Introducción
library(ALDEx2)
## Loading required package: zCompositions
## Loading required package: MASS
## Loading required package: NADA
## Loading required package: survival
##
## Attaching package: 'NADA'
## The following object is masked from 'package:stats':
##
## cor
## Loading required package: truncnorm
#installed.packages()[1:5,] # que paquetes estan instalados
#Verificar si se cargó el paquete
#a<-installed.packages()
#packages<-a[,1]
#is.element("ALDEx2",packages) # está este paquete en el elemento 'packages´
#getwd() #para ver en que directorio estamos (path)
#setwd("/home/betterlab/GIT/Intro_R") #crea un directorio especifico para trabajar,si queremos cambiar de getwd()
#getwd()
data <- read.csv("D:/Users/hayde/Documents/R_sites/Analisis_estadistico_de_datos_de_Microbioma_con_R/data/hsb2demo.csv")
boxplot(write~female,data,main="High School Students Data",xlab="Gender",ylab="writing scoreby gender")
tab1 <- read.table("D:/Users/hayde/Documents/R_sites/Analisis_estadistico_de_datos_de_Microbioma_con_R/data/hsb2demo.csv",header=TRUE,row.names=1,sep="\t")
tab1
## data frame with 0 columns and 200 rows
raw <-"https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_data.csv"
tab2 <- read.table(raw,sep='\t',header=TRUE,row.names=1,check.names=FALSE,stringsAsFactors=FALSE)
tab2
## data frame with 0 columns and 1704 rows
tab3 <- read.delim("D:/Users/hayde/Documents/R_sites/Analisis_estadistico_de_datos_de_Microbioma_con_R/data/hsb2demo.csv", header=T, row.names=1)
tab3
## data frame with 0 columns and 200 rows
tab4 <- read.csv('D:/Users/hayde/Documents/R_sites/Analisis_estadistico_de_datos_de_Microbioma_con_R/data/hsb2demo.csv',head=T,row.names=1,sep=',',dec='.')
tab5 <- read.csv2('D:/Users/hayde/Documents/R_sites/Analisis_estadistico_de_datos_de_Microbioma_con_R/data/hsb2demo.csv',head=T,row.names=1,sep =';',dec=',')
#install.packages("gdata") # este paquete nos ayuda con la lectura de .xls directamente.
library(gdata)
## gdata: Unable to locate valid perl interpreter
## gdata:
## gdata: read.xls() will be unable to read Excel XLS and XLSX files
## gdata: unless the 'perl=' argument is used to specify the location of a
## gdata: valid perl intrpreter.
## gdata:
## gdata: (To avoid display of this message in the future, please ensure
## gdata: perl is installed and available on the executable search path.)
## gdata: Unable to load perl libaries needed by read.xls()
## gdata: to support 'XLX' (Excel 97-2004) files.
##
## gdata: Unable to load perl libaries needed by read.xls()
## gdata: to support 'XLSX' (Excel 2007+) files.
##
## gdata: Run the function 'installXLSXsupport()'
## gdata: to automatically download and install the perl
## gdata: libaries needed to support Excel XLS and XLSX formats.
##
## Attaching package: 'gdata'
## The following object is masked from 'package:stats':
##
## nobs
## The following object is masked from 'package:utils':
##
## object.size
## The following object is masked from 'package:base':
##
## startsWith
#tab6 <- read.xls(“table.xlsx”,sheet=1,header=TRUE)
#tab7 <- read.xls(“table.xlsx”,sheet=1,perl=“C:/Perl64/bin/perl.exe”)
#install.packages ("XLConnect") # este paquete sirve para manipular archivos de excel en windows.
