Grid

1.checkoutWrapper {
2  display: grid;
3  grid-template-columns: 50% 50%;
4}
5
6.coverIMGLast {
7  width: 70%;
8  height: 100vh;
9}
10
11/* this .phone class should be dynamically  located partially on first image */
12.phone {
13  position: absolute;
14  height: 90%;
15  top: 5%;
16  bottom: 5%;
17  margin-left: 18%;
18}
19
20.CheckoutProcess {
21  padding: 5.8rem 6.4rem;
22}
23
24.someContent {
25  border: 1px solid red;
26}
27
28/* removing for demo purposes
29@media screen and (max-width: 768px) {
30  .checkoutWrapper {
31    display: grid;
32    grid-template-columns: 100%;
33  }
34  img {
35    display: none;
36  }
37  .CheckoutProcess {
38    padding: 0;
39  }
40}
41*/<div class="checkoutWrapper">
42  <img src="https://via.placeholder.com/300" class="coverIMGLast" />
43  <img src="https://via.placeholder.com/300/ff0000" class="phone" />
44
45  <div class="CheckoutProcess">
46    <Content />
47  </div>
48</div>.checkoutWrapper {
49  display: grid;
50  grid-template-columns: 50% 50%;
51}
52
53.checkoutImgs {
54  position: relative;
55  border: 2px solid red;
56}
57
58img{
59  object-fit:cover;
60  display:block;
61}
62
63.coverIMGLast {
64  width: 70%;
65  height: 100vh;
66  
67}
68
69  
70.phone {
71  position: absolute;
72  height: 80%;
73  width:40%;
74  top: 10%;
75  right: 0;
76
77}
78
79.CheckoutProcess {
80  padding: 5.8rem 6.4rem;
81  border: 2px solid blue;
82}
83
84
85
86/* removing for demo purposes
87@media screen and (max-width: 768px) {
88  .checkoutWrapper {
89    display: grid;
90    grid-template-columns: 100%;
91  }
92  img {
93    display: none;
94  }
95  .CheckoutProcess {
96    padding: 0;
97  }
98}
99*/

1.checkoutWrapper {
2  display: grid;
3  grid-template-columns: 50% 50%;
4}
5
6.coverIMGLast {
7  width: 70%;
8  height: 100vh;
9}
10
11/* this .phone class should be dynamically  located partially on first image */
12.phone {
13  position: absolute;
14  height: 90%;
15  top: 5%;
16  bottom: 5%;
17  margin-left: 18%;
18}
19
20.CheckoutProcess {
21  padding: 5.8rem 6.4rem;
22}
23
24.someContent {
25  border: 1px solid red;
26}
27
28/* removing for demo purposes
29@media screen and (max-width: 768px) {
30  .checkoutWrapper {
31    display: grid;
32    grid-template-columns: 100%;
33  }
34  img {
35    display: none;
36  }
37  .CheckoutProcess {
38    padding: 0;
39  }
40}
41*/<div class="checkoutWrapper">
42  <img src="https://via.placeholder.com/300" class="coverIMGLast" />
43  <img src="https://via.placeholder.com/300/ff0000" class="phone" />
44
45  <div class="CheckoutProcess">
46    <Content />
47  </div>
48</div>.checkoutWrapper {
49  display: grid;
50  grid-template-columns: 50% 50%;
51}
52
53.checkoutImgs {
54  position: relative;
55  border: 2px solid red;
56}
57
58img{
59  object-fit:cover;
60  display:block;
61}
62
63.coverIMGLast {
64  width: 70%;
65  height: 100vh;
66  
67}
68
69  
70.phone {
71  position: absolute;
72  height: 80%;
73  width:40%;
74  top: 10%;
75  right: 0;
76
77}
78
79.CheckoutProcess {
80  padding: 5.8rem 6.4rem;
81  border: 2px solid blue;
82}
83
84
85
86/* removing for demo purposes
87@media screen and (max-width: 768px) {
88  .checkoutWrapper {
89    display: grid;
90    grid-template-columns: 100%;
91  }
92  img {
93    display: none;
94  }
95  .CheckoutProcess {
96    padding: 0;
97  }
98}
99*/<div class="checkoutWrapper">
100  <div class="checkoutImgs">
101    <img src="https://via.placeholder.com/300" class="coverIMGLast" />
102    <img src="https://via.placeholder.com/300/ff0000" class="phone" />
103  </div>
104
105  <div class="CheckoutProcess">
106    <Content />
107  </div>
108</div>

1library(ggplot2)
2library(magrittr)
3library(dplyr)
4library(ggrepel)
5library(tibble)
6library(cowplot)
7library(patchwork)
8
9StatTriangles <- ggproto("StatTriangles", Stat,
10  required_aes = c('x', 'y', 'z'),
11  compute_group = function(data, scales, params, width = 1, height_scale = .05, width_scale = .05, angle = 0) {
12
13    # specify default width
14    if (is.null(data$width)) data$width <- 1
15
16    # for each row of the data, create the 3 points that will make up our
17    # triangle based on the z, width, height_scale, and width_scale given.
18        triangle_df <-
19            tibble::tibble(
20                group = 1:nrow(data),
21                point1 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] - width[[i]]/2*width_scale, y[[i]]))}),
22                point2 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] + width[[i]]/2*width_scale, y[[i]]))}),
23                point3 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]], y[[i]] + z[[i]]*height_scale))})
24            )
25
26        # pivot the data into a long format so that each coordinate pair (e.g. vertex)
27        # will be its own row
28        triangle_df <- triangle_df %>% tidyr::pivot_longer(
29            cols = c(point1, point2, point3),
30            names_to = 'vertex',
31            values_to = 'coordinates'
32        )
33
34        # extract the coordinates -- this must be done rowwise because
35        # coordinates is a list where each element is a c(x,y) coordinate pair
36        triangle_df <- triangle_df %>% rowwise() %>% mutate(
37            x = coordinates[[1]],
38            y = coordinates[[2]])
39
40        # save the original x and y so we can perform rotations by the
41        # given angle with reference to (orig_x, orig_y) as the fixed point
42        # of the rotation transformation
43    triangle_df$orig_x <- rep(data$x, each = 3)
44    triangle_df$orig_y <- rep(data$y, each = 3)
45
46    # i'm not sure exactly why, but if the group isn't interacted with linetype
47    # then the edges of the triangles get messed up when rendered when linetype
48    # is used in an aesthetic
49    # triangle_df$group <-
50    #   paste0(triangle_df$orig_x, triangle_df$orig_y, triangle_df$group, rep(data$group, each = 3))
51
52        # fill in aesthetics to the dataframe
53    triangle_df$colour <- rep(data$colour, each = 3)
54    triangle_df$size <- rep(data$size, each = 3)
55    triangle_df$fill <- rep(data$fill, each = 3)
56    triangle_df$linetype <- rep(data$linetype, each = 3)
57    triangle_df$alpha <- rep(data$alpha, each = 3)
58    triangle_df$angle <- rep(data$angle, each = 3)
59
60    # determine scaling factor in going from y to x
61    # scale_factor <- diff(range(data$x)) / diff(range(data$y))
62    scale_factor <- diff(scales$x$get_limits()) / diff(scales$y$get_limits())
63    if (! is.finite(scale_factor) | is.na(scale_factor)) scale_factor <- 1
64
65    # rotate the data according to the angle by first subtracting out the
66    # (orig_x, orig_y) component, applying coordinate rotations, and then
67    # adding the (orig_x, orig_y) component back in.
68        new_coords <- triangle_df %>% mutate(
69      x_diff = x - orig_x,
70      y_diff = (y - orig_y) * scale_factor,
71      x_new = x_diff * cos(angle) - y_diff * sin(angle),
72      y_new = x_diff * sin(angle) + y_diff * cos(angle),
73      x_new = orig_x + x_new*scale_factor,
74      y_new = (orig_y + y_new)
75        )
76
77        # overwrite the x,y coordinates with the newly computed coordinates
78        triangle_df$x <- new_coords$x_new
79        triangle_df$y <- new_coords$y_new
80
81    triangle_df
82  }
83)
84
85stat_triangles <- function(mapping = NULL, data = NULL, geom = "polygon",
86                       position = "identity", na.rm = FALSE, show.legend = NA,
87                       inherit.aes = TRUE, ...) {
88  layer(
89    stat = StatTriangles, data = data, mapping = mapping, geom = geom,
90    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
91    params = list(na.rm = na.rm, ...)
92  )
93}
94
95GeomTriangles <- ggproto("GeomTriangles", GeomPolygon,
96    default_aes = aes(
97            color = 'black', fill = "black", size = 0.5, linetype = 1, alpha = 1, angle = 0, width = 1
98        )
99)
100
101geom_triangles <- function(mapping = NULL, data = NULL,
102                       position = "identity", na.rm = FALSE, show.legend = NA,
103                       inherit.aes = TRUE, ...) {
104  layer(
105    stat = StatTriangles, geom = GeomTriangles, data = data, mapping = mapping,
106    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
107    params = list(na.rm = na.rm, ...)
108  )
109}
110
111# here's an example using mtcars 
112
113plt_orig <- mtcars %>%
114  tibble::rownames_to_column('name') %>%
115  ggplot(aes(x = mpg, y = disp, z = cyl, width = wt, color = hp, fill = hp, label = name)) +
116  geom_triangles(width_scale = 10, height_scale = 15, alpha = .7) +
117  geom_point(color = 'black', size = 1) +
118  ggrepel::geom_text_repel(color = 'black', size = 2, nudge_y = -10) +
119  scale_fill_viridis_c(end = .6) +
120  scale_color_viridis_c(end = .6) +
121  xlab("miles per gallon") +
122  ylab("engine displacement (cu. in.)") +
123  labs(fill = 'horsepower', color = 'horsepower') +
124  ggtitle("MPG, Engine Displacement, # of Cylinders, Weight, and Horsepower of Cars from the 1974 Motor Trends Magazine",
125  "Cylinders shown in height, weight in width, horsepower in color") +
126  theme_bw() +
127  theme(plot.title = element_text(size = 10), plot.subtitle = element_text(size = 8), legend.title = element_text(size = 10))
128
129plt_orig
130draw_geom_triangles_height_legend <- function(
131  width = 1,
132  width_scale = .1,
133  height_scale = .1,
134  z_values = 1:3,
135  n.breaks = 3,
136  labels = c("low", "medium", "high"),
137  color = 'black',
138  fill = 'black'
139) {
140  ggplot(
141    data = data.frame(x = rep(0, times = n.breaks),
142                      y = seq(1,n.breaks),
143                      z = quantile(z_values, seq(0, 1, length.out = n.breaks)) %>% as.vector(),
144                      width = width,
145                      label = labels,
146                      color = color,
147                      fill = fill
148    ),
149    mapping = aes(x = x, y = y, z = z, label = label, width = width)
150  ) +
151    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
152    geom_text(mapping = aes(x = x + .5), size = 3) +
153    expand_limits(x = c(-.25, 3/4)) +
154    theme_void() +
155    theme(plot.title = element_text(size = 10, hjust = .5))
156}
157
158draw_geom_triangles_width_legend <- function(
159  width = 1:3,
160  width_scale = .1,
161  height_scale = .1,
162  z_values = 1,
163  n.breaks = 3,
164  labels = c("low", "medium", "high"),
165  color = 'black',
166  fill = 'black'
167) {
168  ggplot(
169    data = data.frame(x = rep(0, times = n.breaks),
170                      y = seq(1, n.breaks),
171                      z = rep(1, n.breaks),
172                      width = width,
173                      label = labels,
174                      color = color,
175                      fill = fill
176    ),
177    mapping = aes(x = x, y = y, z = z, label = label, width = width)
178  ) +
179    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
180    geom_text(mapping = aes(x = x + .5), size = 3) +
181    expand_limits(x = c(-.25, 3/4)) +
182    theme_void() +
183    theme(plot.title = element_text(size = 10, hjust = .5))
184}
185
186# extract the original legend - this is for the color and fill (hp)
187legend_hp <- cowplot::get_legend(plt_orig)
188
189# remove the legend from the plot
190plt <- plt_orig + theme(legend.position = 'none')
191
192# create a height legend using draw_geom_triangles_height_legend
193height_legend <- 
194  draw_geom_triangles_height_legend(z_values = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl)),
195                                    labels = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl))
196                                    ) +
197                                    ggtitle("cylinders\n")
198
199
200# create a width legend using draw_geom_triangles_width_legend
201width_legend <- 
202  draw_geom_triangles_width_legend(
203  width = quantile(mtcars$wt, c(.33, .66, 1)),
204  labels = round(quantile(mtcars$wt, c(.33, .66, 1)), 2),
205  width_scale = .2
206  ) +
207  ggtitle("weight\n(1000 lbs)\n")
208
209blank_plot <- ggplot() + theme_void()
210  
211# create a legend column layout
212# 
213# whitespace is used above, below, and in-between the legend components to
214# make sure the legend column pieces don't appear too densely stacked.
215# 
216legend_component <-
217  (blank_plot /  cowplot::plot_grid(legend_hp) / blank_plot /  height_legend / blank_plot / width_legend / blank_plot) +
218  plot_layout(heights = c(1, 1, .5, 1, .5, 1, 1))
219
220# create the layout with the plot and the legend component
221(plt + legend_component) + 
222  plot_layout(nrow = 1, widths = c(1, .15))
223> sessionInfo()
224R version 4.1.2 (2021-11-01)
225Platform: x86_64-apple-darwin17.0 (64-bit)
226Running under: macOS Mojave 10.14.2
227
228Matrix products: default
229BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
230LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
231
232locale:
233[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
234
235attached base packages:
236[1] stats     graphics  grDevices utils     datasets  methods   base     
237
238other attached packages:
239[1] patchwork_1.1.1 cowplot_1.1.1   tibble_3.1.6    ggrepel_0.9.1   dplyr_1.0.7     magrittr_2.0.1  ggplot2_3.3.5   colorout_1.2-2 
240
241loaded via a namespace (and not attached):
242 [1] Rcpp_1.0.7        tidyselect_1.1.1  munsell_0.5.0     viridisLite_0.4.0 colorspace_2.0-2  R6_2.5.1          rlang_0.4.12      fansi_0.5.0      
243 [9] tools_4.1.2       grid_4.1.2        gtable_0.3.0      utf8_1.2.2        DBI_1.1.2         withr_2.4.3       ellipsis_0.3.2    digest_0.6.29    
244[17] yaml_2.2.1        assertthat_0.2.1  lifecycle_1.0.1   crayon_1.4.2      tidyr_1.1.4       farver_2.1.0      purrr_0.3.4       vctrs_0.3.8      
245[25] glue_1.6.0        labeling_0.4.2    compiler_4.1.2    pillar_1.6.4      generics_0.1.1    scales_1.1.1      pkgconfig_2.0.3  
246library(ggplot2)
247
248GeomTriangles <- ggproto(
249  "GeomTriangles", GeomPoint,
250  default_aes = aes(
251    colour = "black", fill = "black", size = 0.5, linetype = 1, 
252    alpha = 1, angle = 0, width = 0.5, height = 0.5
253  ),
254  
255  draw_panel = function(
256    data, panel_params, coord, na.rm = FALSE
257  ) {
258    # Apply coordinate transform
259    df <- coord$transform(data, panel_params)
260    
261    # Repeat every row 3x
262    idx <- rep(seq_len(nrow(df)), each = 3)
263    rep_df <- df[idx, ]
264    # Calculate offsets from origin
265    x_off <- as.vector(outer(c(-0.5, 0, 0.5), df$width))
266    y_off <- as.vector(outer(c(0, 1, 0), df$height))
267    
268    # Rotate offsets
269    ang <- rep_df$angle * (pi / 180)
270    x_new <- x_off * cos(ang) - y_off * sin(ang)
271    y_new <- x_off * sin(ang) + y_off * cos(ang)
272    
273    # Combine offsets with origin
274    x <- unit(rep_df$x, "npc") + unit(x_new, "cm")
275    y <- unit(rep_df$y, "npc") + unit(y_new, "cm")
276    
277    grid::polygonGrob(
278      x = x, y = y, id = idx,
279      gp = grid::gpar(
280        col  = alpha(df$colour, df$alpha),
281        fill = alpha(df$fill, df$alpha),
282        lwd  = df$size * .pt,
283        lty  = df$linetype
284      )
285    )
286  }
287)
288

