difficulty Level: Beginner

In this beginner-level R project, you'll step into the role of a data analyst exploring the global COVID-19 pandemic using real-world data. Leveraging R and the powerful dplyr library, you'll manipulate, filter, and aggregate a comprehensive dataset containing information on COVID-19 cases, tests, and hospitalizations across different countries. By applying data wrangling techniques such as grouping and summarizing, you'll uncover which countries have the highest rates of positive COVID-19 tests relative to their testing numbers. This hands-on project will not only strengthen your R programming skills and analytical thinking but also provide valuable experience in deriving actionable insights from real-world health data – a crucial skill in today's data-driven healthcare landscape.

Tools and Technologies

Prerequisites.

To successfully complete this project, you should be comfortable with data structures in R such as:

Creating and working with vectors, matrices, and lists in R
Indexing data structures to extract elements for analysis
Applying functions to data structures to perform calculations
Manipulating and analyzing data using dataframes

Step-by-Step Instructions

Load and explore the COVID-19 dataset using readr and tibble
Filter and select relevant data using dplyr functions
Aggregate data by country and calculate summary statistics
Identify top countries by testing numbers and positive case ratios
Create vectors and matrices to store key findings
Compile results into a comprehensive list structure

Expected Outcomes

Upon completing this project, you'll have gained valuable skills and experience, including:

Analyzing a real-world COVID-19 dataset using R and dplyr
Applying data manipulation techniques to filter and aggregate data
Identifying trends and insights from data using grouping and summarizing
Creating and manipulating different R data structures (vectors, matrices, lists)
Interpreting results to answer specific questions about COVID-19 testing and positive rates

Relevant Links and Resources

R Project Example Solution
Original COVID19 Worldwide Testing dataset on Kaggle

Additional Resources

WHO Coronavirus (COVID-19) Dashboard

2. Creating An Efficient Data Analysis Workflow

In this hands-on, beginner-level project with R, you'll step into the role of a data analyst for a company selling programming books. Using R and RStudio, you'll analyze their sales data to determine which titles are most profitable. By applying key R programming concepts like control flow, loops, and functions, you'll develop an efficient data analysis workflow. This project provides valuable practice in data cleaning, transformation, and analysis, culminating in a structured report of your findings and recommendations.

To successfully complete this project, you should be comfortable with control flow, iteration, and functions in R including:

Implementing control flow using if-else statements
Employing for loops and while loops for iteration
Writing custom functions to modularize code
Combining control flow, loops, and functions in R
Load and explore the book sales dataset using tidyverse
Clean the data by handling missing values and inconsistent labels
Transform the review data into numerical format
Analyze the cleaned data to identify top-performing titles
Summarize findings and provide data-driven recommendations
Applying R programming concepts to real-world data analysis
Developing an efficient, reproducible data analysis workflow
Cleaning and preparing messy data for analysis using tidyverse
Analyzing sales data to derive actionable business insights
Communicating findings and recommendations to stakeholders
Getting Started with R and RStudio - Dataquest Blog

In this beginner-level R project, you'll step into the role of a data analyst at a book company tasked with evaluating the impact of a new program launched on July 1, 2019 to encourage customers to buy more books. Using R and powerful packages like dplyr, stringr, and lubridate, you'll clean and analyze the company's 2019 sales data to determine if the program successfully boosted book purchases and improved review quality. You'll handle missing data, process text reviews, and compare key metrics before and after the program launch. This project offers hands-on experience in applying data manipulation techniques to real-world business data, strengthening your skills in efficient data analysis and deriving actionable insights.

tidyverse (including dplyr)

To successfully complete this project, you should be comfortable with specialized data processing techniques in R , including:

Manipulating strings using stringr functions
Working with dates and times using lubridate
Applying the map function to vectorize custom functions
Understanding and employing regular expressions for pattern matching
Load and explore the book company's 2019 sales data
Clean the data by handling missing values and inconsistencies
Process text reviews to determine positive/negative sentiment
Compare key sales metrics before and after the program launch date
Analyze differences in sales between customer segments
Evaluate changes in review sentiment and summarize findings
Cleaning and preparing a real-world business dataset for analysis using R
Applying powerful R packages to manipulate and process data efficiently
Analyzing sales data to quantify the impact of a new business initiative
Translating data analysis findings into meaningful business insights
Project Dataset

4. Analyzing Forest Fire Data

In this beginner-level data analysis project in R, you'll analyze a dataset on forest fires in Portugal to uncover patterns in fire occurrence and severity. Using R and powerful data visualization techniques, you'll explore factors such as temperature, humidity, and wind speed to understand their relationship with fire spread. You'll create engaging visualizations, including bar charts, box plots, and scatter plots, to reveal trends over time and across different variables. By completing this project, you'll gain valuable insights into the ecological impact of forest fires while strengthening your skills in data manipulation, exploratory data analysis, and creating meaningful visualizations using R and ggplot2.

tidyverse (including ggplot2)

To successfully complete this project, you should be comfortable with data visualization techniques in R and have experience with:

Working with variables, data types, and data structures in R
Importing and manipulating data using R data frames
Creating basic plots using ggplot2 (e.g., bar charts, scatter plots)
Transforming and preparing data for visualization
Load and explore the forest fires dataset using R and tidyverse
Process the data, converting relevant columns to appropriate data types (e.g., factors for month and day)
Create bar charts to analyze fire occurrence patterns by month and day of the week
Use box plots to explore relationships between environmental factors and fire severity
Implement scatter plots to investigate potential outliers and their impact on the analysis
Summarize findings and discuss implications for forest fire prevention strategies
Cleaning and preparing real-world ecological data for analysis using R
Creating various types of plots (bar charts, box plots, scatter plots) using ggplot2
Interpreting visualizations to identify trends in forest fire occurrence and severity
Handling outliers and understanding their impact on data analysis and visualization
Communicating data-driven insights for environmental decision-making
UCI Machine Learning Repository: Forest Fires Dataset

5. NYC Schools Perceptions

In this beginner-level R project, you'll explore real-world survey data on school quality perceptions in New York City. Using R and various data manipulation packages, you'll clean, reshape, and visualize responses from students, parents, and teachers to uncover insights about school performance. You'll work with a large, complex dataset to build valuable data wrangling and exploration skills while creating an impactful analysis of NYC school quality perceptions across different stakeholder groups.

R Notebooks
tidyverse (dplyr, tidyr, ggplot2)

To successfully complete this project, you should be comfortable with data cleaning techniques in R including:

Manipulating DataFrames using dplyr
Joining and combining relational data
Handling missing data through various techniques
Reshaping data between wide and long formats using tidyr
Creating visualizations with ggplot2
Load and clean the NYC school survey datasets
Join survey data with school performance data
Create a correlation matrix to identify relationships between variables
Visualize strong correlations using scatter plots
Reshape the data to compare perceptions across stakeholder groups
Analyze and visualize differences in perceptions using box plots
Cleaning and wrangling complex, real-world datasets using tidyverse tools
Joining multiple datasets to create a comprehensive analysis
Identifying correlations and visualizing relationships in data
Reshaping data to facilitate comparisons across different groups
Creating informative visualizations to communicate insights about school quality perceptions
Interpreting results to draw meaningful conclusions about NYC schools
NYC School Survey Data on NYC Open Data

6. Analyzing Movie Ratings

In this beginner-level project with R, you'll analyze movie ratings data from IMDb using web scraping techniques in R. You'll extract information such as titles, release years, runtimes, genres, ratings, and vote counts for the top 30 movies released between March and July 2020. Using packages like rvest and dplyr, you'll practice loading web pages, identifying CSS selectors, and extracting specific data elements. You'll also gain experience in data cleaning by handling missing values. Finally, you'll use ggplot2 to visualize the relationship between user ratings and number of votes, uncovering trends in movie popularity and reception. This project offers hands-on experience in web scraping, data manipulation, and visualization using R, skills that are highly valuable in real-world data analysis scenarios.

