library(dplyr)
Anexando pacote: ‘dplyr’
Os seguintes objetos são mascarados por ‘package:stats’:
filter, lag
Os seguintes objetos são mascarados por ‘package:base’:
intersect, setdiff, setequal, union
getwd()
[1] "C:/Users/joao_/Desktop/tela/Pós DS/Ciclo de Vida e Introdução à Linguagem r/aula3"
#entrada de dados
limpos <- read_excel("dirty_data.xlsx")
New names:
mode(limpos)
[1] "list"
str(limpos)
tibble [14 × 11] (S3: tbl_df/tbl/data.frame)
$ Data most recently refreshed on:: chr [1:14] "First Name" "Jason" "Jason" "Alicia" ...
$ ...2 : chr [1:14] "Last Name" "Bourne" "Bourne" "Keys" ...
$ ...3 : chr [1:14] "Employee Status" "Teacher" "Teacher" "Teacher" ...
$ Dec-27 2020 : chr [1:14] "Subject" "PE" "Drafting" "Music" ...
$ ...5 : chr [1:14] "Hire Date" "39690" "43479" "37118" ...
$ ...6 : chr [1:14] "% Allocated" "0.75" "0.25" "1" ...
$ ...7 : chr [1:14] "Full time?" "Yes" "Yes" "Yes" ...
$ ...8 : chr [1:14] "do not edit! --->" NA NA NA ...
$ ...9 : chr [1:14] "Certification" "Physical ed" "Physical ed" "Instr. music" ...
$ ...10 : chr [1:14] "Certification" "Theater" "Theater" "Vocal music" ...
$ ...11 : chr [1:14] "Active?" "YES" "YES" "YES" ...
#Corrigindo os nomes das colunas
#exibe os nomes e os tipos das variáveis
glimpse(limpos)
Rows: 14
Columns: 11
$ `Data most recently refreshed on:` <chr> "First Name", "Jason", "Jason", "Alicia",…
$ ...2 <chr> "Last Name", "Bourne", "Bourne", "Keys", …
$ ...3 <chr> "Employee Status", "Teacher", "Teacher", …
$ `Dec-27 2020` <chr> "Subject", "PE", "Drafting", "Music", NA,…
$ ...5 <chr> "Hire Date", "39690", "43479", "37118", "…
$ ...6 <chr> "% Allocated", "0.75", "0.25", "1", "1", …
$ ...7 <chr> "Full time?", "Yes", "Yes", "Yes", "Yes",…
$ ...8 <chr> "do not edit! --->", NA, NA, NA, NA, NA, …
$ ...9 <chr> "Certification", "Physical ed", "Physical…
$ ...10 <chr> "Certification", "Theater", "Theater", "V…
$ ...11 <chr> "Active?", "YES", "YES", "YES", "YES", "Y…
#corrigindo os nomes das colunas
limpos <- limpos %>%
row_to_names(row_number = 1) %>%
clean_names() #retira espaços e coloca em minúsculas
Aviso: Row 1 does not provide unique names. Consider running clean_names() after row_to_names().
#Tabela cruzada - conta o total por sobrenome para verificar duplicações
limpos %>% tabyl(last_name, employee_status)
last_name Administration Coach Teacher
Boozer 0 2 0
Bourne 0 0 2
Joyce 0 0 1
Keys 0 0 1
Lamarr 0 0 1
Larsen 0 0 1
Lovelace 0 0 1
Nice 1 0 0
Wu 0 0 2
#Ocorrências válidas
#% de ocorrências geral e válidas (sem NA)
limpos %>%
tabyl(subject)
subject n percent valid_percent
Basketball 1 0.08333333 0.1
Chemistry 1 0.08333333 0.1
Dean 1 0.08333333 0.1
Drafting 1 0.08333333 0.1
English 2 0.16666667 0.2
Music 1 0.08333333 0.1
PE 1 0.08333333 0.1
Physics 1 0.08333333 0.1
Science 1 0.08333333 0.1
<NA> 2 0.16666667 NA
#o R tem a seguinte abordagem com a função table():
table(limpos $subject)
Basketball Chemistry Dean Drafting English Music PE
1 1 1 1 2 1 1
Physics Science
1 1
#Ocorrências válidas – 2 variáveis
limpos %>%
filter(employee_status != is.na(employee_status)) %>%
tabyl(employee_status, full_time)
employee_status No Yes
Administration 0 1
Coach 2 0
Teacher 3 6
#Ocorrências válidas – 3 variáveis
limpos %>%
tabyl(full_time, subject, employee_status, show_missing_levels = FALSE)
$Administration
full_time Dean
Yes 1
$Coach
full_time Basketball NA_
No 1 1
$Teacher
full_time Chemistry Drafting English Music PE Physics Science NA_
No 0 0 2 0 0 0 1 0
Yes 1 1 0 1 1 1 0 1
#Tabela resumo
limpos %>%
tabyl(employee_status, full_time) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting() %>%
adorn_ns() %>%
adorn_title("combined")
employee_status/full_time No Yes
Administration 0.0% (0) 100.0% (1)
Coach 100.0% (2) 0.0% (0)
Teacher 33.3% (3) 66.7% (6)
Total 41.7% (5) 58.3% (7)
#Datas seriais
excel_numeric_to_date(as.numeric(as.character (limpos$hire_date)), date_system = "modern")
[1] "2008-08-30" "2019-01-14" "2001-08-15" "2005-08-08" "2017-02-25" "1930-03-20"
[7] "1930-03-20" "1999-09-20" "1976-06-08" "2015-08-05" "1995-01-01" "2009-09-15"
library(magrittr) #permite o uso de um operador de canal, %>%, para encadear o código
Anexando pacote: ‘magrittr’
O seguinte objeto é mascarado por ‘package:purrr’:
set_names
O seguinte objeto é mascarado por ‘package:tidyr’:
extract
#entrada de dados
gettysburg <- readr::read_csv("gettysburg.csv")
Rows: 590 Columns: 26── Column specification ──────────────────────────────────────────────────────────────
Delimiter: ","
chr (9): type, state, regiment_or_battery, brigade, division, corps, army, july1_...
dbl (17): men, killed, wounded, captured, missing, total_casualties, 3inch_rifles,...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#exibe o total de duplicados - 3 duplicados
table(dupes)
dupes
FALSE TRUE
587 3
dupes
