@@ -9,9 +9,6 @@ test_that(
99 data <- data.frame (x = 1 : 5 , y = rep(1 , 5 ), height = c(0 , 1 , 3 , 4 , 2 ))
1010 p <- ggplot(data , aes(x , y , height = height )) + geom_ridgeline()
1111
12- expect_no_error(
13- p2 <- plotly_build(p )
14- )
1512 p2 <- ggplotly(p )
1613
1714 expect_doppelganger(p2 , ' basic_ridgeline'
@@ -23,15 +20,13 @@ test_that(
2320
2421 # # Negative height cut off
2522 p <- plot_base + geom_ridgeline()
26- expect_no_error(plotly_build(p ))
2723
2824 p2 <- ggplotly(p )
2925 expect_doppelganger(p2 , ' negative_height_cut'
3026
3127
3228 # # Negative height allowed
3329 p <- plot_base + geom_ridgeline(min_height = - 2 )
34- expect_no_error(plotly_build(p ))
3530
3631 p2 <- ggplotly(p )
3732 expect_doppelganger(p2 , ' negative_height_retained'
@@ -46,15 +41,13 @@ test_that(
4641
4742 p <- ggplot(d , aes(x , y , height = height , group = y )) +
4843 geom_ridgeline(fill = " lightblue"
49- expect_no_error(plotly_build(p ))
5044
5145 p2 <- ggplotly(p )
5246 expect_doppelganger(p2 , ' multiple_ridgelines'
5347
5448 # stat = identity (works)
5549 p <- ggplot(d , aes(x , y , height = height , group = y )) +
5650 geom_density_ridges(stat = " identity" scale = 1 )
57- expect_no_error(plotly_build(p ))
5851
5952 p2 <- ggplotly(p )
6053 expect_doppelganger(p2 , ' stat_identity'
@@ -69,13 +62,11 @@ test_that(
6962
7063 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) +
7164 geom_density_ridges()
72- expect_no_error(plotly_build(p ))
7365 p2 <- ggplotly(p )
7466 expect_doppelganger(p2 , ' density_ridgeline'
7567
7668 # geom_density_ridges2 (closed polygon)
7769 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) + geom_density_ridges2()
78- expect_no_error(plotly_build(p ))
7970 p2 <- ggplotly(p )
8071 expect_doppelganger(p2 , ' density_ridgeline2'
8172
@@ -88,47 +79,39 @@ test_that(
8879 y = Species_num ,
8980 group = Species_num )) +
9081 geom_density_ridges()
91- expect_no_error(plotly_build(p ))
9282 p2 <- ggplotly(p )
9383 expect_doppelganger(p2 , ' numeric_grouping'
9484
9585 # Cutting trailing tails (works)
9686 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) +
9787 geom_density_ridges(rel_min_height = 0.01 )
98- expect_no_error(plotly_build(p ))
9988 p2 <- ggplotly(p )
10089 expect_doppelganger(p2 , ' cutting_tails'
101- ggplotly(p )
102- 10390
10491 # Non-overlapping ridges (Works)
10592 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) +
10693 geom_density_ridges(scale = 0.9 )
107- expect_no_error(plotly_build(p ))
10894 p2 <- ggplotly(p )
10995 expect_doppelganger(p2 , ' overlapping_none'
11096
11197
11298 # Exactly touching (Works)
11399 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) +
114100 geom_density_ridges(scale = 1 )
115- expect_no_error(plotly_build(p ))
116101 p2 <- ggplotly(p )
117102 expect_doppelganger(p2 , ' overlapping_touching'
118103
119104
120105 # scale = 5, substantial overlap (Works)
121106 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) +
122107 geom_density_ridges(scale = 5 )
123- expect_no_error(plotly_build(p ))
124108 p2 <- ggplotly(p )
125109 expect_doppelganger(p2 , ' overlapping_lot'
126110
127111
128112 # Panel scaling (Works)
129113 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) +
130114 geom_density_ridges(scale = 1 ) + facet_wrap(~ Species )
131- expect_no_error(plotly_build(p ))
132115 p2 <- ggplotly(p )
133116 expect_doppelganger(p2 , ' overlapping_facet_touching'
134117
@@ -150,7 +133,6 @@ test_that(
150133 p <- ggplot(d , aes(x , y , height = height , group = y , fill = factor (x + y ))) +
151134 geom_ridgeline_gradient() +
152135 scale_fill_viridis_d(direction = - 1 , guide = " none"
153- expect_no_error(plotly_build(p ))
154136 p2 <- ggplotly(p )
155137 expect_doppelganger(p2 , ' varying_fill_colours'
156138
@@ -168,14 +150,12 @@ test_that(
168150 # quantile multiple lines
169151 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) +
