Program

Question 1

what are core tidyverse packages?

Answer 1

the ggplot2, tibble, tidyr, readr, purrr, and dplyr packages

Question 2

Y master one thing at a time?

Answer 2

However, we strongly believe that it’s best to master one tool at a time. You will get better faster if you dive deep, rather than spreading yourself thinly over many topics. This doesn’t mean you should only know one thing, just that you’ll generally learn faster if you stick to one thing at a time. You should strive to learn new things throughout your career, but make sure your understanding is solid before you move on to the next interesting thing.

Question 3

Where does pipe package comes from?

Answer 3

The pipe, %>%, comes from the magrittr package by Stefan Milton Bache

Question 4

if you load tidyverse do u need to load magrittr?

Answer 4

Packages in the tidyverse load %>% for you automatically, so you don’t usually load magrittr explicitly

Question 5

Pipe cant work for 2 classes ?

Answer 5

1. Functions that use the current environment. For example, assign() will create a new variable with the given name in the current environment: 2. Functions that use lazy evaluation. In R, function arguments are only computed when the function uses them, not prior to calling the function. The pipe computes each element in turn, so you can’t rely on this behaviour.

Question 6

Pipes are most useful for rewriting a fairly short linear sequence of operations. when not use pipe?

Answer 6

1. Your pipes are longer than (say) ten steps. In that case, create intermediate objects with meaningful names. That will make debugging easier, because you can more easily check the intermediate results, and it makes it easier to understand your code, because the variable names can help communicate intent. 2. You have multiple inputs or outputs. If there isn’t one primary object being transformed, but two or more objects being combined together, don’t use the pipe. 3. You are starting to think about a directed graph with a complex dependency structure. Pipes are fundamentally linear and expressing complex relationships with them will typically yield confusing code.

Question 7

For assignment magrittr provides the %<>% operator which allows you to do what?

Answer 7

replace code like: mtcars % transform(cyl = cyl * 2) with mtcars %<>% transform(cyl = cyl * 2) I’m not a fan of this operator because I think assignment is such a special operation that it should always be clear when it’s occurring. In my opinion, a little bit of duplication (i.e. repeating the name of the object twice) is fine in return for making assignment more explicit.

Question 8

Functions. why?

Answer 8

Functions allow you to automate common tasks in a more powerful and general way than copy-and-pasting. three advantages 1. You can give a function an evocative name that makes your code easier to understand. 2. As requirements change, you only need to update code in one place, instead of many. 3. You eliminate the chance of making incidental mistakes when you copy and paste (i.e. updating a variable name in one place, but not in another).

Question 9

On writting function do we need tidyverse or base R?

Answer 9

On R4DSC the focus is just to use function for base R , so no need of using any library.

Question 10

When should you consider writting a function?

Answer 10

You should consider writing a function whenever you’ve copied and pasted a block of code more than twice (i.e. you now have three copies of the same code). For example, take a look at this code. What does it do?

Question 11

To use tibble to create data.frame , u need to specify it explicitly if u dont import it from library.

Answer 11

df

Question 12

There are 3 key steps to create a function. what are they?

Answer 12

There are three key steps to creating a new function: 1. You need to pick a name for the function. Here I’ve used rescale01 because this function rescales a vector to lie between 0 and 1. 2. You list the inputs, or arguments, to the function inside function. Here we have just one argument. If we had more the call would look like function(x, y, z). 2. You place the code you have developed in body of the function, a { block that immediately follows function(…).

Question 13

It’s easier to start with working code and turn it into a function; it’s harder what?

Answer 13

it’s harder to create a function and then try to make it work.

Question 14

Generally function names should be what ? argument should be what?

Answer 14

Generally, function names should be verbs, and arguments should be nouns. There are some exceptions: nouns are ok if the function computes a very well known noun (i.e. mean() is better than compute_mean()), or accessing some property of an object (i.e. coef() is better than get_coefficients()). A good sign that a noun might be a better choice is if you’re using a very broad verb like “get”, “compute”, “calculate”, or “determine”. Use your best judgement and don’t be afraid to rename a function if you figure out a better name later.

Question 15

What is recommendation for naming function in R?

Answer 15

If your function name is composed of multiple words, I recommend using “snake_case” where each lowercase word is separated by an underscore.camelCase is a popular alternative. It doesn’t really matter which one you pick, the important thing is to be consistent: # Never do this! col_mins

Question 16

How to name family of function that do similar things?

