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sibyvt/Assignment: swirl Lesson 1: lapply and sapply

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Assignment: swirl Lesson 1: lapply and sapply
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.
> head(flags)
name landmass zone area population language religion bars stripes
1 Afghanistan 5 1 648 16 10 2 0 3
2 Albania 3 1 29 3 6 6 0 0
3 Algeria 4 1 2388 20 8 2 2 0
4 American-Samoa 6 3 0 0 1 1 0 0
5 Andorra 3 1 0 0 6 0 3 0
6 Angola 4 2 1247 7 10 5 0 2
colours red green blue gold white black orange mainhue circles crosses
1 5 1 1 0 1 1 1 0 green 0 0
2 3 1 0 0 1 0 1 0 red 0 0
3 3 1 1 0 0 1 0 0 green 0 0
4 5 1 0 1 1 1 0 1 blue 0 0
5 3 1 0 1 1 0 0 0 gold 0 0
6 3 1 0 0 1 0 1 0 red 0 0
saltires quarters sunstars crescent triangle icon animate text topleft
1 0 0 1 0 0 1 0 0 black
2 0 0 1 0 0 0 1 0 red
3 0 0 1 1 0 0 0 0 green
4 0 0 0 0 1 1 1 0 blue
5 0 0 0 0 0 0 0 0 blue
6 0 0 1 0 0 1 0 0 red
botright
1 green
2 red
3 white
4 red
5 red
6 black
| Great job!
|
|======== | 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 (>).
...viewinfo()
|
|=========== | 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"
| You got it!
|
|============= | 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're the best!
|
|================= | 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"
| Nice work!
|
|================== | 26%
| The 'l' in 'lapply' stands for 'list'. Type class(cls_list) to confirm that
| lapply() returned a list.
> class(cls_list)
[1] "list"
| Excellent 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" "integer"
[8] "integer" "integer" "integer" "integer" "integer" "integer" "integer"
[15] "integer" "integer" "integer" "factor" "integer" "integer" "integer"
[22] "integer" "integer" "integer" "integer" "integer" "integer" "integer"
[29] "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.
> ?sapply
> cls_list <- sapply(flags, class)
| That's not the answer I was looking for, but try again. Or, type info() for
| more options.
| Type cls_vect <- sapply(flags, class) to store the column classes in a
| character vector called cls_vect.
> 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"
| That's the answer I was looking for.
|
|========================= | 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
| Your dedication is inspiring!
|
|=============================== | 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 are doing so well!
|
|================================== | 48%
| Use the head() function to look at the first 6 lines of flag_colors.
> head()
Error in head.default() : argument "x" is missing, with no default
> head(flags)
name landmass zone area population language religion bars stripes
1 Afghanistan 5 1 648 16 10 2 0 3
2 Albania 3 1 29 3 6 6 0 0
3 Algeria 4 1 2388 20 8 2 2 0
4 American-Samoa 6 3 0 0 1 1 0 0
5 Andorra 3 1 0 0 6 0 3 0
6 Angola 4 2 1247 7 10 5 0 2
colours red green blue gold white black orange mainhue circles crosses
1 5 1 1 0 1 1 1 0 green 0 0
2 3 1 0 0 1 0 1 0 red 0 0
3 3 1 1 0 0 1 0 0 green 0 0
4 5 1 0 1 1 1 0 1 blue 0 0
5 3 1 0 1 1 0 0 0 gold 0 0
6 3 1 0 0 1 0 1 0 red 0 0
saltires quarters sunstars crescent triangle icon animate text topleft
1 0 0 1 0 0 1 0 0 black
2 0 0 1 0 0 0 1 0 red
3 0 0 1 1 0 0 0 0 green
4 0 0 0 0 1 1 1 0 blue
5 0 0 0 0 0 0 0 0 blue
6 0 0 1 0 0 1 0 0 red
botright
1 green
2 red
3 white
4 red
5 red
6 black
| You almost had it, but not quite. Try again. Or, type info() for more
| options.
| head(flag_colors) will display the first six lines of flag_colors.
> head(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
| That's the answer I was looking for.
|
|=================================== | 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.
> cls_vect <- lapply(flag_colors, class)
| Not exactly. Give it another go. Or, type info() for more options.
| Use lapply(flag_colors, sum) to return the sum of each column.
> 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're the best!
|
|==================================== | 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.
> cls_vect <- sapply(flag_colors, sum)
| You're close...I can feel it! Try it again. Or, type info() for more options.
| Use sapply(flag_colors, sum) to return a vector of column sums.
> 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 orange
0.7886598 0.4690722 0.5103093 0.4690722 0.7525773 0.2680412 0.1340206
| Keep up the great work!
|
|========================================== | 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 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.
> rang()
Error: could not find function "rang"
> range()
[1] Inf -Inf
Warning messages:
1: In min(x, na.rm = na.rm) :
no non-missing arguments to min; returning Inf
2: In max(x, na.rm = na.rm) :
no non-missing arguments to max; returning -Inf
| One more time. You can do it! Or, type info() for more options.
| Try lapply(flag_shapes, range) to apply the range() function to each column
| of flag_shapes.
> lapply(flag_shapes, range)
$circles
[1] 0 4
$crosses
[1] 0 2
$saltires
[1] 0 1
$quarters
[1] 0 4
$sunstars
[1] 0 50
| Perseverance, that's the answer.
|
|================================================ | 68%
| Do the same operation, but using sapply() and store the result in a variable
| called shape_mat.
> sshaapply(flag_shapes, range)
> sapply(shape_mat, range)
Error in lapply(X = X, FUN = FUN, ...) : object 'shape_mat' not found
> sapply(flag_shapes, range)
circles crosses saltires quarters sunstars
[1,] 0 0 0 0 0
[2,] 4 2 1 4 50
| Not quite! Try again. Or, type info() for more options.
