Atividade 4
Jhenifer Victória Mattos Ramos
08/12/2021
Carregar a base de dados
Aqui vamos carregar base de dados “Questionario_Estresse” pelo Excel.
library(readxl)
Questionario_Estresse <- read_excel("C:/Users/jheni/Base_de_dados-master/Questionario_Estresse.xls")Análise dos quartis das variáveis quantitativas da base
Aqui usamos o comando “summary” para analisar a base de dados “Questionario_Estresse” através de um resumo.
summary(Questionario_Estresse)## Aluno Turma Mora_pais RJ Namorado_a
## Min. : 1.0 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:24.5 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :48.0 Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :48.0 Mean :2.074 Mean :1.537 Mean :1.653 Mean :1.505
## 3rd Qu.:71.5 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :95.0 Max. :3.000 Max. :2.000 Max. :2.000 Max. :2.000
##
## Trabalha Desempenho Estresse Créditos
## Min. :1.000 Min. :5.820 Min. :12.00 Min. :15.00
## 1st Qu.:1.000 1st Qu.:8.500 1st Qu.:22.50 1st Qu.:23.00
## Median :2.000 Median :8.700 Median :27.00 Median :24.00
## Mean :1.621 Mean :8.594 Mean :27.82 Mean :24.95
## 3rd Qu.:2.000 3rd Qu.:9.050 3rd Qu.:33.00 3rd Qu.:27.00
## Max. :2.000 Max. :9.700 Max. :44.00 Max. :49.00
## NA's :1
## Horas_estudo
## Min. :19.00
## 1st Qu.:25.00
## Median :30.00
## Mean :30.73
## 3rd Qu.:35.00
## Max. :60.00
## Inspecionar os dados
Aqui inspecionamos os dados da base de dados através do comando “str” e podemos observar que as variáveis são numerais.
str(Questionario_Estresse)## tibble [95 x 10] (S3: tbl_df/tbl/data.frame)
## $ Aluno : num [1:95] 1 2 3 4 5 6 7 8 9 10 ...
## $ Turma : num [1:95] 1 1 1 1 1 1 1 1 1 1 ...
## $ Mora_pais : num [1:95] 2 1 2 2 2 2 2 1 1 1 ...
## $ RJ : num [1:95] 2 1 2 2 2 2 2 1 1 1 ...
## $ Namorado_a : num [1:95] 2 2 2 1 2 1 2 1 2 2 ...
## $ Trabalha : num [1:95] 2 2 2 1 1 1 2 1 1 1 ...
## $ Desempenho : num [1:95] 8.89 8.8 8 8.8 8.9 8.1 9.2 8.5 8.7 8.3 ...
## $ Estresse : num [1:95] 23 24 25 38 41 25 41 20 26 36 ...
## $ Créditos : num [1:95] 27 28 25 21 18 29 26 24 20 49 ...
## $ Horas_estudo: num [1:95] 27 28 25 30 20 32 25 25 25 59 ...gráfico de pizza
Aqui fizemos um gráfico de pizza sobre a variável créditos pelo comando “pie”, nele podemos identificar que a maioria dos alunos recebe 24 de crédito.
pie(table(Questionario_Estresse$Créditos))
# tabelas Aqui fizemos a tabela de estresse e créditos comparada à horas de estudos. Podemos observar que a média de horas para estudo são de 30 horas, sendo o mínimo de 19 horas e o máximo de 60 horas, já na variável estresse temos a media de 27.82, sendo o mínimo de 12 e o máximo de 44 e nos créditos temos a média de 24.95, com mínimo de 15 e máximo de 49.
