Running my first program

My program is based on the Gapminder data set:

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly;
DATA new; set mydata.gapminder;
LABEL incomeperperson="Per capita GDP"
    co2emissions="CO2 emissions (in metric tons)"
    urbanrate="Percentage of people in urban areas";
IF incomeperperson LE 16000;
PROC SORT; BY COUNTRY;
PROC FREQ; TABLES co2emissions urbanrate incomeperperson;
RUN;

Since the Gapminder dataset has unique quantitative values for all observations, the frequency distribution tables don't provide any insight to the data. I would therefore like to create subsets within the variables, such as per capita GDP, to group countries as lower, middle and higher income. I was not able to find a way to do this. Therefore I ran a query for countries with per capita GDP of less than and equal to 16,000 USD, which is roughly the baseline for middle and lower income countries.

Results:

CO2 emissions (in metric tons)
co2emissions	Frequency	Percent	Cumulative Frequency	Cumulative Percent
132000	1	0.59	1	0.59
850666.66667	1	0.59	2	1.18
1045000	1	0.59	3	1.76
1111000	1	0.59	4	2.35
1206333.3333	1	0.59	5	2.94
1723333.3333	1	0.59	6	3.53
2251333.3333	1	0.59	7	4.12
2335666.6667	1	0.59	8	4.71
2368666.6667	1	0.59	9	5.29
2401666.6667	1	0.59	10	5.88
2907666.6667	1	0.59	11	6.47
2977333.3333	1	0.59	12	7.06
3659333.3333	1	0.59	13	7.65
4352333.3333	1	0.59	14	8.24
4774000	1	0.59	15	8.82
4814333.3333	1	0.59	16	9.41
5210333.3333	1	0.59	17	10.00
5214000	1	0.59	18	10.59
6024333.3333	1	0.59	19	11.18
7315000	1	0.59	20	11.76
7355333.3333	1	0.59	21	12.35
7388333.3333	1	0.59	22	12.94
7601000	1	0.59	23	13.53
7608333.3333	1	0.59	24	14.12
7813666.6667	1	0.59	25	14.71
8092333.3333	1	0.59	26	15.29
8231666.6667	1	0.59	27	15.88
8338000	1	0.59	28	16.47
8968666.6667	1	0.59	29	17.06
9155666.6667	1	0.59	30	17.65
14054333.333	1	0.59	31	18.24
14058000	1	0.59	32	18.82
14241333.333	1	0.59	33	19.41
16225000	1	0.59	34	20.00
16379000	1	0.59	35	20.59
17515666.667	1	0.59	36	21.18
19800000	1	0.59	37	21.76
20152000	1	0.59	38	22.35
20628666.667	1	0.59	39	22.94
21332666.667	1	0.59	40	23.53
21351000	1	0.59	41	24.12
22704000	1	0.59	42	24.71
26125000	1	0.59	43	25.29
26209333.333	1	0.59	44	25.88
28490000	1	0.59	45	26.47
29758666.667	1	0.59	46	27.06
30800000	1	0.59	47	27.65
32233666.667	1	0.59	48	28.24
35717000	1	0.59	49	28.82
35871000	1	0.59	50	29.41
36160666.667	1	0.59	51	30.00
37950000	1	0.59	52	30.59
38991333.333	1	0.59	53	31.18
40857666.667	1	0.59	54	31.76
45411666.667	1	0.59	55	32.35
45778333.333	1	0.59	56	32.94
46306333.333	1	0.59	57	33.53
46684000	1	0.59	58	34.12
49793333.333	1	0.59	59	34.71
51219666.667	1	0.59	60	35.29
52657000	1	0.59	61	35.88
55146666.667	1	0.59	62	36.47
56162333.333	1	0.59	63	37.06
56818666.667	1	0.59	64	37.65
59473333.333	1	0.59	65	38.24
62777000	1	0.59	66	38.82
69329333.333	1	0.59	67	39.41
73784333.333	1	0.59	68	40.00
75944000	1	0.59	69	40.59
78943333.333	1	0.59	70	41.18
