Tarea Bioinformática

Medida de Grasa Corporal para 436 personas

Sexo y Edad de los Encuestados

Sex	n	percent
F	184	42.2%
M	252	57.8%
Total	436	100.0%

Age	n	percent
1	1	0.2%
18	15	3.4%
19	34	7.8%
20	54	12.4%
21	38	8.7%
22	23	5.3%
23	15	3.4%
24	13	3.0%
25	5	1.1%
26	6	1.4%
27	7	1.6%
28	7	1.6%
29	2	0.5%
30	2	0.5%
31	4	0.9%
32	4	0.9%
33	3	0.7%
34	4	0.9%
35	10	2.3%
36	2	0.5%
37	3	0.7%
38	2	0.5%
39	5	1.1%
40	17	3.9%
41	10	2.3%
42	12	2.8%
43	13	3.0%
44	9	2.1%
45	2	0.5%
46	6	1.4%
47	11	2.5%
48	5	1.1%
49	9	2.1%
50	7	1.6%
51	5	1.1%
52	4	0.9%
53	2	0.5%
54	8	1.8%
55	10	2.3%
56	4	0.9%
57	4	0.9%
58	3	0.7%
60	1	0.2%
61	4	0.9%
62	5	1.1%
63	1	0.2%
64	4	0.9%
65	3	0.7%
66	2	0.5%
67	4	0.9%
68	1	0.2%
69	2	0.5%
70	2	0.5%
72	5	1.1%
74	1	0.2%
81	1	0.2%
Total	436	100.0%

Características físicas de los encuestados

PESO

Peso	n	percent
42	1	0.2%
44	3	0.7%
48	4	0.9%
49	12	2.8%
50	6	1.4%
51	4	0.9%
52	5	1.1%
53	5	1.1%
54	9	2.1%
55	8	1.8%
56	8	1.8%
57	9	2.1%
58	14	3.2%
59	12	2.8%
60	13	3.0%
61	6	1.4%
62	13	3.0%
63	7	1.6%
64	12	2.8%
65	12	2.8%
66	8	1.8%
67	8	1.8%
68	12	2.8%
69	12	2.8%
70	12	2.8%
71	8	1.8%
72	10	2.3%
73	12	2.8%
74	11	2.5%
75	9	2.1%
76	17	3.9%
77	11	2.5%
78	7	1.6%
79	5	1.1%
80	11	2.5%
81	8	1.8%
82	7	1.6%
83	7	1.6%
84	8	1.8%
85	6	1.4%
86	4	0.9%
87	7	1.6%
88	5	1.1%
89	6	1.4%
90	6	1.4%
91	6	1.4%
92	5	1.1%
93	4	0.9%
94	3	0.7%
95	5	1.1%
96	3	0.7%
98	6	1.4%
99	5	1.1%
101	2	0.5%
102	4	0.9%
103	2	0.5%
104	2	0.5%
106	3	0.7%
109	1	0.2%
110	1	0.2%
111	1	0.2%
112	1	0.2%
119	1	0.2%
165	1	0.2%
Total	436	100.0%

ALTURA

Altura	n	percent
0.75	1	0.2%
1.54	2	0.5%
1.56	4	0.9%
1.57	5	1.1%
1.59	3	0.7%
1.6	14	3.2%
1.61	12	2.8%
1.62	2	0.5%
1.63	19	4.4%
1.64	11	2.5%
1.65	17	3.9%
1.66	13	3.0%
1.67	13	3.0%
1.68	14	3.2%
1.69	13	3.0%
1.7	19	4.4%
1.71	27	6.2%
1.72	8	1.8%
1.73	29	6.7%
1.74	12	2.8%
1.75	17	3.9%
1.76	12	2.8%
1.77	23	5.3%
1.78	21	4.8%
1.79	9	2.1%
1.8	15	3.4%
1.81	7	1.6%
1.82	20	4.6%
1.83	7	1.6%
1.84	17	3.9%
1.85	10	2.3%
1.86	5	1.1%
1.87	11	2.5%
1.88	4	0.9%
1.89	11	2.5%
1.9	2	0.5%
1.91	2	0.5%
1.92	1	0.2%
1.93	2	0.5%
1.97	2	0.5%
Total	436	100.0%

CIRCUNFERENCIA DEL CUELLO EN CM

cuello	n	percent
26	1	0.2%
27	1	0.2%
28	3	0.7%
29	6	1.4%
30	43	9.9%
31	33	7.6%
32	59	13.5%
33	22	5.0%
34	30	6.9%
35	20	4.6%
36	40	9.2%
37	28	6.4%
38	60	13.8%
39	27	6.2%
40	22	5.0%
41	24	5.5%
42	13	3.0%
43	2	0.5%
44	1	0.2%
51	1	0.2%
Total	436	100.0%

CIRCUNFERENCIA DEL PECHO EN CM

pecho	n	percent
43	1	0.2%
50	1	0.2%
74	1	0.2%
75	1	0.2%
76	6	1.4%
78	5	1.1%
79	7	1.6%
80	7	1.6%
81	9	2.1%
82	18	4.1%
83	13	3.0%
84	24	5.5%
85	12	2.8%
86	19	4.4%
87	9	2.1%
88	15	3.4%
89	14	3.2%
90	18	4.1%
91	13	3.0%
92	15	3.4%
93	15	3.4%
94	17	3.9%
95	7	1.6%
96	9	2.1%
97	15	3.4%
98	14	3.2%
99	18	4.1%
100	15	3.4%
101	9	2.1%
102	11	2.5%
103	11	2.5%
104	14	3.2%
105	10	2.3%
106	8	1.8%
107	8	1.8%
108	11	2.5%
109	1	0.2%
110	3	0.7%
111	4	0.9%
112	4	0.9%
113	2	0.5%
114	2	0.5%
115	5	1.1%
116	2	0.5%
117	1	0.2%
118	4	0.9%
119	1	0.2%
120	4	0.9%
122	1	0.2%
128	1	0.2%
136	1	0.2%
Total	436	100.0%

