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a/_bookdown_files/03_exploring-your-data_files/figure-html/unnamed-chunk-7-1.png b/_bookdown_files/03_exploring-your-data_files/figure-html/unnamed-chunk-7-1.png new file mode 100644 index 0000000000000000000000000000000000000000..121d39bdfca0c790e4138e6c6c6579a7e3a07028 GIT binary patch literal 3250 zcmeAS@N?(olHy`uVBq!ia0y~yV0B<%U_QXX3=}!ML-{q3VhivIaee*z_5c6>+Z9~0 zfg+p*9+AZi46^MY%-H|*V=4m!PmrgJV@SoEw^ufD9xxC%aNztu@7?AzBuo_x%p0}N zGCLT>vorje+Q{U>#w8%B;gGU`VIp46ZwK}UW@gq79uWnjfQAzej7p=N(J&ZI2cvmm zv^*Fs6G!WX(duxtNifQ*LQREWHc+Z9~0 zfg+p*9+AZi46^MY%-H|*V=4m!PmrgJV@SoEw^ufD9xxC%aNztu@7?AzBuo_x%p0}N zGCLT>vorje+Q{U>#w8%B;gGU`VIp46ZwK}UW@gq79uWnjfQAzej7p=N(J&ZI2cvmm zv^*Fs6G!WX(duxtNifQ*LQREWHc+Z9~0 zfg+p*9+AZi46^MY%-H|*V=4m!PmrgJV@SoEw^ufD9xxC%aNztu@7?AzBuo_x%p0}N zGCLT>vorje+Q{U>#w8%B;gGU`VIp46ZwK}UW@gq79uWnjfQAzej7p=N(J&ZI2cvmm zv^*Fs6G!WX(duxtNifQ*LQREWHc - + @@ -33,7 +33,7 @@ - + diff --git a/docs/cleaning-messy-data.html b/docs/cleaning-messy-data.html index d556473..0367669 100644 --- a/docs/cleaning-messy-data.html +++ b/docs/cleaning-messy-data.html @@ -24,7 +24,7 @@ - + @@ -33,7 +33,7 @@ - + @@ -314,14 +314,14 @@

4.1.1 For-loops

For example, let’s make a loop that repeatedly adds a number to a running total and squares the new total. We’ll use a variable total to keep track of the running total as the loop iterates:

-
numbers = c(-1, 1, -3, 2)
-
-total = 0
-for (number in numbers) {
-  total = (total + number)^2
-}
-
-total
+
numbers = c(-1, 1, -3, 2)
+
+total = 0
+for (number in numbers) {
+  total = (total + number)^2
+}
+
+total
## [1] 9

Use for-loops when some or all of the iterations depend on results from other iterations. If the iterations are not dependent, use one of:

@@ -332,7 +332,7 @@

4.1.1 For-loops

In some cases, you can use vectorization even when the iterations are dependent. For example, you can use vectorization to compute the sum of the cubes of several numbers:

-
sum(numbers^3)
+
sum(numbers^3)
## [1] -19
@@ -348,15 +348,15 @@

4.1.2 While-loops

the body contains multiple lines of code.

For example, suppose you want to add up numbers from 0 to 50, but stop as soon as the total is greater than 50:

-
num50 = seq(0, 50)
-
-total = 0
-i = 1
-while (total < 50) {
-  total = total + num50[i]
-  message("i is ", i, " total is ", total)
-  i = i + 1
-}
+
num50 = seq(0, 50)
+
+total = 0
+i = 1
+while (total < 50) {
+  total = total + num50[i]
+  message("i is ", i, " total is ", total)
+  i = i + 1
+}
## i is 1 total is 0
## i is 2 total is 1
## i is 3 total is 3
@@ -368,9 +368,9 @@

4.1.2 While-loops

## i is 9 total is 36
## i is 10 total is 45
## i is 11 total is 55
-
total
+
total
## [1] 55
-
i
+
i
## [1] 12

While-loops are a generalization of for-loops. They tend to be most useful when you don’t know how many iterations will be necessary. For example, suppose you @@ -385,7 +385,7 @@

4.1.3 Saving Multiple Results

seq_along function returns a congruent index vector when passed a vector or list. For instance, let’s make in index for the numbers vector from Section 4.1.1:

-
index = seq_along(numbers)
+
index = seq_along(numbers)

The loop will iterate over the index rather than the input, so the induction variable will track the current iteration number. On the first iteration, the induction variable will be 1, on the second it will be 2, and so on. Then you @@ -403,15 +403,15 @@

4.1.3 Saving Multiple Results

Empty vectors are filled with FALSE, 0, or "", depending on the type of the vector. Here are some examples:

-
logical(3)
+
logical(3)
## [1] FALSE FALSE FALSE
-
numeric(4)
+
numeric(4)
## [1] 0 0 0 0
-
rep(c(1, 2), 2)
+
rep(c(1, 2), 2)
## [1] 1 2 1 2

Let’s create an empty numeric vector congruent to numbers:

-
n = length(numbers)
-result = numeric(n)
+
n = length(numbers)
+result = numeric(n)

As with the input, you can use the induction variable and indexing to set the output for each iteration.

Creating a vector or list in advance to store something, as we’ve just done, is @@ -420,11 +420,11 @@

4.1.3 Saving Multiple Results

Finally, write the loop, making sure to get the input and set the output. Here’s the loop for the squared sums example:

-
for (i in index) {
-  prev = if (i > 1) result[i - 1] else 0
-  result[i] = (numbers[i] + prev)^2
-}
-result
+
for (i in index) {
+  prev = if (i > 1) result[i - 1] else 0
+  result[i] = (numbers[i] + prev)^2
+}
+result
## [1] 1 4 1 9
@@ -433,26 +433,26 @@

4.1.4 Break & Next

use break inside of an if-statement.

