I'm trying to figure out how to create a plot of training and test data in R. Please see the problem description below.

Data:

age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
62	1	2	130	231	0	1	146	0	1.8	1	3	3	1
58	0	0	130	197	0	1	131	0	0.6	1	0	2	1
60	0	3	150	240	0	1	171	0	0.9	2	0	2	1
63	1	0	140	187	0	0	144	1	4	2	2	3	0
62	1	0	120	267	0	1	99	1	1.8	1	2	3	0
63	0	2	135	252	0	0	172	0	0	2	0	2	1
43	1	0	150	247	0	1	171	0	1.5	2	0	2	1
42	1	2	120	240	1	1	194	0	0.8	0	0	3	1
59	1	2	126	218	1	1	134	0	2.2	1	1	1	0
48	1	0	124	274	0	0	166	0	0.5	1	0	3	0
51	1	0	140	261	0	0	186	1	0	2	0	2	1
64	1	0	145	212	0	0	132	0	2	1	2	1	0
44	1	0	112	290	0	0	153	0	0	2	1	2	0
48	0	2	130	275	0	1	139	0	0.2	2	0	2	1
60	0	2	120	178	1	1	96	0	0	2	0	2	1
68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
52	1	3	152	298	1	1	178	0	1.2	1	0	3	1
62	0	0	160	164	0	0	145	0	6.2	0	3	3	0
70	1	2	160	269	0	1	112	1	2.9	1	1	3	0
59	1	3	134	204	0	1	162	0	0.8	2	2	2	0
62	0	2	130	263	0	1	97	0	1.2	1	1	3	0
50	0	2	120	219	0	1	158	0	1.6	1	0	2	1
38	1	3	120	231	0	1	182	1	3.8	1	0	3	0
58	0	3	150	283	1	0	162	0	1	2	0	2	1
52	1	3	118	186	0	0	190	0	0	1	0	1	1
68	0	2	120	211	0	0	115	0	1.5	1	0	2	1
57	1	2	128	229	0	0	150	0	0.4	1	1	3	0
56	1	1	120	240	0	1	169	0	0	0	0	2	1
71	0	2	110	265	1	0	130	0	0	2	1	2	1
59	1	0	135	234	0	1	161	0	0.5	1	0	3	1
60	0	0	150	258	0	0	157	0	2.6	1	2	3	0
66	0	0	178	228	1	1	165	1	1	1	2	3	0
54	1	0	110	239	0	1	126	1	2.8	1	1	3	0
54	1	2	120	258	0	0	147	0	0.4	1	0	3	1
57	1	0	152	274	0	1	88	1	1.2	1	1	3	0
65	0	0	150	225	0	0	114	0	1	1	3	3	0
57	0	1	130	236	0	0	174	0	0	1	1	2	0
58	0	1	136	319	1	0	152	0	0	2	2	2	0
52	1	0	125	212	0	1	168	0	1	2	2	3	0
50	0	1	120	244	0	1	162	0	1.1	2	0	2	1
62	0	0	140	268	0	0	160	0	3.6	0	2	2	0
42	1	0	140	226	0	1	178	0	0	2	0	2	1
62	1	1	120	281	0	0	103	0	1.4	1	1	3	0
64	0	2	140	313	0	1	133	0	0.2	2	0	3	1
65	0	2	155	269	0	1	148	0	0.8	2	0	2	1
41	1	1	135	203	0	1	132	0	0	1	0	1	1
68	1	2	180	274	1	0	150	1	1.6	1	0	3	0
56	1	3	120	193	0	0	162	0	1.9	1	0	3	1
56	0	1	140	294	0	0	153	0	1.3	1	0	2	1
52	0	2	136	196	0	0	169	0	0.1	1	0	2	1
67	1	0	100	299	0	0	125	1	0.9	1	2	2	0
41	1	1	120	157	0	1	182	0	0	2	0	2	1
64	1	2	125	309	0	1	131	1	1.8	1	0	3	0
55	1	0	132	353	0	1	132	1	1.2	1	1	3	0
43	1	0	132	247	1	0	143	1	0.1	1	4	3	0
58	1	0	150	270	0	0	111	1	0.8	2	0	3	0
57	1	1	124	261	0	1	141	0	0.3	2	0	3	0
77	1	0	125	304	0	0	162	1	0	2	3	2	0
41	1	0	110	172	0	0	158	0	0	2	0	3	0
57	1	0	110	335	0	1	143	1	3	1	1	3	0
