Introduction
The motivation for writing these stats and geoms is that at the moment it is in many cases not possible to set breakpoints inside the code of stats and geoms. This can make it tedious to see how these functions work, as one may need to add print statements to their source code to achieve this. I wrote these functions as tools to help in the development of this package itself, and as a way of learning myself how data are passed around within the different components of a ggplot object when it is printed.
The stats described in this vignette are very simple and print a summary of their data input to the console. In addition they also return a data frame containing labels suitable for plotting with geom “text” or geom “label”. However, starting from version 0.2.7 of the package the default geom is “null”. The values are listed to the console at the time when the ggplot object is printed.
As shown here, no other geom or stat is required, however in the remaining examples we include geom_point() to make the data on the plot visible.
ggplot(my.data, aes(x, y)) + stat_debug_group()## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 100 × 4
## x y PANEL group
## * <dbl> <dbl> <int> <int>
## 1 1 -27205.450 1 -1
## 2 2 -14242.651 1 -1
## 3 3 45790.918 1 -1
## 4 4 53731.420 1 -1
## 5 5 -8028.578 1 -1
## 6 6 102863.943 1 -1
## 7 7 -18547.282 1 -1
## 8 8 13080.521 1 -1
## 9 9 79924.325 1 -1
## 10 10 -44711.499 1 -1
## # ... with 90 more rows
In the absence of facets or groups we get just get the summary from one data frame.
ggplot(my.data, aes(x, y)) + geom_point() + stat_debug_group()## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 100 × 4
## x y PANEL group
## * <dbl> <dbl> <int> <int>
## 1 1 -27205.450 1 -1
## 2 2 -14242.651 1 -1
## 3 3 45790.918 1 -1
## 4 4 53731.420 1 -1
## 5 5 -8028.578 1 -1
## 6 6 102863.943 1 -1
## 7 7 -18547.282 1 -1
## 8 8 13080.521 1 -1
## 9 9 79924.325 1 -1
## 10 10 -44711.499 1 -1
## # ... with 90 more rows
ggplot(my.data, aes(x, y)) + geom_point() + stat_debug_panel()## [1] "Input 'data' to 'compute_panel()':"
## # A tibble: 100 × 4
## x y PANEL group
## <dbl> <dbl> <int> <int>
## 1 1 -27205.450 1 -1
## 2 2 -14242.651 1 -1
## 3 3 45790.918 1 -1
## 4 4 53731.420 1 -1
## 5 5 -8028.578 1 -1
## 6 6 102863.943 1 -1
## 7 7 -18547.282 1 -1
## 8 8 13080.521 1 -1
## 9 9 79924.325 1 -1
## 10 10 -44711.499 1 -1
## # ... with 90 more rows
In the case of grouping then one data frame is summarized for each group in the ggplot object.
ggplot(my.data, aes(x, y, colour = group)) + geom_point() +
stat_debug_group()## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 50 × 5
## x y colour PANEL group
## * <dbl> <dbl> <fctr> <int> <int>
## 1 1 -27205.450 A 1 1
## 2 3 45790.918 A 1 1
## 3 5 -8028.578 A 1 1
## 4 7 -18547.282 A 1 1
## 5 9 79924.325 A 1 1
## 6 11 -2823.736 A 1 1
## 7 13 -78016.690 A 1 1
## 8 15 -74281.234 A 1 1
## 9 17 9903.674 A 1 1
## 10 19 -94022.623 A 1 1
## # ... with 40 more rows
## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 50 × 5
## x y colour PANEL group
## * <dbl> <dbl> <fctr> <int> <int>
## 1 2 -14242.65 B 1 2
## 2 4 53731.42 B 1 2
## 3 6 102863.94 B 1 2
## 4 8 13080.52 B 1 2
## 5 10 -44711.50 B 1 2
## 6 12 23839.55 B 1 2
## 7 14 75601.96 B 1 2
## 8 16 104676.72 B 1 2
## 9 18 -68746.93 B 1 2
## 10 20 -39230.19 B 1 2
## # ... with 40 more rows
Without facets, we still have only one panel.
ggplot(my.data, aes(x, y, colour = group)) + geom_point() +
stat_debug_panel()## [1] "Input 'data' to 'compute_panel()':"
## # A tibble: 100 × 5
## x y colour PANEL group
## <dbl> <dbl> <fctr> <int> <int>
## 1 1 -27205.450 A 1 1
## 2 2 -14242.651 B 1 2
## 3 3 45790.918 A 1 1
## 4 4 53731.420 B 1 2
## 5 5 -8028.578 A 1 1
## 6 6 102863.943 B 1 2
## 7 7 -18547.282 A 1 1
## 8 8 13080.521 B 1 2
## 9 9 79924.325 A 1 1
## 10 10 -44711.499 B 1 2
## # ... with 90 more rows
The data are similar, except for the column named after the aesthetic, for the aesthetics used for grouping.
