Practica 6 Analizando Datos de Salarios

Utilizar la lirberia dplyr para analizar datos de salarios

Cargamos las librerias

library(readr)
library(dplyr)

## 
## Attaching package: 'dplyr'

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

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

Cargamos los datos de Salarios

salarios <- read.csv("C:/Users/Gerencia Banthai/Documents/Ciencia de los datos/Datos/Salaries.csv",
                      encoding = "UTF-8")
# salarios Ya vimos que se cargo y lo mostramos ya no lo queremos ver mas

#Verificamos los registros en Head para ver los primeros registros

head(salarios)

##   Id      EmployeeName                                       JobTitle
## 1  1    NATHANIEL FORD GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY
## 2  2      GARY JIMENEZ                CAPTAIN III (POLICE DEPARTMENT)
## 3  3    ALBERT PARDINI                CAPTAIN III (POLICE DEPARTMENT)
## 4  4 CHRISTOPHER CHONG           WIRE ROPE CABLE MAINTENANCE MECHANIC
## 5  5   PATRICK GARDNER   DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)
## 6  6    DAVID SULLIVAN                      ASSISTANT DEPUTY CHIEF II
##    BasePay OvertimePay OtherPay Benefits TotalPay TotalPayBenefits Year
## 1 167411.2        0.00 400184.2       NA 567595.4         567595.4 2011
## 2 155966.0   245131.88 137811.4       NA 538909.3         538909.3 2011
## 3 212739.1   106088.18  16452.6       NA 335279.9         335279.9 2011
## 4  77916.0    56120.71 198306.9       NA 332343.6         332343.6 2011
## 5 134401.6     9737.00 182234.6       NA 326373.2         326373.2 2011
## 6 118602.0     8601.00 189082.7       NA 316285.7         316285.7 2011
##   Notes        Agency Status
## 1    NA San Francisco       
## 2    NA San Francisco       
## 3    NA San Francisco       
## 4    NA San Francisco       
## 5    NA San Francisco       
## 6    NA San Francisco

Utilizando funcion str Ve los atributos o variables

str(salarios)

## 'data.frame':    148654 obs. of  13 variables:
##  $ Id              : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ EmployeeName    : Factor w/ 110810 levels "A Bernard  Fatooh",..: 77636 34712 1560 17232 81101 23164 3271 22709 73975 47938 ...
##  $ JobTitle        : Factor w/ 2159 levels "Account Clerk",..: 836 298 298 2149 594 135 246 609 246 370 ...
##  $ BasePay         : num  167411 155966 212739 77916 134402 ...
##  $ OvertimePay     : num  0 245132 106088 56121 9737 ...
##  $ OtherPay        : num  400184 137811 16453 198307 182235 ...
##  $ Benefits        : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ TotalPay        : num  567595 538909 335280 332344 326373 ...
##  $ TotalPayBenefits: num  567595 538909 335280 332344 326373 ...
##  $ Year            : int  2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
##  $ Notes           : logi  NA NA NA NA NA NA ...
##  $ Agency          : Factor w/ 1 level "San Francisco": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Status          : Factor w/ 3 levels "","FT","PT": 1 1 1 1 1 1 1 1 1 1 ...

Utilizando funcion summary pára determinar el minimo, el maximo, la media y mediana de la variable de TotalPayBenefits

summary(salarios$TotalPayBenefits)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   -618.1  44065.7  92404.1  93692.6 132876.5 567595.4

Utilizando otras funciones para analisis elemental

maximo <- max(salarios$TotalPayBenefits)
minimo <- min(salarios$TotalPayBenefits)
media <- mean(salarios$TotalPayBenefits)
desvstd <- sd(salarios$TotalPayBenefits)

Mostrar los valores estadisticos elementales

paste("Valor maximo de Ingreso Total",maximo)

## [1] "Valor maximo de Ingreso Total 567595.43"

paste("Valor maximo de Ingreso Total",minimo)

## [1] "Valor maximo de Ingreso Total -618.13"

paste("Valor maximo de Ingreso Total",media)

## [1] "Valor maximo de Ingreso Total 93692.5548105668"

paste("Valor maximo de Ingreso Total",desvstd)

## [1] "Valor maximo de Ingreso Total 62793.5334832377"