#library (XLConnect)
data()
attach(iris)
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
#crear data frame usando indices de columna
df <- iris[,c(1,2,3)]
head(df)
## Sepal.Length Sepal.Width Petal.Length
## 1 5.1 3.5 1.4
## 2 4.9 3.0 1.4
## 3 4.7 3.2 1.3
## 4 4.6 3.1 1.5
## 5 5.0 3.6 1.4
## 6 5.4 3.9 1.7
# crear data frame usando indices de columna con secuencias
df <- iris[,c(1:2,4:5)]
head(df)
## Sepal.Length Sepal.Width Petal.Width Species
## 1 5.1 3.5 0.2 setosa
## 2 4.9 3.0 0.2 setosa
## 3 4.7 3.2 0.2 setosa
## 4 4.6 3.1 0.2 setosa
## 5 5.0 3.6 0.2 setosa
## 6 5.4 3.9 0.4 setosa
# crear data frame usando subset() e indices de columnas
df<- subset(iris, select=c(1,2, 4:5))
head(df)
## Sepal.Length Sepal.Width Petal.Width Species
## 1 5.1 3.5 0.2 setosa
## 2 4.9 3.0 0.2 setosa
## 3 4.7 3.2 0.2 setosa
## 4 4.6 3.1 0.2 setosa
## 5 5.0 3.6 0.2 setosa
## 6 5.4 3.9 0.4 setosa
# crear data frame usando subset() e nombres de columnas
df <- subset(iris, select=c("Sepal.Width","Petal.Length", "Petal.Width"))
head(df)
## Sepal.Width Petal.Length Petal.Width
## 1 3.5 1.4 0.2
## 2 3.0 1.4 0.2
## 3 3.2 1.3 0.2
## 4 3.1 1.5 0.2
## 5 3.6 1.4 0.2
## 6 3.9 1.7 0.4
# crear data frame por seleccion de nombres de columnas
df <- iris[,c("Sepal.Width","Petal.Length","Petal.Width")]
head(df)
## Sepal.Width Petal.Length Petal.Width
## 1 3.5 1.4 0.2
## 2 3.0 1.4 0.2
## 3 3.2 1.3 0.2
## 4 3.1 1.5 0.2
## 5 3.6 1.4 0.2
## 6 3.9 1.7 0.4
# crear data frame usando dataframe()
df <- data.frame(iris$Sepal.Width,iris$Petal.Length,iris$Petal.Width)
head(df)
## iris.Sepal.Width iris.Petal.Length iris.Petal.Width
## 1 3.5 1.4 0.2
## 2 3.0 1.4 0.2
## 3 3.2 1.3 0.2
## 4 3.1 1.5 0.2
## 5 3.6 1.4 0.2
## 6 3.9 1.7 0.4
# crear data frame usando c() manualmente
Sepal.Width = c(3.5, 3.0, 3.2, 3.1,3.6,3.9)
Petal.Length = c(1.4,1.4,1.3,1.5,1.4,1.7)
Petal.Width = c(0.2,0.2,0.2,0.2,0.2,0.4)
df = data.frame(Sepal.Width,Petal.Length,Petal.Width)
df
## Sepal.Width Petal.Length Petal.Width
## 1 3.5 1.4 0.2
## 2 3.0 1.4 0.2
## 3 3.2 1.3 0.2
## 4 3.1 1.5 0.2
## 5 3.6 1.4 0.2
## 6 3.9 1.7 0.4
head(iris) #nos muestra una pequeña parte de los datos
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
attributes(iris) #imprime los nombres de las filas y columnas,y la clase de los datos
## $names
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150
class(iris)
## [1] "data.frame"
dim(iris)
## [1] 150 5
nrow(iris)
## [1] 150
ncol(iris)
## [1] 5
length(iris[,"Species"])
## [1] 150
colnames(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
rownames(iris)
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
## [13] "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24"
## [25] "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36"
## [37] "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48"
## [49] "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72"
## [73] "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84"
## [85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96"
## [97] "97" "98" "99" "100" "101" "102" "103" "104" "105" "106" "107" "108"
## [109] "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132"
## [133] "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143" "144"
## [145] "145" "146" "147" "148" "149" "150"
#print(iris)
Species <- iris[,"Species"]
Species
