<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/purrr-like-a-kitten-till-the-lake-pipes-roar/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="https://www.finex.co/purrr-like-a-kitten-till-the-lake-pipes-roar/"> R – FinEx.Co</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>I really should make a minimal effort to resist opening a data analysis blog post with Beach Boys’ lyrics, but this time the combination is too apt. We use the <em>purrr</em> package to show how to let your pipes roar in <em>R</em>.</p>
<p>The <a rel="nofollow" href="https://github.com/tidyverse/purrr" target="_blank"><em>tidyverse</em> GitHub site</a> contains a simple example illustrating how well pipes and <em>purrr</em> work together. For more learning, try Jenny Bryan’s <a rel="nofollow" href="https://jennybc.github.io/purrr-tutorial/" target="_blank"><em>purrr</em> tutorial</a>.</p>
<p>First, load the <em>tidyverse</em> and the <em>purrr</em> package.</p>
<pre><code>library(tidyverse)
library(purrr)
#devtools::install_github(&quot;jennybc/repurrrsive&quot;)
#library(repurrrsive)


</code></pre>
<p>If you want to be more adventurous, you can download Jenny’s <em>repurrrsive</em> package from GitHub. The code is hashed out in the chunk above.</p>
<blockquote>
<p>You Don’t Know What I Got</p>
</blockquote>
<p>The great thing about using pipes is you can tie together a series of steps to produce a single output of exactly what you want. In this case, we are going to start with the base <em>R</em> dataset <em>airquality</em> and apply a number of functions to come up with the adjusted R-squared value for the <em>Ozone</em> data for each month between May and September. Along the way we will run a linear regression on the data to generate the adjusted R-squared values.</p>
<p>Here is what the full process looks like, from beginning to end.</p>
<pre><code>airquality %>% 
         split(.$Month) %>% 
         map(~ lm(Ozone ~ Temp, data = .)) %>% 
         map(summary) %>% 
         map_dbl('adj.r.squared')

     5      6      7      8      9 
0.2781 0.3676 0.5024 0.3307 0.6742
</code></pre>
<p>The problem, of course, is the output generated by the intermediate steps stays behind the scenes. For a beginner, this can be a bit confusing because it isn’t clear what is going on. So let’s break the full chunk into its constituent pieces so we can see what <em>purrr</em> is doing and how pipes tie the whole thing together.</p>
<p>Start at the beginning and take things step-by-step.</p>
<p>Run the <em>airquality</em> data set to see what we are dealing with. We can see the data is collected daily for five months. <em>Ozone</em> looks to be the target, so it is natural to wonder if there is any relationship between it and the other variables.</p>
<pre><code>airquality

