Getting Started

using TidierPlots
using DataFrames

penguins = penguins_data()

333×7 DataFrame

Row	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
	String	String	Float64	Float64	Int64	Int64	String
1	Adelie	Torgersen	39.1	18.7	181	3750	male
2	Adelie	Torgersen	39.5	17.4	186	3800	female
3	Adelie	Torgersen	40.3	18.0	195	3250	female
4	Adelie	Torgersen	36.7	19.3	193	3450	female
5	Adelie	Torgersen	39.3	20.6	190	3650	male
6	Adelie	Torgersen	38.9	17.8	181	3625	female
7	Adelie	Torgersen	39.2	19.6	195	4675	male
8	Adelie	Torgersen	41.1	17.6	182	3200	female
9	Adelie	Torgersen	38.6	21.2	191	3800	male
10	Adelie	Torgersen	34.6	21.1	198	4400	male
11	Adelie	Torgersen	36.6	17.8	185	3700	female
12	Adelie	Torgersen	38.7	19.0	195	3450	female
13	Adelie	Torgersen	42.5	20.7	197	4500	male
14	Adelie	Torgersen	34.4	18.4	184	3325	female
15	Adelie	Torgersen	46.0	21.5	194	4200	male
16	Adelie	Biscoe	37.8	18.3	174	3400	female
17	Adelie	Biscoe	37.7	18.7	180	3600	male
18	Adelie	Biscoe	35.9	19.2	189	3800	female
19	Adelie	Biscoe	38.2	18.1	185	3950	male
20	Adelie	Biscoe	38.8	17.2	180	3800	male
21	Adelie	Biscoe	35.3	18.9	187	3800	female
22	Adelie	Biscoe	40.6	18.6	183	3550	male
23	Adelie	Biscoe	40.5	17.9	187	3200	female
24	Adelie	Biscoe	37.9	18.6	172	3150	female
25	Adelie	Biscoe	40.5	18.9	180	3950	male
26	Adelie	Dream	39.5	16.7	178	3250	female
27	Adelie	Dream	37.2	18.1	178	3900	male
28	Adelie	Dream	39.5	17.8	188	3300	female
29	Adelie	Dream	40.9	18.9	184	3900	male
30	Adelie	Dream	36.4	17.0	195	3325	female
31	Adelie	Dream	39.2	21.1	196	4150	male
32	Adelie	Dream	38.8	20.0	190	3950	male
33	Adelie	Dream	42.2	18.5	180	3550	female
34	Adelie	Dream	37.6	19.3	181	3300	female
35	Adelie	Dream	39.8	19.1	184	4650	male
36	Adelie	Dream	36.5	18.0	182	3150	female
37	Adelie	Dream	40.8	18.4	195	3900	male
38	Adelie	Dream	36.0	18.5	186	3100	female
39	Adelie	Dream	44.1	19.7	196	4400	male
40	Adelie	Dream	37.0	16.9	185	3000	female
41	Adelie	Dream	39.6	18.8	190	4600	male
42	Adelie	Dream	41.1	19.0	182	3425	male
43	Adelie	Dream	36.0	17.9	190	3450	female
44	Adelie	Dream	42.3	21.2	191	4150	male
45	Adelie	Biscoe	39.6	17.7	186	3500	female
46	Adelie	Biscoe	40.1	18.9	188	4300	male
47	Adelie	Biscoe	35.0	17.9	190	3450	female
48	Adelie	Biscoe	42.0	19.5	200	4050	male
49	Adelie	Biscoe	34.5	18.1	187	2900	female
50	Adelie	Biscoe	41.4	18.6	191	3700	male
51	Adelie	Biscoe	39.0	17.5	186	3550	female
52	Adelie	Biscoe	40.6	18.8	193	3800	male
53	Adelie	Biscoe	36.5	16.6	181	2850	female
54	Adelie	Biscoe	37.6	19.1	194	3750	male
55	Adelie	Biscoe	35.7	16.9	185	3150	female
56	Adelie	Biscoe	41.3	21.1	195	4400	male
57	Adelie	Biscoe	37.6	17.0	185	3600	female
58	Adelie	Biscoe	41.1	18.2	192	4050	male
59	Adelie	Biscoe	36.4	17.1	184	2850	female
60	Adelie	Biscoe	41.6	18.0	192	3950	male
61	Adelie	Biscoe	35.5	16.2	195	3350	female
62	Adelie	Biscoe	41.1	19.1	188	4100	male
63	Adelie	Torgersen	35.9	16.6	190	3050	female
64	Adelie	Torgersen	41.8	19.4	198	4450	male
