title: "Desarrollo ejecicio Data Analysis Workshop"
author: "ingrid castaño"
date: "2024-08-11"
output: html_document
#install.packages("plotly")
#install.packages("UsingR")
#install.packages("MASS")
library(dplyr)
##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(dslabs)
library(ggplot2)
library(plotly)
##
## Adjuntando el paquete: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(UsingR)
## Cargando paquete requerido: MASS
##
## Adjuntando el paquete: 'MASS'
## The following object is masked from 'package:plotly':
##
## select
## The following object is masked from 'package:dplyr':
##
## select
## Cargando paquete requerido: HistData
## Cargando paquete requerido: Hmisc
##
## Adjuntando el paquete: 'Hmisc'
## The following object is masked from 'package:plotly':
##
## subplot
## The following objects are masked from 'package:dplyr':
##
## src, summarize
## The following objects are masked from 'package:base':
##
## format.pval, units
library(MASS)
library(readr)
data("brightness")
head(brightness)
## [1] 9.10 9.27 6.61 8.06 8.55 12.31
# A
#install.packages("moments")
library(moments)
brightness_df <- data.frame(brightness)
#head(brightness_df)
# Histograma
ggplot(data=brightness_df,aes(x=brightness,freq =FALSE))+
geom_histogram(aes(y = ..density..),bins = 30,binwidth = 0.5, fill = "skyblue", color = "black", alpha = 0.5)+
geom_density(color = "blue", size = 1,alpha = 0.2) +
labs(
title="Histograma de brightness",
x="brightness",
y="Frecuencia"
)+
theme_light(base_size = 15) +
theme(
plot.title = element_text(hjust = 0.5),
axis.title = element_text(face = "bold")
)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# modos
asimetria<- skewness(brightness_df$brightness)
print(paste("Asimetría:", asimetria))
## [1] "Asimetría: -0.260186782151443"
# B
# Crear el diagrama de caja
ggplot(data = brightness_df, aes(y = brightness)) +
geom_boxplot(fill = "skyblue", color = "black") +
labs(
title = "Diagrama de Caja de Brightness",
x = "",
y = "Brightness"
) +
theme_classic(base_size = 15) +
theme(
plot.title = element_text(hjust = 0.5),
axis.title = element_text(face = "bold"),
axis.text.x = element_blank()
)
## valores atipicos
boxplot_atipi <- boxplot.stats(brightness_df$brightness)
valores_atipicos <- boxplot_atipi$out