    Ozone Solar.R Wind Temp Month Day
1      41     190  7.4   67     5   1
2      36     118  8.0   72     5   2
3      12     149 12.6   74     5   3
4      18     313 11.5   62     5   4
5      NA      NA 14.3   56     5   5
6      28      NA 14.9   66     5   6
7      23     299  8.6   65     5   7
8      19      99 13.8   59     5   8
9       8      19 20.1   61     5   9
10     NA     194  8.6   69     5  10
11      7      NA  6.9   74     5  11
12     16     256  9.7   69     5  12
13     11     290  9.2   66     5  13
14     14     274 10.9   68     5  14
15     18      65 13.2   58     5  15
16     14     334 11.5   64     5  16
17     34     307 12.0   66     5  17
18      6      78 18.4   57     5  18
19     30     322 11.5   68     5  19
20     11      44  9.7   62     5  20
21      1       8  9.7   59     5  21
22     11     320 16.6   73     5  22
23      4      25  9.7   61     5  23
24     32      92 12.0   61     5  24
25     NA      66 16.6   57     5  25
26     NA     266 14.9   58     5  26
27     NA      NA  8.0   57     5  27
28     23      13 12.0   67     5  28
29     45     252 14.9   81     5  29
30    115     223  5.7   79     5  30
31     37     279  7.4   76     5  31
32     NA     286  8.6   78     6   1
33     NA     287  9.7   74     6   2
34     NA     242 16.1   67     6   3
35     NA     186  9.2   84     6   4
36     NA     220  8.6   85     6   5
37     NA     264 14.3   79     6   6
38     29     127  9.7   82     6   7
39     NA     273  6.9   87     6   8
40     71     291 13.8   90     6   9
41     39     323 11.5   87     6  10
42     NA     259 10.9   93     6  11
43     NA     250  9.2   92     6  12
44     23     148  8.0   82     6  13
45     NA     332 13.8   80     6  14
46     NA     322 11.5   79     6  15
47     21     191 14.9   77     6  16
48     37     284 20.7   72     6  17
49     20      37  9.2   65     6  18
50     12     120 11.5   73     6  19
51     13     137 10.3   76     6  20
52     NA     150  6.3   77     6  21
53     NA      59  1.7   76     6  22
54     NA      91  4.6   76     6  23
55     NA     250  6.3   76     6  24
56     NA     135  8.0   75     6  25
57     NA     127  8.0   78     6  26
58     NA      47 10.3   73     6  27
59     NA      98 11.5   80     6  28
60     NA      31 14.9   77     6  29
61     NA     138  8.0   83     6  30
62    135     269  4.1   84     7   1
63     49     248  9.2   85     7   2
64     32     236  9.2   81     7   3
65     NA     101 10.9   84     7   4
66     64     175  4.6   83     7   5
67     40     314 10.9   83     7   6
68     77     276  5.1   88     7   7
69     97     267  6.3   92     7   8
70     97     272  5.7   92     7   9
71     85     175  7.4   89     7  10
72     NA     139  8.6   82     7  11
73     10     264 14.3   73     7  12
74     27     175 14.9   81     7  13
75     NA     291 14.9   91     7  14
76      7      48 14.3   80     7  15
77     48     260  6.9   81     7  16
78     35     274 10.3   82     7  17
79     61     285  6.3   84     7  18
80     79     187  5.1   87     7  19
81     63     220 11.5   85     7  20
82     16       7  6.9   74     7  21
83     NA     258  9.7   81     7  22
84     NA     295 11.5   82     7  23
85     80     294  8.6   86     7  24
86    108     223  8.0   85     7  25
87     20      81  8.6   82     7  26
88     52      82 12.0   86     7  27
89     82     213  7.4   88     7  28
90     50     275  7.4   86     7  29
91     64     253  7.4   83     7  30
92     59     254  9.2   81     7  31
93     39      83  6.9   81     8   1
94      9      24 13.8   81     8   2
95     16      77  7.4   82     8   3
96     78      NA  6.9   86     8   4
97     35      NA  7.4   85     8   5
98     66      NA  4.6   87     8   6
99    122     255  4.0   89     8   7
100    89     229 10.3   90     8   8
101   110     207  8.0   90     8   9
102    NA     222  8.6   92     8  10
103    NA     137 11.5   86     8  11
104    44     192 11.5   86     8  12
105    28     273 11.5   82     8  13
106    65     157  9.7   80     8  14
107    NA      64 11.5   79     8  15
108    22      71 10.3   77     8  16
109    59      51  6.3   79     8  17
110    23     115  7.4   76     8  18
111    31     244 10.9   78     8  19
112    44     190 10.3   78     8  20
113    21     259 15.5   77     8  21
114     9      36 14.3   72     8  22
115    NA     255 12.6   75     8  23
116    45     212  9.7   79     8  24
117   168     238  3.4   81     8  25
118    73     215  8.0   86     8  26
119    NA     153  5.7   88     8  27
120    76     203  9.7   97     8  28
121   118     225  2.3   94     8  29
122    84     237  6.3   96     8  30
123    85     188  6.3   94     8  31
124    96     167  6.9   91     9   1
125    78     197  5.1   92     9   2
126    73     183  2.8   93     9   3
127    91     189  4.6   93     9   4
128    47      95  7.4   87     9   5
129    32      92 15.5   84     9   6
130    20     252 10.9   80     9   7
131    23     220 10.3   78     9   8
132    21     230 10.9   75     9   9
133    24     259  9.7   73     9  10
134    44     236 14.9   81     9  11
135    21     259 15.5   76     9  12
136    28     238  6.3   77     9  13
137     9      24 10.9   71     9  14
138    13     112 11.5   71     9  15
139    46     237  6.9   78     9  16
140    18     224 13.8   67     9  17
141    13      27 10.3   76     9  18
142    24     238 10.3   68     9  19
143    16     201  8.0   82     9  20
144    13     238 12.6   64     9  21
145    23      14  9.2   71     9  22
146    36     139 10.3   81     9  23
147     7      49 10.3   69     9  24
148    14      20 16.6   63     9  25
149    30     193  6.9   70     9  26
150    NA     145 13.2   77     9  27
151    14     191 14.3   75     9  28
152    18     131  8.0   76     9  29
153    20     223 11.5   68     9  30
</code></pre>
<p>The first step is to break the data up by month, so we make use of base <em>R</em>‘s <code>split()</code> function. Notice all the data is grouped by month.</p>
<pre><code>airquality %>% 
    split(.$Month)

