# swirl Lesson 10: lapply and sapply

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5: Missing Values             6: Subsetting Vectors
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|                                                          |   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
| 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
1    Afghanistan        5    1  648         16       10        2
2        Albania        3    1   29          3        6        6
3        Algeria        4    1 2388         20        8        2
4 American-Samoa        6    3    0          0        1        1
5        Andorra        3    1    0          0        6        0
6         Angola        4    2 1247          7       10        5
bars stripes colours red green blue gold white black orange
1    0       3       5   1     1    0    1     1     1      0
2    0       0       3   1     0    0    1     0     1      0
3    2       0       3   1     1    0    0     1     0      0
4    0       0       5   1     0    1    1     1     0      1
5    3       0       3   1     0    1    1     0     0      0
6    0       2       3   1     0    0    1     0     1      0
mainhue circles crosses saltires quarters sunstars crescent
1   green       0       0        0        0        1        0
2     red       0       0        0        0        1        0
3   green       0       0        0        0        1        1
4    blue       0       0        0        0        0        0
5    gold       0       0        0        0        0        0
6     red       0       0        0        0        1        0
triangle icon animate text topleft botright
1        0    1       0    0   black    green
2        0    0       1    0     red      red
3        0    0       0    0   green    white
4        1    1       1    0    blue      red
5        0    0       0    0    blue      red
6        0    1       0    0     red    black
| You're the best!
|=======                                                   |  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
| All that hard work is paying off!
|========                                                  |  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"
| All that hard work is paying off!
|==========                                                |  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)
| You got it right!
|==============                                            |  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"
| That's a job well done!
|===============                                           |  26%
| The 'l' in 'lapply' stands for 'list'. Type class(cls_list) to
| confirm that lapply() returned a list.
> class(cls_list)
[1] "list"
| Great job!
|================                                          |  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"
[7] "integer" "integer" "integer" "integer" "integer" "integer"
[13] "integer" "integer" "integer" "integer" "integer" "factor"
[19] "integer" "integer" "integer" "integer" "integer" "integer"
[25] "integer" "integer" "integer" "integer" "factor"  "factor"
| Keep up the great work!
|===================                                       |  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 <- sapply(flags, class)
| You nailed it! Good job!
|====================                                      |  34%
| Use class(cls_vect) to confirm that sapply() simplified the
| result to a character vector.
> class(cls_vect)
[1] "character"
| You are amazing!
|=====================                                     |  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 quite good my friend!
|==========================                                |  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]
| You're the best!
|============================                              |  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,sum)
$red [1] 153$green
[1] 91
$blue [1] 99$gold
[1] 91
$white [1] 146$black
[1] 52
$orange [1] 26 | You are doing so well! |============================== | 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 | You're the best! |================================ | 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 0.7886598 0.4690722 0.5103093 0.4690722 0.7525773 0.2680412 orange 0.1340206 | All that practice is paying off! |=================================== | 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] | Excellent job! |===================================== | 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
| Excellent work!
|=======================================                   |  68%
| Do the same operation, but using sapply() and store the result in
| a variable called shape_mat.
> shape_mat <- sapply(flag_shapes, range)
| That's the answer I was looking for.
|=========================================                 |  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
| You're the best!
|==========================================                |  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"
| You nailed it! Good 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)).
> 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)
| Excellent work!
|================================================          |  82%
| Print the value of unique_vals to the console.
