Parcial2
Argeli Dirley Sanchez Cruz
2025-04-11
Base de datos
# Cargar base de datos desde una URL
url <- "https://raw.githubusercontent.com/ankita1112/House-Prices-Advanced-Regression/master/train.csv"
datos <- read.csv(url)1 Describir la data:
Cantidad de variables, cantidad de observaciones usando dplyr. Limpiar la base de datos en caso de que sea requerido para la descripción
library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.3##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionglimpse(datos)## Rows: 1,460
## Columns: 81
## $ Id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
## $ MSSubClass <int> 60, 20, 60, 70, 60, 50, 20, 60, 50, 190, 20, 60, 20, 20,…
## $ MSZoning <chr> "RL", "RL", "RL", "RL", "RL", "RL", "RL", "RL", "RM", "R…
## $ LotFrontage <int> 65, 80, 68, 60, 84, 85, 75, NA, 51, 50, 70, 85, NA, 91, …
## $ LotArea <int> 8450, 9600, 11250, 9550, 14260, 14115, 10084, 10382, 612…
## $ Street <chr> "Pave", "Pave", "Pave", "Pave", "Pave", "Pave", "Pave", …
## $ Alley <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ LotShape <chr> "Reg", "Reg", "IR1", "IR1", "IR1", "IR1", "Reg", "IR1", …
## $ LandContour <chr> "Lvl", "Lvl", "Lvl", "Lvl", "Lvl", "Lvl", "Lvl", "Lvl", …
## $ Utilities <chr> "AllPub", "AllPub", "AllPub", "AllPub", "AllPub", "AllPu…
## $ LotConfig <chr> "Inside", "FR2", "Inside", "Corner", "FR2", "Inside", "I…
## $ LandSlope <chr> "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", …
## $ Neighborhood <chr> "CollgCr", "Veenker", "CollgCr", "Crawfor", "NoRidge", "…
## $ Condition1 <chr> "Norm", "Feedr", "Norm", "Norm", "Norm", "Norm", "Norm",…
## $ Condition2 <chr> "Norm", "Norm", "Norm", "Norm", "Norm", "Norm", "Norm", …
## $ BldgType <chr> "1Fam", "1Fam", "1Fam", "1Fam", "1Fam", "1Fam", "1Fam", …
## $ HouseStyle <chr> "2Story", "1Story", "2Story", "2Story", "2Story", "1.5Fi…
## $ OverallQual <int> 7, 6, 7, 7, 8, 5, 8, 7, 7, 5, 5, 9, 5, 7, 6, 7, 6, 4, 5,…
## $ OverallCond <int> 5, 8, 5, 5, 5, 5, 5, 6, 5, 6, 5, 5, 6, 5, 5, 8, 7, 5, 5,…
## $ YearBuilt <int> 2003, 1976, 2001, 1915, 2000, 1993, 2004, 1973, 1931, 19…
## $ YearRemodAdd <int> 2003, 1976, 2002, 1970, 2000, 1995, 2005, 1973, 1950, 19…
## $ RoofStyle <chr> "Gable", "Gable", "Gable", "Gable", "Gable", "Gable", "G…
## $ RoofMatl <chr> "CompShg", "CompShg", "CompShg", "CompShg", "CompShg", "…
## $ Exterior1st <chr> "VinylSd", "MetalSd", "VinylSd", "Wd Sdng", "VinylSd", "…
## $ Exterior2nd <chr> "VinylSd", "MetalSd", "VinylSd", "Wd Shng", "VinylSd", "…
## $ MasVnrType <chr> "BrkFace", "None", "BrkFace", "None", "BrkFace", "None",…
## $ MasVnrArea <int> 196, 0, 162, 0, 350, 0, 186, 240, 0, 0, 0, 286, 0, 306, …
## $ ExterQual <chr> "Gd", "TA", "Gd", "TA", "Gd", "TA", "Gd", "TA", "TA", "T…
## $ ExterCond <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "T…
## $ Foundation <chr> "PConc", "CBlock", "PConc", "BrkTil", "PConc", "Wood", "…
## $ BsmtQual <chr> "Gd", "Gd", "Gd", "TA", "Gd", "Gd", "Ex", "Gd", "TA", "T…
## $ BsmtCond <chr> "TA", "TA", "TA", "Gd", "TA", "TA", "TA", "TA", "TA", "T…
## $ BsmtExposure <chr> "No", "Gd", "Mn", "No", "Av", "No", "Av", "Mn", "No", "N…
## $ BsmtFinType1 <chr> "GLQ", "ALQ", "GLQ", "ALQ", "GLQ", "GLQ", "GLQ", "ALQ", …
## $ BsmtFinSF1 <int> 706, 978, 486, 216, 655, 732, 1369, 859, 0, 851, 906, 99…
## $ BsmtFinType2 <chr> "Unf", "Unf", "Unf", "Unf", "Unf", "Unf", "Unf", "BLQ", …
## $ BsmtFinSF2 <int> 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ BsmtUnfSF <int> 150, 284, 434, 540, 490, 64, 317, 216, 952, 140, 134, 17…
## $ TotalBsmtSF <int> 856, 1262, 920, 756, 1145, 796, 1686, 1107, 