r filter assignment

Keep rows that match a condition

The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA the row will be dropped, unlike base subsetting with [ .

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods , below, for more details.

< data-masking > Expressions that return a logical value, and are defined in terms of the variables in .data . If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

< tidy-select > Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by() . For details and examples, see ?dplyr_by .

Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

An object of the same type as .data . The output has the following properties:

Rows are a subset of the input, but appear in the same order.

Columns are not modified.

The number of groups may be reduced (if .preserve is not TRUE ).

Data frame attributes are preserved.

The filter() function is used to subset the rows of .data , applying the expressions in ... to the column values to determine which rows should be retained. It can be applied to both grouped and ungrouped data (see group_by() and ungroup() ). However, dplyr is not yet smart enough to optimise the filtering operation on grouped datasets that do not need grouped calculations. For this reason, filtering is often considerably faster on ungrouped data.

Useful filter functions

There are many functions and operators that are useful when constructing the expressions used to filter the data:

== , > , >= etc

& , | , ! , xor()

between() , near()

Grouped tibbles

Because filtering expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped filtering:

With the grouped equivalent:

In the ungrouped version, filter() compares the value of mass in each row to the global average (taken over the whole data set), keeping only the rows with mass greater than this global average. In contrast, the grouped version calculates the average mass separately for each gender group, and keeps rows with mass greater than the relevant within-gender average.

This function is a generic , which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: dbplyr ( tbl_lazy ), dplyr ( data.frame , ts ) .

Other single table verbs: arrange () , mutate () , reframe () , rename () , select () , slice () , summarise ()

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How to filter in r: a detailed introduction to the dplyr filter function.

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Data wrangling. It’s the process of getting your raw data transformed into a format that’s easier to work with for analysis.

It’s not the sexiest or the most exciting work.

In our dreams, all datasets come to us perfectly formatted and ready for all kinds of sophisticated analysis! In real life, not so much.

It’s estimated that as much as 75% of a data scientist’s time is spent data wrangling. To be an effective data scientist, you need to be good at this, and you need to be FAST.

One of the most basic data wrangling tasks is filtering data. Starting from a large dataset, and reducing it to a smaller, more manageable dataset, based on some criteria.

Think of filtering your sock drawer by color, and pulling out only the black socks.

Whenever I need to filter in R, I turn to the dplyr filter function.

As is often the case in programming, there are many ways to filter in R. But the dplyr filter function is by far my favorite, and it’s the method I use the vast majority of the time.

Why do I like it so much? It has a user-friendly syntax, is easy to work with, and it plays very nicely with the other dplyr functions.

A brief introduction to dplyr

Before I go into detail on the dplyr filter function, I want to briefly introduce dplyr as a whole to give you some context.

dplyr is a cohesive set of data manipulation functions that will help make your data wrangling as painless as possible.

dplyr, at its core, consists of 5 functions, all serving a distinct data wrangling purpose:

  • filter() selects rows based on their values
  • mutate() creates new variables
  • select() picks columns by name
  • summarise() calculates summary statistics
  • arrange() sorts the rows

The beauty of dplyr is that the syntax of all of these functions is very similar, and they all work together nicely.

If you master these 5 functions, you’ll be able to handle nearly any data wrangling task that comes your way. But we need to tackle them one at a time, so now: let’s learn to filter in R using dplyr!

Loading Our Data

In this post, I’ll be using the diamonds dataset, a dataset built into the ggplot package, to illustrate the best use of the dplyr filter function. To start, let’s take a look at the data:

We can see that the dataset gives characteristics of individual diamonds, including their carat, cut, color, clarity, and price.

Our First dplyr Filter Operation

I’m a big fan of learning by doing, so we’re going to dive in right now with our first dplyr filter operation.

From our diamonds dataset, we’re going to filter only those rows where the diamond cut is ‘Ideal’:

As you can see, every diamond in the returned data frame is showing a cut of ‘Ideal’. It worked! We’ll cover exactly what’s happening here in more detail, but first let’s briefly review how R works with logical and relational operators, and how we can use those to efficiently filter in R.

A brief aside on logical and relational operators in R and dplyr

In dplyr, filter takes in 2 arguments:

  • The dataframe you are operating on
  • A conditional expression that evaluates to TRUE or FALSE

In the example above, we specified diamonds as the dataframe, and cut == 'Ideal' as the conditional expression

Conditional expression? What am I talking about?

Under the hood, dplyr filter works by testing each row against your conditional expression and mapping the results to TRUE and FALSE . It then selects all rows that evaluate to TRUE .

In our first example above, we checked that the diamond cut was Ideal with the conditional expression cut == 'Ideal' . For each row in our data frame, dplyr checked whether the column cut was set to 'Ideal' , and returned only those rows where cut == 'Ideal' evaluated to TRUE .

In our first filter, we used the operator == to test for equality. That’s not the only way we can use dplyr to filter our data frame, however. We can use a number of different relational operators to filter in R.

Relational operators are used to compare values. In R generally (and in dplyr specifically), those are:

  • == (Equal to)
  • != (Not equal to)
  • < (Less than)
  • <= (Less than or equal to)
  • > (Greater than)
  • >= (Greater than or equal to)

These are standard mathematical operators you're used to, and they work as you'd expect. One quick note: make sure you use the double equals sign ( == ) for comparisons! By convention, a single equals sign ( = ) is used to assign a value to a variable, and a double equals sign ( == ) is used to check whether two values are equal. Using a single equals sign will often give an error message that is not intuitive, so make sure you check for this common error!

dplyr can also make use of the following logical operators to string together multiple different conditions in a single dplyr filter call!

  • ! (logical NOT)
  • & (logical AND)
  • | (logical OR)

There are two additional operators that will often be useful when working with dplyr to filter:

  • %in% (Checks if a value is in an array of multiple values)
  • is.na() (Checks whether a value is NA)

In our first example above, we tested for equality when we said cut == 'Ideal' . Now, let's expand our capabilities with different relational parameters in our filter:

Here, we select only the diamonds where the price is greater than 2000.

And here, we select all the diamonds whose cut is NOT equal to 'Ideal'. Note that this is the exact opposite of what we filtered before.

You can use < , > , <= , >= , == , and != in similar ways to filter your data. Try a few examples on your own to get comfortable with the different filtering options!

A note on storing your results

By default, dplyr filter will perform the operation you ask and then print the result to the screen. If you prefer to store the result in a variable, you'll need to assign it as follows:

Note that you can also overwrite the dataset (that is, assign the result back to the diamonds data frame) if you don't want to retain the unfiltered data. In this case I want to keep it, so I'll store this result in e_diamonds . In any case, it's always a good idea to preview your dplyr filter results before you overwrite any data!

Filtering Numeric Variables

Numeric variables are the quantitative variables in a dataset. In the diamonds dataset, this includes the variables carat and price, among others. When working with numeric variables, it is easy to filter based on ranges of values. For example, if we wanted to get any diamonds priced between 1000 and 1500, we could easily filter as follows:

In general, when working with numeric variables, you'll most often make use of the inequality operators, > , < , >= , and <= . While it is possible to use the == and != operators with numeric variables, I generally recommend against it.

The issue with using == is that it will only return true of the value is exactly equal to what you're testing for. If the dataset you're testing against consists of integers, this is possible, but if you're dealing with decimals, this will often break down. For example, 1.0100000001 == 1.01 will evaluate to FALSE . This is technically true, but it's easy to get into trouble with decimal precision. I never use == when working with numerical variables unless the data I am working with consists of integers only!

Filtering Categorical Variables

Categorical variables are non-quantitative variables. In our example dataset, the columns cut, color, and clarity are categorical variables. In contrast to numerical variables, the inequalities > , < , >= and <= have no meaning here. Instead, you'll make frequent use of the == , != , and %in% operators when filtering categorical variables.

Above, we filtered the dataset to include only the diamonds whose cut was Ideal using the == operator. Let's say that we wanted to expand this filter to also include diamonds where the cut is Premium. To accomplish this, we would use the %in% operator:

How does this work? First, we create a vector of our desired cut options, c('Ideal', 'Premium') . Then, we use %in% to filter only those diamonds whose cut is in that vector. dplyr will filter out BOTH those diamonds whose cut is Ideal AND those diamonds whose cut is Premium. The vector you check against for the %in% function can be arbitrarily long, which can be very useful when working with categorical data.

It's also important to note that the vector can be defined before you perform the dplyr filter operation:

This helps to increase the readability of your code when you're filtering against a larger set of potential options. This also means that if you have an existing vector of options from another source, you can use this to filter your dataset. This can come in very useful as you start working with multiple datasets in a single analysis!

Chaining together multiple filtering operations with logical operators

The real power of the dplyr filter function is in its flexibility. Using the logical operators &, |, and !, we can group many filtering operations in a single command to get the exact dataset we want!

Let's say we want to select all diamonds where the cut is Ideal and the carat is greater than 1:

BOTH conditions must evaluate to TRUE for the data to be selected. That is, the cut must be Ideal, and the carat must be greater than 1.

You don't need to limit yourself to two conditions either. You can have as many as you want! Let's say we also wanted to make sure the color of the diamond was E. We can extend our example:

What if we wanted to select rows where the cut is ideal OR the carat is greater than 1? Then we'd use the | operator!

Any time you want to filter your dataset based on some combination of logical statements, this is possibly using the dplyr filter function and R's built-in logical parameters. You just need to figure out how to combine your logical expressions to get exactly what you want!

dplyr filter is one of my most-used functions in R in general, and especially when I am looking to filter in R. With this article you should have a solid overview of how to filter a dataset, whether your variables are numerical, categorical, or a mix of both. Practice what you learned right now to make sure you cement your understanding of how to effectively filter in R using dplyr!

Did you find this post useful? I frequently write tutorials like this one to help you learn new skills and improve your data science. If you want to be notified of new tutorials, sign up here!

I help technology companies to leverage their data to produce branded, influential content to share with their clients. I work on everything from investor newsletters to blog posts to research papers. If you enjoy the work I do and are interested in working together, you can visit my consulting website or contact me at [email protected] !

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Basic Analytics in R

Lesson 4 filtering data.

We often want to “subset” our data. That is, we only want to look at data for a certain year, or from a certain class products or customers. We generally call this process “filtering” in Excel or “selection” in SQL. The key idea is that we use some criteria to extract a subset of rows from our data and use only those rows in subsequent analysis.

There are two ways to subset data in R:

  • Use R’s built in data manipulation tools. These are easily identified by their square bracket [] syntax.
  • Use the dplyr library. Think of dplyr as “data pliers” (where pliers are very useful tools around the house).

I personally find dplyr much easier to use than the square bracket notation, so that is what we will use.

4.1 Preliminaries

4.1.1 import data.

Import the Bank data in the normal way in R Studio. You can either use Tools –> Import Dataset from within R Studio or run the command line version of the import functions from the tidyverse. I typically use the menu the first time but then save the command line version created by R Studio.

Click on the Bank tibble in the panel at the top right of R Studio to inspect the contents of the imported file.

4.2 Filters

4.2.1 using a logical critereon.

The easiest way to filter is to call dplyr’s filter function to create a new, smaller tibble: <new tibble> <- filter(<tibble>, <critereon>)

For example:

The new tibble is called FemaleEmployees (although you can call it anything). The source tibble is, of course, the Bank tibble. The logical criterion is Gender=="Female" . A few things to note about the logical criterion:

  • Gender is the name of a column in the Bank tibble.
  • The logical comparison operator for equals is == , not = . This is the convention in many computer programming languages in which the single equals sign is the assignment operator. In R, <- is the assignment operator and == is the equals comparison operator. If you make a mistake in filtering, it is almost always because you use = instead of == .
  • “Female” is a literal string. It means: Only keep rows in which the value of Gender is exactly equal to “Female”. The string “female” is not close enough. A literal string is a literal string.

4.2.2 Filtering Using a List

One very powerful trick in R is to extract rows that match a list of values. For example, say we wanted to extract a list of managers. In this dataset, managers have a value of JobGrade >= 4, so we could use a logical criterion:

Note that there is no assignment operator here, so I have not created a new tibble. R simply summarizes the results in the console window.

The problem with this approach is that it requires job grades to be numeric (and thus ordinal). I could accomplish the same thing in a more general way using a list of the job grades I want to include:

  • Create a new vector of managerial job grades using the “combine” function, c() . I call the resulting vector “Mgmt”.
  • Use the is.element() function to test membership in the list for each employee. The full syntax is: is.element(x, y) . The function returns TRUE if x is a member of y and FALSE otherwise.