65	Adelie	Torgersen	33.5	19.0	190	3600	female
66	Adelie	Torgersen	39.7	18.4	190	3900	male
67	Adelie	Torgersen	39.6	17.2	196	3550	female
68	Adelie	Torgersen	45.8	18.9	197	4150	male
69	Adelie	Torgersen	35.5	17.5	190	3700	female
70	Adelie	Torgersen	42.8	18.5	195	4250	male
71	Adelie	Torgersen	40.9	16.8	191	3700	female
72	Adelie	Torgersen	37.2	19.4	184	3900	male
73	Adelie	Torgersen	36.2	16.1	187	3550	female
74	Adelie	Torgersen	42.1	19.1	195	4000	male
75	Adelie	Torgersen	34.6	17.2	189	3200	female
76	Adelie	Torgersen	42.9	17.6	196	4700	male
77	Adelie	Torgersen	36.7	18.8	187	3800	female
78	Adelie	Torgersen	35.1	19.4	193	4200	male
79	Adelie	Dream	37.3	17.8	191	3350	female
80	Adelie	Dream	41.3	20.3	194	3550	male
81	Adelie	Dream	36.3	19.5	190	3800	male
82	Adelie	Dream	36.9	18.6	189	3500	female
83	Adelie	Dream	38.3	19.2	189	3950	male
84	Adelie	Dream	38.9	18.8	190	3600	female
85	Adelie	Dream	35.7	18.0	202	3550	female
86	Adelie	Dream	41.1	18.1	205	4300	male
87	Adelie	Dream	34.0	17.1	185	3400	female
88	Adelie	Dream	39.6	18.1	186	4450	male
89	Adelie	Dream	36.2	17.3	187	3300	female
90	Adelie	Dream	40.8	18.9	208	4300	male
91	Adelie	Dream	38.1	18.6	190	3700	female
92	Adelie	Dream	40.3	18.5	196	4350	male
93	Adelie	Dream	33.1	16.1	178	2900	female
94	Adelie	Dream	43.2	18.5	192	4100	male
95	Adelie	Biscoe	35.0	17.9	192	3725	female
96	Adelie	Biscoe	41.0	20.0	203	4725	male
97	Adelie	Biscoe	37.7	16.0	183	3075	female
98	Adelie	Biscoe	37.8	20.0	190	4250	male
99	Adelie	Biscoe	37.9	18.6	193	2925	female
100	Adelie	Biscoe	39.7	18.9	184	3550	male
101	Adelie	Biscoe	38.6	17.2	199	3750	female
102	Adelie	Biscoe	38.2	20.0	190	3900	male
103	Adelie	Biscoe	38.1	17.0	181	3175	female
104	Adelie	Biscoe	43.2	19.0	197	4775	male
105	Adelie	Biscoe	38.1	16.5	198	3825	female
106	Adelie	Biscoe	45.6	20.3	191	4600	male
107	Adelie	Biscoe	39.7	17.7	193	3200	female
108	Adelie	Biscoe	42.2	19.5	197	4275	male
109	Adelie	Biscoe	39.6	20.7	191	3900	female
110	Adelie	Biscoe	42.7	18.3	196	4075	male
111	Adelie	Torgersen	38.6	17.0	188	2900	female
112	Adelie	Torgersen	37.3	20.5	199	3775	male
113	Adelie	Torgersen	35.7	17.0	189	3350	female
114	Adelie	Torgersen	41.1	18.6	189	3325	male
115	Adelie	Torgersen	36.2	17.2	187	3150	female
116	Adelie	Torgersen	37.7	19.8	198	3500	male
117	Adelie	Torgersen	40.2	17.0	176	3450	female
118	Adelie	Torgersen	41.4	18.5	202	3875	male
119	Adelie	Torgersen	35.2	15.9	186	3050	female
120	Adelie	Torgersen	40.6	19.0	199	4000	male
121	Adelie	Torgersen	38.8	17.6	191	3275	female
122	Adelie	Torgersen	41.5	18.3	195	4300	male
123	Adelie	Torgersen	39.0	17.1	191	3050	female
124	Adelie	Torgersen	44.1	18.0	210	4000	male
125	Adelie	Torgersen	38.5	17.9	190	3325	female
126	Adelie	Torgersen	43.1	19.2	197	3500	male
127	Adelie	Dream	36.8	18.5	193	3500	female
128	Adelie	Dream	37.5	18.5	199	4475	male
129	Adelie	Dream	38.1	17.6	187	3425	female
130	Adelie	Dream	41.1	17.5	190	3900	male
131	Adelie	Dream	35.6	17.5	191	3175	female
132	Adelie	Dream	40.2	20.1	200	3975	male