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica
iris[1,3] #se puede acceder por posicion
## [1] 1.4
iris["1","Petal.Length"] #o por nombre de fila y columna
## [1] 1.4
tab = read.csv("D:/Users/hayde/Documents/R_sites/Analisis_estadistico_de_datos_de_Microbioma_con_R/data/hsb2demo.csv",row.names=1,check.names=FALSE)
sum(tab == 0) # podemos contar cuantos elementos del archivo son cero
## [1] 91
sum(tab != 0) #y cuantos son diferentes de cero
## [1] 1909
# layout(matrix, widths=w; heights=h)
# diseño(matriz, ancho=w; alto=h)
ng <- layout(matrix(c(1,3,2,3),2,2, byrow=TRUE), widths=c(5,2),height=c(3,4))
layout.show(ng)
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
iris_1 <- (iris[,-5])
head(apply(iris_1, 1, mean))
## [1] 2.550 2.375 2.350 2.350 2.550 2.850
apply(iris_1, 1, mean)
## [1] 2.550 2.375 2.350 2.350 2.550 2.850 2.425 2.525 2.225 2.400 2.700 2.500
## [13] 2.325 2.125 2.800 3.000 2.750 2.575 2.875 2.675 2.675 2.675 2.350 2.650
## [25] 2.575 2.450 2.600 2.600 2.550 2.425 2.425 2.675 2.725 2.825 2.425 2.400
## [37] 2.625 2.500 2.225 2.550 2.525 2.100 2.275 2.675 2.800 2.375 2.675 2.350
## [49] 2.675 2.475 4.075 3.900 4.100 3.275 3.850 3.575 3.975 2.900 3.850 3.300
## [61] 2.875 3.650 3.300 3.775 3.350 3.900 3.650 3.400 3.600 3.275 3.925 3.550
## [73] 3.800 3.700 3.725 3.850 3.950 4.100 3.725 3.200 3.200 3.150 3.400 3.850
## [85] 3.600 3.875 4.000 3.575 3.500 3.325 3.425 3.775 3.400 2.900 3.450 3.525
## [97] 3.525 3.675 2.925 3.475 4.525 3.875 4.525 4.150 4.375 4.825 3.400 4.575
## [109] 4.200 4.850 4.200 4.075 4.350 3.800 4.025 4.300 4.200 5.100 4.875 3.675
## [121] 4.525 3.825 4.800 3.925 4.450 4.550 3.900 3.950 4.225 4.400 4.550 5.025
## [133] 4.250 3.925 3.925 4.775 4.425 4.200 3.900 4.375 4.450 4.350 3.875 4.550
## [145] 4.550 4.300 3.925 4.175 4.325 3.950
apply(iris_1, 2, mean,na.rm = TRUE)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 5.843333 3.057333 3.758000 1.199333
#apply(DataFrame, dimension = Son identificadas con números, 1 son renglones y 2 son colummnas,funcion que
tab_perc <- apply(tab, 2, function(x){x/sum(x)})
tab_perc
## female race ses schtyp prog read
## 70 0.000000000 0.005830904 0.00243309 0.004310345 0.002469136 0.005456634
## 121 0.009174312 0.005830904 0.00486618 0.004310345 0.007407407 0.006509669
## 86 0.000000000 0.005830904 0.00729927 0.004310345 0.002469136 0.004212139
## 141 0.000000000 0.005830904 0.00729927 0.004310345 0.007407407 0.006031017
## 172 0.000000000 0.005830904 0.00486618 0.004310345 0.004938272 0.004499330
## 113 0.000000000 0.005830904 0.00486618 0.004310345 0.004938272 0.004212139
## 50 0.000000000 0.004373178 0.00486618 0.004310345 0.002469136 0.004786521
## 11 0.000000000 0.001457726 0.00486618 0.004310345 0.004938272 0.003254834
## 84 0.000000000 0.005830904 0.00486618 0.004310345 0.002469136 0.006031017
## 48 0.000000000 0.004373178 0.00486618 0.004310345 0.004938272 0.005456634
## 75 0.000000000 0.005830904 0.00486618 0.004310345 0.007407407 0.005743825
## 60 0.000000000 0.005830904 0.00486618 0.004310345 0.004938272 0.005456634
## 95 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.006988321
## 104 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.005169443
## 38 0.000000000 0.004373178 0.00243309 0.004310345 0.004938272 0.004307869
## 115 0.000000000 0.005830904 0.00243309 0.004310345 0.002469136 