Appendix: Alternative table formats

This is a temporary appendix intended for gathering feedback about the use of alternative table formats only. I’m curious to know which format for displaying data frames readers find most useful. I think it’s possible that the answer partially depends on the size of the data frame (and possibly other factors), so I have two examples below – one smaller data frame and one larger data frame.

40.13 Smaller data frame

demo <- tibble(
  id       = c("001", "002", "003", "004"),
  age      = c(30, 67, 52, 56),
  edu      = c(3, 1, 4, 2),
  edu_char = c("Some college", "Less than high school", "College graduate", 
                     "High school graduate")
)

40.13.1 Default method for printing the data frame to the screen

demo

## # A tibble: 4 × 4
##   id      age   edu edu_char             
##   <chr> <dbl> <dbl> <chr>                
## 1 001      30     3 Some college         
## 2 002      67     1 Less than high school
## 3 003      52     4 College graduate     
## 4 004      56     2 High school graduate

This method produces the least aesthetically pleasing results, but they are also the most similar to the results you actually see when you run the code on your computer. For example, this output shows the type of each column.

However, the two methods below produce much more attractive looking tables. Perhaps that’s more desirable to readers?

40.13.2 Using the kable function

knitr::kable(demo)

id	age	edu	edu_char
001	30	3	Some college
002	67	1	Less than high school
003	52	4	College graduate
004	56	2	High school graduate

40.13.3 Using the datatable function

DT::datatable(demo)

40.14 Larger data frame

aws <- readxl::read_excel("data/aws.xlsx")

40.14.1 Default method for printing the data frame to the screen

head(aws, 100)

## # A tibble: 100 × 71
##        id   obs  days ndays years age_days   age   agec agestrat physfun tag_firstphysfun firstphysfun tag_spi spi_baseline spi_ever tag_lastphysfun
##     <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl> <dbl>  <dbl>    <dbl>   <dbl>            <dbl>        <dbl>   <dbl>        <dbl>    <dbl>           <dbl>
##  1 100001     1     0     0     0    19722    54 -6.01         1      70                1           70       1            1        1               0
##  2 100003     1     0     0     0    22645    62  2.00         2      95                1           95       0            0        0               0
##  3 100004     1     0     0     0    23375    64  4.00         2     100                1          100       0            0        1               0
##  4 100005     1     0     0     0    25158    69  8.88         2      95                1           95       0            0        1               0
##  5 100006     1     0     0     0    19343    53 -7.04         1     100                1          100       0            0        0               0
##  6 100008     1     0     0     0    22646    62  2            2      80                1           80       1            1        1               0
##  7 100009     1     0     0     0    20816    57 -3.01         1      NA                0           95       0            0        1               0
##  8 100010     1     0     0     0    22568    62  1.79         2      90                1           90       0            0        1               0
##  9 100012     1     0     0     0    21873    60 -0.115        2      95                1           95       0            0        1               0
## 10 100013     1     0     0     0    19686    54 -6.10         1      85                1           85       0            0        1               0
## # ℹ 90 more rows
## # ℹ 55 more variables: lastphysfun <dbl>, physfundiff <dbl>, f393mse <dbl>, phyab <dbl>, phyab_d <dbl>, phyab_ever <dbl>, firstphyabobs <dbl>,
## #   firstphyab <dbl>, firstphyab_d <dbl>, verbab <dbl>, verbab_d <dbl>, verbab_ever <dbl>, firstverbabobs <dbl>, firstverbab <dbl>, firstverbab_d <dbl>,
## #   abuse_d <dbl>, abuse_ever <dbl>, firstabobs <dbl>, firstab <dbl>, abuse4cat <dbl>, abuse4cat_ij <dbl>, abuse4cat_ever <dbl>, age_abusegroup <dbl>,
## #   age_abusegroup4 <dbl>, raceeth <dbl>, married <dbl>, income6cat <dbl>, education <dbl>, edu3ms <dbl>, livalor <dbl>, ctos <dbl>, smokenw <dbl>,
## #   hvydrnk <dbl>, care <dbl>, socsupp <dbl>, hostil <dbl>, optimism <dbl>, bmi5cat <dbl>, goodhealth <dbl>, falls <dbl>, depression <dbl>, bone <dbl>,
## #   death <lgl>, deathcause <lgl>, finalobs <dbl>, finalage <dbl>, numobs <dbl>, finalyears <dbl>, missing_covars <dbl>, final_sample <dbl>, …

