"Tarea individual
Alejandra Arellanes"
## [1] "Tarea individual\nAlejandra Arellanes"
library(readxl)
## Warning: package 'readxl' was built under R version 3.6.1
Huatabampo <- read_excel("Huatabampo.xlsx")
View(Huatabampo)
ph = (Huatabampo$PH)
salinidad = (Huatabampo$PPM)
profundidad =(Huatabampo$SNM)
pairs(salinidad~ph) #Grafico de correlacion
library(PerformanceAnalytics) #Activar libreria
## Warning: package 'PerformanceAnalytics' was built under R version 3.6.1
## Loading required package: xts
## Warning: package 'xts' was built under R version 3.6.1
## Loading required package: zoo
## Warning: package 'zoo' was built under R version 3.6.1
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Registered S3 method overwritten by 'xts':
## method from
## as.zoo.xts zoo
##
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
##
## legend
#Integrar los vectores en un marco de datos
dat1 = data.frame(salinidad,ph)
chart.Correlation(dat1)
# Grafica que correlaciona dos variables
#Indice de correlacion
cor(salinidad,ph)
## [1] -0.544348
#Prueba de significancia
#t de student
cor.test(salinidad,ph)
##
## Pearson's product-moment correlation
##
## data: salinidad and ph
## t = -11.07, df = 291, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6202473 -0.4583503
## sample estimates:
## cor
## -0.544348
#Representa los datos catagorizados por modulo en graficos
#Anaslisis exploratorios de datos
#require(Huatabampo)
nrow(Huatabampo)
## [1] 293
ncol(Huatabampo)
## [1] 10
dim(Huatabampo)
## [1] 293 10
colnames(Huatabampo)
## [1] "MODULO" "POZO" "X" "Y" "SNM" "DELNF" "CE"
## [8] "PPM" "PH" "TEMP"
table(Huatabampo$SNM)
##
## 0.9855 1.012 1.162 1.254 1.489 1.766 1.8115 1.842 2.023 2.113
## 1 1 1 1 1 1 1 1 1 1
## 2.1845 2.419 2.472 2.523 2.611 2.796 2.817 2.8215 2.863 3.009
## 1 1 1 1 1 1 1 1 1 1
## 3.059 3.088 3.297 3.316 3.383 3.43 3.454 3.473 3.49 3.525
## 1 1 1 1 1 1 1 1 1 1
## 3.631 3.64 3.641 3.755 3.806 3.864 3.867 3.911 3.9245 4.004
## 1 1 1 1 1 1 1 1 1 1
## 4.052 4.072 4.137 4.164 4.167 4.169 4.181 4.186 4.201 4.207
## 1 1 1 1 1 1 1 1 1 1
## 4.228 4.2635 4.279 4.319 4.3775 4.38 4.394 4.395 4.417 4.462
## 1 1 1 1 1 1 1 1 1 1
## 4.495 4.53 4.5435 4.562 4.582 4.609 4.705 4.721 4.733 4.808
## 1 1 1 1 1 1 1 1 1 1
## 4.8125 4.842 4.85 4.861 4.909 4.9245 4.958 4.988 5.011 5.014
## 1 1 1 1 1 1 1 1 1 1
## 5.032 5.045 5.05 5.055 