56	1	1	130	221	0	0	163	0	0	2	0	3	1
51	0	0	130	305	0	1	142	1	1.2	1	0	3	0
56	1	0	130	283	1	0	103	1	1.6	0	0	3	0
56	1	0	125	249	1	0	144	1	1.2	1	1	2	0
52	1	1	134	201	0	1	158	0	0.8	2	1	2	1
64	1	3	170	227	0	0	155	0	0.6	1	0	3	1
58	0	0	170	225	1	0	146	1	2.8	1	2	1	0
50	1	2	129	196	0	1	163	0	0	2	0	2	1
64	1	3	110	211	0	0	144	1	1.8	1	0	2	1
46	0	1	105	204	0	1	172	0	0	2	0	2	1
58	1	2	105	240	0	0	154	1	0.6	1	0	3	1
41	1	1	110	235	0	1	153	0	0	2	0	2	1
59	1	1	140	221	0	1	164	1	0	2	0	2	1
61	1	0	138	166	0	0	125	1	3.6	1	1	2	0
57	1	2	150	126	1	1	173	0	0.2	2	1	3	1
68	1	2	118	277	0	1	151	0	1	2	1	3	1
44	1	1	120	263	0	1	173	0	0	2	0	3	1
45	1	0	104	208	0	0	148	1	3	1	0	2	1
42	0	2	120	209	0	1	173	0	0	1	0	2	1
41	0	2	112	268	0	0	172	1	0	2	0	2	1
54	0	1	132	288	1	0	159	1	0	2	1	2	1
34	0	1	118	210	0	1	192	0	0.7	2	0	2	1
59	1	3	160	273	0	0	125	0	0	2	0	2	0
59	1	0	164	176	1	0	90	0	1	1	2	1	0
44	1	2	120	226	0	1	169	0	0	2	0	2	1
39	0	2	138	220	0	1	152	0	0	1	0	2	1
49	1	2	118	149	0	0	126	0	0.8	2	3	2	0
59	1	0	140	177	0	1	162	1	0	2	1	3	0
66	1	0	112	212	0	0	132	1	0.1	2	1	2	0
35	1	0	126	282	0	0	156	1	0	2	0	3	0
53	0	0	130	264	0	0	143	0	0.4	1	0	2	1
47	1	2	108	243	0	1	152	0	0	2	0	2	0
46	1	1	101	197	1	1	156	0	0	2	0	3	1
42	1	2	130	180	0	1	150	0	0	2	0	2	1
58	1	0	125	300	0	0	171	0	0	2	2	3	0
56	1	2	130	256	1	0	142	1	0.6	1	1	1	0
41	1	2	130	214	0	0	168	0	2	1	0	2	1
51	1	2	100	222	0	1	143	1	1.2	1	0	2	1
57	1	1	154	232	0	0	164	0	0	2	1	2	0
42	0	0	102	265	0	0	122	0	0.6	1	0	2	1
52	1	0	128	255	0	1	161	1	0	2	1	3	0
54	0	2	110	214	0	1	158	0	1.6	1	0	2	1
49	0	0	130	269	0	1	163	0	0	2	0	2	1
53	1	2	130	197	1	0	152	0	1.2	0	0	2	1
64	0	0	130	303	0	1	122	0	2	1	2	2	1
47	1	2	138	257	0	0	156	0	0	2	0	2	1
45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
65	1	3	138	282	1	0	174	0	1.4	1	1	2	0
45	1	1	128	308	0	0	170	0	0	2	0	2	1
65	0	2	160	360	0	0	151	0	0.8	2	0	2	1
45	1	0	115	260	0	0	185	0	0	2	0	2	1
55	1	1	130	262	0	1	155	0	0	2	0	2	1
66	1	1	160	246	0	1	120	1	0	1	3	1	0
69	1	2	140	254	0	0	146	0	2	1	3	3	0
51	1	2	125	245	1	0	166	0	2.4	1	0	2	1
70	1	0	145	174	0	1	125	1	2.6	0	0	3	0
58	1	2	132	224	0	0	173	0	3.2	2	2	3	0
65	0	2	140	417	1	0	157	0	0.8	2	1	2	1
37	0	2	120	215	0	1	170	0	0	2	0	2	1
57	1	0	140	192	0	1	148	0	0.4	1	0	1	1
61	1	3	134	234	0	1	145	0	2.6	1	2	2	0
60	1	0	130	206	0	0	132	1	2.4	1	2	3	0
39	1	0	118	219	0	1	140	0	1.2	1	0	3	0
63	0	0	124	197	0	1	136	1	0	1	0	2	0
46	1	2	150	231	0	1	147	0	3.6	1	0	2	0