40.14.2 Using the kable function

knitr::kable(head(aws, 100)) %>% 
  kableExtra::scroll_box(height = "300px", width = "100%")

id	obs	age_days	age	agec	agestrat	physfun	tag_firstphysfun	firstphysfun	tag_spi	spi_baseline	spi_ever	tag_lastphysfun	lastphysfun	physfundiff	f393mse	firstphyabobs	verbab	verbab_d	verbab_ever	firstverbabobs	firstverbab	firstverbab_d	abuse_d	abuse_ever	firstabobs	firstab	abuse4cat	abuse4cat_ij	abuse4cat_ever	age_abusegroup	age_abusegroup4	raceeth	married	income6cat	education	edu3ms	livalor	ctos	smokenw	hvydrnk	care	socsupp	hostil	optimism	bmi5cat	goodhealth	falls	depression	bone	death	deathcause	finalage	numobs	finalyears	missing_covars	final_sample	abuse1	abuse2	socsupp5cat
100001	1	19722	54	-6.0054741	1	70	1	70	1	1	1	0	85	15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	3	4	2	0	2	0	0	0	35	1	24	4	0	1	0	0	NA	NA	69.99452	18	16	0	1	1	0	3
100003	1	22645	62	1.9972610	2	95	1	95	0	0	0	0	95	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	4	1	2	0	2	0	0	0	45	1	24	2	1	0	0	0	NA	NA	71.10336	13	9	0	1	1	0	5
100004	1	23375	64	3.9972610	2	100	1	100	0	0	1	0	60	-40	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	2	2	2	0	1	0	0	1	45	0	28	2	0	0	0	0	NA	NA	80.86243	32	17	0	1	1	0	5
100005	1	25158	69	8.8795319	2	95	1	95	0	0	1	0	60	-35	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	3	3	2	0	1	0	0	0	36	2	28	5	1	0	0	1	NA	NA	83.92129	26	15	0	1	1	0	3
100006	1	19343	53	-7.0410690	1	100	1	100	0	0	0	0	100	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	5	4	2	0	2	0	0	0	36	5	19	1	1	0	0	0	NA	NA	58.93840	10	6	0	1	1	0	3
100008	1	22646	62	2.0000000	2	80	1	80	1	1	1	0	60	-20	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	2	0	1	3	2	0	1	1	0	0	39	7	26	1	0	0	1	0	NA	NA	75.93292	29	14	0	1	1	0	3
100009	1	20816	57	-3.0082130	1	NA	0	95	0	0	1	0	65	-30	NA	1	0	0	1	1	0	0	0	1	1	0	0	0	1	1	1	1	1	5	3	2	0	2	0	0	0	42	2	26	1	NA	0	0	0	NA	NA	74.07050	20	17	1	1	1	0	4
100010	1	22568	62	1.7864494	2	90	1	90	0	0	1	0	35	-55	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	3	3	2	0	1	0	0	1	33	6	23	NA	0	0	0	1	NA	NA	77.97810	30	16	1	1	1	0	2
100012	1	21873	60	-0.1149902	2	95	1	95	0	0	1	0	85	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	4	4	2	0	1	0	0	0	33	4	26	2	1	0	0	1	NA	NA	74.01232	23	14	0	1	1	0	2
100013	1	19686	54	-6.1040382	1	85	1	85	0	0	1	0	55	-30	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	4	3	2	0	1	0	0	1	45	5	27	5	1	1	0	0	NA	NA	69.97536	28	16	0	1	1	0	5
100016	1	25201	69	8.9972610	2	90	1	90	0	0	1	0	30	-60	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	4	3	2	0	2	1	0	1	35	0	24	1	1	0	0	0	NA	NA	84.00616	21	15	0	1	1	0	3
