Wrangle

Question 1

What is the package for string manipulation?

Answer 1

stringr package for string manipulation.

Question 2

Is Stringr part of Tidyverse? If not , why?

Answer 2

stringr is not part of the core tidyverse because you don’t always have textual data, so we need to load it explicitly.

Question 3

we can use single or double quote to create string in R. Is there any diff btw the 2? Which one is always recommended? when is it recommended to use single quote?

Answer 3

You can create strings with either single quotes or double quotes. Unlike other languages, there is no difference in behaviour. I recommend always using “, unless you want to create a string that contains multiple “.

example:
string1

Question 4

if you forget to close a quote , what will you see?

Answer 4

If you forget to close a quote, you’ll see +, the continuation character:

> “This is a string without a closing quote
+
+
+ HELP I’M STUCK
If this happen to you, press Escape and try again

Question 5

how to include single quote or double quote?

Answer 5

To include a literal single or double quote in a string you can use \ to “escape” it:

double_quote

Question 6

printed representation of a string is not the same as string itself?

Answer 6

because the printed representation shows the escapes. To see the raw contents of the string, use writeLines():

x [1] “"” “\”
writeLines(x)
#> “
#> \

Question 7

how to create multiple strings? why are they store in character vector?

Answer 7

Multiple strings are often stored in a character vector, which you can create with c():

Question 8

why prefer function from stringr rather than base R?

Answer 8

Base R contains many functions to work with strings but we’ll avoid them because they can be inconsistent, which makes them hard to remember.

Instead we’ll use functions from stringr. These have more intuitive names, and all start with str_. For example, str_length() tells you the number of characters in a string:

str_length(c("a", "R for data science", NA))
#> [1]  1 18 NA
The common str_ prefix is particularly useful if you use RStudio, because typing str_ will trigger autocomplete, allowing you to see all stringr functions:

Question 9

How to combine Stringr?

Answer 9

To combine two or more strings, use str_c():

str_c("x", "y")
#> [1] "xy"

str_c("x", "y", "z")
#> [1] "xyz"

Use the sep argument to control how they’re separated:

str_c("x", "y", sep = ", ")
#> [1] "x, y"

Question 10

Note : str_c() is is vectorised, and it automatically recycles shorter vectors to the same length as the longest:

Answer 10

str_c("prefix-", c("a", "b", "c"), "-suffix")
#> [1] "prefix-a-suffix" "prefix-b-suffix" "prefix-c-suffix"

”” means is vector of length 1

Question 11

How to collapse a vector of strings into single one?

Answer 11

To collapse a vector of strings into a single string, use collapse:

str_c(c("x", "y", "z"), collapse = ", ")
#> [1] "x, y, z"

Question 12

How to Subset strings?

Answer 12

using str_sub() takes start and end arguments which give the (inclusive) position of the substring:

x [1] “App” “Ban” “Pea”

# negative numbers count backwards from end
str_sub(x, -3, -1)
#> [1] "ple" "ana" "ear"

Question 13

How to use use the assignment form of str_sub() to modify strings:?

Answer 13

str_sub(x, 1, 1) [1] “apple” “banana” “pear”

also : str_to_upper()

Question 14

What is the relationship between paste() , paste0() , str_c()

Answer 14

The function paste() separates strings by spaces by default, while paste0() does not separate strings with spaces by default.

paste("foo", "bar")
#> [1] "foo bar"
paste0("foo", "bar")
#> [1] "foobar"
Since str_c() does not separate strings with spaces by default it is closer in behavior to paste0().

str_c("foo", "bar")
#> [1] "foobar"

In simple words,

paste() is like concatenation using separation factor, whereas,
paste0() is like append function using separation facto

> paste(“a”,”b”) #Here default separation factor is “ “ i.e. a space

[1] “a b”

> paste0(“a”,”b”) #Here default separation factor is “” i.e a null

[1] “ab”

Question 15

How str_c and paste function handle NA?

Answer 15

However, str_c() and the paste function handle NA differently. The function str_c() propagates NA, if any argument is a missing value, it returns a missing value. This is in line with how the numeric R functions, e.g. sum(), mean(), handle missing values. However, the paste functions, convert NA to the string “NA” and then treat it as any other character vector.

str_c("foo", NA)
#> [1] NA
paste("foo", NA)
#> [1] "foo NA"
paste0("foo", NA)
#> [1] "fooNA"

Question 16

describe the difference between the sep and collapse arguments to str_c().

Answer 16

The sep argument is the string inserted between arguments to str_c(), while collapse is the string used to separate any elements of the character vector into a character vector of length one.

Question 17

paste using sep and collapse in R

Answer 17

# The difference between the `sep` and `collapse` arguments
# in paste can be thought of like this:
# 
# paste can accept multiple *vectors* as input, and will
# concatenate the ith entries of each vector pairwise 
# (or tuplewise), if it can.
# 
# When you pass paste multiple vectors, sep defines what
# separates the entries in those tuple-wise concatenations.
# 
# When you pass paste a *collapse* value, it will return
# any concatenated pairs as part of a single length-1 
# character vector, with the tuples separated by 
# the string you passed to `collapse`

x

Question 18

What are Regexp?

Answer 18

Regexps are a very terse language that allow you to describe patterns in strings. They take a little while to get your head around, but once you understand them, you’ll find them extremely useful.

Question 19

2 simplest Regrex functions?

Answer 19

we’ll use str_view() and str_view_all(). These functions take a character vector and a regular expression, and show you how they match

Question 20

how str_view works?

Answer 20

The simplest patterns match exact strings:

x

Question 21

What does . means in regrex?

Answer 21

The next step up in complexity is ., which matches any character (except a newline):

str_view(x, “.a.”)
apple
banana
pear

Question 22

How to match . ?

Answer 22

You need to use an “escape” to tell the regular expression you want to match it exactly, not use its special behaviour. Like strings, regexps use the backslash, \, to escape special behaviour. So to match an ., you need the regexp .. Unfortunately this creates a problem. We use strings to represent regular expressions, and \ is also used as an escape symbol in strings. So to create the regular expression . we need the string “\.”.

# To create the regular expression, we need \\
dot

Question 23

What are anchors in

Answer 23

By default, regular expressions will match any part of a string. It’s often useful to anchor the regular expression so that it matches from the start or end of the string. You can use:

^ to match the start of the string.
$ to match the end of the string.
x

Question 24

How to force a regular expression to match a complete string ?

Answer 24

To force a regular expression to only match a complete string, anchor it with both ^ and $:

Question 25

\d ,\s ,[abc] , [^abc]

Answer 25

There are four other useful tools:

\d: matches any digit.
\s: matches any whitespace (e.g. space, tab, newline).
[abc]: matches a, b, or c.
[^abc]: matches anything except a, b, or c.