1library(ggplot2)
2library(magrittr)
3library(dplyr)
4library(ggrepel)
5library(tibble)
6library(cowplot)
7library(patchwork)
8
9StatTriangles <- ggproto("StatTriangles", Stat,
10  required_aes = c('x', 'y', 'z'),
11  compute_group = function(data, scales, params, width = 1, height_scale = .05, width_scale = .05, angle = 0) {
12
13    # specify default width
14    if (is.null(data$width)) data$width <- 1
15
16    # for each row of the data, create the 3 points that will make up our
17    # triangle based on the z, width, height_scale, and width_scale given.
18        triangle_df <-
19            tibble::tibble(
20                group = 1:nrow(data),
21                point1 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] - width[[i]]/2*width_scale, y[[i]]))}),
22                point2 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] + width[[i]]/2*width_scale, y[[i]]))}),
23                point3 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]], y[[i]] + z[[i]]*height_scale))})
24            )
25
26        # pivot the data into a long format so that each coordinate pair (e.g. vertex)
27        # will be its own row
28        triangle_df <- triangle_df %>% tidyr::pivot_longer(
29            cols = c(point1, point2, point3),
30            names_to = 'vertex',
31            values_to = 'coordinates'
32        )
33
34        # extract the coordinates -- this must be done rowwise because
35        # coordinates is a list where each element is a c(x,y) coordinate pair
36        triangle_df <- triangle_df %>% rowwise() %>% mutate(
37            x = coordinates[[1]],
38            y = coordinates[[2]])
39
40        # save the original x and y so we can perform rotations by the
41        # given angle with reference to (orig_x, orig_y) as the fixed point
42        # of the rotation transformation
43    triangle_df$orig_x <- rep(data$x, each = 3)
44    triangle_df$orig_y <- rep(data$y, each = 3)
45
46    # i'm not sure exactly why, but if the group isn't interacted with linetype
47    # then the edges of the triangles get messed up when rendered when linetype
48    # is used in an aesthetic
49    # triangle_df$group <-
50    #   paste0(triangle_df$orig_x, triangle_df$orig_y, triangle_df$group, rep(data$group, each = 3))
51
52        # fill in aesthetics to the dataframe
53    triangle_df$colour <- rep(data$colour, each = 3)
54    triangle_df$size <- rep(data$size, each = 3)
55    triangle_df$fill <- rep(data$fill, each = 3)
56    triangle_df$linetype <- rep(data$linetype, each = 3)
57    triangle_df$alpha <- rep(data$alpha, each = 3)
58    triangle_df$angle <- rep(data$angle, each = 3)
59
60    # determine scaling factor in going from y to x
61    # scale_factor <- diff(range(data$x)) / diff(range(data$y))
62    scale_factor <- diff(scales$x$get_limits()) / diff(scales$y$get_limits())
63    if (! is.finite(scale_factor) | is.na(scale_factor)) scale_factor <- 1
64
65    # rotate the data according to the angle by first subtracting out the
66    # (orig_x, orig_y) component, applying coordinate rotations, and then
67    # adding the (orig_x, orig_y) component back in.
68        new_coords <- triangle_df %>% mutate(
69      x_diff = x - orig_x,
70      y_diff = (y - orig_y) * scale_factor,
71      x_new = x_diff * cos(angle) - y_diff * sin(angle),
72      y_new = x_diff * sin(angle) + y_diff * cos(angle),
73      x_new = orig_x + x_new*scale_factor,
74      y_new = (orig_y + y_new)
75        )
76
77        # overwrite the x,y coordinates with the newly computed coordinates
78        triangle_df$x <- new_coords$x_new
79        triangle_df$y <- new_coords$y_new
80
81    triangle_df
82  }
83)
84
85stat_triangles <- function(mapping = NULL, data = NULL, geom = "polygon",
86                       position = "identity", na.rm = FALSE, show.legend = NA,
87                       inherit.aes = TRUE, ...) {
88  layer(
89    stat = StatTriangles, data = data, mapping = mapping, geom = geom,
90    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
91    params = list(na.rm = na.rm, ...)
92  )
93}
94
95GeomTriangles <- ggproto("GeomTriangles", GeomPolygon,
96    default_aes = aes(
97            color = 'black', fill = "black", size = 0.5, linetype = 1, alpha = 1, angle = 0, width = 1
98        )
99)
100
101geom_triangles <- function(mapping = NULL, data = NULL,
102                       position = "identity", na.rm = FALSE, show.legend = NA,
103                       inherit.aes = TRUE, ...) {
104  layer(
105    stat = StatTriangles, geom = GeomTriangles, data = data, mapping = mapping,
106    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
107    params = list(na.rm = na.rm, ...)
108  )
109}
110
111# here's an example using mtcars 
112
113plt_orig <- mtcars %>%
114  tibble::rownames_to_column('name') %>%
115  ggplot(aes(x = mpg, y = disp, z = cyl, width = wt, color = hp, fill = hp, label = name)) +
116  geom_triangles(width_scale = 10, height_scale = 15, alpha = .7) +
117  geom_point(color = 'black', size = 1) +
118  ggrepel::geom_text_repel(color = 'black', size = 2, nudge_y = -10) +
119  scale_fill_viridis_c(end = .6) +
120  scale_color_viridis_c(end = .6) +
121  xlab("miles per gallon") +
122  ylab("engine displacement (cu. in.)") +
123  labs(fill = 'horsepower', color = 'horsepower') +
124  ggtitle("MPG, Engine Displacement, # of Cylinders, Weight, and Horsepower of Cars from the 1974 Motor Trends Magazine",
125  "Cylinders shown in height, weight in width, horsepower in color") +
126  theme_bw() +
127  theme(plot.title = element_text(size = 10), plot.subtitle = element_text(size = 8), legend.title = element_text(size = 10))
128
129plt_orig
130draw_geom_triangles_height_legend <- function(
131  width = 1,
132  width_scale = .1,
133  height_scale = .1,
134  z_values = 1:3,
135  n.breaks = 3,
136  labels = c("low", "medium", "high"),
137  color = 'black',
138  fill = 'black'
139) {
140  ggplot(
141    data = data.frame(x = rep(0, times = n.breaks),
142                      y = seq(1,n.breaks),
143                      z = quantile(z_values, seq(0, 1, length.out = n.breaks)) %>% as.vector(),
144                      width = width,
145                      label = labels,
146                      color = color,
147                      fill = fill
148    ),
149    mapping = aes(x = x, y = y, z = z, label = label, width = width)
150  ) +
151    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
152    geom_text(mapping = aes(x = x + .5), size = 3) +
153    expand_limits(x = c(-.25, 3/4)) +
154    theme_void() +
155    theme(plot.title = element_text(size = 10, hjust = .5))
156}
157
158draw_geom_triangles_width_legend <- function(
159  width = 1:3,
160  width_scale = .1,
161  height_scale = .1,
162  z_values = 1,
163  n.breaks = 3,
164  labels = c("low", "medium", "high"),
165  color = 'black',
166  fill = 'black'
167) {
168  ggplot(
169    data = data.frame(x = rep(0, times = n.breaks),
170                      y = seq(1, n.breaks),
171                      z = rep(1, n.breaks),
172                      width = width,
173                      label = labels,
174                      color = color,
175                      fill = fill
176    ),
177    mapping = aes(x = x, y = y, z = z, label = label, width = width)
178  ) +
179    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
180    geom_text(mapping = aes(x = x + .5), size = 3) +
181    expand_limits(x = c(-.25, 3/4)) +
182    theme_void() +
183    theme(plot.title = element_text(size = 10, hjust = .5))
184}
185
186# extract the original legend - this is for the color and fill (hp)
187legend_hp <- cowplot::get_legend(plt_orig)
188
189# remove the legend from the plot
190plt <- plt_orig + theme(legend.position = 'none')
191
192# create a height legend using draw_geom_triangles_height_legend
193height_legend <- 
194  draw_geom_triangles_height_legend(z_values = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl)),
195                                    labels = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl))
196                                    ) +
197                                    ggtitle("cylinders\n")
198
199
200# create a width legend using draw_geom_triangles_width_legend
201width_legend <- 
202  draw_geom_triangles_width_legend(
203  width = quantile(mtcars$wt, c(.33, .66, 1)),
204  labels = round(quantile(mtcars$wt, c(.33, .66, 1)), 2),
205  width_scale = .2
206  ) +
207  ggtitle("weight\n(1000 lbs)\n")
208
209blank_plot <- ggplot() + theme_void()
210  
211# create a legend column layout
212# 
213# whitespace is used above, below, and in-between the legend components to
214# make sure the legend column pieces don't appear too densely stacked.
215# 
216legend_component <-
217  (blank_plot /  cowplot::plot_grid(legend_hp) / blank_plot /  height_legend / blank_plot / width_legend / blank_plot) +
218  plot_layout(heights = c(1, 1, .5, 1, .5, 1, 1))
219
220# create the layout with the plot and the legend component
221(plt + legend_component) + 
222  plot_layout(nrow = 1, widths = c(1, .15))
223> sessionInfo()
224R version 4.1.2 (2021-11-01)
225Platform: x86_64-apple-darwin17.0 (64-bit)
226Running under: macOS Mojave 10.14.2
227
228Matrix products: default
229BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
230LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
231
232locale:
233[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
234
235attached base packages:
236[1] stats     graphics  grDevices utils     datasets  methods   base     
237
238other attached packages:
239[1] patchwork_1.1.1 cowplot_1.1.1   tibble_3.1.6    ggrepel_0.9.1   dplyr_1.0.7     magrittr_2.0.1  ggplot2_3.3.5   colorout_1.2-2 
240
241loaded via a namespace (and not attached):
242 [1] Rcpp_1.0.7        tidyselect_1.1.1  munsell_0.5.0     viridisLite_0.4.0 colorspace_2.0-2  R6_2.5.1          rlang_0.4.12      fansi_0.5.0      
243 [9] tools_4.1.2       grid_4.1.2        gtable_0.3.0      utf8_1.2.2        DBI_1.1.2         withr_2.4.3       ellipsis_0.3.2    digest_0.6.29    
244[17] yaml_2.2.1        assertthat_0.2.1  lifecycle_1.0.1   crayon_1.4.2      tidyr_1.1.4       farver_2.1.0      purrr_0.3.4       vctrs_0.3.8      
245[25] glue_1.6.0        labeling_0.4.2    compiler_4.1.2    pillar_1.6.4      generics_0.1.1    scales_1.1.1      pkgconfig_2.0.3  
246library(ggplot2)
247
248GeomTriangles <- ggproto(
249  "GeomTriangles", GeomPoint,
250  default_aes = aes(
251    colour = "black", fill = "black", size = 0.5, linetype = 1, 
252    alpha = 1, angle = 0, width = 0.5, height = 0.5
253  ),
254  
255  draw_panel = function(
256    data, panel_params, coord, na.rm = FALSE
257  ) {
258    # Apply coordinate transform
259    df <- coord$transform(data, panel_params)
260    
261    # Repeat every row 3x
262    idx <- rep(seq_len(nrow(df)), each = 3)
263    rep_df <- df[idx, ]
264    # Calculate offsets from origin
265    x_off <- as.vector(outer(c(-0.5, 0, 0.5), df$width))
266    y_off <- as.vector(outer(c(0, 1, 0), df$height))
267    
268    # Rotate offsets
269    ang <- rep_df$angle * (pi / 180)
270    x_new <- x_off * cos(ang) - y_off * sin(ang)
271    y_new <- x_off * sin(ang) + y_off * cos(ang)
272    
273    # Combine offsets with origin
274    x <- unit(rep_df$x, "npc") + unit(x_new, "cm")
275    y <- unit(rep_df$y, "npc") + unit(y_new, "cm")
276    
277    grid::polygonGrob(
278      x = x, y = y, id = idx,
279      gp = grid::gpar(
280        col  = alpha(df$colour, df$alpha),
281        fill = alpha(df$fill, df$alpha),
282        lwd  = df$size * .pt,
283        lty  = df$linetype
284      )
285    )
286  }
287)
288geom_triangles <- function(mapping = NULL, data = NULL,
289                           position = "identity", na.rm = FALSE, show.legend = NA,
290                           inherit.aes = TRUE, ...) {
291  layer(
292    stat = "identity", geom = GeomTriangles, data = data, mapping = mapping,
293    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
294    params = list(na.rm = na.rm, ...)
295  )
296}
297

