# vapply and tapply

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What shall I call you? Krishnakanth Allika

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1: R Programming
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1: Basic Building Blocks 2: Workspace and Files 3: Sequences of Numbers
4: Vectors 5: Missing Values 6: Subsetting Vectors
7: Matrices and Data Frames 8: Logic 9: Functions
10: lapply and sapply 11: vapply and tapply 12: Looking at Data
13: Simulation 14: Dates and Times 15: Base Graphics

Selection: 11

| | 0%

| In the last lesson, you learned about the two most fundamental members of R's *apply
| family of functions: lapply() and sapply(). Both take a list as input, apply a function
| to each element of the list, then combine and return the result. lapply() always
| returns a list, whereas sapply() attempts to simplify the result.

...

|=== | 4%
| In this lesson, you'll learn how to use vapply() and tapply(), each of which serves a
| very specific purpose within the Split-Apply-Combine methodology. For consistency,
| we'll use the same dataset we used in the 'lapply and sapply' lesson.

...

|====== | 8%
| The Flags dataset from the UCI Machine Learning Repository contains details of various
| nations and their flags. More information may be found here:
| http://archive.ics.uci.edu/ml/datasets/Flags

...

|========== | 12%
| I've stored the data in a variable called flags. If it's been a while since you
| completed the 'lapply and sapply' lesson, you may want to reacquaint yourself with the
| data by using functions like dim(), head(), str(), and summary() when you return to the
| prompt (>). You can also type viewinfo() at the prompt to bring up some documentation
| for the dataset. Let's get started!

...

|============= | 16%
| As you saw in the last lesson, the unique() function returns a vector of the unique
| values contained in the object passed to it. Therefore, sapply(flags, unique) returns a
| list containing one vector of unique values for each column of the flags dataset. Try
| it again now.

sapply(flags, unique)
$name [1] Afghanistan Albania Algeria [4] American-Samoa Andorra Angola [7] Anguilla Antigua-Barbuda Argentina [10] Argentine Australia Austria [13] Bahamas Bahrain Bangladesh [16] Barbados Belgium Belize [19] Benin Bermuda Bhutan [22] Bolivia Botswana Brazil [25] British-Virgin-Isles Brunei Bulgaria [28] Burkina Burma Burundi [31] Cameroon Canada Cape-Verde-Islands [34] Cayman-Islands Central-African-Republic Chad [37] Chile China Colombia [40] Comorro-Islands Congo Cook-Islands [43] Costa-Rica Cuba Cyprus [46] Czechoslovakia Denmark Djibouti [49] Dominica Dominican-Republic Ecuador [52] Egypt El-Salvador Equatorial-Guinea [55] Ethiopia Faeroes Falklands-Malvinas [58] Fiji Finland France [61] French-Guiana French-Polynesia Gabon [64] Gambia Germany-DDR Germany-FRG [67] Ghana Gibraltar Greece [70] Greenland Grenada Guam [73] Guatemala Guinea Guinea-Bissau [76] Guyana Haiti Honduras [79] Hong-Kong Hungary Iceland [82] India Indonesia Iran [85] Iraq Ireland Israel [88] Italy Ivory-Coast Jamaica [91] Japan Jordan Kampuchea [94] Kenya Kiribati Kuwait [97] Laos Lebanon Lesotho [100] Liberia Libya Liechtenstein [103] Luxembourg Malagasy Malawi [106] Malaysia Maldive-Islands Mali [109] Malta Marianas Mauritania [112] Mauritius Mexico Micronesia [115] Monaco Mongolia Montserrat [118] Morocco Mozambique Nauru [121] Nepal Netherlands Netherlands-Antilles [124] New-Zealand Nicaragua Niger [127] Nigeria Niue North-Korea [130] North-Yemen Norway Oman [133] Pakistan Panama Papua-New-Guinea [136] Parguay Peru Philippines [139] Poland Portugal Puerto-Rico [142] Qatar Romania Rwanda [145] San-Marino Sao-Tome Saudi-Arabia [148] Senegal Seychelles Sierra-Leone [151] Singapore Soloman-Islands Somalia [154] South-Africa South-Korea South-Yemen [157] Spain Sri-Lanka St-Helena [160] St-Kitts-Nevis St-Lucia St-Vincent [163] Sudan Surinam Swaziland [166] Sweden Switzerland Syria [169] Taiwan Tanzania Thailand [172] Togo Tonga Trinidad-Tobago [175] Tunisia Turkey Turks-Cocos-Islands [178] Tuvalu UAE Uganda [181] UK Uruguay US-Virgin-Isles [184] USA USSR Vanuatu [187] Vatican-City Venezuela Vietnam [190] Western-Samoa Yugoslavia Zaire [193] Zambia Zimbabwe 194 Levels: Afghanistan Albania Algeria American-Samoa Andorra Angola ... Zimbabwe$landmass
[1] 5 3 4 6 1 2