Answer 16

If you have a family of functions that do similar things, make sure they have consistent names and arguments. Use a common prefix to indicate that they are connected. That’s better than a common suffix because autocomplete allows you to type the prefix and see all the members of the family. # Good input_select() input_checkbox() input_text() # Not so good select_input() checkbox_input() text_input() A good example of this design is the stringr package: if you don’t remember exactly which function you need, you can type str_ and jog your memory

Question 17

How do we use comment in function?

Answer 17

Use comments, lines starting with #, to explain the “why” of your code. You generally should avoid comments that explain the “what” or the “how”. If you can’t understand what the code does from reading it, you should think about how to rewrite it to be more clearly. Another important use of comments is to break up your file into easily readable chunks. Use long lines of - and = to make it easy to spot the breaks. # Load data ————————————– # Plot data ————————————–

Question 18

?if does not give u help menu. How to do it?

Answer 18

?if To get help on if you need to surround it in backticks:

Question 19

What || (or) and && (and) use for?

Answer 19

You can use || (or) and && (and) to combine multiple logical expressions. Dont use I . You should never use | or & in an if statement: these are vectorised operations that apply to multiple values (that’s why you use them in filter())

Question 20

be careful when testing equality with == . why?

Answer 20

Be careful when testing for equality. == is vectorised, which means that it’s easy to get more than one output Either check the length is already 1, collapse with all() or any(), or use the non-vectorised identical(). identical() is very strict: it always returns either a single TRUE or a single FALSE, and doesn’t coerce types. This means that you need to be careful when comparing integers and doubles:

Question 21

near() and ==

Answer 21

There’s another common problem you might encounter when using ==: floating point numbers. These results might surprise you! remember that every number you see is an approximation. Instead of relying on ==, use near() near(sqrt(2) ^ 2, 2) #> [1] TRUE near(1 / 49 * 49, 1) #> [1] TRUE sqrt(2) ^ 2 == 2 #> [1] FALSE 1 / 49 * 49 == 1 #> [1] FALSE

Question 22

Multiple condition

Answer 22

use if else if or switch . But if you end up with a very long series of chained if statements, you should consider rewriting. One useful technique is the switch() function. It allows you to evaluate selected code based on position or name.

Question 23

if u have a logical vector , how can you collapse it to single value?

Answer 23

If you do have a logical vector, you can use any() or all() to collapse it to a single value.

Question 24

Code style

Answer 24

Both if and function should (almost) always be followed by squiggly brackets ({}), and the contents should be indented by two spaces An opening curly brace should never go on its own line and should always be followed by a new line. A closing curly brace should always go on its own line, unless it’s followed by else. Always indent the code inside curly braces. # Good if (y < 0 && debug) { message(“Y is negative”) } if (y == 0) { log(x) } else { y ^ x }

Question 25

argument of function fall into 2.

Answer 25

The arguments to a function typically fall into two broad sets: one set supplies the data to compute on, and the other supplies arguments that control the details of the computation. For example: n mean(), the data is x, and the details are how much data to trim from the ends (trim) and how to handle missing values (na.rm). Generally, data arguments should come first. Detail arguments should go on the end, and usually should have default values

Question 26

spaces in function call and after = , what is best way of using them ?

Answer 26

Notice that when you call a function, you should place a space around = in function calls, and always put a space after a comma, not before (just like in regular English) # Good average

Question 27

how to name arguments?

Answer 27

The names of the arguments are also important. R doesn’t care, but the readers of your code (including future-you!) will. Generally you should prefer longer, more descriptive names, but there are a handful of very common, very short names. It’s worth memorising these: x, y, z: vectors. w: a vector of weights. df: a data frame. i, j: numeric indices (typically rows and columns). n: length, or number of rows. p: number of columns.

Question 28

Checking values in function

Answer 28

It’s good practice to check important preconditions, and throw an error (with stop()), if they are not true: wt_mean

Question 29

What is NA in R?

Answer 29

Missing data in R appears as NA. NA is not a string or a numeric value, but an indicator of missingness. We can create vectors with missing values. x1

Question 30

What is na.omit and na.exclude?