To successfully complete this project, you should be familiar with web scraping techniques in R and have experience with:

Understanding HTML structure and using CSS selectors to locate specific elements
Using the rvest package to extract data from web pages
Basic data manipulation and cleaning using dplyr and stringr
Working with vectors and data frames in R
Load the IMDb web page and extract movie titles and release years
Extract additional movie features such as runtimes and genres
Scrape user ratings, metascores, and vote counts for each movie
Clean the extracted data and handle missing values
Create a data frame combi ning all extracted information
Visualize the relationship between user ratings and vote counts using ggplot2
Implementing web scraping techniques to extract structured data from IMDb
Cleaning and preprocessing scraped data for analysis
Creating a comprehensive dataset of movie information from multiple web elements
Visualizing relationships between movie ratings and popularity
Applying R programming skills to solve real-world data extraction and analysis problems
IMDb Top 30 Movies (March-July 2020)

7. New York Solar Resource Data

Difficulty Level: Intermediate

In this beginner-friendly R project, you'll step into the role of a data analyst tasked with extracting solar resource data for New York City using the Data Gov API. Using R, you'll apply your skills in API querying, JSON parsing, and data structure manipulation to retrieve the data and convert it into a format suitable for analysis. This project provides hands-on experience in working with real-world data from web APIs, a crucial skill for data scientists working with diverse data sources.

To successfully complete this project, you should be comfortable with working with APIs in R and have experience with:

Making API requests using the httr package
Parsing JSON responses with jsonlite
Manipulating data frames using dplyr
Creating basic visualizations with ggplot2
Working with complex list structures in R
Set up the API request parameters and make a GET request to the NREL API
Parse the JSON response and extract relevant data into R objects
Convert the extracted data into a structured dataframe
Create a custom function to streamline the data extraction process
Visualize the solar resource data using ggplot2
Extracting data from web APIs using R and the httr package
Parsing and manipulating complex JSON data structures
Creating custom functions to automate data retrieval and processing
Visualizing time-series data related to solar resources
Applying data wrangling techniques to prepare API data for analysis
NREL Solar Resource Data API Documentation
Data.gov - Open Data Source

8. Investigating Fandango Movie Ratings

In this beginner-friendly project with R, you'll investigate potential bias in Fandango's movie rating system. A 2015 analysis revealed that Fandango's ratings were inflated. Your task is to compare movie ratings data from 2015 and 2016 to determine if Fandango's system changed after the bias was exposed. Using R and statistical analysis techniques, you'll explore rating distributions, calculate summary statistics, and visualize changes in rating patterns. This project provides hands-on experience with a real-world data integrity investigation, strengthening your skills in data manipulation, statistical analysis, and data visualization.

To successfully complete this project, you should be familiar with fundamental statistics concepts in R and have experience with:

Data manipulation using dplyr (filtering, selecting, mutating, summarizing)
Working with string data using stringr functions
Reshaping data with tidyr (gather, spread)
Calculating summary statistics (mean, median, mode)
Creating and customizing plots with ggplot2 (density plots, bar plots)
Interpreting frequency distributions and probability density functions
Basic hypothesis testing and statistical inference
Load and explore the 2015 and 2016 Fandango movie ratings datasets
Clean and preprocess the data, isolating relevant samples for analysis
Compare distribution shapes of 2015 and 2016 ratings using kernel density plots
Calculate and compare summary statistics for both years
Visualize changes in rating patterns using bar plots
Interpret results and draw conclusions about changes in Fandango's rating system
Conducting a comparative analysis of rating distributions using R
Applying statistical techniques to investigate potential bias in ratings
Creating informative visualizations to illustrate changes in rating patterns
Drawing and communicating data-driven conclusions about rating system integrity
Implementing end-to-end data analysis workflow in R, from data loading to insight generation
Original Fandango Ratings Dataset
Original FiveThirtyEight Article on Fandango Ratings

9. Finding the Best Markets to Advertise In

In this beginner-friendly R project, you'll step into the role of an analyst for an e-learning company offering programming courses. Your task is to analyze survey data from freeCodeCamp to determine the two best markets for advertising your company's products. Using R, you'll explore factors such as new coder locations, market densities, and willingness to pay for learning. By applying statistical concepts and data analysis techniques, you'll provide actionable insights to optimize your company's advertising strategy and drive growth.

To successfully complete this project, you should be comfortable with intermediate statistics concepts in R such as:

Summarizing distributions using measures of central tendency
Calculating variance and standard deviation
Standardizing values using z-scores
Locating specific values in distributions using z-scores
Load and explore the freeCodeCamp survey data
Analyze the locations and densities of new coders in different markets
Calculate and compare average monthly spending on learning across countries
Identify and handle outliers in the spending data
Determine the two best markets based on audience size and willingness to pay
Summarize findings and make recommendations for the advertising strategy
Applying statistical concepts to inform strategic business decisions
Using R to analyze real-world survey data and derive actionable insights
Handling outliers and cleaning data for more accurate analysis
Translating data analysis results into clear recommendations for stakeholders
Developing a data-driven approach to optimizing marketing strategies
The 2017 freeCodeCamp New Coder Survey Data
freeCodeCamp's New Coder Survey Results

10. Mobile App for Lottery Addiction

In this beginner-friendly data science project in R, you'll develop the logical core of a mobile app designed to help lottery addicts understand their chances of winning. As a data analyst at a medical institute, you'll use R programming, probability theory, and combinatorics to analyze historical data from the Canadian 6/49 lottery. You'll create functions to calculate various winning probabilities, check for previous winning combinations, and provide users with a realistic view of their odds. This project offers hands-on experience in applying statistical concepts to a real-world problem while building your R programming portfolio.

tidyverse package
sets package

To successfully complete this project, you should be comfortable with fundamental probability concepts in R such as:

Calculating theoretical and empirical probabilities
Applying basic probability rules
Working with permutations and combinations
Using R functions for complex probability calculations
Manipulating data with tidyverse packages
Implement core probability functions for lottery calculations
Calculate the probability of winning the jackpot with a single ticket
Analyze historical lottery data to check for previous winning combinations
Develop functions to calculate probabilities for multiple tickets and partial matches
Create user-friendly outputs to communicate lottery odds effectively
Applying probability and combinatorics concepts to a real-world scenario
Implementing complex probability calculations using R functions
Working with historical data to inform statistical analysis
Developing logical components for a mobile application
Communicating statistical concepts to a non-technical audience
6/49 Lottery Dataset on Kaggle

11. Building a Spam Filter with Naive Bayes

In this beginner-friendly project with R, you'll build an SMS spam filter using the Naive Bayes algorithm. Working with a dataset of labeled SMS messages, you'll apply text preprocessing techniques, implement the Naive Bayes classifier from scratch, and evaluate its performance. This project offers hands-on experience in applying probability theory to a real-world text classification problem, providing valuable skills for aspiring data scientists in natural language processing and spam detection. You'll gain practical experience in data preparation, probability calculations, and implementing machine learning algorithms in R.