tabela_estresse <- table(Questionario_Estresse$Horas_estudo, Questionario_Estresse$Estresse)
tabela_creditos <- table(Questionario_Estresse$Horas_estudo, Questionario_Estresse$Créditos)
tabela_estresse##
## 12 13 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
## 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 20 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 24 0 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 1 1 1 0 0 1 1 1 0 0 1 0 0 0 0 0 0 1
## 26 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
## 27 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0
## 30 0 0 0 1 0 0 0 1 0 0 2 0 1 2 1 1 1 1 0 1 2 0 2 0 1
## 32 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 0 0
## 33 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0
## 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 0 0 1 1 0 0 1 0 0 0
## 36 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 1 0 0 1 0 0
## 40 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0
## 44 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0
## 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
##
## 38 39 40 41 42 44
## 19 0 0 0 0 0 0
## 20 0 0 1 1 0 0
## 21 0 0 0 0 0 0
## 23 0 0 0 0 0 0
## 24 0 0 0 0 0 0
## 25 0 0 0 1 0 0
## 26 0 0 0 0 0 0
## 27 0 0 0 0 0 0
## 28 0 0 0 0 1 1
## 29 0 0 0 0 0 0
## 30 1 0 0 0 0 0
## 32 0 0 0 0 0 1
## 33 0 0 0 0 1 0
## 34 0 0 0 0 0 0
## 35 0 0 0 0 0 0
## 36 0 0 0 0 0 0
## 40 1 1 0 0 0 0
## 44 1 0 0 0 0 0
## 59 0 0 0 0 0 0
## 60 0 0 0 0 0 0tabela_creditos##
## 15 17 18 19 20 21 23 24 25 26 27 28 29 30 32 49
## 19 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 20 0 1 1 0 2 0 1 1 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 23 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0
## 24 0 0 0 0 0 0 0 4 0 1 1 0 0 0 0 0
## 25 0 0 0 0 2 1 2 2 1 1 0 0 0 0 0 0
## 26 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 28 0 0 0 0 0 0 1 1 0 1 0 2 0 0 0 0
## 29 0 0 0 0 1 0 0 3 0 0 0 0 0 0 0 0
## 30 0 0 0 0 1 1 2 6 1 3 1 2 0 0 1 0
## 32 0 0 0 0 1 0 0 1 0 0 0 2 1 0 0 0
## 33 0 0 0 0 0 0 0 0 0 0 3 2 0 1 0 0
## 34 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 35 0 0 0 0 2 0 1 0 0 0 2 0 0 1 0 0
## 36 0 0 0 0 2 0 0 2 0 0 0 1 0 1 0 0
## 40 0 0 0 0 0 0 1 0 0 3 0 2 1 1 0 0
## 44 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0
## 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 60 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0prop.table(tabela_estresse,1)##
## 12 13 15 16 17 18
## 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
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## 21 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
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## 25 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
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##
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##
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## 35 0.00000000 0.33333333 0.00000000 0.00000000 0.00000000 0.16666667
## 36 0.16666667 0.00000000 0.16666667 0.00000000 0.00000000 0.16666667
## 40 0.12500000 0.12500000 0.00000000 0.00000000 0.00000000 0.00000000
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## 59 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
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##
## 31 32 33 34 35 36
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## 29 0.25000000 0.25000000 0.25000000 0.00000000 0.00000000 0.00000000
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## 32 0.00000000 0.00000000 0.20000000 0.00000000 0.00000000 0.00000000
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## 40 0.00000000 0.12500000 0.00000000 0.12500000 0.00000000 0.00000000
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## 60 1.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
##
## 37 38 39 40 41 42
## 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
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## 21 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
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## 26 0.25000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
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## 28 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.16666667