100017	1	23347	64	3.9206009	2	85	1	85	0	0	1	0	15	-70	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	1	1	1	1	1	0	0	1	45	7	18	2	1	1	0	1	NA	NA	73.61533	26	10	0	1	1	0	5
100018	1	24446	67	6.9288177	2	95	1	95	0	0	1	0	75	-20	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	3	1	2	0	1	0	0	0	45	2	23	1	1	0	0	0	NA	NA	83.00548	30	16	0	1	1	0	5
100019	1	25179	69	8.9370270	2	100	1	100	0	0	0	0	90	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	2	3	2	1	2	0	0	1	38	2	28	1	1	0	0	1	NA	NA	82.95483	19	14	0	1	1	0	3
100021	1	20746	57	-3.1998634	1	70	1	70	1	1	1	0	75	5	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	0	1	2	2	0	1	0	0	0	39	8	18	4	0	0	0	0	NA	NA	71.38741	32	14	0	1	1	0	3
100022	1	19716	54	-6.0219040	1	50	1	50	1	1	1	0	90	40	NA	1	2	1	1	1	2	1	1	1	1	1	1	1	1	2	2	1	1	5	2	2	0	2	0	0	1	34	5	24	4	0	0	1	0	NA	NA	69.96167	21	16	0	1	0	1	2
100023	1	20442	56	-4.0328560	1	90	1	90	0	0	0	0	90	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	4	4	2	1	2	1	0	0	43	2	23	1	0	0	0	0	NA	NA	62.47776	10	6	0	1	1	0	4
100024	1	28124	77	17.0000000	3	100	1	100	0	0	0	0	90	-10	91	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	1	4	4	2	0	1	0	0	1	33	3	22	1	0	0	0	0	NA	NA	81.93361	10	5	0	1	1	0	2
100025	1	24794	68	7.8822708	2	80	1	80	1	1	1	0	90	10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	2	2	2	1	1	0	0	1	31	4	23	4	0	2	0	1	NA	NA	78.74880	26	11	0	1	1	0	2
100026	1	22259	61	0.9425049	2	65	1	65	1	1	1	0	60	-5	NA	1	0	0	1	1	0	0	0	1	1	0	0	0	1	3	5	1	1	3	3	2	0	2	0	0	1	39	1	27	2	1	0	0	1	NA	NA	75.97604	19	15	0	1	1	0	3
100027	1	18985	52	-8.0219040	1	NA	0	100	0	0	0	0	100	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	5	4	2	0	2	0	1	1	43	1	26	1	NA	0	0	0	NA	NA	67.98357	18	16	1	1	1	0	4
100028	1	22610	62	1.9014359	2	80	1	80	1	1	1	0	25	-55	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	2	3	2	0	1	0	0	0	36	2	20	3	1	0	0	1	NA	NA	70.07118	24	8	0	1	1	0	3
100030	1	22539	62	1.7070503	2	90	1	90	0	0	1	0	5	-85	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	2	0	NA	3	2	1	2	0	0	0	45	3	24	2	1	0	0	0	NA	NA	75.95483	19	14	1	1	1	0	5
100033	1	21305	58	-1.6707726	1	95	1	95	0	0	1	0	80	-15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	4	3	2	0	2	NA	NA	1	43	3	23	2	1	0	0	0	NA	NA	75.95551	21	17	1	1	1	0	4
100034	1	21172	58	-2.0355911	1	50	1	50	1	1	1	0	40	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	2	1	2	0	2	0	0	0	45	2	17	1	0	1	0	1	NA	NA	62.96372	10	5	0	1	1	0	5
100036	1	25202	69	9.0000000	2	85	1	85	0	0	1	0	75	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	2	2	2	0	1	0	0	1	41	6	25	4	1	0	0	1	NA	NA	84.10472	26	15	0	1	1	0	4