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[14] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[27] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[40] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[53] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[66] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[79] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[92] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[105] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[118] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[131] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[144] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[170] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[183] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[196] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[209] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[222] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[235] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[248] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[261] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[274] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[287] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[300] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[326] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[339] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[352] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[365] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[378] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[391] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[404] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[417] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[430] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[443] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[456] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[482] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[495] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[508] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[521] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[534] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[547] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[560] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[573] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[586] FALSE FALSE TRUE TRUE TRUE
#exibe quais são os duplicados
which(dupes == "TRUE")
[1] 588 589 590
#identificando as linhas duplicadas
dim(gettysburg) #590 26
[1] 590 26
dim(gettysburg) #587 26
[1] 587 26
#estatística descritiva
summary(gettysburg)
type state regiment_or_battery brigade
Length:587 Length:587 Length:587 Length:587
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
division corps army july1_Commander
Length:587 Length:587 Length:587 Length:587
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
Cdr_casualty men killed wounded
Length:587 Min. : 33.0 Min. : 0.00 Min. : 0.00
Class :character 1st Qu.:144.5 1st Qu.: 2.00 1st Qu.: 7.00
Mode :character Median :275.0 Median : 10.00 Median : 29.00
Mean :269.4 Mean : 17.06 Mean : 43.61
3rd Qu.:356.0 3rd Qu.: 25.00 3rd Qu.: 68.00
Max. :843.0 Max. :172.00 Max. :443.00
NA's :6 NA's :6
captured missing total_casualties 3inch_rifles 4.5inch_rifles
Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. :0.0000 Min. :0
1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 12.00 1st Qu.:0.0000 1st Qu.:0
Median : 0.000 Median : 4.00 Median : 48.00 Median :0.0000 Median :0
Mean : 2.455 Mean : 16.52 Mean : 79.17 Mean :0.3578 Mean :0
3rd Qu.: 0.000 3rd Qu.: 21.00 3rd Qu.:127.50 3rd Qu.:0.0000 3rd Qu.:0
Max. :174.000 Max. :164.00 Max. :687.00 Max. :6.0000 Max. :0
NA's :7 NA's :17 NA's :8
10lb_parrots 12lb_howitzers 12lb_napoleons 6lb_howitzers
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000000
Median :0.0000 Median :0.0000 Median :0.0000 Median :0.000000
Mean :0.1704 Mean :0.0477 Mean :0.4242 Mean :0.001704
3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000000
Max. :6.0000 Max. :4.0000 Max. :6.0000 Max. :1.000000
24lb_howitzers 20lb_parrots 12lb_whitworths 14lb_rifles
Min. :0.000000 Min. :0.00000 Min. :0.000000 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.000000
Median :0.000000 Median :0.00000 Median :0.000000 Median :0.000000
Mean :0.006814 Mean :0.02726 Mean :0.003407 Mean :0.006814
3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.000000
Max. :4.000000 Max. :6.00000 Max. :2.000000 Max. :4.000000
total_guns
Min. :0.000
1st Qu.:0.000
Median :0.000
Mean :1.046
3rd Qu.:0.000
Max. :6.000
#somente de uma coluna - caso necessário
summary(gettysburg$total_guns)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 0.000 0.000 1.046 0.000 6.000
#Contando (group by) de todas as variáveis categóricas
gettysburg_cat %>% dplyr::summarise_all(dplyr::funs(dplyr::n_distinct(.)))
