Lecture 8: Tidy evaluation in R#

Note from Firas

Our series of R lectures will be presented by Dr. Tiffany Timbers, the other option co-director of the Vancouver MDS program.

What Metaprogramming lets you do in R#

write library(purrr) instead of library("purrr")
enable plot(x, sin(x)) to automatically label the axes with x and sin(x)
create a model object via lm(y ~ x1 + x2, data = df)
and much much more (that you will see in Data Wrangling as we explore the tidyverse)

What is metaprogramming?#

Code that writes code/code that mutates code.

Our narrow focus on metaprogramming for this course:#

Tidy evaluation

Why focus on tidy evaluation#

In the rest of MDS you will be relying on functions from the tidyverse to do a lot of:

data wrangling
statistics
data visualization

The functions from the tidyverse are beautiful to use interactively:#

library(gapminder)
library(dplyr)
head(gapminder)

Error in library(gapminder): there is no package called ‘gapminder’
Traceback:

1. library(gapminder)

with base r:

gapminder[gapminder$country == "Canada" & gapminder$year == 1952, ]

A tibble: 1 × 6
country	continent	year	lifeExp	pop	gdpPercap
<fct>	<fct>	<int>	<dbl>	<int>	<dbl>
Canada	Americas	1952	68.75	14785584	11367.16

In the tidyverse:

filter(gapminder, country == "Canada", year == 1952)

How does that even work?#

When functions like filter are called, there is a delay in evaluation and the data frame is temporarily promoted as first class objects, we say the data masks the workspace

This is to allow the promotion of the data frame, such that it masks the workspace (global environment)

When this happens, R can then find the relevant columns for the computation

This is referred to as data masking

We can capture expressions and later evaluate the code:#

We will functions from base R and rlang to demonstrate this:

library(rlang)
x <- 10
y <- expr(x)

eval(y)

We can also manipulate the environment code is evaluated in:#

x <- 10
y <- 2
expr(x + y)
eval(expr(x + y), env(x = 1000))

x + y

1002

You can evaluate in a data mask:#

#head(gapminder)

gapminder$gdpPercap * gapminder$pop

Error in eval(expr, envir, enclos): object 'gdpPercap' not found
Traceback:

x <- expr(gdpPercap * pop)
x

gdpPercap * pop

eval(x, gapminder)

6567086329.95223
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6182644080.95383
8637343287.79464
12665833359.6789
20095073782.0045
15305597343.4934
12156859904.4969
3880312374.05489
4425030274.68152
5052880863.15125
5813537083.14685
9370298237.14433
15377328249.8562
19098490175.8022
22121638706.9449
25125105886.174
33512362498.2854
41793958634.6795
50659874994.2062
3065822956.432
3956861606.256
4969774845.486
6930601540.2
7992264610.72771
8287471946.14845
8593512578.67595
8823720129.4891
10148621483.3682
10089784202.2684
11354618752.0137
14931695863.7838
1253572117.47612
1891590901.34375
2255531193.8844
2846372532.11476
4685169626.25753
4553746742.39019
6024110453.88114
6508240904.92827
7422611852.1295
9037850589.82356
8015110972.06478
5782658337.33873

The data mask allows you to mingle variables from an environment and a data frame in a single expression.

y <- 1000
new_exp <- expr(pop / y)

eval(new_exp, gapminder)