50	0	0	110	254	0	0	159	0	0	2	0	2	1
52	1	0	108	233	1	1	147	0	0.1	2	3	3	1
48	1	0	122	222	0	0	186	0	0	2	0	2	1
53	1	0	123	282	0	1	95	1	2	1	2	3	0
69	1	3	160	234	1	0	131	0	0.1	1	1	2	1
45	0	1	130	234	0	0	175	0	0.6	1	0	2	1
38	1	2	138	175	0	1	173	0	0	2	4	2	1
56	0	0	134	409	0	0	150	1	1.9	1	2	3	0
57	1	0	132	207	0	1	168	1	0	2	0	3	1
69	0	3	140	239	0	1	151	0	1.8	2	2	2	1
42	1	0	136	315	0	1	125	1	1.8	1	0	1	0
65	1	0	135	254	0	0	127	0	2.8	1	1	3	0
44	1	1	130	219	0	0	188	0	0	2	0	2	1
58	1	0	128	216	0	0	131	1	2.2	1	3	3	0
48	1	2	124	255	1	1	175	0	0	2	2	2	1
64	1	0	128	263	0	1	105	1	0.2	1	1	3	1
50	1	0	150	243	0	0	128	0	2.6	1	0	3	0
51	0	2	120	295	0	0	157	0	0.6	2	0	2	1
51	1	2	110	175	0	1	123	0	0.6	2	0	2	1
64	1	0	120	246	0	0	96	1	2.2	0	1	2	0
64	0	0	180	325	0	1	154	1	0	2	0	2	1
63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
62	0	0	140	394	0	0	157	0	1.2	1	0	2	1
67	1	0	120	237	0	1	71	0	1	1	0	2	0
76	0	2	140	197	0	2	116	0	1.1	1	0	2	1
56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
35	1	0	120	198	0	1	130	1	1.6	1	0	3	0
47	1	2	130	253	0	1	179	0	0	2	0	2	1
64	1	2	140	335	0	1	158	0	0	2	0	2	0
62	0	0	150	244	0	1	154	1	1.4	1	0	2	0
51	0	2	140	308	0	0	142	0	1.5	2	1	2	1
66	0	3	150	226	0	1	114	0	2.6	0	0	2	1
61	1	2	150	243	1	1	137	1	1	1	0	2	1
54	0	2	160	201	0	1	163	0	0	2	1	2	1
51	0	2	130	256	0	0	149	0	0.5	2	0	2	1
56	0	0	200	288	1	0	133	1	4	0	2	3	0
34	1	3	118	182	0	0	174	0	0	2	0	2	1
52	1	1	120	325	0	1	172	0	0.2	2	0	2	1
59	1	3	178	270	0	0	145	0	4.2	0	0	3	1
46	0	2	142	177	0	0	160	1	1.4	0	0	2	1
49	0	1	134	271	0	1	162	0	0	1	0	2	1
55	1	0	140	217	0	1	111	1	5.6	0	0	3	0
63	0	0	108	269	0	1	169	1	1.8	1	2	2	0
54	1	2	125	273	0	0	152	0	0.5	0	1	2	1
52	1	1	128	205	1	1	184	0	0	2	0	2	1
43	0	0	132	341	1	0	136	1	3	1	0	3	0
45	0	0	138	236	0	0	152	1	0.2	1	0	2	1
43	1	2	130	315	0	1	162	0	1.9	2	1	2	1
46	1	0	140	311	0	1	120	1	1.8	1	2	3	0
44	0	2	118	242	0	1	149	0	0.3	1	1	2	1
55	0	1	135	250	0	0	161	0	1.4	1	0	2	1
74	0	1	120	269	0	0	121	1	0.2	2	1	2	1
39	0	2	94	199	0	1	179	0	0	2	0	2	1
51	1	3	125	213	0	0	125	1	1.4	2	1	2	1
58	0	0	100	248	0	0	122	0	1	1	0	2	1
66	1	0	160	228	0	0	138	0	2.3	2	0	1	1
58	1	0	146	218	0	1	105	0	2	1	1	3	0
67	0	0	106	223	0	1	142	0	0.3	2	2	2	1
41	0	1	126	306	0	1	163	0	0	2	0	2	1
43	0	2	122	213	0	1	165	0	0.2	1	0	2	1
60	1	0	130	253	0	1	144	1	1.4	2	1	3	0
42	1	1	120	295	0	1	162	0	0	2	0	2	1
60	1	0	117	230	1	1	160	1	1.4	2	2	3	0
50	1	0	144	200	0	0	126	1	0.9	1	0	3	0
58	1	2	112	230	0	0	165	0	2.5	1	1	3	0