Aviso: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
#comparação de baixas de Exércitos. Observe que há uma contagem para cada causa de mortalidade!
gettysburg_cat %>% janitor::tabyl(army, Cdr_casualty)
army captured killed mortally wounded no wounded wounded-captured
Confederate 2 15 13 165 44 17
Union 4 14 11 240 60 2
View(feature_variance)
which(feature_variance$zeroVar == 'TRUE')
[1] 17
#Quais?
row.names(feature_variance[17, ])
[1] "4.5inch_rifles"
#Ou indicando os registros que tem nzv
which(feature_variance$nzv == 'TRUE')
[1] 16 17 18 19 20 21 22 23 24 25
---
title: "comandos_aula_3"
output: html_notebook
---

```{r}
#####################
#AVALIAÇÃO DA QUALIDADE DE DADOS
#Janitor estudo de caso
library(readxl)
#install.packages("janitor")
library(janitor)
library(dplyr)
```

```{r}
#diretório corrente
setwd("C:/Users/joao_/Desktop/tela/Pós DS/Ciclo de Vida e Introdução à Linguagem r/aula3") # setwd("C:/pos_unesa")
```

```{r}
getwd()
```

```{r}
#entrada de dados
limpos <- read_excel("dirty_data.xlsx")
```

```{r}
mode(limpos)
```

```{r}
View(limpos)
```

```{r}
str(limpos)
```

```{r}
#Corrigindo os nomes das colunas
#exibe os nomes e os tipos das variáveis
glimpse(limpos)
```

```{r}
#corrigindo os nomes das colunas
limpos <- limpos %>%
  row_to_names(row_number = 1) %>%
  clean_names() #retira espaços e coloca em minúsculas
```

```{r}
View(limpos)
```

```{r}
#eliminando colunas vazias
limpos = remove_empty(limpos, which = c("rows","cols"))
```

```{r}
View(limpos)
```

```{r}
#Tabela cruzada - conta o total por sobrenome para verificar duplicações
limpos %>% tabyl(last_name, employee_status)
```

```{r}
#Verificando as duplicações
limpos %>% get_dupes(contains("name"))
```

```{r}
#Identificando total por assunto
#coluna sem repeticao
distinct(limpos, subject)
```

```{r}
#agrupando:
library(sqldf)
sqldf("SELECT subject,count(*) as total 
      FROM limpos
      GROUP BY 1")
```

```{r}
#Ocorrências válidas
#% de ocorrências geral e válidas (sem NA)
limpos %>%
  tabyl(subject)
```

```{r}
#o R tem a seguinte abordagem com a função table():
table(limpos $subject)
```

```{r}
#Ocorrências válidas – 2 variáveis
limpos %>%
  filter(employee_status != is.na(employee_status)) %>%
  tabyl(employee_status, full_time)
```

```{r}
#Ocorrências válidas – 3 variáveis
limpos %>%
  tabyl(full_time, subject, employee_status, show_missing_levels = FALSE)
```

```{r}
#Tabela resumo
limpos %>%
  tabyl(employee_status, full_time) %>%
  adorn_totals("row") %>%
  adorn_percentages("row") %>%
  adorn_pct_formatting() %>%
  adorn_ns() %>%
  adorn_title("combined")
```

```{r}
#Datas seriais
excel_numeric_to_date(as.numeric(as.character (limpos$hire_date)), date_system = "modern")
```

```{r}
#acertando as datas
limpos$hire_date <- excel_numeric_to_date(as.numeric(as.character (limpos$hire_date)), date_system = "modern")
```

```{r}
View(limpos)
```

```{r}
#####################
#ESTUDO DE CASO  - Gettysburg:
#install.packages("caret")
library(caret)  #funções que tenta agilizar o processo de criação de modelos preditivos
library(janitor) 
library(readr)  
#install.packages("sjmisc")
library(sjmisc) #Funções de transformação de dados e variáveis