CIRCUNFERENCIA DEL ABDOMEN EN CM

abdomen	n	percent
58	2	0.5%
60	2	0.5%
61	3	0.7%
62	9	2.1%
63	10	2.3%
64	16	3.7%
65	13	3.0%
66	17	3.9%
67	8	1.8%
68	16	3.7%
69	8	1.8%
70	18	4.1%
71	10	2.3%
72	9	2.1%
73	7	1.6%
74	7	1.6%
75	7	1.6%
76	10	2.3%
77	9	2.1%
78	9	2.1%
79	5	1.1%
80	9	2.1%
81	4	0.9%
82	7	1.6%
83	11	2.5%
84	16	3.7%
85	4	0.9%
86	9	2.1%
87	9	2.1%
88	10	2.3%
89	12	2.8%
90	16	3.7%
91	8	1.8%
92	11	2.5%
93	7	1.6%
94	6	1.4%
95	9	2.1%
96	10	2.3%
97	3	0.7%
98	8	1.8%
99	9	2.1%
100	14	3.2%
101	5	1.1%
102	3	0.7%
103	3	0.7%
104	5	1.1%
105	6	1.4%
106	4	0.9%
107	2	0.5%
108	3	0.7%
109	3	0.7%
110	2	0.5%
111	1	0.2%
112	1	0.2%
113	2	0.5%
114	3	0.7%
116	2	0.5%
118	1	0.2%
122	1	0.2%
126	1	0.2%
148	1	0.2%
Total	436	100.0%

# NOTAS DE VIDEO

La función tabyl se encuentra en la paquetería janitor

df %>% tabyl(Sex)

##  Sex   n   percent
##    F 184 0.4220183
##    M 252 0.5779817

Mejorando

df %>% tabyl(Sex) %>%
  adorn_pct_formatting() %>%
  flextable() %>%
  fontsize(size=16) %>%
  autofit()

Sex	n	percent
F	184	42.2%
M	252	57.8%

df %>% tabyl(Sex) %>%
  adorn_pct_formatting() %>% #porcentaje
  flextable() %>%
  fontsize(size=16) %>%
  autofit() %>%
  theme_box()

Sex	n	percent
F	184	42.2%
M	252	57.8%

Tablas de Frecuencias (Fromato “final”)

df %>% tabyl(Sex) %>%
  adorn_totals("row")%>%
  adorn_pct_formatting() %>% #porcentaje
  flextable() %>%
  fontsize(size=16) %>%
  autofit() %>%
  theme_box()

Sex	n	percent
F	184	42.2%
M	252	57.8%
Total	436	100.0%

df %>% tabyl(Sex) %>%
  ggplot(aes(x=Sex,y=n,fill=Sex)) +
  geom_col()

df %>% tabyl(Sex) %>%
  ggplot(aes(x=Sex,y=n,fill=Sex)) +
  geom_col() +
  labs(x="Sexo",y="Frecuencia",title="Encuestados Grasa Corporal") +
  guides(fill=FALSE)

df %>% tabyl(Sex) %>%
  ggplot(aes(x=Sex,y=n,fill=Sex)) +
  geom_col() +
  labs(x="Sexo",y="Frecuencia",title="Encuestados Grasa Corporal") +
  geom_text(aes(label=n),vjust=1.5,col="white",fontface="bold")

Grafica de Barras con Frecuencias (Fromato “final”)

df %>% tabyl(Sex) %>%
  ggplot(aes(x=Sex,y=n,fill=Sex)) +
  geom_col() +
  labs(x="Sexo",y="Frecuencia",title="Encuestados Grasa Corporal") +
  geom_text(aes(label=sprintf("%.2f%%",100*percent)),vjust=1.5,col="white",fontface="bold")

Ejemplo de un Histograma

n=100
numeros=rnorm(n=n,mean=20,sd=1)
numeros

##   [1] 21.87062 18.40974 20.61069 19.10839 19.46377 19.52325 18.87887 20.86757
##   [9] 17.43876 19.37294 18.49215 20.35423 20.60978 18.70847 18.70990 22.10176
##  [17] 22.34833 18.50470 19.21824 20.96021 19.71521 19.90850 19.66948 19.02419
##  [25] 18.89305 20.50806 20.37574 21.88304 21.54125 19.52818 20.26335 19.55952
##  [33] 19.59887 20.24997 20.93415 21.08240 20.61188 20.73739 20.38025 17.68439
##  [41] 19.80904 19.56840 20.49275 18.74146 20.26727 18.86651 22.24585 22.28867
##  [49] 18.64660 20.12515 20.96974 20.81428 21.38699 19.86358 20.09173 20.40503
##  [57] 21.18788 19.72227 20.00003 19.92626 21.55132 19.23405 19.81874 19.57776
##  [65] 18.47724 20.03942 20.41645 21.79500 17.45714 19.33574 20.64222 21.13170
##  [73] 18.61268 19.97854 20.23234 18.43701 19.90132 19.82356 18.96481 20.22405
##  [81] 20.31828 20.16685 20.47617 19.30996 21.76690 21.17873 20.36178 17.99275
##  [89] 20.38878 21.00214 20.49501 20.83869 20.52324 19.78266 22.35055 19.23573
##  [97] 19.93648 20.16971 19.91702 18.49338

df1=data.frame(numeros)
df1 %>%
  ggplot(aes(x=numeros)) +
  geom_histogram(color="hotpink",fill="pink") +
  labs(x="Numeros",y="Frecuencia",title="Campana de Gauss Experimental")