# lapply and sapply

swirl()

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What shall I call you? Krishnakanth Allika

| Please choose a course, or type 0 to exit swirl.

1: R Programming
2: Take me to the swirl course repository!

Selection: 1

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: 10

| | 0%

| In this lesson, you'll learn how to use lapply() and sapply(), the two most important
| members of R's *apply family of functions, also known as loop functions.

...

|== | 2%
| These powerful functions, along with their close relatives (vapply() and tapply(),
| among others) offer a concise and convenient means of implementing the
| Split-Apply-Combine strategy for data analysis.

...

|=== | 4%
| Each of the *apply functions will SPLIT up some data into smaller pieces, APPLY a
| function to each piece, then COMBINE the results. A more detailed discussion of this
| strategy is found in Hadley Wickham's Journal of Statistical Software paper titled 'The
| Split-Apply-Combine Strategy for Data Analysis'.

...

|===== | 6%
| Throughout this lesson, we'll use the Flags dataset from the UCI Machine Learning
| Repository. This dataset contains details of various nations and their flags. More
| information may be found here: http://archive.ics.uci.edu/ml/datasets/Flags

...

|====== | 8%
| Let's jump right in so you can get a feel for how these special functions work!

...

|======== | 10%
| I've stored the dataset in a variable called flags. Type head(flags) to preview the
| first six lines (i.e. the 'head') of the dataset.

name landmass zone area population language religion bars stripes colours red
1 Afghanistan 5 1 648 16 10 2 0 3 5 1
2 Albania 3 1 29 3 6 6 0 0 3 1
3 Algeria 4 1 2388 20 8 2 2 0 3 1
4 American-Samoa 6 3 0 0 1 1 0 0 5 1
5 Andorra 3 1 0 0 6 0 3 0 3 1
6 Angola 4 2 1247 7 10 5 0 2 3 1
green blue gold white black orange mainhue circles crosses saltires quarters sunstars
1 1 0 1 1 1 0 green 0 0 0 0 1
2 0 0 1 0 1 0 red 0 0 0 0 1
3 1 0 0 1 0 0 green 0 0 0 0 1
4 0 1 1 1 0 1 blue 0 0 0 0 0
5 0 1 1 0 0 0 gold 0 0 0 0 0
6 0 0 1 0 1 0 red 0 0 0 0 1
crescent triangle icon animate text topleft botright
1 0 0 1 0 0 black green
2 0 0 0 1 0 red red
3 1 0 0 0 0 green white
4 0 1 1 1 0 blue red
5 0 0 0 0 0 blue red
6 0 0 1 0 0 red black

| Keep up the great work!

|========== | 12%
| You may need to scroll up to see all of the output. Now, let's check out the dimensions
| of the dataset using dim(flags).

dim(flags)
[1] 194 30

|=========== | 14%
| This tells us that there are 194 rows, or observations, and 30 columns, or variables.
| Each observation is a country and each variable describes some characteristic of that
| country or its flag. To open a more complete description of the dataset in a separate
| text file, type viewinfo() when you are back at the prompt (>).

...

|============= | 16%
| As with any dataset, we'd like to know in what format the variables have been stored.
| In other words, what is the 'class' of each variable? What happens if we do
| class(flags)? Try it out.

class(flags)
[1] "data.frame"

| Nice work!

|============== | 18%
| That just tells us that the entire dataset is stored as a 'data.frame', which doesn't
| answer our question. What we really need is to call the class() function on each
| individual column. While we could do this manually (i.e. one column at a time) it's
| much faster if we can automate the process. Sounds like a loop!

...

|================ | 20%
| The lapply() function takes a list as input, applies a function to each element of the
| list, then returns a list of the same length as the original one. Since a data frame is
| really just a list of vectors (you can see this with as.list(flags)), we can use
| lapply() to apply the class() function to each column of the flags dataset. Let's see
| it in action!

...

|================== | 22%
| Type cls_list <- lapply(flags, class) to apply the class() function to each column of
| the flags dataset and store the result in a variable called cls_list. Note that you
| just supply the name of the function you want to apply (i.e. class), without the usual
| parentheses after it.

cls_list<-lapply(flags,class)

| Excellent work!

|=================== | 24%
| Type cls_list to view the result.

cls_list
$name [1] "factor"$landmass
[1] "integer"

$zone [1] "integer"$area
[1] "integer"

$population [1] "integer"$language
[1] "integer"

$religion [1] "integer"$bars
[1] "integer"

$stripes [1] "integer"$colours
[1] "integer"

$red [1] "integer"$green
[1] "integer"

$blue [1] "integer"$gold
[1] "integer"

$white [1] "integer"$black
[1] "integer"