133	Adelie	Dream	37.0	16.5	185	3400	female
134	Adelie	Dream	39.7	17.9	193	4250	male
135	Adelie	Dream	40.2	17.1	193	3400	female
136	Adelie	Dream	40.6	17.2	187	3475	male
137	Adelie	Dream	32.1	15.5	188	3050	female
138	Adelie	Dream	40.7	17.0	190	3725	male
139	Adelie	Dream	37.3	16.8	192	3000	female
140	Adelie	Dream	39.0	18.7	185	3650	male
141	Adelie	Dream	39.2	18.6	190	4250	male
142	Adelie	Dream	36.6	18.4	184	3475	female
143	Adelie	Dream	36.0	17.8	195	3450	female
144	Adelie	Dream	37.8	18.1	193	3750	male
145	Adelie	Dream	36.0	17.1	187	3700	female
146	Adelie	Dream	41.5	18.5	201	4000	male
147	Gentoo	Biscoe	46.1	13.2	211	4500	female
148	Gentoo	Biscoe	50.0	16.3	230	5700	male
149	Gentoo	Biscoe	48.7	14.1	210	4450	female
150	Gentoo	Biscoe	50.0	15.2	218	5700	male
151	Gentoo	Biscoe	47.6	14.5	215	5400	male
152	Gentoo	Biscoe	46.5	13.5	210	4550	female
153	Gentoo	Biscoe	45.4	14.6	211	4800	female
154	Gentoo	Biscoe	46.7	15.3	219	5200	male
155	Gentoo	Biscoe	43.3	13.4	209	4400	female
156	Gentoo	Biscoe	46.8	15.4	215	5150	male
157	Gentoo	Biscoe	40.9	13.7	214	4650	female
158	Gentoo	Biscoe	49.0	16.1	216	5550	male
159	Gentoo	Biscoe	45.5	13.7	214	4650	female
160	Gentoo	Biscoe	48.4	14.6	213	5850	male
161	Gentoo	Biscoe	45.8	14.6	210	4200	female
162	Gentoo	Biscoe	49.3	15.7	217	5850	male
163	Gentoo	Biscoe	42.0	13.5	210	4150	female
164	Gentoo	Biscoe	49.2	15.2	221	6300	male
165	Gentoo	Biscoe	46.2	14.5	209	4800	female
166	Gentoo	Biscoe	48.7	15.1	222	5350	male
167	Gentoo	Biscoe	50.2	14.3	218	5700	male
168	Gentoo	Biscoe	45.1	14.5	215	5000	female
169	Gentoo	Biscoe	46.5	14.5	213	4400	female
170	Gentoo	Biscoe	46.3	15.8	215	5050	male
171	Gentoo	Biscoe	42.9	13.1	215	5000	female
172	Gentoo	Biscoe	46.1	15.1	215	5100	male
173	Gentoo	Biscoe	47.8	15.0	215	5650	male
174	Gentoo	Biscoe	48.2	14.3	210	4600	female
175	Gentoo	Biscoe	50.0	15.3	220	5550	male
176	Gentoo	Biscoe	47.3	15.3	222	5250	male
177	Gentoo	Biscoe	42.8	14.2	209	4700	female
178	Gentoo	Biscoe	45.1	14.5	207	5050	female
179	Gentoo	Biscoe	59.6	17.0	230	6050	male
180	Gentoo	Biscoe	49.1	14.8	220	5150	female
181	Gentoo	Biscoe	48.4	16.3	220	5400	male
182	Gentoo	Biscoe	42.6	13.7	213	4950	female
183	Gentoo	Biscoe	44.4	17.3	219	5250	male
184	Gentoo	Biscoe	44.0	13.6	208	4350	female
185	Gentoo	Biscoe	48.7	15.7	208	5350	male
186	Gentoo	Biscoe	42.7	13.7	208	3950	female
187	Gentoo	Biscoe	49.6	16.0	225	5700	male
188	Gentoo	Biscoe	45.3	13.7	210	4300	female
189	Gentoo	Biscoe	49.6	15.0	216	4750	male
190	Gentoo	Biscoe	50.5	15.9	222	5550	male
191	Gentoo	Biscoe	43.6	13.9	217	4900	female
192	Gentoo	Biscoe	45.5	13.9	210	4200	female
193	Gentoo	Biscoe	50.5	15.9	225	5400	male
194	Gentoo	Biscoe	44.9	13.3	213	5100	female
195	Gentoo	Biscoe	45.2	15.8	215	5300	male
196	Gentoo	Biscoe	46.6	14.2	210	4850	female
197	Gentoo	Biscoe	48.5	14.1	220	5300	male
198	Gentoo	Biscoe	45.1	14.4	210	4400	female
199	Gentoo	Biscoe	50.1	15.0	225	5000	male
200	Gentoo	Biscoe	46.5	14.4	217	4900	female