For example, suppose we want to print each string in a vector, but stop at the first missing value. We can do this with break:

-
my_messages = c("Hi", "Hello", NA, "Goodbye")
-
-for (msg in my_messages) {
-  if (is.na(msg))
-    break
-
-  message(msg)
-}
+
my_messages = c("Hi", "Hello", NA, "Goodbye")
+
+for (msg in my_messages) {
+  if (is.na(msg))
+    break
+
+  message(msg)
+}
## Hi
## Hello

The next keyword causes a loop to immediately go to the next iteration. As with break, it only makes sense to use next inside of an if-statement.

Let’s modify the previous example so that missing values are skipped, but don’t cause printing to stop. Here’s the code:

-
for (msg in my_messages) {
-  if (is.na(msg))
-    next
-
-  message(msg)
-}
+
for (msg in my_messages) {
+  if (is.na(msg))
+    next
+
+  message(msg)
+}
## Hi
## Hello
## Goodbye
@@ -466,27 +466,27 @@

4.1.5 Example: Fibonacci

1 1 2 3 5 8 13

We can use a loop to compute the first n Fibonacci numbers. Make sure to choose an appropriate index and to preallocate for the output:

-
n = 10
-fib = c(1, 1, numeric(n - 2))
-
-for (i in 3:n) {
-  fib[i] = fib[i - 1] + fib[i - 2]
-}
-
-fib
-
##  [1]  1  1  2  3  5  8 13 21 34 55
-

If you want to reuse this code, you could make it a function:

-
fibonacci = function(n = 10) {
-  fib = c(1, 1, numeric(n - 2))
+
n = 10
+fib = c(1, 1, numeric(n - 2))
 
-  for (i in 3:n) {
-    fib[i] = fib[i - 1] + fib[i - 2]
-  }
+for (i in 3:n) {
+  fib[i] = fib[i - 1] + fib[i - 2]
+}
 
-  fib
-}
-
-fibonacci(30)

+fib
+
##  [1]  1  1  2  3  5  8 13 21 34 55
+

If you want to reuse this code, you could make it a function:

+
fibonacci = function(n = 10) {
+  fib = c(1, 1, numeric(n - 2))
+
+  for (i in 3:n) {
+    fib[i] = fib[i - 1] + fib[i - 2]
+  }
+
+  fib
+}
+
+fibonacci(30)
##  [1]      1      1      2      3      5      8     13     21     34     55
 ## [11]     89    144    233    377    610    987   1597   2584   4181   6765
 ## [21]  10946  17711  28657  46368  75025 121393 196418 317811 514229 832040
@@ -539,8 +539,8 @@

4.3 String Processing

The first time you use stringr, you’ll have to install it with install.packages (the same as any other package). Then you can load the package with the library function:

-
# install.packages("stringr")
-library(stringr)
+
# install.packages("stringr")
+library(stringr)

The typical syntax of a stringr function is:

str_NAME(string, pattern, ...)

Where:

@@ -552,22 +552,22 @@

4.3 String Processing

The str_detect function detects whether the pattern appears within the string. Here’s an example:

-
str_detect("hello", "el")
+
str_detect("hello", "el")
## [1] TRUE
-
str_detect("hello", "ol")
+
str_detect("hello", "ol")
## [1] FALSE

Most of the stringr functions are vectorized in the string parameter. For instance:

-
str_detect(c("hello", "goodbye", "lo"), "lo")
+
str_detect(c("hello", "goodbye", "lo"), "lo")
## [1]  TRUE FALSE  TRUE

Most of the stringr functions also have support for regular expressions, a powerful language for describing patterns. Several punctuation characters, such as . and ? have special meanings in the regular expressions language. You can disable these special meanings by putting the pattern in a call to fixed:

-
str_detect("a", ".")
+
str_detect("a", ".")
## [1] TRUE
-
str_detect("a", fixed("."))
+
str_detect("a", fixed("."))
## [1] FALSE

You can learn more about regular expressions here.

There are a lot of stringr functions. We’ll focus on two that are especially @@ -584,16 +584,16 @@

4.3.1 Splitting Strings

pattern. The characters that match are thrown away.

For example, suppose we want to split a sentence into words. Since there’s a space between each word, we can use a space as the pattern:

-
x = "The students in this workshop are great!"
-
-result = str_split(x, " ")
-result
+
x = "The students in this workshop are great!"
+
+result = str_split(x, " ")
+result
## [[1]]
 ## [1] "The"      "students" "in"       "this"     "workshop" "are"      "great!"

The str_split function always returns a list with one element for each input string. Here the list only has one element because x only has one element. We can get the first element with:

-
result[[1]]
+
result[[1]]
## [1] "The"      "students" "in"       "this"     "workshop" "are"      "great!"

We have to use the extraction operator [[ here because x is a list (for a vector, we could use the indexing operator [ instead). Notice that in the @@ -601,12 +601,12 @@

4.3.1 Splitting Strings

[[1]].

To see why the function returns a list, consider what happens if we try to split two different sentences at once:

-
x = c(x, "Are you listening?")
-
-result = str_split(x, " ")
-result[[1]]
+
x = c(x, "Are you listening?")
+
+result = str_split(x, " ")
+result[[1]]
## [1] "The"      "students" "in"       "this"     "workshop" "are"      "great!"
-
result[[2]]
+
result[[2]]
## [1] "Are"        "you"        "listening?"

Each sentence has a different number of words, so the vectors in the result have different lengths. So a list is the only way to store both.

@@ -614,7 +614,7 @@

4.3.1 Splitting Strings

third argument for the maximum number of splits to make. Because the number of splits is fixed, the function can return the result in a matrix instead of a list. For example:

-
str_split_fixed(x, " ", 3)
+
str_split_fixed(x, " ", 3)
##      [,1]  [,2]       [,3]                         
 ## [1,] "The" "students" "in this workshop are great!"
 ## [2,] "Are" "you"      "listening?"
@@ -622,10 +622,10 @@

4.3.1 Splitting Strings

because the nth piece of each input string is just the nth column of the result.