ggplot(my.data, aes(x, y, shape = group)) + geom_point() +
stat_debug_group()## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 50 × 5
## x y shape PANEL group
## * <dbl> <dbl> <fctr> <int> <int>
## 1 1 -27205.450 A 1 1
## 2 3 45790.918 A 1 1
## 3 5 -8028.578 A 1 1
## 4 7 -18547.282 A 1 1
## 5 9 79924.325 A 1 1
## 6 11 -2823.736 A 1 1
## 7 13 -78016.690 A 1 1
## 8 15 -74281.234 A 1 1
## 9 17 9903.674 A 1 1
## 10 19 -94022.623 A 1 1
## # ... with 40 more rows
## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 50 × 5
## x y shape PANEL group
## * <dbl> <dbl> <fctr> <int> <int>
## 1 2 -14242.65 B 1 2
## 2 4 53731.42 B 1 2
## 3 6 102863.94 B 1 2
## 4 8 13080.52 B 1 2
## 5 10 -44711.50 B 1 2
## 6 12 23839.55 B 1 2
## 7 14 75601.96 B 1 2
## 8 16 104676.72 B 1 2
## 9 18 -68746.93 B 1 2
## 10 20 -39230.19 B 1 2
## # ... with 40 more rows
If we use as geom "label" or "text" a debug summary is added to the plot itself, we can use other arguments valid for the geom used, in this case vjust.
ggplot(my.data, aes(x, y, shape = group)) + geom_point() +
stat_debug_group(geom = "label", vjust = c(-0.5,1.5))## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 50 × 5
## x y shape PANEL group
## * <dbl> <dbl> <fctr> <int> <int>
## 1 1 -27205.450 A 1 1
## 2 3 45790.918 A 1 1
## 3 5 -8028.578 A 1 1
## 4 7 -18547.282 A 1 1
## 5 9 79924.325 A 1 1
## 6 11 -2823.736 A 1 1
## 7 13 -78016.690 A 1 1
## 8 15 -74281.234 A 1 1
## 9 17 9903.674 A 1 1
## 10 19 -94022.623 A 1 1
## # ... with 40 more rows
## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 50 × 5
## x y shape PANEL group
## * <dbl> <dbl> <fctr> <int> <int>
## 1 2 -14242.65 B 1 2
## 2 4 53731.42 B 1 2
## 3 6 102863.94 B 1 2
## 4 8 13080.52 B 1 2
## 5 10 -44711.50 B 1 2
## 6 12 23839.55 B 1 2
## 7 14 75601.96 B 1 2
## 8 16 104676.72 B 1 2
## 9 18 -68746.93 B 1 2
## 10 20 -39230.19 B 1 2
## # ... with 40 more rows
The summary function can be a user defined one, which allows lots of flexibility.
ggplot(my.data, aes(x, y)) + geom_point() +
stat_debug_group(summary.fun = summary)## [1] "Input 'data' to 'compute_group()':"
## x y PANEL group
## Min. : 1.00 Min. : -94023 Min. :1 Min. :-1
## 1st Qu.: 25.75 1st Qu.: 40345 1st Qu.:1 1st Qu.:-1
## Median : 50.50 Median : 154036 Median :1 Median :-1
## Mean : 50.50 Mean : 266433 Mean :1 Mean :-1
## 3rd Qu.: 75.25 3rd Qu.: 422069 3rd Qu.:1 3rd Qu.:-1
## Max. :100.00 Max. :1077469 Max. :1 Max. :-1
ggplot(my.data, aes(x, y)) + geom_point() +
stat_debug_group(summary.fun = head)## [1] "Input 'data' to 'compute_group()':"
## x y PANEL group
## 1 1 -27205.450 1 -1
## 2 2 -14242.651 1 -1
## 3 3 45790.918 1 -1
## 4 4 53731.420 1 -1
## 5 5 -8028.578 1 -1
## 6 6 102863.943 1 -1
ggplot(my.data, aes(x, y)) + geom_point() +
stat_debug_group(summary.fun = nrow)## [1] "Input 'data' to 'compute_group()':"
## [1] 100
The default.
ggplot(my.data, aes(x, y)) + geom_point() +
stat_debug_group(summary.fun = as_data_frame)## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 100 × 4
## x y PANEL group
## * <dbl> <dbl> <int> <int>
## 1 1 -27205.450 1 -1
## 2 2 -14242.651 1 -1
## 3 3 45790.918 1 -1
## 4 4 53731.420 1 -1
## 5 5 -8028.578 1 -1
## 6 6 102863.943 1 -1
## 7 7 -18547.282 1 -1
## 8 8 13080.521 1 -1
## 9 9 79924.325 1 -1
## 10 10 -44711.499 1 -1
## # ... with 90 more rows
ggplot(my.data, aes(x, y)) + geom_point() +
stat_debug_group(summary.fun = head, summary.fun.args = list(n = 3))## [1] "Input 'data' to 'compute_group()':"
## x y PANEL group
## 1 1 -27205.45 1 -1
## 2 2 -14242.65 1 -1
## 3 3 45790.92 1 -1
This next chunk showing how to print the whole data frame is not run as its output is more than 100 lines long as the data set contains 100 observations.