201	Gentoo	Biscoe	45.0	15.4	220	5050	male
202	Gentoo	Biscoe	43.8	13.9	208	4300	female
203	Gentoo	Biscoe	45.5	15.0	220	5000	male
204	Gentoo	Biscoe	43.2	14.5	208	4450	female
205	Gentoo	Biscoe	50.4	15.3	224	5550	male
206	Gentoo	Biscoe	45.3	13.8	208	4200	female
207	Gentoo	Biscoe	46.2	14.9	221	5300	male
208	Gentoo	Biscoe	45.7	13.9	214	4400	female
209	Gentoo	Biscoe	54.3	15.7	231	5650	male
210	Gentoo	Biscoe	45.8	14.2	219	4700	female
211	Gentoo	Biscoe	49.8	16.8	230	5700	male
212	Gentoo	Biscoe	49.5	16.2	229	5800	male
213	Gentoo	Biscoe	43.5	14.2	220	4700	female
214	Gentoo	Biscoe	50.7	15.0	223	5550	male
215	Gentoo	Biscoe	47.7	15.0	216	4750	female
216	Gentoo	Biscoe	46.4	15.6	221	5000	male
217	Gentoo	Biscoe	48.2	15.6	221	5100	male
218	Gentoo	Biscoe	46.5	14.8	217	5200	female
219	Gentoo	Biscoe	46.4	15.0	216	4700	female
220	Gentoo	Biscoe	48.6	16.0	230	5800	male
221	Gentoo	Biscoe	47.5	14.2	209	4600	female
222	Gentoo	Biscoe	51.1	16.3	220	6000	male
223	Gentoo	Biscoe	45.2	13.8	215	4750	female
224	Gentoo	Biscoe	45.2	16.4	223	5950	male
225	Gentoo	Biscoe	49.1	14.5	212	4625	female
226	Gentoo	Biscoe	52.5	15.6	221	5450	male
227	Gentoo	Biscoe	47.4	14.6	212	4725	female
228	Gentoo	Biscoe	50.0	15.9	224	5350	male
229	Gentoo	Biscoe	44.9	13.8	212	4750	female
230	Gentoo	Biscoe	50.8	17.3	228	5600	male
231	Gentoo	Biscoe	43.4	14.4	218	4600	female
232	Gentoo	Biscoe	51.3	14.2	218	5300	male
233	Gentoo	Biscoe	47.5	14.0	212	4875	female
234	Gentoo	Biscoe	52.1	17.0	230	5550	male
235	Gentoo	Biscoe	47.5	15.0	218	4950	female
236	Gentoo	Biscoe	52.2	17.1	228	5400	male
237	Gentoo	Biscoe	45.5	14.5	212	4750	female
238	Gentoo	Biscoe	49.5	16.1	224	5650	male
239	Gentoo	Biscoe	44.5	14.7	214	4850	female
240	Gentoo	Biscoe	50.8	15.7	226	5200	male
241	Gentoo	Biscoe	49.4	15.8	216	4925	male
242	Gentoo	Biscoe	46.9	14.6	222	4875	female
243	Gentoo	Biscoe	48.4	14.4	203	4625	female
244	Gentoo	Biscoe	51.1	16.5	225	5250	male
245	Gentoo	Biscoe	48.5	15.0	219	4850	female
246	Gentoo	Biscoe	55.9	17.0	228	5600	male
247	Gentoo	Biscoe	47.2	15.5	215	4975	female
248	Gentoo	Biscoe	49.1	15.0	228	5500	male
249	Gentoo	Biscoe	46.8	16.1	215	5500	male
250	Gentoo	Biscoe	41.7	14.7	210	4700	female
251	Gentoo	Biscoe	53.4	15.8	219	5500	male
252	Gentoo	Biscoe	43.3	14.0	208	4575	female
253	Gentoo	Biscoe	48.1	15.1	209	5500	male
254	Gentoo	Biscoe	50.5	15.2	216	5000	female
255	Gentoo	Biscoe	49.8	15.9	229	5950	male
256	Gentoo	Biscoe	43.5	15.2	213	4650	female
257	Gentoo	Biscoe	51.5	16.3	230	5500	male
258	Gentoo	Biscoe	46.2	14.1	217	4375	female
259	Gentoo	Biscoe	55.1	16.0	230	5850	male
260	Gentoo	Biscoe	48.8	16.2	222	6000	male
261	Gentoo	Biscoe	47.2	13.7	214	4925	female
262	Gentoo	Biscoe	46.8	14.3	215	4850	female
263	Gentoo	Biscoe	50.4	15.7	222	5750	male
264	Gentoo	Biscoe	45.2	14.8	212	5200	female
265	Gentoo	Biscoe	49.9	16.1	213	5400	male
266	Chinstrap	Dream	46.5	17.9	192	3500	female
267	Chinstrap	Dream	50.0	19.5	196	3900	male
268	Chinstrap	Dream	51.3	19.2	193	3650	male