valores_atipicos
## [1] 12.31 11.71 5.53 11.28 4.78 5.13 4.37 5.04 12.43 12.04 4.55 11.55
## [13] 12.14 11.63 4.99 11.67 4.61 11.99 12.04 5.55 12.17 11.55 11.79 12.19
## [25] 2.07 11.65 11.73 2.28 5.42 3.88 5.54 5.29 5.01 11.55 4.89 11.80
## [37] 5.41 5.24
# Segundo valor atipico mas pequeño
segundo_atipico <- sort(valores_atipicos)[2]
print(paste("el segundo atipico es:",segundo_atipico))
## [1] "el segundo atipico es: 2.28"
# quitar los valores atipicos
brightness_without <- brightness_df[!brightness_df$brightness %in% valores_atipicos,]
# Display the new variable
brightness_without
## [1] 9.10 9.27 6.61 8.06 8.55 9.64 9.05 8.59 8.59 7.34 8.43 8.80
## [13] 7.25 8.60 8.15 11.03 6.53 8.51 7.55 8.69 7.57 9.05 6.28 9.13
## [25] 9.32 8.83 9.14 8.26 7.63 9.09 8.10 6.43 9.07 7.68 10.44 8.65
## [37] 7.46 8.70 10.61 8.20 6.18 7.91 9.59 8.57 10.78 7.31 9.53 6.49
## [49] 8.94 8.56 10.96 10.57 7.40 8.12 8.27 7.05 9.09 8.34 8.86 8.27
## [61] 6.36 8.08 11.00 8.55 7.83 8.79 8.33 10.42 8.26 8.97 6.90 9.93
## [73] 7.42 9.03 8.41 8.06 8.69 8.40 8.57 9.50 8.85 9.61 10.62 8.05
## [85] 7.80 5.71 7.87 7.64 7.66 8.68 8.12 10.10 8.67 10.46 9.87 9.48
## [97] 7.04 8.44 9.88 7.05 8.29 9.34 7.73 6.22 8.53 7.23 8.61 10.76
## [109] 8.93 7.95 7.46 8.60 8.55 9.20 6.82 8.29 6.83 7.21 5.58 8.70
## [121] 8.06 10.86 6.50 9.32 9.14 8.13 10.62 6.62 9.96 8.64 6.60 6.25
## [133] 7.83 10.03 9.04 8.47 7.33 8.66 10.35 8.96 8.49 11.26 8.15 7.04
## [145] 10.02 8.90 7.78 9.93 8.60 8.51 7.09 6.93 8.68 8.98 9.84 8.98
## [157] 7.98 10.16 8.86 8.58 9.56 9.24 9.63 5.80 9.05 8.45 8.86 7.84
## [169] 8.86 8.93 7.97 6.90 8.47 6.77 8.55 8.48 8.53 6.33 8.99 8.64
## [181] 9.55 8.74 8.16 9.46 5.70 7.62 8.95 8.97 8.94 7.24 10.32 8.24
## [193] 8.62 9.18 8.53 8.54 8.56 9.41 5.87 7.20 9.05 9.52 10.24 7.70
## [205] 8.17 7.29 9.26 7.94 8.42 8.56 7.52 7.74 8.85 9.01 7.17 9.04
## [217] 10.30 9.86 7.64 8.27 8.44 9.58 8.43 8.49 9.64 9.17 8.09 9.00
## [229] 6.25 8.56 10.81 8.76 7.76 7.82 7.90 8.52 9.73 9.19 8.10 8.75
## [241] 8.14 8.65 10.30 6.46 6.73 7.96 9.53 8.87 6.59 8.65 9.64 9.15
## [253] 9.04 8.42 8.09 9.06 8.09 8.18 8.77 7.36 9.16 8.82 11.14 6.24
## [265] 9.44 7.49 6.96 7.94 8.69 8.15 8.45 7.92 7.45 9.01 8.55 9.23
## [277] 9.16 7.90 8.68 7.78 8.21 