8425.333
9240.934
10267.083
11537.966
13079.46
14880.372
12881.816
13867.957
16317.921
22227.415
25268.405
31889.923
1282.697
1476.505
1728.137
1984.06
2263.554
2509.048
2780.097
3075.321
3326.498
3428.038
3508.512
3600.523
9279.525
10270.856
11000.948
12760.499
14760.787
17152.804
20033.753
23254.956
26298.373
29072.015
31287.142
33333.216
4232.095
4561.361
4826.015
5247.469
5894.858
6162.675
7016.384
7874.23
8735.988
9875.024
10866.106
12420.476
17876.956
19610.538
21283.783
22934.225
24779.799
26983.828
29341.374
31620.918
33958.947
36203.463
38331.121
40301.927
8691.212
9712.569
10794.968
11872.264
13177
14074.1
15184.2
16257.249
17481.977
18565.243
19546.792
20434.176
6927.772
6965.86
7129.864
7376.998
7544.201
7568.43
7574.613
7578.903
7914.969
8069.876
8148.312
8199.783
120.447
138.655
171.863
202.182
230.8
297.41
377.967
454.612
529.491
598.561
656.397
708.573
46886.859
51365.468
56839.289
62821.884
70759.295
80428.306
93074.406
103764.241
113704.579
123315.288
135656.79
150448.339
8730.405
8989.111
9218.4
9556.5
9709.1
9821.8
9856.303
9870.2
10045.622
10199.787
10311.97
10392.226
1738.315
1925.173
2151.895
2427.334
2761.407
3168.267
3641.603
4243.788
4981.671
6066.08
7026.113
8078.314
2883.315
3211.738
3593.918
4040.665
4565.872
5079.716
5642.224
6156.369
6893.451
7693.188
8445.134
9119.152
2791
3076
3349
3585
3819
4086
4172.693
4338.977
4256.013
3607
4165.416
4552.198
442.308
474.639
512.764
553.541
619.351
781.472
970.347
1151.184
1342.614
1536.536
1630.347
1639.131
56602.56
65551.171
76039.39
88049.823
100840.058
114313.951
128962.939
142938.076
155975.974
168546.719
179914.212
190010.647
7274.9
7651.254
8012.946
8310.226
8576.2
8797.022
8892.098
8971.958
8658.506
8066.057
7661.799
7322.858
4469.979
4713.416
4919.632
5127.935
5433.886
5889.574
6634.596
7586.551
8878.303
10352.843
12251.209
14326.203
2445.618
2667.518
2961.915
3330.989
3529.983
3834.415
4580.41
5126.023
5809.236
6121.61
7021.078
8390.505
4693.836
5322.536
6083.619
6960.067
7450.606
6978.607
7272.485
8371.791
10150.094
11782.962
12926.707
14131.858
5009.067
5359.923
5793.633
6335.506
7021.028
7959.865
9250.831
10780.667
12467.171
14195.809
15929.988
17696.293
14785.584
17010.154
18985.849
20819.767
22284.5
23796.4
25201.9
26549.7
28523.502
30305.843
31902.268
33390.141
1291.695
1392.284
1523.478
1733.638
1927.26
2167.533
2476.971
2840.009
3265.124
3696.513
4048.013
4369.038
2682.462
2894.855
3150.417
3495.967
3899.068
4388.26
4875.118
5498.955
6429.417
7562.011
8835.739
10238.807
6377.619
7048.426
7961.258
8858.908
9717.524
10599.793
11487.112
12463.354
13572.994
14599.929
15497.046
16284.741
556263.527
637408
665770
754550
862030
943455
1000281
1084035
1164970
1230075
1280400
1318683.096
12350.771
14485.993
17009.885
19764.027
22542.89
25094.412
27764.644
30964.245
34202.721
37657.83
41008.227
44227.55
153.936
170.928
191.689
217.378
250.027
304.739
348.643
395.114
454.429
527.982
614.382
710.96
14100.005
15577.932
17486.434
19941.073
23007.669
26480.87
30646.495
35481.645
41672.143
47798.986
55379.852
64606.759
854.885
940.458
1047.924
1179.76
1340.458
1536.769
1774.735
2064.095
2409.073
2800.947
3328.795
3800.61
926.317
1112.3
1345.187
1588.717
1834.796
2108.457
2424.367
2799.811
3173.216
3518.107
3834.934
4133.884
2977.019
3300
3832.408
4744.87
6071.696
7459.574
9025.951
10761.098
12772.596
14625.967
16252.726
18013.409
3882.229
3991.242
4076.557
4174.366
4225.31
4318.673
4413.368
4484.31
4494.013
4444.595
4481.02
4493.312
6007.797
6640.752
7254.373
8139.332
8831.348
9537.988
9789.224
10239.839
10723.26
10983.007
11226.999
11416.987
9125.183
9513.758
9620.282
9835.109
9862.158
10161.915
10303.704
10311.597
10315.702
10300.707
10256.295
10228.744
4334
4487.831
4646.899
4838.8
4991.596
5088.419
5117.81
5127.024
5171.393
5283.663
5374.693
5468.12
63.149
71.851
89.898
127.617
178.848
228.694
305.991
311.025
384.156
417.908
447.416
496.374
2491.346
2923.186
3453.434
4049.146
4671.329
5302.8
5968.349
6655.297
7351.181
7992.357
8650.322
9319.622
3548.753
4058.385
4681.707
5432.424
6298.651
7278.866
8365.85
9545.158
10748.394
11911.819
12921.234
13755.68
22223.309
25009.741
28173.309
31681.188
34807.417
38783.863
45681.811
52799.062
59402.198
66134.291
73312.559
80264.543
2042.865
2355.805
2747.687
3232.927
3790.903
4282.586
4474.873
4842.194
5274.649
5783.439
6353.681
6939.688
216.964
232.922
249.22
259.864
277.603
192.675
285.483
341.244
387.838
439.971
495.627
551.201
1438.76
1542.611
1666.618
1820.319
2260.187
2512.642
2637.297
2915.959
3668.44
4058.319
4414.865
4906.585
20860.941
22815.614
25145.372
27860.297
30770.372
34617.799
38111.756
42999.53
52088.559
59861.301
67946.797
76511.887
4090.5
4324
4491.443
4605.744
4639.657
4738.902
4826.933
4931.729
5041.039
5134.406
5193.039
5238.46
42459.667
44310.863
47124
49569
51732
53165.019
54433.565
55630.1
57374.179
58623.428
59925.035
61083.916
420.702
434.904
455.661
489.004
537.977
706.367
753.874
880.397
985.739
1126.189
1299.304
1454.867
284.32
323.15
374.02
439.593
517.101
608.274
715.523
848.406
1025.384
1235.767
1457.766
1688.359
69145.952
71019.069
73739.117
76368.453