269	Chinstrap	Dream	45.4	18.7	188	3525	female
270	Chinstrap	Dream	52.7	19.8	197	3725	male
271	Chinstrap	Dream	45.2	17.8	198	3950	female
272	Chinstrap	Dream	46.1	18.2	178	3250	female
273	Chinstrap	Dream	51.3	18.2	197	3750	male
274	Chinstrap	Dream	46.0	18.9	195	4150	female
275	Chinstrap	Dream	51.3	19.9	198	3700	male
276	Chinstrap	Dream	46.6	17.8	193	3800	female
277	Chinstrap	Dream	51.7	20.3	194	3775	male
278	Chinstrap	Dream	47.0	17.3	185	3700	female
279	Chinstrap	Dream	52.0	18.1	201	4050	male
280	Chinstrap	Dream	45.9	17.1	190	3575	female
281	Chinstrap	Dream	50.5	19.6	201	4050	male
282	Chinstrap	Dream	50.3	20.0	197	3300	male
283	Chinstrap	Dream	58.0	17.8	181	3700	female
284	Chinstrap	Dream	46.4	18.6	190	3450	female
285	Chinstrap	Dream	49.2	18.2	195	4400	male
286	Chinstrap	Dream	42.4	17.3	181	3600	female
287	Chinstrap	Dream	48.5	17.5	191	3400	male
288	Chinstrap	Dream	43.2	16.6	187	2900	female
289	Chinstrap	Dream	50.6	19.4	193	3800	male
290	Chinstrap	Dream	46.7	17.9	195	3300	female
291	Chinstrap	Dream	52.0	19.0	197	4150	male
292	Chinstrap	Dream	50.5	18.4	200	3400	female
293	Chinstrap	Dream	49.5	19.0	200	3800	male
294	Chinstrap	Dream	46.4	17.8	191	3700	female
295	Chinstrap	Dream	52.8	20.0	205	4550	male
296	Chinstrap	Dream	40.9	16.6	187	3200	female
297	Chinstrap	Dream	54.2	20.8	201	4300	male
298	Chinstrap	Dream	42.5	16.7	187	3350	female
299	Chinstrap	Dream	51.0	18.8	203	4100	male
300	Chinstrap	Dream	49.7	18.6	195	3600	male
301	Chinstrap	Dream	47.5	16.8	199	3900	female
302	Chinstrap	Dream	47.6	18.3	195	3850	female
303	Chinstrap	Dream	52.0	20.7	210	4800	male
304	Chinstrap	Dream	46.9	16.6	192	2700	female
305	Chinstrap	Dream	53.5	19.9	205	4500	male
306	Chinstrap	Dream	49.0	19.5	210	3950	male
307	Chinstrap	Dream	46.2	17.5	187	3650	female
308	Chinstrap	Dream	50.9	19.1	196	3550	male
309	Chinstrap	Dream	45.5	17.0	196	3500	female
310	Chinstrap	Dream	50.9	17.9	196	3675	female
311	Chinstrap	Dream	50.8	18.5	201	4450	male
312	Chinstrap	Dream	50.1	17.9	190	3400	female
313	Chinstrap	Dream	49.0	19.6	212	4300	male
314	Chinstrap	Dream	51.5	18.7	187	3250	male
315	Chinstrap	Dream	49.8	17.3	198	3675	female
316	Chinstrap	Dream	48.1	16.4	199	3325	female
317	Chinstrap	Dream	51.4	19.0	201	3950	male
318	Chinstrap	Dream	45.7	17.3	193	3600	female
319	Chinstrap	Dream	50.7	19.7	203	4050	male
320	Chinstrap	Dream	42.5	17.3	187	3350	female
321	Chinstrap	Dream	52.2	18.8	197	3450	male
322	Chinstrap	Dream	45.2	16.6	191	3250	female
323	Chinstrap	Dream	49.3	19.9	203	4050	male
324	Chinstrap	Dream	50.2	18.8	202	3800	male
325	Chinstrap	Dream	45.6	19.4	194	3525	female
326	Chinstrap	Dream	51.9	19.5	206	3950	male
327	Chinstrap	Dream	46.8	16.5	189	3650	female
328	Chinstrap	Dream	45.7	17.0	195	3650	female
329	Chinstrap	Dream	55.8	19.8	207	4000	male
330	Chinstrap	Dream	43.5	18.1	202	3400	female
331	Chinstrap	Dream	49.6	18.2	193	3775	male
332	Chinstrap	Dream	50.8	19.0	210	4100	male
333	Chinstrap	Dream	50.2	18.7	198	3775	female