Answer 30

na.omit and na.exclude: returns the object with observations removed if they contain any missing values; differences between omitting and excluding NAs can be seen in some prediction and residual functions

Question 31

How to use R’s ellipsis feature when writing your own function?

Answer 31

https://stackoverflow.com/questions/3057341/how-to-use-rs-ellipsis-feature-when-writing-your-own-function

Question 32

What deos that means R is lazy evaluation programming language?

Answer 32

Arguments in R are lazily evaluated: they’re not computed until they’re needed. That means if they’re never used, they’re never called. This is an important property of R as a programming language, but is generally not important when you’re writing your own functions for data analysis

Question 33

How many types of vectors?

Answer 33

1. Atomic vectors, of which there are six types: logical, integer, double, character, complex, and raw. Integer and double vectors are collectively known as numeric vectors. 2. Lists, which are sometimes called recursive vectors because lists can contain other lists. The chief difference between atomic vectors and lists is that atomic vectors are homogeneous, while lists can be heterogeneous

Question 34

What is relationship between NULL and NA ?

Answer 34

NULL is often used to represent the absence of a vector (as opposed to NA which is used to represent the absence of a value in a vector). NULL typically behaves like a vector of length 0

Question 35

Every vector has two key properties, what are they?

Answer 35

1. Its type, which you can determine with typeof(). 2.Its length, which you can determine with length().

Question 36

There are three important types of augmented vector:?

Answer 36

1. Factors are built on top of integer vectors. 2. Dates and date-times are built on top of numeric vectors. 3. Data frames and tibbles are built on top of lists.

Question 37

What are the 4 most important of atomic vector>

Answer 37

The four most important types of atomic vector are logical, integer, double, and character. Raw and complex are rarely used during a data analysis,

Question 38

Logical Vector?

Answer 38

Logical vectors are the simplest type of atomic vector because they can take only three possible values: FALSE, TRUE, and NA. 1:10 %% 3 == 0 #> [1] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE FALSE

Question 39

What are Numeric Vector?

Answer 39

nteger and double vectors are known collectively as numeric vectors. In R, numbers are doubles by default. To make an integer, place an L after the number: typeof(1) #> [1] “double” typeof(1L) #> [1] “integer” 1.5L #> [1] 1.5

Question 40

Diff btw Integer and double. Double are approximation. What does that means?

Answer 40

Doubles are approximations. Doubles represent floating point numbers that can not always be precisely represented with a fixed amount of memory. This means that you should consider all doubles to be approximations. For example, what is square of the square root of two? x [1] 2 x - 2 #> [1] 4.44e-16 This behaviour is common when working with floating point numbers: most calculations include some approximation error. Instead of comparing floating point numbers using ==, you should use dplyr::near() which allows for some numerical tolerance.

Question 41

What is diff btwn special values for Integer and double?

Answer 41

Integers have one special value: NA, while doubles have four: NA, NaN, Inf and -Inf. All three special values NaN, Inf and -Inf can arise during division: c(-1, 0, 1) / 0 #> [1] -Inf NaN Inf Avoid using == to check for these other special values. Instead use the helper functions is.finite(), is.infinite(), and is.nan():

Question 42

Whata re character vectors?

Answer 42

Character vectors are the most complex type of atomic vector, because each element of a character vector is a string, and a string can contain an arbitrary amount of data.

Question 43

R uses global string fool. What does that mean?

Answer 43

Here I wanted to mention one important feature of the underlying string implementation: R uses a global string pool. This means that each unique string is only stored in memory once, and every use of the string points to that representation. This reduces the amount of memory needed by duplicated strings.

Question 44

What are diff missing string values in vectors?

Answer 44

Note that each type of atomic vector has its own missing value: NA # logical #> [1] NA NA_integer_ # integer #> [1] NA NA_real_ # double #> [1] NA NA_character_ # character #> [1] NA Normally you don’t need to know about these different types because you can always use NA and it will be converted to the correct type using the implicit coercion rules

Question 45

Coercion ? how many in R>?

Answer 45

There are two ways to convert, or coerce, one type of vector to another: 1.Explicit coercion happens when you call a function like as.logical(), as.integer(), as.double(), or as.character(). Whenever you find yourself using explicit coercion, you should always check whether you can make the fix upstream, so that the vector never had the wrong type in the first place. For example, you may need to tweak your readr col_types specification. 2. Implicit coercion happens when you use a vector in a specific context that expects a certain type of vector. For example, when you use a logical vector with a numeric summary function, or when you use a double vector where an integer vector is expected.

