PLOT
astrid Muñoz
2024-08-01
#VARIABLES CUALITATIVAS
VARIABLES CATEGORICAS Y SE DIVIDEN EN DOS: ORDINALES (tienen orden) Y NOMINALES (no tienen orden)
##variables cuantitativas
expresar numericamente y se dividen en dos: discretas y continuas
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, unionlibrary(dslabs)
library(viridisLite)#dataset
data("muders")## Warning in data("muders"): data set 'muders' not found#Graficos
#Diagramas de barras
levels(murders$region)## [1] "Northeast" "South" "North Central" "West"tabla <- table(murders$region)
tabla##
## Northeast South North Central West
## 9 17 12 13miGraficoBarras <-barplot(tabla, main="Grafico de barras",
ylab = "Frecuencia",
xlab = "Region",
ylim = c(0,20),
col = "red"
)
#Usamos la funcion text para agregar las etiquetas de datos de cada barra
text(x = miGraficoBarras, y = tabla, labels = tabla, pos = 3, cex = 1.0, col = "black")
#Calculamos los porcentajes
porcentajes <- round(tabla/sum(tabla)*100,2)
#Creamos las etiquetas con el porcentaje
etiquetas <- paste(porcentajes, "%")
#Creo mi grafico
pie(tabla,
labels = etiquetas,
col = rainbow(4),
main="Diagrama de torta"
)
legend("topleft",
legend = names(tabla),fill = rainbow(4))
#Histograma
#Creamos nuestro dataset con la base de datos murders agregando el campo rate (tasa de asesinatos)
dsMurders <- murders %>%
mutate(murders,rate = total/population*100000)
#Verifico mi dataset para ver si se agrego la columna rate
dsMurders## state abb region population total rate
## 1 Alabama AL South 4779736 135 2.8244238
## 2 Alaska AK West 710231 19 2.6751860
## 3 Arizona AZ West 6392017 232 3.6295273
## 4 Arkansas AR South 2915918 93 3.1893901
## 5 California CA West 37253956 1257 3.3741383
## 6 Colorado CO West 5029196 65 1.2924531
## 7 Connecticut CT Northeast 3574097 97 2.7139722
## 8 Delaware DE South 897934 38 4.2319369
## 9 District of Columbia DC South 601723 99 16.4527532
## 10 Florida FL South 19687653 669 3.3980688
## 11 Georgia GA South 9920000 376 3.7903226
## 12 Hawaii HI West 1360301 7 0.5145920
## 13 Idaho ID West 1567582 12 0.7655102
## 14 Illinois IL North Central 12830632 364 2.8369608
## 15 Indiana IN North Central 6483802 142 2.1900730
## 16 Iowa IA North Central 3046355 21 0.6893484
## 17 Kansas KS North Central 2853118 63 2.2081106
## 18 Kentucky KY South 4339367 116 2.6732010
## 19 Louisiana LA South 4533372 351 7.7425810
## 20 Maine ME Northeast 1328361 11 0.8280881
## 21 Maryland MD South 5773552 293 5.0748655
## 22 Massachusetts MA Northeast 6547629 118 1.8021791
## 23 Michigan MI North Central 9883640 413 4.1786225
## 24 Minnesota MN North Central 5303925 53 0.9992600
## 25 Mississippi MS South 2967297 120 4.0440846
## 26 Missouri MO North Central 5988927 321 5.3598917
## 27 Montana MT West 989415 12 1.2128379
## 28 Nebraska NE North Central 1826341 32 1.7521372
## 29 Nevada NV West 2700551 84 3.1104763
## 30 New Hampshire NH Northeast 1316470 5 0.3798036
## 31 New Jersey NJ Northeast 8791894 246 2.7980319
## 32 New Mexico NM West 2059179 67 3.2537239
## 33 New York NY Northeast 19378102 517 2.6679599
## 34 North Carolina NC South 9535483 286 2.9993237
## 35 North Dakota ND North Central 672591 4 0.5947151
## 36 Ohio OH North Central 11536504 310 2.6871225
## 37 Oklahoma OK South 3751351 111 2.9589340
## 38 Oregon OR West 3831074 36 0.9396843
## 39 Pennsylvania PA Northeast 12702379 457 3.5977513
## 40 Rhode Island RI Northeast 1052567 16 1.5200933
## 41 South Carolina SC South 4625364 207 4.4753235
## 42 South Dakota SD North Central 814180 8 0.9825837
## 43 Tennessee TN South 6346105 219 3.4509357
## 44 Texas TX South 25145561 805 3.2013603
## 45 Utah UT West 2763885 22 0.7959810
## 46 Vermont VT Northeast 625741 2 0.3196211
## 47 Virginia VA South 8001024 250 3.1246001
## 48 Washington WA West 6724540 93 1.3829942
## 49 West Virginia WV South 1852994 27 1.4571013
## 50 Wisconsin WI North Central 5686986 97 1.7056487
## 51 Wyoming WY West 563626 5 0.8871131#Creamos el grafico de tipo histograma usando la funcion hist y definimos cada una de sus caracteristicas
miHistograma <- hist(dsMurders$rate,
main="Histograma de asesinatos por arma de fuego",
labels = TRUE,
xlab = "Tasa",
ylim = c(0,30),
xlim = c(0,20),
col = "green"
)
#Usamos la funcion axis para modificar el tamaño de los intervalos que se muestran en el eje x
axis(1,at=seq(0,20,by=1))
#Boxplot
boxplot(dsMurders$rate, col="Gray",ylab="Tasa de asesinatos",outline=FALSE,main="Boxplot",ylim=c(0,6))
#Adicionar la media
points(mean(dsMurders$rate),col="black",pch=20)
text(paste(" ", round(min(dsMurders$rate), 2)," min"),x=1.3,y=0.3)
text(paste(" ", round(quantile(dsMurders$rate,0.25),2)," Q1"),x=1.3,y=1.3)
text(paste(" ", round(median(dsMurders$rate), 2)," mediana"),x=1.3,y=2.3)
text(paste(" ", round(mean(dsMurders$rate), 2)," media"),x=1.3,y=2.6)
text(paste(" ", round(quantile(dsMurders$rate,0.75),2)," Q2"),x=1.3,y=3.3)
text(paste(" ", round(max(dsMurders$rate), 2)," max"),x=1.3,y=5.3)
summary(dsMurders$rate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3196 1.2526 2.6871 2.7791 3.3861 16.4528#haga el grafico
#Use la paleta de colores