ggplot(my.data, aes(x, y)) + geom_point() +
stat_debug_group(summary.fun = function(x) {x})With grouping, for each group the compute_group() function is called with a subset of the data.
ggplot(my.data, aes(x, y, colour = group)) + geom_point() +
stat_debug_group(summary.fun = head, summary.fun.args = list(n = 3))## [1] "Input 'data' to 'compute_group()':"
## x y colour PANEL group
## 1 1 -27205.450 A 1 1
## 3 3 45790.918 A 1 1
## 5 5 -8028.578 A 1 1
## [1] "Input 'data' to 'compute_group()':"
## x y colour PANEL group
## 2 2 -14242.65 B 1 2
## 4 4 53731.42 B 1 2
## 6 6 102863.94 B 1 2
In this example with grouping and facets, within each panel the compute_group() function is called for each group, in total four times.
ggplot(my.data, aes(x, y, colour = group)) + geom_point() +
stat_debug_group(summary.fun = nrow) +
facet_wrap(~block)## [1] "Input 'data' to 'compute_group()':"
## [1] 25
## [1] "Input 'data' to 'compute_group()':"
## [1] 25
## [1] "Input 'data' to 'compute_group()':"
## [1] 25
## [1] "Input 'data' to 'compute_group()':"
## [1] 25
With facets, for each panel the compute_panel() function is called with a subset of the data that is not split by groups. For our example, it is called twice.
ggplot(my.data, aes(x, y, colour = group)) + geom_point() +
stat_debug_panel(summary.fun = nrow) +
facet_wrap(~block)## [1] "Input 'data' to 'compute_panel()':"
## [1] 50
## [1] "Input 'data' to 'compute_panel()':"
## [1] 50
Finally we show how geom_debug() can be used. First to print to the console the data as passed to geoms.
ggplot(my.data, aes(x, y, colour = group)) + geom_point() +
geom_debug(summary.fun = head)## Input 'data' to 'geom_debug()':## colour x y PANEL group
## 1 #F8766D 1 -27205.450 1 1
## 2 #00BFC4 2 -14242.651 1 2
## 3 #F8766D 3 45790.918 1 1
## 4 #00BFC4 4 53731.420 1 2
## 5 #F8766D 5 -8028.578 1 1
## 6 #00BFC4 6 102863.943 1 2
And also to print to the console the data returned by a stat.
ggplot(my.data, aes(x, y, colour = group)) + geom_point() +
stat_smooth(method = "lm",
geom = "debug",
summary.fun = as_data_frame,
summary.fun.args = list())## Input 'data' to 'geom_debug()':## # A tibble: 160 × 8
## colour x y ymin ymax se PANEL group
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 #F8766D 1.000000 -188136.10 -254710.6 -121561.62 33111.18 1 1
## 2 #F8766D 2.240506 -176933.14 -242260.5 -111605.80 32490.90 1 1
## 3 #F8766D 3.481013 -165730.18 -229819.0 -101641.36 31874.92 1 1
## 4 #F8766D 4.721519 -154527.23 -217386.7 -91667.76 31263.50 1 1
## 5 #F8766D 5.962025 -143324.27 -204964.1 -81684.46 30656.89 1 1
## 6 #F8766D 7.202532 -132121.31 -192551.7 -71690.88 30055.40 1 1
## 7 #F8766D 8.443038 -120918.36 -180150.3 -61686.39 29459.34 1 1
## 8 #F8766D 9.683544 -109715.40 -167760.5 -51670.30 28869.04 1 1
## 9 #F8766D 10.924051 -98512.44 -155383.0 -41641.90 28284.87 1 1
## 10 #F8766D 12.164557 -87309.48 -143018.6 -31600.41 27707.21 1 1
## # ... with 150 more rows
ggplot(my.data, aes(x, y, colour = group)) + geom_point() +
stat_peaks(span = NULL,
geom = "debug",
summary.fun = as_data_frame,
summary.fun.args = list())## Input 'data' to 'geom_debug()':## # A tibble: 2 × 10
## colour xintercept yintercept label x y PANEL group x.label
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <int> <int> <chr>
## 1 #F8766D 95 984858 95 95 984858 1 1 95
## 2 #00BFC4 100 1077468 100 100 1077468 1 2 100
## # ... with 1 more variables: y.label <chr>
formula <- y ~ poly(x, 3, raw = TRUE)
ggplot(my.data, aes(x, y)) +