8.11 8.29 7.89 9.67 8.24 6.80 8.18
## [289] 8.44 7.45 6.31 8.15 8.27 7.66 8.59 7.09 8.54 9.58 8.44 8.59
## [301] 8.01 8.29 9.62 7.26 7.91 9.45 8.19 8.93 7.65 8.53 7.38 8.56
## [313] 8.76 9.56 7.09 9.83 5.90 10.80 8.41 9.05 8.79 8.88 7.59 9.60
## [325] 10.66 8.55 8.11 9.44 9.60 5.78 10.66 6.38 8.80 7.79 8.60 7.77
## [337] 10.37 9.80 10.42 9.22 8.43 7.33 8.93 9.09 9.26 8.73 9.18 8.12
## [349] 9.26 8.94 6.11 9.13 7.90 9.34 7.13 10.82 7.46 8.72 7.02 9.08
## [361] 8.37 5.59 7.37 5.68 8.56 8.72 9.06 8.82 8.18 9.39 9.10 8.46
## [373] 9.15 8.28 8.18 7.93 9.21 6.09 8.31 7.83 8.72 6.61 6.25 7.82
## [385] 8.66 8.15 8.97 8.15 7.47 8.63 8.13 8.23 8.41 6.47 9.83 8.64
## [397] 7.73 8.64 8.94 8.84 6.32 5.80 8.97 7.53 7.41 7.80 8.14 6.71
## [409] 8.73 9.37 8.69 9.95 7.10 8.09 6.88 9.48 9.04 9.30 8.49 8.30
## [421] 7.95 7.08 6.93 8.38 8.56 8.78 7.42 8.26 7.71 6.91 9.16 8.99
## [433] 8.63 9.90 7.59 7.39 7.78 7.47 6.97 8.82 9.13 7.86 7.13 9.45
## [445] 8.78 7.23 9.73 7.36 7.36 8.47 9.37 6.99 8.20 8.36 8.22 9.91
## [457] 9.67 8.60 10.07 10.15 7.75 9.21 9.66 8.47 9.37 9.44 9.99 10.38
## [469] 7.51 8.91 7.45 9.57 8.99 8.58 6.90 7.55 7.93 9.71 9.57 8.55
## [481] 6.62 7.89 7.51 7.36 8.66 8.51 6.65 9.67 7.80 8.21 7.90 8.94
## [493] 9.82 8.69 8.57 8.89 5.98 7.92 7.60 8.22 5.70 8.75 6.93 7.97
## [505] 8.06 10.13 7.31 8.35 5.57 9.85 9.16 9.03 10.07 9.76 9.35 10.95
## [517] 8.87 6.68 9.69 8.05 10.30 6.07 8.51 7.71 8.56 8.26 8.62 10.92
## [529] 10.51 9.83 9.84 9.74 8.21 8.72 8.03 9.00 6.19 8.22 7.93 10.18
## [541] 8.98 9.13 6.91 8.79 8.23 10.24 8.83 7.62 8.96 10.41 8.97 9.61
## [553] 8.29 8.30 8.26 7.44 9.52 8.20 8.68 8.65 10.52 8.41 9.18 8.42
## [565] 8.86 7.92 10.97 8.85 9.31 10.28 7.56 7.88 7.99 8.23 8.52 9.14
## [577] 6.20 7.64 8.95 7.48 7.06 7.33 8.98 8.24 8.53 8.40 7.48 8.46
## [589] 9.29 8.57 8.70 8.50 8.37 6.87 7.50 7.39 8.19 7.56 8.37 7.39
## [601] 6.73 8.66 8.25 8.47 8.01 6.83 9.06 8.79 7.44 6.43 5.93 8.85
## [613] 9.86 8.55 7.66 7.82 9.08 10.10 8.21 8.85 7.79 7.58 7.85 7.18
## [625] 7.54 9.72 7.12 9.77 8.84 5.67 8.15 9.61 8.19 7.27 8.51 8.36
## [637] 10.00 8.74 6.18 10.26 10.16 8.31 8.58 7.04 8.81 5.99 8.22 9.86
## [649] 8.00 9.40 9.10 8.11 8.89 9.43 7.59 8.72 9.86 9.23 9.50 10.73
## [661] 7.59 7.41 9.26 7.78 7.76 8.94 8.95 6.41 