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 x 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 x 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 x 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 x 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 x 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 x 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 x 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 x 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 x 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 x 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_tibble)## [1] "Input 'data' to 'compute_group()':"
## # A tibble: 100 x 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)## 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
We may even save the data input of the geom to a variable in the global environment, but this takes place at the time the ggplot object is printed.
tidy.assign <- function(value, name, pos = .GlobalEnv, ...) {
assign(x = name, value = value, inherits = FALSE, pos = pos, ...)
}
ggplot(my.data, aes(x, y, colour = group)) +
geom_point() +
geom_debug(summary.fun = tidy.assign,
summary.fun.args = list(name = "debug_data"),
print.fun = NULL)
debug_data## colour x y PANEL group
## 1 #F8766D 1 -27205.45039 1 1
## 2 #00BFC4 2 -14242.65108 1 2
## 3 #F8766D 3 45790.91787 1 1
## 4 #00BFC4 4 53731.42037 1 2
## 5 #F8766D 5 -8028.57848 1 1
## 6 #00BFC4 6 102863.94292 1 2
## 7 #F8766D 7 -18547.28227 1 1
## 8 #00BFC4 8 13080.52133 1 2
## 9 #F8766D 9 79924.32504 1 1
## 10 #00BFC4 10 -44711.49916 1 2
## 11 #F8766D 11 -2823.73559 1 1
## 12 #00BFC4 12 23839.55471 1 2
## 13 #F8766D 13 -78016.69001 1 1
## 14 #00BFC4 14 75601.95706 1 2
## 15 #F8766D 15 -74281.23373 1 1
## 16 #00BFC4 16 104676.72111 1 2
## 17 #F8766D 17 9903.67373 1 1
## 18 #00BFC4 18 -68746.93124 1 2
## 19 #F8766D 19 -94022.62267 1 1
## 20 #00BFC4 20 -39230.19259 1 2
## 21 #F8766D 21 40550.54085 1 1
## 22 #00BFC4 22 10961.10296 1 2
## 23 #F8766D 23 12149.63105 1 1
## 24 #00BFC4 24 52254.25671 1 2
## 25 #F8766D 25 9950.24731 1 1
## 26 #00BFC4 26 3101.82898 1 2
## 27 #F8766D 27 23485.44314 1 1
## 28 #00BFC4 28 41668.54023 1 2
## 29 #F8766D 29 -27901.59355 1 1
## 30 #00BFC4 30 -59669.17456 1 2
## 31 #F8766D 31 39726.66001 1 1
## 32 #00BFC4 32 76038.66842 1 2
## 33 #F8766D 33 109169.84766 1 1
## 34 #00BFC4 34 10202.63915 1 2
## 35 #F8766D 35 98481.72725 1 1
## 36 #00BFC4 36 73.97657 1 2
## 37 #F8766D 37 40859.18209 1 1
## 38 #00BFC4 38 104296.43202 1 2
## 39 #F8766D 39 76002.67764 1 1
## 40 #00BFC4 40 146450.81373 1 2
## 41 #F8766D 41 61283.85767 1 1
## 42 #00BFC4 42 151336.13587 1 2
## 43 #F8766D 43 108122.81907 1 1
## 44 #00BFC4 44 38005.48600 1 2
## 45 #F8766D 45 138750.60638 1 1
## 46 #00BFC4 46 105880.33312 1 2
## 47 #F8766D 47 172220.31845 1 1
## 48 #00BFC4 48 166652.15369 1 2
## 49 #F8766D 49 125860.04342 1 1
## 50 #00BFC4 50 128807.63745 1 2
## 51 #F8766D 51 150030.22974 1 1
## 52 #00BFC4 52 156734.97163 1 2