0.004020678
## 76 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.004499330
## 195 0.000000000 0.005830904 0.00486618 0.008620690 0.002469136 0.005456634
## 114 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.006509669
## 85 0.000000000 0.005830904 0.00486618 0.004310345 0.002469136 0.005265173
## 167 0.000000000 0.005830904 0.00486618 0.004310345 0.002469136 0.006031017
## 143 0.000000000 0.005830904 0.00486618 0.004310345 0.007407407 0.006031017
## 41 0.000000000 0.004373178 0.00486618 0.004310345 0.004938272 0.004786521
## 20 0.000000000 0.001457726 0.00729927 0.004310345 0.004938272 0.005743825
## 12 0.000000000 0.001457726 0.00486618 0.004310345 0.007407407 0.003542026
## 53 0.000000000 0.004373178 0.00486618 0.004310345 0.007407407 0.003254834
## 154 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.006222478
## 178 0.000000000 0.005830904 0.00486618 0.008620690 0.007407407 0.004499330
## 196 0.000000000 0.005830904 0.00729927 0.008620690 0.004938272 0.004212139
## 29 0.000000000 0.002915452 0.00243309 0.004310345 0.002469136 0.004977982
## 126 0.000000000 0.005830904 0.00486618 0.004310345 0.002469136 0.004020678
## 103 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.007275512
## 192 0.000000000 0.005830904 0.00729927 0.008620690 0.004938272 0.006222478
## 150 0.000000000 0.005830904 0.00486618 0.004310345 0.007407407 0.004020678
## 199 0.000000000 0.005830904 0.00729927 0.008620690 0.004938272 0.004977982
## 144 0.000000000 0.005830904 0.00729927 0.004310345 0.002469136 0.005743825
## 200 0.000000000 0.005830904 0.00486618 0.008620690 0.004938272 0.006509669
## 80 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.006222478
## 16 0.000000000 0.001457726 0.00243309 0.004310345 0.007407407 0.004499330
## 153 0.000000000 0.005830904 0.00486618 0.004310345 0.007407407 0.003733487
## 176 0.000000000 0.005830904 0.00486618 0.008620690 0.004938272 0.004499330
## 177 0.000000000 0.005830904 0.00486618 0.008620690 0.004938272 0.005265173
## 168 0.000000000 0.005830904 0.00486618 0.004310345 0.004938272 0.004977982
## 40 0.000000000 0.004373178 0.00243309 0.004310345 0.002469136 0.004020678
## 62 0.000000000 0.005830904 0.00729927 0.004310345 0.002469136 0.006222478
## 169 0.000000000 0.005830904 0.00243309 0.004310345 0.002469136 0.005265173
## 49 0.000000000 0.004373178 0.00729927 0.004310345 0.007407407 0.004786521
## 136 0.000000000 0.005830904 0.00486618 0.004310345 0.004938272 0.006222478
## 189 0.000000000 0.005830904 0.00486618 0.008620690 0.004938272 0.004499330
## 7 0.000000000 0.001457726 0.00486618 0.004310345 0.004938272 0.005456634
## 27 0.000000000 0.002915452 0.00486618 0.004310345 0.004938272 0.005073712
## 128 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.003733487
## 21 0.000000000 0.001457726 0.00486618 0.004310345 0.002469136 0.004212139
## 183 0.000000000 0.005830904 0.00486618 0.008620690 0.004938272 0.006031017
## 132 0.000000000 0.005830904 0.00486618 0.004310345 0.004938272 0.006988321
## 15 0.000000000 0.001457726 0.00729927 0.004310345 0.007407407 0.003733487
## 67 0.000000000 0.005830904 0.00243309 0.004310345 0.007407407 0.003542026
## 22 0.000000000 0.001457726 0.00486618 0.004310345 0.007407407 0.004020678
## 185 0.000000000 0.005830904 0.00486618 0.008620690 0.004938272 0.006031017
## 9 0.000000000 0.001457726 