> unique_vals
$name [1] Afghanistan Albania [3] Algeria American-Samoa [5] Andorra Angola [7] Anguilla Antigua-Barbuda [9] Argentina Argentine [11] Australia Austria [13] Bahamas Bahrain [15] Bangladesh Barbados [17] Belgium Belize [19] Benin Bermuda [21] Bhutan Bolivia [23] Botswana Brazil [25] British-Virgin-Isles Brunei [27] Bulgaria Burkina [29] Burma Burundi [31] Cameroon Canada [33] Cape-Verde-Islands Cayman-Islands [35] Central-African-Republic Chad [37] Chile China [39] Colombia Comorro-Islands [41] Congo Cook-Islands [43] Costa-Rica Cuba [45] Cyprus Czechoslovakia [47] Denmark Djibouti [49] Dominica Dominican-Republic [51] Ecuador Egypt [53] El-Salvador Equatorial-Guinea [55] Ethiopia Faeroes [57] Falklands-Malvinas Fiji [59] Finland France [61] French-Guiana French-Polynesia [63] Gabon Gambia [65] Germany-DDR Germany-FRG [67] Ghana Gibraltar [69] Greece Greenland [71] Grenada Guam [73] Guatemala Guinea [75] Guinea-Bissau Guyana [77] Haiti Honduras [79] Hong-Kong Hungary [81] Iceland India [83] Indonesia Iran [85] Iraq Ireland [87] Israel Italy [89] Ivory-Coast Jamaica [91] Japan Jordan [93] Kampuchea Kenya [95] Kiribati Kuwait [97] Laos Lebanon [99] Lesotho Liberia [101] Libya Liechtenstein [103] Luxembourg Malagasy [105] Malawi Malaysia [107] Maldive-Islands Mali [109] Malta Marianas [111] Mauritania Mauritius [113] Mexico Micronesia [115] Monaco Mongolia [117] Montserrat Morocco [119] Mozambique Nauru [121] Nepal Netherlands [123] Netherlands-Antilles New-Zealand [125] Nicaragua Niger [127] Nigeria Niue [129] North-Korea North-Yemen [131] Norway Oman [133] Pakistan Panama [135] Papua-New-Guinea Parguay [137] Peru Philippines [139] Poland Portugal [141] Puerto-Rico Qatar [143] Romania Rwanda [145] San-Marino Sao-Tome [147] Saudi-Arabia Senegal [149] Seychelles Sierra-Leone [151] Singapore Soloman-Islands [153] Somalia South-Africa [155] South-Korea South-Yemen [157] Spain Sri-Lanka [159] St-Helena St-Kitts-Nevis [161] St-Lucia St-Vincent [163] Sudan Surinam [165] Swaziland Sweden [167] Switzerland Syria [169] Taiwan Tanzania [171] Thailand Togo [173] Tonga Trinidad-Tobago [175] Tunisia Turkey [177] Turks-Cocos-Islands Tuvalu [179] UAE Uganda [181] UK Uruguay [183] US-Virgin-Isles USA [185] USSR Vanuatu [187] Vatican-City Venezuela [189] Vietnam Western-Samoa [191] Yugoslavia Zaire [193] Zambia Zimbabwe 194 Levels: Afghanistan Albania Algeria American-Samoa ... 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
[11]   143    31    23   113    47  1099   600  8512     6   111
[21]   274   678    28   474  9976     4   623  1284   757  9561
[31]  1139     2   342    51   115     9   128    43    22    49
[41]   284  1001    21  1222    12    18   337   547    91   268
[51]    10   108   249   239   132  2176   109   246    36   215
[61]   112    93   103  3268  1904  1648   435    70   301   323
[71]    11   372    98   181   583   236    30  1760     3   587
[81]   118   333  1240  1031  1973  1566   447   783   140    41
[91]  1267   925   121   195   324   212   804    76   463   407
[101]  1285   300   313    92   237    26  2150   196    72   637
[111]  1221    99   288   505    66  2506    63    17   450   185
[121]   945   514    57     5   164   781   245   178  9363 22402
[131]    15   912   256   905   753   391
$population [1] 16 3 20 0 7 28 15 8 90 10 1 6 [13] 119 9 35 4 24 2 11 1008 5 47 31 54 [25] 17 61 14 684 157 39 57 118 13 77 12 56 [37] 18 84 48 36 22 29 38 49 45 231 274 60$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
194          6          4        136         48         10
religion       bars    stripes    colours        red      green
8          5         12          8          2          2
blue       gold      white      black     orange    mainhue
2          2          2          2          2          8
circles    crosses   saltires   quarters   sunstars   crescent
4          3          2          3         14          2
triangle       icon    animate       text    topleft   botright
2          2          2          2          7          8
| All that hard work is paying off!