I did not have to put the members of Mgmt in quotation marks because JobGrade is an integer. If my list contains text I have to use quotation marks:

Animals <- c("cat", "dog", "horse", "pig")

4.3 Syntatic sugar

Many computer languages offer “syntactic sugar”: shortcuts that make long or complex commands a bit easier to type. The tidyverse packages offers a couple of sweeteners. The important thing to remember about these shortcuts is that they (generally) only work in tidyverse packages.

4.3.1 Membership

Instead of remembering the syntax of is.element(x, y) , you can use the alternative %in% . This makes the filter syntax a bit more readable. As you see from the output, the results are identical to the un-sweetened version.

4.3.2 Pipes

Pipes are use to solve the problem of nested function calls. A nested function occurs whenever the argument of f() is itself a function g() . As you have probably discovered, it is hard to keep the parentheses straight when you write long statements of the form: f(g(x)) .

A pipe takes the result of the interior function then pass it along to the exterior function. So f(g(x)) can be rewritten using a pipe: g(x) %>% f . This can be helpful for very long, multi-line statements in R. Just read the pipe operator %>% as “THEN”.

To illustrate, start with the tibble Bank THEN filter it THEN view it:

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Filter rows in R with dplyr

Filter rows in R with dplyr

The filter function from dplyr subsets rows of a data frame based on a single or multiple conditions. In this tutorial you will learn how to select rows using comparison and logical operators and how to filter by row number with slice .

Sample data

The examples inside this tutorial will use the women data set provided by R. This data set contains two numeric columns: height and weight .

Filtering rows based on a single condition

The filter function allows to subset rows of a data frame based on a condition . You can filter the values equal to, not equal to, lower than or greater than a value by specifying the desired condition within the function.

The following table contains the comparison operators in R and their descriptions.

Comparison operator Description
> Greater than
< Lower than
>= Greater or equal than
<= Lower or equal than
== Equal to
!= Not equal to

For example, if you want to filter the rows where the height column is greater than 68 you can write the following:

Filter rows with dplyr in R

The filtering can be based on a function . The following example selects the rows of the data frame where the height is equal or lower to the mean of the column.

Filter rows with dplyr using the mean function

It is also possible to filter rows using logical operators or functions that return TRUE or FALSE or a combination of them. The most common are shown in the table below.

Operator/function Description
! Logical negation ‘NOT’
%in% In the set
!(x %in% y) x not in y
is.na() Is NA
!is.na() Is not NA
grepl() Contains a pattern
!grepl() Does not contain a pattern

Consider that you want to filter the rows in which the height column takes the value 65, 70 and 72. For this you can use the %in% operator and filter the rows by a vector.

Select rows with dplyr in R

The opposite of a condition can be selected with the logical negation operator ! . The example below shows how to select the opposite of the filtering made on the previous code.

Filter rows not in a vector with dplyr

To filter rows containing a specific string you can use grepl or str_detect . The following example filters the rows containing a specific pattern (e.g. rows of height containing a 5).

Subset rows that contain a string with the filter function from dplyr in R

Multiple conditions

Row filtering can also be based on multiple conditions to filter, for instance, rows where a value is in a specific range or to filter between dates. For this you will need to use logical operators, such as & to specify one AND another condition , and | to specify one OR another condition .

Logical operator Description
& Elementwise logical ‘AND’
| Elementwise logical ‘OR’
xor() Elementwise exclusive ‘OR’. Equivalent to !(x | y)

The example below selects rows whose values in the height column are greater than 65 and lower than 68.

Filter rows by multiple conditions with dplyr

The multiple conditions can be based on multiple columns . In the following block of code we are selecting the rows whose values in height are greater than 65 and whose values in weight are lower or equal to 150.

Filter rows using dplyr based on several conditions

In case you need to subset rows based on a condition OR on another you can use | . The example below filters the rows whose values in height area greater than 65 or whose values in weight are greater or equal to 150.

Subset rows based on a condition or on another with the filter function from dplyr package

Filter by row number with slice

A similar function related to filter is slice , which allows to filter rows based on its index/position . The function takes a sequence or vector of indices (integers) as input, as shown below.

First rows of a data frame with the slice function from dplyr

In addition, the slice_head function allows to select the first row of the data frame. This function provides an argument named n to select the n first rows.

First row of a data frame with the slice_head function from dplyr

Finally, if you need to select the last row you can use slice_tail . This function also provides an argument named n to select the last n rows of the data frame.

Last row of a data frame with the slice_tail function from dplyr

slice_sample selects rows randomly and slice_min and slice_max selects the rows with the lowest or highest values of a variable, respectively.

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Select columns in R with dplyr

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Select or remove columns from a data frame with the select function from dplyr and learn how to use the contains, matches, all_of, any_of, starts_with, ends_with, last_col, where and everything functions

Create and modify columns in R with the mutate() function from dplyr

Create and modify columns in R with the mutate() function from dplyr

The mutate function in the dplyr package is used to create new columns or modify existing columns in a data frame, while retaining the original structure

Order rows in R with the arrange() function from dplyr

Order rows in R with the arrange() function from dplyr

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r filter assignment

Sharp Sight

A Quick and Dirty Guide to the Dplyr Filter Function

You’ve probably heard it before: 80% of your work as a data scientist will be data wrangling .

While that’s sort of a rough number, experience bears out that data wrangling is a massive part of your job as a data scientist.

As such, it pays to know data manipulation. In fact, it pays to be really f*king good at data manipulation.

And when I say that it “pays,” I sort of mean that literally. If you want to get hired and get paid , your data wrangling skills should be solid.

At minimum, you need to know how to do several key data wrangling skills:

  • Create new variables
  • Summarise data (i.e. calculating summary statistics)
  • Select specific columns
  • Subset rows

In this blog post, we’ll talk about the last one: how to subset rows and filter your data.

r filter assignment

What is the filter() function?

There are several ways to subset your data in R.

For better or for worse though, some ways of subsetting your data are better than others. Hands down, my preferred method is the filter() function from dplyr .

In this blog post, I’ll explain how the filter() function works.

Before I do that though, let’s talk briefly about dplyr , just so you understand what dplyr is, how it relates to data manipulation. This will give you some context for learning about filter() .

A quick introduction to dplyr

For those of you who don’t know, dplyr is a package for the R programing language.

dplyr is a set of tools strictly for data manipulation . In fact, there are only 5 primary functions in the dplyr toolkit:

  • filter() … for filtering rows
  • select() … for selecting columns
  • mutate() … for adding new variables
  • summarise() … for calculating summary stats
  • arrange() … for sorting data

dplyr also has a set of helper functions, so there’s more than these 5 tools, but these 5 are the core tools that you should know.

Subsetting data with dplyr filter

Let’s talk about some details.

How does filter() work?

How the dplyr filter function works

filter() and the rest of the functions of dplyr all essentially work in the same way.

When you use the dplyr functions, there’s a dataframe that you want to operate on. There’s also something specific that you want to do.

The dplyr functions have a syntax that reflects this.

First, you just call the function by the function name.

Then inside of the function, there are at least two arguments.

A simple example explaining how the dplyr filter function works

The first argument is the name of the dataframe that you want to modify. In the above example, you can see that immediately inside the function, the first argument is the dataframe.

Next, is exactly how we want to filter the data. To specify how, we will use set of logical conditions to specify the rows that we want to keep. Everything else will get “filtered” out.

Using logic to filter your rows

Since we need to use logic to specify how to filter our data, let’s briefly review how logic works in R.

In R, we can make logic statements that are evaluated as true or false. Remember that R has special values for true and false: True or False .

Ok. Here’s a quick example of a logic statement that you can type into R:

Is 10 greater than 1? Yes! Of course.

As such, R will evaluate a this logic statement as TRUE .

Although this logic statement is fairly simple, logic statements can be more complicated. We can use operators to combine simple logic statements into more complex logic statements.

The simple logical operators are:

We can use these to combine simple logic conditions into expressions that are more complex. For example:

In the above example we have two simple logic expressions that have been combined with the ‘ & ‘ operator.

Essentially, this statement is evaluating the following: Is 10 greater than 1 AND is 1 not equal to 2.

This statement is true. It’s true that 10 is greater than 1 and it’s also true that 1 is not equal to 2. Since both are true, the overall statement will be evaluated as true (remember … the & operator requires both sides to be True ).

This sort of logic is important if you want to use the dplyr filter function. That being the case, make sure that you understand logic in R. It’s beyond the scope of this blog post to completely explain logic, so if you’re confused by this, you’ll need to do more reading about logic in R.

Why we need logic to use dplyr’s filter function

So why the digression and review of logic in R?

Because you need to use logical expressions to use the filter() function properly.

In some cases, we might want to filter the data with a simple expression … like keeping rows where a variable is greater than 10.

In other cases, we use much more complicated expressions.

But either way, logic is critical.

Examples of how to use the filter() function

So far, the explanation might seem a little abstract, so let’s take a look at some concrete examples.

We’ll start simple, and then increase the complexity.

Having said that, even before we actually filter the data, we’ll perform some preliminary work.

Load packages

First, we’ll just load the tidyverse package. Keep in mind that the tidyverse package is a collection of packages that contains dplyr , ggplot2 , and several other important data science packages.

In this blog post, dplyr and ggplot2 are important because we’ll be using both. Obviously, we’ll need dplyr because we’re going to practice using the filter() function from dplyr .

Additionally, we’re going to work with the txhousing dataset, which is included in the ggplot2 package. So, we’ll need to have ggplot2 loaded as well.

Again, loading the tidyverse package will automatically load both dplyr and ggplot2 , so we have them both covered.

Inspect data

Next, we’ll quickly inspect the data.

There are a few ways to do this, but I often use the glimpse() function. glimpse() provides quite a bit of information (like data types, row counts, etc) and the output is well formatted.

r filter assignment

When inspecting your data, you’ll want to pay attention to a few things.

First, you’ll want to look at the variables. Knowing the variables and what they contain will give you a few ideas about how you can filter your data. For example, when looking at the data, I immediately think about filtering the data down to a particular year, or filtering to return records above a particular value for median . Essentially, looking at the data will spark some ideas about how you might want to subset.

Second, pay attention to the number of rows. The total number of rows in a dataset can be a useful piece of information to capture. You might want to write it down in a little notebook as you’re analyzing your data. The reason is that as you filter, subset, and otherwise wrangle your data, it can be useful to know the original number of records.

For example, if you split your data and then perform some manipulations on that data, you might need to check that your two different datasets still collectively contain the same number of rows as the original dataset.

Essentially, knowing the original number of rows can help you “check your work” as you move through an analysis. I won’t discuss data analysis workflow in detail here, but understand that you should pay attention to the number of records.

A simple example of filter()

Let’s start with a very simple example.

Simple example of the dplyr filter function, filtering for year == 2001

Pay attention to a few things.

First, at a quick glance, it appears that the records were filtered correctly. All of the rows have year equal to 2001 .

Second, there are 552 records in the output dataframe. At the very least, this tells us that the filter() operation did create a subset.

As a quick check, we can take a look at the number of observations for every value of year in txhousing :

Summary of the count of records for every value of year in the txhousing dataframe.

By summarizing the data by year , we can look specifically at the number of records for the year 2001 . We’re doing this to check that our filter operation worked correctly. This summary table shows that there is 522 records for the year 2001 , which matches the number of records when we filtered our data with filter(txhousing, year == 2001) . So, it looked like our use of filter() worked correctly.

Keep in mind, checking your data like this can be useful when you’re performing data manipulation.

Filter data using two logical conditions

In our last example, we filtered the data on a very simple logical condition. We filtered the data and kept only the records where year is exactly 2001 .

What if we want to filter on several conditions?

To do that, we need to use logical operators.

Example: year equal to 2001 AND city equal to ‘Abilene’

Let’s take a look at a concrete example. In this new example, let’s extend the previous example. Previously, we filtered the data to keep only the records where year == 2001 .

Now, we’ll keep records where year == 2001 and city == 'Abilene' .

Syntactically, here’s what that looks like:

And here’s the output:

r filter assignment

This should make sense if you already understood the previous examples.

We’re still applying the dplyr filter() function. The first argument is the dataframe that we’re manipulating, txhousing .

The next argument (after the comma) is a mildly complex logical statement. Here, we’re telling the filter() function that we only want to return rows of data where the year variable is equal to 2001 , and the city variable is equal to 'Abilene' .