952, 991, 10…
## $ Heating <chr> "GasA", "GasA", "GasA", "GasA", "GasA", "GasA", "GasA", …
## $ HeatingQC <chr> "Ex", "Ex", "Ex", "Gd", "Ex", "Ex", "Ex", "Ex", "Gd", "E…
## $ CentralAir <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "…
## $ Electrical <chr> "SBrkr", "SBrkr", "SBrkr", "SBrkr", "SBrkr", "SBrkr", "S…
## $ X1stFlrSF <int> 856, 1262, 920, 961, 1145, 796, 1694, 1107, 1022, 1077, …
## $ X2ndFlrSF <int> 854, 0, 866, 756, 1053, 566, 0, 983, 752, 0, 0, 1142, 0,…
## $ LowQualFinSF <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ GrLivArea <int> 1710, 1262, 1786, 1717, 2198, 1362, 1694, 2090, 1774, 10…
## $ BsmtFullBath <int> 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,…
## $ BsmtHalfBath <int> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ FullBath <int> 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1, 3, 1, 2, 1, 1, 1, 2, 1,…
## $ HalfBath <int> 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,…
## $ BedroomAbvGr <int> 3, 3, 3, 3, 4, 1, 3, 3, 2, 2, 3, 4, 2, 3, 2, 2, 2, 2, 3,…
## $ KitchenAbvGr <int> 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1,…
## $ KitchenQual <chr> "Gd", "TA", "Gd", "Gd", "Gd", "TA", "Gd", "TA", "TA", "T…
## $ TotRmsAbvGrd <int> 8, 6, 6, 7, 9, 5, 7, 7, 8, 5, 5, 11, 4, 7, 5, 5, 5, 6, 6…
## $ Functional <chr> "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", …
## $ Fireplaces <int> 0, 1, 1, 1, 1, 0, 1, 2, 2, 2, 0, 2, 0, 1, 1, 0, 1, 0, 0,…
## $ FireplaceQu <chr> NA, "TA", "TA", "Gd", "TA", NA, "Gd", "TA", "TA", "TA", …
## $ GarageType <chr> "Attchd", "Attchd", "Attchd", "Detchd", "Attchd", "Attch…
## $ GarageYrBlt <int> 2003, 1976, 2001, 1998, 2000, 1993, 2004, 1973, 1931, 19…
## $ GarageFinish <chr> "RFn", "RFn", "RFn", "Unf", "RFn", "Unf", "RFn", "RFn", …
## $ GarageCars <int> 2, 2, 2, 3, 3, 2, 2, 2, 2, 1, 1, 3, 1, 3, 1, 2, 2, 2, 2,…
## $ GarageArea <int> 548, 460, 608, 642, 836, 480, 636, 484, 468, 205, 384, 7…
## $ GarageQual <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "Fa", "G…
## $ GarageCond <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "T…
## $ PavedDrive <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "…
## $ WoodDeckSF <int> 0, 298, 0, 0, 192, 40, 255, 235, 90, 0, 0, 147, 140, 160…
## $ OpenPorchSF <int> 61, 0, 42, 35, 84, 30, 57, 204, 0, 4, 0, 21, 0, 33, 213,…
## $ EnclosedPorch <int> 0, 0, 0, 272, 0, 0, 0, 228, 205, 0, 0, 0, 0, 0, 176, 0, …
## $ X3SsnPorch <int> 0, 0, 0, 0, 0, 320, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ScreenPorch <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 176, 0, 0, 0, 0, 0, …
## $ PoolArea <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ PoolQC <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ Fence <chr> NA, NA, NA, NA, NA, "MnPrv", NA, NA, NA, NA, NA, NA, NA,…
## $ MiscFeature <chr> NA, NA, NA, NA, NA, "Shed", NA, "Shed", NA, NA, NA, NA, …
## $ MiscVal <int> 0, 0, 0, 0, 0, 700, 0, 350, 0, 0, 0, 0, 0, 0, 0, 0, 700,…
## $ MoSold <int> 2, 5, 9, 2, 12, 10, 8, 11, 4, 1, 2, 7, 9, 8, 5, 7, 3, 10…
## $ YrSold <int> 2008, 2007, 2008, 2006, 2008, 2009, 2007, 2009, 2008, 20…
## $ SaleType <chr> "WD", "WD", "WD", "WD", "WD", "WD", "WD", "WD", "WD", "W…
## $ SaleCondition <chr> "Normal", "Normal", "Normal", "Abnorml", "Normal", "Norm…
## $ SalePrice <int> 208500, 181500, 223500, 140000, 250000, 143000, 307000, …Analisis de valores perdidos
Para el analisis de la base de datos lo primero que se hace es identificar si existen valores perdidos, entonces primeramente se halla la cantidad de los valores perdidos y depues se analisan esos valores por columnas y por filas.
