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    <p>Riffing off of @James example</p> <pre><code>p &lt;- qplot(hp,wt,data=mtcars) + stat_smooth() </code></pre> <p>You can use the intermediate stages of the ggplot building process to pull out the plotted data. The results of <code>ggplot_build</code> is a list, one component of which is <code>data</code> which is a list of dataframes which contain the computed values to be plotted. In this case, the list is two dataframes since the original <code>qplot</code> creates one for points and the <code>stat_smooth</code> creates a smoothed one.</p> <pre><code>&gt; ggplot_build(p)$data[[2]] geom_smooth: method="auto" and size of largest group is &lt;1000, so using loess. Use 'method = x' to change the smoothing method. x y ymin ymax se PANEL group 1 52.00000 1.993594 1.149150 2.838038 0.4111133 1 1 2 55.58228 2.039986 1.303264 2.776709 0.3586695 1 1 3 59.16456 2.087067 1.443076 2.731058 0.3135236 1 1 4 62.74684 2.134889 1.567662 2.702115 0.2761514 1 1 5 66.32911 2.183533 1.677017 2.690049 0.2465948 1 1 6 69.91139 2.232867 1.771739 2.693995 0.2244980 1 1 7 73.49367 2.282897 1.853241 2.712552 0.2091756 1 1 8 77.07595 2.333626 1.923599 2.743652 0.1996193 1 1 9 80.65823 2.385059 1.985378 2.784740 0.1945828 1 1 10 84.24051 2.437200 2.041282 2.833117 0.1927505 1 1 11 87.82278 2.490053 2.093808 2.886297 0.1929096 1 1 12 91.40506 2.543622 2.145018 2.942225 0.1940582 1 1 13 94.98734 2.597911 2.196466 2.999355 0.1954412 1 1 14 98.56962 2.652852 2.249260 3.056444 0.1964867 1 1 15 102.15190 2.708104 2.303465 3.112744 0.1969967 1 1 16 105.73418 2.764156 2.357927 3.170385 0.1977705 1 1 17 109.31646 2.821771 2.414230 3.229311 0.1984091 1 1 18 112.89873 2.888224 2.478136 3.298312 0.1996493 1 1 19 116.48101 2.968745 2.531045 3.406444 0.2130917 1 1 20 120.06329 3.049545 2.552102 3.546987 0.2421773 1 1 21 123.64557 3.115893 2.573577 3.658208 0.2640235 1 1 22 127.22785 3.156368 2.601664 3.711072 0.2700548 1 1 23 130.81013 3.175495 2.625951 3.725039 0.2675429 1 1 24 134.39241 3.181411 2.645191 3.717631 0.2610560 1 1 25 137.97468 3.182252 2.658993 3.705511 0.2547460 1 1 26 141.55696 3.186155 2.670350 3.701961 0.2511175 1 1 27 145.13924 3.201258 2.687208 3.715308 0.2502626 1 1 28 148.72152 3.235698 2.721744 3.749652 0.2502159 1 1 29 152.30380 3.291766 2.782767 3.800765 0.2478037 1 1 30 155.88608 3.353259 2.857911 3.848607 0.2411575 1 1 31 159.46835 3.418409 2.938257 3.898561 0.2337596 1 1 32 163.05063 3.487074 3.017321 3.956828 0.2286972 1 1 33 166.63291 3.559111 3.092367 4.025855 0.2272319 1 1 34 170.21519 3.634377 3.165426 4.103328 0.2283065 1 1 35 173.79747 3.712729 3.242093 4.183364 0.2291263 1 1 36 177.37975 3.813399 3.347232 4.279565 0.2269509 1 1 37 180.96203 3.910849 3.447572 4.374127 0.2255441 1 1 38 184.54430 3.977051 3.517784 4.436318 0.2235917 1 1 39 188.12658 4.037302 3.583959 4.490645 0.2207076 1 1 40 191.70886 4.091635 3.645111 4.538160 0.2173882 1 1 41 195.29114 4.140082 3.700184 4.579981 0.2141624 1 1 42 198.87342 4.182676 3.748159 4.617192 0.2115424 1 1 43 202.45570 4.219447 3.788162 4.650732 0.2099688 1 1 44 206.03797 4.250429 3.819579 4.681280 0.2097573 1 1 45 209.62025 4.275654 3.842137 4.709171 0.2110556 1 1 46 213.20253 4.295154 3.855951 4.734357 0.2138238 1 1 47 216.78481 4.308961 3.861497 4.756425 0.2178456 1 1 48 220.36709 4.317108 3.859541 4.774675 0.2227644 1 1 49 223.94937 4.319626 3.851025 4.788227 0.2281358 1 1 50 227.53165 4.316548 3.836964 4.796132 0.2334829 1 1 51 231.11392 4.308435 3.818728 4.798143 0.2384117 