| shape_mat <- sapply(flag_shapes, range) will apply the range() function to
| each column of flag_shapes and store the result in shape_mat.
> shape_mat <- sapply(flag_shapes, range)
| You are quite good my friend!
|
|================================================= | 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
| That's a job well done!
|
|================================================== | 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()
Error in class() : 0 arguments passed to 'class' which requires 1
>
> cls_vect <- lapply(shape_mat, class)
| Almost! Try again. Or, type info() for more options.
| class(shape_mat) returns the class of shape_mat.
> class(shape_mat)
[1] "matrix"
| All that practice is paying off!
|
|===================================================== | 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
| You got it right!
|
|======================================================== | 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.
Error: object 'unique_vals.' not found
> unique_vals
Error: object 'unique_vals' not found
> lapply()
Error in match.fun(FUN) : argument "FUN" is missing, with no default
> unique_vals <- lapply(flags, unique)
| You are amazing!
|
|========================================================= | 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 ... 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
[13] 23 113 47 1099 600 8512 6 111 274 678 28 474
[25] 9976 4 623 1284 757 9561 1139 2 342 51 115 9
[37] 128 43 22 49 284 1001 21 1222 12 18 337 547
[49] 91 268 10 108 249 239 132 2176 109 246 36 215
[61] 112 93 103 3268 1904 1648 435 70 301 323 11 372
[73] 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
[97] 804 76 463 407 1285 300 313 92 237 26 2150 196
[109] 72 637 1221 99 288 505 66 2506 63 17 450 185
[121] 945 514 57 5 164 781 245 178 9363 22402 15 912
[133] 256 905 753 391
$population
[1] 16 3 20 0 7 28 15 8 90 10 1 6 119 9 35
[16] 4 24 2 11 1008 5 47 31 54 17 61 14 684 157 39
[31] 57 118 13 77 12 56 18 84 48 36 22 29 38 49 45
[46] 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 are amazing!
|
|=========================================================== | 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.
> length()
Error in length() : 0 arguments passed to 'length' which requires 1
> length(unique_vals)
[1] 30
| You're close...I can feel it! Try it again. Or, type info() for more options.
| Apply the length() function to each element of the unique_vals list using
| sapply(). Remember, no parentheses after the name of the function you are
| applying (i.e. length).
> sapply(unique_vals, length)
name landmass zone area population language religion
194 6 4 136 48 10 8
bars stripes colours red green blue gold
5 12 8 2 2 2 2
white black orange mainhue circles crosses saltires
2 2 2 8 4 3 2
quarters sunstars crescent triangle icon animate text
3 14 2 2 2 2 2
topleft botright
7 8
| You nailed it! Good job!
|
|============================================================ | 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 ... 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
[13] 23 113 47 1099 600 8512 6 111 274 678 28 474
[25] 9976 4 623 1284 757 9561 1139 2 342 51 115 9
[37] 128 43 22 49 284 1001 21 1222 12 18 337 547
[49] 91 268 10 108 249 239 132 2176 109 246 36 215
[61] 112 93 103 3268 1904 1648 435 70 301 323 11 372
[73] 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
[97] 804 76 463 407 1285 300 313 92 237 26 2150 196
[109] 72 637 1221 99 288 505 66 2506 63 17 450 185
[121] 945 514 57 5 164 781 245 178 9363 22402 15 912
[133] 256 905 753 391
$population
[1] 16 3 20 0 7 28 15 8 90 10 1 6 119 9 35
[16] 4 24 2 11 1008 5 47 31 54 17 61 14 684 157 39
[31] 57 118 13 77 12 56 18 84 48 36 22 29 38 49 45
[46] 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
| Nice work!
|
|=============================================================== | 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 ... 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
| That's a job well done!
|
|=================================================================== | 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%
| Would you like to receive credit for completing this course on Coursera.org?
1: Yes
2: No
Selection: 1
What is your email address? siby.thomas@capgemini.com
What is your assignment token? 5hvUvjvcVgp1DLRY
Error in curl::curl_fetch_memory(url, handle = handle) :
Peer certificate cannot be authenticated with given CA certificates
| Leaving swirl now. Type swirl() to resume.
@20sc
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20sc commented Dec 9, 2020

very nice experience

@ZestyCat
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mapply() is another very useful function that would be good to cover

@twelvetake
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twelvetake commented Apr 21, 2022 •
edited
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This theme was the hardest for me. But thanks to this course I understood it! I hope that I'll start writing my codes so soon. Lapply and sapply aren't as hard as I think. I'm a beginner, but I have mastered that good code can look neat, clean, and short! I'm just trying to write now while taking a course. Fortunately, senior students advised me of this service https://essayreviewexpert.com/review/edusson/ because you can get high-quality assignment help there. I also love GitHub, and this service is generally invaluable. I would thank you, guys, for your big job! Your courses allow me to become who I want to be.

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