$`5`
   Ozone Solar.R Wind Temp Month Day
1     41     190  7.4   67     5   1
2     36     118  8.0   72     5   2
3     12     149 12.6   74     5   3
4     18     313 11.5   62     5   4
5     NA      NA 14.3   56     5   5
6     28      NA 14.9   66     5   6
7     23     299  8.6   65     5   7
8     19      99 13.8   59     5   8
9      8      19 20.1   61     5   9
10    NA     194  8.6   69     5  10
11     7      NA  6.9   74     5  11
12    16     256  9.7   69     5  12
13    11     290  9.2   66     5  13
14    14     274 10.9   68     5  14
15    18      65 13.2   58     5  15
16    14     334 11.5   64     5  16
17    34     307 12.0   66     5  17
18     6      78 18.4   57     5  18
19    30     322 11.5   68     5  19
20    11      44  9.7   62     5  20
21     1       8  9.7   59     5  21
22    11     320 16.6   73     5  22
23     4      25  9.7   61     5  23
24    32      92 12.0   61     5  24
25    NA      66 16.6   57     5  25
26    NA     266 14.9   58     5  26
27    NA      NA  8.0   57     5  27
28    23      13 12.0   67     5  28
29    45     252 14.9   81     5  29
30   115     223  5.7   79     5  30
31    37     279  7.4   76     5  31

$`6`
   Ozone Solar.R Wind Temp Month Day
32    NA     286  8.6   78     6   1
33    NA     287  9.7   74     6   2
34    NA     242 16.1   67     6   3
35    NA     186  9.2   84     6   4
36    NA     220  8.6   85     6   5
37    NA     264 14.3   79     6   6
38    29     127  9.7   82     6   7
39    NA     273  6.9   87     6   8
40    71     291 13.8   90     6   9
41    39     323 11.5   87     6  10
42    NA     259 10.9   93     6  11
43    NA     250  9.2   92     6  12
44    23     148  8.0   82     6  13
45    NA     332 13.8   80     6  14
46    NA     322 11.5   79     6  15
47    21     191 14.9   77     6  16
48    37     284 20.7   72     6  17
49    20      37  9.2   65     6  18
50    12     120 11.5   73     6  19
51    13     137 10.3   76     6  20
52    NA     150  6.3   77     6  21
53    NA      59  1.7   76     6  22
54    NA      91  4.6   76     6  23
55    NA     250  6.3   76     6  24
56    NA     135  8.0   75     6  25
57    NA     127  8.0   78     6  26
58    NA      47 10.3   73     6  27
59    NA      98 11.5   80     6  28
60    NA      31 14.9   77     6  29
61    NA     138  8.0   83     6  30

$`7`
   Ozone Solar.R Wind Temp Month Day
62   135     269  4.1   84     7   1
63    49     248  9.2   85     7   2
64    32     236  9.2   81     7   3
65    NA     101 10.9   84     7   4
66    64     175  4.6   83     7   5
67    40     314 10.9   83     7   6
68    77     276  5.1   88     7   7
69    97     267  6.3   92     7   8
70    97     272  5.7   92     7   9
71    85     175  7.4   89     7  10
72    NA     139  8.6   82     7  11
73    10     264 14.3   73     7  12
74    27     175 14.9   81     7  13
75    NA     291 14.9   91     7  14
76     7      48 14.3   80     7  15
77    48     260  6.9   81     7  16
78    35     274 10.3   82     7  17
79    61     285  6.3   84     7  18
80    79     187  5.1   87     7  19
81    63     220 11.5   85     7  20
82    16       7  6.9   74     7  21
83    NA     258  9.7   81     7  22
84    NA     295 11.5   82     7  23
85    80     294  8.6   86     7  24
86   108     223  8.0   85     7  25
87    20      81  8.6   82     7  26
88    52      82 12.0   86     7  27
89    82     213  7.4   88     7  28
90    50     275  7.4   86     7  29
91    64     253  7.4   83     7  30
92    59     254  9.2   81     7  31