Again, this is pretty easy to understand, because the syntax almost reads like pseudocode.

A critical part of this syntax that you need to understand is the “and” operator: & . This requires that both conditions be true. Year must be equal to 2001 and the city must be Abilene. If both conditions are not met for a particular row, that row will be “filtered” out.

So that’s how filter() works. It’s checking logical conditions. If the logical condition or conditions are not met, then the row is filtered out.

Example: city equal Austin OR city equals Houston

Let’s try another example.

Here, we’ll keep rows where city equals Austin or city equals Houston.

To do this, we will use the ‘or’ operator, which is the vertical bar character: |.

Example of dplyr filter where city == Austin or city == Houston.

This is very straightforward.

filter() will keep any row where city == 'Austin' or city == 'Houston' . All of the other rows will be filtered out.

Filtering using the %in% operator

Let’s say that you want to filter your data so that it’s in one of three values.

For example, let’s filter the data so the returned rows are for Austin, Houston, or Dallas.

One way of doing this is stringing together a series of statements using the ‘or’ operator, like this:

This works, but frankly, it’s a bit of a pain in the ass. It’s a little verbose.

There’s another way to do this in R using the %in% operator:

Dplyr filter example where city is Austin, Houston, or Dallas.

Basically, this returns the records where city is “in” the set of options consisting of Austin, Houston, or Dallas.

It can get more complicated, but master the basics first

Filtering data can get more complicated.

You can filter by using even more complicated logical expressions. There are also other more complicated techniques that you can perform by using functions and dplyr helper functions along with filter() .

Having said that, make sure you master the basic techniques first before you start working on more complicated techniques.

A quick warning: save your new dataframes with new names

Before wrapping this up, I want to mention one “quirk” about the filter() function.

This is important, so pay attention …

When you use the filter() function, it does not modify the original dataframe.

Let me show you an example. Here, we’ll use the filter operation on txhousing to filter the data to rows where city == 'Houston' .

Now, let’s inspect the data using glimpse() .

A view of the TX housing data.

There’s still 8,602 rows. You can immediately see that the data still contains records where the city variable is Abilene.

What the hell?! Didn’t we just filter the data?

Yes, but you need to understand that the filter() function doesn’t change the original dataset. I repeat: filter() does not filter rows out of the input dataset.

Instead, filter() returns a new dataset that contains the appropriate rows.

What that means is that if you run the examples I’ve shown you so far in this blog post, they will not change the original dataset. The new filtered data is just returned and sent directly to the terminal.

If you want to save this new filtered data (instead of having it sent directly to the terminal), you need to save it with a new name using the assignment operator.

For example, you could perform the filter operation above and give the output dataframe a new name: txhousing_houston .

Now, if we examine txhousing_houston , you'll see that it contains the appropriate filtered rows.

Filtered txhousing data with rows only where city is Houston.

The data now have 187 rows, and at a quick glance, it appears that they are all for records where city is Houston. This looks correct.

Basically, I just want to remind you and reiterate that if you want to save and continue working with the filtered data that comes out of the filter() function, you need to save that data with a new name.

Questions? Leave a comment below

Still confused about the dplyr filter function?

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14 thoughts on “A Quick and Dirty Guide to the Dplyr Filter Function”

Say I have a dataset with an ID variable “ID.” And there are 3 other columns, the first is a binomial variable “X” with the next “Y” being a further much more specific identifier with thousands of options. And finally “Z” being the value of interest for each row. So there’s

“ID” “X” “Y” Z”

Is there a way for me to use dplyr filter function to subset only the rows (with all 4 variables) in which for EACH ID, “Y” is represented in both values of “X?”

You’ve got to give me a working example so I can see what you’re talking about more specifically.

If you can give me a working dataset, I can probably help.

I would like to master the Data Manipulation and EDA but in the part of Data Manipulation, I’m struggling in terms of base R. Also, on the other hand, I’m finding dplyr very easy for manipulation. How I should I master the manipulation part using both base R and dplyr ? Please give me some guideline about how to practise and how should I start ? I will be very thankful as I’ve become very nervous because, I’m getting errors in small datasets !

Thank you @sharpsight !

For the most part, you should forget about data manipulation with base R.

Learn the 5 major “verbs” of dplyr , and practice them over and over with very simple examples until you have the basic techniques completely memorized.

After you’ve memorized the basic techniques, increase the complexity of your practice examples … make things slightly more difficult over time.

Then start combining dplyr with ggplot2 (which you should also memorize by practicing on simple examples).

The basic process: – Start with simple examples – Repeat your practice activities until you have the syntax memorized – Learn new (less common) techniques – Increase the complexity of your practice activities.

Hi there, I am learning R with tidy verse. I have a question about comparing base R & dplyr. I try to use filter() from dplyr and data[x == 0 ,] on same data set. however, both ways returns different result. what could be the reason?

It’s difficult to tell the exact reason without seeing a working dataset and the results.

If you can post a working example that shows the differences, I might be able to give some insight.

I’ve been trying to do operations such as this, do you know why these won’t work after the filter function?

test %>% filter(Name==’Jason’) %>% select(Counter) %>% max()

It’s hard to say exactly because I can’t run your code. To really check this out, you’d need to provide me with a working example that I can run (i.e., a dataframe and some code).

Having said that, take a look at the single quotes surrounding the value Jason .

When I paste your code into R studio, those single quote are “curly” quotes, and don’t function properly.

Straight quotes might fix your problem:

Thanks for the reply, forgot to get back!

But essentially, the filter part of dplyr works but I am having trouble applying a function (like max()) onto the filtered result

oops! just found it, i can use summarise( max = max(Counter))

Thanks though!

Can i use the filter() function in R with out using dplyr package?

Not really. At the very least, you’ll need to have dplyr installed .

Once it’s installed, we typically load it with the code library(dplyr) .

Alternatively, if you don’t want to load the whole package, you can call filter alone by using dplyr::filter()

Hi, Are there helpers for filters similar to the Contains and Starts with helpers in the Select function?

It really depends on what data type you’re working with.

If you’re working with a column that contains string data, then you can use the string manipulation functions from Stringr .

For example:

The above will retrieve rows where the clarity variable contains the letter “V.”

You can use str_starts() to detect strings that start with a particular substring, and you can use str_ends() to detect strings that start with a particular substring. You can also use str_detect to detect strings based on regular expressions, which can be quite complex.

These will probably help you, but again, it really depends on what type of data you’re working with.

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filter: Subset rows using column values

Description.

The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA the row will be dropped, unlike base subsetting with [ .

An object of the same type as .data . The output has the following properties:

Rows are a subset of the input, but appear in the same order.

Columns are not modified.

The number of groups may be reduced (if .preserve is not TRUE ).

Data frame attributes are preserved.

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods , below, for more details.

< data-masking > Expressions that return a logical value, and are defined in terms of the variables in .data . If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

Useful filter functions

There are many functions and operators that are useful when constructing the expressions used to filter the data:

== , > , >= etc

& , | , ! , xor()

between() , near()

Grouped tibbles

Because filtering expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped filtering:

With the grouped equivalent:

In the ungrouped version, filter() compares the value of mass in each row to the global average (taken over the whole data set), keeping only the rows with mass greater than this global average. In contrast, the grouped version calculates the average mass separately for each gender group, and keeps rows with mass greater than the relevant within-gender average.

This function is a generic , which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: dplyr:::methods_rd("filter").

The filter() function is used to subset the rows of .data , applying the expressions in ... to the column values to determine which rows should be retained. It can be applied to both grouped and ungrouped data (see group_by() and ungroup() ). However, dplyr is not yet smart enough to optimise the filtering operation on grouped datasets that do not need grouped calculations. For this reason, filtering is often considerably faster on ungrouped data.

Other single table verbs: arrange() , mutate() , rename() , select() , slice() , summarise()

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Introduction to R - tidyverse

5 manipulating data with dplyr.

The dplyr package, part of the tidyverse, is designed to make manipulating and transforming data as simple and intuitive as possible.

A guiding principle for tidyverse packages (and RStudio), is to minimize the number of keystrokes and characters required to get the results you want. To this end, as for ggplot, in dplyr, quotation marks for the column names of data frames are often not required. Another key feature of the tidyverse data wrangling packages such as dplyr, is that the input to and output from all functions, are data frames.

dplyr features a handful of key functions, also termed ‘verbs’, which can be combined to achieve very specific results. You will notice similarities to the functions available in Microsoft Excel.

We will explore the first of these verbs using the mpg_df dataset created earlier. If starting from a new Rstudio session you should open Week_2_tidyverse.R and run the following code:

5.1 filter()

The filter() function subsets the rows in a data frame by testing against a conditional statement. The output from a successful filter() will be a data frame with fewer rows than the input data frame.

Let’s filter the mpg_df data for cars manufactured in the year 1999:

Here we are ‘sending’ the mpg_df data frame into the function filter(), which tests each value in the year column for the number 1999, and returns those rows where the filter() condition is TRUE.

If you are working in an R text document (.R format) or directly in the console, after running this command you will see the dimensions of the output data frame printed in grey text above the column names.

Alternatively you can ‘send’ the output of filter (a data frame) into the dim() function.

We can also filter on character data. For example, let’s take all vehicles in the ‘midsize’ class:

Can you filter mpg_df for all vehicles except the Hyundais?

5.1.1 Logical operations

5.1.1.1 & and.

We can achieve more specific filters by combining conditions across columns. For example, we use the “&” sign to filter for vehicles built in 1999 and with mileage in the city (cty) greater than 18.

To see the entire output you can pipe the output from filter into a View() command

5.1.1.2 | or

Alternatively we might want to filter for vehicles (i.e., rows) where the manufacturer is Chevrolet or the class is ‘suv’. This requires the “|” symbol (shift + \)

5.1.1.3 and/or

To take it a step further we can combine & and | in the same filter command. Adding curved brackets will help to clarify the order of operations.

Let’s filter for the vehicles where the manufacturer is Chevrolet or the class is ‘suv’, and all vehicles with highway mileage less than 20.

5.1.2 str_detect() helper function

Often we want to capture rows containing a particular sequence of letters. For example, there are 10 different vehicle models containing the letters ‘4wd’. We don’t want to have to write an ‘or’ command with 10 alternatives.

A much better way is to ‘detect’ the letters ‘4wd’ in the model column, and return all rows where they are present, using str_detect().

str_detect() is a command within filter() which requires the column name, followed by the letters (in quotes) to search for

Note that the letter order and case have to be matched exactly.

How would you filter for all vehicles with automatic transmission?

5.1.3 %in% helper

When we are interested in a subset of rows that can contain several different values, instead of writing a long OR command, its useful to just give a vector of values of interest. For example, to take the subset of the vehicles in mpg_df that have 4, 5, or 6 cylinders, we can specify cyl %in% c(4,5,6)

5.1.4 is.na() helper

If there are NA (missing) values in a particular column, we can inspect or drop them using the is.na() helper.

To check for the presence of NA values in the year column, for example:

The mpg data set doesn’t contain any missing values, however in later chapters we will encounter them.

Any rows with a missing value in the year column would be dropped using the code

5.1.5 complete.cases() helper

Similar to is.na(), we can check for the presence of NA values across all columns of a dataframe using complete.cases(). This function is not part of the tidyverse package, so it requires a period . within the brackets, to indicate that we want to search across the entire dataframe. To filter for only the rows with no missing values:

And to filter for all rows with a missing value in at least one column:

5.2 select()

Whereas filter() subsets a dataframe by row, select() returns a subset of the columns.

This function can take column names (even without quotes), or the column position number beginning at left. Further, unlike in base R, commands within the brackets in select() do not need to be concatenated using c().

Let’s extract the car model, engine volume (displ) and highway mileage (hwy) from mpg_df:

We can use ‘-’ to extract all except particular column(s). For example, to drop the model and year columns:

We can also specify column positions . Take the data in columns number 1,5 and 11

Or combine column positions and names:

5.2.1 contains() helper function

contains() is a helper function used with select(), which is analogous to the str_detect() helper used with filter().

To select only columns with names containing the letter ‘y’:

contains() is also useful for selecting all column names featuring a certain character, e.g. contains(’_’)

5.2.2 starts_with() helper function

start_with() and ends_with() offer more specificity for select(). If we want all columns beginning with the letter ‘c’:

Happily we can even mix these helper functions with the standard select commands:

5.2.3 everything() helper function

Lastly for select(), a very useful helper is the everything() function, which returns all column names that have not been specified. It is often used when reordering all columns in a dataframe:

Note that the dimensions of the dataframe have not changed, merely the column order.