54	1	1	108	309	0	1	156	0	0	2	0	3	1
57	1	2	150	168	0	1	174	0	1.6	2	0	2	1
71	0	0	112	149	0	1	125	0	1.6	1	0	2	1
58	1	1	125	220	0	1	144	0	0.4	1	4	3	1
44	1	2	130	233	0	1	179	1	0.4	2	0	2	1
44	0	2	108	141	0	1	175	0	0.6	1	0	2	1
57	1	0	110	201	0	1	126	1	1.5	1	0	1	1
35	0	0	138	183	0	1	182	0	1.4	2	0	2	1
58	1	2	140	211	1	0	165	0	0	2	0	2	1
61	0	0	130	330	0	0	169	0	0	2	0	2	0
43	1	0	120	177	0	0	120	1	2.5	1	0	3	0
46	1	0	120	249	0	0	144	0	0.8	2	0	3	0
55	0	0	180	327	0	2	117	1	3.4	1	0	2	0
60	1	2	140	185	0	0	155	0	3	1	0	2	0
61	1	0	140	207	0	0	138	1	1.9	2	1	3	0
50	1	2	140	233	0	1	163	0	0.6	1	1	3	0
49	1	1	130	266	0	1	171	0	0.6	2	0	2	1
57	0	0	120	354	0	1	163	1	0.6	2	0	2	1
66	0	2	146	278	0	0	152	0	0	1	1	2	1
56	1	0	132	184	0	0	105	1	2.1	1	1	1	0
61	0	0	145	307	0	0	146	1	1	1	0	3	0
61	1	0	148	203	0	1	161	0	0	2	1	3	0
51	1	0	140	299	0	1	173	1	1.6	2	0	3	0
54	1	2	150	232	0	0	165	0	1.6	2	0	3	1
65	1	0	120	177	0	1	140	0	0.4	2	0	3	1
59	1	0	110	239	0	0	142	1	1.2	1	1	3	0
67	1	0	120	229	0	0	129	1	2.6	1	2	3	0
67	1	2	152	212	0	0	150	0	0.8	1	0	3	0
60	1	0	145	282	0	0	142	1	2.8	1	2	3	0
39	1	2	140	321	0	0	182	0	0	2	0	2	1
44	1	1	120	220	0	1	170	0	0	2	0	2	1
51	1	0	140	298	0	1	122	1	4.2	1	3	3	0
46	0	0	138	243	0	0	152	1	0	1	0	2	1
40	1	3	140	199	0	1	178	1	1.4	2	0	3	1
43	1	0	115	303	0	1	181	0	1.2	1	0	2	1
67	0	2	115	564	0	0	160	0	1.6	1	0	3	1
54	1	1	192	283	0	0	195	0	0	2	1	3	0
40	1	0	152	223	0	1	181	0	0	2	0	3	0
65	1	0	110	248	0	0	158	0	0.6	2	2	1	0
59	1	2	150	212	1	1	157	0	1.6	2	0	2	1
70	1	1	156	245	0	0	143	0	0	2	0	2	1
48	1	1	110	229	0	1	168	0	1	0	0	3	0
57	1	0	165	289	1	0	124	0	1	1	3	3	0
53	0	2	128	216	0	0	115	0	0	2	0	0	1
38	1	2	138	175	0	1	173	0	0	2	4	2	1
35	1	1	122	192	0	1	174	0	0	2	0	2	1
54	1	0	120	188	0	1	113	0	1.4	1	1	3	0
60	1	0	140	293	0	0	170	0	1.2	1	2	3	0
70	1	0	130	322	0	0	109	0	2.4	1	3	2	0
62	0	0	138	294	1	1	106	0	1.9	1	3	2	0
48	1	1	130	245	0	0	180	0	0.2	1	0	2	1
44	1	0	120	169	0	1	144	1	2.8	0	0	1	0
53	1	0	142	226	0	0	111	1	0	2	0	3	1
54	1	0	122	286	0	0	116	1	3.2	1	2	2	0
54	0	2	108	267	0	0	167	0	0	2	0	2	1
42	1	3	148	244	0	0	178	0	0.8	2	2	2	1
53	0	0	138	234	0	0	160	0	0	2	0	2	1
47	1	0	112	204	0	1	143	0	0.1	2	0	2	1
58	1	1	120	284	0	0	160	0	1.8	1	0	2	0
53	1	0	140	203	1	0	155	1	3.1	0	0	3	0
41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
48	1	0	130	256	1	0	150	1	0	2	2	3	0
71	0	1	160	302	0	1	162	0	0.4	2	2	2	1
63	1	0	130	330	1	0	132	1	1.8	2	3	3	0