6.11 7.76 7.38 6.21
## [673] 7.05 7.44 8.50 7.84 11.01 7.88 9.10 8.65 8.41 7.81 7.43 8.76
## [685] 7.58 9.55 6.82 10.24 6.24 7.31 10.52 9.27 7.13 9.14 8.48 8.57
## [697] 7.21 9.05 7.72 8.03 6.47 5.57 6.32 7.78 8.58 10.37 9.23 9.20
## [709] 6.93 9.32 7.11 9.79 8.21 8.42 7.05 9.26 8.77 9.25 9.30 10.63
## [721] 9.90 9.89 9.33 7.78 7.02 11.26 8.89 9.60 7.07 6.01 9.11 8.24
## [733] 8.97 8.59 7.17 7.94 7.27 9.59 7.94 8.52 7.59 9.17 8.08 9.80
## [745] 8.92 9.91 9.42 8.84 10.15 8.37 9.33 9.35 7.40 8.35 9.53 9.59
## [757] 10.05 8.57 8.48 8.43 8.45 8.84 11.18 8.64 8.42 6.34 7.93 8.36
## [769] 8.32 7.77 6.84 8.78 7.19 8.50 8.82 9.04 7.93 7.66 10.07 9.03
## [781] 8.13 7.51 9.08 7.10 7.88 9.40 9.06 8.38 10.65 7.77 8.50 8.61
## [793] 10.05 8.71 9.37 6.97 8.56 9.34 9.47 8.11 8.91 7.83 8.95 7.20
## [805] 9.37 5.84 9.81 9.27 9.50 9.32 8.92 8.38 7.74 8.60 9.49 8.35
## [817] 7.11 9.87 8.98 7.75 8.24 6.74 6.83 7.70 6.70 8.67 9.94 8.73
## [829] 9.63 6.66 8.29 8.47 8.16 8.97 7.51 8.97 8.55 5.84 7.85 8.68
## [841] 8.05 8.27 7.68 9.40 7.77 6.89 7.55 8.27 8.16 8.07 7.91 7.71
## [853] 10.16 8.41 8.88 9.64 7.93 7.78 8.90 8.55 9.15 10.86 9.08 7.44
## [865] 10.35 6.68 8.85 8.90 8.24 6.74 10.75 8.44 7.69 8.88 7.70 8.60
## [877] 8.44 9.50 9.03 7.15 7.95 8.23 9.81 8.48 9.33 8.97 8.08 7.47
## [889] 8.34 7.75 8.34 7.56 6.93 10.03 8.69 9.04 8.32 7.85 7.21 8.98
## [901] 7.09 8.85 9.21 8.61 7.91 7.47 8.65 8.53 9.92 8.09 7.06 8.45
## [913] 8.73 7.45 9.02 7.51 7.32 8.17 9.45 9.72 9.34 8.75 9.32 7.91
## [925] 7.49 6.53 6.18 8.69
# Calcular media
media <- mean(brightness_df$brightness)
print(paste("Media:", media))
## [1] "Media: 8.41774327122153"
## diagrama de caja sin valores atipicos
ggplot(data = brightness_df, aes(y = brightness)) +
geom_boxplot(fill = "skyblue", color = "black",outlier.shape = NA) + # Hide outliers
geom_hline(yintercept = media, color = "red", linetype = "dashed")+
labs(
title = "Diagrama de Caja de Brightness",
x = "",
y = "Brightness"
) +
theme_classic(base_size = 15) +
theme(
plot.title = element_text(hjust = 0.5),
axis.title = element_text(face = "bold"),
axis.text.x = element_blank()
)+
annotate("text", x = 1, y = media, label = paste("Media =", round(media,2)), color = "red")
# 2.UScereal
data("UScereal")
str(UScereal)