0.00486618 0.004310345 0.007407407 0.004595060
## 181 0.000000000 0.005830904 0.00486618 0.008620690 0.004938272 0.004786521
## 170 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.004499330
## 134 0.000000000 0.005830904 0.00243309 0.004310345 0.002469136 0.004212139
## 108 0.000000000 0.005830904 0.00486618 0.004310345 0.002469136 0.003254834
## 197 0.000000000 0.005830904 0.00729927 0.008620690 0.004938272 0.004786521
## 140 0.000000000 0.005830904 0.00486618 0.004310345 0.007407407 0.004212139
## 171 0.000000000 0.005830904 0.00486618 0.004310345 0.004938272 0.005743825
## 107 0.000000000 0.005830904 0.00243309 0.004310345 0.007407407 0.004499330
## 81 0.000000000 0.005830904 0.00243309 0.004310345 0.004938272 0.006031017
## 18 0.000000000 0.001457726 0.00486618 0.004310345 0.007407407 0.004786521
## 155 0.000000000 0.005830904 0.00486618 0.004310345 0.002469136 0.004212139
## 97 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.005743825
## 68 0.000000000 0.005830904 0.00486618 0.004310345 0.004938272 0.006988321
## 157 0.000000000 0.005830904 0.00486618 0.004310345 0.002469136 0.006509669
## 56 0.000000000 0.005830904 0.00486618 0.004310345 0.007407407 0.005265173
## 5 0.000000000 0.001457726 0.00243309 0.004310345 0.004938272 0.004499330
## 159 0.000000000 0.005830904 0.00729927 0.004310345 0.004938272 0.005265173
## 123 0.000000000 0.005830904 0.00729927 0.004310345 0.002469136 0.006509669
## 164 0.000000000 0.005830904 0.00486618 0.004310345 0.007407407 0.002967643
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## write math science socst
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## 91 0.004642350 0.005318644 0.004532305 0.004388894
## 45 0.003315964 0.003894007 0.002796528 0.002480679
## 116 0.005589768 0.005128692 0.004821601 0.005343002
## 33 0.006158219 0.006838256 0.005207329 0.005343002
## 66 0.005873993 0.005318644 0.004821601 0.004865948
## 72 0.005116059 0.004463862 0.004532305 0.004388894
## 77 0.005589768 0.004653813 0.004243009 0.006297109
## 61 0.005968735 0.005698547 0.006460945 0.006297109
## 190 0.005589768 0.005128692 0.005593057 0.004388894
## 42 0.004926575 0.005223668 0.004243009 0.005343002
## 2 0.003884415 0.003134201 0.004050145 0.003911840
## 55 0.004642350 0.004653813 0.004243009 0.005820055
## 19 0.004358124 0.004083959 0.004243009 0.004865948
## 90 0.005116059 0.004748789 0.004821601 0.004961359
## 142 0.003979157 0.004938741 0.003760849 0.004865948
## 17 0.005400284 0.004558837 0.004243009 0.003911840
## 122 0.005589768 0.005508595 0.005110897 0.006297109
## 191 0.004926575 0.004083959 0.004628737 0.005820055
## 83 0.005873993 0.003894007 0.005303761 0.002957733
## 182 0.004926575 0.004083959 0.004243009 0.004865948
## 6 0.003884415 0.004368886 0.003857281 0.003911840
## 46 0.005210801 0.004178934 0.003278689 0.003911840
## 43 0.003505448 0.004083959 0.004050145 0.004388894
## 96 0.005116059 0.005793523 0.005593057 0.005343002
## 138 0.005400284 0.003799031 0.004821601 0.004865948
## 10 0.005116059 0.004653813 0.005110897 0.005820055
## 71 0.005873993 0.005318644 0.005593057 0.006297109
## 139 0.005589768 0.005793523 0.005303761 0.006774163
## 110 0.005210801 0.004748789 0.005207329 0.005820055
## 148 0.005400284 0.004843765 0.004532305 0.005820055
## 109 0.003694931 0.003988983 0.004050145 0.003911840
## 