From de4434a4faef49311fd27c5b9169e1f10f450d7d Mon Sep 17 00:00:00 2001 From: rmonajemi Date: Fri, 8 Oct 2021 15:42:48 +0200 Subject: [PATCH] updated reshape0 --- R/Doc.R | 6 + docs/Boerhaave_202110/S00L01c_toc.html | 61 +------- .../S02L01s_basic_vectors0.tasks.code.html | 56 +++---- .../S02L02l_useful_functions0a.html | 94 ++++++------ .../S02L03s_basic_vectors0a.tasks.code.html | 58 ++++---- .../S02L04s_basic_factors0.tasks.code.html | 54 +++---- .../S03L05p_dplyr_mutate0.tasks.nocode.html | 140 +++++------------- .../S03L05s_dplyr_mutate0.tasks.code.html | 140 +++++------------- .../S04L03p_dplyr_group0.tasks.nocode.html | 2 +- .../S04L03s_dplyr_group0.tasks.code.html | 2 +- .../S05L01s_basic_lists0.tasks.code.html | 32 ++-- .../S05L02l_basic_formulas0.html | 2 +- .../S05L03s_basic_matrices0.tasks.code.html | 38 ++--- .../S06L04l_ggplot_geoms0.html | 10 +- .../Boerhaave_202110/S07L01l_dplyr_join0.html | 10 +- .../S07L01p_dplyr_join0.tasks.nocode.html | 5 +- .../S07L01s_dplyr_join0.tasks.code.html | 16 +- .../S07L02l_tidyr_reshape0.html | 34 ++++- .../S07L03l_advanced_user_functions0.html | 2 +- .../S08L01l_useful_functions0.html | 96 ++++++------ rcourse/_site.yml | 6 +- rcourse/config.yml | 8 +- rcourse/dplyr_group0.tasks.Rmd | 2 +- rcourse/dplyr_join0.Rmd | 11 +- rcourse/dplyr_join0.tasks.Rmd | 13 +- rcourse/dplyr_pipe0.Rmd | 3 - rcourse/images/gather-spread.png | Bin 132897 -> 0 bytes rcourse/images/joins.png | Bin 0 -> 24747 bytes rcourse/images/tidyr_reshape.png | Bin 0 -> 122741 bytes rcourse/tidyr_reshape0.Rmd | 43 +++++- rcourse/tidyr_reshape0.tasks.Rmd | 2 +- 31 files changed, 432 insertions(+), 514 deletions(-) delete mode 100644 rcourse/images/gather-spread.png create mode 100644 rcourse/images/joins.png create mode 100644 rcourse/images/tidyr_reshape.png diff --git a/R/Doc.R b/R/Doc.R index f7c18ac..0c4eb25 100644 --- a/R/Doc.R +++ b/R/Doc.R @@ -625,6 +625,12 @@ BaseRenderer <- R6Class( id2url = function( id ) stop( "Not inherited." ), makeAll = function( course, makeZip = TRUE, ... ) { self$clearDir() + # Copy rcourse/{footer.html,fold.html,styles.css} to self$outDir(). This to + # avoid relative path issue in _site.yml whene render is called on default + # .doc or docs/. + # make a list of files to be copied in the config.yml + file.copy(file.path(course$dir(), c("footer.html","fold.html","styles.css")), self$outDir()) + # allFiles <- c( self$makeLectures( course, ... ), self$makeTasks( course, enableCode = FALSE, ... ), diff --git a/docs/Boerhaave_202110/S00L01c_toc.html b/docs/Boerhaave_202110/S00L01c_toc.html index d2ae673..3b25362 100644 --- a/docs/Boerhaave_202110/S00L01c_toc.html +++ b/docs/Boerhaave_202110/S00L01c_toc.html @@ -577,7 +577,7 @@

Contents

Solutions - + Data manipulation 1/3 (Wednesday, Oct 13, 09:00-12:30) @@ -616,24 +616,10 @@

Contents

-― break ― - - -10:00-10:15 - - - - - - - - - - Select variables/cols -10:15-11:00 +10:00-10:45 Lecture @@ -647,27 +633,10 @@

Contents

-Filter observations/rows - - -11:00-11:15 - - -Lecture - - -Practice - - -Solutions - - - - ― break ― -11:15-11:30 +10:45-11:00 @@ -681,7 +650,7 @@

Contents

Filter observations/rows -11:30-12:00 +11:00-11:45 Lecture @@ -698,7 +667,7 @@

Contents

Add/modify variables -12:00-12:30 +11:45-12:30 Lecture @@ -991,17 +960,17 @@

Contents

- + Data manipulation 3/3 and functions (Friday, Oct 15, 09:00-12:30) -Merging/joining tables +Joining tables -09:00-09:45 +09:00-10:00 Lecture @@ -1015,20 +984,6 @@

Contents

-― break ― - - -09:45-10:00 - - - - - - - - - - Reshaping tables diff --git a/docs/Boerhaave_202110/S02L01s_basic_vectors0.tasks.code.html b/docs/Boerhaave_202110/S02L01s_basic_vectors0.tasks.code.html index 18b196a..98deea0 100644 --- a/docs/Boerhaave_202110/S02L01s_basic_vectors0.tasks.code.html +++ b/docs/Boerhaave_202110/S02L01s_basic_vectors0.tasks.code.html @@ -390,34 +390,34 @@

Primary exercises

v <- rnorm( 10 ) # a vector of random numbers
 v
-
 [1] -1.62291325  0.95847246  0.08196443  0.54409205  1.13142631 -0.50743260  0.25513347  0.20318319
- [9]  0.97402405  0.38997078
+
 [1]  0.7937472 -1.0087640 -1.3903875 -0.4187800 -1.3761140  0.1322108 -0.5739516 -1.3789547 -0.0075223
+[10] -0.8189675
v <- rnorm( 10 ) # another vector of random numbers
 v
-
 [1]  1.2902434 -0.7014971  1.5156270 -0.3275760 -1.3415029  1.8590176  0.6984202 -1.0443753  2.3604989
-[10] -1.1413431
+
 [1] -1.2473570  1.3865308 -0.1580330 -0.6584027  0.1566449  0.3764169 -0.4764282  1.2185440  1.4531192
+[10]  0.3159742
min( v )
-
[1] -1.341503
+
[1] -1.247357
max( v )
-
[1] 2.360499
+
[1] 1.453119
mean( v )
-
[1] 0.3167513
+
[1] 0.2367009
median( v )
-
[1] 0.1854221
+
[1] 0.2363095
sd( v )
-
[1] 1.385105
+
[1] 0.9108005
  1. Selecting and counting elements by a condition. Type v < 0 and understand the result.
    How to interpret the number produced by sum( v < 0 )? How to interpret the number produced by sum( !( v < 0 ) )?
v < 0             # TRUE corresponds to elements of vector v smaller than 0
-
 [1] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE
+
 [1]  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
sum( v < 0 )      # calculates the number of negative elements in vector v
-
[1] 5
+
[1] 4
sum( !( v < 0 ) ) # calculates the number of non-negative (so, positive OR ZERO) elements in vector v 
-
[1] 5
+
[1] 6
sum( v >= 0 )     # same as above
-
[1] 5
+
[1] 6
  1. Head and tail.
    Often there is a need to quickly look at the beginning (head) or at the end (tail) of a vector.
    @@ -425,13 +425,13 @@

    Primary exercises

v <- rnorm( 20 )
 v
-
 [1]  0.46891296 -0.46498371 -1.01569568  0.08983452  1.89631016 -0.90828665  1.82875359 -1.35528564
- [9] -0.32686536 -0.77723603  1.94866475 -0.74793928  0.30711259  0.16227810 -0.18126833  0.77305165
-[17]  0.31382362  0.86717220  0.01348908 -1.68611814
+
 [1]  0.49168494 -0.71588185 -2.29895757 -0.92007654 -1.44030517  0.07771069 -0.48200707  0.60780228
+ [9] -0.20809487 -1.52883530  1.03345287 -0.65104507  0.38363254  0.76943290  0.41423687  1.96510485
+[17] -0.71668923  0.46486082  1.82292165 -0.38959121
head( v, 5 )
-
[1]  0.46891296 -0.46498371 -1.01569568  0.08983452  1.89631016
+
[1]  0.4916849 -0.7158818 -2.2989576 -0.9200765 -1.4403052
tail( v, 7 )
-
[1]  0.16227810 -0.18126833  0.77305165  0.31382362  0.86717220  0.01348908 -1.68611814
+
[1]  0.7694329  0.4142369  1.9651048 -0.7166892  0.4648608  1.8229216 -0.3895912