100037	1	25166	69	8.9014359	2	55	1	55	1	1	1	0	15	-40	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	4	4	2	0	1	NA	NA	1	40	5	22	3	0	NA	0	0	NA	NA	87.00958	33	18	1	1	1	0	4
100038	1	26663	73	13.0000000	3	100	1	100	0	0	0	0	85	-15	97	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	0	1	2	2	1	1	0	0	0	38	5	23	2	1	0	0	1	NA	NA	86.98494	24	14	0	1	1	0	3
100041	1	24819	68	7.9507217	2	90	1	90	0	0	1	0	15	-75	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	2	3	2	0	2	0	0	1	43	5	23	3	0	0	NA	0	NA	NA	83.20602	23	15	1	1	1	0	4
100042	1	22277	61	0.9917870	2	85	1	85	0	0	0	0	85	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	4	1	5	3	2	0	2	1	0	1	36	3	22	2	1	0	0	0	NA	NA	68.98083	12	8	0	1	1	0	3
100043	1	23313	64	3.8275146	2	100	1	100	0	0	0	0	100	0	NA	1	2	1	1	1	2	1	1	1	1	1	1	1	1	4	6	1	1	4	3	2	0	2	0	0	0	30	6	19	3	1	1	0	0	NA	NA	80.93908	19	17	0	1	0	1	2
100044	1	22271	61	0.9753609	2	70	1	70	1	1	1	1	70	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	3	3	2	0	2	0	0	1	26	10	23	5	0	1	0	1	NA	NA	67.14374	9	6	0	1	1	0	1
100045	1	25886	71	10.8713226	3	95	1	95	0	0	1	0	70	-25	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	1	3	2	2	0	1	0	0	0	41	1	23	1	1	2	0	0	NA	NA	85.97330	26	15	0	1	1	0	4
100046	1	26970	74	13.8384705	3	75	1	75	1	1	1	0	45	-30	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	0	3	4	2	1	1	NA	NA	0	35	3	26	3	1	NA	0	0	NA	NA	89.92882	33	16	1	1	1	0	3
100049	1	23330	64	3.8740578	2	100	1	100	0	0	1	0	55	-45	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	4	4	2	1	1	1	0	0	36	7	20	1	1	1	0	1	NA	NA	77.92197	31	14	0	1	1	0	3
100051	1	25889	71	10.8795319	3	90	1	90	0	0	1	0	75	-15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	1	5	3	2	0	1	0	1	0	35	8	26	1	1	0	0	0	NA	NA	83.59686	25	13	0	1	1	0	3
100052	1	19316	53	-7.1149902	1	100	1	100	0	0	0	0	100	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	3	0	4	4	2	1	1	0	0	0	35	5	25	2	1	NA	0	0	NA	NA	69.11499	34	16	0	1	1	0	3
100053	1	22280	61	1.0000000	2	95	1	95	0	0	0	0	95	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	5	3	2	0	2	0	1	0	44	NA	25	2	1	1	0	1	NA	NA	74.89733	16	14	0	1	1	0	5
100054	1	24411	67	6.8329926	2	65	1	65	1	1	1	0	60	-5	NA	1	1	1	1	1	1	1	1	1	1	1	1	1	1	4	6	1	1	2	2	2	0	1	0	0	0	38	0	22	2	1	0	0	0	NA	NA	68.47570	8	1	0	1	0	1	3
100055	1	24801	68	7.9014359	2	90	1	90	0	0	1	0	75	-15	NA	1	0	0	1	1	0	0	0	1	1	0	0	0	1	3	5	1	1	1	2	2	1	2	1	0	1	33	5	NA	1	0	1	0	0	NA	NA	73.94387	11	6	0	1	1	0	2
100056	1	26662	73	12.9972610	3	95	1	95	0	0	1	0	75	-20	99	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	1	5	4	2	0	1	0	0	0	41	NA	25	1	0	0	0	1	NA	NA	86.92744	26	14	0	1	1	0	4