78717.088
78160.773
78335.266
77718.298
80597.764
82011.073
82350.671
82400.996
5581.001
6391.288
7355.248
8490.213
9354.12
10538.093
11400.338
14168.101
16278.738
18418.288
20550.751
22873.338
7733.25
8096.218
8448.233
8716.441
8888.628
9308.479
9786.48
9974.49
10325.429
10502.372
10603.863
10706.29
3146.381
3640.876
4208.858
4690.773
5149.581
5703.43
6395.63
7326.406
8486.949
9803.875
11178.65
12572.928
2664.249
2876.726
3140.003
3451.418
3811.387
4227.026
4710.497
5650.262
6990.574
8048.834
8807.818
9947.814
580.653
601.095
627.82
601.287
625.361
745.228
825.987
927.524
1050.938
1193.708
1332.459
1472.041
3201.488
3507.701
3880.13
4318.137
4698.301
4908.554
5198.399
5756.203
6326.682
6913.545
7607.651
8502.814
1517.453
1770.39
2090.162
2500.689
2965.146
3055.235
3669.448
4372.203
5077.347
5867.957
6677.328
7483.763
2125.9
2736.3
3305.2
3722.8
4115.7
4583.7
5264.5
5584.51
5829.696
6495.918
6762.476
6980.412
9504
9839
10063
10223.422
10394.091
10637.171
10705.535
10612.74
10348.684
10244.684
10083.313
9956.108
147.962
165.11
182.053
198.676
209.275
221.823
233.997
244.676
259.012
271.192
288.03
301.931
372000
409000
454000
506000
567000
634000
708000
788000
872000
959000
1034172.547
1110396.331
82052
90124
99028
109343
121282
136725
153343
169276
184816
199278
211060
223547
17272
19792
22874
26538
30614
35480.679
43072.751
51889.696
60397.973
63327.987
66907.826
69453.57
5441.766
6248.643
7240.26
8519.282
10061.506
11882.916
14173.318
16543.189
17861.905
20775.703
24001.816
27499.638
2952.156
2878.22
2830
2900.1
3024.4
3271.9
3480
3539.9
3557.761
3667.233
3879.155
4109.086
1620.914
1944.401
2310.904
2693.585
3095.893
3495.918
3858.421
4203.148
4936.55
5531.387
6029.529
6426.679
47666
49182
50843.2
52667.1
54365.564
56059.245
56535.636
56729.703
56840.847
57479.469
57926.999
58147.733
1426.095
1535.09
1665.128
1861.096
1997.616
2156.814
2298.309
2326.606
2378.618
2531.311
2664.659
2780.132
86459.025
91563.009
95831.757
100825.279
107188.273
113872.473
118454.974
122091.325
124329.269
125956.499
127065.841
127467.972
607.914
746.559
933.559
1255.058
1613.551
1937.652
2347.031
2820.042
3867.409
4526.235
5307.47
6053.193
6464.046
7454.779
8678.557
10191.512
12044.785
14500.404
17661.452
21198.082
25020.539
28263.827
31386.842
35610.177
8865.488
9411.381
10917.494
12617.009
14781.241
16325.32
17647.518
19067.554
20711.375
21585.105
22215.365
23301.725
20947.571
22611.552
26420.307
30131
33505
36436
39326
41622
43805.45
46173.816
47969.15
49044.79
160
212.846
358.266
575.003
841.934
1140.357
1497.494
1891.487
1418.095
1765.345
2111.561
2505.559
1439.529
1647.412
1886.848
2186.894
2680.018
3115.787
3086.876
3089.353
3219.994
3430.388
3677.78
3921.278
748.747
813.338
893.143
996.38
1116.779
1251.524
1411.807
1599.2
1803.195
1982.823
2046.772
2012.649
863.308
975.95
1112.796
1279.406
1482.628
1703.617
1956.875
2269.414
1912.974
2200.725
2814.651
3193.942
1019.729
1201.578
1441.863
1759.224
2183.877
2721.783
3344.074
3799.845
4364.501
4759.67
5368.585
6036.914
4762.912
5181.679
5703.324
6334.556
7082.43
8007.166
9171.477
10568.642
12210.395
14165.114
16473.477
19167.654
2917.802
3221.238
3628.608
4147.252
4730.997
5637.246
6502.825
7824.747
10014.249
10419.991
11824.495
13327.079
6748.378
7739.235
8906.385
10154.878
11441.462
12845.381
14441.916
16331.785
18319.502
20476.091
22662.365
24821.286
3838.168
4241.884
4690.372
5212.416
5828.158
6491.649
6998.256
7634.008
8416.215
9384.984
10580.176
12031.795
1022.556
1076.852
1146.757
1230.542
1332.786
1456.688
1622.136
1841.24
2119.465
2444.741
2828.858
3270.065
516.556
609.816
701.016
789.309
851.334
913.025
992.04
1042.663
1096.202
1149.818
1200.206
1250.882
30144.317
35015.548
41121.485
47995.559
55984.294
63759.976
71640.904
80122.492
88111.03
95895.146
102479.927
108700.891
800.663
882.134
1010.28
1149.5
1320.5
1528
1756.032
2015.133
2312.802
2494.803
2674.234
2874.127
413.834
442.829
474.528
501.035
527.678
560.073
562.548
569.473
621.621
692.651
720.23
684.736
9939.217
11406.35
13056.604
14770.296
16660.67
18396.941
20198.73
22987.397
25798.239
28529.501
31167.783
33757.175
6446.316
7038.035
7788.944
8680.909
9809.596
11127.868
12587.223
12891.952
13160.731
16603.334
18473.78
19951.656
20092.996
21731.844
23634.436
25870.271
28466.39
31528.087
34680.442
38028.578
40546.538
43247.867
45598.081
47761.98
485.831
548.08
621.392
706.64
821.782
977.026
1099.01
1278.184
1554.253
1774.766
1972.153
2055.08
9182.536
9682.338
10332.057
11261.69
12412.593
13933.198
15796.314
17917.18
20326.209
23001.113
25873.917
28901.79
10381.988
11026.383
11805.689
12596.822
13329.874
13852.989
14310.401
14665.278
15174.244
15604.464
16122.83
16570.613
1994.794
2229.407
2488.55
2728.15
2929.1
3164.9
3210.65
3317.166
3437.674
3676.187
3908.037
4115.771
1165.79
1358.828
1590.597
1865.49
2182.908
2554.598
2979.423
3344.353
4017.939
4609.572
5146.848
5675.356
3379.468
3692.184
4076.008
4534.062
5060.262
5682.086
6437.188
7332.638
8392.818
9666.252
11140.655
12894.865
33119.096
37173.34
41871.351
47287.752
53740.085
62209.173
73039.376
81551.52
93364.244
106207.839
119901.274