Extra exercises

@@ -535,18 +535,18 @@

Extra exercises

fileName <- "test.txt"
 v <- sample( 1:100, 10 )
 v
-
 [1] 52 49 93 78 38 23 45 11 48 62
+
 [1] 90 25 41 86 69 45 26 38  1 65
writeLines( as.character( v ) )                 # conversion to character needed
-
52
-49
-93
-78
-38
-23
+
90
+25
+41
+86
+69
 45
-11
-48
-62
+26 +38 +1 +65
writeLines( as.character( v ), con = fileName )
 w <- readLines( con = fileName )
 identical( v, w )     # numbers are not the same as their text representation
diff --git a/docs/Boerhaave_202110/S02L02l_useful_functions0a.html b/docs/Boerhaave_202110/S02L02l_useful_functions0a.html index 388f407..b682ff3 100644 --- a/docs/Boerhaave_202110/S02L02l_useful_functions0a.html +++ b/docs/Boerhaave_202110/S02L02l_useful_functions0a.html @@ -306,20 +306,20 @@

Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
 manyNumbers
-
 [1] 387 958   7 346 620 605 533 138 170 674 367 626 411 833 901  32 788 284 132 168
+
 [1] 759 806 445 784 315 224 526 429 825  76 841 595 880 171 403 473 845 298 251 663
manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
 manyNumbersWithNA
-
 [1] 533 620 788  NA 901  32 674  NA 958 411 833 605 168 138 284  NA   7 626 132 346 387 170 367
+
 [1] 806 403 663 759 224 595  76 784 526 429 841 315 171 845 880  NA 445  NA 298 251 473 825  NA
duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
 duplicatedNumbers
-
 [1] 4 5 3 5 1 5 3 4 4 3
+
 [1] 3 4 4 2 2 5 4 2 5 3
letters
 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"
LETTERS
 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"
mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
 mixedLetters
-
 [1] "e" "m" "p" "u" "z" "P" "O" "X" "W" "N"
+
 [1] "d" "v" "a" "p" "o" "Z" "S" "H" "C" "O"

Are all/any elements TRUE

@@ -345,11 +345,11 @@

Are all/any elements TRUE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )
-
[1]  2 15
+
integer(0)
which( manyNumbersWithNA > 900 )
-
[1] 5 9
+
integer(0)
which( is.na( manyNumbersWithNA ) )
-
[1]  4  8 16
+
[1] 16 18 23

Filtering vector elements

@@ -359,12 +359,12 @@

Filtering vector elements

  • Note: several ways to get the same effect
  • manyNumbers[ manyNumbers > 900 ] # indexing by logical vector
    -
    [1] 958 901
    +
    integer(0)
    manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions
    -
    [1] 958 901
    +
    integer(0)
    somePositions <- which( manyNumbers > 900 )
     manyNumbers[ somePositions ]
    -
    [1] 958 901
    +
    integer(0)

    Are some elements among other elements

    @@ -383,16 +383,16 @@

    Are some elements among other elements

    any( mixedLetters %in% LETTERS )
    [1] TRUE
    mixedLetters[ mixedLetters %in% LETTERS ]
    -
    [1] "P" "O" "X" "W" "N"
    +
    [1] "Z" "S" "H" "C" "O"
    mixedLetters[ !( mixedLetters %in% LETTERS ) ]
    -
    [1] "e" "m" "p" "u" "z"
    +
    [1] "d" "v" "a" "p" "o"
    manyNumbers %in% 300:600
    -
     [1]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
    +
     [1] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE
     [18] FALSE FALSE FALSE
    which( manyNumbers %in% 300:600 )
    -
    [1]  1  4  7 11 13
    +
    [1]  3  5  7  8 12 15 16
    sum( manyNumbers %in% 300:600 )
    -
    [1] 5
    +
    [1] 7

    Pick one of two (three) depending on condition

    @@ -402,16 +402,16 @@

    Pick one of two (three) depending on condition

  • Note: it can take care of NAs
  • if_else( manyNumbersWithNA >= 500, "large", "small" )
    -
     [1] "large" "large" "large" NA      "large" "small" "large" NA      "large" "small" "large" "large" "small"
    -[14] "small" "small" NA      "small" "large" "small" "small" "small" "small" "small"
    +
     [1] "large" "small" "large" "large" "small" "large" "small" "large" "large" "small" "large" "small" "small"
    +[14] "large" "large" NA      "small" NA      "small" "small" "small" "large" NA     
    if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )
    -
     [1] "large"   "large"   "large"   "UNKNOWN" "large"   "small"   "large"   "UNKNOWN" "large"   "small"  
    -[11] "large"   "large"   "small"   "small"   "small"   "UNKNOWN" "small"   "large"   "small"   "small"  
    -[21] "small"   "small"   "small"  
    +
     [1] "large"   "small"   "large"   "large"   "small"   "large"   "small"   "large"   "large"   "small"  
    +[11] "large"   "small"   "small"   "large"   "large"   "UNKNOWN" "small"   "UNKNOWN" "small"   "small"  
    +[21] "small"   "large"   "UNKNOWN"
    # here integer 0L is needed instead of real 0.0 
     # manyNumbersWithNA contains integer numbers and the method complains
     if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 
    -
     [1] 533 620 788  NA 901   0 674  NA 958   0 833 605   0   0   0  NA   0 626   0   0   0   0   0
    +
     [1] 806   0 663 759   0 595   0 784 526   0 841   0   0 845 880  NA   0  NA   0   0   0 825  NA

    Duplicates and unique elements

    @@ -419,70 +419,70 @@

    Duplicates and unique elements

  • Input: a vector
  • unique( duplicatedNumbers )
    -
    [1] 4 5 3 1
    +
    [1] 3 4 2 5
    unique( c( NA, duplicatedNumbers, NA ) )
    -
    [1] NA  4  5  3  1
    +
    [1] NA  3  4  2  5
    duplicated( duplicatedNumbers )
    -
     [1] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
    +
     [1] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE

    Positions of max/min elements

    which.max( manyNumbersWithNA )
    -
    [1] 9
    +
    [1] 15
    manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]
    -
    [1] 958
    +
    [1] 880
    which.min( manyNumbersWithNA )
    -
    [1] 17
    -
    manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]
    [1] 7
    +
    manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]
    +
    [1] 76
    range( manyNumbersWithNA, na.rm = TRUE )
    -
    [1]   7 958
    +
    [1]  76 880

    Sorting/ordering of vectors

    manyNumbersWithNA
    -
     [1] 533 620 788  NA 901  32 674  NA 958 411 833 605 168 138 284  NA   7 626 132 346 387 170 367
    +
     [1] 806 403 663 759 224 595  76 784 526 429 841 315 171 845 880  NA 445  NA 298 251 473 825  NA
    sort( manyNumbersWithNA )
    -
     [1]   7  32 132 138 168 170 284 346 367 387 411 533 605 620 626 674 788 833 901 958
    +
     [1]  76 171 224 251 298 315 403 429 445 473 526 595 663 759 784 806 825 841 845 880
    sort( manyNumbersWithNA, na.last = TRUE )
    -
     [1]   7  32 132 138 168 170 284 346 367 387 411 533 605 620 626 674 788 833 901 958  NA  NA  NA
    +
     [1]  76 171 224 251 298 315 403 429 445 473 526 595 663 759 784 806 825 841 845 880  NA  NA  NA
    sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )
    -
     [1] 958 901 833 788 674 626 620 605 533 411 387 367 346 284 170 168 138 132  32   7  NA  NA  NA
    +
     [1] 880 845 841 825 806 784 759 663 595 526 473 445 429 403 315 298 251 224 171  76  NA  NA  NA
    manyNumbersWithNA[1:5]
    -
    [1] 533 620 788  NA 901
    +
    [1] 806 403 663 759 224
    order( manyNumbersWithNA[1:5] )
    -
    [1] 1 2 3 5 4
    +
    [1] 5 2 3 4 1
    rank( manyNumbersWithNA[1:5] )
    -
    [1] 1 2 3 5 4
    +
    [1] 5 2 3 4 1
    sort( mixedLetters )
    -
     [1] "e" "m" "N" "O" "p" "P" "u" "W" "X" "z"
    +
     [1] "a" "C" "d" "H" "o" "O" "p" "S" "v" "Z"

    Ranking of vectors

    manyDuplicates <- sample( 10:15, 10, replace = TRUE )
     rank( manyDuplicates )
    -
     [1]  1.5  1.5  4.5  4.5  4.5 10.0  4.5  7.5  9.0  7.5
    +
     [1]  8.5 10.0  8.5  2.0  4.0  6.0  6.0  2.0  6.0  2.0
    rank( manyDuplicates, ties.method = "min" )
    -
     [1]  1  1  3  3  3 10  3  7  9  7
    +
     [1]  8 10  8  1  4  5  5  1  5  1
    rank( manyDuplicates, ties.method = "random" )
    -
     [1]  1  2  4  5  6 10  3  7  9  8
    +
     [1]  9 10  8  3  4  5  6  2  7  1