For example, suppose we want to get the area code from some phone numbers:

-
phones = c("717-555-3421", "629-555-8902", "903-555-6781")
-result = str_split_fixed(phones, "-", 3)
-
-result[, 1]
+
phones = c("717-555-3421", "629-555-8902", "903-555-6781")
+result = str_split_fixed(phones, "-", 3)
+
+result[, 1]
## [1] "717" "629" "903"
@@ -633,17 +633,17 @@

4.3.2 Replacing Parts of Strings<

The str_replace function replaces the pattern the first time it appears in the string. The replacement goes in the third argument.

For instance, suppose we want to change the word "dog" to "cat":

-
x = c("dogs are great, dogs are fun", "dogs are fluffy")
-str_replace(x, "dog", "cat")
+
x = c("dogs are great, dogs are fun", "dogs are fluffy")
+str_replace(x, "dog", "cat")
## [1] "cats are great, dogs are fun" "cats are fluffy"

The str_replace_all function replaces the pattern every time it appears in the string:

-
str_replace_all(x, "dog", "cat")
+
str_replace_all(x, "dog", "cat")
## [1] "cats are great, cats are fun" "cats are fluffy"

We can also use the str_replace and str_replace_all functions to delete part of a string by setting the replacement to the empty string "".

For example, suppose we want to delete the comma:

-
str_replace(x, ",", "")
+
str_replace(x, ",", "")
## [1] "dogs are great dogs are fun" "dogs are fluffy"

In general, stringr functions with the _all suffix affect all matches. Functions without _all only affect the first match.

@@ -667,8 +667,8 @@

4.4 Date Processing

We’ll focus on the lubridate package. As always, you’ll have to install the package if you haven’t already, and then load it:

-
# install.packages("lubridate")
-library(lubridate)
+
# install.packages("lubridate")
+library(lubridate)
## 
 ## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
@@ -676,8 +676,8 @@ 
4.4 Date Processing

 ##     date, intersect, setdiff, union

The most common task is to convert a string into a date or time class. For instance, when you load a data set, you might have dates that look like this:

-
dates = c("Jan 10, 2021", "Sep 3, 2018", "Feb 28, 1982")
-dates
+
dates = c("Jan 10, 2021", "Sep 3, 2018", "Feb 28, 1982")
+dates
## [1] "Jan 10, 2021" "Sep 3, 2018"  "Feb 28, 1982"

These are strings, so it’s relatively difficult to sort the dates, do arithmetic on them, or extract just one part (such as the year). There are @@ -685,10 +685,10 @@

4.4 Date Processing

are named with one letter for each part of the date. For instance, the dates in the example have the month (m), then the day (d), and then the year (y), so we can use the mdy function:

-
result = mdy(dates)
-result
+
result = mdy(dates)
+result
## [1] "2021-01-10" "2018-09-03" "1982-02-28"
-
class(result)
+
class(result)
## [1] "Date"

Notice that the dates now have class Date, one of R’s built-in classes for representing dates, and that they print differently. You can find a full list @@ -753,8 +753,8 @@

4.4 Date Processing

format string do not have any special meaning. Write the format string so that it matches the format of the dates you want to convert.

For example, let’s try converting an unusual time format:

-
odd_time = "6 minutes, 32 seconds after 10 o'clock"
-fast_strptime(odd_time, "%M minutes, %S seconds after %H o'clock")
+
odd_time = "6 minutes, 32 seconds after 10 o'clock"
+fast_strptime(odd_time, "%M minutes, %S seconds after %H o'clock")
## [1] "0-01-01 10:06:32 UTC"

R usually represents dates with the class Date, and date-times with the classes POSIXct and POSIXlt. The difference between the two date-time @@ -765,9 +765,9 @@

4.4 Date Processing

Once you’ve converted a string to a date, the lubridate package provides a variety of functions to get or set the parts individually. Here are a few examples:

-
day(result)
+
day(result)
## [1] 10  3 28
-
month(result)
+
month(result)
## [1] 1 9 2

You can find a complete list in the lubridate documentation.

diff --git a/docs/data-structures.html b/docs/data-structures.html index 77b06bf..d76d502 100644 --- a/docs/data-structures.html +++ b/docs/data-structures.html @@ -24,7 +24,7 @@ - + @@ -33,7 +33,7 @@ - + @@ -314,6 +314,11 @@

2.1 Data Types & Classes

The built-in classes you’ll use all the time correspond to vectors and lists (which we’ll learn more about in Section 2.1.1):

+++++ @@ -392,6 +397,10 @@

2.1 Data Types & Classes

Besides the classes for vectors and lists, there are several built-in classes that represent more sophisticated data structures:

Class
++++ @@ -982,6 +991,11 @@

Short-circuiting

TRUE || TRUE
## [1] TRUE
c(TRUE, FALSE) && c(TRUE, TRUE)
+
## Warning in c(TRUE, FALSE) && c(TRUE, TRUE): 'length(x) = 2 > 1' in coercion to
+## 'logical(1)'
+
+## Warning in c(TRUE, FALSE) && c(TRUE, TRUE): 'length(x) = 2 > 1' in coercion to
+## 'logical(1)'
## [1] TRUE

For the final expression, notice R only combines the first element of each vector. The others are ignored. In other words, the short-circuit operators are @@ -996,19 +1010,19 @@

2.5.6 Indexing Lists

element from a list, you can either keep the container (the list) or discard it. The indexing operator [ almost always keeps containers.

As an example, let’s get some elements from a small list:

-
x = list(first = c(1, 2, 3), second = sin, third = c("hi", "hello"))
-y = x[c(1, 3)]
-y
+
x = list(first = c(1, 2, 3), second = sin, third = c("hi", "hello"))
+y = x[c(1, 3)]
+y
## $first
 ## [1] 1 2 3
 ## 
 ## $third
 ## [1] "hi"    "hello"
-
class(y)
+
class(y)
## [1] "list"

The result is still a list. Even if we get just one element, the result of indexing a list with [ is a list:

-
class(x[1])
+
class(x[1])
## [1] "list"

Sometimes this will be exactly what we want. But what if we want to get the first element of x so that we can use it in a vectorized function? Or in a @@ -1019,9 +1033,9 @@

2.5.6 Indexing Lists

primary way to get and set elements of lists and other containers.