#sum(is.na(datos)) #total NA
#colSums(is.na(datos)) #total NA columnas
#datos[!complete.cases(datos), ] #total NA filasManejo de los valores perdidos
Despues de encontrar los valores perdidos hacemos una limpieza de la base de datos, hay distintas formas de hacerla, eliminando los NA
datos_sin_na <- na.omit(datos)# Elimina todas las filas con al menos un NA
datos_sin_na_col <- datos[, colSums(is.na(datos)) == 0] # Elimina columnas con valores faltanteO remplazandolos por la media
library(dplyr)
datos <- datos %>% mutate(across(where(is.numeric), ~ ifelse(is.na(.), mean(., na.rm=TRUE), .)))2. SalePrice
Calcule la media, mediana y desviación estándar del precio de venta de las viviendas (SalePrice).
mean(datos$SalePrice, na.rm = TRUE) #Media## [1] 180921.2median(datos$SalePrice, na.rm = TRUE) #mediana## [1] 163000sd(datos$SalePrice, na.rm = TRUE) #desviacion estandar ## [1] 79442.5¿Que indica la distribucion entre la media y la mediana sobre la distribucion de los datos?
vemos que la media es mayor que la mediana, lo que significa que algunas casa tienen precios extremandamente altos, lo que hace que la distribucion de los datos no sea simetrica. pues hay valores muy altos que estan elevando la media.
en otras palabras, la mayoria de las casas se venden por menos de 180921.2 pero unas pocas se venden por mucho mas, lo que hace que el promedio se distorsione.
3. GrLivArea
Calcule la media del área habitable (GrLivArea)
median(datos$GrLivArea, na.rm = TRUE) #mediana## [1] 1464¿Qué puedes concluir al compararla con la media de precios de venta?
La media de los precios de venta es mayor que el area habitable, lo que da indicios de que el area habitable es un factor importante, en el precio de las ventas, pero no es lo unico que se tiene en cuenta, pues este precio no depende solo del area habitable.
4. GarageArea
Calcule el rango y varianza del tamaño del garaje en pies cuadrados (GarageArea)
# Rango
rango_garaje <- range(datos$GarageArea, na.rm = TRUE)
rango <- diff(rango_garaje)
# Varianza
varianza <- var(datos$GarageArea, na.rm = TRUE)¿Consideras que es una variable homogénea o heterogénea? ¿Por qué?
si es heterogenea, pues existe una gran diversidad de tamaños de garaje entre las viviendad del conjunto de datos
5. Resumen
Realice un resumen de las medidas de tendencia central.