    Rounding numbers

    v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
     v
    -
     [1] -1.00000000 -0.50000000  0.00000000  0.50000000  1.00000000  0.03115283  0.57468386 -0.52143619
    - [9]  0.07749757  1.22583009 -1.44897350 -0.51782610  0.54115096 -0.45035968 -0.07287477
    +
     [1] -1.00000000 -0.50000000  0.00000000  0.50000000  1.00000000 -0.15909255 -0.18501522  1.68895313
    + [9]  0.50640422  1.94035697 -0.07700514  1.26848332  0.46908529  0.05367367  0.70700205
    round( v, 0 )
    -
     [1] -1  0  0  0  1  0  1 -1  0  1 -1 -1  1  0  0
    +
     [1] -1  0  0  0  1  0  0  2  1  2  0  1  0  0  1
    round( v, 1 )
    -
     [1] -1.0 -0.5  0.0  0.5  1.0  0.0  0.6 -0.5  0.1  1.2 -1.4 -0.5  0.5 -0.5 -0.1
    +
     [1] -1.0 -0.5  0.0  0.5  1.0 -0.2 -0.2  1.7  0.5  1.9 -0.1  1.3  0.5  0.1  0.7
    round( v, 2 )
    -
     [1] -1.00 -0.50  0.00  0.50  1.00  0.03  0.57 -0.52  0.08  1.23 -1.45 -0.52  0.54 -0.45 -0.07
    +
     [1] -1.00 -0.50  0.00  0.50  1.00 -0.16 -0.19  1.69  0.51  1.94 -0.08  1.27  0.47  0.05  0.71
    floor( v )
    -
     [1] -1 -1  0  0  1  0  0 -1  0  1 -2 -1  0 -1 -1
    +
     [1] -1 -1  0  0  1 -1 -1  1  0  1 -1  1  0  0  0
    ceiling( v )
    -
     [1] -1  0  0  1  1  1  1  0  1  2 -1  0  1  0  0
    +
     [1] -1  0  0  1  1  0  0  2  1  2  0  2  1  1  1

    Naming vector elements

    diff --git a/docs/Boerhaave_202110/S02L03s_basic_vectors0a.tasks.code.html b/docs/Boerhaave_202110/S02L03s_basic_vectors0a.tasks.code.html index 63a7932..42c2aed 100644 --- a/docs/Boerhaave_202110/S02L03s_basic_vectors0a.tasks.code.html +++ b/docs/Boerhaave_202110/S02L03s_basic_vectors0a.tasks.code.html @@ -390,34 +390,34 @@

    Primary exercises

    v <- rnorm( 10 ) # a vector of random numbers
     v
    -
     [1]  0.1214615 -2.1173771 -1.6411241 -1.4047809  0.6549639 -0.9250667 -1.7505583  0.3150473 -1.9336665
    -[10] -0.1494375
    +
     [1]  0.4364696  0.1191291 -1.2061461  0.6182888 -0.5705215 -0.4650489 -0.2979098 -0.1049737 -1.2189937
    +[10]  0.4996053
    v <- rnorm( 10 ) # another vector of random numbers
     v
    -
     [1] -1.0064713  0.7622756 -1.0852171  1.4496796  1.1492568 -1.0777601 -0.1083510 -1.1404464  0.0585023
    -[10]  0.6150167
    +
     [1] -0.85687298 -2.20718200  0.58772715  1.10500507 -0.36152670  0.39801510 -0.05927304 -1.15140336
    + [9] -0.66929035  0.52588750
    min( v )
    -
    [1] -1.140446
    +
    [1] -2.207182
    max( v )
    -
    [1] 1.44968
    +
    [1] 1.105005
    mean( v )
    -
    [1] -0.03835149
    +
    [1] -0.2688914
    median( v )
    -
    [1] -0.02492436
    +
    [1] -0.2103999
    sd( v )
    -
    [1] 1.001942
    +
    [1] 0.9883873
    1. Selecting and counting elements by a condition. Type v < 0 and understand the result.
      How to interpret the number produced by sum( v < 0 )? How to interpret the number produced by sum( !( v < 0 ) )?
    v < 0             # TRUE corresponds to elements of vector v smaller than 0
    -
     [1]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
    +
     [1]  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
    sum( v < 0 )      # calculates the number of negative elements in vector v
    -
    [1] 5
    +
    [1] 6
    sum( !( v < 0 ) ) # calculates the number of non-negative (so, positive OR ZERO) elements in vector v 
    -
    [1] 5
    +
    [1] 4
    sum( v >= 0 )     # same as above
    -
    [1] 5
    +
    [1] 4
    1. Head and tail.
      Often there is a need to quickly look at the beginning (head) or at the end (tail) of a vector.
      @@ -425,13 +425,13 @@

      Primary exercises

    v <- rnorm( 20 )
     v
    -
     [1] -2.52880676  0.83857235  0.86918472  1.09159741  0.39034847  0.45404967  1.83554359  1.59547997
    - [9] -0.30064379  0.06647138  1.81340842 -0.71959965  0.76996736  1.99194088 -0.26927706 -1.14545463
    -[17]  0.51865883  0.24371368 -1.61445570  0.97500062
    +
     [1]  0.35383100 -0.08145983  0.44432246  0.44481558 -2.17068659  0.42460350 -1.54477532  1.66380797
    + [9] -0.15001006 -0.37878836  0.81847974  0.33187413  0.94330443  0.13714706  1.16031581 -0.40407871
    +[17]  0.03492894  1.22273388 -0.42220448 -0.19066694
    head( v, 5 )
    -
    [1] -2.5288068  0.8385723  0.8691847  1.0915974  0.3903485
    +
    [1]  0.35383100 -0.08145983  0.44432246  0.44481558 -2.17068659
    tail( v, 7 )
    -
    [1]  1.9919409 -0.2692771 -1.1454546  0.5186588  0.2437137 -1.6144557  0.9750006
    +
    [1]  0.13714706  1.16031581 -0.40407871  0.03492894  1.22273388 -0.42220448 -0.19066694

    Extra exercises

    @@ -535,18 +535,18 @@

    Extra exercises

    fileName <- "test.txt"
     v <- sample( 1:100, 10 )
     v
    -
     [1] 17 41 50 21 36 70 24 53 37 25
    +
     [1] 28 67 13 71 56 63 65 17 62 85
    writeLines( as.character( v ) )                 # conversion to character needed
    -
    17
    -41
    -50
    -21
    -36
    -70
    -24
    -53
    -37
    -25
    +
    28
    +67
    +13
    +71
    +56
    +63
    +65
    +17
    +62
    +85
    writeLines( as.character( v ), con = fileName )
     w <- readLines( con = fileName )
     identical( v, w )     # numbers are not the same as their text representation
    diff --git a/docs/Boerhaave_202110/S02L04s_basic_factors0.tasks.code.html b/docs/Boerhaave_202110/S02L04s_basic_factors0.tasks.code.html index f75bb96..5c03d09 100644 --- a/docs/Boerhaave_202110/S02L04s_basic_factors0.tasks.code.html +++ b/docs/Boerhaave_202110/S02L04s_basic_factors0.tasks.code.html @@ -351,40 +351,40 @@