Unlike the indexing operator [, the extraction operator always discards the container:

-
x[[1]]
+
x[[1]]
## [1] 1 2 3
-
class(x[[1]])
+
class(x[[1]])
## [1] "numeric"

The tradeoff is that the extraction operator can only get or set one element at a time. Note that the element can be a vector, as above. Because it can only @@ -1030,14 +1044,14 @@

2.5.6 Indexing Lists

The final difference between the index operator [ and the extraction operator [[ has to do with how they handle invalid indexes. The index operator [ returns NA for invalid vector elements, and NULL for invalid list elements:

-
c(1, 2)[10]
+
c(1, 2)[10]
## [1] NA
-
x[10]
+
x[10]
## $<NA>
 ## NULL

On the other hand, the extraction operator [[ raises an error for invalid elements:

-
x[[10]]
+
x[[10]]
## Error in x[[10]]: subscript out of bounds

The indexing operator [ and the extraction operator [[ both work with any data structure that has elements. However, you’ll generally use the indexing @@ -1051,7 +1065,7 @@

2.5.7 Indexing Data Frames

dimension. The rows come first:

DATA[ROWS, COLUMNS]

For instance, let’s get the first 3 rows and all columns of the earnings data:

-
earn[1:3, ]
+
earn[1:3, ]
##          sex      race ethnic_origin               age year quarter n_persons
 ## 1 Both Sexes All Races   All Origins 16 years and over 2010       1  96821000
 ## 2 Both Sexes All Races   All Origins 16 years and over 2010       2  99798000
@@ -1063,21 +1077,21 @@ 
2.5.7 Indexing Data Frames

 
As we saw in Section 2.5.1, leaving an index blank means all
 elements.

 
As another example, let’s get the 3rd and 5th row, and the 2nd and 4th column:

-
earn[c(3, 5), c(2, 4)]

+
earn[c(3, 5), c(2, 4)]

 
##        race               age
 ## 3 All Races 16 years and over
 ## 5 All Races 16 years and over

 
Mixing several different ways of indexing is allowed. So for example, we can
 get the same above, but use column names instead of positions:

-
earn[c(3, 5), c("race", "age")]

+
earn[c(3, 5), c("race", "age")]

 
##        race               age
 ## 3 All Races 16 years and over
 ## 5 All Races 16 years and over

 
For data frames, it’s especially common to index the rows by condition and the
 columns by name. For instance, let’s get the sex, age, and n_persons
 columns for all rows that pertain to women:

-
result = earn[earn$sex == "Women", c("sex", "age", "n_persons")]
-head(result)

+
result = earn[earn$sex == "Women", c("sex", "age", "n_persons")]
+head(result)

 
##      sex               age n_persons
 ## 89 Women 16 years and over  43794000
 ## 90 Women 16 years and over  44562000
@@ -1089,14 +1103,14 @@ 
2.5.7 Indexing Data Frames

 
The Drop Parameter

 
If you use two-dimensional indexing with [ to select exactly one column, you
 get a vector:

-
result = earn[1:3, 2]
-class(result)

+
result = earn[1:3, 2]
+class(result)

 
## [1] "character"

 
The container is dropped, even though the indexing operator [ usually keeps
 containers. This also occurs for matrices. You can control this behavior with
 the drop parameter:

-
result = earn[1:3, 2, drop = FALSE]
-class(result)

+
result = earn[1:3, 2, drop = FALSE]
+class(result)

 
## [1] "data.frame"

 
The default is drop = TRUE.

 
diff --git a/docs/exploring-your-data.html b/docs/exploring-your-data.html
index 3ec5e24..0f1e662 100644
--- a/docs/exploring-your-data.html
+++ b/docs/exploring-your-data.html
@@ -24,7 +24,7 @@
 
 
 
-
+
 
   
   
@@ -33,7 +33,7 @@
   
 
 
-
+
 
 
 
@@ -288,11 +288,11 @@ 
3.1 Apply Functions

 
The class function is an example of a function that’s not vectorized. If we
 call the class function on the earnings data set, we get just one result for
 the data set as a whole:

-
class(earn)

+
class(earn)

 
## [1] "data.frame"

 
What if we want to get the class of each column? We can get the class for a
 single column by selecting the column with $, the dollar sign operator:

-
class(earn$age)

+
class(earn$age)

 
## [1] "character"

 
But what if we want the classes for all the columns? We could write a call to
 class for each column, but that would be tedious. When you’re working with a
@@ -310,7 +310,7 @@ 
3.1 Apply Functions

 the first argument. The ... is for additional arguments to FUN, which are
 held constant across all the elements.

 
Let’s try this out with the earnings data and the class function:

-
lapply(earn, class)

+
lapply(earn, class)

 
## $sex
 ## [1] "character"
 ## 
@@ -337,11 +337,11 @@ 
3.1 Apply Functions

 
The result is similar to if the class function was vectorized. In fact, if we
 use a vector and a vectorized function with lapply, the result is nearly
 identical to the result from vectorization:

-
x = c(1, 2, pi)
-
-sin(x)

+
x = c(1, 2, pi)
+
+sin(x)

 
## [1] 8.414710e-01 9.092974e-01 1.224647e-16

-
lapply(x, sin)

+
lapply(x, sin)

 
## [[1]]
 ## [1] 0.841471
 ## 
@@ -374,14 +374,14 @@ 
3.1.1 The sapply Fun
 “s” in sapply stands for “simplify”.

 
For instance, if we use sapply to find the classes of the columns in the
 earnings data, we get a character vector:

-
sapply(earn, class)

+
sapply(earn, class)

 
##                sex               race      ethnic_origin                age 
 ##        "character"        "character"        "character"        "character" 
 ##               year            quarter          n_persons median_weekly_earn 
 ##          "integer"          "integer"          "integer"          "integer"

 
Likewise, if we use sapply to compute the sin values, we get a numeric
 vector, the same as from vectorization:

-
sapply(x, sin)

+
sapply(x, sin)

 
## [1] 8.414710e-01 9.092974e-01 1.224647e-16

 
In spite of that, vectorization is still more efficient than sapply, so use
 vectorization instead when possible.