135031.164
3327.728
3491.938
3638.919
3786.019
3933.004
4043.205
4114.787
4186.147
4286.357
4405.672
4535.591
4627.926
507.833
561.977
628.164
714.775
829.05
1004.533
1301.048
1593.882
1915.208
2283.635
2713.462
3204.897
41346.56
46679.944
53100.671
60641.899
69325.921
78152.686
91462.088
105186.881
120065.004
135564.834
153403.524
169270.617
940.08
1063.506
1215.725
1405.486
1616.384
1839.782
2036.305
2253.639
2484.997
2734.531
2990.875
3242.173
1555.876
1770.902
2009.813
2287.985
2614.104
2984.494
3366.439
3886.512
4483.945
5154.123
5884.491
6667.147
8025.7
9146.1
10516.5
12132.2
13954.7
15990.099
18125.129
20195.924
22430.449
24748.122
26769.436
28674.757
22438.691
26072.194
30325.264
35356.6
40850.141
46850.962
53456.774
60017.788
67185.766
75012.988
82995.088
91077.287
25730.551
28235.346
30329.617
31785.378
33039.545
34621.254
36227.381
37740.71
38370.697
38654.957
38625.976
38518.241
8526.05
8817.65
9019.8
9103
8970.45
9662.6
9859.65
9915.289
9927.68
10156.415
10433.867
10642.836
2227
2260
2448.046
2648.961
2847.132
3080.828
3279.001
3444.468
3585.176
3759.43
3859.606
3942.491
257.7
308.7
358.9
414.024
461.633
492.095
517.81
562.035
622.191
684.81
743.981
798.094
16630
17829.327
18680.721
19284.814
20662.648
21658.597
22356.726
22686.371
22797.027
22562.458
22404.337
22276.056
2534.927
2822.082
3051.242
3451.079
3992.121
4657.072
5507.565
6349.365
7290.203
7212.583
7852.401
8860.588
60.011
61.325
65.345
70.787
76.595
86.796
98.593
110.812
125.911
145.608
170.372
199.579
4005.677
4419.65
4943.029
5618.198
6472.756
8128.505
11254.672
14619.745
16945.857
21229.759
24501.53
27601.038
2755.589
3054.547
3430.243
3965.841
4588.696
5260.855
6147.783
7171.347
8307.92
9535.314
10870.037
12267.493
6860.147
7271.135
7616.06
7971.222
8313.288
8686.367
9032.824
9230.783
9826.397
10336.594
10111.559
10150.265
2143.249
2295.678
2467.895
2662.19
2879.013
3140.897
3464.522
3868.905
4260.884
4578.212
5359.092
6144.562
1127
1445.929
1750.2
1977.6
2152.4
2325.3
2651.869
2794.552
3235.865
3802.309
4197.776
4553.009
3558.137
3844.277
4237.384
4442.238
4593.433
4827.803
5048.043
5199.318
5302.888
5383.01
5410.052
5447.502
1489.518
1533.07
1582.962
1646.912
1694.51
1746.919
1861.252
1945.87
1999.21
2011.612
2011.497
2009.245
2526.994
2780.415
3080.153
3428.839
3840.161
4353.666
5828.892
6921.858
6099.799
6633.514
7753.31
9118.773
14264.935
16151.549
18356.657
20997.321
23935.81
27129.932
31140.029
35933.379
39964.159
42835.005
44433.622
43997.828
28549.87
29841.614
31158.061
32850.275
34513.161
36439
37983.31
38880.702
39549.438
39855.442
40152.517
40448.191
7982.342
9128.546
10421.936
11737.396
13016.733
14116.836
15410.151
16495.304
17587.06
18698.655
19576.783
20378.239
8504.667
9753.392
11183.227
12716.129
14597.019
17104.986
20367.053
24725.96
28227.588
32160.729
37090.298
42292.929
290.243
326.741
370.006
420.69
480.105
551.425
649.901
779.348
962.344
1054.486
1130.269
1133.066
7124.673
7363.802
7561.588
7867.931
8122.293
8251.648
8325.26
8421.403
8718.867
8897.619
8954.175
9031.088
4815
5126
5666
6063
6401.4
6316.424
6468.126
6649.942
6995.447
7193.761
7361.757
7554.661
3661.549
4149.908
4834.621
5680.812
6701.172
7932.503
9410.494
11242.847
13219.062
15081.016
17155.814
19314.747
8550.362
10164.215
11918.938
13648.692
15226.039
16785.196
18501.39
19757.799
20686.918
21628.605
22454.239
23174.294
8322.925
9452.826
10863.958
12607.312
14706.593
17129.565
19844.382
23040.63
26605.473
30686.889
34593.779
38139.64
21289.402
25041.917
29263.397
34024.249
39276.153
44148.285
48827.16
52910.342
56667.095
60216.677
62806.748
65068.149
1219.113
1357.445
1528.098
1735.55
2056.351
2308.582
2644.765
3154.264
3747.553
4320.89
4977.378
5701.579
662.85
764.9
887.498
960.155
975.199
1039.009
1116.479
1191.336
1183.669
1138.101
1101.832
1056.608
3647.735
3950.849
4286.552
4786.986
5303.507
6005.061
6734.098
7724.976
8523.077
9231.669
9770.575
10276.158
22235.677
25670.939
29788.695
33411.317
37492.953
42404.033
47328.791
52881.328
58179.144
63047.647
67308.928
71158.647
5824.797
6675.501
7688.797
8900.294
10190.285
11457.758
12939.4
15283.05
18252.19
21210.254
24739.869
29170.398
50430
51430
53292
54959
56079
56179
56339.704
56981.62
57866.349
58808.266
59912.431
60776.238
157553
171984
186538
198712
209896
220239
232187.835
242803.533
256894.189
272911.76
287675.526
301139.947
2252.965
2424.959
2598.466
2748.579
2829.526
2873.52
2953.997
3045.153
3149.262
3262.838
3363.085
3447.496
5439.568
6702.668
8143.375
9709.552
11515.649
13503.563
15620.766
17910.182
20265.563
22374.398
24287.67
26084.662
26246.839
28998.543
33796.14
39463.91
44655.014
50533.506
56142.181
62826.491
69940.728
76048.996
80908.147
85262.356
1030.585
1070.439
1133.134
1142.636
1089.572
1261.091
1425.876
1691.21
2104.779
2826.046
3389.578
4018.332
4963.829
5498.09
6120.081
6740.785
7407.075
8403.99
9657.618
11219.34
13367.997
15826.497
18701.257
22211.743
2672
3016
3421
3900
4506.497
5216.55
6100.407
7272.406
8381.163
9417.789
10595.811
11746.035
3080.907
3646.34
4277.736
4995.432
5861.135
6642.107
7636.524
9216.418
10704.34
11404.948
11926.563
12311.143