    Primary exercises

    sample( lvs, 3 )
    [1] "severalPerWeek" "none"           "oncePerWeek"   
    sample( lvs, 3 )
    -
    [1] "severalPerWeek" "none"           "daily"         
    +
    [1] "none"        "oncePerWeek" "daily"      
    sample( lvs, 3 )
    -
    [1] "severalPerWeek" "oncePerWeek"    "none"          
    +
    [1] "daily"          "severalPerWeek" "oncePerWeek"   
    sample( lvs, 100 )
    Error in sample.int(length(x), size, replace, prob): cannot take a sample larger than the population when 'replace = FALSE'
    v <- sample( lvs, 100, replace = TRUE )
     w <- factor( v, levels = lvs )
     w
    -
      [1] oncePerWeek    none           severalPerWeek severalPerWeek severalPerWeek oncePerWeek   
    -  [7] none           oncePerWeek    none           daily          severalPerWeek none          
    - [13] none           oncePerWeek    oncePerWeek    none           severalPerWeek oncePerWeek   
    - [19] none           none           daily          severalPerWeek none           none          
    - [25] daily          oncePerWeek    daily          daily          severalPerWeek oncePerWeek   
    - [31] severalPerWeek daily          none           none           oncePerWeek    oncePerWeek   
    - [37] severalPerWeek severalPerWeek none           severalPerWeek oncePerWeek    none          
    - [43] oncePerWeek    oncePerWeek    oncePerWeek    oncePerWeek    oncePerWeek    oncePerWeek   
    - [49] severalPerWeek daily          oncePerWeek    daily          none           none          
    - [55] none           daily          oncePerWeek    none           severalPerWeek daily         
    - [61] severalPerWeek none           oncePerWeek    none           oncePerWeek    oncePerWeek   
    - [67] severalPerWeek severalPerWeek oncePerWeek    oncePerWeek    none           oncePerWeek   
    - [73] severalPerWeek none           daily          severalPerWeek oncePerWeek    oncePerWeek   
    - [79] oncePerWeek    daily          oncePerWeek    none           daily          oncePerWeek   
    - [85] severalPerWeek oncePerWeek    daily          daily          none           daily         
    - [91] severalPerWeek oncePerWeek    daily          daily          oncePerWeek    severalPerWeek
    - [97] daily          severalPerWeek none           none          
    +
      [1] daily          none           severalPerWeek severalPerWeek oncePerWeek    oncePerWeek   
    +  [7] daily          severalPerWeek oncePerWeek    severalPerWeek severalPerWeek severalPerWeek
    + [13] oncePerWeek    severalPerWeek severalPerWeek daily          none           daily         
    + [19] oncePerWeek    daily          daily          none           severalPerWeek severalPerWeek
    + [25] severalPerWeek oncePerWeek    oncePerWeek    severalPerWeek oncePerWeek    oncePerWeek   
    + [31] none           none           severalPerWeek severalPerWeek daily          severalPerWeek
    + [37] oncePerWeek    none           none           severalPerWeek oncePerWeek    daily         
    + [43] none           severalPerWeek oncePerWeek    daily          oncePerWeek    none          
    + [49] daily          oncePerWeek    oncePerWeek    severalPerWeek severalPerWeek daily         
    + [55] daily          oncePerWeek    severalPerWeek severalPerWeek oncePerWeek    oncePerWeek   
    + [61] none           daily          daily          severalPerWeek severalPerWeek daily         
    + [67] oncePerWeek    oncePerWeek    oncePerWeek    oncePerWeek    oncePerWeek    severalPerWeek
    + [73] none           none           severalPerWeek severalPerWeek severalPerWeek daily         
    + [79] oncePerWeek    daily          daily          none           severalPerWeek daily         
    + [85] severalPerWeek daily          severalPerWeek daily          oncePerWeek    none          
    + [91] daily          severalPerWeek daily          daily          daily          oncePerWeek   
    + [97] severalPerWeek none           oncePerWeek    daily         
     Levels: none oncePerWeek severalPerWeek daily
    fct_count( w )
    # A tibble: 4 x 2
       f                  n
       <fct>          <int>
    -1 none              26
    -2 oncePerWeek       33
    -3 severalPerWeek    22
    -4 daily             19
    +1 none 15 +2 oncePerWeek 27 +3 severalPerWeek 32 +4 daily 26
    1. Reordering factor levels.
      When a factor is shown on an axis of a plot, the order is given by its levels.
      @@ -401,10 +401,10 @@

      Primary exercises

      # A tibble: 4 x 2
         f                  n
         <fct>          <int>
      -1 daily             19
      -2 severalPerWeek    22
      -3 oncePerWeek       33
      -4 none              26
      +1 daily 26 +2 severalPerWeek 32 +3 oncePerWeek 27 +4 none 15

    Extra exercises

    diff --git a/docs/Boerhaave_202110/S03L05p_dplyr_mutate0.tasks.nocode.html b/docs/Boerhaave_202110/S03L05p_dplyr_mutate0.tasks.nocode.html index 9fda3df..5758d84 100644 --- a/docs/Boerhaave_202110/S03L05p_dplyr_mutate0.tasks.nocode.html +++ b/docs/Boerhaave_202110/S03L05p_dplyr_mutate0.tasks.nocode.html @@ -325,61 +325,31 @@

    Extra exercises

    - - - - + + + - - - + + + - - - + + + - - - + + + - - - + + +
    - -classification - -bmi -
    classificationbmi
    -1 - -underweight - -<18.5 -1underweight<18.5
    -2 - -normal - -18.5-24.9 -2normal18.5-24.9
    -3 - -overweight - -25-29.9 -3overweight25-29.9
    -4 - -obese - ->=30 -4obese\>=30
    @@ -391,76 +361,36 @@

    Extra exercises

    - - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + +
    - -classification - -age - -notation -
    classificationagenotation
    -1 - -adult - ->19 - -(19,∞) -1adult\>19(19,∞)
    -2 - -adolescent - ->=10 and <=19 - -[10,19] -2adolescent\>=10 and <=19[10,19]
    -3 - -child - ->=1 and <=9 - -[1,9] -3child\>=1 and <=9[1,9]
    -4 - -infant - -<1 - -(-∞,1) -4infant<1(-∞,1)
    diff --git a/docs/Boerhaave_202110/S03L05s_dplyr_mutate0.tasks.code.html b/docs/Boerhaave_202110/S03L05s_dplyr_mutate0.tasks.code.html index 9ff9ef4..001ce94 100644 --- a/docs/Boerhaave_202110/S03L05s_dplyr_mutate0.tasks.code.html +++ b/docs/Boerhaave_202110/S03L05s_dplyr_mutate0.tasks.code.html @@ -441,61 +441,31 @@

    Extra exercises

    - - - - + + + - - - + + + - - - + + + - - - + + + - - - + + +
    - -classification - -bmi -
    classificationbmi
    -1 - -underweight - -<18.5 -1underweight<18.5
    -2 - -normal - -18.5-24.9 -2normal18.5-24.9
    -3 - -overweight - -25-29.9 -3overweight25-29.9
    -4 - -obese - ->=30 -4obese\>=30
    @@ -529,76 +499,36 @@

    Extra exercises

    - - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + +
    - -classification - -age - -notation -
    classificationagenotation
    -1 - -adult - ->19 - -(19,∞) -1adult\>19(19,∞)
    -2 - -adolescent - ->=10 and <=19 - -[10,19] -2adolescent\>=10 and <=19[10,19]
    -3 - -child - ->=1 and <=9 - -[1,9] -3child\>=1 and <=9[1,9]
    -4 - -infant - -<1 - -(-∞,1) -4infant<1(-∞,1)
    diff --git a/docs/Boerhaave_202110/S04L03p_dplyr_group0.tasks.nocode.html b/docs/Boerhaave_202110/S04L03p_dplyr_group0.tasks.nocode.html index 1227ac7..d53b784 100644 --- a/docs/Boerhaave_202110/S04L03p_dplyr_group0.tasks.nocode.html +++ b/docs/Boerhaave_202110/S04L03p_dplyr_group0.tasks.nocode.html @@ -332,7 +332,7 @@

    Extra exercises

    1 female left 47 2 female neither 6 3 female right 64 -

    1.c most popular name initials in descending order of popularity.

    +

    1.c most popular name initials in descending order of popularity. See ?stringr::str_sub for extracting initials from a character vector.

    # A tibble: 20 x 2
        initial     n
        <chr>   <int>
    diff --git a/docs/Boerhaave_202110/S04L03s_dplyr_group0.tasks.code.html b/docs/Boerhaave_202110/S04L03s_dplyr_group0.tasks.code.html
    index 538ca04..42bbd73 100644
    --- a/docs/Boerhaave_202110/S04L03s_dplyr_group0.tasks.code.html
    +++ b/docs/Boerhaave_202110/S04L03s_dplyr_group0.tasks.code.html
    @@ -413,7 +413,7 @@ 

    Extra exercises

    # larger set including male observations. # # survey %>% group_by(gender,fold) %>% summarise(n=n(), .groups = "drop") %>% filter(gender=="female")
    -

    1.c most popular name initials in descending order of popularity.