5.059 5.087 5.123 5.151 5.157 5.178
## 1 1 1 2 1 1 1 1 1 1
## 5.183 5.186 5.245 5.285 5.302 5.35 5.366 5.394 5.405 5.43
## 1 1 1 1 1 1 1 2 1 1
## 5.448 5.505 5.531 5.537 5.557 5.565 5.583 5.614 5.651 5.659
## 1 1 1 1 1 1 1 1 1 1
## 5.674 5.702 5.753 5.792 5.855 5.901 5.921 5.936 5.974 5.981
## 1 1 1 2 1 1 1 1 1 1
## 6.024 6.051 6.0705 6.072 6.085 6.09 6.135 6.153 6.174 6.189
## 1 1 1 1 1 1 1 1 1 1
## 6.203 6.213 6.234 6.292 6.314 6.315 6.331 6.339 6.4008 6.401
## 1 1 1 1 1 1 1 1 1 2
## 6.427 6.476 6.507 6.544 6.578 6.62 6.648 6.658 6.671 6.691
## 1 1 1 1 1 1 1 1 1 1
## 6.695 6.734 6.785 6.799 6.881 6.882 6.897 6.941 6.974 7.056
## 1 1 2 1 1 1 1 1 1 1
## 7.142 7.152 7.179 7.225 7.315 7.395 7.4 7.405 7.454 7.508
## 1 1 1 1 1 1 1 1 1 1
## 7.56 7.575 7.617 7.637 7.696 7.71 7.712 7.805 7.852 7.857
## 1 1 1 1 1 1 1 1 1 1
## 7.873 7.876 7.889 7.914 7.936 7.956 8.0001 8.094 8.103 8.127
## 1 1 1 1 1 1 1 1 1 1
## 8.183 8.218 8.264 8.268 8.27 8.316 8.371 8.403 8.406 8.423
## 1 1 1 1 1 1 1 1 1 1
## 8.452 8.464 8.482 8.499 8.512 8.534 8.554 8.583 8.615 8.632
## 1 1 1 1 1 1 1 1 1 1
## 8.64 8.648 8.667 8.696 8.697 8.705 8.706 8.708 8.754 8.788
## 2 1 1 1 1 1 1 1 1 1
## 8.868 8.919 8.925 8.932 8.945 8.962 8.986 8.988 9.02 9.067
## 1 1 1 1 1 1 1 1 1 1
## 9.1 9.123 9.128 9.131 9.21 9.213 9.274 9.317 9.355 9.402
## 1 1 1 1 1 1 1 1 1 1
## 9.473 9.475 9.522 9.592 9.635 9.65 9.689 9.758 9.761 9.83
## 1 1 1 1 1 1 1 1 1 1
## 9.891 9.934 9.945 9.993 10.005 10.068 10.081 10.124 10.234 10.251
## 1 1 1 1 2 1 1 1 1 1
## 10.257 10.267 10.268 10.302 10.351 10.403 10.428 10.446 10.5 10.529
## 1 1 1 1 1 1 1 1 1 1
## 10.609 10.714 10.791 10.939 10.991 11.028 11.096 11.198 11.269 11.327
## 1 1 1 1 1 1 1 1 1 1
## 11.553 11.592 11.778 11.792 11.92 12.144
## 1 1 1 1 1 1
table(Huatabampo$`DEL N.F.`)
## Warning: Unknown or uninitialised column: 'DEL N.F.'.
## < table of extent 0 >
table(Huatabampo$CE)
##
## 0.49 0.61 0.68 0.69 0.7 0.76 0.78 0.79 0.81 0.82 0.89 0.9
## 1 1 1 1 1 1 1 2 2 2 2 1
## 0.91 0.92 0.95 0.96 0.97 1.04 1.05 1.06 1.08 1.09 1.1 1.12
## 1 1 1 1 1 1 1 2 1 2 1 1
## 1.14 1.15 1.18 1.2 1.21 1.22 1.23 1.26 1.29 1.3 1.31 1.32
## 1 3 2 1 1 1 1 2 1 1 1 1
## 1.34 1.36 1.38 1.39 1.4 1.41 1.42 1.43 1.44 1.45 1.48 1.52
## 1 1 1 1 1 1 1 1 1 2 1 3
## 1.53 1.54 1.55 1.56 1.58 1.59 1.6 1.63 1.64 1.65 1.66 1.68
## 1 3 1 1 1 2 1 1 1 2 2 1
## 1.7 1.71 1.72 