@@ -393,8 +393,8 @@ 
3.1.1 The sapply Fun
 its argument is a real number, not whether it its argument is a numeric vector.
 In other words, it also returns true for integer values. We can use sapply to
 apply this function to all of the columns in the earnings data set:

-
is_not_number = !sapply(earn, is.numeric)
-is_not_number

+
is_not_number = !sapply(earn, is.numeric)
+is_not_number

 
##                sex               race      ethnic_origin                age 
 ##               TRUE               TRUE               TRUE               TRUE 
 ##               year            quarter          n_persons median_weekly_earn 
@@ -408,7 +408,7 @@ 
3.1.1 The sapply Fun
 
Now that we know which columns are non-numeric, we can use the table function
 to compute frequencies. We only want to compute frequencies for those columns,
 so we need to subset the data:

-
lapply(earn[, is_not_number], table)

+
lapply(earn[, is_not_number], table)

 
## $sex
 ## 
 ## Both Sexes        Men      Women 
@@ -446,8 +446,8 @@ 
3.1.2 The Split-Apply Pattern
For example, the earnings data set has three different categories in the sex
 column. If we want all of the rows in one category, one way to get them is by
 indexing:

-
women = earn[earn$sex == "Women", ]
-head(women)

+
women = earn[earn$sex == "Women", ]
+head(women)

 
##      sex      race ethnic_origin               age year quarter n_persons
 ## 89 Women All Races   All Origins 16 years and over 2010       1  43794000
 ## 90 Women All Races   All Origins 16 years and over 2010       2  44562000
@@ -466,7 +466,7 @@ 
3.1.2 The Split-Apply Pattern
n_persons column, we
 also have to repeat that computation three times. Here’s what it would look
 like for just the women category:

-
mean(women$n_persons)

+
mean(women$n_persons)

 
## [1] 10758771

 
If the categories were elements, we could avoid writing code to index each
 category, and just use the sapply (or lapply) function to apply the mean
@@ -477,18 +477,18 @@ 
3.1.2 The Split-Apply Pattern
We can use split to elegantly compute means of n_persons broken down by
 sex. First, we split the data by category. Since we only want to compute on the
 n_persons column, we only split that column:

-
by_sex = split(earn$n_persons, earn$sex)
-class(by_sex)

+
by_sex = split(earn$n_persons, earn$sex)
+class(by_sex)

 
## [1] "list"

-
names(by_sex)

+
names(by_sex)

 
## [1] "Both Sexes" "Men"        "Women"

 
The result from split is a list with one element for each category. The
 individual elements contain pieces of the original n_persons column:

-
head(by_sex$Women)

+
head(by_sex$Women)

 
## [1] 43794000 44562000 44912000 44620000 44077000 44539000

 
Since the categories are elements in the split data, now we can use sapply
 the same way we did in previous examples:

-
sapply(by_sex, mean)

+
sapply(by_sex, mean)

 
## Both Sexes        Men      Women 
 ##   24402515   13643727   10758771

 
This two-step process is an R idiom called the split-apply pattern. First you
@@ -502,7 +502,7 @@ 
3.1.2 The Split-Apply Pattern
sapply.

 
We can use tapply to compute the n_persons means by sex for the earnings
 data:

-
tapply(earn$n_persons, earn$sex, mean)

+
tapply(earn$n_persons, earn$sex, mean)

 
## Both Sexes        Men      Women 
 ##   24402515   13643727   10758771

 
Notice that the result is identical to the one we computed before.

@@ -517,7 +517,7 @@ 
3.1.2 The Split-Apply Pattern

 
As an example, here’s the result of using aggregate to compute the
 n_persons means:

-
aggregate(earn$n_persons, list(earn$sex), mean)

+
aggregate(earn$n_persons, list(earn$sex), mean)

 
##      Group.1        x
 ## 1 Both Sexes 24402515
 ## 2        Men 13643727
@@ -533,75 +533,74 @@ 
3.2 Conditional Expressions

 
Sometimes you’ll need code to do different things, depending on a condition.
 If-statements provide a way to write conditional code.

 
For example, suppose we want to greet one person differently from the others:

-
name = "Nick"
-if (name == "Nick") {
-   # If name is Nick:
-   message("We went down the TRUE branch")
-   msg = "Hi Nick, nice to see you again!"
-} else {
-   # Anything else:
-   msg = "Nice to meet you!"
-}

+
name = "Nick"
+if (name == "Nick") {
+   # If name is Nick:
+   message("We went down the TRUE branch")
+   msg = "Hi Nick, nice to see you again!"
+} else {
+   # Anything else:
+   msg = "Nice to meet you!"
+}

 
## We went down the TRUE branch

 
Indent code inside of the if-statement by 2 or 4 spaces. Indentation makes your
 code easier to read.

 
The condition in an if-statement has to be a scalar:

-
name = c("Nick", "Susan")
-if (name == "Nick") {
-   msg = "Hi Nick!"
-} else {
-   msg = "Nice to meet you!"
-}

-
## Warning in if (name == "Nick") {: the condition has length > 1 and only the
-## first element will be used

+
name = c("Nick", "Susan")
+if (name == "Nick") {
+   msg = "Hi Nick!"
+} else {
+   msg = "Nice to meet you!"
+}

+
## Error in if (name == "Nick") {: the condition has length > 1

 
You can chain together if-statements:

-
name = "Susan"
-if (name == "Nick") {
-   msg = "Hi Nick, nice to see you again!"
-} else if (name == "Peter") {
-   msg = "Go away Peter, I'm busy!"
-} else {
-   msg = "Nice to meet you!"
-}
-msg

+
name = "Susan"
+if (name == "Nick") {
+   msg = "Hi Nick, nice to see you again!"
+} else if (name == "Peter") {
+   msg = "Go away Peter, I'm busy!"
+} else {
+   msg = "Nice to meet you!"
+}
+msg

 
## [1] "Nice to meet you!"

 
If-statements return the value of the last expression in the evaluated block:

-
name = "Tom"
-msg = if (name == "Nick") {
-   "Hi Nick, nice to see you again!"
-} else {
-   "Nice to meet you!"
-}
-msg

+
name = "Tom"
+msg = if (name == "Nick") {
+   "Hi Nick, nice to see you again!"
+} else {
+   "Nice to meet you!"
+}
+msg

 
## [1] "Nice to meet you!"

 
Curly braces { } are optional for single-line expressions:

-
name = "Nick"
-msg = if (name == "Nick") "Hi Nick, nice to see you again!" else
-   "Nice to meet you!"
-msg

+
name = "Nick"
+msg = if (name == "Nick") "Hi Nick, nice to see you again!" else
+   "Nice to meet you!"
+msg

 
## [1] "Hi Nick, nice to see you again!"

 
But you have to be careful if you don’t use them:

-
# NO GOOD:
-msg = if (name == "Nick")
-   "Hi Nick, nice to see you again!"
-else
-   "Nice to meet you!"

+
# NO GOOD:
+msg = if (name == "Nick")
+   "Hi Nick, nice to see you again!"
+else
+   "Nice to meet you!"

 
## Error: <text>:4:1: unexpected 'else'
 ## 3:    "Hi Nick, nice to see you again!"
 ## 4: else
 ##    ^

 
The else block is optional:

-
msg = "Hi"
-name = "Tom"
-if (name == "Nick")
-   msg = "Hi Nick, nice to see you again!"
-msg

+
msg = "Hi"
+name = "Tom"
+if (name == "Nick")
+   msg = "Hi Nick, nice to see you again!"
+msg

 
## [1] "Hi"

 
When there’s no else block, the value of the else block is NULL:

-
name = "Tom"
-msg = if (name == "Nick")
-   "Hi Nick, nice to see you again!"
-msg

+
name = "Tom"
+msg = if (name == "Nick")
+   "Hi Nick, nice to see you again!"
+msg

 
## NULL

 
 

@@ -636,7 +635,7 @@ 
3.3 Functions

 braces by 2-4 spaces also helps make it visually distinct from other code.

 
For example, let’s look at the body of the append function, which appends a
 value to the end of a list or vector:

-
append

+
append

 
## function (x, values, after = length(x)) 
 ## {
 ##     lengx <- length(x)
@@ -646,7 +645,7 @@ 
3.3 Functions

 ##         c(x, values)
 ##     else c(x[1L:after], values, x[(after + 1L):lengx])
 ## }
-## <bytecode: 0x55ed8bc33190>
+## <bytecode: 0x5624ae762a20>
 ## <environment: namespace:base>

 
Don’t worry if you can’t understand everything the append function’s code
 does yet. It will make more sense later on, after you’ve written a few
@@ -655,7 +654,7 @@ 
3.3 Functions

 these by calls to the special .Primitive or .Internal functions in their
 code.

 
For instance, the sum function is not written in R code:

-
sum

+
sum

 
## function (..., na.rm = FALSE)  .Primitive("sum")

 
The function keyword creates a new function. Here’s the syntax:

 
function(parameter1, parameter2, ...) {
@@ -672,25 +671,25 @@ 
3.3 Functions

 
Let’s create a function that detects negative numbers. It should take a vector
 of numbers as input, compare them to zero, and then return the logical result
 from the comparison as output. Here’s the code to do that:

-
is_negative = function(x) {
-  x < 0
-}
-
-# Since this is a one-line function, the curly braces are optional:
-is_negative = function(x) x < 0

+
is_negative = function(x) {
+  x < 0
+}
+
+# Since this is a one-line function, the curly braces are optional:
+is_negative = function(x) x < 0

 
The name of the function, is_negative, describes what the function does and
 includes a verb. The parameter x is the input. The return value is the result
 of x < 0, since we put that expression on the last line.

 
Any time you write a function, the first thing you should do afterwards is test
 that it actually works. Let’s try the is_negative function on a few test
 cases:

-
x = c(5, -1, -2, 0, 3)
-
-is_negative(6)

+
x = c(5, -1, -2, 0, 3)
+
+is_negative(6)

 
## [1] FALSE

-
is_negative(-1.1)

+
is_negative(-1.1)

 
## [1] TRUE

-
is_negative(x)

+
is_negative(x)

 
## [1] FALSE  TRUE  TRUE FALSE FALSE

 
Notice that the parameter x inside the function is different from the
 variable x we created outside the function. We’ll learn more about this in
@@ -703,15 +702,15 @@ 
3.3 Functions

 
For example, let’s write a function that gets the largest values in a vector.
 We’ll make the number of values to get a parameter, with a default argument of
 5. Here’s the code and some test cases:

-
top = function(x, n = 5) {
-  sorted = sort(x, decreasing = TRUE)
-  head(sorted, n)
-}
-
-y = c(-6, 7, 10, 3, 1, 15, -2)
-top(y, 3)

+
top = function(x, n = 5) {
+  sorted = sort(x, decreasing = TRUE)
+  head(sorted, n)
+}
+
+y = c(-6, 7, 10, 3, 1, 15, -2)
+top(y, 3)

 
## [1] 15 10  7

-
top(y)

+
top(y)

 
## [1] 15 10  7  3  1

 

 
3.3.1 Returning Values

@@ -725,15 +724,15 @@ 
3.3.1 Returning Values

 argument isn’t a number. This is an ideal case to use return, since we skip
 the computation x < 0 when the argument isn’t a number. Here’s the new
 version of the function, as well as a new test case:

-
is_negative = function(x) {
-  if (!is.numeric(x))
-    return (NA)
-  
-  # TRUE for negative numbers, FALSE otherwise
-  x < 0
-}
-
-is_negative("hi")

+
is_negative = function(x) {
+  if (!is.numeric(x))
+    return (NA)
+  
+  # TRUE for negative numbers, FALSE otherwise
+  x < 0
+}
+
+is_negative("hi")

 
## [1] NA

 
It’s idiomatic to only use return when strictly necessary.