1library(ggplot2)
2library(magrittr)
3library(dplyr)
4library(ggrepel)
5library(tibble)
6library(cowplot)
7library(patchwork)
8
9StatTriangles <- ggproto("StatTriangles", Stat,
10  required_aes = c('x', 'y', 'z'),
11  compute_group = function(data, scales, params, width = 1, height_scale = .05, width_scale = .05, angle = 0) {
12
13    # specify default width
14    if (is.null(data$width)) data$width <- 1
15
16    # for each row of the data, create the 3 points that will make up our
17    # triangle based on the z, width, height_scale, and width_scale given.
18        triangle_df <-
19            tibble::tibble(
20                group = 1:nrow(data),
21                point1 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] - width[[i]]/2*width_scale, y[[i]]))}),
22                point2 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] + width[[i]]/2*width_scale, y[[i]]))}),
23                point3 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]], y[[i]] + z[[i]]*height_scale))})
24            )
25
26        # pivot the data into a long format so that each coordinate pair (e.g. vertex)
27        # will be its own row
28        triangle_df <- triangle_df %>% tidyr::pivot_longer(
29            cols = c(point1, point2, point3),
30            names_to = 'vertex',
31            values_to = 'coordinates'
32        )
33
34        # extract the coordinates -- this must be done rowwise because
35        # coordinates is a list where each element is a c(x,y) coordinate pair
36        triangle_df <- triangle_df %>% rowwise() %>% mutate(
37            x = coordinates[[1]],
38            y = coordinates[[2]])
39
40        # save the original x and y so we can perform rotations by the
41        # given angle with reference to (orig_x, orig_y) as the fixed point
42        # of the rotation transformation
43    triangle_df$orig_x <- rep(data$x, each = 3)
44    triangle_df$orig_y <- rep(data$y, each = 3)
45
46    # i'm not sure exactly why, but if the group isn't interacted with linetype
47    # then the edges of the triangles get messed up when rendered when linetype
48    # is used in an aesthetic
49    # triangle_df$group <-
50    #   paste0(triangle_df$orig_x, triangle_df$orig_y, triangle_df$group, rep(data$group, each = 3))
51
52        # fill in aesthetics to the dataframe
53    triangle_df$colour <- rep(data$colour, each = 3)
54    triangle_df$size <- rep(data$size, each = 3)
55    triangle_df$fill <- rep(data$fill, each = 3)
56    triangle_df$linetype <- rep(data$linetype, each = 3)
57    triangle_df$alpha <- rep(data$alpha, each = 3)
58    triangle_df$angle <- rep(data$angle, each = 3)
59
60    # determine scaling factor in going from y to x
61    # scale_factor <- diff(range(data$x)) / diff(range(data$y))
62    scale_factor <- diff(scales$x$get_limits()) / diff(scales$y$get_limits())
63    if (! is.finite(scale_factor) | is.na(scale_factor)) scale_factor <- 1
64
65    # rotate the data according to the angle by first subtracting out the
66    # (orig_x, orig_y) component, applying coordinate rotations, and then
67    # adding the (orig_x, orig_y) component back in.
68        new_coords <- triangle_df %>% mutate(
69      x_diff = x - orig_x,
70      y_diff = (y - orig_y) * scale_factor,
71      x_new = x_diff * cos(angle) - y_diff * sin(angle),
72      y_new = x_diff * sin(angle) + y_diff * cos(angle),
73      x_new = orig_x + x_new*scale_factor,
74      y_new = (orig_y + y_new)
75        )
76
77        # overwrite the x,y coordinates with the newly computed coordinates
78        triangle_df$x <- new_coords$x_new
79        triangle_df$y <- new_coords$y_new
80
81    triangle_df
82  }
83)
84
85stat_triangles <- function(mapping = NULL, data = NULL, geom = "polygon",
86                       position = "identity", na.rm = FALSE, show.legend = NA,
87                       inherit.aes = TRUE, ...) {
88  layer(
89    stat = StatTriangles, data = data, mapping = mapping, geom = geom,
90    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
91    params = list(na.rm = na.rm, ...)
92  )
93}
94
95GeomTriangles <- ggproto("GeomTriangles", GeomPolygon,
96    default_aes = aes(
97            color = 'black', fill = "black", size = 0.5, linetype = 1, alpha = 1, angle = 0, width = 1
98        )
99)
100
101geom_triangles <- function(mapping = NULL, data = NULL,
102                       position = "identity", na.rm = FALSE, show.legend = NA,
103                       inherit.aes = TRUE, ...) {
104  layer(
105    stat = StatTriangles, geom = GeomTriangles, data = data, mapping = mapping,
106    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
107    params = list(na.rm = na.rm, ...)
108  )
109}
110
111# here's an example using mtcars 
112
113plt_orig <- mtcars %>%
114  tibble::rownames_to_column('name') %>%
115  ggplot(aes(x = mpg, y = disp, z = cyl, width = wt, color = hp, fill = hp, label = name)) +
116  geom_triangles(width_scale = 10, height_scale = 15, alpha = .7) +
117  geom_point(color = 'black', size = 1) +
118  ggrepel::geom_text_repel(color = 'black', size = 2, nudge_y = -10) +
119  scale_fill_viridis_c(end = .6) +
120  scale_color_viridis_c(end = .6) +
121  xlab("miles per gallon") +
122  ylab("engine displacement (cu. in.)") +
123  labs(fill = 'horsepower', color = 'horsepower') +
124  ggtitle("MPG, Engine Displacement, # of Cylinders, Weight, and Horsepower of Cars from the 1974 Motor Trends Magazine",
125  "Cylinders shown in height, weight in width, horsepower in color") +
126  theme_bw() +
127  theme(plot.title = element_text(size = 10), plot.subtitle = element_text(size = 8), legend.title = element_text(size = 10))
128
129plt_orig
130draw_geom_triangles_height_legend <- function(
131  width = 1,
132  width_scale = .1,
133  height_scale = .1,
134  z_values = 1:3,
135  n.breaks = 3,
136  labels = c("low", "medium", "high"),
137  color = 'black',
138  fill = 'black'
139) {
140  ggplot(
141    data = data.frame(x = rep(0, times = n.breaks),
142                      y = seq(1,n.breaks),
143                      z = quantile(z_values, seq(0, 1, length.out = n.breaks)) %>% as.vector(),
144                      width = width,
145                      label = labels,
146                      color = color,
147                      fill = fill
148    ),
149    mapping = aes(x = x, y = y, z = z, label = label, width = width)
150  ) +
151    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
152    geom_text(mapping = aes(x = x + .5), size = 3) +
153    expand_limits(x = c(-.25, 3/4)) +
154    theme_void() +
155    theme(plot.title = element_text(size = 10, hjust = .5))
156}
157
158draw_geom_triangles_width_legend <- function(
159  width = 1:3,
160  width_scale = .1,
161  height_scale = .1,
162  z_values = 1,
163  n.breaks = 3,
164  labels = c("low", "medium", "high"),
165  color = 'black',
166  fill = 'black'
167) {
168  ggplot(
169    data = data.frame(x = rep(0, times = n.breaks),
170                      y = seq(1, n.breaks),
171                      z = rep(1, n.breaks),
172                      width = width,
173                      label = labels,
174                      color = color,
175                      fill = fill
176    ),
177    mapping = aes(x = x, y = y, z = z, label = label, width = width)
178  ) +
179    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
180    geom_text(mapping = aes(x = x + .5), size = 3) +
181    expand_limits(x = c(-.25, 3/4)) +
182    theme_void() +
183    theme(plot.title = element_text(size = 10, hjust = .5))
184}
185
186# extract the original legend - this is for the color and fill (hp)
187legend_hp <- cowplot::get_legend(plt_orig)
188
189# remove the legend from the plot
190plt <- plt_orig + theme(legend.position = 'none')
191
192# create a height legend using draw_geom_triangles_height_legend
193height_legend <- 
194  draw_geom_triangles_height_legend(z_values = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl)),
195                                    labels = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl))
196                                    ) +
197                                    ggtitle("cylinders\n")
198
199
200# create a width legend using draw_geom_triangles_width_legend
201width_legend <- 
202  draw_geom_triangles_width_legend(
203  width = quantile(mtcars$wt, c(.33, .66, 1)),
204  labels = round(quantile(mtcars$wt, c(.33, .66, 1)), 2),
205  width_scale = .2
206  ) +
207  ggtitle("weight\n(1000 lbs)\n")
208
209blank_plot <- ggplot() + theme_void()
210  
211# create a legend column layout
212# 
213# whitespace is used above, below, and in-between the legend components to
214# make sure the legend column pieces don't appear too densely stacked.
215# 
216legend_component <-
217  (blank_plot /  cowplot::plot_grid(legend_hp) / blank_plot /  height_legend / blank_plot / width_legend / blank_plot) +
218  plot_layout(heights = c(1, 1, .5, 1, .5, 1, 1))
219
220# create the layout with the plot and the legend component
221(plt + legend_component) + 
222  plot_layout(nrow = 1, widths = c(1, .15))
223> sessionInfo()
224R version 4.1.2 (2021-11-01)
225Platform: x86_64-apple-darwin17.0 (64-bit)
226Running under: macOS Mojave 10.14.2
227
228Matrix products: default
229BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
230LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
231
232locale:
233[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
234
235attached base packages:
236[1] stats     graphics  grDevices utils     datasets  methods   base     
237
238other attached packages:
239[1] patchwork_1.1.1 cowplot_1.1.1   tibble_3.1.6    ggrepel_0.9.1   dplyr_1.0.7     magrittr_2.0.1  ggplot2_3.3.5   colorout_1.2-2 
240
241loaded via a namespace (and not attached):
242 [1] Rcpp_1.0.7        tidyselect_1.1.1  munsell_0.5.0     viridisLite_0.4.0 colorspace_2.0-2  R6_2.5.1          rlang_0.4.12      fansi_0.5.0      
243 [9] tools_4.1.2       grid_4.1.2        gtable_0.3.0      utf8_1.2.2        DBI_1.1.2         withr_2.4.3       ellipsis_0.3.2    digest_0.6.29    
244[17] yaml_2.2.1        assertthat_0.2.1  lifecycle_1.0.1   crayon_1.4.2      tidyr_1.1.4       farver_2.1.0      purrr_0.3.4       vctrs_0.3.8      
245[25] glue_1.6.0        labeling_0.4.2    compiler_4.1.2    pillar_1.6.4      generics_0.1.1    scales_1.1.1      pkgconfig_2.0.3  
246library(ggplot2)
247
248GeomTriangles <- ggproto(
249  "GeomTriangles", GeomPoint,
250  default_aes = aes(
251    colour = "black", fill = "black", size = 0.5, linetype = 1, 
252    alpha = 1, angle = 0, width = 0.5, height = 0.5
253  ),
254  
255  draw_panel = function(
256    data, panel_params, coord, na.rm = FALSE
257  ) {
258    # Apply coordinate transform
259    df <- coord$transform(data, panel_params)
260    
261    # Repeat every row 3x
262    idx <- rep(seq_len(nrow(df)), each = 3)
263    rep_df <- df[idx, ]
264    # Calculate offsets from origin
265    x_off <- as.vector(outer(c(-0.5, 0, 0.5), df$width))
266    y_off <- as.vector(outer(c(0, 1, 0), df$height))
267    
268    # Rotate offsets
269    ang <- rep_df$angle * (pi / 180)
270    x_new <- x_off * cos(ang) - y_off * sin(ang)
271    y_new <- x_off * sin(ang) + y_off * cos(ang)
272    
273    # Combine offsets with origin
274    x <- unit(rep_df$x, "npc") + unit(x_new, "cm")
275    y <- unit(rep_df$y, "npc") + unit(y_new, "cm")
276    
277    grid::polygonGrob(
278      x = x, y = y, id = idx,
279      gp = grid::gpar(
280        col  = alpha(df$colour, df$alpha),
281        fill = alpha(df$fill, df$alpha),
282        lwd  = df$size * .pt,
283        lty  = df$linetype
284      )
285    )
286  }
287)
288geom_triangles <- function(mapping = NULL, data = NULL,
289                           position = "identity", na.rm = FALSE, show.legend = NA,
290                           inherit.aes = TRUE, ...) {
291  layer(
292    stat = "identity", geom = GeomTriangles, data = data, mapping = mapping,
293    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
294    params = list(na.rm = na.rm, ...)
295  )
296}
297ggplot(mtcars, aes(mpg, disp, height = cyl, width = wt, colour = hp, fill = hp)) +
298  geom_triangles() +
299  geom_point(colour = "black") +
300  continuous_scale("width", "wscale",  
301                   palette = scales::rescale_pal(c(0.1, 0.5))) +
302  continuous_scale("height", "hscale", 
303                   palette = scales::rescale_pal(c(0.1, 0.5)))
304