5.3 arrange()

arrange() is the simplest of the dplyr functions, which orders rows according to values in a given column. The default is to order numbers from lowest -> highest.

Let’s try ordering the vehicles by engine size (displ)

We can refine the order by giving additional columns of data. To order rows by manufacturer name (alphabetical), then by engine size then by city mileage:

5.3.1 desc() helper function

To invert the standard order, we can use the ‘descending’ desc() helper function. To find the most fuel-efficient vehicles when on the highway, we could use:

5.4 Chaining dplyr functions

Coding from left-to-right using the pipe %>% allows us to make ‘chains’ of commands to achieve very specific results.

Let’s filter for the midsize vehicles, then select the columns class, manufacturer, displ and year, and arrange on engine size (displ):

Using line-breaks makes the order of operations very easy to read (and fix if necessary). Once we’re happy with the output of this chain of functions, we can assign it to a new object (aka variable) in the environment:

Note that all of the functions will be performed before the output is assigned into mpg_slim. Therefore even though mpg_slim is at the top of the code, it will contain the final output dataframe.

5.5 Writing data to a file

The new mpg_slim data frame could be saved to a file outside of the R session using write_tsv()

write_tsv() creates a tab-separated file that can be read by applications like Excel. We first give the variable name, then the file name (ideally with a full directory location):

We will learn how to read data in to R in the next chapter.

5.6 Chaining dplyr and ggplot

We can also send the dplyr output directly into ggplot!

r filter assignment

Whereas this is very useful for quickly manipulating and plotting data, for readability you might prefer to separate the dplyr commands from the ggplot commands like so:

r filter assignment

5.7 mutate()

Whereas the the verbs we’ve covered so far modify the dimensions and order of the existing data frame, mutate() adds new columns of data, thus ‘mutating’ the contents and dimensions of the input data frame.

To explore mutate we will use the diamond_df data frame from earlier. You can recreate if necessary:

The price column for these diamonds is in US dollars. If we want to convert the price to Australian dollars we can (optimistically) multiply USD by 1.25. Here we create a new column called AUD, which will contain a new column where each row = price * 1.25.

Because the number of columns is expanding, to easily see the results we can first drop the x/y/z dimension columns using select()

We can also perform operations using only the data in existing columns. Here as above, the newly created column will contain the results of a mathematical operation, performed row by row. Let’s calculate the US dollars per carat (‘ppc’) by dividing the price column by the carat column

5.7.1 Challenge

One carat weighs 0.2 grams. Can you chain multiple mutate() functions together to calculate for each diamond, the Australian Dollars per gram?

5.7.2 Solution

5.7.3 ifelse() helper.

The mutate() function is very useful for making a new column of labels for the existing data. For example, to label outliers, or a sub-set of genes with particular characteristics. This is where ifelse() comes in. ifelse() is a function that tests each value in a column of data for a particular condition (a logical test), and returns one answer when the condition==TRUE, and another when the condition==FALSE.

Specifically, ifelse() takes three commands: the condition to test, the output when TRUE, and the output when FALSE. To see how this works let’s create a label for each diamond depending on whether we consider it ‘expensive’ (> $5000) or ‘cheap’ (< $5000).

Remember that we need two closing brackets, one for the mutate() function, and one for the ifelse() inside it.

It seems that the ifelse() function has worked. All the rows we can see are price < 5000 and labelled ‘cheap’. But how can we be sure? One option to check the new labels is to plot the price column as a histogram, and fill the bars according to price_label:

r filter assignment

5.7.4 case_when() helper

This function is useful but quite involved. I’m including it here for completeness, however beginners can feel free to skip down to the summarize() section and return to case_when() later.

At times we want to create a label column that tests multiple conditions. We can either put multiple ifelse() commands inside each other (and go mad), or use case_when()!

This command takes multiple conditions and tests them in order. This is important to remember as all rows that satisfy the first condition will be tagged as such. There may be rows that satisfy more than one condition, so you should order the tests from specific to general, and keep track of how those ambiguous rows are being treated.

case_when() takes a conditional command in the same format as the first command in ifelse(), however only the action for the TRUE condition is given, separated with a tilde ~ . The catch-all command for rows that do not satisfy any other conditions, is given at the end. Let’s use case_when() to make a label for diamonds based on their clarity super-groups. For simplicity, we select only the clarity column as input. The current clarity categories are:

IF: internally flawless VVS1 and 2: very very slight impurity 1 and 2 VS1 and 2: very slight impurity 1 and 2 SI1 and 2: slight impurity 1 and 2 I1: impurity

Note that we are searching for similar conditions (‘VVS’ contains ‘VS’) and will have to be careful with the order of conditions. To create the super-groupings we will use a combination of str_detect() and equality == conditions.

Note that both VS1 and VS2 diamonds are now tagged as ‘V_slight’, and similarly VVS1 and VVS2 are tagged as ‘VV_slight’. Because we have captured all clarity categories within the list of conditions, we don’t expect the catch-all output, “other”, to be present in the clarity_group column. We could use %>% count(clarity_group) , introduced below, to check for the presence of unintended values such as ‘other’ or NA. These super-groups could now be used for colouring or faceting data in a plot, or creating summary statistics (see below).

5.8 summarize()

The last of the dplyr verbs is summarize(), which as the name suggests, creates individual summary statistics from larger data sets.

As for mutate(), the output of summarize() is qualitatively different from the input: it is generally a smaller dataframe with a reduced representation of the input data. Importantly, even though the output of summarize() can be very small, it is still a dataframe. Although not essential, it is also a good idea to specify new column names for the summary statistics that this function creates.

First we will calculate the mean price for the diamond_df dataframe by specifying a name for the new data, and then the function we want to apply to the price column:

The output is the smallest possible dataframe: 1 row X 1 column.

We can create additional summary statistics by adding them in a comma-separated sequence. For example, to calculate the standard deviation, minimum and maximum values, we create three additional columns: “sd_price”, “min_price”, and “max_price”

5.8.1 n() helper

When using summarize(), we can also count the number of rows being summarized, which can be important for interpreting the associated statistics. The simple function n() never takes any additional code, but simply counts rows:

So far so good, however this seems like quite a lot of code to get the simple summary statistics. The power of this function is really amplified in conjunction with the group_by() helper.

5.9 group_by() helper

Although I’ve called group_by() a helper function, it is key to unleashing the power of nearly all dplyr functions. group_by() allows us to create sub-groups based on labels in a particular column, and to run subsequent functions on all sub-groups. It is conceptually similar to facet_wrap() in ggplot, which applies the same plotting command to multiple subsets of the input dataframe.

For example the figure below is using group_by() as the first arrow, and summarize() as the second arrow. Three sub-groups, corresponding to e.g. three categories in column 1, are represented in the light grey, blue and green rows. A summarize() command is then run on each sub-group, producing a results dataframe with only three rows, and new (dark blue) column names indicating the summary statistic.

r filter assignment

For those interested in more details, group_by() is essentially creating a separate dataframe for each category in a specified column. To see this at work, look the structure str() of the diamonds data before and after grouping:

We have a single dataframe with 54K rows.

Now we group by cut:

The output of group_by() is a ‘grouped_df’ and all functions following will be applied separately to each sub-dataframe.

5.9.1 group_by() %>% summarize()

Returning to the above summarize() function, we can now quickly generate summary statistics for the diamonds in each clarity category by first grouping on this column name.

By adding this simple command before summarize() we’ve created detailed statistics on each clarity category. We could split the input data further by grouping on more than one column. For example, what are the summary statistics for each clarity category within each cut?

We now have 40 rows of summary statistics which gives a higher-resolution representation of the input data.

5.9.2 group_by() %>% mutate()

As mentioned, group_by() is compatible with all other dplyr functions. Sometimes we want both the original data and the summary statistics in the output data frame. To do this, group_by() can be combined with mutate(), to make a new column of summary statistics (repeated many times) corresponding to the sub-grouping of interest. The new column of summary statistics is represented in darker colours in the right panel below.

To create a column containing the mean price for diamonds in each cut category in addition to the input data, we can use group_by() before mutate():

The new column now contains one of five possible values depending on the cut column. From this we could then use a second mutate() to calculate the difference between each diamond price and the mean price for its cut category:

5.9.3 ungroup() helper

When running longer dplyr chains it is good practice to ungroup the data after the group_by() operations are run. To do this simply add %>% ungroup() at the end of the code block. Inappropriate preservation of groupings can sometimes cause your code to run very slowly and give unexpected results.

5.9.4 count() helper

count() is a shortcut function that combines group_by() and summarize(), which is useful for counting ‘character data’, e.g. labels.

To quickly count the number of diamonds in each cut category:

And to count the number of diamonds in each cut and clarity category:

Note that the count summary output column name is ‘n’. This reflects that count() is running summarize(n = n()) in the background.

5.9.5 sample_n() helper

The final helper for this session is sample_n() which takes a random sample of rows according to the number specified. To sample 10 rows from the entire diamond_df dataset:

It can be more useful to sample rows from within sub-groups, by combining group_by() and sample_n(). Let’s take 2 rows at random from each cut category:

5.10 Challenges

What is the weight of the most expensive diamond in each clarity category?

Summarize the standard deviation of diamond weight in each cut category.

A z score is the (sample value - mean)/sd.

Can you create a z score for the weight of each diamond relative to others of that cut?

What does the density distribution of z scores look like for each cut?

5.11 Solutions

r filter assignment

5.12 Summary

Now you have worked through the key verbs of dplyr, and the associated helper functions which, together, allow you to efficiently subset, transform and summarize your data. Whereas the diamond_df and mpg_df dataframes we have worked with so far are self-contained, readily available within R and clean, in the next chapter we will learn to read in external datasets, join different datasets and clean data.

5.13 Cheat sheets!

Most of the figures in this chapter are taken from the dplyr cheat sheet. You can pull up a number of cheat sheets by clicking e.g. Help >> Cheatsheets >> Data Visualization with ggplot2

These are fantastic resources compiled by RStudio contributors. You could print these and have them on hand during your R coding work. While these cheat sheets are packed with information, its not immediate obvious how to use them.

5.13.1 ggplot example

Say you want to try out geom_text() from the ‘Two Variables’ family of geoms in page 1. The pictogram at left gives a simple example of the shape of this geom, in place of a text description. To test out this geom, we first have to create the variable ‘e’ in bold text. At the top of the panel there is a code snippet for creating e:

Next we can run the bold code and everything between the bold brackets for geom_text():

After the bold brackets are a list of sub-commands (known as ‘arguments’) that can be modified for geom_text(). x, y, alpha and colour will be familiar to you from Week 1. There are many additional arguments we don’t have space to cover, but which have example code in the ?geom_text() Help page.

Having created ‘e’, you can also test out geom_quantile(), geom_rug() etc.

5.13.2 dplyr example

Now pull up the dplyr cheat sheet: Help >> Cheatsheets >> Data Transformation with dplyr To take the example of sample_n in page 1 of the dplyr cheat sheet.

There is a lot of text here, but it can be split up into three parts:

The bold text indicates the function name: sample_n . The text inside the bold brackets are the main sub-commands (known as ‘arguments’) that the function requires: sample_n( tbl, size, replace = FALSE, weight = NULL, .env = parent.frame() ) The first argument is often tbl, .tbl or .data, referring to the input data frame. The values (= FALSE, = NULL etc.) following each argument are the ‘default’ values - they will be set this way unless the user changes their value. You will see the same argument structure at the top of the Help tab if you run ?sample_n() in RStudio.

The normal font text briefly describes what the function does: ‘Randomly select size rows.’ NB this doesn’t really make sense in isolation but will become clearer.

The italic font text gives a toy example of working code for this function. If you run the italic code in R you should get a result. The iris, mpg and diamonds data sets come pre-packaged with R, and are ready for use despite not being displayed in the Environment pane. These are the most common data-sets used in the cheat sheets. Note that in this book, the input data is given first, followed by a pipe %>% into a particular function. It is also possible (and more compact) to give the input dataframe as the first argument, which is how the cheat sheet examples are written.

So based on the cheat sheet explanation, the more elaborate code for sample_n() would be:

Finally, although the explanation in 2. is hard to understand, look for ‘size’ in the function argument names, and where that argument appears in the example code. It is set to 10, and the example code returns 10 rows. Given more space, the explanation might read: ‘Randomly select a sample of rows from an input dataframe, of size (n rows) as specified in the size = argument’.