## 'data.frame': 65 obs. of 11 variables:
## $ mfr : Factor w/ 6 levels "G","K","N","P",..: 3 2 2 1 2 1 6 4 5 1 ...
## $ calories : num 212 212 100 147 110 ...
## $ protein : num 12.12 12.12 8 2.67 2 ...
## $ fat : num 3.03 3.03 0 2.67 0 ...
## $ sodium : num 394 788 280 240 125 ...
## $ fibre : num 30.3 27.3 28 2 1 ...
## $ carbo : num 15.2 21.2 16 14 11 ...
## $ sugars : num 18.2 15.2 0 13.3 14 ...
## $ shelf : int 3 3 3 1 2 3 1 3 2 1 ...
## $ potassium: num 848.5 969.7 660 93.3 30 ...
## $ vitamins : Factor w/ 3 levels "100%","enriched",..: 2 2 2 2 2 2 2 2 2 2 ...
# 2.UScereal
datos_agrupados<- UScereal%>%
group_by(mfr) %>%
summarise(almacenamiento_total= sum(shelf))%>%
mutate(porcentaje = (almacenamiento_total/sum(almacenamiento_total))*100)
ggplot(data=datos_agrupados,aes(x=mfr,y=porcentaje,fill = mfr))+
geom_col()+
geom_text(aes(label=sprintf("%.1f%%", porcentaje)),vjust=1,colour="black")+
scale_x_discrete(limits = c("K","G","P","Q","R","N"))+
theme()+
labs(
title="Distribucion de cereales por fabricante",
x="fabricante",
y="almacenamiento")+
theme_light(base_size=15)+
theme(
plot.title=element_text(hjust=0.5),
axis.title=element_text(face="bold")
)
# 2.UScereal
ggplot(data=UScereal,aes(x=vitamins,y=fat,fill=vitamins))+
geom_violin()+
geom_boxplot(width = 0.1)+
labs(
title="Grasas y vitaminas en cereales",
x="Vitaminas",
y="Grasas")+
theme_minimal(base_size=15)+
theme(
plot.title=element_text(hjust=0.5),
axis.title=element_text(face="bold")
)
ggplot(data=UScereal,aes(x=shelf,y=fat,fill=shelf))+
geom_point(alpha = 0.6)+
geom_jitter(width = 0.2, height = 0.2)+
geom_smooth(method = "lm")+
labs(
title="Cantidad de grasas en cereales",
x="Cantidad en cereales",
y="Grasa")+
theme_minimal(base_size=15)+
theme(
plot.title=element_text(hjust=0.5),
axis.title=element_text(face="bold")
)
## `geom_smooth()` using formula = 'y ~ x'
## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
ggplot(data=UScereal,aes(x=sugars,y=carbo))+
geom_point(alpha = 0.7)+ #trasparencia 70%
geom_smooth(method = "loess")+
labs(
title="Relacion entre azucares y carbohidratos en cereales",
x="Azucar",
y="Carbohidratos")+
theme_minimal(base_size=15)+
theme(
legend.position = "bottom",
plot.title=element_text(hjust=0.5),
axis.title=element_text(face="bold")
)
## `geom_smooth()` using formula = 'y ~ x'
#los cereales con mayor azucar también tienden a tener un mayor carbohidratos
#coeficiente de relacion
correlacion <- cor(UScereal$sugars, UScereal$carbo)
print(correlacion)
## [1] -0.04082599
# el coeficiente de correlacion es negativa, lo que indica que hay una corrrelacion debil entre los azucares y carbohidratos puede ser a otros factores que no se estan teniendo en cuenta
datos_agru<- UScereal%>%
group_by(mfr) %>%
summarise(almacenamiento_total= sum(fibre))%>%
mutate(porcentaje = (almacenamiento_total/sum(almacenamiento_total))*100)
ggplot(data=datos_agru,aes(x=mfr,y=porcentaje,fill=mfr))+
geom_col()+
geom_text(aes(label=sprintf("%.1f%%", porcentaje)),vjust=1,colour="black")+
labs(
title="cantidad de fibra por fabricantes",
x="Fabricantes",
y="Fibra")+
theme_minimal(base_size=15)+
theme(
plot.title=element_text(hjust=0.5),
axis.title=element_text(face="bold")
)
ggplot(data=UScereal,aes(x=sugars,y=sodium))+
geom_point(alpha = 0.7)+ #trasparencia 70%
geom_smooth(method = "loess")+
labs(
title="Relacion entre azucares y Sodio en cereales",
x="Azucar",
y="Sodio")+
theme_minimal(base_size=15)+
theme(
legend.position = "bottom",
plot.title=element_text(hjust=0.5),
axis.title=element_text(face="bold")
)
## `geom_smooth()` using formula = 'y ~ x'
#los cereales con mayor azucar también tienden a tener un mayor carbohidratos
#coeficiente de relacion
correlacion <- cor(UScereal$sugars, UScereal$sodium)
print(correlacion)
## [1] 0.2112437
# 0.2112437 es una correlacion positiva lo cual indica que a medida que aumenta la cantida d de azuicar en un cereal tiende a aumentar el contenido de sodio, a apesar de ser positivo la relacion no es suficientemente fuerte y pueden haber otros factores que influyan
# En general en promedio, los cereales con mayor contenido de azúcar tienden a tener un poco más de sodio
data("mammals")
str(mammals)
