richarddmorey/_scatter.R

## _scatter.R
d = read.csv("https://gist.githubusercontent.com/richarddmorey/f7c3ed9fe3f9f1fc0520f332b4a8efd7/raw/900d75f765a086dba65a316504c926da1c1e894a/golf.csv")

plot(d$score, d$size, xlab = "Course score", ylab = "Perceived size", axes = FALSE, ylim = c(0,9), xlim = c(60,140), pch = 19)
axis(2, at = 1:9, las = 1)
axis(1)
abline(lm(size~score, data = d))

cor(d$size, d$score, method = "spearman")
# edge of significance
cor.test(d$size, d$score, method = "spearman")
# edge of significance
cor.test(d$size, d$score, method = "kendall")

## golf.csv

          
            
            score
            size

            
              1
              114.749175254226
              2

            
              2
              101.709689487467
              3

            
              3
              102.701084923307
              3

            
              4
              87.8253919668061
              3

            
              5
              88.8171275039962
              3

            
              6
              109.496650001701
              4

            
              7
              77.7607727102676
              4

            
              8
              79.9098731421964
              4

            
              9
              82.8850797537666
              4

            
              10
              87.6784681835187
              4

            
              11
              89.6615991565486
              4

            
              12
              93.7941706628575
              4

            
              13
              81.0849233071455
              5

            
              14
              81.7464204332891
              5

            
              15
              84.8869163010577
              5

            
              16
              88.8541985511683
              5

            
              17
              97.6145291296806
              5

            
              18
              114.473693160562
              5

            
              19
              122.407917559433
              5

            
              20
              76.8601843349318
              9

            
              21
              81.8192021222324
              9

            
              22
              89.7530864197531
              9

            
              23
              87.7515899738122
              8

            
              24
              81.7644458048498
              6

            
              25
              82.5908920858416
              6

            
              26
              85.7313879536102
              6

            
              27
              87.7148590279903
              6

            
              28
              89.6983301023705
              6

            
              29
              98.4586606808829
              6

            
              30
              104.078155290276
              6

            
              31
              84.8314797809747
              2

            
              32
              86.9802401115532
              2

            
              33
              88.7984219297351
              2

            
              34
              90.7818930041152
              2

            
              35
              91.608339285107
              2

            
              36
              92.2694963099003
              2

            
              37
              92.9306533346937
              2

            
              38
              100.699248376016
              2

            
              39
              90.6900656395606
              6

            
              40
              91.351222664354
              6

            
              41
              92.0123796891474
              6

            
              42
              92.6735367139407
              6

            
              43
              97.7240417644458
              2

            
              44
              98.3851987892392
              2

            
              45
              98.8810665578342
              2
	d = read.csv("https://gist.githubusercontent.com/richarddmorey/f7c3ed9fe3f9f1fc0520f332b4a8efd7/raw/900d75f765a086dba65a316504c926da1c1e894a/golf.csv")

	plot(d$score, d$size, xlab = "Course score", ylab = "Perceived size", axes = FALSE, ylim = c(0,9), xlim = c(60,140), pch = 19)
	axis(2, at = 1:9, las = 1)
	axis(1)
	abline(lm(size~score, data = d))

	cor(d$size, d$score, method = "spearman")
	# edge of significance
	cor.test(d$size, d$score, method = "spearman")
	# edge of significance
	cor.test(d$size, d$score, method = "kendall")
	score	size
1	114.749175254226	2
2	101.709689487467	3
3	102.701084923307	3
4	87.8253919668061	3
5	88.8171275039962	3
6	109.496650001701	4
7	77.7607727102676	4
8	79.9098731421964	4
9	82.8850797537666	4
10	87.6784681835187	4
11	89.6615991565486	4
12	93.7941706628575	4
13	81.0849233071455	5
14	81.7464204332891	5
15	84.8869163010577	5
16	88.8541985511683	5
17	97.6145291296806	5
18	114.473693160562	5
19	122.407917559433	5
20	76.8601843349318	9
21	81.8192021222324	9
22	89.7530864197531	9
23	87.7515899738122	8
24	81.7644458048498	6
25	82.5908920858416	6
26	85.7313879536102	6
27	87.7148590279903	6
28	89.6983301023705	6
29	98.4586606808829	6
30	104.078155290276	6
31	84.8314797809747	2
32	86.9802401115532	2
33	88.7984219297351	2
34	90.7818930041152	2
35	91.608339285107	2
36	92.2694963099003	2
37	92.9306533346937	2
38	100.699248376016	2
39	90.6900656395606	6
40	91.351222664354	6
41	92.0123796891474	6
42	92.6735367139407	6
43	97.7240417644458	2
44	98.3851987892392	2
45	98.8810665578342	2