5.14 Extra resources

There are several great resources for consolidating and building on the material above.

R for Data Science Ch. 5 ‘Data transformation’

Tidyverse resources

Introduction to open data science (Ocean Health Index)

Jenny Bryan’s STAT545 course notes

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R dplyr filter() – Subset DataFrame Rows

  • Post author: Naveen Nelamali
  • Post category: R Programming
  • Post last modified: May 24, 2024
  • Reading time: 14 mins read

You are currently viewing R dplyr filter() – Subset DataFrame Rows

The filter() function from dplyr package is used to filter the data frame rows in R. Note that filter() doesn’t filter the data instead it retains all rows that satisfy the specified condition.

dplyr is an R package that offers a grammar for data manipulation and includes a widely-used set of verbs to help data science analysts address common data manipulation tasks. To use this, you have to install it first using  install.packages('dplyr')  and load it using  library(dplyr) .

  • dplyr filter() Syntax
  • Filter by Row Name
  • Filter by Column Value
  • Filter by Multiple Conditions
  • Filter by Row Number

1. Create DataFrame

Let’s create a data frame,

Yields below output.

2. dplyr filter() Syntax

Following is the syntax of the filter() function from the dplyr package.

  • x – Object you wanted to apply a filter on. In our case, it will be a data frame object.
  • condition – condition you wanted to apply to filter the df.

3. Filter Data Frame Rows based on Row

If you have row names on the data frame and want to filter rows by row name in R data frame , use the below approach. By default, row names in an R data frame are incremental sequence numbers assigned at creation. You can also assign custom row names during the creation of the data frame or by using the rownames() function on an existing data frame. To set column names, use the colnames() function.

Yields below output. This example returns a row that matches with row name 'r3'

4. Filter by Column Value

You can also  filter dataframe based on column value  by specifying the conditions. In the following examples, I have covered how to filter the data frame based on column value. The following example retains rows that gender is equal to 'M' .

5. Filter Rows by List of Values

If you want to choose the rows that match with the list of values, use the %in% operator in the condition. The following example retains all rows where the state is in the list of values.

6. Filter Rows by Multiple Conditions

You can also filter data frame rows by multiple conditions in R, all you need to do is use logical operators between the conditions in the expression.

The expressions include comparison operators (==, >, >= ), logical operators (&, |, !, xor()), range operators (between(), near()) as well as NA value checks against the column values.

7. Filter Data Frame Rows by Row Number

To filter data frame rows by row number or positions in R, we have to use the slice() function. this function takes the data frame object as the first argument and the row number that you want to filter as the second argument.

Frequently Asked Questions on dplyr filter() Function in R

The filter() function from dplyr is used to subset or filter rows from a data frame based on specified conditions. It retains only the rows that satisfy specified conditions.

The basic syntax of the filter() function is filter(x, condition,…) where, x is the original data frame and condition specifies to filter the data.

You can use the filter() function to filter data frame rows by multiple conditions in R, to do this use logical operators such as & , | , and ! between the conditions in the expression.

Use the column name within the condition to filter the rows based on a specific column value. For example, filter(df, column_name == 'column_value') .

The common conditions include comparison operators (==, >, >= ), logical operators (&, |, !, xor()), range operators (between(), near()) as well as NA value check against the column values.

8. Conclusion

In this article, you have learned the syntax and usage of the R filter() function from the dplyr package that is used to filter data frame rows by column value, row name, row number, multiple conditions, etc.

Related Articles

  • How to Get Rows by Index in R with Examples
  • How to Get Rows by Condition in R with Examples
  • How to Get Rows by Column Values in R
  • R Subset DataFrame by Column Value
  • R subset() function from the dplyr package
  • R select() function from dplyr package
  • R mutate() function from dplyr package
  • How to subset a matrix in R?
  • https://www.rdocumentation.org/packages/dplyr/versions/0.7.8/topics/filter

Statology

Statistics Made Easy

R: How to Use %in% to Filter for Rows with Value in List

You can use the following basic syntax with the %in% operator in R to filter for rows that contain a value in a list:

This particular syntax filters a data frame to only keep the rows where the value in the team column is equal to one of the three values in the team_names vector that we specified.

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

Example: Using %in% to Filter for Rows with Value in List

Suppose we have the following data frame in R that contains information about various basketball teams:

Suppose we would like to filter the data frame to only contain rows where the value in the team column is equal to one of the following team names:

We can use the following syntax with the %in% operator to do so:

Notice that only the rows with a value of Mavs, Pacers or Nets in the team column are kept.

If you would like to filter for rows where the team name is not in a list of team names, simply add an exclamation point ( ! ) in front of the column name:

Notice that only the rows with a value not equal to Mavs, Pacers or Nets in the team column are kept.

Note : You can find the complete documentation for the filter function in dplyr here .

Additional Resources

The following tutorials explain how to perform other common operations in dplyr:

How to Select the First Row by Group Using dplyr How to Filter by Multiple Conditions Using dplyr How to Filter Rows that Contain a Certain String Using dplyr

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r filter assignment

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

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  • Data Visualization
  • Statistics in R
  • Machine Learning in R
  • Data Science in R
  • Packages in R

Filter data by multiple conditions in R using Dplyr

  • How to filter R dataframe by multiple conditions?
  • Subset or Filter data with multiple conditions in PySpark
  • Filter Pandas Dataframe with multiple conditions
  • Pyspark - Filter dataframe based on multiple conditions
  • Filter multiple values on a string column in R using Dplyr
  • Delete rows in PySpark dataframe based on multiple conditions
  • Filter Rows Based on Conditions in a DataFrame in R
  • Subset Data Frames Using Logical Conditions In R
  • Drop multiple columns using Dplyr package in R
  • Filter DataFrame columns in R by given condition
  • Summarise multiple columns using dplyr in R
  • Remove duplicate rows based on multiple columns using Dplyr in R
  • Group data.table by Multiple Columns in R
  • Sort a given DataFrame by multiple column(s) in R
  • How to Replace Multiple Values in Data Frame Using dplyr
  • Dplyr - Find Mean for multiple columns in R
  • Apply a Function (or functions) across Multiple Columns using dplyr in R
  • Merge multiple CSV files using R
  • Python PySpark - DataFrame filter on multiple columns

In this article , we will learn how can we filter dataframe by multiple conditions in R programming language using dplyr package.

The filter() function is used to produce a subset of the data frame, retaining all rows that satisfy the specified conditions. The filter() method in R programming language can be applied to both grouped and ungrouped data. The expressions include comparison operators (==, >, >= ) , logical operators (&, |, !, xor()) , range operators (between(), near()) as well as NA value check against the column values. The subset data frame has to be retained in a separate variable.

Method 1: Using filter() directly

For this simply the conditions to check upon are passed to the filter function, this function automatically checks the dataframe and retrieves the rows which satisfy the conditions.

Syntax : filter(df , condition) Parameter : df:  The data frame object condition: filtering based upon this condition

Example : R program to filter rows using filter() function

Method 2: Using %>% with filter()

This approach is considered to be a cleaner approach when you are working with a large set of conditions because the dataframe is being referred to using %>% and then the condition is being applied through the filter() function.

Syntax: df  %>%  filter ( condition ) Parameter:  df :  The data frame object condition :  filtering based upon this condition

Example : R program to filter using %>%  

Method 3: Using NA with filter()

is.na() function accepts a value and returns TRUE if it’s a NA value and returns FALSE if it’s not a NA value.

Syntax: df %>% filter(!is.na(x)) Parameters : is.na() : reqd to check whether the value is NA or not x : column of dataframe object.

Example: R program to filter dataframe using NA

Method 4: Using ‘ %in%’ o perator with filter()

The %in% operator is used to filter out only the columns which contain the data provided in the vector.

Syntax: filter( column %in% c(“data1”, “data2″….”data N” )) Parameters:  column : column name of the dataframe c( “data1”, “data2″….”data N” ): A vector containing the names of data to be found and printed.

Example: R program to filter dataframe using %in%   

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How to filter a dataframe in R

how to filter a dataframe in r

A data frame filter in R is a way to select a subset of rows from a data frame based on specific conditions. Filtering a data frame can be done using the square bracket notation or the `subset()` function. In both cases, specify a condition that must be met for a filter row in R to be included in the filtered data frame.

For example, it can filter rows in R where a specific column has a value greater than a certain threshold:

  • df[df$column_name > threshold, ]
  • subset(df, column_name > threshold)

The resulting filtered rows in R where the condition is true. Filtering data frames in R is a common data manipulation task and can be useful for exploring and analysing data.

Why There Is A Need For Filtering Dataframe In R

Filtering a dataframe in R is an important operation because it allows you to extract a subset of the data based on certain criteria. Filtering is useful when you have a large dataset and you want to extract only a specific subset of rows that meet certain conditions or criteria.

For example, you may want to filter a dataframe to only include rows where a certain variable meets a specific condition, such as selecting only the rows where the value of a variable is greater than a certain threshold or within a specific range.

Filtering can also help you clean your data by removing rows that contain missing or erroneous values.

Overall, filtering a dataframe in R  allows you to work with a smaller and more relevant subset of your data, and can help you uncover meaningful patterns and insights.

Four Methods To Filter A Dataframe In R:

  • Square bracket notation
  • `subset()` function
  • `filter()` function from the `dplyr` package
  • `which ()` function

Different Approaches to filter a dataframe in R:

1. Square bracket notation: The most common way to filter a data frame in R is to use the square bracket notation and specify a condition for selecting rows especially if users want to filter rows in R.

2. subset()` function: Another approach is to use the `subset()` function, which allows  to specify the filter data frame and the condition for selecting filter row in R:

3. `filter()` function from the `dplyr` package: The `dplyr` package provides a convenient `filter()` function for filtering data frames in R:

4. `which ()` function: The `which()` function can be used to return the indices of the rows in the data frame that meet the specified condition. These indices can then be used to extract the filtered rows in R:

These are some of the most common approaches for filtering data frames in R. Each approach has its own advantages and limitations, and the best approach will depend on the specific needs and requirements of the data analysis task.

Approach 1 – Using the square bracket notation for filtering data frames in R.

The first approach for filtering data frames in R is the square bracket notation. This approach allows the user to select a subset of rows or filter rows from a data frame based on a specific condition. The general syntax for filtering a data frame using this approach is:

In this example, `df` is the name of the data frame and `column_name` is the name of the column used for filtering. The condition `df$column_name > threshold` specifies that only rows where the value in the `column_name` column is greater than `threshold` will be selected. The comma and empty square brackets at the end are used to return the selected rows as a new data frame.

The square bracket notation is evaluated in the following order:

  • The condition `df$column_name > threshold` is evaluated, which returns a logical vector indicating whether each row in the data frame meets the condition.
  • The logical vector is used to index the data frame, selecting only the filter rows where the condition is true.
  • The resulting filtered data frame is returned as the output.

The square bracket notation is a simple and efficient way to filter data frames in R, and is widely used by data analysts and data scientists.

Sample Code:

Explanation:

  • In this example, the input is a data frame `df` with two columns `x` and `y`.
  • The code uses the square bracket notation to filter the data frame to only include rows where the value in the `y` column is equal to “A”.
  • The resulting filtered data frame contains only two rows where the condition is true. The comma and empty square brackets at the end are used to return the filtered data frame as the output.

Approach 2 – Using the `subset()` function for filtering data frames in R

The second approach for filtering data frames in R is the `subset()` function. This approach allows the user to select a subset of rows or filter rows from a data frame based on a specific condition. The general syntax for filtering a data frame using this approach is:

 In this example, `df` is the name of the data frame, `column_name` is the name of the column used for filtering, and `threshold` is a value used as the cut-off for selecting rows. The condition `column_name > threshold` specifies that only rows where the value in the `column_name` column is greater than `threshold` will be selected.

The `subset()` function works in the following order:

  • The function takes two arguments: the filter data frame and the condition for selecting filter rows.
  • The condition `column_name > threshold` is evaluated, which returns a logical vector indicating whether each row in the data frame meets the condition.
  • The logical vector is used to index the data frame, selecting only the rows where the condition is true.

The `subset()` function is similar to the square bracket notation in terms of functionality, but provides a slightly different syntax. Some data analysts and data scientists prefer the `subset()` function because it is more readable and easier to understand, especially for more complex filtering conditions.