81191000	1	0.59	71	41.76
86317000	1	0.59	72	42.35
87970666.667	1	0.59	73	42.94
88337333.333	1	0.59	74	43.53
90269666.667	1	0.59	75	44.12
95256333.333	1	0.59	76	44.71
100782000	1	0.59	77	45.29
102538333.33	1	0.59	78	45.88
104170000	1	0.59	79	46.47
107096000	1	0.59	80	47.06
109681000	1	0.59	81	47.65
119958666.67	1	0.59	82	48.24
125172666.67	1	0.59	83	48.82
125755666.67	1	0.59	84	49.41
127108666.67	1	0.59	85	50.00
131703000	1	0.59	86	50.59
132025666.67	1	0.59	87	51.18
143586666.67	1	0.59	88	51.76
148470666.67	1	0.59	89	52.35
149904333.33	1	0.59	90	52.94
168883000	1	0.59	91	53.53
169180000	1	0.59	92	54.12
170404666.67	1	0.59	93	54.71
170804333.33	1	0.59	94	55.29
183535000	1	0.59	95	55.88
188268666.67	1	0.59	96	56.47
214368000	1	0.59	97	57.06
223747333.33	1	0.59	98	57.65
225019666.67	1	0.59	99	58.24
226255333.33	1	0.59	100	58.82
228748666.67	1	0.59	101	59.41
234864666.67	1	0.59	102	60.00
236419333.33	1	0.59	103	60.59
242594000	1	0.59	104	61.18
248358000	1	0.59	105	61.76
253854333.33	1	0.59	106	62.35
254939666.67	1	0.59	107	62.94
275744333.33	1	0.59	108	63.53
277170666.67	1	0.59	109	64.12
283583666.67	1	0.59	110	64.71
300934333.33	1	0.59	111	65.29
310024000	1	0.59	112	65.88
322960000	1	0.59	113	66.47
340090666.67	1	0.59	114	67.06
377303666.67	1	0.59	115	67.65
428006333.33	1	0.59	116	68.24
446365333.33	1	0.59	117	68.82
487993000	1	0.59	118	69.41
503994333.33	1	0.59	119	70.00
511107666.67	1	0.59	120	70.59
525891666.67	1	0.59	121	71.18
531303666.67	1	0.59	122	71.76
590219666.67	1	0.59	123	72.35
590674333.33	1	0.59	124	72.94
598774000	1	0.59	125	73.53
692039333.33	1	0.59	126	74.12
953051000	1	0.59	127	74.71
999874333.33	1	0.59	128	75.29
1146277000	1	0.59	129	75.88
1286670000	1	0.59	130	76.47
1321661000	1	0.59	131	77.06
1414031666.7	1	0.59	132	77.65
1425435000	1	0.59	133	78.24
1436893333.3	1	0.59	134	78.82
1712755000	1	0.59	135	79.41
1718339333.3	1	0.59	136	80.00
1776016000	1	0.59	137	80.59
1839471333.3	1	0.59	138	81.18
1865922666.7	1	0.59	139	81.76
2008116000	1	0.59	140	82.35
2269806000	1	0.59	141	82.94
2329308666.7	1	0.59	142	83.53
2386820333.3	1	0.59	143	84.12
2421917666.7	1	0.59	144	84.71
2484925666.7	1	0.59	145	85.29
2670950333.3	1	0.59	146	85.88
2712915333.3	1	0.59	147	86.47
2932108666.7	1	0.59	148	87.06
3157700333.3	1	0.59	149	87.65
3341129000	1	0.59	150	88.24
4200940333.3	1	0.59	151	88.82
4244009000	1	0.59	152	89.41
5248815000	1	0.59	153	90.00
5418886000	1	0.59	154	90.59
5584766000	1	0.59	155	91.18
5675629666.7	1	0.59	156	91.76
5872119000	1	0.59	157	92.35
5896388666.7	1	0.59	158	92.94
6710201666.7	1	0.59	159	93.53
7104137333.3	1	0.59	160	94.12
7861553333.3	1	0.59	161	94.71
9183548000	1	0.59	162	95.29
9580226333.3	1	0.59	163	95.88
10822529667	1	0.59	164	96.47
13304503667	1	0.59	165	97.06
14609848000	1	0.59	166	97.65
23053598333	1	0.59	167	98.24
23404568000	1	0.59	168	98.82
30391317000	1	0.59	169	99.41
101386215333	1	0.59	170	100.00
Frequency Missing = 9

 
More tables: 
Urbanisation rate
Per capita GDP