1library(ggplot2)
2library(magrittr)
3library(dplyr)
4library(ggrepel)
5library(tibble)
6library(cowplot)
7library(patchwork)
8
9StatTriangles <- ggproto("StatTriangles", Stat,
10  required_aes = c('x', 'y', 'z'),
11  compute_group = function(data, scales, params, width = 1, height_scale = .05, width_scale = .05, angle = 0) {
12
13    # specify default width
14    if (is.null(data$width)) data$width <- 1
15
16    # for each row of the data, create the 3 points that will make up our
17    # triangle based on the z, width, height_scale, and width_scale given.
18        triangle_df <-
19            tibble::tibble(
20                group = 1:nrow(data),
21                point1 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] - width[[i]]/2*width_scale, y[[i]]))}),
22                point2 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] + width[[i]]/2*width_scale, y[[i]]))}),
23                point3 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]], y[[i]] + z[[i]]*height_scale))})
24            )
25
26        # pivot the data into a long format so that each coordinate pair (e.g. vertex)
27        # will be its own row
28        triangle_df <- triangle_df %>% tidyr::pivot_longer(
29            cols = c(point1, point2, point3),
30            names_to = 'vertex',
31            values_to = 'coordinates'
32        )
33
34        # extract the coordinates -- this must be done rowwise because
35        # coordinates is a list where each element is a c(x,y) coordinate pair
36        triangle_df <- triangle_df %>% rowwise() %>% mutate(
37            x = coordinates[[1]],
38            y = coordinates[[2]])
39
40        # save the original x and y so we can perform rotations by the
41        # given angle with reference to (orig_x, orig_y) as the fixed point
42        # of the rotation transformation
43    triangle_df$orig_x <- rep(data$x, each = 3)
44    triangle_df$orig_y <- rep(data$y, each = 3)
45
46    # i'm not sure exactly why, but if the group isn't interacted with linetype
47    # then the edges of the triangles get messed up when rendered when linetype
48    # is used in an aesthetic
49    # triangle_df$group <-
50    #   paste0(triangle_df$orig_x, triangle_df$orig_y, triangle_df$group, rep(data$group, each = 3))
51
52        # fill in aesthetics to the dataframe
53    triangle_df$colour <- rep(data$colour, each = 3)
54    triangle_df$size <- rep(data$size, each = 3)
55    triangle_df$fill <- rep(data$fill, each = 3)
56    triangle_df$linetype <- rep(data$linetype, each = 3)
57    triangle_df$alpha <- rep(data$alpha, each = 3)
58    triangle_df$angle <- rep(data$angle, each = 3)
59
60    # determine scaling factor in going from y to x
61    # scale_factor <- diff(range(data$x)) / diff(range(data$y))
62    scale_factor <- diff(scales$x$get_limits()) / diff(scales$y$get_limits())
63    if (! is.finite(scale_factor) | is.na(scale_factor)) scale_factor <- 1
64
65    # rotate the data according to the angle by first subtracting out the
66    # (orig_x, orig_y) component, applying coordinate rotations, and then
67    # adding the (orig_x, orig_y) component back in.
68        new_coords <- triangle_df %>% mutate(
69      x_diff = x - orig_x,
70      y_diff = (y - orig_y) * scale_factor,
71      x_new = x_diff * cos(angle) - y_diff * sin(angle),
72      y_new = x_diff * sin(angle) + y_diff * cos(angle),
73      x_new = orig_x + x_new*scale_factor,
74      y_new = (orig_y + y_new)
75        )
76
77        # overwrite the x,y coordinates with the newly computed coordinates
78        triangle_df$x <- new_coords$x_new
79        triangle_df$y <- new_coords$y_new
80
81    triangle_df
82  }
83)
84
85stat_triangles <- function(mapping = NULL, data = NULL, geom = "polygon",
86                       position = "identity", na.rm = FALSE, show.legend = NA,
87                       inherit.aes = TRUE, ...) {
88  layer(
89    stat = StatTriangles, data = data, mapping = mapping, geom = geom,
90    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
91    params = list(na.rm = na.rm, ...)
92  )
93}
94
95GeomTriangles <- ggproto("GeomTriangles", GeomPolygon,
96    default_aes = aes(
97            color = 'black', fill = "black", size = 0.5, linetype = 1, alpha = 1, angle = 0, width = 1
98        )
99)
100
101geom_triangles <- function(mapping = NULL, data = NULL,
102                       position = "identity", na.rm = FALSE, show.legend = NA,
103                       inherit.aes = TRUE, ...) {
104  layer(
105    stat = StatTriangles, geom = GeomTriangles, data = data, mapping = mapping,
106    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
107    params = list(na.rm = na.rm, ...)
108  )
109}
110
111# here's an example using mtcars 
112
113plt_orig <- mtcars %>%
114  tibble::rownames_to_column('name') %>%
115  ggplot(aes(x = mpg, y = disp, z = cyl, width = wt, color = hp, fill = hp, label = name)) +
116  geom_triangles(width_scale = 10, height_scale = 15, alpha = .7) +
117  geom_point(color = 'black', size = 1) +
118  ggrepel::geom_text_repel(color = 'black', size = 2, nudge_y = -10) +
119  scale_fill_viridis_c(end = .6) +
120  scale_color_viridis_c(end = .6) +
121  xlab("miles per gallon") +
122  ylab("engine displacement (cu. in.)") +
123  labs(fill = 'horsepower', color = 'horsepower') +
124  ggtitle("MPG, Engine Displacement, # of Cylinders, Weight, and Horsepower of Cars from the 1974 Motor Trends Magazine",
125  "Cylinders shown in height, weight in width, horsepower in color") +
126  theme_bw() +
127  theme(plot.title = element_text(size = 10), plot.subtitle = element_text(size = 8), legend.title = element_text(size = 10))
128
129plt_orig
130draw_geom_triangles_height_legend <- function(
131  width = 1,
132  width_scale = .1,
133  height_scale = .1,
134  z_values = 1:3,
135  n.breaks = 3,
136  labels = c("low", "medium", "high"),
137  color = 'black',
138  fill = 'black'
139) {
140  ggplot(
141    data = data.frame(x = rep(0, times = n.breaks),
142                      y = seq(1,n.breaks),
143                      z = quantile(z_values, seq(0, 1, length.out = n.breaks)) %>% as.vector(),
144                      width = width,
145                      label = labels,
146                      color = color,
147                      fill = fill
148    ),
149    mapping = aes(x = x, y = y, z = z, label = label, width = width)
150  ) +
151    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
152    geom_text(mapping = aes(x = x + .5), size = 3) +
153    expand_limits(x = c(-.25, 3/4)) +
154    theme_void() +
155    theme(plot.title = element_text(size = 10, hjust = .5))
156}
157
158draw_geom_triangles_width_legend <- function(
159  width = 1:3,
160  width_scale = .1,
161  height_scale = .1,
162  z_values = 1,
163  n.breaks = 3,
164  labels = c("low", "medium", "high"),
165  color = 'black',
166  fill = 'black'
167) {
168  ggplot(
169    data = data.frame(x = rep(0, times = n.breaks),
170                      y = seq(1, n.breaks),
171                      z = rep(1, n.breaks),
172                      width = width,
173                      label = labels,
174                      color = color,
175                      fill = fill
176    ),
177    mapping = aes(x = x, y = y, z = z, label = label, width = width)
178  ) +
179    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
180    geom_text(mapping = aes(x = x + .5), size = 3) +
181    expand_limits(x = c(-.25, 3/4)) +
182    theme_void() +
183    theme(plot.title = element_text(size = 10, hjust = .5))
184}
185
186# extract the original legend - this is for the color and fill (hp)
187legend_hp <- cowplot::get_legend(plt_orig)
188
189# remove the legend from the plot
190plt <- plt_orig + theme(legend.position = 'none')
191
192# create a height legend using draw_geom_triangles_height_legend
193height_legend <- 
194  draw_geom_triangles_height_legend(z_values = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl)),
195                                    labels = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl))
196                                    ) +
197                                    ggtitle("cylinders\n")
198
199
200# create a width legend using draw_geom_triangles_width_legend
201width_legend <- 
202  draw_geom_triangles_width_legend(
203  width = quantile(mtcars$wt, c(.33, .66, 1)),
204  labels = round(quantile(mtcars$wt, c(.33, .66, 1)), 2),
205  width_scale = .2
206  ) +
207  ggtitle("weight\n(1000 lbs)\n")
208
209blank_plot <- ggplot() + theme_void()
210  
211# create a legend column layout
212# 
213# whitespace is used above, below, and in-between the legend components to
214# make sure the legend column pieces don't appear too densely stacked.
215# 
216legend_component <-
217  (blank_plot /  cowplot::plot_grid(legend_hp) / blank_plot /  height_legend / blank_plot / width_legend / blank_plot) +
218  plot_layout(heights = c(1, 1, .5, 1, .5, 1, 1))
219
220# create the layout with the plot and the legend component
221(plt + legend_component) + 
222  plot_layout(nrow = 1, widths = c(1, .15))
223> sessionInfo()
224R version 4.1.2 (2021-11-01)
225Platform: x86_64-apple-darwin17.0 (64-bit)
226Running under: macOS Mojave 10.14.2
227
228Matrix products: default
229BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
230LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
231
232locale:
233[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
234
235attached base packages:
236[1] stats     graphics  grDevices utils     datasets  methods   base     
237
238other attached packages:
239[1] patchwork_1.1.1 cowplot_1.1.1   tibble_3.1.6    ggrepel_0.9.1   dplyr_1.0.7     magrittr_2.0.1  ggplot2_3.3.5   colorout_1.2-2 
240
241loaded via a namespace (and not attached):
242 [1] Rcpp_1.0.7        tidyselect_1.1.1  munsell_0.5.0     viridisLite_0.4.0 colorspace_2.0-2  R6_2.5.1          rlang_0.4.12      fansi_0.5.0      
243 [9] tools_4.1.2       grid_4.1.2        gtable_0.3.0      utf8_1.2.2        DBI_1.1.2         withr_2.4.3       ellipsis_0.3.2    digest_0.6.29    
244[17] yaml_2.2.1        assertthat_0.2.1  lifecycle_1.0.1   crayon_1.4.2      tidyr_1.1.4       farver_2.1.0      purrr_0.3.4       vctrs_0.3.8      
245[25] glue_1.6.0        labeling_0.4.2    compiler_4.1.2    pillar_1.6.4      generics_0.1.1    scales_1.1.1      pkgconfig_2.0.3  
246library(ggplot2)
247
248GeomTriangles <- ggproto(
249  "GeomTriangles", GeomPoint,
250  default_aes = aes(
251    colour = "black", fill = "black", size = 0.5, linetype = 1, 
252    alpha = 1, angle = 0, width = 0.5, height = 0.5
253  ),
254  
255  draw_panel = function(
256    data, panel_params, coord, na.rm = FALSE
257  ) {
258    # Apply coordinate transform
259    df <- coord$transform(data, panel_params)
260    
261    # Repeat every row 3x
262    idx <- rep(seq_len(nrow(df)), each = 3)
263    rep_df <- df[idx, ]
264    # Calculate offsets from origin
265    x_off <- as.vector(outer(c(-0.5, 0, 0.5), df$width))
266    y_off <- as.vector(outer(c(0, 1, 0), df$height))
267    
268    # Rotate offsets
269    ang <- rep_df$angle * (pi / 180)
270    x_new <- x_off * cos(ang) - y_off * sin(ang)
271    y_new <- x_off * sin(ang) + y_off * cos(ang)
272    
273    # Combine offsets with origin
274    x <- unit(rep_df$x, "npc") + unit(x_new, "cm")
275    y <- unit(rep_df$y, "npc") + unit(y_new, "cm")
276    
277    grid::polygonGrob(
278      x = x, y = y, id = idx,
279      gp = grid::gpar(
280        col  = alpha(df$colour, df$alpha),
281        fill = alpha(df$fill, df$alpha),
282        lwd  = df$size * .pt,
283        lty  = df$linetype
284      )
285    )
286  }
287)
288geom_triangles <- function(mapping = NULL, data = NULL,
289                           position = "identity", na.rm = FALSE, show.legend = NA,
290                           inherit.aes = TRUE, ...) {
291  layer(
292    stat = "identity", geom = GeomTriangles, data = data, mapping = mapping,
293    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
294    params = list(na.rm = na.rm, ...)
295  )
296}
297ggplot(mtcars, aes(mpg, disp, height = cyl, width = wt, colour = hp, fill = hp)) +
298  geom_triangles() +
299  geom_point(colour = "black") +
300  continuous_scale("width", "wscale",  
301                   palette = scales::rescale_pal(c(0.1, 0.5))) +
302  continuous_scale("height", "hscale", 
303                   palette = scales::rescale_pal(c(0.1, 0.5)))
304draw_key_triangle <- function(data, params, size) {
305  # browser()
306  idx <- rep(seq_len(nrow(data)), each = 3)
307  rep_data <- data[idx, ]
308  
309  x_off <- as.vector(outer(
310    c(-0.5, 0, 0.5),
311    data$width
312  ))
313  
314  y_off <- as.vector(outer(
315    c(0, 1, 0),
316    data$height
317  ))
318  
319  ang <- rep_data$angle * (pi / 180)
320  x_new <- x_off * cos(ang) - y_off * sin(ang)
321  y_new <- x_off * sin(ang) + y_off * cos(ang)
322  
323  # Origin x and y have fixed values
324  x <- unit(0.5, "npc") + unit(x_new, "cm")
325  y <- unit(0.2, "npc") + unit(y_new, "cm")
326  
327  grid::polygonGrob(
328    x = x, y = y, id = idx,
329    gp = grid::gpar(
330      col  = alpha(data$colour, data$alpha),
331      fill = alpha(data$fill, data$alpha),
332      lwd  = data$size * .pt,
333      lty  = data$linetype
334    )
335  )
336  
337}
338