stat_fit_residuals(formula = formula,
geom = "debug",
summary.fun = as_data_frame,
summary.fun.args = list())## Input 'data' to 'geom_debug()':## # A tibble: 100 × 6
## x y y.resid y.resid.abs PANEL group
## <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 1 -23513.687 -23513.687 23513.687 1 -1
## 2 2 -11663.732 -11663.732 11663.732 1 -1
## 3 3 47289.460 47289.460 47289.460 1 -1
## 4 4 54175.231 54175.231 54175.231 1 -1
## 5 5 -8620.676 -8620.676 8620.676 1 -1
## 6 6 101247.937 101247.937 101247.937 1 -1
## 7 7 -21182.019 -21182.019 21182.019 1 -1
## 8 8 9425.407 9425.407 9425.407 1 -1
## 9 9 75240.365 75240.365 75240.365 1 -1
## 10 10 -50439.597 -50439.597 50439.597 1 -1
## # ... with 90 more rows
formula <- y ~ x + I(x^2) + I(x^3)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_fit_augment(method = "lm",
method.args = list(formula = formula),
geom = "debug",
summary.fun = tibble::as_data_frame,
summary.fun.args = list()) +
stat_fit_augment(method = "lm",
method.args = list(formula = formula),
geom = "smooth")## Input 'data' to 'geom_debug()':## # A tibble: 100 × 17
## ymin ymax y x I.x.2. I.x.3. .fitted .se.fit
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -48698.27 41314.74 -3691.7638 1 1 1 -3691.7638 22673.48
## 2 -44308.35 39150.51 -2578.9186 2 4 8 -2578.9186 21022.55
## 3 -40181.60 37184.51 -1498.5419 3 9 27 -1498.5419 19487.84
## 4 -36313.71 35426.09 -443.8109 4 16 64 -443.8109 18070.62
## 5 -32700.69 33884.88 592.0973 5 25 125 592.0973 16772.32
## 6 -29338.59 32570.60 1616.0056 6 36 216 1616.0056 15594.38
## 7 -26223.08 31492.56 2634.7369 7 49 343 2634.7369 14538.06
## 8 -23348.80 30659.03 3655.1141 8 64 512 3655.1141 13604.10
## 9 -20708.60 30076.52 4683.9600 9 81 729 4683.9600 12792.32
## 10 -18292.59 29748.78 5728.0975 10 100 1000 5728.0975 12101.20
## # ... with 90 more rows, and 9 more variables: .resid <dbl>, .hat <dbl>,
## # .sigma <dbl>, .cooksd <dbl>, .std.resid <dbl>, y.observed <dbl>,
## # t.value <dbl>, PANEL <int>, group <int>
formula <- y ~ x + I(x^2) + I(x^3)
ggplot(my.data, aes(x, y2, colour = group)) +
geom_point() +
stat_fit_augment(method = "lm",
method.args = list(formula = formula),
geom = "debug",
summary.fun = tibble::as_data_frame,
summary.fun.args = list()) +
stat_fit_augment(method = "lm",
method.args = list(formula = formula),
geom = "smooth")## Input 'data' to 'geom_debug()':## # A tibble: 100 × 19
## colour ymin ymax .rownames y x I.x.2. I.x.3.
## <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 #F8766D -41080.41 20399.43 1 -10340.4868 1 1 1
## 2 #F8766D -35273.92 17554.72 3 -8859.5995 3 9 27
## 3 #F8766D -30156.45 15352.63 5 -7401.9119 5 25 125
## 4 #F8766D -25719.57 13832.64 7 -5943.4676 7 49 343
## 5 #F8766D -21946.27 13025.65 9 -4460.3100 9 81 729
## 6 #F8766D -18793.72 12936.75 11 -2928.4828 11 121 1331
## 7 #F8766D -16175.63 13527.57 13 -1324.0295 13 169 2197
## 8 #F8766D -13958.28 14712.29 15 377.0064 15 225 3375
## 9 #F8766D -11978.53 16375.69 17 2198.5812 17 289 4913
## 10 #F8766D -10073.31 18402.61 19 4164.6515 19 361 6859
## # ... with 90 more rows, and 11 more variables: .fitted <dbl>,
## # .se.fit <dbl>, .resid <dbl>, .hat <dbl>, .sigma <dbl>, .cooksd <dbl>,
## # .std.resid <dbl>, y.observed <dbl>, t.value <dbl>, PANEL <int>,
## # group <int>
The package also defines a "null" geom, which is used as default by the debug stats described above. Currently this geom is similar to the recently added ggplot2::geom_blank().
ggplot(my.data, aes(x, y, colour = group)) + geom_null()