Question 46

implicit coercion example in code?

Answer 46

You may see some code (typically older) that relies on implicit coercion in the opposite direction, from integer to logical: if (length(x)) { # do something } You may see some code (typically older) that relies on implicit coercion in the opposite direction, from integer to logical: if (length(x)) { # do something }

Question 47

Higher precedence always win in vector?

Answer 47

typeof(c(TRUE, 1L)) #> [1] “integer” typeof(c(1L, 1.5)) #> [1] “double” typeof(c(1.5, “a”)) #> [1] “character”

Question 48

Test functions in R?

Answer 48

One option is to use typeof(). Another is to use a test function which returns a TRUE or FALSE Base R provides many functions like is.vector() and is.atomic(), but they often return surprising results. Instead, it’s safer to use the is_* functions provided by purrr is_logical() x is_integer() x is_double() x is_numeric() x x is_character() x is_atomic() x x x x is_list() x is_vector() x x x x x Each predicate also comes with a “scalar” version, like is_scalar_atomic(), which checks that the length is 1. This is useful, for example, if you want to check that an argument to your function is a single logical value

Question 49

R does not have scalar, what it does have?

Answer 49

R doesn’t actually have scalars: instead, a single number is a vector of length 1. Because there are no scalars, most built-in functions are vectorised, meaning that they will operate on a vector of numbers. That’s why, for example, this code works: sample(10) + 100 #> [1] 109 108 104 102 103 110 106 107 105 101

Question 50

what happens if you add two vectors of different lengths?

Answer 50

1:10 + 1:2 #> [1] 2 4 4 6 6 8 8 10 10 12 1:10 + 1:3 #> Warning in 1:10 + 1:3: longer object length is not a multiple of shorter #> object length #> [1] 2 4 6 5 7 9 8 10 12 11 Here, R will expand the shortest vector to the same length as the longest, so called recycling. This is silent except when the length of the longer is not an integer multiple of the length of the shorter:

Question 51

Tidyverse throw error if vectors of diff length? but what is solution?

Answer 51

the vectorised functions in tidyverse will throw errors when you recycle anything other than a scalar. If you do want to recycle, you’ll need to do it yourself with rep() tibble(x = 1:4, y = 1:2) #> Error: Tibble columns must have consistent lengths, only values of length one are recycled: #> * Length 2: Column y #> * Length 4: Column x tibble(x = 1:4, y = rep(1:2, 2)) #> # A tibble: 4 x 2 #> x y #> #> 1 1 1 #> 2 2 2 #> 3 3 1 #> 4 4 2 tibble(x = 1:4, y = rep(1:2, each = 2)) #> # A tibble: 4 x 2 #> x y #> #> 1 1 1 #> 2 2 1 #> 3 3 2 #> 4 4 2

Question 52

How to name vectors in R?

Answer 52

All types of vectors can be named. You can name them during creation with c(): c(x = 1, y = 2, z = 4) #> x y z #> 1 2 4 Or after the fact with purrr::set_names(): set_names(1:3, c(“a”, “b”, “c”)) #> a b c #> 1 2 3

Question 53

Can we use diplyr::filter() to for vectors no tibble?

Answer 53

So far we’ve used dplyr::filter() to filter the rows in a tibble. filter() only works with tibble, so we’ll need new tool for vectors: . [is the subsetting function, and is called like x[a]

Question 54

How can you subset vector with integer?

Answer 54

1. A numeric vector containing only integers. The integers must either be all positive, all negative, or zero. Subsetting with positive integers keeps the elements at those positions: x [1] “two” “four” It’s an error to mix positive and negative values: x[c(1, -1)] #> Error in x[c(1, -1)]: only 0’s may be mixed with negative subscripts

Question 55

There are4 ways to subsect vector. How to subset vector with logical vector?

Answer 55

Subsetting with a logical vector keeps all values corresponding to a TRUE value. This is most often useful in conjunction with the comparison functions. x [1] 10 3 5 8 1 # All even (or missing!) values of x x[x %% 2 == 0] #> [1] 10 NA 8 NA

Question 56

There are4 ways to subsect vector.