Naive Bayes algorithm

To successfully complete this project, you should be familiar with conditional probability concepts in R and have experience with:

Basic R programming and data manipulation using tidyverse
Understanding and applying conditional probability rules
Calculating probabilities based on prior knowledge using Bayes' theorem
Text preprocessing techniques in R
Load and preprocess the SMS dataset, creating training, cross-validation, and test sets
Clean the text data and build a vocabulary from the training set
Calculate probability parameters for the Naive Bayes classifier
Implement the Naive Bayes algorithm to classify new messages
Evaluate the model's performance and tune hyperparameters using cross-validation
Test the final model on the test set and interpret results
Implementing text preprocessing techniques for machine learning tasks
Building a Naive Bayes classifier from scratch in R
Applying probability calculations in a real-world text classification problem
Evaluating and optimizing machine learning model performance
Interpreting classification results in the context of spam detection
UCI Machine Learning Repository: SMS Spam Collection Dataset

12. Winning Jeopardy

In this beginner-friendly R project, you'll analyze a dataset of over 20,000 Jeopardy questions to uncover patterns that could give you an edge in the game. Using R and statistical techniques, you'll explore question categories, identify terms associated with high-value clues, and develop data-driven strategies to improve your odds of winning. You'll apply chi-squared tests and text analysis methods to determine which categories appear most frequently and which topics are associated with higher-value questions. This project will strengthen your skills in hypothesis testing, string manipulation, and deriving actionable insights from text data.

Chi-squared test

To successfully complete this project, you should be familiar with hypothesis testing in R and have experience with:

Performing chi-squared tests on categorical data
Manipulating strings and text data in R
Data cleaning and preprocessing techniques
Basic data visualization in R
Load and preprocess the Jeopardy dataset, cleaning text and converting data types
Normalize dates to make them more accessible for analysis
Analyze the frequency of question categories using chi-squared tests
Identify unique terms in questions and associate them with question values
Perform statistical tests to determine which terms are associated with high-value questions
Visualize and interpret the results to develop game strategies
Applying chi-squared tests to analyze categorical data in a real-world context
Implementing text preprocessing and analysis techniques in R
Interpreting statistical results to derive actionable insights
Developing data-driven strategies for game show success
Original Jeopardy Dataset

13. Predicting Condominium Sale Prices

Difficulty Level: Advanced

In this challenging project with R, you'll analyze New York City condominium sales data to predict prices based on property size. Using R and linear regression modeling techniques, you'll clean and explore the dataset, visualize relationships between variables, and build predictive models. You'll compare model performance across NYC's five boroughs (Manhattan, Brooklyn, Queens, The Bronx, and Staten Island), gaining valuable experience in real estate data analysis and statistical modeling. This project will strengthen your skills in data cleaning, exploratory analysis, and interpreting regression results in a practical business context.

Linear regression

To successfully complete this project, you should be familiar with linear regression modeling in R and have experience with:

Data manipulation and cleaning using tidyverse functions
Creating scatterplots and other visualizations with ggplot2
Fitting and interpreting linear regression models in R
Evaluating model performance using metrics like R-squared and RMSE
Basic understanding of real estate market dynamics
Load and clean the NYC condominium sales dataset
Perform exploratory data analysis, visualizing relationships between property size and sale price
Identify and handle outliers that may impact model performance
Build a linear regression model for all NYC boroughs combined
Create separate models for each borough and compare their performance
Interpret results and draw conclusions about price prediction across different areas of NYC
Cleaning and preparing real estate data for analysis in R
Visualizing and interpreting relationships between property features and prices
Building and comparing linear regression models across different market segments
Evaluating model performance and understanding limitations in real estate price prediction
Translating statistical results into actionable insights for real estate analysis
R-bloggers: A great resource for R programming tips and tutorials

14. Predicting Car Prices

In this challenging R project, you'll step into the role of a data scientist tasked with developing a model to predict car prices for a leading automotive company. Using a dataset of various car attributes such as make, fuel type, body style, and engine specifications, you'll apply the k-nearest neighbors algorithm in R to build an optimized prediction model. You'll go through the complete machine learning workflow - from data exploration and preprocessing to model evaluation and interpretation. This project will strengthen your skills in examining relationships between predictors, implementing cross-validation, performing hyperparameter optimization, and comparing different models to create an effective price prediction tool that could be used in real-world automotive market analysis.

caret package
k-nearest neighbors algorithm

To successfully complete this project, you should be comfortable with fundamental machine learning concepts in R such as:

Understanding the key steps in a typical machine learning workflow
Implementing k-nearest neighbors for regression tasks
Using the caret library for machine learning model training and evaluation in R
Evaluating model performance using error metrics (e.g., RMSE) and k-fold cross validation
Basic data manipulation and visualization using dplyr and ggplot2
Load and preprocess the car features and prices dataset, handling missing values and non-numerical columns
Explore relationships between variables using feature plots and identify potential predictors
Prepare training and test sets by splitting the data using createDataPartition
Implement k-nearest neighbors models using caret, experimenting with different values of k
Conduct 5-fold cross-validation and hyperparameter tuning to optimize model performance
Evaluate the final model on the test set, interpret results, and discuss potential improvements
Applying the end-to-end machine learning workflow in R to a real-world prediction problem
Implementing and optimizing k-nearest neighbors models for regression tasks using caret
Using resampling techniques like k-fold cross validation for robust model evaluation
Interpreting model performance metrics (e.g., RMSE) in the context of car price prediction
Gaining practical experience in feature selection, preprocessing, and hyperparameter tuning
Developing intuition for model selection and performance optimization in regression tasks
Original Automobile Dataset on UCI Machine Learning Repository

15. Creating a Project Portfolio

In this challenging project with R, you'll be tasked with creating an impressive interactive portfolio to showcase your R programming and data analysis skills to potential employers. Using Shiny, you'll compile your guided projects from Dataquest R courses into one cohesive portfolio app. You'll apply your Shiny skills to incorporate R Markdown files, customize your app's appearance, and deploy it for easy sharing. This project will strengthen your ability to create interactive web applications, integrate multiple data projects, and effectively present your work to enhance your job prospects in the data analysis field.