Back to our example:#

What is going on here?

code evaluation is delayed
the filter function quotes columns country and year
the filter function then creates a data mask (to mingle variables from the environment and the data frame)
the columns country and year and unquoted and evaluated within the data mask

filter(gapminder, country == "Canada", year == 1952)

Trade off of lovely interactivity of tidyverse functions…#

programming with them can be more challenging.#

Let’s try writing a function which wraps filter for gapminder:

#filter(gapminder, country == "Canada")

filter_gap <- function(col, val) {
    filter(gapminder, col == val)
}

filter_gap(country, "Canada")

Error: object 'country' not found
Traceback:

1. filter_gap(country, "Canada")
2. filter(gapminder, col == val)   # at line 4 of file <text>
3. filter.tbl_df(gapminder, col == val)
4. filter_impl(.data, quo)

Why does filter work with non-quoted variable names, but our function filter_gap fail?

Proper way of defining this function:#

Use enquo to quote the column names, and then !! to unquote them in context.

filter_gap <- function(col, val) {
    col <- enquo(col)
    filter(gapminder, !!col == val)
}

filter_gap(country, "Canada")

A tibble: 12 × 6
country	continent	year	lifeExp	pop	gdpPercap
<fct>	<fct>	<int>	<dbl>	<int>	<dbl>
Canada	Americas	1952	68.750	14785584	11367.16
Canada	Americas	1957	69.960	17010154	12489.95
Canada	Americas	1962	71.300	18985849	13462.49
Canada	Americas	1967	72.130	20819767	16076.59
Canada	Americas	1972	72.880	22284500	18970.57
Canada	Americas	1977	74.210	23796400	22090.88
Canada	Americas	1982	75.760	25201900	22898.79
Canada	Americas	1987	76.860	26549700	26626.52
Canada	Americas	1992	77.950	28523502	26342.88
Canada	Americas	1997	78.610	30305843	28954.93
Canada	Americas	2002	79.770	31902268	33328.97
Canada	Americas	2007	80.653	33390141	36319.24

Evaluating functions and quoting functions in R#

differ in the way they get their arguments

evaluating functions take arguments as values

a quoting function is not passed the value of an expression, it is passed the expression itself

Evaluating functions#

take arguments as values:

identity(6)

identity(2 * 3)

a <- 2
b <- 3
identity(a * b)

Quoting functions#

take the expression itself, not the value

typeof(quote(6))

'double'

typeof(quote(2 * 3))

'language'

typeof(quote(a * b))
identity(quote(a * b))

'language'

a * b

You get the code! Not the value!

Not always one or the other:#

In practice some functions take both arguments that are evaluated and quoted, for example:

select(iris, Species)

Here iris is an evaluated argument, and Species is a quoted argument.

How can you tell if an argument is quoted?#

The argument will not work correctly outside of its original context and ordinary indirect references do not work, some examples:

library(dplyr)

temp <- dplyr

Error in eval(expr, envir, enclos): object 'dplyr' not found
Traceback:

temp <- "dplyr"
library(temp)

Error in library(temp): there is no package called ‘temp’
Traceback:

1. library(temp)

We get these errors because there is no dplyr object for R to find, and temp is interpreted by library directly as a package name rather than as an indirect reference.

filter(mtcars, cyl == 4)

temp <- cyl == 4

Error in eval(expr, envir, enclos): object 'cyl' not found
Traceback:

temp <- "cyl" == 4
filter(mtcars, temp)

A data.frame: 0 × 11
mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>

sum(mtcars2$am)

temp <- mtcars$am

sum(temp)

It worked! sum() is an evaluating function and the indirect reference was resolved in the ordinary way.

R cannot find cyl because we haven’t specified where to find it. This object exists only inside the mtcars data frame. And then when we put temp in the subset function it tries to use temp as a column name but it doesn’t exist and so we get nothing returned.

Challenge 1#

Which of the function arguments in the function call below are quoted? Which are evaluated?

arrange(mtcars, cyl)

Let’s try some quoting and unquoting so we can use indirect references in a dplyr function:#

Unquoting is accomplished using the !! (pronounced “bang bang”) operator.

col1 <- quote(country)
val1 <- "Canada"
col2 <- quote(year)
val2 <- 2007

typeof(col1)
col2

'symbol'

'name'

year

filter(gapminder, !!col1 == val1, !!col2 == val2)

A tibble: 1 × 6
country	continent	year	lifeExp	pop	gdpPercap
<fct>	<fct>	<int>	<dbl>	<int>	<dbl>
Canada	Americas	2007	80.653	33390141	36319.24

Viewing the unquoted expression:#

The qq_show function from the rlang package performs unquoting and prints the result to the screen:

qq_show(filter(gapminder, !!col1 == val1, !!col2 == val2))

filter(gapminder, country == val1, year == val2)

Challenge 2#

Re-write the code below using quoting and unquoting so that you can create two variables, var_1 and var_2, to indirectly reference the column names in this function call. Also use rlang::qq_show to check your expresion.

arrange(mtcars, hp, mpg)

var_1 <- quote(hp)
var_2 <- quote(mpg)

qq_show(arrange(mtcars, !!var_1, !!var_2))

arrange(mtcars, hp, mpg)

`enquo` vs `quote` when writing a function#

quote quotes what you typed

enquo quotes what your user typed (i.e., it makes a function argument automatically quote its input)

filter_gap <- function(col, val) {
    col <- enquo(col)
    filter(gapminder, !!col == val)
}

filter_gap(country, "Canada")

A tibble: 12 × 6
country	continent	year	lifeExp	pop	gdpPercap
<fct>	<fct>	<int>	<dbl>	<int>	<dbl>
Canada	Americas	1952	68.750	14785584	11367.16
Canada	Americas	1957	69.960	17010154	12489.95
Canada	Americas	1962	71.300	18985849	13462.49
Canada	Americas	1967	72.130	20819767	16076.59
Canada	Americas	1972	72.880	22284500	18970.57
Canada	Americas	1977	74.210	23796400	22090.88
Canada	Americas	1982	75.760	25201900	22898.79
Canada	Americas	1987	76.860	26549700	26626.52
Canada	Americas	1992	77.950	28523502	26342.88
Canada	Americas	1997	78.610	30305843	28954.93
Canada	Americas	2002	79.770	31902268	33328.97
Canada	Americas	2007	80.653	33390141	36319.24

Quote - unquote pattern all in one step: interpolation#

In the newest release of rlang, there has been the introduction of the {{ (pronounced “curly curly”) operator.
Does the same thing as enguo and !! but (hopefully) easier to use.

filter_gap <- function(col, val) {
    filter(gapminder, {{col}} == val)
}

filter_gap(country, "Canada")