100057	1	23735	65	4.9835739	2	90	1	90	0	0	1	0	85	-5	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	4	3	2	0	2	0	0	0	44	NA	23	2	0	1	0	0	NA	NA	80.92608	19	16	0	1	1	0	5
100058	1	24033	66	5.7974014	2	95	1	95	0	0	1	0	65	-30	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	4	4	2	0	1	0	0	0	32	4	22	2	0	1	0	0	NA	NA	81.99178	30	16	0	1	1	0	2
100059	1	24452	67	6.9452438	2	70	1	70	1	1	1	0	60	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	2	1	2	4	2	0	1	0	0	0	41	4	24	3	0	1	0	1	NA	NA	74.29911	21	7	0	1	1	0	4
100060	1	21892	60	-0.0629692	2	95	1	95	0	0	0	0	90	-5	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	5	4	2	0	2	0	0	1	44	2	26	1	0	0	0	1	NA	NA	76.17249	19	16	0	1	1	0	5
100061	1	23362	64	3.9616699	2	100	1	100	0	0	0	0	100	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	4	1	5	3	2	0	2	0	0	0	45	1	26	1	1	0	0	1	NA	NA	77.97673	18	14	0	1	1	0	5
100063	1	18259	50	-10.0109520	1	65	1	65	1	1	1	0	65	0	NA	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2	2	1	1	3	4	2	0	2	0	0	1	21	4	21	3	0	2	1	1	NA	NA	65.94798	19	16	0	1	0	1	1
100064	1	24804	68	7.9096527	2	55	1	55	1	1	1	1	55	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	2	0	4	4	2	0	2	0	0	0	42	9	22	5	0	2	0	0	NA	NA	72.41067	8	4	0	1	1	0	4
100066	1	20082	55	-5.0191650	1	10	1	10	1	1	1	0	35	25	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	2	1	5	3	2	0	2	0	0	0	43	2	23	1	0	0	0	1	NA	NA	64.40178	15	9	0	1	1	0	4
100067	1	24404	67	6.8138275	2	50	1	50	1	1	1	0	65	15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	4	4	2	0	1	0	0	0	44	10	25	4	0	0	0	0	NA	NA	83.94730	26	17	0	1	1	0	5
100068	1	25529	70	9.8932266	3	75	1	75	1	1	1	0	75	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	1	4	1	1	0	1	0	0	1	38	2	27	1	0	0	0	0	NA	NA	80.59548	24	11	0	1	1	0	3
100070	1	24094	66	5.9644089	2	95	1	95	0	0	1	0	80	-15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	3	3	2	0	2	0	0	0	37	1	21	1	1	0	0	0	NA	NA	77.95619	18	12	0	1	1	0	3
100072	1	24107	66	6.0000000	2	100	1	100	0	0	0	0	85	-15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	2	0	3	3	2	1	2	0	NA	0	NA	1	24	2	0	0	0	0	NA	NA	79.94661	17	14	1	1	1	0	NA
100073	1	23349	64	3.9260788	2	90	1	90	0	0	1	0	80	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	3	3	2	1	1	0	1	0	36	5	NA	1	0	0	0	1	NA	NA	78.98699	26	15	0	1	1	0	3
100074	1	21503	59	-1.1286774	1	55	1	55	1	1	1	0	80	25	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	5	3	2	0	1	NA	NA	0	45	0	24	5	0	1	0	1	NA	NA	73.97330	29	15	1	1	1	0	5
100078	1	19984	55	-5.2874756	1	95	1	95	0	0	0	0	95	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	4	2	2	0	1	NA	NA	0	37	6	18	1	1	NA	0	0	NA	NA	72.97125	32	18	1	1	1	0	3