To successfully complete this project, you should be comfortable with building interactive web applications in Shiny and have experience with:

Understanding the structure and components of a Shiny app
Creating inputs and outputs in the Shiny user interface
Programming the server logic to connect inputs and outputs
Extending Shiny apps with additional features
Basic R Markdown usage for creating dynamic reports
Plan the structure and content of your portfolio app
Build the user interface with a navigation bar and project pages
Incorporate R Markdown files for individual project showcases
Develop server logic to handle user interactions and display content
Create a utility function to efficiently generate project pages
Design an engaging splash page and interactive resume section
Deploy your portfolio app to shinyapps.io for easy sharing
Building a comprehensive, interactive portfolio app using Shiny
Integrating multiple R projects and analyses into a cohesive presentation
Creating utility functions to streamline app development
Customizing Shiny app appearance and functionality for professional presentation
Deploying a Shiny app to a public hosting platform for easy access
Effectively showcasing your R programming and data analysis skills to potential employers
Resolved R Shiny app issue regarding images in the Dataquest Community
Non-Guided Project: Making an R Shiny App to track moths | Dataquest Community

How to Prepare for an R Programming Job

Looking to land your first R programming job? Let's walk through the key steps to prepare yourself for success in this field.

Understand Market Demands

Start by researching what employers want. Browse R programming job listings on popular job listing sites like the ones below. They'll give you a clear picture of the skills and qualifications currently in demand.

Once you have a good idea of the skills employers are looking for, take on projects that help you develop and demonstrate those in-demand skills.

Develop Essential Skills

For entry-level positions, focus on being able to demonstrate these skills:

Data manipulation (using packages like dplyr )
Data analysis and visualization (with tools like ggplot2 )
Basic statistical analysis
Fundamental machine learning concepts
Core programming principles

To build these skills:

Enroll in structured learning paths or bootcamps
Work on hands-on coding projects
Participate in coding competitions to enhance problem-solving skills

As you learn, you might find some concepts challenging. Don't get discouraged. Instead:

Practice coding regularly to improve your speed and accuracy
Seek feedback from peers or mentors to refine your code quality and problem-solving approach

Showcase Your Work

Create a portfolio that highlights your R projects. Include examples demonstrating your data analysis, visualization, and statistical computing skills. Consider using GitHub to host your work , ensuring each project is well-documented.

Prepare for the Job Hunt

Tailor your resume to emphasize relevant technical skills and project experiences. For interviews, be ready to discuss your projects in detail . Practice explaining how you've applied specific R functions and packages to solve real-world problems.

Remember, becoming job-ready in R programming is a journey that combines technical skill development, practical experience, and effective self-presentation. By following these steps and persistently honing your skills, you'll be well-equipped to pursue opportunities in the data science field using R.

Bottom line: R programming projects are essential for building real-world skills and advancing your data science career. Here's why they matter and how to get started:

Practical application : Projects help you apply theory to actual problems.
Career advancement : They showcase your abilities to potential employers.
Skill development : Start simple and gradually tackle more complex challenges.

If you're new to R, begin with basic projects focusing on data cleaning and visualization. This approach builds your confidence and expertise gradually. As you progress, adopt good coding practices. Clear, well-organized code is easier to read and maintain, especially when collaborating with others.

Consider exploring Dataquest's Data Analyst in R path . This program covers everything from basic concepts to advanced data techniques.

R projects do more than beef up your portfolio. They sharpen your problem-solving skills and prepare you for real data science challenges. Start with a project that interests you and matches your current skills. Then, step by step, move to more complex problems. Let your interest in data guide your learning journey.

Remember, every R project you complete brings you closer to your data science goals. So, pick a project and start coding!

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Athletics Release Aledmys Díaz, Sean Newcomb

By Darragh McDonald | July 5, 2024 at 7:49pm CDT

Today: The Athletics have released Aledmys Díaz and Sean Newcomb according to their player pages on MLB.com.

July 2: The Athletics announced today that they have recalled right-hander Tyler Ferguson and infielder Brett Harris from Triple-A Las Vegas. Left-hander Sean Newcomb and infielder Aledmys Díaz were designated for assignment in corresponding moves.

Díaz, 33, had a solid run as a productive major leaguer from 2016 to 2022, suiting up for the Cardinals, Blue Jays and Astros. The A’s signed him going into last year, giving him a $14.5MM guarantee over two years, ideally hoping that he could serve as a stable veteran presence on a rebuilding club full of younger and less experienced players.

The first season of the deal was undoubtedly a disappointment. Díaz carried a career batting line of .266/.320/.443 into the year but ended up slashing just .229/.280/.337 in 2023. While the former line translates to a wRC+ of 105, the latter put him at just 72, going from a bit above average to well below.

This year has been far worse. Díaz dealt with a groin and a calf strain in Spring Training and landed on the 60-day injured list to start the year. He was reinstated at the end of May but has slashed .103/.133/.103 since then. He wasn’t getting much playing time, with just 30 plate appearances so far this year and just three in the past week.

Rather than keeping him on the bench for the final three months of his contract, the club will let him pursue opportunities elsewhere while giving his roster spot and playing time to younger players that could perhaps be part of the club’s future. Max Schuemann has been getting the bulk of the playing time at shortstop and will likely continue to do so, with players like Harris and Armando Alvarez capable of filling in from time to time. Darell Hernaiz is on the IL but Nick Allen is on the 40-man and could be recalled at some point. Jacob Wilson is a shortstop and one of the club’s top prospects. He only has 11 games of Triple-A experience and just 22 at Double-A, but he keeps putting up huge numbers and could be an option at some point.

The A’s will technically have five days to work out a trade with Díaz but the interest from other clubs figures to be close to zero, given his contract and recent performance. He has more than enough service time to reject an outright assignment and will likely be released in the coming days, leaving the A’s on the hook for the remainder of his salary.

At that point, another club could sign him and would only have to pay him the prorated league minimum for any time spent on the roster. That amount would be subtracted from what the A’s pay. Though given his recent form, he will likely have to settle for minor league offers.

As for Newcomb, 31, he also began the year on the 60-day injured list. In his case, it was due to irritation in his surgically-repaired left knee. He was reinstated from the IL about a month ago and has since tossed 10 innings over seven appearances, allowing seven earned runs. He struck out seven opponents but also issued eight walks.

He once seemed like a building block in Atlanta earlier in his career, tossing 264 innings in 2017 and 2018 with an earned run average of 4.06. He pitched primarily out of the bullpen in 2019 but continued having good results with a 3.16 ERA.

Unfortunately, things took a nosedive from there. He posted a 7.45 ERA over the 2020 to 2022 seasons, including a stint with the Cubs. The A’s gave him some time on the roster late last year with encouraging results, as he logged 15 innings with an ERA of 3.00.

The A’s took a shot on Newcomb carrying that over, avoiding arbitration by agreeing to give him a $1MM salary for 2024, even after he had undergone surgery on that left knee. But things haven’t gone according to plan and he’s been bumped off the roster.

Newcomb came into this year with his service time count at four years and 113 days, leaving him 59 days shy of the five-year mark. Since players collect service time on the minor league IL, he crossed over that threshold earlier this year. That means he now has the right to reject an outright assignment in favor of free agency while keeping his salary in place.

That makes it likely that, just like Díaz, he will end up released in the coming days. The A’s will be on the hook for what’s left of his salary and any other club could potentially sign him and only pay him the prorated league minimum, with that amount subtracted from what the A’s pay.

18 Comments

About time for Diaz. But why is Jacob Wilson in AAA? (Probably because he’s good)

Aledmys ranks right up there with Billy Butler in terms of poor A’s free agent signings.

Trevor Rosenthal got $11M on a one-year Deal and never played for us. That has to be the worst Signing.