Approach 3 – Using the `filter()` function from the `dplyr` package for filtering data frames in R

The third approach for filtering data frames in R is the `filter()` function from the dplyr library. This approach allows you to easily select a subset of rows or filter rows from a data frame based on a specific condition. The general syntax for filtering a data frame using this approach is:

In this example, `df` is the name of the data frame, `column_name` is the name of the column used for filtering, and `threshold` is a value used as the cut-off for selecting rows. The condition `column_name > threshold` specifies that only rows where the value in the `column_name` column is greater than `threshold` will be selected.

The `filter()` function from the dplyr library is similar to the `subset()` function and the square bracket notation in terms of functionality. The advantage of using the `filter()` function is that it is part of a larger suite of data manipulation functions from the dplyr library, which makes it easier to perform a wide range of data manipulation tasks in a consistent and readable manner. The `filter()` function works in the following order:

  • The function takes two arguments: the data frame and the condition for selecting rows.
  •  In this example, the input is a data frame `df` with two columns `x` and `y`. The first line loads the dplyr library.
  • The code uses the `filter()` function from the dplyr library to filter the data frame to only include rows where the value in the `y` column is equal to “A”.
  • The resulting filtered data frame contains only two rows where the condition is true. The filtered data frame is returned as the output.

Best Approach filtering a dataframe in R

The filter() function from the dplyr package is considered one of the best methods for filtering dataframes in R for several reasons:

  • Concise and readable syntax: The syntax of the filter() function is intuitive and easy to read, making it easier to write and understand complex filter conditions.
  • Efficient execution: The filter() function is designed to be highly efficient, which means that it can handle large datasets with minimal computing time.
  • Wide range of filter conditions: The filter() function allows you to specify a wide range of filter conditions using a variety of logical operators, making it flexible and adaptable to different filtering requirements.
  • Integration with other dplyr functions: The filter() function is part of the dplyr package, which includes a range of other functions for data manipulatio n and analysis. This integration allows for seamless integration of filtering operations with other data wrangling tasks.

Overall, the filter() function provides a powerful and efficient way to extract subsets of data from dataframes, making it an ideal method for filtering data in R.

Sample Problems for filtering a Dataframe in R

Sample Problem 1

A data analyst has a data frame in R that contains information about various stocks traded on the stock market. The data frame contains the following columns: `Date`, `Ticker`, `Open`, `High`, `Low`, and `Close`. The analyst wants to filter the data frame to only include rows where the `Ticker` column is equal to “AAPL” and the `Close` column is greater than 150.

  Explanation:

  • This code first creates a data frame called `df` that contains information about stocks traded on the stock market. Then, it creates a new data frame called `filtered_df` that only contains rows where the `Ticker` column is equal to “AAPL” and the `Close` column is greater than 150.
  • The filtered data frame is created by using the square bracket notation to extract only the rows that meet the specified conditions. Finally, the filtered data frame is printed to the console to verify that the correct rows have been extracted.

Sample Problem 2

A data analyst has a data frame in R that contains information about various cars and their specifications. The data frame contains the following columns: `Car`, `Type`, `Year`, `Price`, and `MPG`. The analyst wants to filter the data frame to only include rows where the `Type` column is equal to “SUV” and the `Price` column is greater than 30,000.

 Explanation:

  • This code first creates a data frame called `df` that contains information about cars and their specifications. Then, it creates a new data frame called `filtered_df` that only contains rows where the `Type` column is equal to “SUV” and the `Price` column is greater than 30,000.
  • The filtered data frame is created by using the `subset()` function to extract only the rows that meet the specified conditions. Finally, the filtered data frame is printed to the console to verify that the correct rows have been extracted.

Sample Problem 3

You have a data frame called `df` with three columns: `Name`, `Age`, and `Gender`. You want to filter the data frame to only include rows where the value in the `Age` column is greater than 30.

  • In this example, the data frame `df` contains information about five individuals, including their name, age, and gender.
  • The code uses the `filter()` function from the dplyr library to filter the data frame to only include rows where the value in the `Age` column is greater than 30 which was a filtered row data in R.
  • The resulting filtered data frame contains three rows where the condition is true, and is returned as the output.

Conclusion:

In conclusion, there are three approaches to filtering a data frame in R: `subset()`, square bracket notation, and `filter()` function from the dplyr library. Each approach has its own advantages and limitations, and the best approach for a particular use case will depend on the specific requirements and constraints of the project.

It is recommended to try each approach and consider factors such as readability, ease of use, compatibility with other functions, and performance before making a final decision.

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Banasree Ghosh

Introductory R Tutorial 3: Filtering and Plotting

Shane t. mueller [email protected] michigan technological university.

Return to main site | Lesson 1 | Lesson 2 | Lesson 3 | Lesson 4 | Lesson 5 Download Lesson 3 files here

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Filtering and plotting: Goals

The goals of this session are to introduce you to data handling and visualization. We will start by looking a plotting, which you are now ready to learn because you have understood data types and functions.

The main topics covered will include:

  • Making simple plots of data
  • Filtering rows of data/cases.
  • Overlaying plots with filtered data.
  • Combining multiple filters with logical operations

Plotting a single data variable

A real data set.

The data file physio.csv was collected from a system that records heart rate, breathing rate, and skin temperature and writes this to a file every 15 seconds. We will look at just the first few rows of the data here, using the head() function:

It actually recorded a number of other columns, but I have deleted these to make them easier to manage. Along with a time counter in seconds, it records a body position and a classification of whether the participant was moving or not (in this data, they wer always Stationary). It also provides some categories for whether the person has high physio measures which could be of a concern, but because this was always stationary, that is not a problem.

Inspecting the data

We already know how to select a specific variable using the $ operator. For example, we can look at the temperature:

However, it is hard to see if there is a pattern here.

The plot() function

We can use the plot() function to make a simple plot. Notice the arguments for the code block to fix the size of the figure.

r filter assignment

What we didn’t probably see in the numbers that after a sharp rise, the skin temperature slowly increases throughout the session, and then fell sharply. There are also some nosy data, and an area around sample 25 where maybe the sensor was removed or adjusted for a minute. But how long is the session? Maybe if we plot by time, that would be easier. We can give the plot function two arguments, and it makes an x/y plot. We will divide time by 60 to give it as time in minutes, so it is easier to comprehend.

r filter assignment

Exercise 1.

Look up the the plot and the par functions in the help, and create a new plot that includes axis labels, a main label, changes this to a line plot or a line + symbol plot, changes the symbol being plotted, and changes the color being plotted.

Selecting rows from a data frame

Our initial plot of the data looks fine, but there are some obvious problems: it appears to include data at the beginning and the end which is bad–probably the sensor had not yet been put on, and then it was taken off before we stopped collecting data. Also, there are some points that look like blips; probably sensor errors or something we might want to clean up.

We’d like to restrict the data to just the values we think are valid, How might we do that? Here are some ideas, and we might have to do all of them.

Try to select a range of rows from the middle, cutting out the ends.

Count the time periods/indexes of the bad data, and figure out a way to remove them. This is a bit tedious, and would not help us a lot if we needed to handle dozens of these data sets, but it is likely to work, as long as we don’t make mistakes. But we are likely to make mistakes.

Figure out a valid range of temperatures, and throw out any data outside that range–maybe 32 to 35. This will work for the initial data and some of the noisy data, but not for all the data points, which are noisy but within the range.

Come up with a rule for detecting outliers, like if the change between two data points is large, and filter these out. This might work for the pops, but maybe not for the smooth decline at the end.

We will try a few different approaches to explore filtering options.

Selecting rows by row index

The first option is to simply figure out the middle range and use that. Just like we used the [] to select a single row or column of a matrix, we can select a range. We might guess that we want to keep rows 4 through 150. We can put the sequence of rows, and then a comma, indicating we want to keep all the columns, but only access a subset of rows. Here, I create a new data frame by selecting the rows using this method.

r filter assignment

That looks a lot better, but I’m not going to be able to get rid of the pops that easily. To to that, I can count the indexes I want to remove. To make it easier, I will first create a list of the indexes to remove, and then use another filter method to make a logical vector of 169 T/F values, so we can filter out, or plot things differently, and apply the same filter to all of the data variables in the data frame.

First, I will record the rows that are bad in a vector. Then I will make a vector of boolean values that are all TRUE. Next, I need to do something we haven’t done before which can look like black magic to new users. I will select rows on the left side of the assignment operator, and assign F to those values. If I plot this vector, we can see it jumps between 0/1 or F/T, with a few F and mostly T values.

r filter assignment

Here, we seemed to catch the right values. If you look carefully, you will see that the line gets connected seamlessly over the missing data, because we have simply edited it out. But I also made at least one mistake: I missed a bad data point near the end. Did I miscode any others and remove them incorrectly? Clearly, I might not know unless I check.

To do this, lets think about filtering differently. What if we plotted every value, but changed the color of ones we remove? To do this, we need a vector of color names–one for each of the data rows. To do this, we will use the filter we just made, which is a bunch of TRUE / FALSE values. We would like a bunch of ‘navy’ and red values maybe, which we can put in a new vector, and use the [] to select from that. But here we will use another trick–we can specify the same row multiple times when we use the selection. Let’s say we wanted 5 “navy”, 4 “red”, and 3 “navy” in a row. We can do that like this:

Now it is just a matter of turning the T/F filter vector into 1/2 values. Remember that F=0 and T=1, so we can just add 1 to the filter and get what we want. However, we will need to reverse the colors vector.

Now, we can give the function this vector in the color argument, instead of a single value of ‘navy’.

r filter assignment

Plotting multiple layers

To do this, we will use one of the nice features of R plotting–overlaying. Overlaying lets you build a plot layer by layer. This is exactly how we made the grid on top of the function. The points() function works just like the plot() function, but it does not plot axis or axis labels, and plots on top of the plot we previously made. Our strategy here will be to make the plot we just made with plot, but without the lines. Then we will add lines connecting the data we did no not want to throw out. Although the filter is wrong, we will keep using that same filter for now. Instead of making a new filtered data set, we will just filter it directly in the points command. I will also use the [keep+1] filter trick for point size and symbol.

r filter assignment

This helps me see both the main pattern of data, and the filtered points clearly.

Filter by a calculation

Finally, maybe we want an automatic outlier detection instead of the one by hand. We could maybe combine that with a hand-coded start/end filter. An easy filter is to look at the change between any pair of points. To do this, we can subtract each data point from the previous row, and take the absolute value. We can remove a single row with the [] by referring to the index with a negative value, which we need to do to accomplish this. That will be one row too short, so we will add a single T at the beginning. Maybe we will try to remove any points that change by .2 or more. I will reverse the remove vector by using the negative operator ! , and combine several filters with the & operator. We will look at any value that differs from the one before AND the one after by .2 or more, and remove it.

r filter assignment

This removes most of the bad data, but not the regions around minute 7 and 15. How might we handle those?

The samples near minute 7 are all less than 31.5, which is 88.7 F; clearly too low for valid data. Similarly, near minute 15, there are two measures above 36, which is higher than all of our data by a large amount. Create another filter based on normal temperature range of 31.5 to 36, and use that to remove any outlying data points.

Plotting multiple variables from a data frame

We’d like to plot the heart rate and breathing rate data as well. Luckily, although these are different measures, they all fall into a similar range, so we can plot them all on one figure. The matplot function is helpful for doing this–it plots several series of data as distinct lines. But we need to select the columns we want first. We can use the [] operator, and maybe just select the columns we want, which would be data[,3:5] , but it might be more foolproof to select by the name of the variables. We can do that too:

r filter assignment

This needs to be cleaned up as well, For now, let’s just filter out the beginning and end points, but we’d like to truncate the range so it doesn’t go above 100 as well. Finally, we’d like a legend to know what the different series are. We will filter a slightly different way this time

r filter assignment

We can overlay a normal points plot on this. Remember that there is an indicator for each row called ‘Alert’, which is either Blue, Green, Yellow, or Red. This gets read in as a factor:

We can convert these to characters and use them directly for the color. However, since these are factors, they have an implicit order, and we can use the same trick we used before for color and point size, but use the factor level as an index. Then we could use a different set of colors. For example, the default green is almost lime, and I like to use dark green. Here are two methods, both plotted near the bottom.

r filter assignment

Use the keeprows variable to filter out the beginning and end of the data to remove bad data. Then, instead of plotting the $Alert twice, plot both the $Alert and $PWI variables as separate strips near the bottom. Finally, look up text() in the help, and label these two rows directly. Adjust the x and y range if necessary to fit all the labels you need.