1 1 52 234.69620 4.302276 3.802201 4.802351 0.2434590 1 1 53 238.27848 4.297902 3.787395 4.808409 0.2485379 1 1 54 241.86076 4.292303 3.772103 4.812503 0.2532567 1 1 55 245.44304 4.282505 3.754087 4.810923 0.2572576 1 1 56 249.02532 4.269040 3.733184 4.804896 0.2608786 1 1 57 252.60759 4.253361 3.710042 4.796680 0.2645121 1 1 58 256.18987 4.235474 3.684476 4.786473 0.2682509 1 1 59 259.77215 4.215385 3.656265 4.774504 0.2722044 1 1 60 263.35443 4.193098 3.625161 4.761036 0.2764974 1 1 61 266.93671 4.168621 3.590884 4.746357 0.2812681 1 1 62 270.51899 4.141957 3.553134 4.730781 0.2866658 1 1 63 274.10127 4.113114 3.511593 4.714635 0.2928472 1 1 64 277.68354 4.082096 3.465939 4.698253 0.2999729 1 1 65 281.26582 4.048910 3.415849 4.681971 0.3082025 1 1 66 284.84810 4.013560 3.361010 4.666109 0.3176905 1 1 67 288.43038 3.976052 3.301132 4.650972 0.3285813 1 1 68 292.01266 3.936392 3.235952 4.636833 0.3410058 1 1 69 295.59494 3.894586 3.165240 4.623932 0.3550782 1 1 70 299.17722 3.850639 3.088806 4.612473 0.3708948 1 1 71 302.75949 3.804557 3.006494 4.602619 0.3885326 1 1 72 306.34177 3.756345 2.918191 4.594499 0.4080510 1 1 73 309.92405 3.706009 2.823813 4.588205 0.4294926 1 1 74 313.50633 3.653554 2.723308 4.583801 0.4528856 1 1 75 317.08861 3.598987 2.616650 4.581325 0.4782460 1 1 76 320.67089 3.542313 2.503829 4.580796 0.5055805 1 1 77 324.25316 3.483536 2.384853 4.582220 0.5348886 1 1 78 327.83544 3.422664 2.259739 4.585589 0.5661643 1 1 79 331.41772 3.359701 2.128512 4.590891 0.5993985 1 1 80 335.00000 3.294654 1.991200 4.598107 0.6345798 1 1 </code></pre> <p>Knowing <em>a priori</em> where the one you want is in the list isn't easy, but if nothing else you can look at the column names.</p> <p>It is still better to do the smoothing outside the <code>ggplot</code> call, though.</p> <p>EDIT:</p> <p>It turns out replicating what <code>ggplot2</code> does to make the <code>loess</code> is not as straightforward as I thought, but this will work. I copied it out of some internal functions in <code>ggplot2</code>.</p> <pre><code>model &lt;- loess(wt ~ hp, data=mtcars) xrange &lt;- range(mtcars$hp) xseq &lt;- seq(from=xrange[1], to=xrange[2], length=80) pred &lt;- predict(model, newdata = data.frame(hp = xseq), se=TRUE) y = pred$fit ci &lt;- pred$se.fit * qt(0.95 / 2 + .5, pred$df) ymin = y - ci ymax = y + ci loess.DF &lt;- data.frame(x = xseq, y, ymin, ymax, se = pred$se.fit) ggplot(mtcars, aes(x=hp, y=wt)) + geom_point() + geom_smooth(aes_auto(loess.DF), data=loess.DF, stat="identity") </code></pre> <p>That gives a plot that looks identical to</p> <pre><code>ggplot(mtcars, aes(x=hp, y=wt)) + geom_point() + geom_smooth() </code></pre> <p>(which is the expanded form of the original <code>p</code>).</p>
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    1. CONice, it's a lot more useful to get the x values too.
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    2. COThanks. Rookie curious, why do you say it is better to do smoothing outside the ggplot call? Seems like if you need to produce the plots anyway, it would be repetitive to do a sepreate fit just to extract the fit values.
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    3. COI wouldn't do the fit outside the ggplot call in addition to one inside of it, but rather instead of. I would form the fit data into an appropriate format/dataframe and then plot that. There is an example of that buried in this answer: http://stackoverflow.com/a/9742012/892313
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