$orange [1] "integer"$mainhue
[1] "factor"

$circles [1] "integer"$crosses
[1] "integer"

$saltires [1] "integer"$quarters
[1] "integer"

$sunstars [1] "integer"$crescent
[1] "integer"

$triangle [1] "integer"$icon
[1] "integer"

$animate [1] "integer"$text
[1] "integer"

$topleft [1] "factor"$botright
[1] "factor"

| You are really on a roll!

|===================== | 26%
| The 'l' in 'lapply' stands for 'list'. Type class(cls_list) to confirm that lapply()
| returned a list.

play()

| Entering play mode. Experiment as you please, then type nxt() when you are ready to
| resume the lesson.

str(cls_list)
List of 30
$name : chr "factor"$ landmass : chr "integer"
$zone : chr "integer"$ area : chr "integer"
$population: chr "integer"$ language : chr "integer"
$religion : chr "integer"$ bars : chr "integer"
$stripes : chr "integer"$ colours : chr "integer"
$red : chr "integer"$ green : chr "integer"
$blue : chr "integer"$ gold : chr "integer"
$white : chr "integer"$ black : chr "integer"
$orange : chr "integer"$ mainhue : chr "factor"
$circles : chr "integer"$ crosses : chr "integer"
$saltires : chr "integer"$ quarters : chr "integer"
$sunstars : chr "integer"$ crescent : chr "integer"
$triangle : chr "integer"$ icon : chr "integer"
$animate : chr "integer"$ text : chr "integer"
$topleft : chr "factor"$ botright : chr "factor"
cls_list[1,]
Error in cls_list[1, ] : incorrect number of dimensions
as.data.frame(cls_list)[1,]
name landmass zone area population language religion bars stripes colours
1 factor integer integer integer integer integer integer integer integer integer
red green blue gold white black orange mainhue circles crosses
1 integer integer integer integer integer integer integer factor integer integer
saltires quarters sunstars crescent triangle icon animate text topleft botright
1 integer integer integer integer integer integer integer integer factor factor
nxt()

| Resuming lesson...

| The 'l' in 'lapply' stands for 'list'. Type class(cls_list) to confirm that lapply()
| returned a list.

class(cls_list)
[1] "list"

| You got it!

|====================== | 28%
| As expected, we got a list of length 30 -- one element for each variable/column. The
| output would be considerably more compact if we could represent it as a vector instead
| of a list.

...

|======================== | 30%
| You may remember from a previous lesson that lists are most helpful for storing
| multiple classes of data. In this case, since every element of the list returned by
| lapply() is a character vector of length one (i.e. "integer" and "vector"), cls_list
| can be simplified to a character vector. To do this manually, type
| as.character(cls_list).

as.character(cls_list)
[1] "factor" "integer" "integer" "integer" "integer" "integer" "integer" "integer"
[9] "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer"
[17] "integer" "factor" "integer" "integer" "integer" "integer" "integer" "integer"
[25] "integer" "integer" "integer" "integer" "factor" "factor"

| You nailed it! Good job!

|========================== | 32%
| sapply() allows you to automate this process by calling lapply() behind the scenes, but
| then attempting to simplify (hence the 's' in 'sapply') the result for you. Use
| sapply() the same way you used lapply() to get the class of each column of the flags
| dataset and store the result in cls_vect. If you need help, type ?sapply to bring up
| the documentation.

cls_vect<-sappy(flags,class)
Error in sappy(flags, class) : could not find function "sappy"
cls_vect<-sapply(flags,class)

| That's a job well done!

|=========================== | 34%
| Use class(cls_vect) to confirm that sapply() simplified the result to a character
| vector.

class(cls_vect)
[1] "character"

| You're the best!

|============================= | 36%
| In general, if the result is a list where every element is of length one, then sapply()
| returns a vector. If the result is a list where every element is a vector of the same
| length (> 1), sapply() returns a matrix. If sapply() can't figure things out, then it
| just returns a list, no different from what lapply() would give you.

...

|============================== | 38%
| Let's practice using lapply() and sapply() some more!

...

|================================ | 40%
| Columns 11 through 17 of our dataset are indicator variables, each representing a
| different color. The value of the indicator variable is 1 if the color is present in a
| country's flag and 0 otherwise.

...

|================================== | 42%
| Therefore, if we want to know the total number of countries (in our dataset) with, for
| example, the color orange on their flag, we can just add up all of the 1s and 0s in the
| 'orange' column. Try sum(flags$orange) to see this. sum(flags$orange)
[1] 26

| You are doing so well!

|=================================== | 44%
| Now we want to repeat this operation for each of the colors recorded in the dataset.