summary(datos)## Id MSSubClass MSZoning LotFrontage
## Min. : 1.0 Min. : 20.0 Length:1460 Min. : 21.00
## 1st Qu.: 365.8 1st Qu.: 20.0 Class :character 1st Qu.: 60.00
## Median : 730.5 Median : 50.0 Mode :character Median : 70.05
## Mean : 730.5 Mean : 56.9 Mean : 70.05
## 3rd Qu.:1095.2 3rd Qu.: 70.0 3rd Qu.: 79.00
## Max. :1460.0 Max. :190.0 Max. :313.00
## LotArea Street Alley LotShape
## Min. : 1300 Length:1460 Length:1460 Length:1460
## 1st Qu.: 7554 Class :character Class :character Class :character
## Median : 9478 Mode :character Mode :character Mode :character
## Mean : 10517
## 3rd Qu.: 11602
## Max. :215245
## LandContour Utilities LotConfig LandSlope
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Neighborhood Condition1 Condition2 BldgType
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## HouseStyle OverallQual OverallCond YearBuilt
## Length:1460 Min. : 1.000 Min. :1.000 Min. :1872
## Class :character 1st Qu.: 5.000 1st Qu.:5.000 1st Qu.:1954
## Mode :character Median : 6.000 Median :5.000 Median :1973
## Mean : 6.099 Mean :5.575 Mean :1971
## 3rd Qu.: 7.000 3rd Qu.:6.000 3rd Qu.:2000
## Max. :10.000 Max. :9.000 Max. :2010
## YearRemodAdd RoofStyle RoofMatl Exterior1st
## Min. :1950 Length:1460 Length:1460 Length:1460
## 1st Qu.:1967 Class :character Class :character Class :character
## Median :1994 Mode :character Mode :character Mode :character
## Mean :1985
## 3rd Qu.:2004
## Max. :2010
## Exterior2nd MasVnrType MasVnrArea ExterQual
## Length:1460 Length:1460 Min. : 0.0 Length:1460
## Class :character Class :character 1st Qu.: 0.0 Class :character
## Mode :character Mode :character Median : 0.0 Mode :character
## Mean : 103.7
## 3rd Qu.: 164.2
## Max. :1600.0
## ExterCond Foundation BsmtQual BsmtCond
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2
## Length:1460 Length:1460 Min. : 0.0 Length:1460
## Class :character Class :character 1st Qu.: 0.0 Class :character
## Mode :character Mode :character Median : 383.5 Mode :character
## Mean : 443.6
## 3rd Qu.: 712.2
## Max. :5644.0
## BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating
## Min. : 0.00 Min. : 0.0 Min. : 0.0 Length:1460
## 1st Qu.: 0.00 1st Qu.: 223.0 1st Qu.: 795.8 Class :character
## Median : 0.00 Median : 477.5 Median : 991.5 Mode :character
## Mean : 46.55 Mean : 567.2 Mean :1057.4
## 3rd Qu.: 0.00 3rd Qu.: 808.0 3rd Qu.:1298.2
## Max. :1474.00 Max. :2336.0 Max. :6110.0
## HeatingQC CentralAir Electrical X1stFlrSF
## Length:1460 Length:1460 Length:1460 Min. : 334
## Class :character Class :character Class :character 1st Qu.: 882
## Mode :character Mode :character Mode :character Median :1087
## Mean :1163
## 3rd Qu.:1391
## Max. :4692
## X2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath
## Min. : 0 Min. : 0.000 Min. : 334 Min. :0.0000
## 1st Qu.: 0 1st Qu.: 0.000 1st Qu.:1130 1st Qu.:0.0000
## Median : 0 Median : 0.000 Median :1464 Median :0.0000
## Mean : 347 Mean : 5.845 Mean :1515 Mean :0.4253
## 3rd Qu.: 728 3rd Qu.: 0.000 3rd Qu.:1777 3rd Qu.:1.0000
## Max. :2065 Max. :572.000 Max. :5642 Max. :3.0000
## BsmtHalfBath FullBath HalfBath BedroomAbvGr
## Min. :0.00000 Min. :0.000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:2.000
## Median :0.00000 Median :2.000 Median :0.0000 Median :3.000
## Mean :0.05753 Mean :1.565 Mean :0.3829 Mean :2.866
## 3rd Qu.:0.00000 3rd Qu.:2.000 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :2.00000 Max. :3.000 Max. :2.0000 Max. :8.000
## KitchenAbvGr KitchenQual TotRmsAbvGrd Functional
## Min. :0.000 Length:1460 Min. : 2.000 Length:1460
## 1st Qu.:1.000 Class :character 1st Qu.: 5.000 Class :character
## Median :1.000 Mode :character Median : 6.000 Mode :character
## Mean :1.047 Mean : 6.518
## 3rd Qu.:1.000 3rd Qu.: 7.000
## Max. :3.000 Max. :14.000
## Fireplaces FireplaceQu GarageType GarageYrBlt
## Min. :0.000 Length:1460 Length:1460 Min. :1900
## 1st Qu.:0.000 Class :character Class :character 1st Qu.:1962
## Median :1.000 Mode :character Mode :character Median :1979
## Mean :0.613 Mean :1979
## 3rd Qu.:1.000 3rd Qu.:2001
## Max. :3.000 Max. :2010
## GarageFinish GarageCars GarageArea GarageQual
## Length:1460 Min. :0.000 Min. : 0.0 Length:1460
## Class :character 1st Qu.:1.000 1st Qu.: 334.5 Class :character
## Mode :character Median :2.000 Median : 480.0 Mode :character
## Mean :1.767 Mean : 473.0
## 3rd Qu.:2.000 3rd Qu.: 576.0
## Max. :4.000 Max. :1418.0
## GarageCond PavedDrive WoodDeckSF OpenPorchSF
## Length:1460 Length:1460 Min. : 0.00 Min. : 0.00
## Class :character Class :character 1st Qu.: 0.00 1st Qu.: 0.00
## Mode :character Mode :character Median : 0.00 Median : 25.00
## Mean : 94.24 Mean : 46.66
## 3rd Qu.:168.00 3rd Qu.: 68.00
## Max. :857.00 Max. :547.00
## EnclosedPorch X3SsnPorch ScreenPorch PoolArea
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.000
