Homework 2

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

library(readxl)
library(MASS) 

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

library(stats)
library(psych)
library(dplyr)
library(openxlsx)
library(ggplot2)

## 
## Attaching package: 'ggplot2'

## The following objects are masked from 'package:psych':
## 
##     %+%, alpha

library(tidyr)
library(tibble)
library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:dplyr':
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##     between, first, last

library(visdat) 
library(assertive)

## 
## Attaching package: 'assertive'

## The following object is masked from 'package:tibble':
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##     has_rownames

data <- c(21, 22, 22, 22, 22, 23, 23, 24, 25, 30, 30, 33, 33, 33, 34, 35, 40, 44, 48, 50)

hist(data, main="Histograma", xlab="Número de Errores por Día")

# 2. Media
mean(data)

## [1] 30.7

# 3. Varianza
var(data)

## [1] 82.85263

# 4. Desviación estándar
sd(data)

## [1] 9.102342

# 5. Moda
ifelse(length(unique(data)) == length(data), "No hay moda", as.numeric(names(sort(table(data), decreasing=TRUE)[1])))

## [1] 22

# 6. IQR
IQR(data)

## [1] 11.5

# 7. Calcular el Percentil
quantile(data, 0.88) # 12% de los días con mayor número de errores

##   88% 
## 42.88