Scatterplots

We have actually been making scatterplots all along, but let’s try plotting HR by BR instead of time by HR or time by BR;

r filter assignment

There seems to be some association here, but how much of it comes from the bad data?

For the scatterplot comparing HR and BR, make a plot showing the bad data at the beginning and end as red. Then, filter this out and make another plot. Use the command par(mfrow=c(1,2)) to plot two figures on one page.

Exercise solutions

r filter assignment

Here, the easiest thing to do is to count the indexes we want to remove, and set remove.ends to T for these.

r filter assignment

Use the keeprows variable to filter out the beginnig and end of the data to remove bad data. Then, instead of plotting the Alert twice, plot both the Alert and PWI variables as separate strips near the bottom. Finally, look up text() in the help, and label these two rows directly. Adjust the x and y range if necessary to fit all the labels you need.

r filter assignment

This should be pretty straightforward. We will reuse one of the filter methods we tried before. I also overlaid two points–a filled and unfilled, to give an outline look.

r filter assignment

It doesn’t look like there is much of a relationship.

Filter Group Operations in R: Missing DataFrame Assignment

Resolving Missing DataFrame Assignment in R: Filter Group Operations

Abstract: Learn how to resolve missing DataFrame assignments when performing filter group operations in R.

Resolving Missing DataFrame Assignment: Filter Group Operations

In data manipulation and analysis, working with DataFrames is a common task. One of the challenges that data analysts and scientists face is handling missing or empty DataFrames after filter group operations. In this article, we will discuss the issue and provide solutions to resolve the problem of missing DataFrame assignments when applying filter group operations.

Context and Key Concepts

When working with DataFrames, filter group operations are used to subset data based on specific conditions. These operations are essential in data preprocessing, cleaning, and analysis. However, there are instances when the resulting DataFrame after filter group operations is empty or missing, causing errors in subsequent operations.

Consider the following example:

In this example, the filter group operation returns an empty DataFrame, causing an error when trying to assign the result to the filtered_df variable:

Resolving Missing DataFrame Assignment

To resolve the missing DataFrame assignment issue, you can add a condition to check if the resulting DataFrame is empty before assigning it to a variable. Here's an updated example:

Missing DataFrame assignments after filter group operations can cause errors and hinder the analysis process. By adding a condition to check if the resulting DataFrame is empty, you can prevent these issues and ensure the smooth execution of your code. It is essential to handle missing DataFrames to ensure the robustness and reliability of your data analysis.

Type: Online Resource

Title: Pandas Documentation - GroupBy: split-apply-combine

URL: https://pandas.pydata.org/docs/reference/groupby.html

Title: Python for Data Analysis

Author: Wes McKinney

Publisher: O'Reilly Media

Tags: :  R DataFrame Filtering

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Assignment linter

Check that <- is always used for assignment.

Logical, default TRUE . If FALSE , <<- and ->> are not allowed.

Logical, default FALSE . If TRUE , -> and ->> are allowed.

Logical, default TRUE . If FALSE then assignments aren't allowed at end of lines.

Logical, default FALSE . If TRUE , magrittr's %<>% assignment is allowed.

linters for a complete list of linters available in lintr.

https://style.tidyverse.org/syntax.html#assignment-1

https://style.tidyverse.org/pipes.html#assignment-2

configurable , consistency , default , style

Red Sox injury updates: Chris Martin returns,…

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IT'S PATCH TIME ONCE AGAIN —

High-severity vulnerabilities affect a wide range of asus router models, many models receive patches; others will need to be replaced..

Dan Goodin - Jun 17, 2024 6:39 pm UTC

High-severity vulnerabilities affect a wide range of Asus router models

Hardware manufacturer Asus has released updates patching multiple critical vulnerabilities that allow hackers to remotely take control of a range of router models with no authentication or interaction required of end users.

The most critical vulnerability, tracked as CVE-2024-3080 is an authentication bypass flaw that can allow remote attackers to log into a device without authentication. The vulnerability, according to the Taiwan Computer Emergency Response Team / Coordination Center (TWCERT/CC), carries a severity rating of 9.8 out of 10. Asus said the vulnerability affects the following routers:

Model name Support Site link
XT8 and XT8_V2
RT-AX88U
RT-AX58U
RT-AX57
RT-AC86U
RT-AC68U

A favorite haven for hackers

A second vulnerability tracked as CVE-2024-3079 affects the same router models. It stems from a buffer overflow flaw and allows remote hackers who have already obtained administrative access to an affected router to execute commands.

TWCERT/CC is warning of a third vulnerability affecting various Asus router models. It’s tracked as CVE-2024-3912 and can allow remote hackers to execute commands with no user authentication required. The vulnerability, carrying a severity rating of 9.8, affects:

Model name Support Site link
DSL-N12U_C1
DSL-N12U_D1
DSL-N14U
DSL-N14U_B1
DSL-N16
DSL-N17U
DSL-N55U_C1
DSL-N55U_D1
DSL-N66U
DSL-AC51/DSL-AC750

DSL-AC52U
DSL-AC55U
DSL-AC56U

Security patches, which have been available since January, are available for those models at the links provided in the table above. CVE-2024-3912 also affects Asus router models that are no longer supported by the manufacturer. Those models include:

  • DSL-N10P_C1
  • DSL-N12E_C1

TWCERT/CC advises owners of these devices to replace them.

Asus has advised all router owners to regularly check their devices to ensure they’re running the latest available firmware. The company also recommended users set a separate password from the wireless network and router-administration page. Additionally, passwords should be strong, meaning 11 or more characters that are unique and randomly generated. Asus also recommended users disable any services that can be reached from the Internet, including remote access from the WAN, port forwarding, DDNS, VPN server, DMZ, and port trigger. The company provided FAQs here and here .

Further Reading

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How to Filter in R: A Detailed Introduction to the dplyr Filter Function

Posted on Mon 08 April 2019 in R

Data wrangling. It's the process of getting your raw data transformed into a format that's easier to work with for analysis.

It's not the sexiest or the most exciting work.

In our dreams, all datasets come to us perfectly formatted and ready for all kinds of sophisticated analysis! In real life, not so much.

It's estimated that as much as 75% of a data scientist's time is spent data wrangling. To be an effective data scientist, you need to be good at this, and you need to be FAST.

One of the most basic data wrangling tasks is filtering data. Starting from a large dataset, and reducing it to a smaller, more manageable dataset, based on some criteria.

Think of filtering your sock drawer by color, and pulling out only the black socks.

Whenever I need to filter in R, I turn to the dplyr filter function.

As is often the case in programming, there are many ways to filter in R. But the dplyr filter function is by far my favorite, and it's the method I use the vast majority of the time.

Why do I like it so much? It has a user-friendly syntax, is easy to work with, and it plays very nicely with the other dplyr functions.

A brief introduction to dplyr

Before I go into detail on the dplyr filter function, I want to briefly introduce dplyr as a whole to give you some context.

dplyr is a cohesive set of data manipulation functions that will help make your data wrangling as painless as possible.

dplyr, at its core, consists of 5 functions, all serving a distinct data wrangling purpose:

  • filter() selects rows based on their values
  • mutate() creates new variables
  • select() picks columns by name
  • summarise() calculates summary statistics
  • arrange() sorts the rows

The beauty of dplyr is that the syntax of all of these functions is very similar, and they all work together nicely.

If you master these 5 functions, you'll be able to handle nearly any data wrangling task that comes your way. But we need to tackle them one at a time, so now: let's learn to filter in R using dplyr!

Loading Our Data

In this post, I'll be using the diamonds dataset, a dataset built into the ggplot package, to illustrate the best use of the dplyr filter function. To start, let's take a look at the data:

We can see that the dataset gives characteristics of individual diamonds, including their carat, cut, color, clarity, and price.

Our First dplyr Filter Operation

I'm a big fan of learning by doing, so we're going to dive in right now with our first dplyr filter operation.

From our diamonds dataset, we're going to filter only those rows where the diamond cut is 'Ideal':

As you can see, every diamond in the returned data frame is showing a cut of 'Ideal'. It worked! We'll cover exactly what's happening here in more detail, but first let's briefly review how R works with logical and relational operators, and how we can use those to efficiently filter in R.

A brief aside on logical and relational operators in R and dplyr

In dplyr, filter takes in 2 arguments:

  • The dataframe you are operating on
  • A conditional expression that evaluates to TRUE or FALSE

In the example above, we specified diamonds as the dataframe, and cut == 'Ideal' as the conditional expression

Conditional expression? What am I talking about?

Under the hood, dplyr filter works by testing each row against your conditional expression and mapping the results to TRUE and FALSE . It then selects all rows that evaluate to TRUE .

In our first example above, we checked that the diamond cut was Ideal with the conditional expression cut == 'Ideal' . For each row in our data frame, dplyr checked whether the column cut was set to 'Ideal' , and returned only those rows where cut == 'Ideal' evaluated to TRUE .

In our first filter, we used the operator == to test for equality. That's not the only way we can use dplyr to filter our data frame, however. We can use a number of different relational operators to filter in R.

Relational operators are used to compare values. In R generally (and in dplyr specifically), those are:

  • == (Equal to)
  • != (Not equal to)
  • < (Less than)
  • <= (Less than or equal to)
  • > (Greater than)
  • >= (Greater than or equal to)

These are standard mathematical operators you're used to, and they work as you'd expect. One quick note: make sure you use the double equals sign ( == ) for comparisons! By convention, a single equals sign ( = ) is used to assign a value to a variable, and a double equals sign ( == ) is used to check whether two values are equal. Using a single equals sign will often give an error message that is not intuitive, so make sure you check for this common error!

dplyr can also make use of the following logical operators to string together multiple different conditions in a single dplyr filter call!

  • ! (logical NOT)
  • & (logical AND)
  • | (logical OR)

There are two additional operators that will often be useful when working with dplyr to filter:

  • %in% (Checks if a value is in an array of multiple values)
  • is.na() (Checks whether a value is NA)

In our first example above, we tested for equality when we said cut == 'Ideal' . Now, let's expand our capabilities with different relational parameters in our filter:

Here, we select only the diamonds where the price is greater than 2000.

And here, we select all the diamonds whose cut is NOT equal to 'Ideal'. Note that this is the exact opposite of what we filtered before.

You can use < , > , <= , >= , == , and != in similar ways to filter your data. Try a few examples on your own to get comfortable with the different filtering options!

A note on storing your results

By default, dplyr filter will perform the operation you ask and then print the result to the screen. If you prefer to store the result in a variable, you'll need to assign it as follows:

Note that you can also overwrite the dataset (that is, assign the result back to the diamonds data frame) if you don't want to retain the unfiltered data. In this case I want to keep it, so I'll store this result in e_diamonds . In any case, it's always a good idea to preview your dplyr filter results before you overwrite any data!

Filtering Numeric Variables

Numeric variables are the quantitative variables in a dataset. In the diamonds dataset, this includes the variables carat and price, among others. When working with numeric variables, it is easy to filter based on ranges of values. For example, if we wanted to get any diamonds priced between 1000 and 1500, we could easily filter as follows:

In general, when working with numeric variables, you'll most often make use of the inequality operators, > , < , >= , and <= . While it is possible to use the == and != operators with numeric variables, I generally recommend against it.

The issue with using == is that it will only return true of the value is exactly equal to what you're testing for. If the dataset you're testing against consists of integers, this is possible, but if you're dealing with decimals, this will often break down. For example, 1.0100000001 == 1.01 will evaluate to FALSE . This is technically true, but it's easy to get into trouble with decimal precision. I never use == when working with numerical variables unless the data I am working with consists of integers only!

Filtering Categorical Variables

Categorical variables are non-quantitative variables. In our example dataset, the columns cut, color, and clarity are categorical variables. In contrast to numerical variables, the inequalities > , < , >= and <= have no meaning here. Instead, you'll make frequent use of the == , != , and %in% operators when filtering categorical variables.

Above, we filtered the dataset to include only the diamonds whose cut was Ideal using the == operator. Let's say that we wanted to expand this filter to also include diamonds where the cut is Premium. To accomplish this, we would use the %in% operator:

How does this work? First, we create a vector of our desired cut options, c('Ideal', 'Premium') . Then, we use %in% to filter only those diamonds whose cut is in that vector. dplyr will filter out BOTH those diamonds whose cut is Ideal AND those diamonds whose cut is Premium. The vector you check against for the %in% function can be arbitrarily long, which can be very useful when working with categorical data.