$`8`
    Ozone Solar.R Wind Temp Month Day
93     39      83  6.9   81     8   1
94      9      24 13.8   81     8   2
95     16      77  7.4   82     8   3
96     78      NA  6.9   86     8   4
97     35      NA  7.4   85     8   5
98     66      NA  4.6   87     8   6
99    122     255  4.0   89     8   7
100    89     229 10.3   90     8   8
101   110     207  8.0   90     8   9
102    NA     222  8.6   92     8  10
103    NA     137 11.5   86     8  11
104    44     192 11.5   86     8  12
105    28     273 11.5   82     8  13
106    65     157  9.7   80     8  14
107    NA      64 11.5   79     8  15
108    22      71 10.3   77     8  16
109    59      51  6.3   79     8  17
110    23     115  7.4   76     8  18
111    31     244 10.9   78     8  19
112    44     190 10.3   78     8  20
113    21     259 15.5   77     8  21
114     9      36 14.3   72     8  22
115    NA     255 12.6   75     8  23
116    45     212  9.7   79     8  24
117   168     238  3.4   81     8  25
118    73     215  8.0   86     8  26
119    NA     153  5.7   88     8  27
120    76     203  9.7   97     8  28
121   118     225  2.3   94     8  29
122    84     237  6.3   96     8  30
123    85     188  6.3   94     8  31

$`9`
    Ozone Solar.R Wind Temp Month Day
124    96     167  6.9   91     9   1
125    78     197  5.1   92     9   2
126    73     183  2.8   93     9   3
127    91     189  4.6   93     9   4
128    47      95  7.4   87     9   5
129    32      92 15.5   84     9   6
130    20     252 10.9   80     9   7
131    23     220 10.3   78     9   8
132    21     230 10.9   75     9   9
133    24     259  9.7   73     9  10
134    44     236 14.9   81     9  11
135    21     259 15.5   76     9  12
136    28     238  6.3   77     9  13
137     9      24 10.9   71     9  14
138    13     112 11.5   71     9  15
139    46     237  6.9   78     9  16
140    18     224 13.8   67     9  17
141    13      27 10.3   76     9  18
142    24     238 10.3   68     9  19
143    16     201  8.0   82     9  20
144    13     238 12.6   64     9  21
145    23      14  9.2   71     9  22
146    36     139 10.3   81     9  23
147     7      49 10.3   69     9  24
148    14      20 16.6   63     9  25
149    30     193  6.9   70     9  26
150    NA     145 13.2   77     9  27
151    14     191 14.3   75     9  28
152    18     131  8.0   76     9  29
153    20     223 11.5   68     9  30
</code></pre>
<p>In the second step, we apply the <em>purrr</em> <code>map()</code> function to the linear regression model we create with <code>lm()</code>. We want the adjusted R-squared value for each month for <em>Ozone</em>. I played around with the variables a bit to find which one illustrated the adjusted R-squared values best and settled on <em>Temp</em>, but you can choose any other besides <em>Month</em> and <em>Day</em>.</p>
<p>The <code>map()</code> command applies the <code>lm()</code> function to each monthy group and yields the typical output for each month. We now have five linear regression models, one for each month, but no adjusted R-squared values.</p>
<pre><code>airquality %>% 
    split(.$Month) %>% 
    map(~ lm(Ozone ~ Temp, data = .))

$`5`

Call:
lm(formula = Ozone ~ Temp, data = .)

Coefficients:
(Intercept)         Temp  
    -102.16         1.88  


$`6`

Call:
lm(formula = Ozone ~ Temp, data = .)

Coefficients:
(Intercept)         Temp  
     -91.99         1.55  


$`7`

Call:
lm(formula = Ozone ~ Temp, data = .)

Coefficients:
(Intercept)         Temp  
    -372.92         5.15  


$`8`

Call:
lm(formula = Ozone ~ Temp, data = .)