A tibble: 12 × 6
country	continent	year	lifeExp	pop	gdpPercap
<fct>	<fct>	<int>	<dbl>	<int>	<dbl>
Canada	Americas	1952	68.750	14785584	11367.16
Canada	Americas	1957	69.960	17010154	12489.95
Canada	Americas	1962	71.300	18985849	13462.49
Canada	Americas	1967	72.130	20819767	16076.59
Canada	Americas	1972	72.880	22284500	18970.57
Canada	Americas	1977	74.210	23796400	22090.88
Canada	Americas	1982	75.760	25201900	22898.79
Canada	Americas	1987	76.860	26549700	26626.52
Canada	Americas	1992	77.950	28523502	26342.88
Canada	Americas	1997	78.610	30305843	28954.93
Canada	Americas	2002	79.770	31902268	33328.97
Canada	Americas	2007	80.653	33390141	36319.24

Creating functions that handle unquoted column names:#

Or do it all in one step with the brand new curly curly {{ operator:

filter_gap <- function(col, val) {
    filter(gapminder, {{col}} == val)
}

filter_gap(country, "Canada")

Creating functions that handle column names as strings:#

Sometimes you want to pass a column name into a function as a string (often useful when you are programming and have the column names as a character vector).

You can do this by using symbols + unquoting with sym + !! :

# example of what we want to wrap: filter(gapminder, country == "Canada")
filter_gap <- function(col, val) {
    col <- sym(col)
    filter(gapminder, !!col == val)
}

filter_gap("country", "Canada")

Another operator is needed when assigning values…#

: is needed before the = when quoting and unquoting:

library(rlang)

r <- quote(c(1, 2, 3))
eval(r)
{{c(1, 2, 3)}}

?eval_tidy

x <- quote(mpg)
typeof(x)
select(mtcars, !!x)

'symbol'

A data.frame: 32 × 1
	mpg
	<dbl>
Mazda RX4	21.0
Mazda RX4 Wag	21.0
Datsun 710	22.8
Hornet 4 Drive	21.4
Hornet Sportabout	18.7
Valiant	18.1
Duster 360	14.3
Merc 240D	24.4
Merc 230	22.8
Merc 280	19.2
Merc 280C	17.8
Merc 450SE	16.4
Merc 450SL	17.3
Merc 450SLC	15.2
Cadillac Fleetwood	10.4
Lincoln Continental	10.4
Chrysler Imperial	14.7
Fiat 128	32.4
Honda Civic	30.4
Toyota Corolla	33.9
Toyota Corona	21.5
Dodge Challenger	15.5
AMC Javelin	15.2
Camaro Z28	13.3
Pontiac Firebird	19.2
Fiat X1-9	27.3
Porsche 914-2	26.0
Lotus Europa	30.4
Ford Pantera L	15.8
Ferrari Dino	19.7
Maserati Bora	15.0
Volvo 142E	21.4

old_col <- quote(mpg)
new_col <- quote(kml)

mutate(mtcars, !!new_col := !!old_col * 0.425144)

#mutate(mtcars, mpg = mpg * 0.425144)

A data.frame: 32 × 12
mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb	kml
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4	8.928024
21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4	8.928024
22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1	9.693283
21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1	9.098082
18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2	7.950193
18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1	7.695106
14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4	6.079559
24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2	10.373514
22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2	9.693283
19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4	8.162765
17.8	6	167.6	123	3.92	3.440	18.90	1	0	4	4	7.567563
16.4	8	275.8	180	3.07	4.070	17.40	0	0	3	3	6.972362
17.3	8	275.8	180	3.07	3.730	17.60	0	0	3	3	7.354991
15.2	8	275.8	180	3.07	3.780	18.00	0	0	3	3	6.462189
10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4	4.421498
10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4	4.421498
14.7	8	440.0	230	3.23	5.345	17.42	0	0	3	4	6.249617
32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1	13.774666
30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2	12.924378
33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1	14.412382
21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1	9.140596
15.5	8	318.0	150	2.76	3.520	16.87	0	0	3	2	6.589732
15.2	8	304.0	150	3.15	3.435	17.30	0	0	3	2	6.462189
13.3	8	350.0	245	3.73	3.840	15.41	0	0	3	4	5.654415
19.2	8	400.0	175	3.08	3.845	17.05	0	0	3	2	8.162765
27.3	4	79.0	66	4.08	1.935	18.90	1	1	4	1	11.606431
26.0	4	120.3	91	4.43	2.140	16.70	0	1	5	2	11.053744
30.4	4	95.1	113	3.77	1.513	16.90	1	1	5	2	12.924378
15.8	8	351.0	264	4.22	3.170	14.50	0	1	5	4	6.717275
19.7	6	145.0	175	3.62	2.770	15.50	0	1	5	6	8.375337
15.0	8	301.0	335	3.54	3.570	14.60	0	1	5	8	6.377160
21.4	4	121.0	109	4.11	2.780	18.60	1	1	4	2	9.098082

old_col <- quote(mpg)
new_col <- quote(kml)

mutate(mtcars, !!new_col := !!old_col * 0.425144)

Pass the dots when you can#

If you are only passing on variable to a tidyverse function, and that variable is not used in logical comparisons, or in variable assignment, you can get away with passing the dots:

sort_gap <- function(...) {
    arrange(gapminder, ...)
}

sort_gap(year)