100079	1	22262	61	0.9507179	2	95	1	95	0	0	1	0	35	-60	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	5	4	2	0	2	0	1	0	45	1	26	NA	0	0	0	0	NA	NA	74.94661	15	14	1	1	1	0	5
100080	1	25922	71	10.9698868	3	100	1	100	0	0	0	0	100	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	0	3	4	2	0	2	0	0	0	19	7	29	2	1	0	0	1	NA	NA	78.51814	11	8	0	1	1	0	1
100083	1	22987	63	2.9342918	2	70	1	70	1	1	1	0	25	-45	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	2	2	2	0	1	0	0	0	37	3	23	3	0	0	0	0	NA	NA	77.94593	30	15	0	1	1	0	3
100084	1	23709	65	4.9123917	2	90	1	90	0	0	1	0	60	-30	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	2	4	2	1	1	0	0	1	36	4	15	1	0	0	1	1	NA	NA	79.92950	29	15	0	1	1	0	3
100085	1	25534	70	9.9069138	3	55	1	55	1	1	1	0	40	-15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	1	3	3	2	0	2	0	0	1	34	0	24	3	0	1	0	0	NA	NA	83.94114	18	14	0	1	1	0	2
100086	1	24800	68	7.8986969	2	60	1	60	1	1	1	0	70	10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	4	1	2	3	2	0	2	0	0	0	37	5	27	1	1	0	0	1	NA	NA	79.91512	16	12	0	1	1	0	3
100087	1	25561	70	9.9808350	3	75	1	75	1	1	1	0	70	-5	NA	1	0	0	1	1	0	0	0	1	1	0	0	0	1	5	9	1	1	3	4	2	0	2	0	1	0	42	7	27	2	1	1	0	0	NA	NA	84.93224	19	15	0	1	1	0	4
100088	1	22224	61	0.8466797	2	85	1	85	0	0	0	0	100	15	NA	1	0	0	1	1	0	0	0	1	1	0	0	0	1	3	5	3	1	9	4	2	0	1	0	0	1	22	NA	27	2	0	0	0	0	NA	NA	67.50240	15	7	0	1	1	0	1
100091	1	20767	57	-3.1423683	1	100	1	100	0	0	1	0	45	-55	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	3	1	4	4	2	0	1	NA	NA	1	40	0	22	1	1	0	0	1	NA	NA	73.04928	27	16	1	1	1	0	4
100092	1	27393	75	14.9972610	3	60	1	60	1	1	1	1	60	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	0	4	3	2	0	2	0	0	0	39	11	26	3	0	1	0	0	NA	NA	76.17728	5	1	0	1	1	0	3
100093	1	22623	62	1.9370308	2	90	1	90	0	0	1	0	95	5	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	2	0	2	4	2	1	1	NA	NA	0	39	2	26	1	1	0	0	0	NA	NA	75.83162	29	14	1	1	1	0	3
100094	1	18985	52	-8.0219040	1	95	1	95	0	0	1	0	75	-20	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	5	3	2	0	2	0	0	1	42	2	23	1	0	0	0	0	NA	NA	69.02670	18	17	0	1	1	0	4
100095	1	25167	69	8.9041748	2	90	1	90	0	0	1	0	0	-90	NA	1	1	1	1	1	1	1	1	1	1	1	1	1	1	4	6	1	0	1	3	2	0	1	0	0	1	20	9	25	2	0	0	1	0	NA	NA	81.30938	29	12	0	1	0	1	1
100096	1	26899	74	13.6440811	3	45	1	45	1	1	1	0	40	-5	NA	1	1	1	1	1	1	1	1	1	1	1	1	1	1	6	10	1	1	3	3	2	0	1	0	0	1	36	2	22	2	0	0	1	1	NA	NA	87.83984	29	14	0	1	0	1	3
100097	1	24458	67	6.9616699	2	90	1	90	0	0	1	0	65	-25	NA	1	1	1	1	1	1	1	1	1	1	1	1	1	1	4	6	1	0	1	3	2	1	2	0	0	1	42	6	22	3	0	1	0	1	NA	NA	75.80219	13	9	0	1	0	1	4