    +

    1.c most popular name initials in descending order of popularity. See ?stringr::str_sub for extracting initials from a character vector.

    survey %>% mutate(initial=str_sub(name, 1,1)) %>% group_by(initial) %>% summarise(n=n()) %>% arrange(desc(n))
    # A tibble: 20 x 2
        initial     n
    diff --git a/docs/Boerhaave_202110/S05L01s_basic_lists0.tasks.code.html b/docs/Boerhaave_202110/S05L01s_basic_lists0.tasks.code.html
    index 6c03aef..32f8f83 100644
    --- a/docs/Boerhaave_202110/S05L01s_basic_lists0.tasks.code.html
    +++ b/docs/Boerhaave_202110/S05L01s_basic_lists0.tasks.code.html
    @@ -439,38 +439,38 @@ 

    Extra exercises

    x <- rnorm( 30 )
     y <- rnorm( 30 )
     x
    -
     [1] -0.70148686  0.11932335 -0.72083867 -0.25285534  0.01406954  1.49590310  0.26524669 -1.21488087
    - [9]  0.60676100 -0.75351803  1.70643194  0.56049939 -0.89019986  0.09544850 -0.96194584  0.14130505
    -[17]  0.14782467  0.80579286 -0.33277742 -0.30128468  1.08231724 -1.68409294 -1.61256495  0.49829969
    -[25]  1.16702635 -2.06816214 -2.09600983  0.49382976  0.05167264  0.32745243
    +
     [1] -0.45908833 -1.05269814 -1.19324122  0.11331330 -1.49442157 -1.17810685  0.12911434 -1.11706045
    + [9]  1.48058442 -1.45697663 -1.34622933  0.47134893 -0.35400617  0.47785922 -0.84360860  0.07407137
    +[17]  0.20576332 -0.02440903  1.55121390 -1.13264009  0.25622827  1.50609562 -0.34694679  0.68369074
    +[25]  2.22868221 -2.46728704 -1.17981619 -1.72301706  1.14342795  1.30303134
    y
    -
     [1]  1.39715981 -2.11016222  0.63900299  0.18670497 -1.54618112  1.03595654 -0.30111533  1.65442302
    - [9]  0.14425990  0.93479356  2.33905352 -0.99020227  0.62668760  0.65415906  0.57457684 -0.39291755
    -[17] -0.04936992  0.69847278  0.34975786 -0.63999332 -1.41238189  0.48037647 -0.53009461 -1.92062571
    -[25] -0.82002411 -0.42679018  0.13746774  0.93024766  1.15204079  0.35412667
    +
     [1]  0.98875528 -0.24958662  1.49300814  0.10615538  0.09800274  1.56793593 -0.22914571 -0.57560307
    + [9] -0.27190217 -0.47277958 -0.85909785 -0.92878728 -1.49738888  0.28388141 -0.19927372 -0.03446519
    +[17]  1.33417521 -0.88644545  0.61378744  0.25970544 -2.20140714  0.82976825  0.90134689 -1.38486252
    +[25]  0.35631293 -1.82481406 -0.45770061  1.08515103 -0.28190320  1.16650470
    plot( x, y )
    -

    +

    h <- cor.test( x, y )
     h
    
         Pearson's product-moment correlation
     
     data:  x and y
    -t = -0.2997, df = 28, p-value = 0.7666
    +t = 0.61284, df = 28, p-value = 0.5449
     alternative hypothesis: true correlation is not equal to 0
     95 percent confidence interval:
    - -0.4084940  0.3100388
    + -0.2558264  0.4563988
     sample estimates:
    -        cor 
    --0.05654657 
    + cor +0.1150463
    names( h )
    [1] "statistic"   "parameter"   "p.value"     "estimate"    "null.value"  "alternative" "method"     
     [8] "data.name"   "conf.int"   
    h[[ 'estimate' ]]
    -
            cor 
    --0.05654657 
    +
          cor 
    +0.1150463 
    h[[ 'p.value' ]]
    -
    [1] 0.7666227
    +
    [1] 0.5449302
    1. A nested list.
      Let’s extend the concept of scores to describe various topics (see the code below).
      diff --git a/docs/Boerhaave_202110/S05L02l_basic_formulas0.html b/docs/Boerhaave_202110/S05L02l_basic_formulas0.html index e455cef..05d3710 100644 --- a/docs/Boerhaave_202110/S05L02l_basic_formulas0.html +++ b/docs/Boerhaave_202110/S05L02l_basic_formulas0.html @@ -313,7 +313,7 @@

      Formula concept

      form <- y ~ x
       form
      y ~ x
      -<environment: 0x7fb4cc9af818>
      +<environment: 0x7f826165ef48>
      class(form)
      [1] "formula"
    diff --git a/docs/Boerhaave_202110/S05L03s_basic_matrices0.tasks.code.html b/docs/Boerhaave_202110/S05L03s_basic_matrices0.tasks.code.html index 71b6f88..dc05401 100644 --- a/docs/Boerhaave_202110/S05L03s_basic_matrices0.tasks.code.html +++ b/docs/Boerhaave_202110/S05L03s_basic_matrices0.tasks.code.html @@ -330,7 +330,7 @@

    Extra exercises

    x <- rnorm( size ) x1 <- x + rnorm( size )/10 cor( x, x1 ) -
    [1] 0.9945626
    +
    [1] 0.9890689
    x2 <- x + rnorm( size )/10
     x3 <- x + rnorm( size )/10
      @@ -346,15 +346,15 @@

      Extra exercises

      class( m )
      [1] "matrix"
      head( m )
      -
                    x1         x2          x3
      -[1,]  1.51389746  1.5155970  1.50477891
      -[2,]  1.40798870  1.3655355  1.59466327
      -[3,] -0.95050756 -1.0012282 -0.89029233
      -[4,]  0.13175349  0.1478531  0.02385004
      -[5,] -0.72146922 -0.6779397 -0.65452518
      -[6,]  0.09893675 -0.1702900  0.06815393
      +
                    x1          x2         x3
      +[1,] -0.76433889 -0.60614083 -0.7693876
      +[2,]  0.68720721  0.57542798  0.7446180
      +[3,]  0.16578338 -0.01080554  0.2716051
      +[4,] -1.22105496 -1.20189261 -1.2121575
      +[5,] -1.23579559 -0.93584988 -1.0846577
      +[6,] -0.08967726 -0.11371192 -0.2078300
      heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
      -

      +

                      # x1, x2, x3 follow similar color pattern, they should be correlated
      1. Matrix of correlated and uncorrelated vectors.
        @@ -369,7 +369,7 @@

        Extra exercises

        y4 <- y + rnorm( size )/10 m <- cbind( y4, y3, x2, y1, x1, x3, y2 ) heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow -

        +

        1. Matrix of correlations.
          Use cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.
          @@ -378,16 +378,16 @@

          Extra exercises

        cc <- cor( m )
         round( cc, 3 ) # 
        -
               y4     y3     x2     y1     x1     x3     y2
        -y4  1.000  0.979 -0.187  0.967 -0.186 -0.239  0.973
        -y3  0.979  1.000 -0.143  0.970 -0.132 -0.189  0.972
        -x2 -0.187 -0.143  1.000 -0.163  0.994  0.991 -0.211
        -y1  0.967  0.970 -0.163  1.000 -0.158 -0.216  0.981
        -x1 -0.186 -0.132  0.994 -0.158  1.000  0.994 -0.210
        -x3 -0.239 -0.189  0.991 -0.216  0.994  1.000 -0.258
        -y2  0.973  0.972 -0.211  0.981 -0.210 -0.258  1.000
        +
              y4    y3    x2    y1    x1    x3    y2
        +y4 1.000 0.974 0.118 0.991 0.135 0.206 0.985
        +y3 0.974 1.000 0.244 0.990 0.251 0.334 0.984
        +x2 0.118 0.244 1.000 0.156 0.979 0.979 0.202
        +y1 0.991 0.990 0.156 1.000 0.170 0.245 0.986
        +x1 0.135 0.251 0.979 0.170 1.000 0.985 0.201
        +x3 0.206 0.334 0.979 0.245 0.985 1.000 0.281
        +y2 0.985 0.984 0.202 0.986 0.201 0.281 1.000
        heatmap( cc, symm = TRUE, scale = "none" )
        -

        +

        # E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
         # Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
         # Correlations between x, x vectors are close to 1.0.
        diff --git a/docs/Boerhaave_202110/S06L04l_ggplot_geoms0.html b/docs/Boerhaave_202110/S06L04l_ggplot_geoms0.html
        index 26aad50..34ef947 100644
        --- a/docs/Boerhaave_202110/S06L04l_ggplot_geoms0.html
        +++ b/docs/Boerhaave_202110/S06L04l_ggplot_geoms0.html
        @@ -299,7 +299,7 @@ 

        Plot types

        Generally speaking, ggplot2 geoms specify plot types.
        Each geom produces a plot layer and multiple layers can be combined.
        @@ -369,7 +369,7 @@

        Boxplots

        geom_jitter( color = "red", height = 0, width = 0.1, alpha = 0.5 )
        Warning: Removed 1 rows containing non-finite values (stat_boxplot).
        Warning: Removed 1 rows containing missing values (geom_point).
        -

        +

        In some contexts violin plots may be more useful than the boxplots.
        Replace geom_boxplot with geom_violin:

        ggplot( pulse ) +
        @@ -378,7 +378,7 @@ 

        Boxplots

        geom_jitter( color = "red", height = 0, width = 0.1, alpha = 0.5 )
        Warning: Removed 1 rows containing non-finite values (stat_ydensity).
        Warning: Removed 1 rows containing missing values (geom_point).
        -

        +

        This can be the final combination of a boxplot, violin plot, and points plot with extra horizontal noise:

        ggplot( pulse ) +
           aes( x = ran, y = pulse2 ) +
        @@ -388,7 +388,7 @@ 

        Boxplots

        Warning: Removed 1 rows containing non-finite values (stat_ydensity).
        Warning: Removed 1 rows containing non-finite values (stat_boxplot).
        Warning: Removed 1 rows containing missing values (geom_point).
        -

        +

        A plot of texts

        @@ -400,7 +400,7 @@

        A plot of texts

        Warning: Removed 1 rows containing missing values (geom_text).

        diff --git a/docs/Boerhaave_202110/S07L01l_dplyr_join0.html b/docs/Boerhaave_202110/S07L01l_dplyr_join0.html index 15a43be..5d8fcda 100644 --- a/docs/Boerhaave_202110/S07L01l_dplyr_join0.html +++ b/docs/Boerhaave_202110/S07L01l_dplyr_join0.html @@ -11,7 +11,7 @@ -Merging/joining tables +Joining tables @@ -293,7 +293,7 @@ -

        Merging/joining tables

        +

        Joining tables

        @@ -304,7 +304,11 @@

        Merging/joining tables

        Combine tibbles

        -

        *_join(...) is a family of functions for combining two tibbles on common variable(s) called key. For example, take a group of people (n=7) for which we have collected age and height data:

        +

        *_join(...) is a family of functions for combining two tibbles on common variable(s) called key.

