Para el ejercicio presente usaremos datos de la cantidad de miel que se produce por año en el estado de Sonora, segun datos oficiales de la FAP STAT, obtenidos del atlas de abejas
https://atlasnacionaldelasabejasmx.github.io/atlas/cap5.html
setwd("~/Esta")
library(readr)
sonora <- read_csv("sonora.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## YEAR = col_double(),
## PROMIEL = col_double()
## )
head(sonora)
## # A tibble: 6 x 2
## YEAR PROMIEL
## <dbl> <dbl>
## 1 2003 542
## 2 2004 452
## 3 2005 743
## 4 2006 378
## 5 2007 369
## 6 2008 387
mean(sonora$PROMIEL)
## [1] 467.1891
median(sonora$PROMIEL)
## [1] 452
sort(sonora$PROMIEL)
## [1] 250.000 340.000 369.000 377.000 378.000 387.000 410.000 432.000 452.000
## [10] 516.000 526.000 528.214 540.000 542.000 569.000 583.000 743.000
library(modeest)
mlv(sonora$PROMIEL, method = "mfv")
## [1] 250.000 340.000 369.000 377.000 378.000 387.000 410.000 432.000 452.000
## [10] 516.000 526.000 528.214 540.000 542.000 569.000 583.000 743.000
maximo <- max(sonora$PROMIEL)
maximo
## [1] 743
minimo <- min(sonora$PROMIEL)
minimo
## [1] 250
rango <- (maximo - minimo)
rango
## [1] 493
summary(sonora$PROMIEL)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 250.0 378.0 452.0 467.2 540.0 743.0
boxplot(sonora$PROMIEL)
RIC = IQR(sonora$PROMIEL)
RIC
## [1] 162
Q3 <- 540
limitesuperior <- (Q3+1.5*RIC)
limitesuperior
## [1] 783
Q1 <- 378
limiteinferior <- (Q1-1.5*RIC)
limiteinferior
## [1] 135
library(fdth)
##
## Attaching package: 'fdth'
## The following object is masked from 'package:modeest':
##
## mfv
## The following objects are masked from 'package:stats':
##
## sd, var
dist <- fdt(sonora, breaks="Sturges")
dist
## YEAR
## Class limits f rf rf(%) cf cf(%)
## [1982.97,1992.34) 0 0.00 0.00 0 0.00
## [1992.34,2001.71) 0 0.00 0.00 0 0.00
## [2001.71,2011.08) 9 0.53 52.94 9 52.94
## [2011.08,2020.45) 8 0.47 47.06 17 100.00
## [2020.45,2029.82) 0 0.00 0.00 17 100.00
## [2029.82,2039.19) 0 0.00 0.00 17 100.00
##
## PROMIEL
## Class limits f rf rf(%) cf cf(%)
## [247.5,331.322) 1 0.06 5.88 1 5.88
## [331.322,415.143) 6 0.35 35.29 7 41.18
## [415.143,498.965) 2 0.12 11.76 9 52.94
## [498.965,582.787) 6 0.35 35.29 15 88.24
## [582.787,666.608) 1 0.06 5.88 16 94.12
## [666.608,750.43) 1 0.06 5.88 17 100.00
#Absoulutos
plot(dist, type = "fh")
plot(dist, type = "fp")
#Acumulados
plot(dist, type = "cfh")
plot(dist, type = "cfp")
# Relativos
plot(dist, type = "rfh")
plot(dist, type = "rfp")