1.74 1.76 1.78 1.8 1.82 1.84 1.85 1.87 1.88
## 1 2 1 1 1 2 1 3 1 1 2 2
## 1.91 1.92 1.93 1.99 2.02 2.04 2.05 2.06 2.08 2.11 2.13 2.14
## 1 1 1 1 1 1 2 1 1 2 1 2
## 2.16 2.17 2.19 2.21 2.28 2.32 2.4 2.43 2.47 2.5 2.54 2.66
## 2 1 1 1 1 1 2 1 1 2 1 1
## 2.68 2.72 2.74 2.77 2.8 2.83 2.85 2.86 3.01 3.05 3.09 3.1
## 2 1 1 1 2 1 1 1 1 1 1 1
## 3.12 3.15 3.19 3.23 3.25 3.29 3.39 3.47 3.49 3.5 3.56 3.59
## 1 1 1 1 1 1 1 1 1 1 2 1
## 3.6 3.61 3.69 3.72 3.87 3.9 3.96 3.98 4.05 4.14 4.17 4.27
## 1 1 1 1 1 1 2 1 2 1 1 1
## 4.28 4.3 4.32 4.33 4.4 4.44 4.48 4.49 4.73 4.78 4.8 4.83
## 1 1 1 1 1 1 2 1 1 1 1 1
## 4.85 4.87 4.92 4.95 4.98 4.99 5.3 5.44 5.55 5.56 5.92 6.02
## 1 1 1 1 1 1 1 1 1 1 1 1
## 6.05 6.1 6.13 6.15 6.19 6.21 6.22 6.23 6.3 6.48 6.57 6.62
## 1 1 1 1 1 1 1 1 1 1 1 1
## 6.66 6.73 6.8 6.82 6.87 7.03 7.04 7.08 7.12 7.17 7.26 7.27
## 2 1 1 1 1 2 1 1 1 1 1 1
## 7.35 7.44 7.58 7.62 7.65 7.68 7.71 7.82 7.88 7.9 7.92 7.98
## 1 1 1 1 1 1 1 1 1 2 1 1
## 8.07 8.08 8.09 8.12 8.19 8.21 8.28 8.29 8.31 8.33 8.34 8.35
## 1 1 1 1 1 1 2 1 1 1 1 1
## 8.42 8.43 8.46 8.48 8.49 8.54 8.61 8.63 8.65 8.66 8.76 8.79
## 1 1 1 1 1 1 1 1 1 1 1 1
## 8.8 8.88 8.89 8.9 8.97 8.99 9.02 9.04 9.06 9.1 9.13 9.17
## 1 1 1 1 1 1 1 1 1 1 1 2
## 9.18 9.22 9.28 9.3 9.47 9.52 9.55 9.58 9.64 9.66 9.67 9.7
## 1 1 1 1 1 1 1 1 1 1 1 1
## 9.73 9.76 9.8 9.83 9.84 9.89 9.95 9.97 10.02 10.05 10.1 10.16
## 1 1 1 1 1 1 1 1 1 1 1 1
table(Huatabampo$PPM)
##
## 313.6 390.4 435.2 441.6 448 486.4 499.2 505.6 518.4 524.8
## 1 1 1 1 1 1 1 2 2 2
## 569.6 576 582.4 588.8 608 614.4 620.8 665.6 672 678.4
## 2 1 1 1 1 1 1 1 1 2
## 691.2 697.6 704 716.8 729.6 736 755.2 768 774.4 780.8
## 1 2 1 1 1 3 2 1 1 1
## 787.2 806.4 825.6 832 838.4 844.8 857.6 870.4 883.2 889.6
## 1 2 1 1 1 1 1 1 1 1
## 896 902.4 908.8 915.2 921.6 928 947.2 972.8 979.2 985.6
## 1 1 1 1 1 2 1 3 1 3
## 992 998.4 1011.2 1017.6 1024 1043.2 1049.6 1056 1062.4 1075.2
## 1 1 1 2 1 1 1 2 2 1
## 1088 1094.4 1100.8 1113.6 1126.4 1139.2 1152 1164.8 1177.6 1184
## 1 2 1 1 1 2 1 3 1 1
## 1196.8 1203.2 1222.4 1228.8 1235.2 1273.6 1292.8 1305.6 1312 1318.4
## 2 2 1 1 1 1 1 1 2 1
## 1331.2 1350.4 1363.2 1369.6 1382.4 1388.8 1401.6 1414.4 1459.2 1484.8
## 1 2 1 2 2 1 1 1 1 1
## 1536 1555.2 1580.8 1600 1625.6 1702.4 1715.2 1740.8 1753.6 1772.8
## 2 1 1 2 1 1 2 1 1 1
## 1792 1811.2 1824 1830.4 1926.4 1952 1977.6 1984 1996.8 