Answer 56

1. integere 2. Logical vector 3. With charcter vector\4. nothing . The simplest type of subsetting is nothing, x[], which returns the complete x. This is not useful for subsetting vectors, but it is useful when subsetting matrices

Question 57

How to subset with character vector:?

Answer 57

If you have a named vector, you can subset it with a character vector: x xyz def #> 5 2

Question 58

What is Recursive vectors ?

Answer 58

Lists are recursive vector

Question 59

What makes List diff from atomic vector and how can we create it?

Answer 59

lists can contain other lists. This makes them suitable for representing hierarchical or tree-like structures. You create a list with list() x [[1]] #> [1] 1

Question 60

What is diff bten subsetiing vectors and list?

Answer 60

Vectors we use [x(1)] , List we use [x[1]] all () and [] works in each case

Question 61

what str() does in list for R?

Answer 61

What is str? Str is a compact way to display the structure of an R object. This allows you to use str as a diagnostic function and an alternative to summary. Str will output the information on one line for each basic structure. Str is best for displaying contents of lists. The goals is to get an output for any R object. str(x) #> List of 3 #> $ : num 1 #> $ : num 2 #> $ : num 3 x_named List of 3 #> $ a: num 1 #> $ b: num 2 #> $ c: num 3 Unlike atomic vectors, list() can contain a mix of objects: y List of 4 #> $ : chr “a” #> $ : int 1 #> $ : num 1.5 #> $ : logi TRUE list can contain other list . Lists can even contain other lists! z

Question 62

how many ways to subset list() ?

Answer 62

There are three ways to subset a list, which I’ll illustrate with a list named a: a List of 2 #> $ a: int [1:3] 1 2 3 #> $ b: chr “a string” Like with vectors, you can subset with a logical, integer, or character vector. [[extracts a single component from a list. It removes a level of hierarchy from the list. str(a[[1]]) #> int [1:3] 1 2 3 str(a[[4]]) #> List of 2 #> $ : num -1 #> $ : num -5 $ is a shorthand for extracting named elements of a list. It works similarly to [[except that you don’t need to use quotes. a$a #> [1] 1 2 3 a[[“a”]] #> [1] 1 2 3

Question 63

Difference between [ and [[ in subsetting

Answer 63

The distinction between [ and [[ is really important for lists, because [[ drills down into the list while [ returns a new, smaller list.

Question 65

What is diff for subsetting tibble and list?

Answer 65

Subsetting a tibble works the same way as a list; a data frame can be thought of as a list of columns. The key difference between a list and a tibble is that all the elements (columns) of a tibble must have the same length (number of rows). Lists can have vectors with different lengths as elements.

Question 66

what are argumented vectors?

Answer 66

Atomic vectors and lists are the building blocks for other important vector types like factors and dates. I call these augmented vectors, because they are vectors with additional attributes, including class. Because augmented vectors have a class, they behave differently to the atomic vector on which they are built.

4 argumented vectors

Factors

Dates

Date-times

Tibbles

Question 67

What are factors?

Answer 67

Factors are designed to represent categorical data that can take a fixed set of possible values. Factors are built on top of integers, and have a levels attribute:

Question 68

what are Dates and date-times argumented vectors?

Answer 68

Dates in R are numeric vectors that represent the number of days since 1 January 1970.

x \<- as.Date("1971-01-01")
unclass(x)
#\> [1] 365

typeof(x)
#\> [1] "double"
attributes(x)
#\> $class
#\> [1] "Date"

Question 69

What are tibble argumnted Lists?

Answer 69

Tibbles

Tibbles are augmented lists: they have class “tbl_df” + “tbl” + “data.frame”, and names (column) and row.names attributes:

The difference between a tibble and a list is that all the elements of a data frame must be vectors with the same length. All functions that work with tibbles enforce this constraint.

Traditional data.frames have a very similar structure:

The main difference is the class. The class of tibble includes “data.frame” which means tibbles inherit the regular data frame behaviour by default.

Question 70

Reducing code duplication has three main benefits:?

Answer 70

1. It’s easier to see the intent of your code, because your eyes are drawn to what’s different, not what stays the same.
2. It’s easier to respond to changes in requirements. As your needs change, you only need to make changes in one place, rather than remembering to change every place that you copied-and-pasted the code.
3. You’re likely to have fewer bugs because each line of code is used in more places.