## 29 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 30 0.05555556 0.05555556 0.00000000 0.00000000 0.00000000 0.00000000
## 32 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
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## 34 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 35 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 36 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 40 0.00000000 0.12500000 0.12500000 0.00000000 0.00000000 0.00000000
## 44 0.00000000 0.33333333 0.00000000 0.00000000 0.00000000 0.00000000
## 59 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 60 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
##
## 44
## 19 0.00000000
## 20 0.00000000
## 21 0.00000000
## 23 0.00000000
## 24 0.00000000
## 25 0.00000000
## 26 0.00000000
## 27 0.00000000
## 28 0.16666667
## 29 0.00000000
## 30 0.00000000
## 32 0.20000000
## 33 0.00000000
## 34 0.00000000
## 35 0.00000000
## 36 0.00000000
## 40 0.00000000
## 44 0.00000000
## 59 0.00000000
## 60 0.00000000prop.table(tabela_estresse,2)##
## 12 13 15 16 17 18 19
## 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 20 0.0000000 1.0000000 0.0000000 0.3333333 0.3333333 0.0000000 0.0000000
## 21 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 23 0.0000000 0.0000000 1.0000000 0.0000000 0.3333333 0.0000000 0.0000000
## 24 0.0000000 0.0000000 0.0000000 0.3333333 0.3333333 1.0000000 0.0000000
## 25 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 26 1.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 27 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 28 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.0000000
## 29 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 30 0.0000000 0.0000000 0.0000000 0.3333333 0.0000000 0.0000000 0.0000000
## 32 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 33 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 34 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 35 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 36 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 40 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 44 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 59 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 60 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
##
## 20 21 22 23 24 25 26
## 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 20 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 21 0.0000000 0.2000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 23 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 24 0.2000000 0.0000000 0.0000000 0.0000000 0.2500000 0.0000000 0.0000000
## 25 0.2000000 0.2000000 0.5000000 0.0000000 0.0000000 0.1666667 0.1250000
## 26 0.2000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1250000
## 27 0.0000000 0.0000000 0.0000000 0.3333333 0.0000000 0.0000000 0.0000000
## 28 0.2000000 0.0000000 0.5000000 0.0000000 0.2500000 0.0000000 0.0000000
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## 30 0.2000000 0.0000000 0.0000000 0.6666667 0.0000000 0.1666667 0.2500000
## 32 0.0000000 0.0000000 0.0000000 0.0000000 0.2500000 0.1666667 0.0000000
## 33 0.0000000 0.2000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
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## 44 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1250000
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##
## 27 28 29 30 31 32 33
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## 30 0.3333333 0.5000000 0.2500000 0.1666667 0.0000000 0.2500000 0.4000000
## 32 0.0000000 0.0000000 0.2500000 0.0000000 0.0000000 0.0000000 0.2000000
## 33 0.0000000 0.5000000 0.0000000 0.1666667 0.2000000 0.0000000 0.0000000
## 34 0.0000000 0.0000000 0.2500000 0.0000000 0.0000000 0.0000000 0.0000000
## 35 0.0000000 0.0000000 0.0000000 0.1666667 0.2000000 0.0000000 0.0000000
## 36 0.3333333 0.0000000 0.0000000 0.1666667 0.2000000 0.2500000 0.0000000
## 40 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.2500000 0.0000000
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## 60 0.0000000 0.0000000 0.0000000 0.0000000 0.2000000 0.0000000 0.0000000
##
## 34 35 36 37 38 39 40
## 19 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
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## 30 0.0000000 0.6666667 0.0000000 0.3333333 0.3333333 0.0000000 0.0000000