...

|===================================== | 46%
| First, use flag_colors <- flags[, 11:17] to extract the columns containing the color
| data and store them in a new data frame called flag_colors. (Note the comma before
| 11:17. This subsetting command tells R that we want all rows, but only columns 11
| through 17.)

flag_colors<-flags[,11:17]

| All that hard work is paying off!

|====================================== | 48%
| Use the head() function to look at the first 6 lines of flag_colors.

red green blue gold white black orange
1 1 1 0 1 1 1 0
2 1 0 0 1 0 1 0
3 1 1 0 0 1 0 0
4 1 0 1 1 1 0 1
5 1 0 1 1 0 0 0
6 1 0 0 1 0 1 0

| You got it right!

|======================================== | 50%
| To get a list containing the sum of each column of flag_colors, call the lapply()
| function with two arguments. The first argument is the object over which we are looping
| (i.e. flag_colors) and the second argument is the name of the function we wish to apply
| to each column (i.e. sum). Remember that the second argument is just the name of the
| function with no parentheses, etc.

lapply(flag_colors,aum)
lapply(flag_colors,sum)
$red [1] 153$green
[1] 91

$blue [1] 99$gold
[1] 91

$white [1] 146$black
[1] 52

$orange [1] 26 | All that hard work is paying off! |========================================== | 52% | This tells us that of the 194 flags in our dataset, 153 contain the color red, 91 | contain green, 99 contain blue, and so on. ... |=========================================== | 54% | The result is a list, since lapply() always returns a list. Each element of this list | is of length one, so the result can be simplified to a vector by calling sapply() | instead of lapply(). Try it now. sapply(flag_colors,sum) red green blue gold white black orange 153 91 99 91 146 52 26 | All that hard work is paying off! |============================================= | 56% | Perhaps it's more informative to find the proportion of flags (out of 194) containing | each color. Since each column is just a bunch of 1s and 0s, the arithmetic mean of each | column will give us the proportion of 1s. (If it's not clear why, think of a simpler | situation where you have three 1s and two 0s -- (1 + 1 + 1 + 0 + 0)/5 = 3/5 = 0.6). ... |============================================== | 58% | Use sapply() to apply the mean() function to each column of flag_colors. Remember that | the second argument to sapply() should just specify the name of the function (i.e. | mean) that you want to apply. sapply(flag_colors,mean) red green blue gold white black orange 0.7886598 0.4690722 0.5103093 0.4690722 0.7525773 0.2680412 0.1340206 | That's the answer I was looking for. |================================================ | 60% | In the examples we've looked at so far, sapply() has been able to simplify the result | to vector. That's because each element of the list returned by lapply() was a vector of | length one. Recall that sapply() instead returns a matrix when each element of the list | returned by lapply() is a vector of the same length (> 1). ... |================================================== | 62% | To illustrate this, let's extract columns 19 through 23 from the flags dataset and | store the result in a new data frame called flag_shapes. flag_shapes <- flags[, 19:23] | will do it. flag_shapes<-flags[,19:23] | Keep up the great work! |=================================================== | 64% | Each of these columns (i.e. variables) represents the number of times a particular | shape or design appears on a country's flag. We are interested in the minimum and | maximum number of times each shape or design appears. ... |===================================================== | 66% | The range() function returns the minimum and maximum of its first argument, which | should be a numeric vector. Use lapply() to apply the range function to each column of | flag_shapes. Don't worry about storing the result in a new variable. By now, we know | that lapply() always returns a list. lapply(flag_shapes,range)$circles
[1] 0 4

$crosses [1] 0 2$saltires
[1] 0 1

$quarters [1] 0 4$sunstars
[1] 0 50

| You are quite good my friend!

|====================================================== | 68%
| Do the same operation, but using sapply() and store the result in a variable called
| shape_mat.

shape_mat<-sapply(flag_shapes,range)

| Nice work!

|======================================================== | 70%
| View the contents of shape_mat.

shape_mat
circles crosses saltires quarters sunstars
[1,] 0 0 0 0 0
[2,] 4 2 1 4 50

|========================================================== | 72%
| Each column of shape_mat gives the minimum (row 1) and maximum (row 2) number of times
| its respective shape appears in different flags.

...

|=========================================================== | 74%
| Use the class() function to confirm that shape_mat is a matrix.

class(shape_mat)
[1] "matrix"

| Excellent job!

|============================================================= | 76%
| As we've seen, sapply() always attempts to simplify the result given by lapply(). It
| has been successful in doing so for each of the examples we've looked at so far. Let's
| look at an example where sapply() can't figure out how to simplify the result and thus
| returns a list, no different from lapply().