Every team swings and misses at signings, but when the lowly AAAA’s do it it just looks so much worse.

Newcomb, Díaz & Sean The newest law firm that the Athletics can’t support (no balls)

Butler was an attempt to win baseball games, Diaz was just a ploy to try and get back into revenue sharing. Utility players are popular at the trade deadline so they probably figured they were only going to have to pay him for a half year.

Designating Diaz was long time coming unfortunately! He just didn’t live up to expectations… But Newcomb? SMH C’mon Sean didn’t have plenty of opportunities to showcase himself…

Diaz and Jace Peterson were GREAT signings! Lol So they are cheap and trade off good players but then spend money on crap players.

Opens up 2 spots on the 40 man roster… for who???

Average age of the roster keeps getting younger – the A’s have designated 6 players 30 or older so far this season

The A’s signed Diaz to a two-year deal but as I recall, it wasn’t worth even close to $14 million.

Yes, it was. $14.5 million to be exact

Nuke back to the Braves???

Tidy piece of business here

I hope the A’s call up Colby Thomas

24 hours ago

If Trevor Bauer had been in the bigs all year, he’d be the sure-fire Cy Young winner. Oakland can right this wrong, though.

Trevor is waiting.

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( Emma McIntyre/Getty Images for BAFTA ; Realtor.com )

Michael Douglas and Catherine Zeta-Jones Put Their 1929 Hudson River Home on the Market for $12M

Hollywood couple Michael Douglas and Catherine Zeta-Jones are trying to sell their huge estate in Irvington, NY , for $12 million.

The mansion boasts eight bedrooms, 11.5 baths, and more than 11,600 square feet on 12 acres.

The power pair sold a much larger home in nearby Bedford, NY , in 2019 before buying this spread on the banks of the Hudson River in Westchester County that same year.

Now this historic home, about 25 miles north of NYC , is up for grabs as the Oscar-winning duo is apparently planning to spend more time in Bermuda and Europe.

Let’s have a look inside the home known as Longmeadow.

(Realtor.com)

At nearly a century old, this grand estate feels modern and comfortable, thanks to a recent interior restoration. The Georgian-style, brick and stone home offers an array of gorgeous details, like glossy wood and stone floors, red lacquer walls in the formal dining room, and numerous fireplaces.

Baronial elegance comes to mind upon entering the home as the gallery is 30 feet in length. A formal living room and dining room are on the first floor, as is the gorgeous kitchen with lots of shiny subway tile, marble counters, and an island with wine storage.

The primary suite is spacious, with room for a seating area by the fireplace. Another fireplace shows up in the primary bathroom, along with a soaking tub and pretty chandelier. There’s also a two-story, paneled library; extra rooms on the lower level for play and recreation; a home gym; and a 21-foot, indoor, heated pool.

Meanwhile, 130 feet of river frontage awaits behind the home, along with a 100-foot terrace that faces the river to the west. The property also comes with a four-car garage.

Douglas and the Welsh-born Zeta-Jones have three Academy Awards between them, a Tony for her, and five Golden Globes for him—a stash few celebrity couple can match.

More recently, Douglas starred in the Apple TV series “Franklin,” while Zeta-Jones had a star turn as Morticia Addams in the supernatural mystery show “Wednesday.”

Homes for sale in Irvington, NY
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Jennifer Kelly Geddes creates content for WhatToExpect.com, American Airlines Vacations, Oxo, Livestrong, and Parade.

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Roster moves

The Pittsburgh Pirates today placed left-handed pitcher Bailey Falter on the 15-day injured list with left triceps tendonitis.

The team has selected the contract of right-handed Brent Honeywell Jr. from Triple-A Indianapolis.

Left-handed pitcher Justin Bruihl has been designated for assignment to make room for Honeywell Jr. on the 40-man roster.

In addition, catcher Henry Davis had his rehab assignment ended, has been activated from the injured list, and optioned to Triple-A.

Honeywell Jr. was 1-3 with seven saves, a 4.85 ERA (39.0ip/21er) and 33 strikeouts in 31 appearances this season with Indianapolis at the time he was promoted to the Pirates.

Honeywell Jr. attended big league camp as a non-roster invitee during spring training with the Pirates.

Last year Honeywell Jr. went a combined 2-4 with a 4.82 ERA (52.1ip/28er), 45 strikeouts and .260 batting average against in 40 appearances between San Diego and Chicago (AL).

He will wear uniform No. 34.

Necas, Luukkonen among 14 players to file for NHL salary arbitration

R. Lindgren, Marchenko also on list; hearings scheduled to run July 20-Aug. 4

Link copied

Martin Necas of the Carolina Hurricanes was one of 14 restricted free agents who filed for salary arbitration Friday, according to the NHL Players' Association.

The 25-year-old forward had 53 points (24 goals, 29 assists) in 77 regular-season games last season, and had nine points (four goals, five assists) in 11 Stanley Cup Playoff games.

Notable players who filed include goalie Ukko-Pekka Luukkonen (Buffalo Sabres), defenseman Ryan Lindgren (New York Rangers) and forward Kirill Marchenko (Columbus Blue Jackets).

The deadline for club-elected salary arbitration notification is Saturday at 5 p.m. ET.

Salary arbitration hearings will be held from July 20-Aug. 4. A player can sign a contract with his current team any time before his hearing.

Last offseason, 22 restricted free agents filed for salary arbitration. Goalies Jeremy Swayman (Boston Bruins) and Ilya Samsonov (Toronto Maple Leafs), and forward Philipp Kurashev (Chicago Blackhawks) were the only players awarded a contract by an independent arbitrator following their hearings.

Here is the list of players who filed for salary arbitration (hearing dates have not been announced):

Buffalo Sabres Beck Malenstyn , F Ukko-Pekka Luukkonen, G

Carolina Hurricanes Jack Drury , F Martin Necas, F

Columbus Blue Jackets Jake Christiansen , D Jet Greaves , G Kirill Marchenko, F

Detroit Red Wings Joe Veleno , F

Nashville Predators Spencer Stastney , D

New York Islanders Oliver Wahlstrom , F

New York Rangers Ryan Lindgren, D

San Jose Sharks Ty Emberson , D

Tampa Bay Lightning J.J. Moser , D

Toronto Maple Leafs Connor Dewar , F

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Texas on alert as Beryl churns closer; landfall as hurricane likely

Editor's note: Read USA TODAY's live coverage of Beryl for Sunday, July 7, including the storm's track as it heads toward Texas.

Beryl , once a hurricane and now a tropical storm, is expected to regain hurricane strength before it hits the south Texas coast late Sunday or early Monday, the National Hurricane Center said Saturday.

The storm weakened after making landfall on Mexico's Yucatan Peninsula Friday morning, after it plowed through the Caribbean and Jamaica earlier this week killing at least 11 people.

“This is a determined storm that is still strong,” Texas Lt. Gov. Dan Patrick said at a news briefing on Friday.

At 4 p.m. local time, Beryl was about 385 miles southeast of Corpus Christi in the Gulf of Mexico, traveling west-northwest at 13 mph. It was expected to continue moving northwest into Sunday, then north-northwest by Sunday night, and make landfall on the Texas coast Monday after re-strengthening to a Category 1 hurricane.

