Facultad de Derecho y Ciencia Politica
Escuela de Ciencia Política
Guia de Clase de ESTADISTICA
NOTAS: Recodificacion
- Leer Datos
# comentario: enlace está entre comillas
link="https://docs.google.com/spreadsheets/d/e/2PACX-1vTFp8tPWkUD3qMcuXsqySAHJUZBoIjiFb_pyIJfTiQAK070YNo8G__7wOD_nl_UPYdWnMbW7I5VbRxr/pub?gid=477181888&single=true&output=csv"
# comentario: funcion read.csv le entrega datos al objeto 'sere19':
escolares=read.csv(link, stringsAsFactors = F,na.strings = '')Verificando tipo de datos:
str(escolares)## 'data.frame': 600 obs. of 13 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ SEX : chr "HOMBRE" "MUJER" "HOMBRE" "HOMBRE" ...
## $ RACE : chr "ASIATICO" "ASIATICO" "ASIATICO" "ASIATICO" ...
## $ SES : chr "ALTO" "ALTO" "ALTO" "MEDIO" ...
## $ SCTYP : chr "PUBLICA" "PUBLICA" "PUBLICA" "PUBLICA" ...
## $ LOCUS : num 0.29 -0.42 0.71 0.06 0.22 0.46 0.44 0.68 0.06 0.05 ...
## $ CONCPT: num 0.88 0.03 0.03 0.03 -0.28 0.03 -0.47 0.25 0.56 0.15 ...
## $ MOT : num 0.67 0.33 0.67 0 0 0 0.33 1 0.33 1 ...
## $ RDG : num 33.6 46.9 41.6 38.9 36.3 49.5 62.7 44.2 46.9 44.2 ...
## $ WRTG : num 43.7 35.9 59.3 41.1 48.9 46.3 64.5 51.5 41.1 49.5 ...
## $ MATH : num 40.2 41.9 41.9 32.7 39.5 46.2 48 36.9 45.3 40.5 ...
## $ SCI : num 39 36.3 44.4 41.7 41.7 41.7 63.4 49.8 47.1 39 ...
## $ CIV : num 40.6 45.6 45.6 40.6 45.6 35.6 55.6 55.6 55.6 50.6 ...Si uno piden resumen estadistico aqui, obtiene esto:
summary(escolares)## ID SEX RACE SES
## Min. : 1.0 Length:600 Length:600 Length:600
## 1st Qu.:150.8 Class :character Class :character Class :character
## Median :300.5 Mode :character Mode :character Mode :character
## Mean :300.5
## 3rd Qu.:450.2
## Max. :600.0
## SCTYP LOCUS CONCPT
## Length:600 Min. :-2.23000 Min. :-2.620000
## Class :character 1st Qu.:-0.37250 1st Qu.:-0.300000
## Mode :character Median : 0.21000 Median : 0.030000
## Mean : 0.09653 Mean : 0.004917
## 3rd Qu.: 0.51000 3rd Qu.: 0.440000
## Max. : 1.36000 Max. : 1.190000
## MOT RDG WRTG MATH
## Min. :0.0000 Min. :28.3 Min. :25.50 Min. :31.80
## 1st Qu.:0.3300 1st Qu.:44.2 1st Qu.:44.30 1st Qu.:44.50
## Median :0.6700 Median :52.1 Median :54.10 Median :51.30
## Mean :0.6608 Mean :51.9 Mean :52.38 Mean :51.85
## 3rd Qu.:1.0000 3rd Qu.:60.1 3rd Qu.:59.90 3rd Qu.:58.38
## Max. :1.0000 Max. :76.0 Max. :67.10 Max. :75.50
## SCI CIV
## Min. :26.00 Min. :25.70
## 1st Qu.:44.40 1st Qu.:45.60
## Median :52.60 Median :50.60
## Mean :51.76 Mean :52.05
## 3rd Qu.:58.65 3rd Qu.:60.50
## Max. :74.20 Max. :70.50Las numericas estan bien, pero si tuvieras que transformarlas:
# de la columa 6 a la 13:
# aplicar funcion as.numeric
escolares[,c(6:13)]=lapply(escolares[,c(6:13)], as.numeric)Aqui si cambiamos la nominales:
# de la columa 1 a la 3 y la 5:
# aplicar as.factor
escolares[,c(1:3,5)]=lapply(escolares[,c(1:3,5)], as.factor)Aqui las Ordinales:
Las vemos:
table(escolares$SES)##
## ALTO BAJO MEDIO
## 139 162 299Usemos dplyr (instalalo si no lo tienes)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionescolares$SES= recode(escolares$SES,
'ALTO'='3_alto',
'MEDIO'='2_medio',
'BAJO'='1_bajo')
# poner numero delante, ayuda a crear una ordinal
escolares$SES=as.ordered(escolares$SES)str(escolares)## 'data.frame': 600 obs. of 13 variables:
## $ ID : Factor w/ 600 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ SEX : Factor w/ 2 levels "HOMBRE","MUJER": 1 2 1 1 1 2 2 1 2 1 ...