$zone [1] 1 3 2 4$area
[1] 648 29 2388 0 1247 2777 7690 84 19 1 143 31 23 113
[15] 47 1099 600 8512 6 111 274 678 28 474 9976 4 623 1284
[29] 757 9561 1139 2 342 51 115 9 128 43 22 49 284 1001
[43] 21 1222 12 18 337 547 91 268 10 108 249 239 132 2176
[57] 109 246 36 215 112 93 103 3268 1904 1648 435 70 301 323
[71] 11 372 98 181 583 236 30 1760 3 587 118 333 1240 1031
[85] 1973 1566 447 783 140 41 1267 925 121 195 324 212 804 76
[99] 463 407 1285 300 313 92 237 26 2150 196 72 637 1221 99
[113] 288 505 66 2506 63 17 450 185 945 514 57 5 164 781
[127] 245 178 9363 22402 15 912 256 905 753 391

$population [1] 16 3 20 0 7 28 15 8 90 10 1 6 119 9 35 4 24 [18] 2 11 1008 5 47 31 54 17 61 14 684 157 39 57 118 13 77 [35] 12 56 18 84 48 36 22 29 38 49 45 231 274 60$language
[1] 10 6 8 1 2 4 3 5 7 9

$religion [1] 2 6 1 0 5 3 4 7$bars
[1] 0 2 3 1 5

$stripes [1] 3 0 2 1 5 9 11 14 4 6 13 7$colours
[1] 5 3 2 8 6 4 7 1

$red [1] 1 0$green
[1] 1 0

$blue [1] 0 1$gold
[1] 1 0

$white [1] 1 0$black
[1] 1 0

$orange [1] 0 1$mainhue
[1] green red blue gold white orange black brown
Levels: black blue brown gold green orange red white

$circles [1] 0 1 4 2$crosses
[1] 0 1 2

$saltires [1] 0 1$quarters
[1] 0 1 4

$sunstars [1] 1 0 6 22 14 3 4 5 15 10 7 2 9 50$crescent
[1] 0 1

$triangle [1] 0 1$icon
[1] 1 0

$animate [1] 0 1$text
[1] 0 1

$topleft [1] black red green blue white orange gold Levels: black blue gold green orange red white$botright
[1] green red white black blue gold orange brown
Levels: black blue brown gold green orange red white

| You got it right!

|================ | 20%
| What if you had forgotten how unique() works and mistakenly thought it returns the
| number of unique values contained in the object passed to it? Then you might have
| incorrectly expected sapply(flags, unique) to return a numeric vector, since each
| element of the list returned would contain a single number and sapply() could then
| simplify the result to a vector.

...

|=================== | 24%
| When working interactively (at the prompt), this is not much of a problem, since you
| see the result immediately and will quickly recognize your mistake. However, when
| working non-interactively (e.g. writing your own functions), a misunderstanding may go
| undetected and cause incorrect results later on. Therefore, you may wish to be more
| careful and that's where vapply() is useful.

...

|====================== | 28%
| Whereas sapply() tries to 'guess' the correct format of the result, vapply() allows you
| to specify it explicitly. If the result doesn't match the format you specify, vapply()
| will throw an error, causing the operation to stop. This can prevent significant
| problems in your code that might be caused by getting unexpected return values from
| sapply().

...

|========================== | 32%
| Try vapply(flags, unique, numeric(1)), which says that you expect each element of the
| result to be a numeric vector of length 1. Since this is NOT actually the case, YOU
| WILL GET AN ERROR. Once you get the error, type ok() to continue to the next question.

vapply(flags, unique, numeric(1))
Error in vapply(flags, unique, numeric(1)) : values must be length 1,
but FUN(X[[1]]) result is length 194
ok()

| That's correct!

|============================= | 36%
| Recall from the previous lesson that sapply(flags, class) will return a character
| vector containing the class of each column in the dataset. Try that again now to see
| the result.

sapply(flags, class)
name landmass zone area population language religion bars
"factor" "integer" "integer" "integer" "integer" "integer" "integer" "integer"
stripes colours red green blue gold white black
"integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer"
orange mainhue circles crosses saltires quarters sunstars crescent
"integer" "factor" "integer" "integer" "integer" "integer" "integer" "integer"
triangle icon animate text topleft botright
"integer" "integer" "integer" "integer" "factor" "factor"

| That's a job well done!

|================================ | 40%
| If we wish to be explicit about the format of the result we expect, we can use
| vapply(flags, class, character(1)). The 'character(1)' argument tells R that we expect
| the class function to return a character vector of length 1 when applied to EACH column
| of the flags dataset. Try it now.