Coefficients:
(Intercept)         Temp  
    -238.86         3.56  


$`9`

Call:
lm(formula = Ozone ~ Temp, data = .)

Coefficients:
(Intercept)         Temp  
    -149.35         2.35  

</code></pre>
<p>To generate the adjusted R-squared values, we need to <code>map()</code> the <code>summary()</code> command to each group. This is the third step. Again, the results are familiar. We have the typical summary of the linear model, one for each month.</p>
<pre><code>airquality %>% 
    split(.$Month) %>% 
    map(~ lm(Ozone ~ Temp, data = .)) %>%
    map(summary)

$`5`

Call:
lm(formula = Ozone ~ Temp, data = .)

Residuals:
   Min     1Q Median     3Q    Max 
-30.32  -8.62  -2.41   5.32  68.26 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept) -102.159     38.750   -2.64   0.0145 * 
Temp           1.885      0.578    3.26   0.0033 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 18.9 on 24 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.307, Adjusted R-squared:  0.278 
F-statistic: 10.6 on 1 and 24 DF,  p-value: 0.00331


$`6`

Call:
lm(formula = Ozone ~ Temp, data = .)

Residuals:
   Min     1Q Median     3Q    Max 
-12.99  -9.34  -6.31  11.08  23.27 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  -91.991     51.312   -1.79    0.116  
Temp           1.552      0.653    2.38    0.049 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.5 on 7 degrees of freedom
  (21 observations deleted due to missingness)
Multiple R-squared:  0.447, Adjusted R-squared:  0.368 
F-statistic: 5.65 on 1 and 7 DF,  p-value: 0.0491


$`7`

Call:
lm(formula = Ozone ~ Temp, data = .)

Residuals:
   Min     1Q Median     3Q    Max 
-32.11 -14.52  -1.16   7.58  75.29 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -372.92      84.45   -4.42  0.00018 ***
Temp            5.15       1.01    5.12    3e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 22.3 on 24 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.522, Adjusted R-squared:  0.502 
F-statistic: 26.2 on 1 and 24 DF,  p-value: 3.05e-05


$`8`

Call:
lm(formula = Ozone ~ Temp, data = .)

Residuals:
   Min     1Q Median     3Q    Max 
-40.42 -17.65  -8.07   9.97 118.58 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept) -238.861     82.023   -2.91   0.0076 **
Temp           3.559      0.974    3.65   0.0013 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 32.5 on 24 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.357, Adjusted R-squared:  0.331 
F-statistic: 13.4 on 1 and 24 DF,  p-value: 0.00126


$`9`

Call:
lm(formula = Ozone ~ Temp, data = .)

Residuals:
   Min     1Q Median     3Q    Max 
-27.45  -8.59  -3.69  11.04  31.39 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -149.347     23.688   -6.30  9.5e-07 ***
Temp           2.351      0.306    7.68  2.9e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 13.8 on 27 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.686, Adjusted R-squared:  0.674 
F-statistic: 58.9 on 1 and 27 DF,  p-value: 2.95e-08
</code></pre>
<blockquote>
<p>She Blows em Outta the Water Like You Never Seen</p>
</blockquote>
<p>Now things get interesting, because we can put it all together.</p>
<p>Step 4 involves using the specialized <code>map_dbl()</code> command to pull the adjusted R-squared values from each month’s linear model summary and output them in a single line.</p>
<p>How do we know the adjusted R-squared value is a double? Well, it looks like one since it consists of a floating decimal. But we could guess, too. If we were to use the <code>map_int()</code> command we would get an error that tells us the value is a double. If we guessed character we could use <code>mapZ_chr</code>. That would work but we would have the output in character form, which isn’t what we want.</p>
<p>So we can recognize its data form or we can figure it out through trial and error. Either way we end up at the same place, back where we started.</p>
<pre><code>airquality %>% 
    split(.$Month) %>% 
    map(~ lm(Ozone ~ Temp, data = .)) %>% 
    map(summary) %>% 
    map_dbl('adj.r.squared')

     5      6      7      8      9 
0.2781 0.3676 0.5024 0.3307 0.6742
</code></pre>
<blockquote>
<p>And if That Ain’t Enough to Make You Flip Your Lid</p>
</blockquote>
<p>I don’t know what will.</p>
	<p>
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