## $ RACE : Factor w/ 4 levels "ASIATICO","BLANCO",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ SES : Ord.factor w/ 3 levels "1_bajo"<"2_medio"<..: 3 3 3 2 2 2 3 3 2 3 ...
## $ SCTYP : Factor w/ 2 levels "PRIVADA","PUBLICA": 2 2 2 2 2 2 2 2 2 2 ...
## $ LOCUS : num 0.29 -0.42 0.71 0.06 0.22 0.46 0.44 0.68 0.06 0.05 ...
## $ CONCPT: num 0.88 0.03 0.03 0.03 -0.28 0.03 -0.47 0.25 0.56 0.15 ...
## $ MOT : num 0.67 0.33 0.67 0 0 0 0.33 1 0.33 1 ...
## $ RDG : num 33.6 46.9 41.6 38.9 36.3 49.5 62.7 44.2 46.9 44.2 ...
## $ WRTG : num 43.7 35.9 59.3 41.1 48.9 46.3 64.5 51.5 41.1 49.5 ...
## $ MATH : num 40.2 41.9 41.9 32.7 39.5 46.2 48 36.9 45.3 40.5 ...
## $ SCI : num 39 36.3 44.4 41.7 41.7 41.7 63.4 49.8 47.1 39 ...
## $ CIV : num 40.6 45.6 45.6 40.6 45.6 35.6 55.6 55.6 55.6 50.6 ...Compara tambien:
summary(escolares)## ID SEX RACE SES SCTYP
## 1 : 1 HOMBRE:327 ASIATICO: 71 1_bajo :162 PRIVADA: 94
## 2 : 1 MUJER :273 BLANCO : 34 2_medio:299 PUBLICA:506
## 3 : 1 HISPANO :437 3_alto :139
## 4 : 1 NEGRO : 58
## 5 : 1
## 6 : 1
## (Other):594
## LOCUS CONCPT MOT RDG
## Min. :-2.23000 Min. :-2.620000 Min. :0.0000 Min. :28.3
## 1st Qu.:-0.37250 1st Qu.:-0.300000 1st Qu.:0.3300 1st Qu.:44.2
## Median : 0.21000 Median : 0.030000 Median :0.6700 Median :52.1
## Mean : 0.09653 Mean : 0.004917 Mean :0.6608 Mean :51.9
## 3rd Qu.: 0.51000 3rd Qu.: 0.440000 3rd Qu.:1.0000 3rd Qu.:60.1
## Max. : 1.36000 Max. : 1.190000 Max. :1.0000 Max. :76.0
##
## WRTG MATH SCI CIV
## Min. :25.50 Min. :31.80 Min. :26.00 Min. :25.70
## 1st Qu.:44.30 1st Qu.:44.50 1st Qu.:44.40 1st Qu.:45.60
## Median :54.10 Median :51.30 Median :52.60 Median :50.60
## Mean :52.38 Mean :51.85 Mean :51.76 Mean :52.05
## 3rd Qu.:59.90 3rd Qu.:58.38 3rd Qu.:58.65 3rd Qu.:60.50
## Max. :67.10 Max. :75.50 Max. :74.20 Max. :70.50
## De aqui ya puedes hacer estadistica!!
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