58	1	0	128	259	0	0	130	1	3	1	2	3	0
52	1	2	172	199	1	1	162	0	0.5	2	0	3	1
59	1	0	138	271	0	0	182	0	0	2	0	2	1
43	1	0	110	211	0	1	161	0	0	2	0	3	1
58	1	0	100	234	0	1	156	0	0.1	2	1	3	0
61	1	0	120	260	0	1	140	1	3.6	1	1	3	0
59	0	0	174	249	0	1	143	1	0	1	0	2	0
41	1	2	112	250	0	1	179	0	0	2	0	2	1
45	1	0	142	309	0	0	147	1	0	1	3	3	0
52	1	0	112	230	0	1	160	0	0	2	1	2	0
44	1	0	110	197	0	0	177	0	0	2	1	2	0
51	1	2	94	227	0	1	154	1	0	2	1	3	1
58	0	2	120	340	0	1	172	0	0	2	0	2	1
54	1	0	110	206	0	0	108	1	0	1	1	2	0
62	0	0	124	209	0	1	163	0	0	2	0	2	1
59	1	3	170	288	0	0	159	0	0.2	1	0	3	0
57	0	0	128	303	0	0	159	0	0	2	1	2	1
60	1	0	125	258	0	0	141	1	2.8	1	1	3	0
29	1	1	130	204	0	0	202	0	0	2	0	2	1
52	1	0	128	204	1	1	156	1	1	1	0	0	0
37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
45	0	1	112	160	0	1	138	0	0	1	0	2	1
49	1	2	120	188	0	1	139	0	2	1	3	3	0
63	0	0	150	407	0	0	154	0	4	1	3	3	0
55	1	0	160	289	0	0	145	1	0.8	1	1	3	0
67	1	0	125	254	1	1	163	0	0.2	1	2	3	0
53	1	2	130	246	1	0	173	0	0	2	3	2	1
54	1	0	124	266	0	0	109	1	2.2	1	1	3	0
63	1	0	130	254	0	0	147	0	1.4	1	1	3	0
59	1	0	170	326	0	0	140	1	3.4	0	0	3	0
62	1	1	128	208	1	0	140	0	0	2	0	2	1
58	1	0	114	318	0	2	140	0	4.4	0	3	1	0
67	1	0	160	286	0	0	108	1	1.5	1	3	2	0
60	0	2	102	318	0	1	160	0	0	2	1	2	1
57	1	0	150	276	0	0	112	1	0.6	1	1	1	0
54	1	0	140	239	0	1	160	0	1.2	2	0	2	1
41	0	1	105	198	0	1	168	0	0	2	1	2	1
54	0	2	135	304	1	1	170	0	0	2	0	2	1
57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
40	1	0	110	167	0	0	114	1	2	1	0	3	0
55	0	0	128	205	0	2	130	1	2	1	1	3	0
52	1	2	138	223	0	1	169	0	0	2	4	2	1
66	1	0	120	302	0	0	151	0	0.4	1	0	2	1
67	0	2	152	277	0	1	172	0	0	2	1	2	1
57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
55	0	1	132	342	0	1	166	0	1.2	2	0	2	1
47	1	0	110	275	0	0	118	1	1	1	1	2	0
44	1	2	140	235	0	0	180	0	0	2	0	2	1
63	0	1	140	195	0	1	179	0	0	2	2	2	1

• Graph the training and testing error against the number of trees using a classification random forest model for the presence of heart disease (target) using variables age (age), sex (sex), chest pain type (cp), resting blood pressure (trestbps), cholesterol measurement (chol), resting electrocardiographic measurement (restecg), exercise-induced angina (exang), slope of peak exercise (slope), and number of major vessels (ca). Use a maximum of 200 trees. Use set.seed(511038).
• 

.0 training set testing set 0.8 Classification Error 0.2 0.0 0 50 100 150 200 Number of Trees