On Saturday, Beryl's winds reached up to 60 mph, with higher gusts.

Hurricane, tropical storm and storm surge watches and warnings were issued Saturday along Texas' Gulf Coast, the hurricane center said. Storm surge in Texas could reach as high as 6 feet from Mesquite Bay to Sargent and Matagorda Bay.

Tornadoes are possible Sunday, and rainfall totals were forecast between 5 to 10 inches, with as much as 15 inches in some locations.

"This rainfall will likely produce areas of flash and urban flooding, some of which may be locally considerable," the hurricane center said. "Minor to isolated moderate river flooding is also possible."

Beryl tracker: See projected path, spaghetti models of path toward Texas

A stunning storm: As Hurricane Beryl tears through the Caribbean, a look at its record-breaking path

Key developments:

∎ At least 11 people were killed in Jamaica, Grenada, St. Vincent and the Grenadines, and northern Venezuela after Beryl tore through, officials have said. There were no casualties in Mexico.

∎ Beryl, the first hurricane of the 2024 Atlantic season, became the earliest Category 5 hurricane on record. With its rapid intensification, Beryl became a hurricane within 24 hours of formation. Within another 24 hours, its winds had increased 55 mph, reaching 130 mph, almost a Category 4 hurricane.

∎ Schools near Texas' coast canceled class activities on Monday, including the Corpus Christi and West Oso school districts. Del Mar College will close its campus on Monday, while Texas A&M University-Corpus Christi will shift to remote status.

How ocean temps have fueled Beryl: Hurricane super-charged by warm seas

'Beat the rush,' be prepared, hardware store owner says

Shop now, not later. That's what Raoul Martinez, who owns a hardware store in Corpus Christi, Texas, reminded his customers Saturday as Beryl took a turn toward the city.

"GET EVERY BATTERY CHARGED in your household," Martinez posted from the No Hassle Tools and More Facebook page early Saturday. "Have a chainsaw on hand to cut away any debris. Cut your grass now so it don’t grow like crazy. Tie down kids playgrounds and animal houses."

Worried but calm customers were buying necessities, he told USA TODAY. Martinez vowed his store will be open through the storm as long he can reach it safely.

"There are people who are going to be in need and we will never know until it happens," he said. "I have tools for everything, and I want to be at people's disposals and it's OK for me."

Martinez encouraged people to be prepared and not wait.

"Beat the rush," Martinez said. "A lot of us small businesses are taking the time out and there are little stores selling water, sand bags, and I'm at least staying open for people."

Some Texas residents asked to evacuate ahead of storm conditions

Voluntary evacuations were in place in some counties along the Texas coast.

Aransas County urged residents to evacuate but made it voluntary. Emergency officials there said anyone who lives in an RV should evacuate, as well as those in low-lying areas.

Matagorda County warned residents that floodwaters from the storm could trap them in their homes and called for voluntary evacuations in coastal areas of the county.

"The biggest concern for this situation is the coastal areas of our County. Low lying areas that normally flood are impacted," the county said in a news release on Friday.

Sandbags were being distributed in the City of Kingsville in Kleberg County as voluntary evacuation was underway in Baffin Bay, Loyola Beach and all low-lying areas of the county.

Sandbag distribution has ended for the day at 1300 E. Corral. It will continue tomorrow morning at 10 a.m. Proof of... Posted by The City of Kingsville, TX on Friday, July 5, 2024

Beryl's effects could start to be felt Saturday in Texas

As Beryl strengthens over the Gulf of Mexico, forecasters said Texans will already be feeling its impact Saturday ahead of its arrival.

"The exact location of Beryl's landfall is uncertain at this point, but what's most important is that heavy rainfall, strong winds and storm surge are expected for much of the state's coastline and portions of the central Gulf Coast beginning tonight into Sunday," the Weather Prediction Center said early Saturday.

Swells and "life-threatening" surf and rip currents caused by the storm are also not far off.

Tropical storm conditions are expected to start hitting Texas' coast on Sunday night.

What areas in Texas are in Beryl's path?

Multiple large urban areas in Texas, including Houston, Austin and San Antonio, lie in the storm's broad path of projection, and it's still uncertain exactly where the hurricane will pass through. Most of the state will feel at least some impacts from the storm.

The weather service in Corpus Christi also said south Texas should prepare for power outages.

About 3 to 5 feet of storm surge is likely around areas along the coast, and "the surge will be accompanied by large and destructive waves," the hurricane center said. The storm surge will impact areas from Baffin Bay to San Luis Pass, Corpus Christi Bay, Matagorda Bay, between the mouth of the Rio Grande to Baffin Bay, San Luis Pass to High Island and Galveston Bay.

Heavy rain and flash flooding are some of the greatest threats, forecasters warned. About 5 to 10 inches of rain and up to 15 inches in some areas will inundate the Texas Gulf Coast and parts of eastern Texas starting late Sunday and into next week. Flooding is likely wherever Beryl passes, Patrick said.

Contributing: Cybele Mayes-Osterman, Doyle Rice and Dinah Voyles Pulver, USA TODAY; Cross Harris and Alexis Simmerman, the Austin American-Statesman ; Reuters

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Everything you need to know about the July 4th Concert and Fireworks

A group of people are smiling and waiving the American Flag at the July 4th Concert

*For the complete list of Wawa Welcome America events and road closures, visit our Welcome America Road Closure Press Release .

On Thursday, July 4, the Wawa Welcome America July 4th Concert and Fireworks returns to the Benjamin Franklin Parkway for the culmination of the sixteen-day independence celebration.

Attendees can explore the 2024 Welcome America story map on the City’s website to review important public safety information. The map helps members of the public find Parkway amenities such as jumbotrons and porta-potties. It also indicates stations for medical help and security. The story map , available at phila.gov/america , also includes road closure maps for the Parkway area and Old City.

Stay connected to important information from the City, like weather and event-related details. Text “ AMERICA ” to 888-777 to receive free Welcome America alerts from the Office of Emergency Management.

July 4th Concert and Fireworks

Concert begins at 7 p.m. | Benjamin Franklin Parkway

Fireworks begin at 9:45 p.m. | Benjamin Franklin Parkway

Philadelphia’s legendary July 4th Concert features performances by three-time GRAMMY® Award-winning R&B hitmaker, iconic songwriter, actor, entrepreneur, and philanthropist Ne-Yo, and Grammy-nominated and award-winning musician, actress and activist Kesha.

Prior to the concert, guests will enjoy a collaborative performance by DJ Diamond Kuts and Snacktime Philly on the Pennsylvania Lottery Groove Stage on the Parkway from 4 p.m. to 6 p.m. The concert will begin on the main stage at 7 p.m.

The main concert will be televised live during primetime in its entirety on NBC10 & simulcast from 7 p.m. to 10 p.m. on NBC Sports Philadelphia+. The free concert will also be live streamed on the NBC Philadelphia News channel on Peacock, Roku, Samsung TV Plus, Amazon Fire TV, Pluto and Xumo Play as well as www.nbc10.com. The concert will be streamed in Spanish on Telemundo Noreste on Roku and Samsung TV Plus as well as www.telemundo62.com enabling viewers to watch on any device.

