ggplot

library(janitor)
library(tidyverse)
library(flextable)
library(moments)
setwd("E:/Proyecto")
df=read.csv("diabetes.csv")
df$Outcome[df$Outcome==0]="Negativo"
df$Outcome[df$Outcome==1]="Positivo"
table(df$Outcome)

## 
## Negativo Positivo 
##      500      268

dim(df)

## [1] 768   9

df %>% tabyl(Outcome)

##   Outcome   n   percent
##  Negativo 500 0.6510417
##  Positivo 268 0.3489583

Version mejorada

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

Outcome	n	percent
Negativo	500	0.6510417
Positivo	268	0.3489583

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

Outcome	n	percent
Negativo	500	65.1%
Positivo	268	34.9%

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

Outcome	n	percent
Negativo	500	65.1%
Positivo	268	34.9%
Total	768	100.0%

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

df %>% tabyl(Outcome) %>%
  ggplot(aes(x=Outcome,y=n, fill=Outcome)) +
  geom_col() +
  labs(x="resultados",y="frecuencia", title = "Resultados positivos") +
  guides(fill=FALSE)

df %>% tabyl(Outcome) %>%
  ggplot(aes(x=Outcome,y=n, fill=Outcome)) +
  geom_col() +
  labs(x="resultados",y="frecuencia", title = "Resultados positivos") +
  geom_text(aes(label=n),vjust=1.5, col="pink",fontface="bold")

n=100
numeros=rnorm(n=n,mean=0,sd=1)
df1=data.frame(numeros)
df1 %>%
  ggplot(aes(x=numeros)) +
  geom_histogram(color="pink",fill="pink3") +
  labs(x="Numeros",y="Frecuencia",title="Campana de gauss experimental")

```