## 53 #F8766D 53 245768.82089 1 1
## 54 #00BFC4 54 293062.82187 1 2
## 55 #F8766D 55 178917.33063 1 1
## 56 #00BFC4 56 288374.87380 1 2
## 57 #F8766D 57 254755.27055 1 1
## 58 #00BFC4 58 123199.36853 1 2
## 59 #F8766D 59 274487.91623 1 1
## 60 #00BFC4 60 222623.20984 1 2
## 61 #F8766D 61 222842.96771 1 1
## 62 #00BFC4 62 241015.87419 1 2
## 63 #F8766D 63 220328.38018 1 1
## 64 #00BFC4 64 318806.51984 1 2
## 65 #F8766D 65 272864.11128 1 1
## 66 #00BFC4 66 265162.41694 1 2
## 67 #F8766D 67 255642.47850 1 1
## 68 #00BFC4 68 322194.31154 1 2
## 69 #F8766D 69 347017.72201 1 1
## 70 #00BFC4 70 359621.14058 1 2
## 71 #F8766D 71 409505.44488 1 1
## 72 #00BFC4 72 295070.27132 1 2
## 73 #F8766D 73 358004.66398 1 1
## 74 #00BFC4 74 459490.18232 1 2
## 75 #F8766D 75 486672.38910 1 1
## 76 #00BFC4 76 409640.42160 1 2
## 77 #F8766D 77 475457.21816 1 1
## 78 #00BFC4 78 508282.47227 1 2
## 79 #F8766D 79 511276.10109 1 1
## 80 #00BFC4 80 459354.48209 1 2
## 81 #F8766D 81 381879.15102 1 1
## 82 #00BFC4 82 594162.52358 1 2
## 83 #F8766D 83 580768.97604 1 1
## 84 #00BFC4 84 718909.76743 1 2
## 85 #F8766D 85 465913.07527 1 1
## 86 #00BFC4 86 680853.27505 1 2
## 87 #F8766D 87 745596.30841 1 1
## 88 #00BFC4 88 620977.11497 1 2
## 89 #F8766D 89 691123.81099 1 1
## 90 #00BFC4 90 739921.53095 1 2
## 91 #F8766D 91 724468.12123 1 1
## 92 #00BFC4 92 720974.71398 1 2
## 93 #F8766D 93 823013.91892 1 1
## 94 #00BFC4 94 829332.45926 1 2
## 95 #F8766D 95 984858.01120 1 1
## 96 #00BFC4 96 977486.03153 1 2
## 97 #F8766D 97 818881.35514 1 1
## 98 #00BFC4 98 1058604.36945 1 2
## 99 #F8766D 99 977556.06367 1 1
## 100 #00BFC4 100 1077468.45449 1 2
ggplot(my.data, aes(x, y, colour = group)) + geom_point() +
geom_debug(summary.fun = NULL)## colour x y PANEL group
## 1 #F8766D 1 -27205.45039 1 1
## 2 #00BFC4 2 -14242.65108 1 2
## 3 #F8766D 3 45790.91787 1 1
## 4 #00BFC4 4 53731.42037 1 2
## 5 #F8766D 5 -8028.57848 1 1
## 6 #00BFC4 6 102863.94292 1 2
## 7 #F8766D 7 -18547.28227 1 1
## 8 #00BFC4 8 13080.52133 1 2
## 9 #F8766D 9 79924.32504 1 1
## 10 #00BFC4 10 -44711.49916 1 2
## 11 #F8766D 11 -2823.73559 1 1
## 12 #00BFC4 12 23839.55471 1 2
## 13 #F8766D 13 -78016.69001 1 1
## 14 #00BFC4 14 75601.95706 1 2
## 15 #F8766D 15 -74281.23373 1 1
## 16 #00BFC4 16 104676.72111 1 2
## 17 #F8766D 17 9903.67373 1 1
## 18 #00BFC4 18 -68746.93124 1 2
## 19 #F8766D 19 -94022.62267 1 1
## 20 #00BFC4 20 -39230.19259 1 2
## 21 #F8766D 21 40550.54085 1 1
## 22 #00BFC4 22 10961.10296 1 2
## 23 #F8766D 23 12149.63105 1 1
## 24 #00BFC4 24 52254.25671 1 2
## 25 #F8766D 25 9950.24731 1 1
## 26 #00BFC4 26 3101.82898 1 2
## 27 #F8766D 27 23485.44314 1 1
## 28 #00BFC4 28 41668.54023 1 2
## 29 #F8766D 29 -27901.59355 1 1
## 30 #00BFC4 30 -59669.17456 1 2
## 31 #F8766D 31 39726.66001 1 1
## 32 #00BFC4 32 76038.66842 1 2
## 33 #F8766D 33 109169.84766 1 1
## 34 #00BFC4 34 10202.63915 1 2
## 35 #F8766D 35 98481.72725 1 1
## 36 #00BFC4 36 73.97657 1 2
## 37 #F8766D 37 40859.18209 1 1
## 38 #00BFC4 38 104296.43202 