2016
## 2 1 1 1 1 1 1 1 1 1
## 2041.6 2067.2 2080 2105.6 2169.6 2220.8 2233.6 2240 2278.4 2297.6
## 1 1 1 1 1 1 1 1 2 1
## 2304 2310.4 2361.6 2380.8 2476.8 2496 2534.4 2547.2 2592 2649.6
## 1 1 1 1 1 1 2 1 2 1
## 2668.8 2732.8 2739.2 2752 2764.8 2771.2 2816 2841.6 2867.2 2873.6
## 1 1 1 1 1 1 1 1 2 1
## 3027.2 3059.2 3072 3091.2 3104 3116.8 3148.8 3168 3187.2 3193.6
## 1 1 1 1 1 1 1 1 1 1
## 3392 3481.6 3552 3558.4 3788.8 3852.8 3872 3904 3923.2 3936
## 1 1 1 1 1 1 1 1 1 1
## 3961.6 3974.4 3980.8 3987.2 4032 4147.2 4204.8 4236.8 4262.4 4307.2
## 1 1 1 1 1 1 1 1 2 1
## 4352 4364.8 4396.8 4499.2 4505.6 4531.2 4556.8 4588.8 4646.4 4652.8
## 1 1 1 2 1 1 1 1 1 1
## 4704 4761.6 4851.2 4876.8 4896 4915.2 4934.4 5004.8 5043.2 5056
## 1 1 1 1 1 1 1 1 1 2
## 5068.8 5107.2 5164.8 5171.2 5177.6 5196.8 5241.6 5254.4 5299.2 5305.6
## 1 1 1 1 1 1 1 1 2 1
## 5318.4 5331.2 5337.6 5344 5388.8 5395.2 5414.4 5427.2 5433.6 5465.6
## 1 1 1 1 1 1 1 1 1 1
## 5510.4 5523.2 5536 5542.4 5606.4 5625.6 5632 5683.2 5689.6 5696
## 1 1 1 1 1 1 1 1 1 1
## 5740.8 5753.6 5772.8 5785.6 5798.4 5824 5843.2 5868.8 5875.2 5900.8
## 1 1 1 1 1 1 1 2 1 1
## 5939.2 5952 6060.8 6092.8 6112 6131.2 6169.6 6182.4 6188.8 6208
## 1 1 1 1 1 1 1 1 1 1
## 6227.2 6246.4 6272 6291.2 6297.6 6329.6 6368 6380.8 6412.8 6432
## 1 1 1 1 1 1 1 1 1 1
## 6464 6502.4
## 1 1
table(Huatabampo$PH)
##
## 6.1 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.5
## 1 1 7 17 23 9 58 44 78 30 12 6 6 1
table(Huatabampo$TEMP)
##
## 25.6 25.8 26.2 26.3 26.4 26.8 26.9 27 27.1 27.2 27.3 27.4 27.5 27.6 27.7
## 1 1 1 2 2 2 1 2 1 2 4 5 12 1 4
## 27.8 27.9 28 28.1 28.2 28.3 28.4 28.5 28.6 28.7 28.8 28.9 29 29.1 29.2
## 11 14 18 3 12 7 7 9 19 13 12 18 14 11 14
## 29.3 29.4 29.5 29.6 29.7 29.8 29.9 30 30.1 30.2 30.3 30.4 30.5 30.6 30.8
## 4 11 9 3 2 6 4 6 4 3 4 1 1 1 1
## 30.9 31.1 31.2 31.4 31.5 31.7 31.9 32.1
## 1 3 1 1 1 1 1 1
#Revisar si los datos son iguales
boxplot(Huatabampo$PPM ~ Huatabampo$PH, col = "light pink")
#Normalidad
shapiro.test(Huatabampo$PPM)
##
## Shapiro-Wilk normality test
##
## data: Huatabampo$PPM
## W = 0.87725, p-value = 1.439e-14
#Marco Muestral
Huatabampo <-(Huatabampo)
dim(Huatabampo)
## [1] 293 10
#como existen los datos
head(Huatabampo)
## # A tibble: 6 x 10
## MODULO POZO X Y SNM DELNF CE PPM PH TEMP
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 620903 2962392 3.91 2.68 2.83 1811. 6.8 28.5