39 0.006347703 0.006363377 0.005882353 0.006297109
## 147 0.005873993 0.005033716 0.005110897 0.005820055
## 74 0.004737091 0.004748789 0.004918033 0.005533823
## 198 0.005779252 0.004843765 0.006075217 0.002957733
## 161 0.005873993 0.006838256 0.005882353 0.005820055
## 112 0.005589768 0.004558837 0.005303761 0.005820055
## 69 0.004168640 0.003799031 0.003857281 0.002957733
## 156 0.005589768 0.005033716 0.005882353 0.005820055
## 111 0.005116059 0.003704055 0.004532305 0.003434787
## 186 0.005873993 0.005983474 0.005303761 0.003911840
## 98 0.005684510 0.004843765 0.005110897 0.003530198
## 119 0.005400284 0.004273910 0.004821601 0.004102662
## 13 0.004358124 0.003704055 0.004532305 0.005820055
## 51 0.003410706 0.003988983 0.002989392 0.003721019
## 26 0.005589768 0.005888498 0.005882353 0.004865948
## 36 0.004642350 0.004178934 0.003375121 0.004865948
## 135 0.005684510 0.006173426 0.005207329 0.006297109
## 59 0.006347703 0.005983474 0.005303761 0.006774163
## 78 0.005116059 0.005128692 0.005110897 0.003911840
## 64 0.004926575 0.004273910 0.005593057 0.003434787
## 63 0.006158219 0.005698547 0.005400193 0.004865948
## 79 0.005873993 0.004653813 0.004821601 0.004865948
## 193 0.004642350 0.004558837 0.003760849 0.004865948
## 92 0.006347703 0.005413620 0.006075217 0.005820055
## 160 0.006158219 0.005223668 0.004821601 0.005820055
## 32 0.006347703 0.006268402 0.006364513 0.005343002
## 23 0.006158219 0.006078450 0.005593057 0.006774163
## 158 0.005116059 0.005223668 0.005110897 0.004865948
## 25 0.004168640 0.003988983 0.004050145 0.003434787
## 188 0.005873993 0.005318644 0.005303761 0.005820055
## 52 0.004358124 0.005033716 0.005110897 0.006297109
## 124 0.005116059 0.003894007 0.004050145 0.003911840
## 175 0.005400284 0.003988983 0.004821601 0.003911840
## 184 0.004926575 0.005033716 0.005303761 0.005343002
## 30 0.005589768 0.003988983 0.003278689 0.004865948
## 179 0.006158219 0.005698547 0.004821601 0.005343002
## 31 0.005589768 0.004938741 0.004050145 0.005343002
## 145 0.004358124 0.003609080 0.003471553 0.004388894
## 187 0.003884415 0.005413620 0.005303761 0.004961359
## 118 0.005873993 0.005508595 0.005593057 0.005820055
## 137 0.006158219 0.006173426 0.005110897 0.005820055
tab_perc <- apply(tab[,1:ncol(tab)-1], 2, function(x){x/sum(x)})
tab_p1 <- tab[apply(tab_perc, 1, max)>0.01,]
tab_p2 <- tab[apply(tab_perc, 1, min)>0.01,]
head(tab_p2)
## [1] female race ses schtyp prog read write math science
## [10] socst
## <0 rows> (or 0-length row.names)
iris_t <-t(iris) #toma la traspuesta del dataframe
iris_t[1:5,1:6]
## [,1] [,2] [,3] [,4] [,5] [,6]
## Sepal.Length "5.1" "4.9" "4.7" "4.6" "5.0" "5.4"
## Sepal.Width "3.5" "3.0" "3.2" "3.1" "3.6" "3.9"
## Petal.Length "1.4" "1.4" "1.3" "1.5" "1.4" "1.7"
## Petal.Width "0.2" "0.2" "0.2" "0.2" "0.2" "0.4"
## Species "setosa" "setosa" "setosa" "setosa" "setosa" "setosa"
# ordenar el dataframe
iris_2 <- (iris[,-c(3:5)])
sorted <- sort(iris_2$Sepal.Length)
#sort() y order(), ordenan(Sepal.Length) de forma ascendente
ordered <- order(iris_2$Sepal.Length)
new_iris<- data.frame(iris_2,sorted,ordered)
head(new_iris)
## Sepal.Length Sepal.Width sorted ordered
## 1 5.1 3.5 4.3 14
## 2 4.9 3.0 4.4 9
## 3 4.7 3.2 4.4 39
## 4 4.6 3.1 4.4 43
## 5 5.0 3.6 4.5 42
## 6 5.4 3.9 4.6 4
rev_iris <- rev(sort(iris_2$Sepal.Length))