#install.packages("skimr")
library(skimr)  #Resumos de dados
library(tidyverse)
#install.packages("vtreat")
library(vtreat)  #prepara dados para modelagem preditiva
library(magrittr) #permite o uso de um operador de canal, %>%, para encadear o código 
library(dplyr)
```

```{r}
#entrada de dados
gettysburg <- readr::read_csv("gettysburg.csv")
```

```{r}
#verificando duplicações
dupes <- duplicated(gettysburg)
```

```{r}
#exibe o total de duplicados - 3 duplicados
table(dupes)
```

```{r}
dupes
```

```{r}
#exibe quais são os duplicados
which(dupes == "TRUE")
```

```{r}
#verificando duplicações com o sqldf()
library(sqldf)
```

```{r}
#verificando as duplicações com SQL
sqldf("SELECT type, state, regiment_or_battery,
      count(*) as total 
      FROM gettysburg 
      GROUP BY 1,2,3
      HAVING count(*) > 1")
```

```{r}
#identificando as linhas duplicadas
dim(gettysburg) #590 26
```

```{r}
df_dupes <- janitor::get_dupes(gettysburg)

df_dupes
```

```{r}
#eliminando as duplicatas!!!!
gettysburg <- dplyr::distinct(gettysburg, .keep_all = TRUE)
```

```{r}
dim(gettysburg) #587 26
```

```{r}
#estatística descritiva
summary(gettysburg)
```

```{r}
#somente de uma coluna - caso necessário
summary(gettysburg$total_guns)
```

```{r}
gettysburg %>%
  dplyr::filter(army == "Confederate" & type == "Infantry") %>%
  sjmisc::descr() -> descr_stats
```

```{r}
#cria uma tabela com estatísticas de acordo com um filtro anterior
readr::write_csv(descr_stats, 'descr_stats.csv')
#clicar no data.frame descr_stats
```

```{r}
#variáveis categóricas
gettysburg_cat <- gettysburg[, sapply(gettysburg, class) == 'character']
```

```{r}
View(gettysburg_cat)
```

```{r}
#Contando (group by) de todas as variáveis categóricas
gettysburg_cat %>% dplyr::summarise_all(dplyr::funs(dplyr::n_distinct(.)))
```

```{r}
#realizando um agrupamento com uma das variáveis
gettysburg_cat %>% 
  dplyr::group_by(Cdr_casualty) %>%
  dplyr::summarize(num_rows = n())
```

```{r}
#comparação de baixas de Exércitos. Observe que há uma contagem para cada causa de mortalidade!
gettysburg_cat %>% janitor::tabyl(army, Cdr_casualty)
```

```{r}
#Em SQL
sqldf("SELECT Cdr_casualty, count(*) as total 
      FROM gettysburg_cat 
      GROUP BY 1")
```

```{r}
#ou comparando com a ordem da maior para a menor causa:
sqldf("SELECT army, Cdr_casualty, count(*) total 
      FROM gettysburg_cat 
      GROUP BY 1,2
      ORDER  BY 1, 3 desc")
```

```{r}
#dados ausentes - conta o NA
na_count <-
  sapply(gettysburg, function(y)
    sum(length(which(is.na(y)))))
na_df <- data.frame(na_count)
```

```{r}
View(na_df)
```

```{r}
sqldf("SELECT killed, count(*) total 
      FROM gettysburg
      GROUP BY 1
      ORDER  BY 1 desc")
```

```{r}
#Variância - lembrado nzv - near zero var
feature_variance <- caret::nearZeroVar(gettysburg, saveMetrics = TRUE)
```

```{r}
View(feature_variance)
```

```{r}
which(feature_variance$zeroVar == 'TRUE')
```

```{r}
#Quais?
row.names(feature_variance[17, ])
```

```{r}
#Ou indicando os registros que tem nzv
which(feature_variance$nzv == 'TRUE')
```

```{r}
gettysburg_fltrd <- gettysburg[, feature_variance$zeroVar == 'FALSE']
```

```{r}
View(gettysburg_fltrd)
```