        +
        + +
        +

        For example, take a group of people (n=7) for which we have collected age and height data:

        # height 
         df1 <- tibble(name = c("Isa","Jaylinn","Mila","Milas","Yara"),
                    height = c(160, 172, 182, 157, 162)) 
        diff --git a/docs/Boerhaave_202110/S07L01p_dplyr_join0.tasks.nocode.html b/docs/Boerhaave_202110/S07L01p_dplyr_join0.tasks.nocode.html
        index c132ecb..fc6fcc3 100644
        --- a/docs/Boerhaave_202110/S07L01p_dplyr_join0.tasks.nocode.html
        +++ b/docs/Boerhaave_202110/S07L01p_dplyr_join0.tasks.nocode.html
        @@ -11,7 +11,7 @@
         
         
         
        -Merging/joining tables (practice)
        +Joining tables (practice)
         
         
         
        @@ -293,7 +293,7 @@
         
         
         
        -

        Merging/joining tables (practice)

        +

        Joining tables (practice)

        @@ -320,6 +320,7 @@

        Extra exercises

        1. The name and span2 were not good combinations as a key, show the rows with identical name and span2 values.

        2. Explain the result if you use only the name variable as the key in the primary exercise (1) .

        3. +
        4. In the previous exercise, using only name as the key, we obtained 78 ‘excess’ observations due to non-unique key name. Calculate this number without applying the join.

        5. Recall Data manipulation 1/3, Tibble exercise 4.2, where you were asked to add a new variable height. Repeat the exercise by using a join functions.

        @@ -453,6 +453,18 @@

        Extra exercises

        name_key_row_count - name_age_key_row_count # number of additional rows due to non-unique key 'name'
        [1] 78
          +
        1. In the previous exercise, using only name as the key, we obtained 78 ‘excess’ observations due to non-unique key name. Calculate this number without applying the join.
        2. +
        +
        survey %>%  
        +  count(name) %>%               # count occurrences of 'name' (n)
        +  filter(n>1) %>%               # filter non-unique 'name' (n>1)
        +  mutate(excess=(n*n)-n) %>%    # excess per observation : product minus exact match
        +  summarise(excess=sum(excess)) # total excess
        +
        # A tibble: 1 x 1
        +  excess
        +   <int>
        +1     78
        +
        1. Recall Data manipulation 1/3, Tibble exercise 4.2, where you were asked to add a new variable height. Repeat the exercise by using a join functions.
        favourite_colour1  <- tibble(name=c("Lucas","Lotte","Noa","Wim"), 
        diff --git a/docs/Boerhaave_202110/S07L02l_tidyr_reshape0.html b/docs/Boerhaave_202110/S07L02l_tidyr_reshape0.html
        index 52885ad..583c8ce 100644
        --- a/docs/Boerhaave_202110/S07L02l_tidyr_reshape0.html
        +++ b/docs/Boerhaave_202110/S07L02l_tidyr_reshape0.html
        @@ -299,13 +299,17 @@ 

        Reshaping tables

        Change layout of values in a table

        -

        -

        Source: data import cheat sheet : tidyr

        + +

        Source: data import cheat sheet : tidyr

        Two main functions to manipulate the layout of the table, pivot_longer transforms the table from wide to long format and pivot_wider, which does the opposite, converts the table from long to wide format.

        pivot_longer : wide to long format

        @@ -395,8 +399,30 @@

        pivot_wider : long to wide format


        + +
        +

        ggplot and reshaping

        +

        Often you will need to reshape your data for ggplot visualisations. For example we would like to compare pulse1 and pulse2 variables with a boxplot. You may plot pulse1 and pulse2 in separate plots one after the other:

        +
        require(gridExtra)
        +
        +bp1 <- ggplot(pulse %>% drop_na()) +  # remove missing
        +  aes(y=pulse1) +
        +  geom_boxplot()
        +bp2 <- ggplot(pulse %>% drop_na()) +  # remove missing
        +  aes(y=pulse2) +
        +  geom_boxplot()
        +grid.arrange(bp1, bp2, ncol=2)
        +

        +

        As you can see the y-scale of each plot is set dynamically and can be misleading. To resolve this, we would need a single plot with aesthetic mapping of x being the categorical variable pulse and y the pulse values as was shown above.

        +
        ggplot(pulse %>% 
        +         drop_na() %>%  # remove missing 
        +         pivot_longer( c(pulse1,pulse2), names_to = "pulse", values_to = "level")
        +       ) +  
        +  aes(x=pulse, y=level) +
        +  geom_boxplot()
        +

        diff --git a/docs/Boerhaave_202110/S07L03l_advanced_user_functions0.html b/docs/Boerhaave_202110/S07L03l_advanced_user_functions0.html index daeeb38..aab1fd8 100644 --- a/docs/Boerhaave_202110/S07L03l_advanced_user_functions0.html +++ b/docs/Boerhaave_202110/S07L03l_advanced_user_functions0.html @@ -424,7 +424,7 @@

        Example 1

        function(x) {
             x + 1
         }
        -<environment: 0x7fb4e07b7520>
        +<environment: 0x7f8253b4ae70>

        What is the class of add_one?

        class( add_one )
        [1] "function"
        diff --git a/docs/Boerhaave_202110/S08L01l_useful_functions0.html b/docs/Boerhaave_202110/S08L01l_useful_functions0.html index eb801ab..7269da3 100644 --- a/docs/Boerhaave_202110/S08L01l_useful_functions0.html +++ b/docs/Boerhaave_202110/S08L01l_useful_functions0.html @@ -306,20 +306,20 @@

        Sandbox vectors

        Let’s define some vectors which can be used for demonstrations:

        manyNumbers <- sample( 1:1000, 20 )
         manyNumbers
        -
         [1] 500 899 100 409 606 309 608 631 721  41 398 561 754 463 706 277 131 888 533 305
        +
         [1] 633 174 212 623 176 527 845 241 462 570 342 644  83 205 969 676 985  50 512 726
        manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
         manyNumbersWithNA
        -
         [1] 899 721  NA 131  NA  41 888 631 500 533 606 305 398 463 100 561  NA 754 608 277 409 309 706
        +
         [1] 462 241  83 985 676 176 633 205 512 726 644 174 623  NA 527  NA 969 845 342  NA 570  50 212
        duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
         duplicatedNumbers
        -
         [1] 1 5 1 5 2 4 1 3 1 5
        +
         [1] 5 3 1 1 5 4 2 2 5 1
        letters
         [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"
        LETTERS
         [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"
        mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
         mixedLetters
        -
         [1] "t" "s" "h" "z" "n" "O" "F" "U" "D" "Q"
        +
         [1] "m" "y" "z" "q" "w" "J" "F" "K" "L" "P"

        Are all/any elements TRUE

        @@ -345,11 +345,11 @@

        Are all/any elements TRUE

        Which elements are TRUE

        Input: logical vector Output: vector of numbers (positions)

        which( manyNumbers > 900 )
        -
        integer(0)
        +
        [1] 15 17
        which( manyNumbersWithNA > 900 )
        -
        integer(0)
        +
        [1]  4 17
        which( is.na( manyNumbersWithNA ) )
        -
        [1]  3  5 17
        +
        [1] 14 16 20

        Filtering vector elements

        @@ -359,12 +359,12 @@

        Filtering vector elements

      2. Note: several ways to get the same effect
      3. manyNumbers[ manyNumbers > 900 ] # indexing by logical vector
        -
        integer(0)
        +
        [1] 969 985
        manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions
        -
        integer(0)
        +
        [1] 969 985
        somePositions <- which( manyNumbers > 900 )
         manyNumbers[ somePositions ]
        -
        integer(0)
        +
        [1] 969 985