## Mean : 21.95 Mean : 3.41 Mean : 15.06 Mean : 2.759
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.000
## Max. :552.00 Max. :508.00 Max. :480.00 Max. :738.000
## PoolQC Fence MiscFeature MiscVal
## Length:1460 Length:1460 Length:1460 Min. : 0.00
## Class :character Class :character Class :character 1st Qu.: 0.00
## Mode :character Mode :character Mode :character Median : 0.00
## Mean : 43.49
## 3rd Qu.: 0.00
## Max. :15500.00
## MoSold YrSold SaleType SaleCondition
## Min. : 1.000 Min. :2006 Length:1460 Length:1460
## 1st Qu.: 5.000 1st Qu.:2007 Class :character Class :character
## Median : 6.000 Median :2008 Mode :character Mode :character
## Mean : 6.322 Mean :2008
## 3rd Qu.: 8.000 3rd Qu.:2009
## Max. :12.000 Max. :2010
## SalePrice
## Min. : 34900
## 1st Qu.:129975
## Median :163000
## Mean :180921
## 3rd Qu.:214000
## Max. :7550006. Histograma
Realice un histograma para la variable SalePrice usando ggplot2, que contenga: • Título: “Distribución del precio de venta” • Interprete la forma de la distribución:
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.4.3# Crear histograma para SalePrice
ggplot(datos, aes(x = SalePrice)) +
geom_histogram(fill = "blue", color = "black", bins = 30) +
labs(title = "Distribucion del precio de venta",
x = "Precio de venta",
y = "Frecuencia") +
theme_minimal()
¿La distribución es simétrica o asimétrica?
hay una distibucion asimetrica pues los datos no estan centrados si no que estan agrupados hacia la izquierda.
¿Hay muchos precios altos o bajos?
hay muchos precios altos, lo cual eleva la media y por eso el grafico se ve mas hacua la izquierda
7. Boxplot
Realice un boxplot del precio de venta (SalePrice) agrupado por la calidad general (OverallQual). • Título: “Precio de venta según calidad general”
# Asegúrate de tener ggplot2 cargado
library(ggplot2)
# Crear boxplot de SalePrice por OverallQual
ggplot(datos, aes(x = factor(OverallQual), y = SalePrice)) +
geom_boxplot(fill = "red", color = "black") +
labs(title = "Precio de venta segun calidad general",
x = "Calidad general",
y = "Precio de venta") +
theme_minimal()
¿Qué conclusiones puedes obtener sobre la relación entre calidad y precio?. Describir el grafico
En el grafico se observa que las clase de variable es distinta pues una es cuantitativa y la otra cualitativa por lo tanto el grfico no se muestra correctamente
8.Grafico de Barras
Genere un gráfico de barras apiladas con el precio promedio (SalePrice) por barrio (Neighborhood).
library(dplyr)
library(ggplot2)
# Agrupar y calcular promedio por barrio y calidad
precio_apilado <- datos %>%
group_by(Neighborhood, OverallQual) %>%
summarise(Promedio = mean(SalePrice, na.rm = TRUE), .groups = "drop")
# Crear gráfico de barras apiladas
ggplot(precio_apilado, aes(x = Neighborhood, y = Promedio, fill = factor(OverallQual))) +
geom_bar(stat = "identity") +
labs(title = "Precio promedio por barrio y calidad general",
x = "Barrio",
y = "Precio promedio de venta",
fill = "Calidad general") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
10. Grafico de Dispersion
Haga un gráfico de dispersión (plot) entre GrLivArea y SalePrice.
library(ggplot2)
# Gráfico de dispersión
ggplot(datos, aes(x = GrLivArea, y = SalePrice)) +
geom_point(color = "darkblue", alpha = 0.6) +
labs(title = "Relacion entre area habitable y precio de venta",
x = "Area habitable (GrLivArea)",
y = "Precio de venta (SalePrice)") +
theme_minimal()
11.OverallQual
. ¿Qué tipo de variable es OverallQual? ¿Es correcto calcular la media de esta variable?
print(class(datos$OverallQual)) ## [1] "integer"mean(datos$OverallQual, na.rm = TRUE) #Media## [1] 6.099315