vapply(flags, class,character(1))
name landmass zone area population language religion bars
"factor" "integer" "integer" "integer" "integer" "integer" "integer" "integer"
stripes colours red green blue gold white black
"integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer"
orange mainhue circles crosses saltires quarters sunstars crescent
"integer" "factor" "integer" "integer" "integer" "integer" "integer" "integer"
triangle icon animate text topleft botright
"integer" "integer" "integer" "integer" "factor" "factor"

| You are quite good my friend!

|=================================== | 44%
| Note that since our expectation was correct (i.e. character(1)), the vapply() result is
| identical to the sapply() result -- a character vector of column classes.

...

|====================================== | 48%
| You might think of vapply() as being 'safer' than sapply(), since it requires you to
| specify the format of the output in advance, instead of just allowing R to 'guess' what
| you wanted. In addition, vapply() may perform faster than sapply() for large datasets.
| However, when doing data analysis interactively (at the prompt), sapply() saves you
| some typing and will often be good enough.

...

|========================================== | 52%
| As a data analyst, you'll often wish to split your data up into groups based on the
| value of some variable, then apply a function to the members of each group. The next
| function we'll look at, tapply(), does exactly that.

...

|============================================= | 56%
| Use ?tapply to pull up the documentation.

?tapply

| All that hard work is paying off!

|================================================ | 60%
| The 'landmass' variable in our dataset takes on integer values between 1 and 6, each of
| which represents a different part of the world. Use table(flags$landmass) to see how | many flags/countries fall into each group. table(flags$landmass)

1 2 3 4 5 6
31 17 35 52 39 20

| You're the best!

|=================================================== | 64%
| The 'animate' variable in our dataset takes the value 1 if a country's flag contains an
| animate image (e.g. an eagle, a tree, a human hand) and 0 otherwise. Use
| table(flags$animate) to see how many flags contain an animate image. table(flags$animate)

0 1
155 39

| Nice work!

|====================================================== | 68%
| This tells us that 39 flags contain an animate object (animate = 1) and 155 do not
| (animate = 0).

...

|========================================================== | 72%
| If you take the arithmetic mean of a bunch of 0s and 1s, you get the proportion of 1s.
| Use tapply(flags$animate, flags$landmass, mean) to apply the mean function to the
| 'animate' variable separately for each of the six landmass groups, thus giving us the
| proportion of flags containing an animate image WITHIN each landmass group.

tapply(flags$animate, flags$landmass, mean)
1 2 3 4 5 6
0.4193548 0.1764706 0.1142857 0.1346154 0.1538462 0.3000000

| You are doing so well!

|============================================================= | 76%
| The first landmass group (landmass = 1) corresponds to North America and contains the
| highest proportion of flags with an animate image (0.4194).

...

|================================================================ | 80%
| Similarly, we can look at a summary of population values (in round millions) for
| countries with and without the color red on their flag with tapply(flags$population, | flags$red, summary).

tapply(flags$population,flags$red, summary)
$0 Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 0.00 3.00 27.63 9.00 684.00$1
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0 0.0 4.0 22.1 15.0 1008.0

| Great job!

|=================================================================== | 84%
| What is the median population (in millions) for countries without the color red on
| their flag?

1: 22.1
2: 27.6
3: 4.0
4: 3.0
5: 0.0
6: 9.0

Selection: 4

| That's correct!

|====================================================================== | 88%
| Lastly, use the same approach to look at a summary of population values for each of the
| six landmasses.

tapply(flags$population,flags$landmass, summary)
$1 Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 0.00 0.00 12.29 4.50 231.00$2
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 1.00 6.00 15.71 15.00 119.00

$3 Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 0.00 8.00 13.86 16.00 61.00$4
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 1.000 5.000 8.788 9.750 56.000

$5 Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 2.00 10.00 69.18 39.00 1008.00$6
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 0.00 0.00 11.30 1.25 157.00

| You are doing so well!

|========================================================================== | 92%
| What is the maximum population (in millions) for the fourth landmass group (Africa)?

1: 5.00
2: 56.00
3: 157.00
4: 119.0
5: 1010.0

Selection: 2

| Nice work!

|============================================================================= | 96%
| In this lesson, you learned how to use vapply() as a safer alternative to sapply(),
| which is most helpful when writing your own functions. You also learned how to use
| tapply() to split your data into groups based on the value of some variable, then apply
| a function to each group. These functions will come in handy on your quest to become a
| better data analyst.

...

|================================================================================| 100%
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ls()
[1] "flags" "ok" "viewinfo"
rm(list=ls())

Last updated 2020-04-18 20:41:25.391453 IST