# ordena de forma descendente
head(rev_iris)
## [1] 7.9 7.7 7.7 7.7 7.7 7.6
head(iris[order(Sepal.Length),])
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 14 4.3 3.0 1.1 0.1 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 4 4.6 3.1 1.5 0.2 setosa
head(iris[order(iris[,'Sepal.Length']),])
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 14 4.3 3.0 1.1 0.1 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 4 4.6 3.1 1.5 0.2 setosa
Intro a dplyr
#instaler y cargar el paquete
#install.packages("dplyr")
library("dplyr")
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:gdata':
##
## combine, first, last
## The following object is masked from 'package:MASS':
##
## select
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
tab <- read.csv('D:/Users/hayde/Documents/R_sites/Analisis_estadistico_de_datos_de_Microbioma_con_R/data/hsb2demo.csv')
head(tab)
## id female race ses schtyp prog read write math science socst
## 1 70 0 4 1 1 1 57 52 41 47 57
## 2 121 1 4 2 1 3 68 59 53 63 61
## 3 86 0 4 3 1 1 44 33 54 58 31
## 4 141 0 4 3 1 3 63 44 47 53 56
## 5 172 0 4 2 1 2 47 52 57 53 61
## 6 113 0 4 2 1 2 44 52 51 63 61
tab %>%
select(id,write) %>%
head
## id write
## 1 70 52
## 2 121 59
## 3 86 33
## 4 141 44
## 5 172 52
## 6 113 52
head(select(tab, id, read, write, math))
## id read write math
## 1 70 57 52 41
## 2 121 68 59 53
## 3 86 44 33 54
## 4 141 63 44 47
## 5 172 47 52 57
## 6 113 44 52 51
head(select(tab, read:socst))
## read write math science socst
## 1 57 52 41 47 57
## 2 68 59 53 63 61
## 3 44 33 54 58 31
## 4 63 44 47 53 56
## 5 47 52 57 53 61
## 6 44 52 51 63 61
head(select(tab, - female))
## id race ses schtyp prog read write math science socst
## 1 70 4 1 1 1 57 52 41 47 57
## 2 121 4 2 1 3 68 59 53 63 61
## 3 86 4 3 1 1 44 33 54 58 31
## 4 141 4 3 1 3 63 44 47 53 56
## 5 172 4 2 1 2 47 52 57 53 61
## 6 113 4 2 1 2 44 52 51 63 61
head(select(tab, - (female:prog )))
## id read write math science socst
## 1 70 57 52 41 47 57
## 2 121 68 59 53 63 61
## 3 86 44 33 54 58 31
## 4 141 63 44 47 53 56
## 5 172 47 52 57 53 61
## 6 113 44 52 51 63 61
head(select(tab, starts_with("s")))
## ses schtyp science socst
## 1 1 1 47 57
## 2 2 1 63 61
## 3 3 1 58 31
## 4 3 1 53 56
## 5 2 1 53 61
## 6 2 1 63 61
#filtra las fila de estudiantes con puntaje de lectura mayor o igual a 70.
filter(tab, read >= 70)
## id female race ses schtyp prog read write math science socst
## 1 95 0 4 3 1 2 73 60 71 61 71
## 2 103 0 4 3 1 2 76 52 64 64 61
## 3 132 0 4 2 1 2 73 62 73 69 66
## 4 68 0 4 2 1 2 73 67 71 63 66
## 5 57 1 4 2 1 2 71 65 72 66 56
## 6 180 1 4 3 2 2 71 65 69 58 71
## 7 34 1 1 3 2 2 73 61 57 55 66
## 8 93 1 4 3 1 2 73 67 62 58 66
## 9 61 1 4 3 1 2 76 63 60 67 66
#Filtra las filas de estudiantes con un puntaje de lectura y matematica mayor o igual a 70
filter(tab, read >= 70, math >= 70)
## id female race ses schtyp prog read write math science socst
## 1 95 0 4 3 1 2 73 60 71 61 71
## 2 132 0 4 2 1 2 73 62 73 69 66
## 3 68 0 4 2 1 2 73 67 71 63 66
## 4 57 1 4 2 1 2 71 65 72 66 56
#ordena por read y luego por write
head(arrange(tab, read, write))
## id female race ses schtyp prog read write math science socst
## 1 19 1 1 1 1 1 28 46 43 44 51
## 2 164 0 4 2 1 3 31 36 46 39 46
## 3 108 0 4 2 1 1 34 33 41 36 36
## 4 45 1 3 1 1 3 34 35 41 29 26
## 5 53 0 3 2 1 3 34 37 46 39 31
## 6 1 1 1 1 1 3 34 44 40 39 41
#Usamos desc() para odenar una columna en orden decreciente