It's also important to note that the vector can be defined before you perform the dplyr filter operation:

This helps to increase the readability of your code when you're filtering against a larger set of potential options. This also means that if you have an existing vector of options from another source, you can use this to filter your dataset. This can come in very useful as you start working with multiple datasets in a single analysis!

Chaining together multiple filtering operations with logical operators

The real power of the dplyr filter function is in its flexibility. Using the logical operators &, |, and !, we can group many filtering operations in a single command to get the exact dataset we want!

Let's say we want to select all diamonds where the cut is Ideal and the carat is greater than 1:

BOTH conditions must evaluate to TRUE for the data to be selected. That is, the cut must be Ideal, and the carat must be greater than 1.

You don't need to limit yourself to two conditions either. You can have as many as you want! Let's say we also wanted to make sure the color of the diamond was E. We can extend our example:

What if we wanted to select rows where the cut is ideal OR the carat is greater than 1? Then we'd use the | operator!

Any time you want to filter your dataset based on some combination of logical statements, this is possibly using the dplyr filter function and R's built-in logical parameters. You just need to figure out how to combine your logical expressions to get exactly what you want!

dplyr filter is one of my most-used functions in R in general, and especially when I am looking to filter in R. With this article you should have a solid overview of how to filter a dataset, whether your variables are numerical, categorical, or a mix of both. Practice what you learned right now to make sure you cement your understanding of how to effectively filter in R using dplyr!

Did you find this post useful? I frequently write tutorials like this one to help you learn new skills and improve your data science. If you want to be notified of new tutorials, sign up here!

I help technology companies to leverage their data to produce branded, influential content to share with their clients. I work on everything from investor newsletters to blog posts to research papers. If you enjoy the work I do and are interested in working together, you can visit my consulting website or contact me at [email protected] !

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Angela Bofill Dies: Hit Singer For ‘I Try’ And ‘Angel Of The Night’ Was 70

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r filter assignment

Angela Bofill , who had numerous hits on the R&B charts in the 1970s and 1980s, died Thursday at her daughter’s home in Vallejo, California at 70. No cause was given by her manager and on her personal Facebook account.

Her hits included I Try, This Time I’ll Be Sweeter, and Angel of the Night.

“ON BEHALF OF MY DEAR FRIEND ANGIE, I AM SADDENED TO ANNOUNCE HER PASSING ON THE MORNING OF JUNE 13TH,” the first post Facebook post read.

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The message was signed by her friend and manager, Rich Engel.

The singer had two strokes, in 2006 and 2007, and had to learn again how to walk and sing.

Speaking to The Denver Post in 2011 after taking off five years to recover, Bofill said she was happy to be back.

“I feel happy performing again,” Bofill says. “I need crowd. In the blood, entertain. Any time a crowd comes to see me, I’m surprised. No sing no more and still people come. Wow. Impressed,” she said in the interview.

Bofill released her first studio album, Angie , in 1978, and continued recording into the 1990s.

Her funeral will be held at St. Dominick’s Church in California on June 28 at 1 p.m.

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r filter assignment

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An algorithm for filtering text files

Imagine you have a .txt file of the following structure:

I would like to read all the data except lines denoted by >>> and lines below the >>> end of file line. So far I've solved this using read.table(comment.char = ">", skip = x, nrow = y) ( x and y are currently fixed). This reads the data between the header and >>> end of file .

However, I would like to make my function a bit more plastic regarding the number of rows. Data may have values larger than 800, and consequently more rows.

I could scan or readLines the file and see which row corresponds to the >>> end of file and calculate the number of lines to be read. What approach would you use?

Jørgen R's user avatar

  • Please provide some dummy data. =) –  aL3xa Commented Jan 7, 2011 at 19:07
  • @aL3xa: is the snippet already shown insufficient? –  Gavin Simpson Commented Jan 7, 2011 at 19:18

2 Answers 2

Here is one way to do it:

Which gives:

On the data snippet you provide (in file foo.txt , and after removing the ... lines).

Gavin Simpson's user avatar

  • 1 +1, nice to learn about rle , which I haven't used before. I'm wondering though if there's a way to modify the definition of read.table (and/or scan and/or readLines ), by adding an optional EOF argument so that it bails when it encounters the EOF string. That way we could do this in one pass rather than 2. –  Prasad Chalasani Commented Jan 7, 2011 at 19:57
  • EOF argument would be a nice addition. –  Roman Luštrik Commented Jan 7, 2011 at 20:34
  • I was hoping there would be a way to add an optional EOF arg to the source definition for scan , but it calls .Internal(scan...) , so the only way is to change the internal (C?) code for scan... –  Prasad Chalasani Commented Jan 7, 2011 at 20:41
  • 2 A tiny side-effect of the textConnection() within a function (lapply) is that connections get gc()-ed, which produces an irritating warning (harmless). This can be solved with closeAllConnections() after a textConnection() call. –  Roman Luštrik Commented Jan 7, 2011 at 22:42
  • @Roman; good point. If I were using the above a lot, I'd wrap it in a function, save the output of con <- textConnection(Lines[want]) and include a on.exit(close(con)) in the function body to ensure only the generated connection was closed, whenever the function exited, normally or abnormally. –  Gavin Simpson Commented Jan 7, 2011 at 23:06

Here are a couple of ways.

1) readLine reads in the lines of the file into L and sets skip to the number of lines to skip at the beginning and end.of.file to the line number of the row marking the end of the data. The read.table command then uses these two variables to re-read the data.

A variation would be to use textConnection in place of File in the read.table line:

2) Another possibility is to use sed or awk/gawk. Consider this one line gawk program. The program exits if it sees the line marking the end of the data; otherwise, it skips the current line if that line starts with >>> and if neither of those happen it prints the line. We can pipe foo.txt through the gawk program and read it using read.table .

A variation of this is that we could omit the /^>>>/ {next}; portion of the gawk program, which skips over the >>> lines at the beginning, and use comment = ">" in the read.table` call instead.

G. Grothendieck's user avatar

  • The awk/gawk solution would be very handy if you couldn't foresee the structure of your file in advance. –  Roman Luštrik Commented Jan 8, 2011 at 9:10

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r filter assignment

IMAGES

  1. Filter Dataframe Based On List Of Values In R

    r filter assignment

  2. How to filter rows in R

    r filter assignment

  3. Arrange, Filter, & Group Rows In R Using dplyr

    r filter assignment

  4. R Filter

    r filter assignment

  5. Filter Function in R Programming

    r filter assignment

  6. Filter Function in R Programming

    r filter assignment

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  1. Keep rows that match a condition

    Keep rows that match a condition. Source: R/filter.R. The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA the row will be dropped, unlike base subsetting with [.

  2. How to Filter in R: A Detailed Introduction to the dplyr Filter

    In our first filter, we used the operator == to test for equality. That's not the only way we can use dplyr to filter our data frame, however. We can use a number of different relational operators to filter in R. Relational operators are used to compare values. In R generally (and in dplyr specifically), those are:

  3. How can I use a parameter as a filter criteria with 'dplyr' in R?

    You assign the string "Liability" to the variable R.cat.1 but use this variable as column name in the filter() expression. You should put it on the other side, i.e. dplyr::filter(a, Note == "N.6.2", R.val.1 == R.cat.1) assuming that R.val.1 is the column in the data.frame a. - Taufi.

  4. Lesson 4 Filtering Data

    4.2.2 Filtering Using a List. One very powerful trick in R is to extract rows that match a list of values. For example, say we wanted to extract a list of managers. In this dataset, managers have a value of JobGrade >= 4, so we could use a logical criterion: filter (Bank, JobGrade >= 4) ## # A tibble: 63 x 9.

  5. filter() and slice() functions in R from dplyr ️ [Select Rows]

    The filter function from dplyr subsets rows of a data frame based on a single or multiple conditions. In this tutorial you will learn how to select rows using comparison and logical operators and how to filter by row number with slice. Sample data The examples inside this tutorial will use the women data set provided by R.

  6. A Quick and Dirty Guide to the Dplyr Filter Function

    dplyr is a set of tools strictly for data manipulation. In fact, there are only 5 primary functions in the dplyr toolkit: filter() … for filtering rows. select() … for selecting columns. mutate() … for adding new variables. summarise() … for calculating summary stats. arrange() … for sorting data.

  7. filter function

    The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA > the row will be dropped, unlike base subsetting with <code>[</code>.</p>

  8. How to Filter Rows in R

    starwars %>% filter(eye_color %in% c(' blue ', ' yellow ', ' red ')) # A tibble: 35 x 13 name height mass hair_color skin_color eye_color birth_year gender 1 Luke~ 172 77 blond fair blue 19 male 2 C-3PO 167 75 <NA> gold yellow 112 <NA> 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA> 4 Dart~ 202 136 none white yellow 41.9 male 5 Owen~ 178 120 brown ...

  9. 5 Manipulating data with dplyr

    If starting from a new Rstudio session you should open Week_2_tidyverse.R and run the following code: library (tidyverse) mpg_df <-mpg. 5.1 filter() The filter() function subsets the rows in a data frame by testing against a conditional statement. The output from a successful filter() will be a data frame with fewer rows than the input data ...

  10. R dplyr filter()

    R dplyr filter () - Subset DataFrame Rows. The filter() function from dplyr package is used to filter the data frame rows in R. Note that filter () doesn't filter the data instead it retains all rows that satisfy the specified condition. dplyr is an R package that offers a grammar for data manipulation and includes a widely-used set of ...

  11. R: How to Use %in% to Filter for Rows with Value in List

    Note: You can find the complete documentation for the filter function in dplyr here. Additional Resources. The following tutorials explain how to perform other common operations in dplyr: How to Select the First Row by Group Using dplyr How to Filter by Multiple Conditions Using dplyr How to Filter Rows that Contain a Certain String Using dplyr

  12. Filter data by multiple conditions in R using Dplyr

    Method 1: Using filter () directly. For this simply the conditions to check upon are passed to the filter function, this function automatically checks the dataframe and retrieves the rows which satisfy the conditions. Syntax: filter (df , condition) Parameter : df: The data frame object. condition: filtering based upon this condition.

  13. R Basics

    The filter() function chooses rows that meet a specific criteria. We can do this with Base R functions or with dplyr`. We can do this with Base R functions or with dplyr`. Let's say that we want to look at the flights data but we are only interested in the data from the first day of the year.

  14. How to filter a dataframe in R

    A data frame filter in R is a way to select a subset of rows from a data frame based on specific conditions. Filtering a data frame can be done using the square bracket notation or the `subset ()` function. In both cases, specify a condition that must be met for a filter row in R to be included in the filtered data frame.

  15. Introductory R Tutorial 3: Filtering and Plotting

    Filtering and plotting: Goals. The goals of this session are to introduce you to data handling and visualization. We will start by looking a plotting, which you are now ready to learn because you have understood data types and functions. The main topics covered will include: Making simple plots of data. Filtering rows of data/cases.

  16. Resolving Missing DataFrame Assignment in R: Filter Group Operations

    Resolving Missing DataFrame Assignment: Filter Group Operations. In data manipulation and analysis, working with DataFrames is a common task. One of the challenges that data analysts and scientists face is handling missing or empty DataFrames after filter group operations.

  17. Assignment linter

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  20. r

    My assignment must satisfy the following conditions: Use filter() on airlines to identify which airline corresponds to the carrier code. Save the result to a variable, fastest_airline.

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    A favorite haven for hackers. A second vulnerability tracked as CVE-2024-3079 affects the same router models. It stems from a buffer overflow flaw and allows remote hackers who have already ...

  23. How to Filter in R: A Detailed Introduction to the dplyr Filter Function

    dplyr is a cohesive set of data manipulation functions that will help make your data wrangling as painless as possible. dplyr, at its core, consists of 5 functions, all serving a distinct data wrangling purpose: filter() selects rows based on their values. mutate() creates new variables. select() picks columns by name.

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  27. r

    I do not know how to program in R at all, I only know python pandas. How to do the following in R: my code has two variables, username and asignment I want to filter my dataframe so that I only ge...

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  30. r

    Here are a couple of ways. 1) readLine reads in the lines of the file into L and sets skip to the number of lines to skip at the beginning and end.of.file to the line number of the row marking the end of the data. The read.table command then uses these two variables to re-read the data. File <- "foo.txt".