## 32 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 33 0.3333333 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
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##
## 41 42 44
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## 40 0.0000000 0.0000000 0.0000000
## 44 0.0000000 0.0000000 0.0000000
## 59 0.0000000 0.0000000 0.0000000
## 60 0.0000000 0.0000000 0.0000000prop.table(tabela_creditos,1)*100##
## 15 17 18 19 20 21
## 19 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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## 26 0.000000 0.000000 0.000000 25.000000 0.000000 0.000000
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## 30 0.000000 0.000000 0.000000 0.000000 5.555556 5.555556
## 32 0.000000 0.000000 0.000000 0.000000 20.000000 0.000000
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##
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## 28 20.000000 20.000000 0.000000 20.000000 0.000000 40.000000
## 29 0.000000 75.000000 0.000000 0.000000 0.000000 0.000000
## 30 11.111111 33.333333 5.555556 16.666667 5.555556 11.111111
## 32 0.000000 20.000000 0.000000 0.000000 0.000000 40.000000
## 33 0.000000 0.000000 0.000000 0.000000 50.000000 33.333333
## 34 0.000000 100.000000 0.000000 0.000000 0.000000 0.000000
## 35 16.666667 0.000000 0.000000 0.000000 33.333333 0.000000
## 36 0.000000 33.333333 0.000000 0.000000 0.000000 16.666667
## 40 12.500000 0.000000 0.000000 37.500000 0.000000 25.000000
## 44 0.000000 66.666667 0.000000 0.000000 0.000000 33.333333
## 59 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 60 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000
##
## 29 30 32 49
## 19 0.000000 0.000000 0.000000 0.000000
## 20 0.000000 0.000000 0.000000 0.000000
## 21 0.000000 0.000000 0.000000 0.000000
## 23 0.000000 0.000000 0.000000 0.000000
## 24 0.000000 0.000000 0.000000 0.000000
## 25 0.000000 0.000000 0.000000 0.000000
## 26 0.000000 0.000000 0.000000 0.000000
## 27 0.000000 0.000000 0.000000 0.000000
## 28 0.000000 0.000000 0.000000 0.000000
## 29 0.000000 0.000000 0.000000 0.000000
## 30 0.000000 0.000000 5.555556 0.000000
## 32 20.000000 0.000000 0.000000 0.000000
## 33 0.000000 16.666667 0.000000 0.000000
## 34 0.000000 0.000000 0.000000 0.000000
## 35 0.000000 16.666667 0.000000 0.000000
## 36 0.000000 16.666667 0.000000 0.000000
## 40 12.500000 12.500000 0.000000 0.000000
## 44 0.000000 0.000000 0.000000 0.000000
## 59 0.000000 0.000000 0.000000 100.000000
## 60 0.000000 0.000000 0.000000 0.000000prop.table(tabela_creditos,2)*100##
## 15 17 18 19 20 21
## 19 100.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 20 0.000000 100.000000 100.000000 0.000000 18.181818 0.000000
## 21 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 23 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 24 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 25 0.000000 0.000000 0.000000 0.000000 18.181818 50.000000
## 26 0.000000 0.000000 0.000000 100.000000 0.000000 0.000000
## 27 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 28 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 29 0.000000 0.000000 0.000000 0.000000 9.090909 0.000000
## 30 0.000000 0.000000 0.000000 0.000000 9.090909 50.000000
## 32 0.000000 0.000000 0.000000 0.000000 9.090909 0.000000
## 33 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 34 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 35 0.000000 0.000000 0.000000 0.000000 18.181818 0.000000
## 36 0.000000 0.000000 0.000000 0.000000 18.181818 0.000000
## 40 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 44 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 59 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 60 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
##
## 23 24 25 26 27 28
## 19 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 20 11.111111 4.166667 0.000000 0.000000 0.000000 0.000000
## 21 0.000000 0.000000 0.000000 0.000000 0.000000 7.142857
## 23 0.000000 4.166667 0.000000 0.000000 10.000000 0.000000
## 24 0.000000 16.666667 0.000000 10.000000 10.000000 0.000000
## 25 22.222222 8.333333 50.000000 10.000000 0.000000 0.000000
## 26 11.111111 0.000000 0.000000 0.000000 10.000000 7.142857
