Intento por subir mis tareas: German y Algas

EduardoHidalgo/algas/00-load.R

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+algas_data <- load()

EduardoHidalgo/algas/01-prepare.R

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+colnames(algas_data) <- algas_colnames
+
+#german_data$good_loan <- as.factor(
+#  ifelse(
+#    german_data$good_loan == 1, 
+#    'GoodLoan', 
+#    'BadLoan'
+#  )
+#)
+
+#german_data  <- german_data %>% 
+#  mutate_all(funs(german_decode))
+
+#german_data  <- german_data %>% 
+#  mutate_at(c(1,3,4,6,7,9,10,12,14,15,17,19,20),funs(as.factor))

EduardoHidalgo/algas/02-clean.R

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+colnames(algas_data) <- algas_clean_colnames(algas_colnames)
+
+problematic_rows <- problems(algas_data)$row
+
+algas_data[problematic_rows,] <- algas_data %>% 
+  slice(problematic_rows) %>% 
+  unite(col="all", -seq(1:6), sep = "/", remove=TRUE) %>% 
+  extract(all, into=c("NO3", "NH4", "resto"), regex="([0-9]*.[0-9]{5})([0-9]*.[0-9]*)/(.*)/NA", remove=TRUE) %>% 
+  separate(resto, into=names(algas_data)[9:18], sep="/", remove=TRUE) 
+
+algas_data <- algas_data %>% mutate_at(c(2,3), funs(algas_clean_data))
+
+algas_data <- readr::type_convert(algas_data)

EduardoHidalgo/algas/algas.R

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+setwd("~/GitHub/MineriaYAnalisisDeDatos/algas")
+
+library(readr)
+library(stringr)
+library(tidyr)
+library(dplyr)
+library(ggplot2)
+library(ggthemes)
+
+source("metadata.R")
+source("utils.R")
+source("00-load.R")
+source("01-prepare.R")
+source("02-clean.R")
+
+summary(algas_data)
+
+glimpse(algas_data)
+
+problems(algas_data)
+
+
+library(mice)
+md.pattern(algas_data)
+
+library("VIM")
+aggr(algas_data, prop=FALSE, numbers=TRUE)
+
+matrixplot(algas_data)
+
+
+x <- as.data.frame(abs(is.na(algas_data))) # df es un data.frame
+
+head(algas_data)
+
+head(x)
+
+# Extrae las variables que tienen algunas celdas con NAs
+y <- x[which(sapply(x, sd) > 0)] 
+
+# Da la correación un valor alto positivo significa que desaparecen juntas.
+cor(y,y) 
+
+summary(algas_data[-grep(colnames(algas_data),pattern = "^a[1-9]")])
+
+algas_con_NAs <- algas_data[!complete.cases(algas_data),]
+
+algas_con_NAs[c('max_ph', 'min_o2', 'cl', 'no3', 'nh4', 'opo4', 'po4', 'chla')]  %>%
+  print(n = 33)
+
+
+algas_data %>%
+  select(-c(1:3)) %>%
+  cor(use="complete.obs") %>%
+  symnum()
+
+ggplot(data=algas_data) + 
+  aes(x=opo4, y=po4) + 
+  geom_point(shape=1) + # Usamos una bolita para los puntos
+  geom_smooth(method=lm, se=FALSE) +
+  theme_hc()
+# Mostramos la linea de la regresión y no mostramos la región de confianza
+
+
+###
+
+
+c2 <- ggplot(algas_data, aes(max_ph)) +
+  geom_histogram(aes(y = ..density..), binwidth=1) +
+  geom_density()+
+  xlab("PH Maximo") + ylab("") + ggtitle("Distribucion Empirica del PH Maximo Por Estacion")+facet_wrap(~ season, nrow = 3)
+
+p2 <- ggplot(father.son, aes(fheight)) +
+  geom_histogram(aes(y = ..density..), binwidth=1) +
+  geom_density() + xlim(58, 80) + ylim(0, 0.16) + 
+  xlab("ht (inches)") + ylab("") +
+  ggtitle("Fathers")
+
+grid.arrange(c2, p2, nrow = 1)