...

|============================================================== | 78%
| When given a vector, the unique() function returns a vector with all duplicate elements
| removed. In other words, unique() returns a vector of only the 'unique' elements. To
| see how it works, try unique(c(3, 4, 5, 5, 5, 6, 6)).

play()

| Entering play mode. Experiment as you please, then type nxt() when you are ready to
| resume the lesson.

as.data.frame(shape_mat)
circles crosses saltires quarters sunstars
1 0 0 0 0 0
2 4 2 1 4 50
nxt()

| Resuming lesson...

| When given a vector, the unique() function returns a vector with all duplicate elements
| removed. In other words, unique() returns a vector of only the 'unique' elements. To
| see how it works, try unique(c(3, 4, 5, 5, 5, 6, 6)).

unique(c(3, 4, 5, 5, 5, 6, 6))
[1] 3 4 5 6

|================================================================ | 80%
| We want to know the unique values for each variable in the flags dataset. To accomplish
| this, use lapply() to apply the unique() function to each column in the flags dataset,
| storing the result in a variable called unique_vals.

unique_vals<-lapply(flags,unique)

| Keep working like that and you'll get there!

|================================================================== | 82%
| Print the value of unique_vals to the console.

unique_vals
$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

|=================================================================== | 84%
| Since unique_vals is a list, you can use what you've learned to determine the length of
| each element of unique_vals (i.e. the number of unique values for each variable).
| Simplify the result, if possible. Hint: Apply the length() function to each element of
| unique_vals.

sapply(unique_vals,length)
name landmass zone area population language religion bars
194 6 4 136 48 10 8 5
stripes colours red green blue gold white black
12 8 2 2 2 2 2 2
orange mainhue circles crosses saltires quarters sunstars crescent
2 8 4 3 2 3 14 2
triangle icon animate text topleft botright
2 2 2 2 7 8

| You got it right!

|===================================================================== | 86%
| The fact that the elements of the unique_vals list are all vectors of different
| length poses a problem for sapply(), since there's no obvious way of simplifying the
| result.

...

|====================================================================== | 88%
| Use sapply() to apply the unique() function to each column of the flags dataset to see
| that you get the same unsimplified list that you got from lapply().

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're the best!

|======================================================================== | 90%
| Occasionally, you may need to apply a function that is not yet defined, thus requiring
| you to write your own. Writing functions in R is beyond the scope of this lesson, but
| let's look at a quick example of how you might do so in the context of loop functions.

...

|========================================================================== | 92%
| Pretend you are interested in only the second item from each element of the unique_vals
| list that you just created. Since each element of the unique_vals list is a vector and
| we're not aware of any built-in function in R that returns the second element of a
| vector, we will construct our own function.

...

|=========================================================================== | 94%
| lapply(unique_vals, function(elem) elem[2]) will return a list containing the second
| item from each element of the unique_vals list. Note that our function takes one
| argument, elem, which is just a 'dummy variable' that takes on the value of each
| element of unique_vals, in turn.

lapply(unique_vals, function(elem) elem[2])
$name [1] Albania 194 Levels: Afghanistan Albania Algeria American-Samoa Andorra Angola ... Zimbabwe$landmass
[1] 3

$zone [1] 3$area
[1] 29

$population [1] 3$language
[1] 6

$religion [1] 6$bars
[1] 2

$stripes [1] 0$colours
[1] 3

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

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

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

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

$circles [1] 1$crosses
[1] 1

$saltires [1] 1$quarters
[1] 1

$sunstars [1] 0$crescent
[1] 1

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

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

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

| Great job!

|============================================================================= | 96%
| The only difference between previous examples and this one is that we are defining and
| using our own function right in the call to lapply(). Our function has no name and
| disappears as soon as lapply() is done using it. So-called 'anonymous functions' can be
| very useful when one of R's built-in functions isn't an option.

...

|============================================================================== | 98%
| In this lesson, you learned how to use the powerful lapply() and sapply() functions to
| apply an operation over the elements of a list. In the next lesson, we'll take a look
| at some close relatives of lapply() and sapply().

...

|================================================================================| 100%
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1: Yes
2: No

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What is your assignment token? xXxXxxXXxXxxXXXx

| That's the answer I was looking for.

| You've reached the end of this lesson! Returning to the main menu...

| Please choose a course, or type 0 to exit swirl.

1: R Programming
2: Take me to the swirl course repository!

Selection: 0

| Leaving swirl now. Type swirl() to resume.

ls()
[1] "cls_list" "cls_vect" "flag_colors" "flag_shapes" "flags" "shape_mat"
[7] "unique_vals" "viewinfo"
rm(list=ls())

Last updated 2020-04-18 20:39:08.401287 IST