 
A function returns one R object, but sometimes computations have multiple
@@ -742,12 +741,12 @@ 
3.3.1 Returning Values

 
For example, let’s make a function that computes the mean and median for a
 vector. We’ll return the results in a named list, although we could also use a
 named vector:

-
compute_mean_med = function(x) {
-  m1 = mean(x)
-  m2 = median(x)
-  list(mean = m1, median = m2)
-}
-compute_mean_med(c(1, 2, 3, 1))

+
compute_mean_med = function(x) {
+  m1 = mean(x)
+  m2 = median(x)
+  list(mean = m1, median = m2)
+}
+compute_mean_med(c(1, 2, 3, 1))

 
## $mean
 ## [1] 1.75
 ## 
@@ -775,22 +774,22 @@ 
3.3.2 Planning Your Functions

 
That means the years 2004 and 2000 are leap years, but the year 2200 is not.
 Here’s the code and a few test cases:

-
# If year is divisible by 4 and not 100 -> leap
-# If year is divisible by 400 -> leap
-year = 2004
-is_leap = function(year) {
-  if (year %% 4 == 0 & year %% 100 != 0) {
-    leap = TRUE
-  } else if (year %% 400 == 0) {
-    leap = TRUE
-  } else {
-    leap = FALSE
-  }
-  leap
-}
-is_leap(400)

+
# If year is divisible by 4 and not 100 -> leap
+# If year is divisible by 400 -> leap
+year = 2004
+is_leap = function(year) {
+  if (year %% 4 == 0 & year %% 100 != 0) {
+    leap = TRUE
+  } else if (year %% 400 == 0) {
+    leap = TRUE
+  } else {
+    leap = FALSE
+  }
+  leap
+}
+is_leap(400)

 
## [1] TRUE

-
is_leap(1997)

+
is_leap(1997)

 
## [1] FALSE

 
Functions are the building blocks for solving larger problems. Take a
 divide-and-conquer approach, breaking large problems into smaller steps. Use a
@@ -807,34 +806,34 @@ 
3.4 Variable Scope & Lookup3.4.1 Local Variables

 
A variable’s scope is the section of code where it exists and is accessible.
 The exists function checks whether a variable is in scope:

-
exists("zz")

+
exists("zz")

 
## [1] FALSE

-
zz = 3
-exists("zz")

+
zz = 3
+exists("zz")

 
## [1] TRUE

 
When you create a function, you create a new scope. Variables defined inside of
 a function are local to the function. Local variables cannot be accessed from
 outside:

-
rescale = function(x, center, scale) {
-  centered = x - center
-  centered / scale
-}
-
-centered

+
rescale = function(x, center, scale) {
+  centered = x - center
+  centered / scale
+}
+
+centered

 
## Error in eval(expr, envir, enclos): object 'centered' not found

-
exists("centered")

+
exists("centered")

 
## [1] FALSE

 
Local variables are reset each time the function is called:

-
f = function() {
-  is_z_in_scope = exists("z")
-  z = 42
-  
-  is_z_in_scope
-}
-
-f()

+
f = function() {
+  is_z_in_scope = exists("z")
+  z = 42
+  
+  is_z_in_scope
+}
+
+f()

 
## [1] FALSE

-
f()

+
f()

 
## [1] FALSE

 

 

@@ -846,50 +845,50 @@ 
3.4.2 Lexical Scoping

 then define a function get_cats in the same place (the top level, not inside
 any functions). As a result, the cats variable is in scope inside of the
 get_cats function:

-
cats = 3
-get_cats = function() cats
-get_cats()

+
cats = 3
+get_cats = function() cats
+get_cats()

 
## [1] 3

 
Now let’s define a variable dogs inside of a function create_dogs. We’ll
 also define a function get_dogs at the top level. The variable dogs is not
 in scope at the top level, so it’s not in scope inside of the get_dogs
 function:

-
create_dogs = function() {
-  dogs = "hello"
-}
-get_dogs = function() dogs
-create_dogs()
-get_dogs()

+
create_dogs = function() {
+  dogs = "hello"
+}
+get_dogs = function() dogs
+create_dogs()
+get_dogs()

 
## Error in get_dogs(): object 'dogs' not found

 
Variables defined directly in the R console are global and available to any
 function.

 
Local variables mask (hide) non-local variables with the same name:

-
get_parrot = function() {
-  parrot = 3
-  
-  parrot
-}
-parrot = 42
-get_parrot()

+
get_parrot = function() {
+  parrot = 3
+  
+  parrot
+}
+parrot = 42
+get_parrot()

 
## [1] 3

 
There’s one exception to this rule. We often use variables that refer to
 functions in calls:

-
#mean()

+
#mean()

 
In this case, the variable must refer to a function, so R ignores local
 variables that aren’t functions. For example:

-
my_mean = function() {
-  mean = 0
-  
-  mean(c(1, 2, 3))
-}
-my_mean()

+
my_mean = function() {
+  mean = 0
+  
+  mean(c(1, 2, 3))
+}
+my_mean()

 
## [1] 2

-
my_get_cats = function() {
-  get_cats = 10
-  
-  get_cats()
-}
-my_get_cats()

+
my_get_cats = function() {
+  get_cats = 10
+  
+  get_cats()
+}
+my_get_cats()

 
## [1] 3

 

 

@@ -898,11 +897,11 @@ 
3.4.3 Dynamic Lookup

 This is called dynamic lookup.

 
For example, the result from get_cats, which accesses the global variable
 cats, changes if we change the value of cats:

-
cats = 10
-get_cats()

+
cats = 10
+get_cats()

 
## [1] 10

-
cats = 20
-get_cats()

+
cats = 20
+get_cats()

 
## [1] 20

 

 

@@ -951,7 +950,7 @@ 
3.5 Packages

 you aren’t sure which mirror to choose, you can use the 0-Cloud mirror, which
 attempts to automatically choose a mirror near you.