head(arrange(tab, desc(read)))
## id female race ses schtyp prog read write math science socst
## 1 103 0 4 3 1 2 76 52 64 64 61
## 2 61 1 4 3 1 2 76 63 60 67 66
## 3 95 0 4 3 1 2 73 60 71 61 71
## 4 132 0 4 2 1 2 73 62 73 69 66
## 5 68 0 4 2 1 2 73 67 71 63 66
## 6 34 1 1 3 2 2 73 61 57 55 66
head(arrange(tab, desc(female),read)) #ejemplo
## id female race ses schtyp prog read write math science socst
## 1 19 1 1 1 1 1 28 46 43 44 51
## 2 1 1 1 1 1 3 34 44 40 39 41
## 3 45 1 3 1 1 3 34 35 41 29 26
## 4 89 1 4 1 1 3 35 35 40 51 33
## 5 106 1 4 2 1 3 36 44 37 42 41
## 6 175 1 4 3 2 1 36 57 42 50 41
tab %>% arrange(female) %>% head
## id female race ses schtyp prog read write math science socst
## 1 70 0 4 1 1 1 57 52 41 47 57
## 2 86 0 4 3 1 1 44 33 54 58 31
## 3 141 0 4 3 1 3 63 44 47 53 56
## 4 172 0 4 2 1 2 47 52 57 53 61
## 5 113 0 4 2 1 2 44 52 51 63 61
## 6 50 0 3 2 1 1 50 59 42 53 61
#Primero selecciona las columnas id, gender, read de tab, luego ordena las filas por gender y luego por
tab%>%
select(id, female, read) %>%
arrange(female, read) %>%
head
## id female read
## 1 164 0 31
## 2 11 0 34
## 3 53 0 34
## 4 108 0 34
## 5 117 0 34
## 6 165 0 36
# Filtramos las filas por 'read' con un puntaje mayor o igual a 70
tab %>% select(id, female, read) %>% arrange(female, read) %>% filter(read >= 70)
## id female read
## 1 95 0 73
## 2 132 0 73
## 3 68 0 73
## 4 103 0 76
## 5 57 1 71
## 6 180 1 71
## 7 34 1 73
## 8 93 1 73
## 9 61 1 76
#realizamos los mismos pasos anteriores, pero con orden descendente
tab %>% select(id, female, read) %>% arrange(female, desc(read)) %>% filter(read >= 70)
## id female read
## 1 103 0 76
## 2 95 0 73
## 3 132 0 73
## 4 68 0 73
## 5 61 1 76
## 6 34 1 73
## 7 93 1 73
## 8 57 1 71
## 9 180 1 71
#Calculamos los puntajes promedio de lectura y escritura
head(mutate(tab, avg_read = sum(read)/n()))
## id female race ses schtyp prog read write math science socst avg_read
## 1 70 0 4 1 1 1 57 52 41 47 57 52.23
## 2 121 1 4 2 1 3 68 59 53 63 61 52.23
## 3 86 0 4 3 1 1 44 33 54 58 31 52.23
## 4 141 0 4 3 1 3 63 44 47 53 56 52.23
## 5 172 0 4 2 1 2 47 52 57 53 61 52.23
## 6 113 0 4 2 1 2 44 52 51 63 61 52.23
tab %>% mutate(avg_read = sum(read/n())) %>% head
## id female race ses schtyp prog read write math science socst avg_read
## 1 70 0 4 1 1 1 57 52 41 47 57 52.23
## 2 121 1 4 2 1 3 68 59 53 63 61 52.23
## 3 86 0 4 3 1 1 44 33 54 58 31 52.23
## 4 141 0 4 3 1 3 63 44 47 53 56 52.23
## 5 172 0 4 2 1 2 47 52 57 53 61 52.23
## 6 113 0 4 2 1 2 44 52 51 63 61 52.23
#contrae un dataframe en una sola fila.
summarise(tab, avg_read = mean(read, na.rm = TRUE))
## avg_read
## 1 52.23
tab %>% summarise(avg_read = mean(read),min_read = min(read),max_read = max(read),n = n())
## avg_read min_read max_read n
## 1 52.23 28 76 200
#primero agruparemos por genero y luego muestra las estadisticas obtenidas (media, minimo y maximo)
by_gender <- group_by(tab, female)
read_by_gender <- summarise(by_gender,
n = n(),
avg_read = mean(read, na.rm = TRUE),
min_read = min(read,na.rm = TRUE),
max_read = max(read,na.rm = TRUE))
read_by_gender
## # A tibble: 2 × 5
## female n avg_read min_read max_read
## <int> <int> <dbl> <int> <int>
## 1 0 91 52.8 31 76
## 2 1 109 51.7 28 76