1 2
## 39 #F8766D 39 76002.67764 1 1
## 40 #00BFC4 40 146450.81373 1 2
## 41 #F8766D 41 61283.85767 1 1
## 42 #00BFC4 42 151336.13587 1 2
## 43 #F8766D 43 108122.81907 1 1
## 44 #00BFC4 44 38005.48600 1 2
## 45 #F8766D 45 138750.60638 1 1
## 46 #00BFC4 46 105880.33312 1 2
## 47 #F8766D 47 172220.31845 1 1
## 48 #00BFC4 48 166652.15369 1 2
## 49 #F8766D 49 125860.04342 1 1
## 50 #00BFC4 50 128807.63745 1 2
## 51 #F8766D 51 150030.22974 1 1
## 52 #00BFC4 52 156734.97163 1 2
## 53 #F8766D 53 245768.82089 1 1
## 54 #00BFC4 54 293062.82187 1 2
## 55 #F8766D 55 178917.33063 1 1
## 56 #00BFC4 56 288374.87380 1 2
## 57 #F8766D 57 254755.27055 1 1
## 58 #00BFC4 58 123199.36853 1 2
## 59 #F8766D 59 274487.91623 1 1
## 60 #00BFC4 60 222623.20984 1 2
## 61 #F8766D 61 222842.96771 1 1
## 62 #00BFC4 62 241015.87419 1 2
## 63 #F8766D 63 220328.38018 1 1
## 64 #00BFC4 64 318806.51984 1 2
## 65 #F8766D 65 272864.11128 1 1
## 66 #00BFC4 66 265162.41694 1 2
## 67 #F8766D 67 255642.47850 1 1
## 68 #00BFC4 68 322194.31154 1 2
## 69 #F8766D 69 347017.72201 1 1
## 70 #00BFC4 70 359621.14058 1 2
## 71 #F8766D 71 409505.44488 1 1
## 72 #00BFC4 72 295070.27132 1 2
## 73 #F8766D 73 358004.66398 1 1
## 74 #00BFC4 74 459490.18232 1 2
## 75 #F8766D 75 486672.38910 1 1
## 76 #00BFC4 76 409640.42160 1 2
## 77 #F8766D 77 475457.21816 1 1
## 78 #00BFC4 78 508282.47227 1 2
## 79 #F8766D 79 511276.10109 1 1
## 80 #00BFC4 80 459354.48209 1 2
## 81 #F8766D 81 381879.15102 1 1
## 82 #00BFC4 82 594162.52358 1 2
## 83 #F8766D 83 580768.97604 1 1
## 84 #00BFC4 84 718909.76743 1 2
## 85 #F8766D 85 465913.07527 1 1
## 86 #00BFC4 86 680853.27505 1 2
## 87 #F8766D 87 745596.30841 1 1
## 88 #00BFC4 88 620977.11497 1 2
## 89 #F8766D 89 691123.81099 1 1
## 90 #00BFC4 90 739921.53095 1 2
## 91 #F8766D 91 724468.12123 1 1
## 92 #00BFC4 92 720974.71398 1 2
## 93 #F8766D 93 823013.91892 1 1
## 94 #00BFC4 94 829332.45926 1 2
## 95 #F8766D 95 984858.01120 1 1
## 96 #00BFC4 96 977486.03153 1 2
## 97 #F8766D 97 818881.35514 1 1
## 98 #00BFC4 98 1058604.36945 1 2
## 99 #F8766D 99 977556.06367 1 1
## 100 #00BFC4 100 1077468.45449 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")## # A tibble: 160 x 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")## # A tibble: 2 x 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 = head, summary.fun.args = list(n = 12))## x y y.resid y.resid.abs PANEL group
## 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
## 11 11 -9618.085 -9618.085 9618.085 1 -1
## 12 12 15950.016 15950.016 15950.016 1 -1
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") +
stat_fit_augment(method = "lm",
method.args = list(formula = formula),
geom = "smooth")## # A tibble: 100 x 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") +
stat_fit_augment(method = "lm",
method.args = list(formula = formula),
geom = "smooth")## # A tibble: 100 x 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 ‘ggpmisc’ 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()