170152 stat_density_ridges(quantile_lines = TRUE )
171- expect_no_error(plotly_build(p ))
172153 p2 <- ggplotly(p )
173154 expect_doppelganger(p2 , ' quantile_lines_multi'
174155
175156 # quantile single line
176157 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) +
177158 stat_density_ridges(quantile_lines = TRUE , quantiles = 2 )
178- expect_no_error(plotly_build(p ))
179159 p2 <- ggplotly(p )
180160 expect_doppelganger(p2 , ' quantile_lines_1'
181161
@@ -184,7 +164,6 @@ test_that(
184164 stat_density_ridges(quantile_lines = TRUE ,
185165 quantiles = c(0.025 , 0.975 ),
186166 alpha = 0.7 )
187- expect_no_error(plotly_build(p ))
188167 p2 <- ggplotly(p )
189168 expect_doppelganger(p2 , ' quantile_cut_points'
190169
@@ -199,10 +178,10 @@ test_that(
199178 ) +
200179 scale_fill_viridis_d(name = " Quartiles"
201180 )
181+ 202182 suppressWarnings(
203- expect_no_error(plotly_build( p ) )
183+ p2 <- ggplotly( p )
204184 )
205- p2 <- ggplotly(p )
206185 expect_doppelganger(p2 , ' quantile_colouring'
207186
208187
@@ -217,7 +196,6 @@ test_that(
217196 name = " Probability" values = c(" #FF0000A0" " #A0A0A0A0" " #0000FFA0"
218197 labels = c(" (0, 0.025]" " (0.025, 0.975]" " (0.975, 1]"
219198 )
220- expect_no_error(plotly_build(p ))
221199 p2 <- ggplotly(p )
222200 expect_doppelganger(p2 , ' quantile_colouring_tails_only'
223201
@@ -236,10 +214,10 @@ test_that(
236214 desc = " ggridges points"
237215 code = {
238216
217+ set.seed(123 ) # make jittering reproducible
239218 # jittering points (works)
240219 p <- ggplot(iris , aes(x = Sepal.Length , y = Species )) +
241220 geom_density_ridges(jittered_points = TRUE )
242- expect_no_error(plotly_build(p ))
243221 p2 <- ggplotly(p )
244222 expect_doppelganger(p2 , ' jittering points'
245223
@@ -249,7 +227,6 @@ test_that(
249227 jittered_points = TRUE , position = " raincloud"
250228 alpha = 0.7 , scale = 0.9
251229 )
252- expect_no_error(plotly_build(p ))
253230 p2 <- ggplotly(p )
254231 expect_doppelganger(p2 , ' raincloud_effect'
255232
@@ -270,7 +247,6 @@ test_that(
270247 ) +
271248 scale_point_color_hue(l = 40 ) +
272249 scale_discrete_manual(aesthetics = " point_shape" values = c(21 , 22 , 23 ))
273- expect_no_error(plotly_build(p ))
274250 p2 <- ggplotly(p )
275251 expect_doppelganger(p2 , ' styling_points'
276252
@@ -282,7 +258,6 @@ test_that(
282258 ) +
283259 scale_point_color_hue(l = 40 ) + scale_point_size_continuous(range = c(0.5 , 4 )) +
284260 scale_discrete_manual(aesthetics = " point_shape" values = c(21 , 22 , 23 ))
285- expect_no_error(plotly_build(p ))
286261 p2 <- ggplotly(p )
287262 expect_doppelganger(p2 , ' styling_points2'
288263
@@ -295,7 +270,6 @@ test_that(
295270 point_size = 0.4 , point_alpha = 1 ,
296271 position = position_raincloud(adjust_vlines = TRUE )
297272 )
298- expect_no_error(plotly_build(p ))
299273 p2 <- ggplotly(p )
300274 expect_doppelganger(p2 , ' raincloud_vertical_line_points'
301275
@@ -312,9 +286,8 @@ test_that(
312286 p <- ggplot(iris , aes(x = Sepal.Length , y = Species , height = stat(density ))) +
313287 geom_density_ridges(stat = " density"
314288
315- expect_no_error(plotly_build( p ) )
289+ p2 <- ggplotly( p )
316290 })
317- p2 <- ggplotly(p )
318291 expect_doppelganger(p2 , ' stat_density'
319292
320293
@@ -326,14 +299,12 @@ test_that(
326299
327300 p <- ggplot(iris_densities , aes(x = Sepal.Length , y = Species , height = density )) +
328301 geom_density_ridges(stat = " identity"
329- expect_no_error(plotly_build(p ))
330302 p2 <- ggplotly(p )
331303 expect_doppelganger(p2 , ' manual_densities_stat_identity'
332304
333305 # # histograms (works)
334306 p <- ggplot(iris , aes(x = Sepal.Length , y = Species , height = stat(density ))) +
335307 geom_density_ridges(stat = " binline" bins = 20 , scale = 0.95 , draw_baseline = FALSE )
336- expect_no_error(plotly_build(p ))
337308 p2 <- ggplotly(p )
338309 expect_doppelganger(p2 , ' histogram_ridges'
339310
0 commit comments