The July 4th Concert will be followed by a large, public firework display over the iconic Benjamin Franklin Parkway courtesy of Wawa beginning at 9:45 p.m.

Please view the Wawa Welcome America Festival Brochure for more details about the events.

How to Enter the Parkway Events

The Wawa Welcome America July 4th Concert & Fireworks is open to the public and free to attend.

Gates for the concert open at 4 p.m. Guests will be directed to enter the event venue at the secured entry point at the following location:

20th Street and Benjamin Franklin Parkway (Logan Circle)

*If you have a ticket for the front section, you must enter at 22nd Street & Pennsylvania Avenue. OR 22nd & Winter Streets .

All guests must go through a security check upon entering, including magnetometers and bag checks.

Guests are encouraged to arrive early to avoid any traffic delays or heavy pedestrian traffic. The Parkway is general admission and occupied on a first-come-first-served basis.

What to Expect

Venue rules and safety information.

The vicinity of the Wawa Welcome America July Fourth Concert & Fireworks (a boundary from at least 19th Street and Benjamin Franklin Parkway to the Philadelphia Museum of Art, and adjacent areas), will be secured and protected for a safe and fun celebration. Throughout the Wawa Welcome America festivities, the City will provide enhanced law enforcement and emergency medical services. The City and Welcome America are coordinating with local, state and federal public safety authorities to ensure everyone can safely enjoy the Fourth of July.

For the safety of event attendees and participants, unauthorized sUAS/drone usage is prohibited over crowds by the FAA. If you see a drone at an event, please report it to the nearest police officer with a location and description of the operator.

Check out this informative video provided by Welcome America that highlights the venue rules.

Know where to find law enforcement and medical help along the Parkway.

The Philadelphia Fire Department’s EMS Division will have medic teams spread across the Parkway at the following locations:

2300 Benjamin Franklin Parkway (Southside)
22nd Street and Benjamin Franklin Parkway (Southside)
21st Street and Benjamin Franklin Parkway (Northside)

Call 9-1-1 if you are having a medical emergency or see someone who may be in need of medical attention.

Private security posts and information booths can also call for help.

Have important phone numbers memorized.

If you see something – say something!

These safety tips and more can be found in the Special Events Safety Guide .

Weather and heat precautions

With the potential for high temperatures, there is a risk of heat-related health issues. Many heat stress or heat exhaustion issues can be avoided by taking simple precautions.

Attendees should follow all proper precautions to protect themselves and their families against the heat. All festival-goers should stay hydrated by drinking lots of water, and avoiding alcoholic beverages, caffeine, and excessive layers of clothing.

Consider the “Three L’s” Rule: light-colored, lightweight and loose-fitting. Wear garments made of natural fibers like cotton.

For more tips on how to beat the heat, visit the City’s heat guide .

Accessibility

Welcome America July 4th Concert and Fireworks is an accessible event and is dedicated to ensuring access to people of all abilities. Benjamin Franklin Parkway is a wheelchair-accessible roadway that features flat surfaces and curb cutouts.

Portable toilets, including ADA-compliant units, will be available throughout the Parkway.

There will be multiple screens throughout the festival grounds for people to see the stage.

Please refer to the 2024 Welcome America story map , which identifies these locations.

What Can I Bring?

The following items are permitted at Welcome America events:

Personal food & beverage (no alcoholic beverages)
Small personal coolers
Pets on leashes
Chairs and blankets (not permitted in the ticketed front section)

Please note: all persons and/or their belongings are subject to search as a condition of entry into the July 4th Concert and Fireworks.

When in doubt, leave it home!

Keep possessions and bags with you always and do not leave them unattended.

Items NOT allowed inside the festival footprint

For the safety of event attendees and staff, the following items are prohibited:

Weapons and contraband of any kind regardless of permitting, e.g. Right-to-Carry permits will not be honored, and entry will be denied.
Fireworks, firecrackers, or explosives (including sparklers)
Open flame of any kind
Illegal or illicit substances of any kind (does not pertain to individual prescribed medication)
Flyers, handbills, posters, stickers (no solicitation allowed)
Full face coverings, such as ski masks and balaclavas (this does not apply to COVID-19 masks or faith-based coverings)
Unauthorized commerce
Items that would obstruct others’ view (large signs, banners, etc.)
Drones and any other unmanned and remote-controlled devices
ATVS, dirt bikes, skateboards, rollerblades, bicycles, segways, hoverboards, and similar items (cannot be brought into the event space)
Glass Containers
Laser pointers
Selfie Sticks
Any other item or action deemed dangerous or inappropriate by production or security management

Lost children

Parents or guardians should report a missing child to the nearest law enforcement officer, medic, private security representative, or information booth as soon as possible.

Public safety officials will coordinate the search for missing children with the full support of the Philadelphia Police Department.

Be sure to take a photo of your child before arriving at the Welcome America events. If separated, this will be very helpful for those searching for your child. It’s also recommended to have children memorize their parent’s cell phone numbers.

Lost and found

If you’ve lost a personal belonging, look for the nearest information booth where you last had the item. Wawa Welcome America staff can check if the item has been returned there or at other information booths.

After July 4, lost items can be reclaimed by contacting the Welcome America office at [email protected] .

Getting to the Parkway events

Philadelphia’s mass transit system, SEPTA, offers two subway lines, regional rail service to and from the surrounding suburbs, and bus service throughout the city.

Regional rail service, the Market Frankford Line, and the Broad Street Line will operate on a Sunday schedule with additional cars added to increase rider capacity at the break of the event. Check the Regional Rail extended train service schedule and the SEPTA website for more details.

Bus Detours: Due to road closures, buses that travel on or near the Parkway will be detoured starting at 3:30 a.m. on Thursday, July 4 through 5 p.m. on Friday, July 5. Routes detoured: 7, 32, 33, 38, 43, 48 and 49.

SEPTA travel tips:

On Thursday, July 4, please refer to the Sunday schedule of the SEPTA service you wish to ride. Routes that do not offer Sunday service will not operate
The Ben Franklin Parkway is best accessed from the 15th Street Market-Frankford Line Station and Race-Vine or City Hall Broad Street Line Station
Visit the Fares section for fare and SEPTA Key information
Download the SEPTA App for iPhone and Android devices. Visit the Google Play or Apple App Store and look for the official SEPTA App.
The SEPTA Customer Service Call Center (215) 580-7800 will open from 8 a.m. to 8 p.m. on Thursday, July 4.

Check SEPTA’s website, Septa.org , for full details on transit options. For information on NJ Transit routes and schedules on July 4, visit NJTransit.com .

Philly PHLASH

The Philly PHLASH Downtown Bus Loop will operate a special holiday service on Thursday, July 4. For updated holiday route detour and schedule information, visit RidePhillyPhlash.com on your mobile device or VisitPhilly’s PhillyPhlash Guide on your desktop.

Taxi & Rideshare

Designated rideshare and taxi drop-off points for the July Fourth Concert will be 17th and Spring Garden Street.

Cyclists and Pedestrians

The Benjamin Franklin Parkway is easily accessible via cycling and walking. Keep in mind that if a road is closed to vehicular traffic, it is also closed to cycling. Walking paths will remain open for pedestrians as conditions allow; however, please follow all posted signs, detours and traffic control devices.

