GG plot

library(tidyverse)
library(janitor)
library(flextable)
library(moments)
getwd()
## [1] "C:/Users/xpadi/OneDrive/Escritorio/Universidad/2023-2/Bioinformatica"
setwd("C:/Users/xpadi/OneDrive/Escritorio/Universidad/2023-2/Bioinformatica")
getwd()
## [1] "C:/Users/xpadi/OneDrive/Escritorio/Universidad/2023-2/Bioinformatica"
df=read.csv("500_Person_Gender_Height_Weight_Index.csv")
table(df$Gender)
## 
## Female   Male 
##    255    245

La función tabyl se encuentra en la paqueteria janitor

table(df$Gender)
## 
## Female   Male 
##    255    245
df %>% tabyl(Gender)
##  Gender   n percent
##  Female 255    0.51
##    Male 245    0.49
#tabyl da cuantos hay y en porcentaje

Versión mejorada

df %>% tabyl(Gender)%>%
  flextable()%>%
  fontsize(size=14)%>%
  autofit()

Gender

n

percent

Female

255

0.51

Male

245

0.49

df%>% tabyl(Gender)%>%
  adorn_pct_formatting()%>%
  flextable()%>%
  fontsize(size=14)%>%
  autofit()%>%
  theme_box()

Gender

n

percent

Female

255

51.0%

Male

245

49.0%

df%>% tabyl(Gender)%>%
  adorn_totals("row")%>%
  adorn_pct_formatting()%>%
  flextable()%>%
  fontsize(size=14)%>%
  autofit()%>%
  theme_box()

Gender

n

percent

Female

255

51.0%

Male

245

49.0%

Total

500

100.0%

df %>% tabyl(Gender)%>%
  ggplot(aes(x=Gender,y=n,fill=Gender))+
  geom_col()+
  labs(x="Genero",y="Frecuencia",title = "Genero de los encuestados")+
    guides(fill=FALSE)

df %>% tabyl(Weight)%>%
  ggplot(aes(x=Weight,y=n,fill=Weight))+
  geom_col()+
  labs(x="Peso",y="Frecuencia",title="Peso de los encuestados")+
  geom_text(aes(label=n),vjust=1.5,col="white",fontface="bold")

df %>% tabyl(Gender)%>%
  ggplot(aes(x=Gender,y=n,fill=Gender))+
  geom_col()+
  labs(x="Genero",y="Frecuencia",title="Genero de los encuestados")+
  geom_text(aes(label=sprintf("%.2f%%",100*percent)),vjust=1.5,col="white",fontface="bold")

n=100
numeros= rnorm(n=n,mean=20,sd=1)
numeros
##   [1] 18.34981 20.90628 21.65883 20.00418 20.38705 19.62184 19.88493 19.54363
##   [9] 18.88072 20.11042 19.73245 21.10461 20.63915 19.09704 20.01025 21.78147
##  [17] 19.86414 20.83799 19.07582 21.54067 19.70331 20.87679 20.20636 20.36104
##  [25] 18.27729 19.71850 19.51813 23.31992 20.17049 20.80231 19.64855 21.59344
##  [33] 21.08385 20.72297 20.00221 18.79489 20.22549 20.70079 19.50620 19.21157
##  [41] 18.03785 20.75006 19.58807 16.66063 19.93704 19.50728 17.84562 18.99696
##  [49] 18.66469 19.09867 18.94722 20.69832 19.33933 18.75053 21.11828 20.17801
##  [57] 19.42060 21.13341 19.46589 19.62398 20.89609 20.39718 19.98148 20.66587
##  [65] 21.25255 18.60555 19.06325 17.74926 18.59314 20.11798 20.38930 18.63804
##  [73] 20.44239 20.04718 21.22896 20.98803 20.58817 19.46253 19.71645 19.21688
##  [81] 20.07555 21.22608 20.68400 21.38023 18.50697 19.10191 19.02500 18.14078
##  [89] 19.80997 20.36756 19.34232 21.27608 19.81637 20.34342 20.48275 19.49632
##  [97] 20.05225 17.26891 19.96078 20.92943
df1=data.frame(numeros)
df1
##      numeros
## 1   18.34981
## 2   20.90628
## 3   21.65883
## 4   20.00418
## 5   20.38705
## 6   19.62184
## 7   19.88493
## 8   19.54363
## 9   18.88072
## 10  20.11042
## 11  19.73245
## 12  21.10461
## 13  20.63915
## 14  19.09704
## 15  20.01025
## 16  21.78147
## 17  19.86414
## 18  20.83799
## 19  19.07582
## 20  21.54067
## 21  19.70331
## 22  20.87679
## 23  20.20636
## 24  20.36104
## 25  18.27729
## 26  19.71850
## 27  19.51813
## 28  23.31992
## 29  20.17049
## 30  20.80231
## 31  19.64855
## 32  21.59344
## 33  21.08385
## 34  20.72297
## 35  20.00221
## 36  18.79489
## 37  20.22549
## 38  20.70079
## 39  19.50620
## 40  19.21157
## 41  18.03785
## 42  20.75006
## 43  19.58807
## 44  16.66063
## 45  19.93704
## 46  19.50728
## 47  17.84562
## 48  18.99696
## 49  18.66469
## 50  19.09867
## 51  18.94722
## 52  20.69832
## 53  19.33933
## 54  18.75053
## 55  21.11828
## 56  20.17801
## 57  19.42060
## 58  21.13341
## 59  19.46589
## 60  19.62398
## 61  20.89609
## 62  20.39718
## 63  19.98148
## 64  20.66587
## 65  21.25255
## 66  18.60555
## 67  19.06325
## 68  17.74926
## 69  18.59314
## 70  20.11798
## 71  20.38930
## 72  18.63804
## 73  20.44239
## 74  20.04718
## 75  21.22896
## 76  20.98803
## 77  20.58817
## 78  19.46253
## 79  19.71645
## 80  19.21688
## 81  20.07555
## 82  21.22608
## 83  20.68400
## 84  21.38023
## 85  18.50697
## 86  19.10191
## 87  19.02500
## 88  18.14078
## 89  19.80997
## 90  20.36756
## 91  19.34232
## 92  21.27608
## 93  19.81637
## 94  20.34342
## 95  20.48275
## 96  19.49632
## 97  20.05225
## 98  17.26891
## 99  19.96078
## 100 20.92943
df1 %>%
  ggplot(aes(x=numeros))+
  geom_histogram(color="blue",fill="lightblue")+
  labs(x="Numeros",y="Frecuencia",tittle="Campana de gauss experimental")