## 2 1 2 620915 2963671 4.53 2.61 8.35 5344 6.9 29.2
## 3 1 3 620943 2964903 2.80 1.3 8.66 5542. 6.8 28.9
## 4 1 4 620879 2965667 3.64 2.14 8.34 5338. 7.1 29.4
## 5 1 5 620888 2966604 3.49 2.01 9.18 5875. 6.6 28.3
## 6 1 6 621213 2968060 4.81 2 7.9 5056 6.8 28.4
#MUESTREO ALEATORIO SIMPLE SIN ESTRATIFICAR
#con remplazamiento (los valores SI se repiten)
set.seed(20170704)
sample(Huatabampo$POZO,size=30, replace= TRUE)
## [1] "140" "146" "183" "216" "11" "247" "160" "32" "242" "56" "80"
## [12] "201" "186" "222" "207" "62" "56" "92" "168" "250" "19" "216"
## [23] "59" "41" "23" "124" "76" "23" "75" "98"
set.seed(20170704)
sample(Huatabampo$POZO,size=30, replace= FALSE)
## [1] "140" "146" "183" "216" "11" "247" "160" "32" "242" "56" "80"
## [12] "201" "186" "222" "207" "62" "56" "92" "250" "19" "174" "59"
## [23] "41" "23" "124" "76" "258" "75" "98" "265"
#MUESTREO POR CONGLOMERADOS
set.seed <-(2017071)
x <- 1:7 ; x
## [1] 1 2 3 4 5 6 7
sample (x, size=5,
replace= TRUE )
## [1] 4 1 3 4 3
crime <- data.frame(crimtab)
dim(crime)
## [1] 924 3
head(crime)
## Var1 Var2 Freq
## 1 9.4 142.24 0
## 2 9.5 142.24 0
## 3 9.6 142.24 0
## 4 9.7 142.24 0
## 5 9.8 142.24 0
## 6 9.9 142.24 0
n <- 35
muestramia <- sample(1:nrow(crime), size=n, replace=FALSE)
muestramia
## [1] 647 69 569 613 298 923 165 874 42 368 763 555 99 23 363 663 617
## [18] 851 16 485 435 859 348 247 853 102 295 430 814 10 680 721 433 213
## [35] 873
#HACER UN DATA FRAME
crimemuestramia <- crime[muestramia,]
head(crimemuestramia)
## Var1 Var2 Freq
## 647 11 180.34 0
## 69 12 144.78 0
## 569 11.6 175.26 12
## 613 11.8 177.8 5
## 298 9.7 160.02 0
## 923 13.4 195.58 0
#Activar bibloteca
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.6.1
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:xts':
##
## first, last
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
#MUESTRA SIN REEMPLAZO
crimemuestramia2 <- crime %>%
sample_n(size=n,replace=FALSE)
head(crimemuestramia2)
## Var1 Var2 Freq
## 1 10.5 177.8 0
## 2 11.6 160.02 13
## 3 12.8 144.78 0
## 4 11.7 142.24 0
## 5 9.6 147.32 0
## 6 11.3 154.94 5
#MUESTREO CON PESOS
crimemuestramia3 <- crime %>%
sample_n(size=n,weight=Freq)
head(crimemuestramia3)
## Var1 Var2 Freq
## 1 11.1 149.86 3
## 2 11.6 172.72 27
## 3 11 172.72 6
## 4 11.8 170.18 34
## 5 11.6 175.26 12
## 6 12.1 170.18 28