The Kelly Drive bike path (river side) between Eakins Oval and Sedgley Drive will be closed to cyclists and pedestrians beginning at 5 p.m. until the conclusion of the fireworks for safety concerns.

RideIndego is an accessible and affordable way to get to and from the July 4th Concert and Fireworks with stations conveniently located along the Parkway. Download the official Indego App to check real-time bike and dock availability at any station. Visit the Google Play or Apple App Store and look for the official Indego App.

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Dietz & Watson Half Marathon Nominated as One of the Country’s Best Half Marathons by USA Today’s 10Best Readers’ Choice Awards

The morning of the Philadelphia Marathon. Tens of thousands of runners are lined up at the start. The tree foliage is orange and red. The grand steps of the Art Museum are in the backgound.

Philadelphia Marathon Team: Reflections and Resolutions

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A Proclamation on the 60th Anniversary of the Civil Rights Act

Sixty years ago, President Lyndon B. Johnson signed into law one of the most significant pieces of civil rights legislation in history — the Civil Rights Act of 1964. It prohibited discrimination on the basis of race, color, religion, sex, or national origin. That day, our Nation moved closer to our North Star, the founding ideal of America: We are all created equal and deserve to be treated equally throughout our lives. We have never fully lived up to that idea, but we have never walked away from it either. On this anniversary, we promise we will not walk away from it now.

On July 2, 1964, when President Johnson signed the Civil Rights Act, a crowd gathered behind him. Among them were some of the Black leaders and activists who had led the movement that made it possible: Dr. Martin Luther King, Jr., A. Philip Randolph, Dr. Dorothy Height, and others, who had fought, protested, and prayed for this moment. It was the work of changemakers like them — from the countless protests, sit-ins, and marches they organized to the many speeches and sermons they gave — and the courageous activism and participation of everyday Americans that brought the bill to President Johnson’s desk. It was an answer to President John F. Kennedy’s call for national action to guarantee the equal treatment of every American regardless of race. Change only rippled out from there: The Civil Rights Act of 1964 also helped set the foundation for other critical legislation like the Voting Rights Act of 1965.

The legacies of those activists and extraordinary Americans have had a profound impact on me and others of my generation. It inspired me to become a public defender and then a county councilman, working to change our State’s politics to embrace the cause of civil rights. Their efforts made us fundamentally better as Americans, opening the doors of opportunity wider and bringing our Nation closer to its promise of liberty, equality, and justice for all.

Despite this critical step forward, securing our civil rights remains the unfinished fight of our time. Our country is still facing attacks to some of our most fundamental civil liberties and rights, including the right to vote and have that vote counted and the right to live free from the threat of violence, hate, and discrimination. That is why my Administration is remaining vigilant — fighting actively to protect the rights of every American. I signed the Electoral Count Reform Act to protect the will of the people and the peaceful transfer of power. I appointed Justice Ketanji Brown Jackson, the first Black woman to be on the Supreme Court, and have appointed more Black women to the Federal appellate court than every other President in history.

We are also working to reverse the shameful legacy of segregation in our country and creating new opportunities for every American to thrive. My Administration is investing more money than ever in Black families and Black communities. We are reconnecting historic business districts and neighborhoods cut off by old highways, redlining, and decades of discrimination and disinvestment. We have invested over $16 billion in Historically Black Colleges and Universities, which will help raise the next generation of Black leaders. At the same time, we are creating good-paying jobs that people can raise a family on; making capital and loans for starting small businesses and buying homes more accessible; and making health insurance and prescription drugs more affordable. I also signed the boldest policing reform Executive Order in history. My Administration is changing our Nation’s failed approach to marijuana, which disproportionately impacts communities of color. I have used executive clemency more than any recent President at this point in my Presidency in pursuit of justice and fairness. And I continue to call on the Congress to pass the John Lewis Voting Rights Advancement Act and the Freedom to Vote Act — it is the only way to fully secure the sacred right to vote.

On this anniversary, may we recommit to continuing the work that the Civil Rights Act began six decades ago — it is still the task of our time to build a democracy where every American is treated with dignity and has an equal opportunity to follow their dreams. We must continue to move forward together, stand with one another, and choose democracy over autocracy and beloved community over chaos. We must choose to be believers, dreamers, and doers.

NOW, THEREFORE, I, JOSEPH R. BIDEN JR., President of the United States of America, by virtue of the authority vested in me by the Constitution and the laws of the United States, do hereby proclaim July 2, 2024, as the 60th Anniversary of the Civil Rights Act. I call upon all Americans to observe this day with programs, ceremonies, and activities that celebrate this accomplishment and advance civil rights in our time.

IN WITNESS WHEREOF, I have hereunto set my hand this first day of July, in the year of our Lord two thousand twenty-four, and of the Independence of the United States of America the two hundred and forty-eighth. JOSEPH R. BIDEN JR.

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What happens when you use assignment inside of dplyr function R

I commonly see the mistake with my students the use of the assignment <- inside of dplyr functions. This results in the column name to be the assignment call.

I believe this behavior stems from the use of assignment in base R dollar assign notation

How should I go about explaining this behavior to my students?

The steps through the logic of how this happens in terms of the non-standard evaluation that dplyr uses are likely too complex for a beginner R class. A straightforward explanation for an R user who has some knowledge of the basics might be something like this:

Any expressions inside summarise are evaluated to get the value(s) that will be written into the column(s) of the resulting data frame. Typically, these expressions will be passed as named arguments so that we can control column names:

If the expression is passed as an unnamed argument, then summarise will capture the expression, convert it into a string, and use that for the column name. This is in addition to evaluating it for use as a value in the column.

The reason why we don't just get an error when we use assignment inside summarise is that assignment silently returns the assigned value:

So in your example, the expression

is evaluated (using the data mask so that Petal.Length is recognised as a column in the iris dataframe), to give the summary value for the column (3.758), but it is also captured to create the name of the column.

The learning points here for R beginners are

Assignment silently returns the assigned value
Tidyverse functions work differently from most R functions due to sophisticated use of non-standard evaluation . This makes many tasks easier, but one needs to learn the syntax rather than applying learning from base R.
If you want to create a new column inside summarise , you must use the = operator rather than the <- operator

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R Lists (With Examples)
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@ClarkThomborson The semantics are fundamentally different because in R assignment is a regular operation which is performed via a function call to an assignment function. However, this is not the case for = in an argument list. In an argument list, = is an arbitrary separator token which is no longer present after parsing. After parsing f(x = 1), R sees (essentially) call("f", 1).
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The LHS of the assignment expression is built using calls to c(). The RHS of the assignment expression may be any expression which returns or is a vector, list, nested list, data frame, character string, date object, or custom objects (assuming there is a destructure implementation).
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What happens when you use assignment inside of dplyr function R
The learning points here for R beginners are. Assignment silently returns the assigned value; Tidyverse functions work differently from most R functions due to sophisticated use of non-standard evaluation. This makes many tasks easier, but one needs to learn the syntax rather than applying learning from base R.

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R – Lists

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6 Lists and data frames

6.2 Constructing and modifying lists

6.2.1 Concatenating lists

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6.3.1 Making data frames

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6.3.3 Working with data frames

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