100099	1	25933	71	11.0000000	3	75	1	75	1	1	1	0	65	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	0	4	4	2	0	2	0	0	0	16	9	21	1	0	0	0	1	NA	NA	78.99452	14	8	0	1	1	0	1
100100	1	21129	58	-2.1533203	1	85	1	85	0	0	1	0	30	-55	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	4	2	2	0	1	0	0	0	45	1	28	5	1	0	0	1	NA	NA	73.89596	27	16	0	1	1	0	5
100101	1	24774	68	7.8275146	2	100	1	100	0	0	1	0	70	-30	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	2	0	1	3	2	1	1	0	0	1	29	4	24	2	1	0	0	0	NA	NA	81.87543	24	14	0	1	1	0	1
100103	1	24770	68	7.8165665	2	85	1	85	0	0	1	0	20	-65	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	2	3	2	0	1	NA	NA	1	42	1	26	3	1	1	0	0	NA	NA	80.70911	26	13	1	1	1	0	4
100104	1	28847	79	18.9780960	3	70	1	70	1	1	1	0	60	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	0	1	3	2	1	2	0	0	1	45	2	28	3	1	2	0	1	NA	NA	89.91307	13	11	0	1	1	0	5
100105	1	20787	57	-3.0876122	1	100	1	100	0	0	1	0	80	-20	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	5	3	2	0	2	0	0	1	43	3	25	2	1	NA	0	1	NA	NA	70.94935	17	14	0	1	1	0	4
100106	1	24807	68	7.9178619	2	85	1	85	0	0	1	0	5	-80	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	3	3	2	0	1	0	0	1	45	0	24	2	1	0	0	0	NA	NA	82.28610	28	14	0	1	1	0	5
100108	1	24818	68	7.9479828	2	55	1	55	1	1	1	0	10	-45	NA	1	0	0	1	1	0	0	0	1	1	0	0	0	1	3	5	1	1	1	2	2	1	1	0	0	0	25	6	20	2	0	0	0	0	NA	NA	76.66530	20	9	0	1	1	0	1
100109	1	25168	69	8.9069138	2	80	1	80	1	1	1	0	65	-15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	4	0	1	3	2	0	1	NA	NA	1	45	2	18	5	1	0	0	0	NA	NA	83.01780	29	14	1	1	1	0	5
100110	1	25926	71	10.9808350	3	85	1	85	0	0	1	0	55	-30	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	1	4	3	2	0	2	0	0	0	44	1	22	1	0	0	0	1	NA	NA	85.01506	17	14	0	1	1	0	5
100111	1	26962	74	13.8165665	3	80	1	80	1	1	1	1	80	0	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	1	1	4	2	0	2	0	0	0	38	4	22	1	0	1	0	0	NA	NA	79.26762	7	5	0	1	1	0	3
100112	1	21868	60	-0.1286774	2	65	1	65	1	1	1	0	35	-30	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	1	3	2	0	1	NA	NA	0	37	7	17	4	0	NA	0	0	NA	NA	75.46612	30	15	1	1	1	0	3
100113	1	27372	75	14.9397659	3	60	1	60	1	1	1	0	50	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	0	1	2	2	1	2	0	0	0	14	9	12	2	0	3	1	1	NA	NA	87.22450	17	12	0	1	1	0	1
100115	1	24462	67	6.9726181	2	85	1	85	0	0	1	0	5	-80	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	3	4	2	0	2	0	0	1	36	0	24	1	1	1	0	0	NA	NA	78.86037	17	12	0	1	1	0	3
100116	1	22617	62	1.9206009	2	70	1	70	1	1	1	0	35	-35	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	4	4	2	0	2	0	0	1	45	0	28	2	1	0	0	0	NA	NA	77.88501	20	16	0	1	1	0	5