## 'data.frame': 62 obs. of 2 variables:
## $ body : num 3.38 0.48 1.35 465 36.33 ...
## $ brain: num 44.5 15.5 8.1 423 119.5 ...
ggplot(data=mammals,aes(x=brain,y=body))+
geom_point(alpha = 0.7)+ #trasparencia 70%
geom_smooth(method = "lm" )+
labs(
title="peso corporal y cerebral en mamiferos",
x="Peso cerebral",
y="Peso corporal")+
theme_minimal(base_size=15)+
theme(
legend.position = "bottom",
plot.title=element_text(hjust=0.5),
axis.title=element_text(face="bold")
)
## `geom_smooth()` using formula = 'y ~ x'
# La línea que se inclina hacia arriba,indica una correlacion positiva entre el peso cerebral y el peso corporal, es decir a medida que aumenta el peso corporal también tiende a aumentar el peso cerebral
# los puntos de dispersion estan cerca de la linea y juntos lo que indica una relacion fuerte
correlacion <- cor(mammals$brain, mammals$body)
print(correlacion)
## [1] 0.9341638
# 0.9341638 S e tiene una correlacion positova y muy alta
# función logarítmica
mammals_log <- mammals %>%
mutate(log_brain = log(brain),
log_body = log(body))
ggplot(data = mammals_log, aes(x = log_brain, y = log_body)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm") +
labs(
title = "peso corporal y cerebral en mamiferos(log)",
x = "Log peso cerebral",
y = "Log peso corporal"
) +
theme_minimal(base_size = 15) +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5),
axis.title = element_text(face = "bold")
)
## `geom_smooth()` using formula = 'y ~ x'
# Gracias a la funcion logaritmica, la relacion de los datos se ven mejor no como en el enaterior que sabiamos que la relacion era grande pero visualmente no era tan claro
data("anorexia")
str(anorexia)
## 'data.frame': 72 obs. of 3 variables:
## $ Treat : Factor w/ 3 levels "CBT","Cont","FT": 2 2 2 2 2 2 2 2 2 2 ...
## $ Prewt : num 80.7 89.4 91.8 74 78.1 88.3 87.3 75.1 80.6 78.4 ...
## $ Postwt: num 80.2 80.1 86.4 86.3 76.1 78.1 75.1 86.7 73.5 84.6 ...
# Calculo de la diferencia de peso y el promedio de esa diferencia por tratamiento usando aggregate
diferencia_peso <- aggregate(diff_peso ~ Treat, data = mutate(anorexia, diff_peso = Postwt - Prewt), FUN = mean)
print(diferencia_peso)
## Treat diff_peso
## 1 CBT 3.006897
## 2 Cont -0.450000
## 3 FT 7.264706
ggplot(diferencia_peso, aes(x = Treat, y = diff_peso,fill=Treat)) +
geom_col() +
geom_text(aes(label=sprintf("%.1fkg", diff_peso)),vjuelst=-0.5,colour="black",position = position_dodge(width = 1))+
scale_x_discrete(limits = c("FT","CBT","Cont"))+
labs(
x = "Tratamiento",
y = "diff peso",
title = "Eficacia de los tratamientos"
)+
theme_minimal(base_size=15)+
theme(
plot.title=element_text(hjust=0.5),
axis.title=element_text(face="bold")
)
## Warning in geom_text(aes(label = sprintf("%.1fkg", diff_peso)), vjuelst = -0.5,
## : Ignoring unknown parameters: `vjuelst`
# el tratamiento FT ha sido el más eficaz en terminos de ganancia de peso, seguido por CBT mientras que Cont muestra una perdida de peso promedio.
# Agregar una nueva variable para el cambio de peso
anorexia <- anorexia %>%
mutate(weight_change = ifelse(Postwt - Prewt > 0, "Gano peso", "Perdio peso"))
# Contar el numero de pacientes que ganaron y perdieron peso
count_weight_change <- table(anorexia$weight_change)
#print(count_weight_change)
# Contar el numero de pacientes que ganaron y perdieron peso por tratamiento
count_weight_change_by_treatment <- table(anorexia$Treat, anorexia$weight_change)
#print(count_weight_change_by_treatment)
count_weight_change_by_treatment_df <- as.data.frame(count_weight_change_by_treatment)
# Verificar la estructura
#str(count_weight_change_by_treatment_df)
ggplot(count_weight_change_by_treatment_df, aes(x = Var1, y = Freq,fill=Var2)) +
geom_bar(stat = "identity", position = "dodge")+
geom_text(aes(label = Freq),
vjust = -0.2,
colour = "black",
hjust = 0.5,
position = position_dodge(width = 0.9))+
labs(
title = "Pacientes que Ganaron y Perdieron Peso",
x = "Tratamiento",
y = "Numero de Pacientes",
fill = "Cambio Peso"
)
theme_minimal(base_size = 10) +
theme(
plot.title = element_text(hjust = 0.5),
axis.title = element_text(face = "bold"),
legend.title = element_text(face = "bold")
)
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