1library(ggplot2)
2library(magrittr)
3library(dplyr)
4library(ggrepel)
5library(tibble)
6library(cowplot)
7library(patchwork)
8
9StatTriangles <- ggproto("StatTriangles", Stat,
10  required_aes = c('x', 'y', 'z'),
11  compute_group = function(data, scales, params, width = 1, height_scale = .05, width_scale = .05, angle = 0) {
12
13    # specify default width
14    if (is.null(data$width)) data$width <- 1
15
16    # for each row of the data, create the 3 points that will make up our
17    # triangle based on the z, width, height_scale, and width_scale given.
18        triangle_df <-
19            tibble::tibble(
20                group = 1:nrow(data),
21                point1 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] - width[[i]]/2*width_scale, y[[i]]))}),
22                point2 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] + width[[i]]/2*width_scale, y[[i]]))}),
23                point3 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]], y[[i]] + z[[i]]*height_scale))})
24            )
25
26        # pivot the data into a long format so that each coordinate pair (e.g. vertex)
27        # will be its own row
28        triangle_df <- triangle_df %>% tidyr::pivot_longer(
29            cols = c(point1, point2, point3),
30            names_to = 'vertex',
31            values_to = 'coordinates'
32        )
33
34        # extract the coordinates -- this must be done rowwise because
35        # coordinates is a list where each element is a c(x,y) coordinate pair
36        triangle_df <- triangle_df %>% rowwise() %>% mutate(
37            x = coordinates[[1]],
38            y = coordinates[[2]])
39
40        # save the original x and y so we can perform rotations by the
41        # given angle with reference to (orig_x, orig_y) as the fixed point
42        # of the rotation transformation
43    triangle_df$orig_x <- rep(data$x, each = 3)
44    triangle_df$orig_y <- rep(data$y, each = 3)
45
46    # i'm not sure exactly why, but if the group isn't interacted with linetype
47    # then the edges of the triangles get messed up when rendered when linetype
48    # is used in an aesthetic
49    # triangle_df$group <-
50    #   paste0(triangle_df$orig_x, triangle_df$orig_y, triangle_df$group, rep(data$group, each = 3))
51
52        # fill in aesthetics to the dataframe
53    triangle_df$colour <- rep(data$colour, each = 3)
54    triangle_df$size <- rep(data$size, each = 3)
55    triangle_df$fill <- rep(data$fill, each = 3)
56    triangle_df$linetype <- rep(data$linetype, each = 3)
57    triangle_df$alpha <- rep(data$alpha, each = 3)
58    triangle_df$angle <- rep(data$angle, each = 3)
59
60    # determine scaling factor in going from y to x
61    # scale_factor <- diff(range(data$x)) / diff(range(data$y))
62    scale_factor <- diff(scales$x$get_limits()) / diff(scales$y$get_limits())
63    if (! is.finite(scale_factor) | is.na(scale_factor)) scale_factor <- 1
64
65    # rotate the data according to the angle by first subtracting out the
66    # (orig_x, orig_y) component, applying coordinate rotations, and then
67    # adding the (orig_x, orig_y) component back in.
68        new_coords <- triangle_df %>% mutate(
69      x_diff = x - orig_x,
70      y_diff = (y - orig_y) * scale_factor,
71      x_new = x_diff * cos(angle) - y_diff * sin(angle),
72      y_new = x_diff * sin(angle) + y_diff * cos(angle),
73      x_new = orig_x + x_new*scale_factor,
74      y_new = (orig_y + y_new)
75        )
76
77        # overwrite the x,y coordinates with the newly computed coordinates
78        triangle_df$x <- new_coords$x_new
79        triangle_df$y <- new_coords$y_new
80
81    triangle_df
82  }
83)
84
85stat_triangles <- function(mapping = NULL, data = NULL, geom = "polygon",
86                       position = "identity", na.rm = FALSE, show.legend = NA,
87                       inherit.aes = TRUE, ...) {
88  layer(
89    stat = StatTriangles, data = data, mapping = mapping, geom = geom,
90    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
91    params = list(na.rm = na.rm, ...)
92  )
93}
94
95GeomTriangles <- ggproto("GeomTriangles", GeomPolygon,
96    default_aes = aes(
97            color = 'black', fill = "black", size = 0.5, linetype = 1, alpha = 1, angle = 0, width = 1
98        )
99)
100
101geom_triangles <- function(mapping = NULL, data = NULL,
102                       position = "identity", na.rm = FALSE, show.legend = NA,
103                       inherit.aes = TRUE, ...) {
104  layer(
105    stat = StatTriangles, geom = GeomTriangles, data = data, mapping = mapping,
106    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
107    params = list(na.rm = na.rm, ...)
108  )
109}
110
111# here's an example using mtcars 
112
113plt_orig <- mtcars %>%
114  tibble::rownames_to_column('name') %>%
115  ggplot(aes(x = mpg, y = disp, z = cyl, width = wt, color = hp, fill = hp, label = name)) +
116  geom_triangles(width_scale = 10, height_scale = 15, alpha = .7) +
117  geom_point(color = 'black', size = 1) +
118  ggrepel::geom_text_repel(color = 'black', size = 2, nudge_y = -10) +
119  scale_fill_viridis_c(end = .6) +
120  scale_color_viridis_c(end = .6) +
121  xlab("miles per gallon") +
122  ylab("engine displacement (cu. in.)") +
123  labs(fill = 'horsepower', color = 'horsepower') +
124  ggtitle("MPG, Engine Displacement, # of Cylinders, Weight, and Horsepower of Cars from the 1974 Motor Trends Magazine",
125  "Cylinders shown in height, weight in width, horsepower in color") +
126  theme_bw() +
127  theme(plot.title = element_text(size = 10), plot.subtitle = element_text(size = 8), legend.title = element_text(size = 10))
128
129plt_orig
130draw_geom_triangles_height_legend <- function(
131  width = 1,
132  width_scale = .1,
133  height_scale = .1,
134  z_values = 1:3,
135  n.breaks = 3,
136  labels = c("low", "medium", "high"),
137  color = 'black',
138  fill = 'black'
139) {
140  ggplot(
141    data = data.frame(x = rep(0, times = n.breaks),
142                      y = seq(1,n.breaks),
143                      z = quantile(z_values, seq(0, 1, length.out = n.breaks)) %>% as.vector(),
144                      width = width,
145                      label = labels,
146                      color = color,
147                      fill = fill
148    ),
149    mapping = aes(x = x, y = y, z = z, label = label, width = width)
150  ) +
151    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
152    geom_text(mapping = aes(x = x + .5), size = 3) +
153    expand_limits(x = c(-.25, 3/4)) +
154    theme_void() +
155    theme(plot.title = element_text(size = 10, hjust = .5))
156}
157
158draw_geom_triangles_width_legend <- function(
159  width = 1:3,
160  width_scale = .1,
161  height_scale = .1,
162  z_values = 1,
163  n.breaks = 3,
164  labels = c("low", "medium", "high"),
165  color = 'black',
166  fill = 'black'
167) {
168  ggplot(
169    data = data.frame(x = rep(0, times = n.breaks),
170                      y = seq(1, n.breaks),
171                      z = rep(1, n.breaks),
172                      width = width,
173                      label = labels,
174                      color = color,
175                      fill = fill
176    ),
177    mapping = aes(x = x, y = y, z = z, label = label, width = width)
178  ) +
179    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
180    geom_text(mapping = aes(x = x + .5), size = 3) +
181    expand_limits(x = c(-.25, 3/4)) +
182    theme_void() +
183    theme(plot.title = element_text(size = 10, hjust = .5))
184}
185
186# extract the original legend - this is for the color and fill (hp)
187legend_hp <- cowplot::get_legend(plt_orig)
188
189# remove the legend from the plot
190plt <- plt_orig + theme(legend.position = 'none')
191
192# create a height legend using draw_geom_triangles_height_legend
193height_legend <- 
194  draw_geom_triangles_height_legend(z_values = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl)),
195                                    labels = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl))
196                                    ) +
197                                    ggtitle("cylinders\n")
198
199
200# create a width legend using draw_geom_triangles_width_legend
201width_legend <- 
202  draw_geom_triangles_width_legend(
203  width = quantile(mtcars$wt, c(.33, .66, 1)),
204  labels = round(quantile(mtcars$wt, c(.33, .66, 1)), 2),
205  width_scale = .2
206  ) +
207  ggtitle("weight\n(1000 lbs)\n")
208
209blank_plot <- ggplot() + theme_void()
210  
211# create a legend column layout
212# 
213# whitespace is used above, below, and in-between the legend components to
214# make sure the legend column pieces don't appear too densely stacked.
215# 
216legend_component <-
217  (blank_plot /  cowplot::plot_grid(legend_hp) / blank_plot /  height_legend / blank_plot / width_legend / blank_plot) +
218  plot_layout(heights = c(1, 1, .5, 1, .5, 1, 1))
219
220# create the layout with the plot and the legend component
221(plt + legend_component) + 
222  plot_layout(nrow = 1, widths = c(1, .15))
223> sessionInfo()
224R version 4.1.2 (2021-11-01)
225Platform: x86_64-apple-darwin17.0 (64-bit)
226Running under: macOS Mojave 10.14.2
227
228Matrix products: default
229BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
230LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
231
232locale:
233[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
234
235attached base packages:
236[1] stats     graphics  grDevices utils     datasets  methods   base     
237
238other attached packages:
239[1] patchwork_1.1.1 cowplot_1.1.1   tibble_3.1.6    ggrepel_0.9.1   dplyr_1.0.7     magrittr_2.0.1  ggplot2_3.3.5   colorout_1.2-2 
240
241loaded via a namespace (and not attached):
242 [1] Rcpp_1.0.7        tidyselect_1.1.1  munsell_0.5.0     viridisLite_0.4.0 colorspace_2.0-2  R6_2.5.1          rlang_0.4.12      fansi_0.5.0      
243 [9] tools_4.1.2       grid_4.1.2        gtable_0.3.0      utf8_1.2.2        DBI_1.1.2         withr_2.4.3       ellipsis_0.3.2    digest_0.6.29    
244[17] yaml_2.2.1        assertthat_0.2.1  lifecycle_1.0.1   crayon_1.4.2      tidyr_1.1.4       farver_2.1.0      purrr_0.3.4       vctrs_0.3.8      
245[25] glue_1.6.0        labeling_0.4.2    compiler_4.1.2    pillar_1.6.4      generics_0.1.1    scales_1.1.1      pkgconfig_2.0.3  
246library(ggplot2)
247
248GeomTriangles <- ggproto(
249  "GeomTriangles", GeomPoint,
250  default_aes = aes(
251    colour = "black", fill = "black", size = 0.5, linetype = 1, 
252    alpha = 1, angle = 0, width = 0.5, height = 0.5
253  ),
254  
255  draw_panel = function(
256    data, panel_params, coord, na.rm = FALSE
257  ) {
258    # Apply coordinate transform
259    df <- coord$transform(data, panel_params)
260    
261    # Repeat every row 3x
262    idx <- rep(seq_len(nrow(df)), each = 3)
263    rep_df <- df[idx, ]
264    # Calculate offsets from origin
265    x_off <- as.vector(outer(c(-0.5, 0, 0.5), df$width))
266    y_off <- as.vector(outer(c(0, 1, 0), df$height))
267    
268    # Rotate offsets
269    ang <- rep_df$angle * (pi / 180)
270    x_new <- x_off * cos(ang) - y_off * sin(ang)
271    y_new <- x_off * sin(ang) + y_off * cos(ang)
272    
273    # Combine offsets with origin
274    x <- unit(rep_df$x, "npc") + unit(x_new, "cm")
275    y <- unit(rep_df$y, "npc") + unit(y_new, "cm")
276    
277    grid::polygonGrob(
278      x = x, y = y, id = idx,
279      gp = grid::gpar(
280        col  = alpha(df$colour, df$alpha),
281        fill = alpha(df$fill, df$alpha),
282        lwd  = df$size * .pt,
283        lty  = df$linetype
284      )
285    )
286  }
287)
288geom_triangles <- function(mapping = NULL, data = NULL,
289                           position = "identity", na.rm = FALSE, show.legend = NA,
290                           inherit.aes = TRUE, ...) {
291  layer(
292    stat = "identity", geom = GeomTriangles, data = data, mapping = mapping,
293    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
294    params = list(na.rm = na.rm, ...)
295  )
296}
297ggplot(mtcars, aes(mpg, disp, height = cyl, width = wt, colour = hp, fill = hp)) +
298  geom_triangles() +
299  geom_point(colour = "black") +
300  continuous_scale("width", "wscale",  
301                   palette = scales::rescale_pal(c(0.1, 0.5))) +
302  continuous_scale("height", "hscale", 
303                   palette = scales::rescale_pal(c(0.1, 0.5)))
304draw_key_triangle <- function(data, params, size) {
305  # browser()
306  idx <- rep(seq_len(nrow(data)), each = 3)
307  rep_data <- data[idx, ]
308  
309  x_off <- as.vector(outer(
310    c(-0.5, 0, 0.5),
311    data$width
312  ))
313  
314  y_off <- as.vector(outer(
315    c(0, 1, 0),
316    data$height
317  ))
318  
319  ang <- rep_data$angle * (pi / 180)
320  x_new <- x_off * cos(ang) - y_off * sin(ang)
321  y_new <- x_off * sin(ang) + y_off * cos(ang)
322  
323  # Origin x and y have fixed values
324  x <- unit(0.5, "npc") + unit(x_new, "cm")
325  y <- unit(0.2, "npc") + unit(y_new, "cm")
326  
327  grid::polygonGrob(
328    x = x, y = y, id = idx,
329    gp = grid::gpar(
330      col  = alpha(data$colour, data$alpha),
331      fill = alpha(data$fill, data$alpha),
332      lwd  = data$size * .pt,
333      lty  = data$linetype
334    )
335  )
336  
337}
338ggplot(mtcars, aes(mpg, disp, height = cyl, width = wt, colour = hp, fill = hp)) +
339  geom_triangles(key_glyph = draw_key_triangle) +
340  geom_point(colour = "black") +
341  continuous_scale("width", "wscale",  
342                   palette = scales::rescale_pal(c(0.1, 0.5))) +
343  continuous_scale("height", "hscale", 
344                   palette = scales::rescale_pal(c(0.1, 0.5)))
345