A tibble: 1704 × 6
country	continent	year	lifeExp	pop	gdpPercap
<fct>	<fct>	<int>	<dbl>	<int>	<dbl>
Afghanistan	Asia	1952	28.801	8425333	779.4453
Albania	Europe	1952	55.230	1282697	1601.0561
Algeria	Africa	1952	43.077	9279525	2449.0082
Angola	Africa	1952	30.015	4232095	3520.6103
Argentina	Americas	1952	62.485	17876956	5911.3151
Australia	Oceania	1952	69.120	8691212	10039.5956
Austria	Europe	1952	66.800	6927772	6137.0765
Bahrain	Asia	1952	50.939	120447	9867.0848
Bangladesh	Asia	1952	37.484	46886859	684.2442
Belgium	Europe	1952	68.000	8730405	8343.1051
Benin	Africa	1952	38.223	1738315	1062.7522
Bolivia	Americas	1952	40.414	2883315	2677.3263
Bosnia and Herzegovina	Europe	1952	53.820	2791000	973.5332
Botswana	Africa	1952	47.622	442308	851.2411
Brazil	Americas	1952	50.917	56602560	2108.9444
Bulgaria	Europe	1952	59.600	7274900	2444.2866
Burkina Faso	Africa	1952	31.975	4469979	543.2552
Burundi	Africa	1952	39.031	2445618	339.2965
Cambodia	Asia	1952	39.417	4693836	368.4693
Cameroon	Africa	1952	38.523	5009067	1172.6677
Canada	Americas	1952	68.750	14785584	11367.1611
Central African Republic	Africa	1952	35.463	1291695	1071.3107
Chad	Africa	1952	38.092	2682462	1178.6659
Chile	Americas	1952	54.745	6377619	3939.9788
China	Asia	1952	44.000	556263527	400.4486
Colombia	Americas	1952	50.643	12350771	2144.1151
Comoros	Africa	1952	40.715	153936	1102.9909
Congo, Dem. Rep.	Africa	1952	39.143	14100005	780.5423
Congo, Rep.	Africa	1952	42.111	854885	2125.6214
Costa Rica	Americas	1952	57.206	926317	2627.0095
⋮	⋮	⋮	⋮	⋮	⋮
Sierra Leone	Africa	2007	42.568	6144562	862.5408
Singapore	Asia	2007	79.972	4553009	47143.1796
Slovak Republic	Europe	2007	74.663	5447502	18678.3144
Slovenia	Europe	2007	77.926	2009245	25768.2576
Somalia	Africa	2007	48.159	9118773	926.1411
South Africa	Africa	2007	49.339	43997828	9269.6578
Spain	Europe	2007	80.941	40448191	28821.0637
Sri Lanka	Asia	2007	72.396	20378239	3970.0954
Sudan	Africa	2007	58.556	42292929	2602.3950
Swaziland	Africa	2007	39.613	1133066	4513.4806
Sweden	Europe	2007	80.884	9031088	33859.7484
Switzerland	Europe	2007	81.701	7554661	37506.4191
Syria	Asia	2007	74.143	19314747	4184.5481
Taiwan	Asia	2007	78.400	23174294	28718.2768
Tanzania	Africa	2007	52.517	38139640	1107.4822
Thailand	Asia	2007	70.616	65068149	7458.3963
Togo	Africa	2007	58.420	5701579	882.9699
Trinidad and Tobago	Americas	2007	69.819	1056608	18008.5092
Tunisia	Africa	2007	73.923	10276158	7092.9230
Turkey	Europe	2007	71.777	71158647	8458.2764
Uganda	Africa	2007	51.542	29170398	1056.3801
United Kingdom	Europe	2007	79.425	60776238	33203.2613
United States	Americas	2007	78.242	301139947	42951.6531
Uruguay	Americas	2007	76.384	3447496	10611.4630
Venezuela	Americas	2007	73.747	26084662	11415.8057
Vietnam	Asia	2007	74.249	85262356	2441.5764
West Bank and Gaza	Asia	2007	73.422	4018332	3025.3498
Yemen, Rep.	Asia	2007	62.698	22211743	2280.7699
Zambia	Africa	2007	42.384	11746035	1271.2116
Zimbabwe	Africa	2007	43.487	12311143	469.7093

Notes on passing the dots#

the dots must be the last function argument
they are useful because you can add multiple arguments

For example:

sort_gap <- function(..., x) {
    print(x + 1)
    arrange(gapminder, ...)
}

sort_gap(year, continent, country, 2)

sort_gap <- function(x, ...) {
    print(x + 1)
    arrange(gapminder, ...)
}

sort_gap(1, year, continent, country)

Pass the dots is not always the solution…#

library(dplyr)
select_n_change <- function(data, ...) {
    out <- select(data, ...)
    mutate(out, ... := ... / mean(...))
}

select_n_change(mtcars, mpg, cyl, disp, hp)

Error: object 'mpg' not found
Traceback:

select_n_change(mtcars, mpg, cyl, disp, hp)
mutate(out, `:=`(..., .../mean(...)))   # at line 4 of file <text>
mutate.data.frame(out, `:=`(..., .../mean(...)))
as.data.frame(mutate(tbl_df(.data), ...))
mutate(tbl_df(.data), ...)
mutate.tbl_df(tbl_df(.data), ...)
mutate_impl(.data, dots, caller_env())

When passing in different column names to different functions, use quote & unquote:#

select_n_change <- function(data, col_range, col_to_change) {
    out <- select(data, {{col_range}})
    mutate(out, {{col_to_change}} := {{col_to_change}} / mean({{col_to_change}}))
}

select_n_change(mtcars, mpg:hp, mpg)

A data.frame: 32 × 4
mpg	cyl	disp	hp
<dbl>	<dbl>	<dbl>	<dbl>
1.0452636	6	160.0	110
1.0452636	6	160.0	110
1.1348577	4	108.0	93
1.0651734	6	258.0	110
0.9307824	8	360.0	175
0.9009177	6	225.0	105
0.7117748	8	360.0	245
1.2144968	4	146.7	62
1.1348577	4	140.8	95
0.9556696	6	167.6	123
0.8859854	6	167.6	123
0.8163011	8	275.8	180
0.8610981	8	275.8	180
0.7565718	8	275.8	180
0.5176544	8	472.0	205
0.5176544	8	460.0	215
0.7316846	8	440.0	230
1.6126925	4	78.7	66
1.5131436	4	75.7	52
1.6873542	4	71.1	65
1.0701509	4	120.1	97
0.7715041	8	318.0	150
0.7565718	8	304.0	150
0.6620003	8	350.0	245
0.9556696	8	400.0	175
1.3588427	4	79.0	66
1.2941359	4	120.3	91
1.5131436	4	95.1	113
0.7864365	8	351.0	264
0.9805569	6	145.0	175
0.7466169	8	301.0	335
1.0651734	4	121.0	109