EduardoHidalgo/algas/metadata.R

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+.## German credit ---------------------------------------------------------------
+
+## Nombres de columnas ---------------------------------------------------------
+german_colnames <- c('Status of existing checking account',
+                     'Duration in month',
+                     'Credit history',
+                     'Purpose',
+                     'Credit amount',
+                     'Savings account/bonds',
+                     'Present employment since',
+                     'Installment rate in percentage of disposable income',
+                     'Personal status and sex',
+                     'Other debtors / guarantors',
+                     'Present residence since',
+                     'Property',
+                     'Age in years',
+                     'Other installment plans',
+                     'Housing',
+                     'Number of existing credits at this bank',
+                     'Job',
+                     'Number of people being liable to provide maintenance for',
+                     'Telephone',
+                     'foreign worker',
+                     'good_loan'
+)
+
+## Códigos ---------------------------------------------------------------------
+german_codes <- list('A11'='... < 0 DM',
+                     'A12'='0 <= ... < 200 DM',
+                     'A13'='... >= 200 DM / salary assignments for at least 1 year',
+                     'A14'='no checking account',
+                     'A30'='no credits taken/all credits paid back duly',
+                     'A31'='all credits at this bank paid back duly',
+                     'A32'='existing credits paid back duly till now',
+                     'A33'='delay in paying off in the past',
+                     'A34'='critical account/other credits existing (not at this bank)',
+                     'A40'='car (new)',
+                     'A41'='car (used)',
+                     'A42'='furniture/equipment',
+                     'A43'='radio/television', 'A44'='domestic appliances', 'A45'='repairs',
+                     'A46'='education', 'A47'='(vacation - does not exist?)',
+                     'A48'='retraining', 'A49'='business', 'A410'='others', 'A61'='... < 100 DM',
+                     'A62'='100 <= ... < 500 DM', 'A63'='500 <= ... < 1000 DM',
+                     'A64'='.. >= 1000 DM', 'A65'='unknown/ no savings account',
+                     'A71'='unemployed', 'A72'='... < 1 year', 'A73'='1 <= ... < 4 years',
+                     'A74'='4 <= ... < 7 years', 'A75'='.. >= 7 years', 'A91'='male : divorced/separated',
+                     'A92'='female : divorced/separated/married',
+                     'A93'='male : single',
+                     'A94'='male : married/widowed',
+                     'A95'='female : single',
+                     'A101'='none',
+                     'A102'='co-applicant',
+                     'A103'='guarantor', 'A121'='real estate',
+                     'A122'='if not A121 : building society savings agreement/life insurance',
+                     'A123'='if not A121/A122 : car or other, not in attribute 6',
+                     'A124'='unknown / no property',
+                     'A141'='bank', 'A142'='stores',  'A143'='none', 'A151'='rent', 'A152'='own',
+                     'A153'='for free', 'A171'='unemployed/ unskilled - non-resident',
+                     'A172'='unskilled - resident', 'A173'='skilled employee / official',
+                     'A174'='management/ self-employed/highly qualified employee/ officer',
+                     'A191'='none', 'A192'='yes, registered under the customers name',
+                     'A201'='yes', 'A202'='no'
+)
+
+
+## Algas -----------------------------------------------------------------------
+
+## Nombre de columnas ----------------------------------------------------------
+algas_colnames <- c('season',
+                    'river_size',
+                    'fluid_velocity',
+                    'max_PH',
+                    'min_O2',
+                    'Cl',
+                    'NO3',
+                    'NH4',
+                    'oPO4',
+                    'PO4',
+                    'Chla',
+                    paste('a', seq(1:7), sep="")
+)

EduardoHidalgo/algas/utils.R

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+
+load <- function(){
+  if(!file.exists('algas.rds')){
+    algas_url <- 'https://archive.ics.uci.edu/ml/machine-learning-databases/coil-mld/analysis.data'
+
+    algas_data <- read_csv(algas_url, 
+                      col_names = algas_colnames,
+                      na = 'XXXXXXX')
+    saveRDS(algas_data, "algas.rds")
+    print('algas.rds se bajó y guardó\n')
+  }
+  else{
+    warning('algas.rds ya existe\n')
+    algas_data <- readRDS("algas.rds")
+  }
+
+  return(algas_data)
+}
+
+algas_decode <- function(columna){
+  if(is.character(columna)){
+    unlist(german_codes[columna],use.names = F)
+  }else{
+    columna
+  }
+}
+
+algas_clean_colnames <- function(x){
+  str_replace_all(tolower(x),"/| ",'_')
+}
+
+algas_clean_data <- function(x){
+  str_replace_all(tolower(x),"_",'')
+}

EduardoHidalgo/german/00-load.R

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+german_data <- load()

EduardoHidalgo/german/01-prepare.R

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+colnames(german_data) <- german_colnames
+
+german_data$good_loan <- as.factor(
+  ifelse(
+    german_data$good_loan == 1, 
+    'GoodLoan', 
+    'BadLoan'
+  )
+)
+
+german_data  <- german_data %>% 
+  mutate_all(funs(german_decode))
+
+german_data  <- german_data %>% 
+  mutate_at(c(1,3,4,6,7,9,10,12,14,15,17,19,20),funs(as.factor))

EduardoHidalgo/german/02-clean.R

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+colnames(german_data) <- german_clean_colnames(german_colnames)