ggplot()

ggplot() is the starting point of any plot. It sets up the initial plot with default settings that can be later customized with geoms, scales, theme settings and other specifications. ggplot usually used with a data source as an argument, and optionally, a set of aesthetics specified by @aes(). The data source is typically a DataFrame.

If a set of aesthetics is specified in the initial ggplot call, these aesthetics apply to all layers added to the plot, unless they are overridden in subsequent layers.

ggplot(penguins, @aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
    geom_point()

@aes()

aes() is used to map variables in your data to visual properties (aesthetics) of the plot. These aesthetics can include things like position (x and y coordinates), color, shape, size, etc. Each aesthetic is a way of visualizing a variable or a statistical transformation of a variable.

Aesthetics are specified in the form aes(aesthetic = variable), where aesthetic is the name of the aesthetic, and variable is the column name in your data that you want to map to the aesthetic. The variable names do not need to be preceded by a colon.

Of note, TidierPlots.jl accepts multiple forms for aes specification, none of which is exactly the same as ggplot2.

• Option 1: @aes macro, aes as in ggplot, e.g. @aes(x = x, y = y)
• Option 2: @es macro, aes as in ggplot, e.g. @es(x = x, y = y)
• Option 3: aes function, julia-style columns, e.g. aes(x = :x, y = :y)
• Option 4: aes function, strings for columns, e.g. aes(x = "x", y = "y")

The examples below will generally use option 1

In the above example, we can see that the x, y, and color aesthetics are being mapped to each subsequent layer of the plot.

In general, aes() can be used within the ggplot to set global aesthetics that apply to all layers, or within individual geoms to set aesthetics that apply only to that layer.