Question 72

What is Another tool after function for reducing duplication in code?

Answer 72

iteration : which helps you when you need to do the same thing to multiple inputs: repeating the same operation on different columns, or on different datasets.

Question 74

What are important iteration paradigms in this book?

Answer 74

imperative programming and functional programming

On the imperative side you have tools like for loops and while loops, which are a great place to start because they make iteration very explicit, so it’s obvious what’s happening. However, for loops are quite verbose, and require quite a bit of bookkeeping code that is duplicated for every for loop.

Functional programming (FP) offers tools to extract out this duplicated code, so each common for loop pattern gets its own function. Once you master the vocabulary of FP, you can solve many common iteration problems with less code, more ease, and fewer errors.

Question 75

Learn loop and discard them

Answer 75

learn about loops. They offer you a detailed view of what it is supposed to happen at the elementary level as well as they provide you with an understanding of the data that you’re manipulating.

And after you have gotten a clear understanding of loops, get rid of them.

Put your effort into learning about vectorized alternatives. It pays off in terms of efficiency.

Question 76

For loop general structire?

Answer 76

Question 77

creating an empty vector ?

Answer 77

A general way of creating an empty vector of given length is the vector() function. It has two arguments: the type of the vector (“logical”, “integer”, “double”, “character”, etc) and the length of the vector.

output <- vector(“double”, length(x)).

output <- vector(“double”, length(x))

Before you start the loop, you must always allocate sufficient space for the output

Question 78

for loop has 3 component?

Answer 78

output <- vector(“double”, ncol(df)) # 1. output
for(i in seq_along(df)) { # 2. sequence
output[[i]] <- median(df[[i]]) # 3. body
}

Question 80

Component of for loop?

Answer 80

1. The output: output <- vector(“double”, length(x)). Before you start the loop, you must always allocate sufficient space for the output
2. The sequence: i in seq_along(df). This determines what to loop over: each run of the for loop will assign i to a different value from seq_along(df) . You might not have seen seq_along() before. It’s a safe version of the familiar 1:length(l)
3. The body: output[[i]] <- median(df[[i]])

Question 81

For loop variations?

Answer 81

There are four variations on the basic theme of the for loop:

1. Modifying an existing object, instead of creating a new object.
2. Looping over names or values, instead of indices.
3. Handling outputs of unknown length.
4. Handling sequences of unknown length.

Question 82

How to get output from Function.

Answer 82

Function automatically return the result of last statement in the body. So even u dont used return or variable to return the result.

Question 85

What are two tools for reducing duplication?

Answer 85

Fuction and iteration(which helps you when you need to do the same thing to multiple inputs: repeating the same operation on different columns, or on different datasets)

Question 87

What makes tranforming tidy data feel natural?

Answer 87

Most built-in R functions work with vectors of values. That makes transforming tidy data feel particularly natural.dplyr, ggplot2, and all the other packages in the tidyverse are designed to work with tidy data

Question 89

count()

Answer 89

Compute cases per year
table1 %\>% 
 count(year, wt =cases)
#\> # A tibble: 2 x 2
#\> year n
#\> <int> <int><br>#&gt; 1 1999 250740<br>#&gt; 2 2000 296920</int></int>

Question 90

how to use Gathering to tidy data?

Answer 90

A common problem is a dataset where some of the column names are not names of variables, but values of a variable

table4a
#\> # A tibble: 3 x 3
#\> country `1999` `2000`
#\> \* <chr> <int> <int><br>#&gt; 1 Afghanistan 745 2666<br>#&gt; 2 Brazil 37737 80488</int></int></chr>

table4a %\>% 
 gather(`1999`, `2000`, key = "year", value = "cases")
#\> # A tibble: 6 x 3
#\> country year cases
#\> <chr> <chr> <int><br>#&gt; 1 Afghanistan 1999 745<br>#&gt; 2 Brazil 1999 37737</int></chr></chr>

Question 91

How can we combine Tibble?

Answer 91

left_join(tidy4a, tidy4b)

Question 92

names function in R, how to use it?

Answer 92

names() function gets or sets the names of an object.

names(x)

names(x) <- valuex:

R object
value: to be assigned to the x, with the same length as x, or NULL
…

Question 93

comment multiple line in R?

Answer 93

command + Shift + C