 
As an example, here’s the code to install the remotes package:

-
install.packages("remotes")

+
install.packages("remotes")

 
If you run the code above, you’ll be asked to select a mirror, and then see
 output that looks something like this:

 
--- Please select a CRAN mirror for use in this session ---
@@ -995,15 +994,13 @@ 
3.5 Packages

 installed.packages function. It does not require any arguments. It returns a
 matrix with one row for each package and columns that contain a variety of
 information. Here’s an example:

-
packages = installed.packages()
-# Just print the version numbers for 10 packages.
-packages[1:10, "Version"]

-
##              abind               brio       CodeAnalysis            diffobj 
-##            "1.4-5"            "1.1.1"            "0.1-1"            "0.3.3" 
-##             httpuv          lcap.sec2            mapproj               maps 
-##            "1.5.5"            "0.1-1"            "1.2.7"            "3.3.0" 
-##            mapview NativeCodeAnalysis 
-##            "2.9.8"            "0.1-0"

+
packages = installed.packages()
+# Just print the version numbers for 10 packages.
+packages[1:10, "Version"]

+
##       abind        brio CodeDepends     diffobj      GGally     ggplot2 
+##     "1.4-5"     "1.1.1"     "0.6.6"     "0.3.3"     "2.1.2"     "3.3.6" 
+##      httpuv   lcap.sec2    lmerTest      magick 
+##     "1.5.5"     "0.1-1"     "3.1-3"     "2.7.3"

 
You’ll see a different set of packages, since you have a different computer.

 
Before you can use the functions (or other resources) in an installed package,
 you must load the package with the library function. R doesn’t load packages
@@ -1012,7 +1009,7 @@ 
3.5 Packages

 it is that you want to do. When you restart R, the loaded packages are cleared
 and you must again load any packages you want to use.

 
Let’s load the remotes package we installed earlier:

-
library("remotes")

+
library("remotes")

 
The library function works with or without quotes around the package name, so
 you may also see people write things like library(remotes). We recommend
 using quotes to make it unambiguous that you are not referring to a variable.

@@ -1093,20 +1090,24 @@ 
3.6 Data Visualization

 
As an example, let’s plot the earnings data. First, we need to load ggplot2. As
 always, if this is your first time using the package, you’ll have to install
 it. Then you can load the package:

-
# install.packages("ggplot2")
-library(ggplot2)

+
# install.packages("ggplot2")
+library(ggplot2)

+
## Registered S3 methods overwritten by 'tibble':
+##   method     from  
+##   format.tbl pillar
+##   print.tbl  pillar

 
What kind of plot should we make? It depends on what data we want the plot to
 show. Let’s make a line plot that shows median earnings for each quarter in
 2019, with separate lines for men and women.

 
Before plotting, we need to take a subset of the earnings that only contains
 information for 2019:

-
earn19 = earn[earn$year == 2019, ]

+
earn19 = earn[earn$year == 2019, ]

 
The data is also broken down across race, ethnic_origin, and age. Since
 we aren’t interested in these categories for the plot, we need to further
 subset the data:

-
earn19 = earn19[earn19$race == "All Races" &
-  earn19$ethnic_origin == "All Origins" &
-  earn19$age == "16 years and over", ]

+
earn19 = earn19[earn19$race == "All Races" &
+  earn19$ethnic_origin == "All Origins" &
+  earn19$age == "16 years and over", ]

 
Now we’re ready to make the plot.

 

 
3.6.1 Layer 1: Data

@@ -1122,7 +1123,7 @@ 
3.6.1 Layer 1: Data

 an untidy data set tidy. Until then, we’ll take it for granted that the data
 sets we work with are tidy.

 
To set up the data layer, call the ggplot function on a data frame:

-
ggplot(earn19)

+
ggplot(earn19)

 

 
This returns a blank plot. We still need to add a few more layers.

 

@@ -1135,8 +1136,9 @@ 
3.6.2 Layer 2: Geometry

 a function for each geometry, always prefixed with geom_.

 
To add a geometry layer to the plot, choose the geom_ function you want and
 add it to the plot with the + operator:

-
ggplot(earn19) + geom_line()

-
## Error: geom_line requires the following missing aesthetics: x and y

+
ggplot(earn19) + geom_line()

+
## Error in `check_required_aesthetics()`:
+## ! geom_line requires the following missing aesthetics: x and y

 
This returns an error message that we’re missing aesthetics x and y. We’ll
 learn more about aesthetics in the next section, but this error message is
 especially helpful: it tells us exactly what we’re missing. When you use a
@@ -1158,24 +1160,24 @@ 
3.6.3 Layer 3: Aesthetics

 
For example, we want to put quarter on the x-axis and median_weekly_earn on
 the y-axis. We also want to use a separate line style for each sex category.
 So the aesthetic layer should be:

-
aes(x = quarter, y = median_weekly_earn, linetype = sex)

+
aes(x = quarter, y = median_weekly_earn, linetype = sex)

 
In the aes function, column names are never quoted.

 
Unlike most layers, the aesthetic layer is not added to the plot with the +
 operator. Instead, you can pass the aesthetic layer as the second argument to
 the ggplot function:

-
ggplot(earn19, aes(x = quarter, y = median_weekly_earn, linetype = sex)) +
-  geom_line()

+
ggplot(earn19, aes(x = quarter, y = median_weekly_earn, linetype = sex)) +
+  geom_line()

 

 
If you want to set an aesthetic to a constant value, rather than one that’s
 data dependent, do so outside of the aesthetic layer. For instance, suppose
 we want to make the lines blue:

-
ggplot(earn19, aes(x = quarter, y = median_weekly_earn, linetype = sex)) +
-  geom_line(color = "blue")

+
ggplot(earn19, aes(x = quarter, y = median_weekly_earn, linetype = sex)) +
+  geom_line(color = "blue")

 

 
If you set an aesthetic to a constant value inside of the aesthetic layer, the
 results you get might not be what you expect:

-
ggplot(earn19, aes(x = quarter, y = median_weekly_earn, linetype = sex,
-    color = "blue")) + geom_line()

+
ggplot(earn19, aes(x = quarter, y = median_weekly_earn, linetype = sex,
+    color = "blue")) + geom_line()
Class