        Are some elements among other elements

        @@ -383,16 +383,16 @@

        Are some elements among other elements

        any( mixedLetters %in% LETTERS )
        [1] TRUE
        mixedLetters[ mixedLetters %in% LETTERS ]
        -
        [1] "O" "F" "U" "D" "Q"
        +
        [1] "J" "F" "K" "L" "P"
        mixedLetters[ !( mixedLetters %in% LETTERS ) ]
        -
        [1] "t" "s" "h" "z" "n"
        +
        [1] "m" "y" "z" "q" "w"
        manyNumbers %in% 300:600
        -
         [1]  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
        -[18] FALSE  TRUE  TRUE
        +
         [1] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
        +[18] FALSE  TRUE FALSE
        which( manyNumbers %in% 300:600 )
        -
        [1]  1  4  6 11 12 14 19 20
        +
        [1]  6  9 10 11 19
        sum( manyNumbers %in% 300:600 )
        -
        [1] 8
        +
        [1] 5

        Pick one of two (three) depending on condition

        @@ -402,16 +402,16 @@

        Pick one of two (three) depending on condition

      4. Note: it can take care of NAs
      5. if_else( manyNumbersWithNA >= 500, "large", "small" )
        -
         [1] "large" "large" NA      "small" NA      "small" "large" "large" "large" "large" "large" "small" "small"
        -[14] "small" "small" "large" NA      "large" "large" "small" "small" "small" "large"
        +
         [1] "small" "small" "small" "large" "large" "small" "large" "small" "large" "large" "large" "small" "large"
        +[14] NA      "large" NA      "large" "large" "small" NA      "large" "small" "small"
        if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )
        -
         [1] "large"   "large"   "UNKNOWN" "small"   "UNKNOWN" "small"   "large"   "large"   "large"   "large"  
        -[11] "large"   "small"   "small"   "small"   "small"   "large"   "UNKNOWN" "large"   "large"   "small"  
        -[21] "small"   "small"   "large"  
        +
         [1] "small"   "small"   "small"   "large"   "large"   "small"   "large"   "small"   "large"   "large"  
        +[11] "large"   "small"   "large"   "UNKNOWN" "large"   "UNKNOWN" "large"   "large"   "small"   "UNKNOWN"
        +[21] "large"   "small"   "small"  
        # here integer 0L is needed instead of real 0.0 
         # manyNumbersWithNA contains integer numbers and the method complains
         if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 
        -
         [1] 899 721  NA   0  NA   0 888 631 500 533 606   0   0   0   0 561  NA 754 608   0   0   0 706
        +
         [1]   0   0   0 985 676   0 633   0 512 726 644   0 623  NA 527  NA 969 845   0  NA 570   0   0

        Duplicates and unique elements

        @@ -419,70 +419,70 @@

        Duplicates and unique elements

      6. Input: a vector
      7. unique( duplicatedNumbers )
        -
        [1] 1 5 2 4 3
        +
        [1] 5 3 1 4 2
        unique( c( NA, duplicatedNumbers, NA ) )
        -
        [1] NA  1  5  2  4  3
        +
        [1] NA  5  3  1  4  2
        duplicated( duplicatedNumbers )
        -
         [1] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE
        +
         [1] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE

        Positions of max/min elements

        which.max( manyNumbersWithNA )
        -
        [1] 1
        +
        [1] 4
        manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]
        -
        [1] 899
        +
        [1] 985
        which.min( manyNumbersWithNA )
        -
        [1] 6
        +
        [1] 22
        manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]
        -
        [1] 41
        +
        [1] 50
        range( manyNumbersWithNA, na.rm = TRUE )
        -
        [1]  41 899
        +
        [1]  50 985

        Sorting/ordering of vectors

        manyNumbersWithNA
        -
         [1] 899 721  NA 131  NA  41 888 631 500 533 606 305 398 463 100 561  NA 754 608 277 409 309 706
        +
         [1] 462 241  83 985 676 176 633 205 512 726 644 174 623  NA 527  NA 969 845 342  NA 570  50 212
        sort( manyNumbersWithNA )
        -
         [1]  41 100 131 277 305 309 398 409 463 500 533 561 606 608 631 706 721 754 888 899
        +
         [1]  50  83 174 176 205 212 241 342 462 512 527 570 623 633 644 676 726 845 969 985
        sort( manyNumbersWithNA, na.last = TRUE )
        -
         [1]  41 100 131 277 305 309 398 409 463 500 533 561 606 608 631 706 721 754 888 899  NA  NA  NA
        +
         [1]  50  83 174 176 205 212 241 342 462 512 527 570 623 633 644 676 726 845 969 985  NA  NA  NA
        sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )
        -
         [1] 899 888 754 721 706 631 608 606 561 533 500 463 409 398 309 305 277 131 100  41  NA  NA  NA
        +
         [1] 985 969 845 726 676 644 633 623 570 527 512 462 342 241 212 205 176 174  83  50  NA  NA  NA
        manyNumbersWithNA[1:5]
        -
        [1] 899 721  NA 131  NA
        +
        [1] 462 241  83 985 676
        order( manyNumbersWithNA[1:5] )
        -
        [1] 4 2 1 3 5
        +
        [1] 3 2 1 5 4
        rank( manyNumbersWithNA[1:5] )
        -
        [1] 3 2 4 1 5
        +
        [1] 3 2 1 5 4
        sort( mixedLetters )
        -
         [1] "D" "F" "h" "n" "O" "Q" "s" "t" "U" "z"
        +
         [1] "F" "J" "K" "L" "m" "P" "q" "w" "y" "z"

        Ranking of vectors

        manyDuplicates <- sample( 10:15, 10, replace = TRUE )
         rank( manyDuplicates )
        -
         [1]  8.0  1.5  4.5  4.5  4.5  8.0  1.5  8.0  4.5 10.0
        +
         [1] 2.0 5.0 7.5 5.0 9.5 5.0 2.0 9.5 2.0 7.5
        rank( manyDuplicates, ties.method = "min" )
        -
         [1]  7  1  3  3  3  7  1  7  3 10
        +
         [1] 1 4 7 4 9 4 1 9 1 7
        rank( manyDuplicates, ties.method = "random" )
        -
         [1]  9  2  5  4  6  8  1  7  3 10
        +
         [1]  1  6  8  5  9  4  2 10  3  7

        Rounding numbers

        v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
         v
        -
         [1] -1.00000000 -0.50000000  0.00000000  0.50000000  1.00000000  1.75914738 -0.14215858 -0.49714693
        - [9]  1.17914620 -0.45587778 -1.86398126 -2.34252198  0.72980966  0.07787878 -2.02964882
        +
         [1] -1.00000000 -0.50000000  0.00000000  0.50000000  1.00000000 -0.55700252  0.28980461  0.02789515
        + [9]  0.27340777  0.43334730 -0.28217503 -0.30836190 -0.48783588 -0.86480465  0.88774686
        round( v, 0 )
        -
         [1] -1  0  0  0  1  2  0  0  1  0 -2 -2  1  0 -2
        +
         [1] -1  0  0  0  1 -1  0  0  0  0  0  0  0 -1  1
        round( v, 1 )
        -
         [1] -1.0 -0.5  0.0  0.5  1.0  1.8 -0.1 -0.5  1.2 -0.5 -1.9 -2.3  0.7  0.1 -2.0
        +
         [1] -1.0 -0.5  0.0  0.5  1.0 -0.6  0.3  0.0  0.3  0.4 -0.3 -0.3 -0.5 -0.9  0.9
        round( v, 2 )
        -
         [1] -1.00 -0.50  0.00  0.50  1.00  1.76 -0.14 -0.50  1.18 -0.46 -1.86 -2.34  0.73  0.08 -2.03
        +
         [1] -1.00 -0.50  0.00  0.50  1.00 -0.56  0.29  0.03  0.27  0.43 -0.28 -0.31 -0.49 -0.86  0.89
        floor( v )
        -
         [1] -1 -1  0  0  1  1 -1 -1  1 -1 -2 -3  0  0 -3
        +
         [1] -1 -1  0  0  1 -1  0  0  0  0 -1 -1 -1 -1  0
        ceiling( v )
        -
         [1] -1  0  0  1  1  2  0  0  2  0 -1 -2  1  1 -2
        +
         [1] -1  0  0  1  1  0  1  1  1  1  0  0  0  0  1