Moving from general rules, to specific plots, let us first explore geom_point()

geom_point() geom_point is used to create a scatter plot. It is typically used with aesthetics mapping variables to x and y positions, and optionally to other aesthetics like color, shape, and size. geom_point can be used to visualize the relationship between two continuous variables, or a continuous and a discrete variable. The following visuals features can be changed within geom_point(), shape, size, stroke, strokecolour, and alpha .

ggplot(penguins, @aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
    geom_point( size = 20,
                stroke = 1,
                strokecolor = "black",
                alpha = 0.2) +
    labs(x = "Bill Length (mm)", y = "Bill Width (mm)") +
    lims(x = c(40, 60), y = c(15, 20)) +
    theme_minimal()

In the example above, a scatter plot is created with the variable billlengthmm mapped to the x position, and billdepthmm mapped to the y position with color mapped to species. Supported optional arguements include:

• size - this is the size of the marker
• alpha (or transparency), is set to a value between 0 and 1.
• strokecolor is the stroke color around the marker. https://juliagraphics.github.io/Colors.jl/stable/namedcolors/ colors can be chosen from any name on this list
• stroke this is the thickeness of the stroke around the marker

lims

lims allows the user to set the ranges for the x and y axises as shown in the example above.

geom_bar, geom_col, and geom_histogram

geom_bar is used to create bar plots for categorical data. geom_col is a special case of geom_bar where the height of the bars is already computed and does not need to be counted. geom_histogram is used to create a histogram, which is essentially a bar plot for continuous data, where the data is divided into bins and the number of data points in each bin is counted.

ggplot(data=penguins, @aes(x=species)) +
  geom_bar(aes(color = "island"), position = "dodge")

geom_bar optional arguments include

• color, alpha as above
• position, when set to "dodge," bar charts will not stack

ggplot(data=penguins, @aes(x = island, y=species)) +
    geom_col()

ggplot() +
  geom_histogram(data=penguins, @aes(x = bill_length_mm))

In the first example, a bar plot is created with the variable CategoricalVar mapped to the x position, and the count of each category is represented by the height of the bars.

In the second example, a column plot is created with the variable CategoricalVar mapped to the x position, and ComputedHeight mapped to the y position.

A histogram is created with the continuous variable, billlengthmm, mapped to the x position, and the data is divided into bins, with the count in each bin represented by the height of the bars.

geom_path and geom_line

The geom_path and geom_line are used to create line plots. geom_path connects the data points in the order they appear in the data, while geom_line connects the data points in order of the x-values.

x_values = 1:10;
y_values = x_values .^ 2;
df_line = DataFrame(X = x_values, Y = y_values);
ggplot(df_line) +
    geom_line(@aes(x = X, y = Y)) +
    labs(title = "Line Plot Example", x = "X axis", y = "Y axis")+
    theme_dark()

geom_step

geom_step creates a step plot, which is similar to a line plot but with a step pattern rather than a direct line from point to point.

ggplot(df_line, @aes(x = X, y = Y)) +
    geom_step() +
    labs(title = "Step Plot Example", x = "X axis", y = "Y axis")+
    theme_minimal()

geom_boxplot

geom_boxplot creates a boxplot.

ggplot()+
  geom_boxplot(data=penguins, @aes(x = island, y = bill_length_mm))

In this example, a boxplot is created where different island of penguins are mapped to the x position, and the bill length is mapped to the y position. Finally, the each species will be mapped to a different color

geom_boxplot supported optinal arguements currently include:

• color - if used within the aes() with a categorical variable it will make each category a different color as shown above. When used outside of the aes() and selected with a color, it will make each boxplot that color.
• alpha - transaparency as above, used outside of the aes()

geom_violin

geom_violin creates a violin plot, which is a combination of a boxplot and a kernel density plot.

ggplot(penguins, @aes(x = species, y = bill_depth_mm)) +
    geom_violin()

In this example, a violin plot is created where different species of penguins are mapped to the x position, and the bill depth is mapped to the y position. geom_violin does not currently support mapping a categorical variable to colors.

geom_tile

The geom_tile creates a tile plot, also known as a heatmap.

x_values = repeat(1:5, inner = 5);
y_values = repeat(1:5, outer = 5);
values = x_values .* y_values;
df_tile = DataFrame(X = x_values, Y = y_values, Value = values);

ggplot(df_tile, @aes(x = X, y = Y, z = Value)) +
    geom_tile() +
    labs(title = "Tile Plot Example", x = "X axis", y = "Y axis")

geom_text and geom_label

geom_text and geom_label are used to add text and labels to a plot.