1library(ggplot2)
2library(magrittr)
3library(dplyr)
4library(ggrepel)
5library(tibble)
6library(cowplot)
7library(patchwork)
8
9StatTriangles <- ggproto("StatTriangles", Stat,
10  required_aes = c('x', 'y', 'z'),
11  compute_group = function(data, scales, params, width = 1, height_scale = .05, width_scale = .05, angle = 0) {
12
13    # specify default width
14    if (is.null(data$width)) data$width <- 1
15
16    # for each row of the data, create the 3 points that will make up our
17    # triangle based on the z, width, height_scale, and width_scale given.
18        triangle_df <-
19            tibble::tibble(
20                group = 1:nrow(data),
21                point1 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] - width[[i]]/2*width_scale, y[[i]]))}),
22                point2 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]] + width[[i]]/2*width_scale, y[[i]]))}),
23                point3 = lapply(1:nrow(data), function(i) {with(data, c(x[[i]], y[[i]] + z[[i]]*height_scale))})
24            )
25
26        # pivot the data into a long format so that each coordinate pair (e.g. vertex)
27        # will be its own row
28        triangle_df <- triangle_df %>% tidyr::pivot_longer(
29            cols = c(point1, point2, point3),
30            names_to = 'vertex',
31            values_to = 'coordinates'
32        )
33
34        # extract the coordinates -- this must be done rowwise because
35        # coordinates is a list where each element is a c(x,y) coordinate pair
36        triangle_df <- triangle_df %>% rowwise() %>% mutate(
37            x = coordinates[[1]],
38            y = coordinates[[2]])
39
40        # save the original x and y so we can perform rotations by the
41        # given angle with reference to (orig_x, orig_y) as the fixed point
42        # of the rotation transformation
43    triangle_df$orig_x <- rep(data$x, each = 3)
44    triangle_df$orig_y <- rep(data$y, each = 3)
45
46    # i'm not sure exactly why, but if the group isn't interacted with linetype
47    # then the edges of the triangles get messed up when rendered when linetype
48    # is used in an aesthetic
49    # triangle_df$group <-
50    #   paste0(triangle_df$orig_x, triangle_df$orig_y, triangle_df$group, rep(data$group, each = 3))
51
52        # fill in aesthetics to the dataframe
53    triangle_df$colour <- rep(data$colour, each = 3)
54    triangle_df$size <- rep(data$size, each = 3)
55    triangle_df$fill <- rep(data$fill, each = 3)
56    triangle_df$linetype <- rep(data$linetype, each = 3)
57    triangle_df$alpha <- rep(data$alpha, each = 3)
58    triangle_df$angle <- rep(data$angle, each = 3)
59
60    # determine scaling factor in going from y to x
61    # scale_factor <- diff(range(data$x)) / diff(range(data$y))
62    scale_factor <- diff(scales$x$get_limits()) / diff(scales$y$get_limits())
63    if (! is.finite(scale_factor) | is.na(scale_factor)) scale_factor <- 1
64
65    # rotate the data according to the angle by first subtracting out the
66    # (orig_x, orig_y) component, applying coordinate rotations, and then
67    # adding the (orig_x, orig_y) component back in.
68        new_coords <- triangle_df %>% mutate(
69      x_diff = x - orig_x,
70      y_diff = (y - orig_y) * scale_factor,
71      x_new = x_diff * cos(angle) - y_diff * sin(angle),
72      y_new = x_diff * sin(angle) + y_diff * cos(angle),
73      x_new = orig_x + x_new*scale_factor,
74      y_new = (orig_y + y_new)
75        )
76
77        # overwrite the x,y coordinates with the newly computed coordinates
78        triangle_df$x <- new_coords$x_new
79        triangle_df$y <- new_coords$y_new
80
81    triangle_df
82  }
83)
84
85stat_triangles <- function(mapping = NULL, data = NULL, geom = "polygon",
86                       position = "identity", na.rm = FALSE, show.legend = NA,
87                       inherit.aes = TRUE, ...) {
88  layer(
89    stat = StatTriangles, data = data, mapping = mapping, geom = geom,
90    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
91    params = list(na.rm = na.rm, ...)
92  )
93}
94
95GeomTriangles <- ggproto("GeomTriangles", GeomPolygon,
96    default_aes = aes(
97            color = 'black', fill = "black", size = 0.5, linetype = 1, alpha = 1, angle = 0, width = 1
98        )
99)
100
101geom_triangles <- function(mapping = NULL, data = NULL,
102                       position = "identity", na.rm = FALSE, show.legend = NA,
103                       inherit.aes = TRUE, ...) {
104  layer(
105    stat = StatTriangles, geom = GeomTriangles, data = data, mapping = mapping,
106    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
107    params = list(na.rm = na.rm, ...)
108  )
109}
110
111# here's an example using mtcars 
112
113plt_orig <- mtcars %>%
114  tibble::rownames_to_column('name') %>%
115  ggplot(aes(x = mpg, y = disp, z = cyl, width = wt, color = hp, fill = hp, label = name)) +
116  geom_triangles(width_scale = 10, height_scale = 15, alpha = .7) +
117  geom_point(color = 'black', size = 1) +
118  ggrepel::geom_text_repel(color = 'black', size = 2, nudge_y = -10) +
119  scale_fill_viridis_c(end = .6) +
120  scale_color_viridis_c(end = .6) +
121  xlab("miles per gallon") +
122  ylab("engine displacement (cu. in.)") +
123  labs(fill = 'horsepower', color = 'horsepower') +
124  ggtitle("MPG, Engine Displacement, # of Cylinders, Weight, and Horsepower of Cars from the 1974 Motor Trends Magazine",
125  "Cylinders shown in height, weight in width, horsepower in color") +
126  theme_bw() +
127  theme(plot.title = element_text(size = 10), plot.subtitle = element_text(size = 8), legend.title = element_text(size = 10))
128
129plt_orig
130draw_geom_triangles_height_legend <- function(
131  width = 1,
132  width_scale = .1,
133  height_scale = .1,
134  z_values = 1:3,
135  n.breaks = 3,
136  labels = c("low", "medium", "high"),
137  color = 'black',
138  fill = 'black'
139) {
140  ggplot(
141    data = data.frame(x = rep(0, times = n.breaks),
142                      y = seq(1,n.breaks),
143                      z = quantile(z_values, seq(0, 1, length.out = n.breaks)) %>% as.vector(),
144                      width = width,
145                      label = labels,
146                      color = color,
147                      fill = fill
148    ),
149    mapping = aes(x = x, y = y, z = z, label = label, width = width)
150  ) +
151    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
152    geom_text(mapping = aes(x = x + .5), size = 3) +
153    expand_limits(x = c(-.25, 3/4)) +
154    theme_void() +
155    theme(plot.title = element_text(size = 10, hjust = .5))
156}
157
158draw_geom_triangles_width_legend <- function(
159  width = 1:3,
160  width_scale = .1,
161  height_scale = .1,
162  z_values = 1,
163  n.breaks = 3,
164  labels = c("low", "medium", "high"),
165  color = 'black',
166  fill = 'black'
167) {
168  ggplot(
169    data = data.frame(x = rep(0, times = n.breaks),
170                      y = seq(1, n.breaks),
171                      z = rep(1, n.breaks),
172                      width = width,
173                      label = labels,
174                      color = color,
175                      fill = fill
176    ),
177    mapping = aes(x = x, y = y, z = z, label = label, width = width)
178  ) +
179    geom_triangles(width_scale = width_scale, height_scale = height_scale, color = color, fill = fill) +
180    geom_text(mapping = aes(x = x + .5), size = 3) +
181    expand_limits(x = c(-.25, 3/4)) +
182    theme_void() +
183    theme(plot.title = element_text(size = 10, hjust = .5))
184}
185
186# extract the original legend - this is for the color and fill (hp)
187legend_hp <- cowplot::get_legend(plt_orig)
188
189# remove the legend from the plot
190plt <- plt_orig + theme(legend.position = 'none')
191
192# create a height legend using draw_geom_triangles_height_legend
193height_legend <- 
194  draw_geom_triangles_height_legend(z_values = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl)),
195                                    labels = c(min(mtcars$cyl), median(mtcars$cyl), max(mtcars$cyl))
196                                    ) +
197                                    ggtitle("cylinders\n")
198
199
200# create a width legend using draw_geom_triangles_width_legend
201width_legend <- 
202  draw_geom_triangles_width_legend(
203  width = quantile(mtcars$wt, c(.33, .66, 1)),
204  labels = round(quantile(mtcars$wt, c(.33, .66, 1)), 2),
205  width_scale = .2
206  ) +
207  ggtitle("weight\n(1000 lbs)\n")
208
209blank_plot <- ggplot() + theme_void()
210  
211# create a legend column layout
212# 
213# whitespace is used above, below, and in-between the legend components to
214# make sure the legend column pieces don't appear too densely stacked.
215# 
216legend_component <-
217  (blank_plot /  cowplot::plot_grid(legend_hp) / blank_plot /  height_legend / blank_plot / width_legend / blank_plot) +
218  plot_layout(heights = c(1, 1, .5, 1, .5, 1, 1))
219
220# create the layout with the plot and the legend component
221(plt + legend_component) + 
222  plot_layout(nrow = 1, widths = c(1, .15))
223> sessionInfo()
224R version 4.1.2 (2021-11-01)
225Platform: x86_64-apple-darwin17.0 (64-bit)
226Running under: macOS Mojave 10.14.2
227
228Matrix products: default
229BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
230LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
231
232locale:
233[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
234
235attached base packages:
236[1] stats     graphics  grDevices utils     datasets  methods   base     
237
238other attached packages:
239[1] patchwork_1.1.1 cowplot_1.1.1   tibble_3.1.6    ggrepel_0.9.1   dplyr_1.0.7     magrittr_2.0.1  ggplot2_3.3.5   colorout_1.2-2 
240
241loaded via a namespace (and not attached):
242 [1] Rcpp_1.0.7        tidyselect_1.1.1  munsell_0.5.0     viridisLite_0.4.0 colorspace_2.0-2  R6_2.5.1          rlang_0.4.12      fansi_0.5.0      
243 [9] tools_4.1.2       grid_4.1.2        gtable_0.3.0      utf8_1.2.2        DBI_1.1.2         withr_2.4.3       ellipsis_0.3.2    digest_0.6.29    
244[17] yaml_2.2.1        assertthat_0.2.1  lifecycle_1.0.1   crayon_1.4.2      tidyr_1.1.4       farver_2.1.0      purrr_0.3.4       vctrs_0.3.8      
245[25] glue_1.6.0        labeling_0.4.2    compiler_4.1.2    pillar_1.6.4      generics_0.1.1    scales_1.1.1      pkgconfig_2.0.3  
246library(ggplot2)
247
248GeomTriangles <- ggproto(
249  "GeomTriangles", GeomPoint,
250  default_aes = aes(
251    colour = "black", fill = "black", size = 0.5, linetype = 1, 
252    alpha = 1, angle = 0, width = 0.5, height = 0.5
253  ),
254  
255  draw_panel = function(
256    data, panel_params, coord, na.rm = FALSE
257  ) {
258    # Apply coordinate transform
259    df <- coord$transform(data, panel_params)
260    
261    # Repeat every row 3x
262    idx <- rep(seq_len(nrow(df)), each = 3)
263    rep_df <- df[idx, ]
264    # Calculate offsets from origin
265    x_off <- as.vector(outer(c(-0.5, 0, 0.5), df$width))
266    y_off <- as.vector(outer(c(0, 1, 0), df$height))
267    
268    # Rotate offsets
269    ang <- rep_df$angle * (pi / 180)
270    x_new <- x_off * cos(ang) - y_off * sin(ang)
271    y_new <- x_off * sin(ang) + y_off * cos(ang)
272    
273    # Combine offsets with origin
274    x <- unit(rep_df$x, "npc") + unit(x_new, "cm")
275    y <- unit(rep_df$y, "npc") + unit(y_new, "cm")
276    
277    grid::polygonGrob(
278      x = x, y = y, id = idx,
279      gp = grid::gpar(
280        col  = alpha(df$colour, df$alpha),
281        fill = alpha(df$fill, df$alpha),
282        lwd  = df$size * .pt,
283        lty  = df$linetype
284      )
285    )
286  }
287)
288geom_triangles <- function(mapping = NULL, data = NULL,
289                           position = "identity", na.rm = FALSE, show.legend = NA,
290                           inherit.aes = TRUE, ...) {
291  layer(
292    stat = "identity", geom = GeomTriangles, data = data, mapping = mapping,
293    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
294    params = list(na.rm = na.rm, ...)
295  )
296}
297ggplot(mtcars, aes(mpg, disp, height = cyl, width = wt, colour = hp, fill = hp)) +
298  geom_triangles() +
299  geom_point(colour = "black") +
300  continuous_scale("width", "wscale",  
301                   palette = scales::rescale_pal(c(0.1, 0.5))) +
302  continuous_scale("height", "hscale", 
303                   palette = scales::rescale_pal(c(0.1, 0.5)))
304draw_key_triangle <- function(data, params, size) {
305  # browser()
306  idx <- rep(seq_len(nrow(data)), each = 3)
307  rep_data <- data[idx, ]
308  
309  x_off <- as.vector(outer(
310    c(-0.5, 0, 0.5),
311    data$width
312  ))
313  
314  y_off <- as.vector(outer(
315    c(0, 1, 0),
316    data$height
317  ))
318  
319  ang <- rep_data$angle * (pi / 180)
320  x_new <- x_off * cos(ang) - y_off * sin(ang)
321  y_new <- x_off * sin(ang) + y_off * cos(ang)
322  
323  # Origin x and y have fixed values
324  x <- unit(0.5, "npc") + unit(x_new, "cm")
325  y <- unit(0.2, "npc") + unit(y_new, "cm")
326  
327  grid::polygonGrob(
328    x = x, y = y, id = idx,
329    gp = grid::gpar(
330      col  = alpha(data$colour, data$alpha),
331      fill = alpha(data$fill, data$alpha),
332      lwd  = data$size * .pt,
333      lty  = data$linetype
334    )
335  )
336  
337}
338ggplot(mtcars, aes(mpg, disp, height = cyl, width = wt, colour = hp, fill = hp)) +
339  geom_triangles(key_glyph = draw_key_triangle) +
340  geom_point(colour = "black") +
341  continuous_scale("width", "wscale",  
342                   palette = scales::rescale_pal(c(0.1, 0.5))) +
343  continuous_scale("height", "hscale", 
344                   palette = scales::rescale_pal(c(0.1, 0.5)))
345GeomTriangles <- ggproto(
346  "GeomTriangles", GeomPoint,
347  ..., # Whatever you want to put in here
348  draw_key = draw_key_triangle
349)
350

1>>> tm = TeamMembership.objects.get(person=alice)
2>>> tm.end_date = date(2022,1,1)
3>>> tm.save()
4from django.db.models.signals import post_save
5from django.dispatch import receiver
6import logging
7
8@receiver(post_save)
9def logg_save(sender, instance, **kwargs):
10    logging.debug("whatever you want to log")
11

1paths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
2paths i j n m k dir
3    | i > n || j > m || k < 0 = 0
4    | i == n && j == m = 1
5    | dir == 0 = paths (i+1) j n m k 1 + paths i (j+1) n m k 2        -- is in grid (1,1)
6    | dir == 1 = paths (i+1) j n m k 1 + paths i (j+1) n m (k-1) 2    -- down was the direction took to reach here
7    | dir == 2 = paths (i+1) j n m (k-1) 1 + paths i (j+1) n m k 2    -- right was the direction took to reach here 
8    | otherwise = -1
9{-# LANGUAGE Rank2Types #-}
10module Solution where
11
12import Control.Monad.ST
13import Control.Monad
14import Data.HashTable.ST.Basic as HT
15

1paths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
2paths i j n m k dir
3    | i > n || j > m || k < 0 = 0
4    | i == n && j == m = 1
5    | dir == 0 = paths (i+1) j n m k 1 + paths i (j+1) n m k 2        -- is in grid (1,1)
6    | dir == 1 = paths (i+1) j n m k 1 + paths i (j+1) n m (k-1) 2    -- down was the direction took to reach here
7    | dir == 2 = paths (i+1) j n m (k-1) 1 + paths i (j+1) n m k 2    -- right was the direction took to reach here 
8    | otherwise = -1
9{-# LANGUAGE Rank2Types #-}
10module Solution where
11
12import Control.Monad.ST
13import Control.Monad
14import Data.HashTable.ST.Basic as HT
15type Mem s = HT.HashTable s (Int, Int, Int, Int) Integer
16

1paths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
2paths i j n m k dir
3    | i > n || j > m || k < 0 = 0
4    | i == n && j == m = 1
5    | dir == 0 = paths (i+1) j n m k 1 + paths i (j+1) n m k 2        -- is in grid (1,1)
6    | dir == 1 = paths (i+1) j n m k 1 + paths i (j+1) n m (k-1) 2    -- down was the direction took to reach here
7    | dir == 2 = paths (i+1) j n m (k-1) 1 + paths i (j+1) n m k 2    -- right was the direction took to reach here 
8    | otherwise = -1
9{-# LANGUAGE Rank2Types #-}
10module Solution where
11
12import Control.Monad.ST
13import Control.Monad
14import Data.HashTable.ST.Basic as HT
15type Mem s = HT.HashTable s (Int, Int, Int, Int) Integer
16runpaths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
17runpaths i j n m k dir = runST $ do
18  mem <- HT.new
19  paths mem i j n m k dir
20

1paths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
2paths i j n m k dir
3    | i > n || j > m || k < 0 = 0
4    | i == n && j == m = 1
5    | dir == 0 = paths (i+1) j n m k 1 + paths i (j+1) n m k 2        -- is in grid (1,1)
6    | dir == 1 = paths (i+1) j n m k 1 + paths i (j+1) n m (k-1) 2    -- down was the direction took to reach here
7    | dir == 2 = paths (i+1) j n m (k-1) 1 + paths i (j+1) n m k 2    -- right was the direction took to reach here 
8    | otherwise = -1
9{-# LANGUAGE Rank2Types #-}
10module Solution where
11
12import Control.Monad.ST
13import Control.Monad
14import Data.HashTable.ST.Basic as HT
15type Mem s = HT.HashTable s (Int, Int, Int, Int) Integer
16runpaths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
17runpaths i j n m k dir = runST $ do
18  mem <- HT.new
19  paths mem i j n m k dir
20mempaths mem i j n m k dir = do
21  res <- HT.lookup mem (i, j, k, dir)
22  case res of
23    Just x -> return x
24    Nothing -> do
25      x <- paths mem i j n m k dir
26      HT.insert mem (i, j, k, dir) x
27      return x
28

1paths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
2paths i j n m k dir
3    | i > n || j > m || k < 0 = 0
4    | i == n && j == m = 1
5    | dir == 0 = paths (i+1) j n m k 1 + paths i (j+1) n m k 2        -- is in grid (1,1)
6    | dir == 1 = paths (i+1) j n m k 1 + paths i (j+1) n m (k-1) 2    -- down was the direction took to reach here
7    | dir == 2 = paths (i+1) j n m (k-1) 1 + paths i (j+1) n m k 2    -- right was the direction took to reach here 
8    | otherwise = -1
9{-# LANGUAGE Rank2Types #-}
10module Solution where
11
12import Control.Monad.ST
13import Control.Monad
14import Data.HashTable.ST.Basic as HT
15type Mem s = HT.HashTable s (Int, Int, Int, Int) Integer
16runpaths :: Int -> Int -> Int -> Int -> Int -> Int -> Integer
17runpaths i j n m k dir = runST $ do
18  mem <- HT.new
19  paths mem i j n m k dir
20mempaths mem i j n m k dir = do
21  res <- HT.lookup mem (i, j, k, dir)
22  case res of
23    Just x -> return x
24    Nothing -> do
25      x <- paths mem i j n m k dir
26      HT.insert mem (i, j, k, dir) x
27      return x
28paths mem i j n m k dir
29    | i > n || j > m || k < 0 = return 0
30    | i == n && j == m = return 1
31    | dir == 0 = do
32        x1 <- mempaths mem (i+1) j n m k 1
33        x2 <- mempaths mem i (j+1) n m k 2        -- is in grid (1,1)
34        return $ x1 + x2
35    | dir == 1 = do 
36        x1 <- mempaths mem (i+1) j n m k 1
37        x2 <- mempaths mem i (j+1) n m (k-1) 2    -- down was the direction took to reach here
38        return $ x1 + x2
39    | dir == 2 = do
40        x1 <- mempaths mem (i+1) j n m (k-1) 1 
41        x2 <- mempaths mem i (j+1) n m k 2    -- right was the direction took to reach here 
42        return $ x1 + x2
43    | otherwise = return (-1)
44