        Naming vector elements

        diff --git a/rcourse/_site.yml b/rcourse/_site.yml index 5785b26..cf94643 100644 --- a/rcourse/_site.yml +++ b/rcourse/_site.yml @@ -37,7 +37,7 @@ output: # toc_float: true # toc_collapsed: true include: - after_body: ../../rcourse/footer.html - in_header: ../../rcourse/fold.html - css: ../../rcourse/styles.css + after_body: footer.html + in_header: fold.html + css: styles.css df_print: paged diff --git a/rcourse/config.yml b/rcourse/config.yml index e61ab84..570044d 100644 --- a/rcourse/config.yml +++ b/rcourse/config.yml @@ -40,13 +40,13 @@ slots: slot_label : "Data manipulation 1/3" slot_date : 1 slot_time : "09:00-12:30" - slot_plan: "l60:b15:l60:b15:l60" + slot_plan: "l105:b15:l90" lectures: - "tidyverse0:Tidyverse library:FALSE:15" - "dplyr_tibble0:Tibble:TRUE:45" - "dplyr_select0:Select variables/cols:TRUE:45" - "dplyr_filter0:Filter observations/rows:TRUE:45" - - "dplyr_mutate0:Add/modify variables:TRUE:30" + - "dplyr_mutate0:Add/modify variables:TRUE:45" slot4: slot_label : "Data manipulation 2/3" slot_date : 1 @@ -79,9 +79,9 @@ slots: slot_label : "Data manipulation 3/3 and functions" slot_date : 3 slot_time : "09:00-12:30" - slot_plan: "l45:b15:l45:b15:l45:b15:l30" + slot_plan: "l60:l45:b15:l45:b15:l30" lectures: - - "dplyr_join0:Merging/joining tables:TRUE:45" + - "dplyr_join0:Joining tables:TRUE:60" - "tidyr_reshape0:Reshaping tables:TRUE:45" - "advanced_user_functions0:User-defined functions:TRUE:45" - "flow_control:Flow control (if/else, for):FALSE:30" diff --git a/rcourse/dplyr_group0.tasks.Rmd b/rcourse/dplyr_group0.tasks.Rmd index b2c78fc..eb3762c 100644 --- a/rcourse/dplyr_group0.tasks.Rmd +++ b/rcourse/dplyr_group0.tasks.Rmd @@ -80,7 +80,7 @@ survey %>% filter(gender=="female") %>% group_by(gender,fold) %>% summarise(n=n # survey %>% group_by(gender,fold) %>% summarise(n=n(), .groups = "drop") %>% filter(gender=="female") ``` -1.c most popular name initials in descending order of popularity. +1.c most popular name initials in descending order of popularity. See `?stringr::str_sub` for extracting initials from a character vector. ```{r eval=TRUE} survey %>% mutate(initial=str_sub(name, 1,1)) %>% group_by(initial) %>% summarise(n=n()) %>% arrange(desc(n)) diff --git a/rcourse/dplyr_join0.Rmd b/rcourse/dplyr_join0.Rmd index 7ad1e9e..e6ab1d7 100644 --- a/rcourse/dplyr_join0.Rmd +++ b/rcourse/dplyr_join0.Rmd @@ -3,12 +3,21 @@ title: 'Join' --- ```{r setup, include=FALSE,eval=TRUE} +# library(rooc) +# source("../R/Doc.R") +# source("../R/Globals.R") pulse <- read_pulse() ``` > Combine tibbles -`*_join(...)` is a family of functions for combining two tibbles on common `variable(s)` called `key`. For example, take a group of people (n=7) for which we have collected age and height data: +`*_join(...)` is a family of functions for combining two tibbles on common `variable(s)` called `key`. + +
        +![](images/joins.png) +
        + +For example, take a group of people (n=7) for which we have collected age and height data: ```{r} # height diff --git a/rcourse/dplyr_join0.tasks.Rmd b/rcourse/dplyr_join0.tasks.Rmd index f2c6cfa..e322a89 100644 --- a/rcourse/dplyr_join0.tasks.Rmd +++ b/rcourse/dplyr_join0.tasks.Rmd @@ -128,6 +128,17 @@ name_key_row_count name_key_row_count - name_age_key_row_count # number of additional rows due to non-unique key 'name' ``` +3. In the previous exercise, using only `name` as the key, we obtained 78 'excess' observations due to non-unique key `name`. Calculate this number without applying the join. + +```{r} +survey %>% + count(name) %>% # count occurrences of 'name' (n) + filter(n>1) %>% # filter non-unique 'name' (n>1) + mutate(excess=(n*n)-n) %>% # excess per observation : product minus exact match + summarise(excess=sum(excess)) # total excess +``` + + ```{r, eval=FALSE, echo=FALSE} # Maybe another time # @@ -141,7 +152,7 @@ freq_non_uniq_names sum(freq_non_uniq_names*freq_non_uniq_names - freq_non_uniq_names) ``` -3. Recall `Data manipulation 1/3, Tibble exercise 4.2`, where you were asked to add a new variable `height`. Repeat the exercise by using a `join` functions. +4. Recall `Data manipulation 1/3, Tibble exercise 4.2`, where you were asked to add a new variable `height`. Repeat the exercise by using a `join` functions. ```{r} diff --git a/rcourse/dplyr_pipe0.Rmd b/rcourse/dplyr_pipe0.Rmd index 898ea8b..721190e 100644 --- a/rcourse/dplyr_pipe0.Rmd +++ b/rcourse/dplyr_pipe0.Rmd @@ -3,9 +3,6 @@ title: 'Pipe operator %>%' --- ```{r setup,include=FALSE,eval=TRUE} -# library(rooc) -# source("../R/Doc.R") -# source("../R/Globals.R") pulse <- read_pulse() survey <- read_survey() ``` diff --git a/rcourse/images/gather-spread.png b/rcourse/images/gather-spread.png deleted file mode 100644 index e6137ca61adbb55fd03b0fa4026e19f407388d62..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 132897 zcma&OWmFu`w(t!M?u6hD2@-5@cSuNrhTzU%!F6zV21_6?xCIFA5?lxO1lJ)r1lJ(% z@IU9Cd)IUChnKZ_O?Q{?+Eu-)_OFOiQ+b1jO^JGZ8+j8F5h} zE<^`>2}i;}{60fm@;NB~6$;SG0sPlJ^7>Om84bC&ZxO#5AQuY@2UlxHH#-q)Fe0m& zPak#Mbd;4uL5}uZrsj@j7F?e8PESdY#63k3xAqopru3fnb`Gwho)V1z#1KW?KfUH= zr2i+1o2>++j4Xf|=Px3Ev+5!Tz40_pphhE-cNu%Dg!&d+QRSI}jHkI8p(pC+9C9_D 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tidyr](https://github.com/rstudio/cheatsheets/raw/master/tidyr.pdf) -*Source:* [data import cheat sheet : tidyr](https://www.rstudio.com/resources/cheatsheets/) Two main functions to manipulate the layout of the table, `pivot_longer` transforms the table from wide to long format and `pivot_wider`, which does the opposite, converts the table from long to wide format. @@ -54,6 +63,7 @@ Let's reshape the pulse dataset with variables `drug` = {smokes,alcohol} and `us pulse %>% pivot_longer(c(smokes,alcohol), names_to = "drug", values_to = "use") ``` + ## `pivot_wider` : long to wide format ```{r} @@ -77,3 +87,30 @@ qa(msg) ``` +## ggplot and reshaping + +Often you will need to reshape your data for ggplot visualisations. For example we would like to compare `pulse1` and `pulse2` variables with a `boxplot`. You may plot pulse1 and pulse2 in separate plots one after the other: + +```{r, message=FALSE, warning=FALSE} +require(gridExtra) + +bp1 <- ggplot(pulse %>% drop_na()) + # remove missing + aes(y=pulse1) + + geom_boxplot() +bp2 <- ggplot(pulse %>% drop_na()) + # remove missing + aes(y=pulse2) + + geom_boxplot() +grid.arrange(bp1, bp2, ncol=2) +``` + +As you can see the y-scale of each plot is set dynamically and can be misleading. To resolve this, we would need a single plot with aesthetic mapping of `x` being the categorical variable `pulse` and `y` the pulse values as was shown above. + +```{r} +ggplot(pulse %>% + drop_na() %>% # remove missing + pivot_longer( c(pulse1,pulse2), names_to = "pulse", values_to = "level") + ) + + aes(x=pulse, y=level) + + geom_boxplot() +``` + diff --git a/rcourse/tidyr_reshape0.tasks.Rmd b/rcourse/tidyr_reshape0.tasks.Rmd index 4f53de0..7b9cb47 100644 --- a/rcourse/tidyr_reshape0.tasks.Rmd +++ b/rcourse/tidyr_reshape0.tasks.Rmd @@ -1,5 +1,5 @@ --- -title: "Reshape (practice)" +title: "Reshaping tables (practice)" #author: "R. Monajemi" ---