|==================================================        |  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 [3] Algeria American-Samoa [5] Andorra Angola [7] Anguilla Antigua-Barbuda [9] Argentina Argentine [11] Australia Austria [13] Bahamas Bahrain [15] Bangladesh Barbados [17] Belgium Belize [19] Benin Bermuda [21] Bhutan Bolivia [23] Botswana Brazil [25] British-Virgin-Isles Brunei [27] Bulgaria Burkina [29] Burma Burundi [31] Cameroon Canada [33] Cape-Verde-Islands Cayman-Islands [35] Central-African-Republic Chad [37] Chile China [39] Colombia Comorro-Islands [41] Congo Cook-Islands [43] Costa-Rica Cuba [45] Cyprus Czechoslovakia [47] Denmark Djibouti [49] Dominica Dominican-Republic [51] Ecuador Egypt [53] El-Salvador Equatorial-Guinea [55] Ethiopia Faeroes [57] Falklands-Malvinas Fiji [59] Finland France [61] French-Guiana French-Polynesia [63] Gabon Gambia [65] Germany-DDR Germany-FRG [67] Ghana Gibraltar [69] Greece Greenland [71] Grenada Guam [73] Guatemala Guinea [75] Guinea-Bissau Guyana [77] Haiti Honduras [79] Hong-Kong Hungary [81] Iceland India [83] Indonesia Iran [85] Iraq Ireland [87] Israel Italy [89] Ivory-Coast Jamaica [91] Japan Jordan [93] Kampuchea Kenya [95] Kiribati Kuwait [97] Laos Lebanon [99] Lesotho Liberia [101] Libya Liechtenstein [103] Luxembourg Malagasy [105] Malawi Malaysia [107] Maldive-Islands Mali [109] Malta Marianas [111] Mauritania Mauritius [113] Mexico Micronesia [115] Monaco Mongolia [117] Montserrat Morocco [119] Mozambique Nauru [121] Nepal Netherlands [123] Netherlands-Antilles New-Zealand [125] Nicaragua Niger [127] Nigeria Niue [129] North-Korea North-Yemen [131] Norway Oman [133] Pakistan Panama [135] Papua-New-Guinea Parguay [137] Peru Philippines [139] Poland Portugal [141] Puerto-Rico Qatar [143] Romania Rwanda [145] San-Marino Sao-Tome [147] Saudi-Arabia Senegal [149] Seychelles Sierra-Leone [151] Singapore Soloman-Islands [153] Somalia South-Africa [155] South-Korea South-Yemen [157] Spain Sri-Lanka [159] St-Helena St-Kitts-Nevis [161] St-Lucia St-Vincent [163] Sudan Surinam [165] Swaziland Sweden [167] Switzerland Syria [169] Taiwan Tanzania [171] Thailand Togo [173] Tonga Trinidad-Tobago [175] Tunisia Turkey [177] Turks-Cocos-Islands Tuvalu [179] UAE Uganda [181] UK Uruguay [183] US-Virgin-Isles USA [185] USSR Vanuatu [187] Vatican-City Venezuela [189] Vietnam Western-Samoa [191] Yugoslavia Zaire [193] Zambia Zimbabwe 194 Levels: Afghanistan Albania Algeria American-Samoa ... 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
[11]   143    31    23   113    47  1099   600  8512     6   111
[21]   274   678    28   474  9976     4   623  1284   757  9561
[31]  1139     2   342    51   115     9   128    43    22    49
[41]   284  1001    21  1222    12    18   337   547    91   268
[51]    10   108   249   239   132  2176   109   246    36   215
[61]   112    93   103  3268  1904  1648   435    70   301   323
[71]    11   372    98   181   583   236    30  1760     3   587
[81]   118   333  1240  1031  1973  1566   447   783   140    41
[91]  1267   925   121   195   324   212   804    76   463   407
[101]  1285   300   313    92   237    26  2150   196    72   637
[111]  1221    99   288   505    66  2506    63    17   450   185
[121]   945   514    57     5   164   781   245   178  9363 22402
[131]    15   912   256   905   753   391
$population [1] 16 3 20 0 7 28 15 8 90 10 1 6 [13] 119 9 35 4 24 2 11 1008 5 47 31 54 [25] 17 61 14 684 157 39 57 118 13 77 12 56 [37] 18 84 48 36 22 29 38 49 45 231 274 60$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
| That's a job well done!
|====================================================      |  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])
$name [1] Albania 194 Levels: Afghanistan Albania Algeria American-Samoa ... Zimbabwe$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
| You nailed it! Good 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%