100118	1	24457	67	6.9589310	2	55	1	55	1	1	1	0	5	-50	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	3	3	2	1	1	0	0	1	28	4	24	5	0	2	0	0	NA	NA	82.00616	27	15	0	1	1	0	1
100120	1	20061	55	-5.0766602	1	95	1	95	0	0	1	0	45	-50	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	0	2	3	2	1	1	0	0	0	33	3	19	1	0	NA	0	0	NA	NA	71.98289	33	17	0	1	1	0	2
100121	1	20067	55	-5.0602341	1	100	1	100	0	0	0	0	95	-5	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	4	4	2	0	1	0	0	0	40	3	26	3	1	0	0	0	NA	NA	62.96988	20	8	0	1	1	0	4
100122	1	24458	67	6.9616699	2	100	1	100	0	0	0	0	95	-5	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	5	4	2	0	2	1	1	0	45	4	23	2	1	0	0	1	NA	NA	73.97057	12	7	0	1	1	0	5
100123	1	21464	59	-1.2354546	1	80	1	80	1	1	1	0	90	10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	5	4	2	0	1	0	0	1	44	5	22	1	1	0	0	0	NA	NA	72.96851	25	14	0	1	1	0	5
100124	1	18235	50	-10.0766602	1	75	1	75	1	1	1	0	100	25	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	0	2	3	2	1	2	NA	NA	0	22	5	23	1	1	1	1	1	NA	NA	65.99178	21	16	1	1	1	0	1
100125	1	24351	67	6.6687164	2	65	1	65	1	1	1	0	50	-15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	9	4	2	0	2	0	0	1	26	7	22	1	1	0	1	0	NA	NA	81.23409	18	14	0	1	1	0	1
100126	1	23355	64	3.9425049	2	100	1	100	0	0	1	0	65	-35	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	2	3	2	1	1	0	0	0	37	6	24	3	1	0	0	0	NA	NA	80.98563	32	17	0	1	1	0	3
100127	1	25548	70	9.9459305	2	85	1	85	0	0	1	0	35	-50	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	1	NA	3	2	NA	2	NA	NA	NA	NA	NA	NA	NA	1	0	0	0	NA	NA	83.97741	20	17	1	1	1	0	NA
100128	1	25533	70	9.9041748	3	100	1	100	0	0	1	0	65	-35	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	0	3	3	2	0	2	0	0	0	32	6	20	2	1	2	0	1	NA	NA	83.90007	16	14	0	1	1	0	2
100130	1	20018	55	-5.1943855	1	65	1	65	1	1	1	0	75	10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	1	1	1	1	4	2	0	1	0	0	1	32	5	17	NA	1	2	0	1	NA	NA	70.98357	29	16	1	1	1	0	2
100131	1	26232	72	11.8193054	3	95	1	95	0	0	1	0	80	-15	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	5	9	1	0	4	4	2	0	2	0	0	0	39	6	24	1	1	0	0	0	NA	NA	87.00343	16	15	0	1	1	0	3
100134	1	24455	67	6.9534531	2	90	1	90	0	0	1	0	80	-10	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	2	4	2	1	2	0	0	0	41	0	22	2	1	0	0	1	NA	NA	80.19096	17	13	0	1	1	0	4
100136	1	24756	68	7.7782364	2	90	1	90	0	0	1	0	20	-70	NA	1	0	0	0	1	0	0	0	0	1	0	0	0	0	3	5	1	0	1	3	2	1	2	0	0	0	44	2	30	2	1	0	0	1	NA	NA	85.06776	20	17	0	1	1	0	5

40.14.3 Using the datatable function

DT::datatable(head(aws, 20), options = list(scrollX = TRUE))