Combining plots

Similar to the patchwork library in R, plots can be combined horizontally using the + and | operators and vertically with the / operator.

plot = ggplot(df_tile, @aes(x = X, y = Y, z = Value)) +
           geom_tile();

plot | plot

plot / plot

These extend beyond two plots, but must be used in functional form for | and /.

plot + plot + plot

/(plot, plot, plot)

Grids of plots can also be combined using parethesis to delimit grid boundaries.

((plot + plot + plot) | plot) / (plot / plot)

scale_x_continuous, scale_y_continuous

scale_x_continuous and scale_y_continuous can apply labels and scales, reverse, or adjust the ticks for the axis.

ggplot(penguins, @aes(x = body_mass_g, y = bill_length_mm)) +
    geom_point() +
    scale_x_continuous(
        name="Mass (g)",
        trans=log10
    ) +
    scale_y_continuous(
        name="Length",
        reversed = true,
        labels=label_number(;suffix="mm")
    )

The trans argument takes as input a function which takes in and outputs numerical values. and labels argument can be a string specification from Format.jl or a function which formats a list of strings. Available label generators are:

• label_bytes
• label_currency
• label_date
• label_log
• label_number
• label_ordinal
• label_percent
• label_pvalue
• label_scientific
• label_wrap

Each of these will accept keywords arguments to generate a label function compatible with the labels argument.

scale_x_log10, scale_y_log10

scale_x_log10 and scale_y_log10 apply a base 10 logarithmic transformation to the x and y axes, respectively.

ggplot(penguins, @aes(x = body_mass_g, y = bill_length_mm)) +
    geom_point() +
    scale_x_log10()

In this example, a scatter plot is created where the body mass of penguins is mapped to the x position and the bill length to the y position. A base 10 logarithmic transformation is then applied to the x-axis.

scale_x_log2, scale_y_log2, scale_x_log, scale_y_log

These work similarly to the previous ones, but apply a base 2 or base e logarithmic transformation to the x and y axes, respectively.

scale_x_logit, scale_y_logit

scale_x_logit and `scaleylogit apply a logit transformation to the x and y axes, respectively. This transformation is often used when visualizing proportions or probabilities.

scale_x_pseudolog10, scale_y_pseudolog10, scale_x_Symlog10, scale_y_Symlog10

These apply different types of logarithmic transformations to the x and y axes. The "pseudo" and "Symlog" transformations are designed to handle zeros and negative values more effectively.

scale_x_reverse, scale_y_reverse

scale_x_reverse and scale_y_reverse reverse the direction of the x and y axes, respectively.

ggplot(penguins, @aes(x = body_mass_g, y = bill_length_mm, color = species)) +
   geom_point() +
   scale_y_reverse() +
   theme_minimal()

In this example, a scatter plot is created where the body mass of penguins is mapped to the x position and the bill length to the y position. The direction of the y-axis is then reversed.

scale_x_sqrt, scale_y_sqrt

scale_x_sqrt and scale_y_sqrt apply a square root transformation to the x and y axes, respectively.

ggplot(penguins, @aes(x = body_mass_g, y = bill_length_mm, color = species)) +
   geom_point() +
   scale_x_sqrt() +
   theme_minimal()

In this example, a scatter plot is created where the body mass of penguins is mapped to the x position and the bill length to the y position. A square root transformation is then applied to the x-axis

geom_errorbar

geom_errorbar creates vertical and error bars .

categories = ["A", "B", "C", "D"];
n = length(categories);

mean_values = rand(n);  # Random mean values for demonstration
errors = rand(n) / 2;   # Random error values for demonstration

LowerBound = mean_values .- errors;
UpperBound = mean_values .+ errors;

df_errorbar = DataFrame(
    Category = categories,
    MeanValue = mean_values,
    LowerBound = LowerBound,
    UpperBound = UpperBound);

ggplot(df_errorbar, @aes(x = Category, y = MeanValue, ymin = LowerBound, ymax = UpperBound)) +
    geom_point() + # to show the mean value
    geom_errorbar() +
    labs(title = "Error Bar Plot Example", x = "Category", y = "Mean Value")

ggsave

ggsave saves a GGPlot to the specified location.

plot = ggplot(penguins, @aes(x = body_mass_g, y = bill_length_mm, color = species)) +
           geom_point()

ggsave(plot, "penguin_points.png")

In this example, plot is saved to penguin_points.png. Acceptable filetypes are all those supported by CairoMakie: svg, pdf, and png.

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