1library(ggplot2)
2library(gridExtra)
3library(dplyr)
4library(formattable)
5library(signal)
6
7#dataset for ggplot
8full.data <- structure(list(error = c(0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 
95, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 
105, 6, 0, 1, 2, 3, 4, 5, 6), prob.ed.n = c(0, 0, 0.2, 0.5, 0.8, 
111, 1, 0, 0, 0.3, 0.7, 1, 1, 1, 0, 0.1, 0.4, 0.9, 1, 1, 1, 0, 
120.1, 0.5, 0.9, 1, 1, 1, 0, 0.1, 0.6, 1, 1, 1, 1, 0, 0.1, 0.6, 
131, 1, 1, 1), N = c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 
143, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 
156, 6, 6, 6, 6, 6, 6)), row.names = c(NA, -42L), class = "data.frame")
16
17#summary table 
18summary.table <- structure(list(prob.fr = c("1.62%", "1.35%", "1.09%", "0.81%", "0.54%", "0.27%"), prob.ed.n = c("87.4%", "82.2%", "74.8%", "64.4%", "49.8%", "29.2%"), N = c(6, 5, 4, 3, 2, 1)), row.names = c(NA, 
19-6L), class = "data.frame")
20
21#table object to beincluded with ggplot
22table <- tableGrob(summary.table %>%
23            rename(
24              `Prb FR` = prob.fr,
25              `Prb ED` = prob.ed.n,
26            ), 
27          rows = NULL)
28#plot
29plot <- ggplot(full.data, aes(x = error, y = prob.ed.n, group = N, colour = as.factor(N))) +
30  geom_vline(xintercept = 2.45, colour = "red", linetype = "dashed") +
31  geom_hline(yintercept = 0.9, linetype = "dashed") +
32  geom_line(data = full.data %>%
33              group_by(N) %>%
34              do({
35                tibble(error = seq(min(.$error), max(.$error),length.out=100),
36                       prob.ed.n = pchip(.$error, .$prob.ed.n, error))
37              }),
38            size = 1) +
39  scale_x_continuous(labels = full.data$error, breaks = full.data$error, expand = c(0, 0.05)) +
40  scale_y_continuous(expand = expansion(add = c(0.01, 0.01))) +
41  scale_color_brewer(palette = "Dark2") +
42  guides(color = guide_legend(reverse=TRUE, nrow = 1)) +
43  theme_bw() +
44  theme(legend.key = element_rect(fill = "white", colour = "black"),
45        legend.direction= "horizontal",
46        legend.position=c(0.8,0.05)
47)
48
49#arrange plot and grid side-by-side
50grid.arrange(plot, table, nrow = 1, widths = c(4,1))
51library(tidyverse)
52library(ggpubr)
53library(cowplot)
54#> 
55#> Attaching package: 'cowplot'
56#> The following object is masked from 'package:ggpubr':
57#> 
58#>     get_legend
59
60full.data <- structure(list(error = c(
61  0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,
62  5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,
63  5, 6, 0, 1, 2, 3, 4, 5, 6
64), prob.ed.n = c(
65  0, 0, 0.2, 0.5, 0.8,
66  1, 1, 0, 0, 0.3, 0.7, 1, 1, 1, 0, 0.1, 0.4, 0.9, 1, 1, 1, 0,
67  0.1, 0.5, 0.9, 1, 1, 1, 0, 0.1, 0.6, 1, 1, 1, 1, 0, 0.1, 0.6,
68  1, 1, 1, 1
69), N = c(
70  1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,
71  3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5,
72  6, 6, 6, 6, 6, 6, 6
73)), row.names = c(NA, -42L), class = "data.frame")
74
75summary.table <-
76  structure(list(
77    prob.fr = c("1.62%", "1.35%", "1.09%", "0.81%", "0.54%", "0.27%"),
78    prob.ed.n = c("87.4%", "82.2%", "74.8%", "64.4%", "49.8%", "29.2%"),
79    N = c(6, 5, 4, 3, 2, 1)
80  ), row.names = c(NA, -6L), class = "data.frame")
81
82## Hack 1 - create some space for the new legend
83spacer <- paste(rep(" ", 6), collapse = "")
84my_table <-
85  summary.table %>%
86  mutate(N = paste(spacer, N))
87
88p1 <-
89  ggplot(full.data, aes(x = error, y = prob.ed.n, group = N, colour = as.factor(N))) +
90  geom_vline(xintercept = 2.45, colour = "red", linetype = "dashed") +
91  geom_hline(yintercept = 0.9, linetype = "dashed") +
92  geom_line(
93    data = full.data %>%
94      group_by(N) %>%
95      do({
96        tibble(
97          error = seq(min(.$error), max(.$error), length.out = 100),
98          prob.ed.n = signal::pchip(.$error, .$prob.ed.n, error)
99        )
100      }),
101    size = 1
102  ) +
103  ## remove the legend labels. You have them in the table already.
104  scale_color_brewer(NULL, palette = "Dark2", labels = rep("", length(unique(full.data$N)))) +
105  ## remove all the legend specs! I've also removed the not so important reverse scale
106  ## I have removed fill and color to make it aesthetically more pleasing
107  theme(
108    legend.key = element_rect(fill = NA, colour = NA),
109    ## hack 2 - hard code legend key spacing
110    legend.spacing.y = unit(1.8, "pt"),
111    legend.background = element_blank()
112  ) +
113  ## make y spacing work
114  guides(color = guide_legend(byrow = TRUE))
115
116## create the plot elements
117p_leg <- cowplot::get_legend(p1)
118p2 <- ggtexttable(my_table, rows = NULL)
119## we don't want the legend twice
120p <- p1 + theme(legend.position = "none")
121
122## hack 3 - hard code the plot element positions
123ggdraw(p, xlim = c(0, 1.7)) +
124  draw_plot(p2, x = .8) +
125  draw_plot(p_leg, x = .97, y = 0.975, vjust = 1)
126

1body {
2  display: flex;
3  justify-content: center;
4  margin: 0
5  width: 100%;
6  height: 100vh;
7}
8
9.wrapper {
10  display: grid;
11  grid-template-columns: 1fr 1fr 1fr;
12  grid-gap: 1rem;
13  max-width: 1250px;
14}
15
16.box {
17  width: 200px;
18  height: 200px;
19  background: red;
20}<div class="wrapper">
21  <div class="box"></div>
22<!--   <div class="box"></div>
23  <div class="box"></div> -->
24</div>body {
25  display: flex;
26  justify-content: center;
27  margin: 0
28  width: 100%;
29  height: 100vh;
30}
31
32.wrapper {
33  display: grid;
34  grid-template-columns: auto auto auto;
35  align-content : start;
36  /*  grid-gap: 1rem;  */ 
37  max-width: 1250px;
38}
39
40.box {
41  width: 200px;
42  height: 200px;
43  background: red;
44  margin: 1rem ; 
45}

1body {
2  display: flex;
3  justify-content: center;
4  margin: 0
5  width: 100%;
6  height: 100vh;
7}
8
9.wrapper {
10  display: grid;
11  grid-template-columns: 1fr 1fr 1fr;
12  grid-gap: 1rem;
13  max-width: 1250px;
14}
15
16.box {
17  width: 200px;
18  height: 200px;
19  background: red;
20}<div class="wrapper">
21  <div class="box"></div>
22<!--   <div class="box"></div>
23  <div class="box"></div> -->
24</div>body {
25  display: flex;
26  justify-content: center;
27  margin: 0
28  width: 100%;
29  height: 100vh;
30}
31
32.wrapper {
33  display: grid;
34  grid-template-columns: auto auto auto;
35  align-content : start;
36  /*  grid-gap: 1rem;  */ 
37  max-width: 1250px;
38}
39
40.box {
41  width: 200px;
42  height: 200px;
43  background: red;
44  margin: 1rem ; 
45}<div class="wrapper">
46  <div class="box"></div>
47<!--   <div class="box"></div>
48  <div class="box"></div> -->
49</div>

1library(ggplot2)
2library(cowplot)
3
4
5df <- data.frame(
6  x = 1:10, y1 = 1:10, y2 = (1:10)^2, y3 = (1:10)^3, y4 = (1:10)^4
7)
8
9p1 <- ggplot(df, aes(x, y1)) + geom_point()
10p2 <- ggplot(df, aes(x, y2)) + geom_point()
11p3 <- ggplot(df, aes(x, y3)) + geom_point()
12p4 <- ggplot(df, aes(x, y4)) + geom_point()
13p5 <- ggplot(df, aes(x, y3)) + geom_point()
14# simple grid
15plot_grid(p1, p2, 
16          p3, p4,
17          p5, p4)
18library(gridExtra)
19
20grid.arrange(p1, p2, p3, p4, p5, 
21             ncol = 2, 
22             layout_matrix = cbind(c(1,3,5), c(2,4,4)))
23

1@import url("https://fonts.googleapis.com/css2?family=Asap:wght@400;700&display=swap");
2* {
3  padding: 0;
4  margin: 0;
5  height: 100%;
6  width: 100%;
7  background-color: #4a6163;
8  font-family: "Asap";
9  -webkit-box-sizing: border-box;
10          box-sizing: border-box;
11}
12
13.main_grid {
14  display: -ms-grid;
15  display: grid;
16  -ms-grid-columns: 0.25fr (1fr)[2];
17      grid-template-columns: 0.25fr repeat(2, 1fr);
18  -ms-grid-rows: 1fr;
19      grid-template-rows: 1fr;
20  grid-column-gap: 0px;
21  grid-row-gap: 0px;
22  max-height: 100%;
23  max-width: 100%;
24}
25
26.nav_section {
27  -ms-grid-row: 1;
28  -ms-grid-column: 1;
29  -ms-grid-column-span: 1;
30  grid-area: 1 / 1 / 1 / 2;
31  border: 3px yellow solid;
32}
33
34.left_column {
35  -ms-grid-row: 1;
36  -ms-grid-column: 2;
37  -ms-grid-column-span: 1;
38  grid-area: 1 / 2 / 1 / 3;
39  border: 1px yellow solid;
40}
41
42.right_colomn {
43  -ms-grid-row: 1;
44  -ms-grid-column: 3;
45  -ms-grid-column-span: 1;
46  grid-area: 1 / 3 / 1 / 4;
47  border: 2px blue solid;
48}
49
50.left_column > h1 {
51  font-family: "Asap";
52  color: #f9faf4;
53  font-size: 13rem;
54  font-style: normal;
55  font-weight: normal;
56  line-height: 15.75rem;
57  text-transform: uppercase;
58  -webkit-transform: rotate(-90deg);
59          transform: rotate(-90deg);
60  display: -webkit-box;
61  display: -ms-flexbox;
62  display: flex;
63  border: red 3px solid;
64  -o-object-fit: contain;
65     object-fit: contain;
66  max-width: 100%;
67  max-height: 100%;
68}
69
70.main_bio {
71  color: #f2c4ce;
72  font-size: 1.75rem;
73  text-decoration: underline;
74}    <main>
75        <div class="main_grid">
76            <div class="nav_section">
77                <nav class="main_nav">
78                    <a href="#">home</a>
79                    <a href="#">work</a>
80                    <a href="#">contact</a>
81                </nav>
82            </div>
83            <div class="left_column">
84                <h1 class="main_title">Hello, I'm Jack</h1>
85            </div>
86           <div class="right_colomn">
87              <p class="main_bio">A 20 YEAR OLD FROM A SMALL TOWN NEAR AMSTERDAM. CURRENTLY STUDYING COMPUTER SCIENCE IN LEIDEN.</p>
88           </div>
89        </div>
90    </main>@import url("https://fonts.googleapis.com/css2?family=Asap:wght@400;700&display=swap");
91* {
92  padding: 0;
93  margin: 0;
94  height: 100%;
95  width: 100%;
96  background-color: #4a6163;
97  font-family: "Asap";
98  -webkit-box-sizing: border-box;
99          box-sizing: border-box;
100}
101
102.main_grid {
103  display: -ms-grid;
104  display: grid;
105  -ms-grid-columns: 0.25fr (1fr)[2];
106      grid-template-columns: 0.25fr repeat(2, 1fr);
107  -ms-grid-rows: 1fr;
108      grid-template-rows: 1fr;
109  grid-column-gap: 0px;
110  grid-row-gap: 0px;
111  max-height: 100%;
112  max-width: 100%;
113}
114
115.nav_section {
116  -ms-grid-row: 1;
117  -ms-grid-column: 1;
118  -ms-grid-column-span: 1;
119  grid-area: 1 / 1 / 1 / 2;
120  border: 3px yellow solid;
121}
122
123.left_column {
124  -ms-grid-row: 1;
125  -ms-grid-column: 2;
126  -ms-grid-column-span: 1;
127  grid-area: 1 / 2 / 1 / 3;
128  border: 1px yellow solid;
129}
130
131.right_colomn {
132  -ms-grid-row: 1;
133  -ms-grid-column: 3;
134  -ms-grid-column-span: 1;
135  grid-area: 1 / 3 / 1 / 4;
136  border: 2px blue solid;
137}
138
139.left_column > h1 {
140  font-family: "Asap";
141  color: #f9faf4;
142  font-size: 13rem;
143  font-style: normal;
144  font-weight: normal;
145  line-height: 15.75rem;
146  text-transform: uppercase;
147  /* Updated the following 3 lines */
148  white-space: nowrap;
149  writing-mode: tb-rl;
150  -webkit-transform: rotate(-180deg);
151          transform: rotate(-180deg);
152  display: -webkit-box;
153  display: -ms-flexbox;
154  display: flex;
155  border: red 3px solid;
156  -o-object-fit: contain;
157     object-fit: contain;
158  max-width: 100%;
159  max-height: 100%;
160}
161
162.main_bio {
163  color: #f2c4ce;
164  font-size: 1.75rem;
165  text-decoration: underline;
166}

1@import url("https://fonts.googleapis.com/css2?family=Asap:wght@400;700&display=swap");
2* {
3  padding: 0;
4  margin: 0;
5  height: 100%;
6  width: 100%;
7  background-color: #4a6163;
8  font-family: "Asap";
9  -webkit-box-sizing: border-box;
10          box-sizing: border-box;
11}
12
13.main_grid {
14  display: -ms-grid;
15  display: grid;
16  -ms-grid-columns: 0.25fr (1fr)[2];
17      grid-template-columns: 0.25fr repeat(2, 1fr);
18  -ms-grid-rows: 1fr;
19      grid-template-rows: 1fr;
20  grid-column-gap: 0px;
21  grid-row-gap: 0px;
22  max-height: 100%;
23  max-width: 100%;
24}
25
26.nav_section {
27  -ms-grid-row: 1;
28  -ms-grid-column: 1;
29  -ms-grid-column-span: 1;
30  grid-area: 1 / 1 / 1 / 2;
31  border: 3px yellow solid;
32}
33
34.left_column {
35  -ms-grid-row: 1;
36  -ms-grid-column: 2;
37  -ms-grid-column-span: 1;
38  grid-area: 1 / 2 / 1 / 3;
39  border: 1px yellow solid;
40}
41
42.right_colomn {
43  -ms-grid-row: 1;
44  -ms-grid-column: 3;
45  -ms-grid-column-span: 1;
46  grid-area: 1 / 3 / 1 / 4;
47  border: 2px blue solid;
48}
49
50.left_column > h1 {
51  font-family: "Asap";
52  color: #f9faf4;
53  font-size: 13rem;
54  font-style: normal;
55  font-weight: normal;
56  line-height: 15.75rem;
57  text-transform: uppercase;
58  -webkit-transform: rotate(-90deg);
59          transform: rotate(-90deg);
60  display: -webkit-box;
61  display: -ms-flexbox;
62  display: flex;
63  border: red 3px solid;
64  -o-object-fit: contain;
65     object-fit: contain;
66  max-width: 100%;
67  max-height: 100%;
68}
69
70.main_bio {
71  color: #f2c4ce;
72  font-size: 1.75rem;
73  text-decoration: underline;
74}    <main>
75        <div class="main_grid">
76            <div class="nav_section">
77                <nav class="main_nav">
78                    <a href="#">home</a>
79                    <a href="#">work</a>
80                    <a href="#">contact</a>
81                </nav>
82            </div>
83            <div class="left_column">
84                <h1 class="main_title">Hello, I'm Jack</h1>
85            </div>
86           <div class="right_colomn">
87              <p class="main_bio">A 20 YEAR OLD FROM A SMALL TOWN NEAR AMSTERDAM. CURRENTLY STUDYING COMPUTER SCIENCE IN LEIDEN.</p>
88           </div>
89        </div>
90    </main>@import url("https://fonts.googleapis.com/css2?family=Asap:wght@400;700&display=swap");
91* {
92  padding: 0;
93  margin: 0;
94  height: 100%;
95  width: 100%;
96  background-color: #4a6163;
97  font-family: "Asap";
98  -webkit-box-sizing: border-box;
99          box-sizing: border-box;
100}
101
102.main_grid {
103  display: -ms-grid;
104  display: grid;
105  -ms-grid-columns: 0.25fr (1fr)[2];
106      grid-template-columns: 0.25fr repeat(2, 1fr);
107  -ms-grid-rows: 1fr;
108      grid-template-rows: 1fr;
109  grid-column-gap: 0px;
110  grid-row-gap: 0px;
111  max-height: 100%;
112  max-width: 100%;
113}
114
115.nav_section {
116  -ms-grid-row: 1;
117  -ms-grid-column: 1;
118  -ms-grid-column-span: 1;
119  grid-area: 1 / 1 / 1 / 2;
120  border: 3px yellow solid;
121}
122
123.left_column {
124  -ms-grid-row: 1;
125  -ms-grid-column: 2;
126  -ms-grid-column-span: 1;
127  grid-area: 1 / 2 / 1 / 3;
128  border: 1px yellow solid;
129}
130
131.right_colomn {
132  -ms-grid-row: 1;
133  -ms-grid-column: 3;
134  -ms-grid-column-span: 1;
135  grid-area: 1 / 3 / 1 / 4;
136  border: 2px blue solid;
137}
138
139.left_column > h1 {
140  font-family: "Asap";
141  color: #f9faf4;
142  font-size: 13rem;
143  font-style: normal;
144  font-weight: normal;
145  line-height: 15.75rem;
146  text-transform: uppercase;
147  /* Updated the following 3 lines */
148  white-space: nowrap;
149  writing-mode: tb-rl;
150  -webkit-transform: rotate(-180deg);
151          transform: rotate(-180deg);
152  display: -webkit-box;
153  display: -ms-flexbox;
154  display: flex;
155  border: red 3px solid;
156  -o-object-fit: contain;
157     object-fit: contain;
158  max-width: 100%;
159  max-height: 100%;
160}
161
162.main_bio {
163  color: #f2c4ce;
164  font-size: 1.75rem;
165  text-decoration: underline;
166}    <main>
167        <div class="main_grid">
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169                <nav class="main_nav">
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175            <div class="left_column">
176                <h1 class="main_title">Hello,<br/>I'm Jack</h1>
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179              <p class="main_bio">A 20 YEAR OLD FROM A SMALL TOWN NEAR AMSTERDAM. CURRENTLY STUDYING COMPUTER SCIENCE IN LEIDEN.</p>
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182    </main>