EduardoHidalgo/german/german.R

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+setwd("~/GitHub/MineriaYAnalisisDeDatos/german")
+
+library(readr)
+library(stringr)
+library(dplyr)
+library(ggplot2)
+library(ggthemes)
+
+source("metadata.R")
+source("utils.R")
+source("00-load.R")
+source("01-prepare.R")
+source("02-clean.R")
+
+ggplot(data = german_data) + 
+  geom_bar(mapping = aes(x = `personal_status_and_sex`, fill = `good_loan`), position = "fill")+
+  theme(axis.text.x = element_text(angle = 60, hjust = 1))
+
+ggplot(data = german_data) + 
+  geom_bar(mapping = aes(x = `good_loan`, fill = `credit_history`), position = "fill")
+
+ggplot(data = german_data) + 
+  geom_bar(mapping = aes(x = `credit_history`, fill = `good_loan`), position = "fill")+
+  theme(axis.text.x = element_text(angle = 70, hjust = 1))
+
+german_data %>% 
+  group_by(credit_history) %>% 
+  dplyr::summarise(count = n()) %>% 
+  arrange(desc(count)) %>% 
+  ggplot(.) + 
+  geom_bar(aes(x=reorder(credit_history, count), y = count), stat="identity", fill="gray") + 
+  coord_flip() + 
+  theme_hc() + 
+  ylab('casos') + 
+  xlab('Historial de crédito')
+
+summary(german_data)

EduardoHidalgo/german/german.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: ISO8859-1
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

EduardoHidalgo/german/metadata.R

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+## German credit ---------------------------------------------------------------
+
+## Nombres de columnas ---------------------------------------------------------
+german_colnames <- c('Status of existing checking account',
+                     'Duration in month',
+                     'Credit history',
+                     'Purpose',
+                     'Credit amount',
+                     'Savings account/bonds',
+                     'Present employment since',
+                     'Installment rate in percentage of disposable income',
+                     'Personal status and sex',
+                     'Other debtors / guarantors',
+                     'Present residence since',
+                     'Property',
+                     'Age in years',
+                     'Other installment plans',
+                     'Housing',
+                     'Number of existing credits at this bank',
+                     'Job',
+                     'Number of people being liable to provide maintenance for',
+                     'Telephone',
+                     'foreign worker',
+                     'good_loan'
+)
+
+## Códigos ---------------------------------------------------------------------
+german_codes <- list('A11'='... < 0 DM',
+                     'A12'='0 <= ... < 200 DM',
+                     'A13'='... >= 200 DM / salary assignments for at least 1 year',
+                     'A14'='no checking account',
+                     'A30'='no credits taken/all credits paid back duly',
+                     'A31'='all credits at this bank paid back duly',
+                     'A32'='existing credits paid back duly till now',
+                     'A33'='delay in paying off in the past',
+                     'A34'='critical account/other credits existing (not at this bank)',
+                     'A40'='car (new)',
+                     'A41'='car (used)',
+                     'A42'='furniture/equipment',
+                     'A43'='radio/television', 'A44'='domestic appliances', 'A45'='repairs',
+                     'A46'='education', 'A47'='(vacation - does not exist?)',
+                     'A48'='retraining', 'A49'='business', 'A410'='others', 'A61'='... < 100 DM',
+                     'A62'='100 <= ... < 500 DM', 'A63'='500 <= ... < 1000 DM',
+                     'A64'='.. >= 1000 DM', 'A65'='unknown/ no savings account',
+                     'A71'='unemployed', 'A72'='... < 1 year', 'A73'='1 <= ... < 4 years',
+                     'A74'='4 <= ... < 7 years', 'A75'='.. >= 7 years', 'A91'='male : divorced/separated',
+                     'A92'='female : divorced/separated/married',
+                     'A93'='male : single',
+                     'A94'='male : married/widowed',
+                     'A95'='female : single',
+                     'A101'='none',
+                     'A102'='co-applicant',
+                     'A103'='guarantor', 'A121'='real estate',
+                     'A122'='if not A121 : building society savings agreement/life insurance',
+                     'A123'='if not A121/A122 : car or other, not in attribute 6',
+                     'A124'='unknown / no property',
+                     'A141'='bank', 'A142'='stores',  'A143'='none', 'A151'='rent', 'A152'='own',
+                     'A153'='for free', 'A171'='unemployed/ unskilled - non-resident',
+                     'A172'='unskilled - resident', 'A173'='skilled employee / official',
+                     'A174'='management/ self-employed/highly qualified employee/ officer',
+                     'A191'='none', 'A192'='yes, registered under the customers name',
+                     'A201'='yes', 'A202'='no'
+)
+
+
+## Algas -----------------------------------------------------------------------
+
+## Nombre de columnas ----------------------------------------------------------
+algas_colnames <- c('season',
+                    'river_size',
+                    'fluid_velocity',
+                    'max_PH',
+                    'min_O2',
+                    'Cl',
+                    'NO3',
+                    'NH4',
+                    'oPO4',
+                    'PO4',
+                    'Chla',
+                    paste('a', seq(1:7), sep="")
+)