Combine quoting & unquoting with pass the dots:#

select_n_change <- function(data, col_to_change, ...) {
    out <- select(data, ...)
    mutate(out, {{col_to_change}} := {{col_to_change}} / mean({{col_to_change}}))
}

select_n_change(mtcars, mpg, mpg, drat, carb)

A data.frame: 32 × 3
mpg	drat	carb
<dbl>	<dbl>	<dbl>
1.0452636	3.90	4
1.0452636	3.90	4
1.1348577	3.85	1
1.0651734	3.08	1
0.9307824	3.15	2
0.9009177	2.76	1
0.7117748	3.21	4
1.2144968	3.69	2
1.1348577	3.92	2
0.9556696	3.92	4
0.8859854	3.92	4
0.8163011	3.07	3
0.8610981	3.07	3
0.7565718	3.07	3
0.5176544	2.93	4
0.5176544	3.00	4
0.7316846	3.23	4
1.6126925	4.08	1
1.5131436	4.93	2
1.6873542	4.22	1
1.0701509	3.70	1
0.7715041	2.76	2
0.7565718	3.15	2
0.6620003	3.73	4
0.9556696	3.08	2
1.3588427	4.08	1
1.2941359	4.43	2
1.5131436	3.77	2
0.7864365	4.22	4
0.9805569	3.62	6
0.7466169	3.54	8
1.0651734	4.11	2

head(mtcars)

Another operator is needed when assigning values…#

mutate(mtcars, kml = mpg * 0.425144)

What did we learn?#

quoting and unquoting
data masking
without and quoting and unquoting function arguments for quoted functions, R will attempt to evaluate the arguments - potentially out of context.
introduction to the tidyverse

Attribution:#

Tidy evaluation by Lionel Henry & Hadley Wickham
Tidy eval in context talk by Jenny Bryan
Programming in the tidyverse

iris[c(2, 4)]

A data.frame: 150 × 2
Sepal.Width	Petal.Width
<dbl>	<dbl>
3.5	0.2
3.0	0.2
3.2	0.2
3.1	0.2
3.6	0.2
3.9	0.4
3.4	0.3
3.4	0.2
2.9	0.2
3.1	0.1
3.7	0.2
3.4	0.2
3.0	0.1
3.0	0.1
4.0	0.2
4.4	0.4
3.9	0.4
3.5	0.3
3.8	0.3
3.8	0.3
3.4	0.2
3.7	0.4
3.6	0.2
3.3	0.5
3.4	0.2
3.0	0.2
3.4	0.4
3.5	0.2
3.4	0.2
3.2	0.2
⋮	⋮
3.2	2.3
2.8	2.0
2.8	2.0
2.7	1.8
3.3	2.1
3.2	1.8
2.8	1.8
3.0	1.8
2.8	2.1
3.0	1.6
2.8	1.9
3.8	2.0
2.8	2.2
2.8	1.5
2.6	1.4
3.0	2.3
3.4	2.4
3.1	1.8
3.0	1.8
3.1	2.1
3.1	2.4
3.1	2.3
2.7	1.9
3.2	2.3
3.3	2.5
3.0	2.3
2.5	1.9
3.0	2.0
3.4	2.3
3.0	1.8

Data 531

Lecture 8: Tidy evaluation in R

Contents

Lecture 8: Tidy evaluation in R#

What Metaprogramming lets you do in R#

What is metaprogramming?#

Our narrow focus on metaprogramming for this course:#

Why focus on tidy evaluation#

The functions from the tidyverse are beautiful to use interactively:#

How does that even work?#

We can capture expressions and later evaluate the code:#

We can also manipulate the environment code is evaluated in:#

You can evaluate in a data mask:#

Back to our example:#

Trade off of lovely interactivity of tidyverse functions…#

programming with them can be more challenging.#

Proper way of defining this function:#

Evaluating functions and quoting functions in R#

Evaluating functions#

Quoting functions#

Not always one or the other:#

How can you tell if an argument is quoted?#

Challenge 1#

Let’s try some quoting and unquoting so we can use indirect references in a dplyr function:#

Viewing the unquoted expression:#

Challenge 2#

`enquo` vs `quote` when writing a function#

Quote - unquote pattern all in one step: interpolation#

Creating functions that handle unquoted column names:#

Creating functions that handle column names as strings:#

Another operator is needed when assigning values…#

Pass the dots when you can#

Notes on passing the dots#

Pass the dots is not always the solution…#

When passing in different column names to different functions, use quote & unquote:#

Combine quoting & unquoting with pass the dots:#

Another operator is needed when assigning values…#

What did we learn?#

Attribution:#

Data 531

Lecture 8: Tidy evaluation in R

Contents

Lecture 8: Tidy evaluation in R#

What Metaprogramming lets you do in R#

What is metaprogramming?#

Our narrow focus on metaprogramming for this course:#

Why focus on tidy evaluation#

The functions from the tidyverse are beautiful to use interactively:#

How does that even work?#

We can capture expressions and later evaluate the code:#

We can also manipulate the environment code is evaluated in:#

You can evaluate in a data mask:#

Back to our example:#

Trade off of lovely interactivity of tidyverse functions…#

programming with them can be more challenging.#

Proper way of defining this function:#

Evaluating functions and quoting functions in R#

Evaluating functions#

Quoting functions#

Not always one or the other:#

How can you tell if an argument is quoted?#

Challenge 1#

Let’s try some quoting and unquoting so we can use indirect references in a dplyr function:#

Viewing the unquoted expression:#

Challenge 2#

enquo vs quote when writing a function#

Quote - unquote pattern all in one step: interpolation#

Creating functions that handle unquoted column names:#

Creating functions that handle column names as strings:#

Another operator is needed when assigning values…#

Pass the dots when you can#

Notes on passing the dots#

Pass the dots is not always the solution…#

When passing in different column names to different functions, use quote & unquote:#

Combine quoting & unquoting with pass the dots:#

Another operator is needed when assigning values…#

What did we learn?#

Attribution:#

`enquo` vs `quote` when writing a function#