1#lets try gridsearchcv
2#https://www.kaggle.com/enespolat/grid-search-with-logistic-regression
3
4from sklearn.model_selection import GridSearchCV
5
6grid={"C":np.logspace(-3,3,7), "penalty":["l2"]}# l1 lasso l2 ridge
7logreg=LogisticRegression(solver = 'liblinear')
8logreg_cv=GridSearchCV(logreg,grid,cv=3,scoring='f1')
9logreg_cv.fit(X_train_vectors_tfidf, y_train)
10
11print("tuned hpyerparameters :(best parameters) ",logreg_cv.best_params_)
12print("best score :",logreg_cv.best_score_)
13
14#tuned hpyerparameters :(best parameters)  {'C': 10.0, 'penalty': 'l2'}
15#best score : 0.7390325593588823
16logreg_cv.predict_proba(X_train_vectors_tfidf)[:,1]
17final_prediction=np.where(logreg_cv.predict_proba(X_train_vectors_tfidf)[:,1]>=0.5,1,0)
18#https://www.statology.org/f1-score-in-python/
19from sklearn.metrics import f1_score
20#calculate F1 score
21f1_score(y_train, final_prediction)
220.9839388145315489
23from sklearn.model_selection import GridSearchCV
24
25grid={"C":np.logspace(-3,3,7), "penalty":["l2"]}# l1 lasso l2 ridge
26logreg=LogisticRegression(solver = 'liblinear')
27logreg_cv=GridSearchCV(logreg,grid,cv=3,scoring='precision')
28logreg_cv.fit(X_train_vectors_tfidf, y_train)
29
30print("tuned hpyerparameters :(best parameters) ",logreg_cv.best_params_)
31print("best score :",logreg_cv.best_score_)
32
33
34
35/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
36  _warn_prf(average, modifier, msg_start, len(result))
37/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
38  _warn_prf(average, modifier, msg_start, len(result))
39/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
40  _warn_prf(average, modifier, msg_start, len(result))
41/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
42  _warn_prf(average, modifier, msg_start, len(result))
43/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
44  _warn_prf(average, modifier, msg_start, len(result))
45/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
46  _warn_prf(average, modifier, msg_start, len(result))
47tuned hpyerparameters :(best parameters)  {'C': 0.1, 'penalty': 'l2'}
48best score : 0.9474200393672962
49from sklearn.model_selection import GridSearchCV
50
51grid={"C":np.logspace(-3,3,7), "penalty":["l2"], "solver":['liblinear','newton-cg'], 'class_weight':[{ 0:0.95, 1:0.05 }, { 0:0.55, 1:0.45 }, { 0:0.45, 1:0.55 },{ 0:0.05, 1:0.95 }]}# l1 lasso l2 ridge
52#logreg=LogisticRegression(solver = 'liblinear')
53logreg=LogisticRegression()
54logreg_cv=GridSearchCV(logreg,grid,cv=3,scoring='f1_micro')
55logreg_cv.fit(X_train_vectors_tfidf, y_train)
56
57tuned hpyerparameters :(best parameters)  {'C': 10.0, 'class_weight': {0: 0.45, 1: 0.55}, 'penalty': 'l2', 'solver': 'newton-cg'}
58best score : 0.7894909688013136
59res = pd.DataFrame(logreg_cv.cv_results_)
60res.iloc[:,res.columns.str.contains("split[0-9]_test_score|params",regex=True)]
61 
62                           params  split0_test_score  split1_test_score  split2_test_score
630   {'C': 0.001, 'penalty': 'l2'}           0.000000           0.000000           0.000000
641    {'C': 0.01, 'penalty': 'l2'}           0.000000           0.000000           0.000000
652     {'C': 0.1, 'penalty': 'l2'}           0.973568           0.952607           0.952174
663     {'C': 1.0, 'penalty': 'l2'}           0.863934           0.851064           0.836449
674    {'C': 10.0, 'penalty': 'l2'}           0.811634           0.769547           0.787838
685   {'C': 100.0, 'penalty': 'l2'}           0.789826           0.762162           0.773438
696  {'C': 1000.0, 'penalty': 'l2'}           0.781003           0.750000           0.763871
70

1#lets try gridsearchcv
2#https://www.kaggle.com/enespolat/grid-search-with-logistic-regression
3
4from sklearn.model_selection import GridSearchCV
5
6grid={"C":np.logspace(-3,3,7), "penalty":["l2"]}# l1 lasso l2 ridge
7logreg=LogisticRegression(solver = 'liblinear')
8logreg_cv=GridSearchCV(logreg,grid,cv=3,scoring='f1')
9logreg_cv.fit(X_train_vectors_tfidf, y_train)
10
11print("tuned hpyerparameters :(best parameters) ",logreg_cv.best_params_)
12print("best score :",logreg_cv.best_score_)
13
14#tuned hpyerparameters :(best parameters)  {'C': 10.0, 'penalty': 'l2'}
15#best score : 0.7390325593588823
16logreg_cv.predict_proba(X_train_vectors_tfidf)[:,1]
17final_prediction=np.where(logreg_cv.predict_proba(X_train_vectors_tfidf)[:,1]>=0.5,1,0)
18#https://www.statology.org/f1-score-in-python/
19from sklearn.metrics import f1_score
20#calculate F1 score
21f1_score(y_train, final_prediction)
220.9839388145315489
23from sklearn.model_selection import GridSearchCV
24
25grid={"C":np.logspace(-3,3,7), "penalty":["l2"]}# l1 lasso l2 ridge
26logreg=LogisticRegression(solver = 'liblinear')
27logreg_cv=GridSearchCV(logreg,grid,cv=3,scoring='precision')
28logreg_cv.fit(X_train_vectors_tfidf, y_train)
29
30print("tuned hpyerparameters :(best parameters) ",logreg_cv.best_params_)
31print("best score :",logreg_cv.best_score_)
32
33
34
35/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
36  _warn_prf(average, modifier, msg_start, len(result))
37/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
38  _warn_prf(average, modifier, msg_start, len(result))
39/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
40  _warn_prf(average, modifier, msg_start, len(result))
41/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
42  _warn_prf(average, modifier, msg_start, len(result))
43/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
44  _warn_prf(average, modifier, msg_start, len(result))
45/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
46  _warn_prf(average, modifier, msg_start, len(result))
47tuned hpyerparameters :(best parameters)  {'C': 0.1, 'penalty': 'l2'}
48best score : 0.9474200393672962
49from sklearn.model_selection import GridSearchCV
50
51grid={"C":np.logspace(-3,3,7), "penalty":["l2"], "solver":['liblinear','newton-cg'], 'class_weight':[{ 0:0.95, 1:0.05 }, { 0:0.55, 1:0.45 }, { 0:0.45, 1:0.55 },{ 0:0.05, 1:0.95 }]}# l1 lasso l2 ridge
52#logreg=LogisticRegression(solver = 'liblinear')
53logreg=LogisticRegression()
54logreg_cv=GridSearchCV(logreg,grid,cv=3,scoring='f1_micro')
55logreg_cv.fit(X_train_vectors_tfidf, y_train)
56
57tuned hpyerparameters :(best parameters)  {'C': 10.0, 'class_weight': {0: 0.45, 1: 0.55}, 'penalty': 'l2', 'solver': 'newton-cg'}
58best score : 0.7894909688013136
59res = pd.DataFrame(logreg_cv.cv_results_)
60res.iloc[:,res.columns.str.contains("split[0-9]_test_score|params",regex=True)]
61 
62                           params  split0_test_score  split1_test_score  split2_test_score
630   {'C': 0.001, 'penalty': 'l2'}           0.000000           0.000000           0.000000
641    {'C': 0.01, 'penalty': 'l2'}           0.000000           0.000000           0.000000
652     {'C': 0.1, 'penalty': 'l2'}           0.973568           0.952607           0.952174
663     {'C': 1.0, 'penalty': 'l2'}           0.863934           0.851064           0.836449
674    {'C': 10.0, 'penalty': 'l2'}           0.811634           0.769547           0.787838
685   {'C': 100.0, 'penalty': 'l2'}           0.789826           0.762162           0.773438
696  {'C': 1000.0, 'penalty': 'l2'}           0.781003           0.750000           0.763871
70lr = LogisticRegression(C=0.01).fit(X_train_vectors_tfidf,y_train)
71np.unique(lr.predict(X_train_vectors_tfidf))
72array([0])
73

1#lets try gridsearchcv
2#https://www.kaggle.com/enespolat/grid-search-with-logistic-regression
3
4from sklearn.model_selection import GridSearchCV
5
6grid={"C":np.logspace(-3,3,7), "penalty":["l2"]}# l1 lasso l2 ridge
7logreg=LogisticRegression(solver = 'liblinear')
8logreg_cv=GridSearchCV(logreg,grid,cv=3,scoring='f1')
9logreg_cv.fit(X_train_vectors_tfidf, y_train)
10
11print("tuned hpyerparameters :(best parameters) ",logreg_cv.best_params_)
12print("best score :",logreg_cv.best_score_)
13
14#tuned hpyerparameters :(best parameters)  {'C': 10.0, 'penalty': 'l2'}
15#best score : 0.7390325593588823
16logreg_cv.predict_proba(X_train_vectors_tfidf)[:,1]
17final_prediction=np.where(logreg_cv.predict_proba(X_train_vectors_tfidf)[:,1]>=0.5,1,0)
18#https://www.statology.org/f1-score-in-python/
19from sklearn.metrics import f1_score
20#calculate F1 score
21f1_score(y_train, final_prediction)
220.9839388145315489
23from sklearn.model_selection import GridSearchCV
24
25grid={"C":np.logspace(-3,3,7), "penalty":["l2"]}# l1 lasso l2 ridge
26logreg=LogisticRegression(solver = 'liblinear')
27logreg_cv=GridSearchCV(logreg,grid,cv=3,scoring='precision')
28logreg_cv.fit(X_train_vectors_tfidf, y_train)
29
30print("tuned hpyerparameters :(best parameters) ",logreg_cv.best_params_)
31print("best score :",logreg_cv.best_score_)
32
33
34
35/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
36  _warn_prf(average, modifier, msg_start, len(result))
37/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
38  _warn_prf(average, modifier, msg_start, len(result))
39/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
40  _warn_prf(average, modifier, msg_start, len(result))
41/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
42  _warn_prf(average, modifier, msg_start, len(result))
43/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
44  _warn_prf(average, modifier, msg_start, len(result))
45/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
46  _warn_prf(average, modifier, msg_start, len(result))
47tuned hpyerparameters :(best parameters)  {'C': 0.1, 'penalty': 'l2'}
48best score : 0.9474200393672962
49from sklearn.model_selection import GridSearchCV
50
51grid={"C":np.logspace(-3,3,7), "penalty":["l2"], "solver":['liblinear','newton-cg'], 'class_weight':[{ 0:0.95, 1:0.05 }, { 0:0.55, 1:0.45 }, { 0:0.45, 1:0.55 },{ 0:0.05, 1:0.95 }]}# l1 lasso l2 ridge
52#logreg=LogisticRegression(solver = 'liblinear')
53logreg=LogisticRegression()
54logreg_cv=GridSearchCV(logreg,grid,cv=3,scoring='f1_micro')
55logreg_cv.fit(X_train_vectors_tfidf, y_train)
56
57tuned hpyerparameters :(best parameters)  {'C': 10.0, 'class_weight': {0: 0.45, 1: 0.55}, 'penalty': 'l2', 'solver': 'newton-cg'}
58best score : 0.7894909688013136
59res = pd.DataFrame(logreg_cv.cv_results_)
60res.iloc[:,res.columns.str.contains("split[0-9]_test_score|params",regex=True)]
61 
62                           params  split0_test_score  split1_test_score  split2_test_score
630   {'C': 0.001, 'penalty': 'l2'}           0.000000           0.000000           0.000000
641    {'C': 0.01, 'penalty': 'l2'}           0.000000           0.000000           0.000000
652     {'C': 0.1, 'penalty': 'l2'}           0.973568           0.952607           0.952174
663     {'C': 1.0, 'penalty': 'l2'}           0.863934           0.851064           0.836449
674    {'C': 10.0, 'penalty': 'l2'}           0.811634           0.769547           0.787838
685   {'C': 100.0, 'penalty': 'l2'}           0.789826           0.762162           0.773438
696  {'C': 1000.0, 'penalty': 'l2'}           0.781003           0.750000           0.763871
70lr = LogisticRegression(C=0.01).fit(X_train_vectors_tfidf,y_train)
71np.unique(lr.predict(X_train_vectors_tfidf))
72array([0])
73# expected probability
74np.exp(lr.intercept_)/(1+np.exp(lr.intercept_))
75array([0.41764462])
76
77lr.predict_proba(X_train_vectors_tfidf)
78 
79array([[0.58732636, 0.41267364],
80       [0.57074279, 0.42925721],
81       [0.57219143, 0.42780857],
82       ...,
83       [0.57215605, 0.42784395],
84       [0.56988186, 0.43011814],
85       [0.58966184, 0.41033816]])
86