#ACTIVIDAD DE R
#install.packages("dslabs")
library(dslabs)
data("movielens")
summary(movielens)## movieId title year
## Min. : 1 Length:100004 Min. :1902
## 1st Qu.: 1028 Class :character 1st Qu.:1987
## Median : 2406 Mode :character Median :1995
## Mean : 12549 Mean :1992
## 3rd Qu.: 5418 3rd Qu.:2001
## Max. :163949 Max. :2016
## NA's :7
## genres userId rating timestamp
## Drama : 7757 Min. : 1 Min. :0.500 Min. :7.897e+08
## Comedy : 6748 1st Qu.:182 1st Qu.:3.000 1st Qu.:9.658e+08
## Comedy|Romance : 3973 Median :367 Median :4.000 Median :1.110e+09
## Drama|Romance : 3462 Mean :347 Mean :3.544 Mean :1.130e+09
## Comedy|Drama : 3272 3rd Qu.:520 3rd Qu.:4.000 3rd Qu.:1.296e+09
## Comedy|Drama|Romance: 3204 Max. :671 Max. :5.000 Max. :1.477e+09
## (Other) :71588#Vector sin los Na
vector_yearsClean <- movielens$year[!is.na(movielens$year)]
#print(vector_yearsClean)
#Vector años Na
vector_yearsNa <- movielens$year[is.na(movielens$year)]
#print(vector_yearsNa)
#Df con valores en year sin Na
df_yearsClean <- movielens[!is.na(movielens$year), ]
#Df con valores en year Na
df_yearsNa <- movielens[is.na(movielens$year), ]
#DF con las peores peliculas
df_peoresPeliculas <- df_yearsClean[df_yearsClean$rating < 3.5,]
summary(df_peoresPeliculas)## movieId title year
## Min. : 1 Length:37895 Min. :1902
## 1st Qu.: 838 Class :character 1st Qu.:1990
## Median : 2405 Mode :character Median :1995
## Mean : 11839 Mean :1993
## 3rd Qu.: 4974 3rd Qu.:2000
## Max. :162672 Max. :2016
##
## genres userId rating
## Comedy : 3292 Min. : 1.0 Min. :0.500
## Drama : 2399 1st Qu.:187.0 1st Qu.:2.000
## Comedy|Romance : 1848 Median :375.0 Median :3.000
## Drama|Romance : 1176 Mean :347.2 Mean :2.435
## Comedy|Drama : 1122 3rd Qu.:514.0 3rd Qu.:3.000
## Comedy|Drama|Romance: 1101 Max. :671.0 Max. :3.000
## (Other) :26957
## timestamp
## Min. :7.897e+08
## 1st Qu.:9.600e+08
## Median :1.077e+09
## Mean :1.113e+09
## 3rd Qu.:1.272e+09
## Max. :1.477e+09
##