## 27 0.000000 0.000000 0.000000 0.000000 10.000000 0.000000
## 28 11.111111 4.166667 0.000000 10.000000 0.000000 14.285714
## 29 0.000000 12.500000 0.000000 0.000000 0.000000 0.000000
## 30 22.222222 25.000000 50.000000 30.000000 10.000000 14.285714
## 32 0.000000 4.166667 0.000000 0.000000 0.000000 14.285714
## 33 0.000000 0.000000 0.000000 0.000000 30.000000 14.285714
## 34 0.000000 4.166667 0.000000 0.000000 0.000000 0.000000
## 35 11.111111 0.000000 0.000000 0.000000 20.000000 0.000000
## 36 0.000000 8.333333 0.000000 0.000000 0.000000 7.142857
## 40 11.111111 0.000000 0.000000 30.000000 0.000000 14.285714
## 44 0.000000 8.333333 0.000000 0.000000 0.000000 7.142857
## 59 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 60 0.000000 0.000000 0.000000 10.000000 0.000000 0.000000
##
## 29 30 32 49
## 19 0.000000 0.000000 0.000000 0.000000
## 20 0.000000 0.000000 0.000000 0.000000
## 21 0.000000 0.000000 0.000000 0.000000
## 23 0.000000 0.000000 0.000000 0.000000
## 24 0.000000 0.000000 0.000000 0.000000
## 25 0.000000 0.000000 0.000000 0.000000
## 26 0.000000 0.000000 0.000000 0.000000
## 27 0.000000 0.000000 0.000000 0.000000
## 28 0.000000 0.000000 0.000000 0.000000
## 29 0.000000 0.000000 0.000000 0.000000
## 30 0.000000 0.000000 100.000000 0.000000
## 32 50.000000 0.000000 0.000000 0.000000
## 33 0.000000 25.000000 0.000000 0.000000
## 34 0.000000 0.000000 0.000000 0.000000
## 35 0.000000 25.000000 0.000000 0.000000
## 36 0.000000 25.000000 0.000000 0.000000
## 40 50.000000 25.000000 0.000000 0.000000
## 44 0.000000 0.000000 0.000000 0.000000
## 59 0.000000 0.000000 0.000000 100.000000
## 60 0.000000 0.000000 0.000000 0.000000gráfico de barras
No gráfico de barras podemos observar melhor as informações já ditas. Usamos o comando Barplot para analisar uma tabela de estresse e uma tabela de creditos. Nele, podemos observar melhor os máximos e mínimos e os númros mais comuns na tabela.
barplot(tabela_estresse)
barplot(tabela_estresse, main= "Gráfico 1 - Estresse por horas", horiz= FALSE,
beside= TRUE, legend= rownames(tabela_estresse))
COR <- c("red", "blue","red", "blue")
barplot(tabela_creditos,main ="Gráfico 2 - Crédito por horas",
col = COR,horiz =FALSE,beside=TRUE,
legend = rownames(tabela_creditos))
# tabela em propoção Aqui fizemos uma tabela de proporção de desempenho e horas de estudos. Nessa tabela podemos observar a média de 8.594, o máximo de 9.700 e o mínimo de 5.820 em relação ao desempenho dos alunos.
library(dplyr)## Warning: package 'dplyr' was built under R version 4.1.1##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniontabela_desempenho <- Questionario_Estresse %>% select(Desempenho, Horas_estudo) %>% table()
tabela_desempenho_prop <- Questionario_Estresse %>% select(Desempenho, Horas_estudo) %>% table() %>%
prop.table(1) %>% round(2)
tabela_desempenho_prop## Horas_estudo
## Desempenho 19 20 21 23 24 25 26 27 28 29 30 32 33
## 5.82 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 5.9 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00
## 6.5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00
## 6.6 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 7 0.00 0.50 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 7.5 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 7.7 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 7.8 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8 0.00 0.00 0.00 0.00 0.00 0.40 0.00 0.00 0.00 0.20 0.00 0.20 0.20
## 8.1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.50 0.00
## 8.2 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.3 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.4 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50
## 8.48 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.5 0.00 0.00 0.00 0.00 0.07 0.07 0.14 0.00 0.14 0.07 0.43 0.00 0.00
## 8.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00
## 8.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00
## 8.6 0.00 0.00 0.00 0.25 0.00 0.25 0.25 0.00 0.00 0.00 0.00 0.00 0.00
## 8.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.7 0.00 0.25 0.00 0.00 0.00 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.79 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00
## 8.8 0.00 0.00 0.00 0.00 0.25 0.00 0.00 0.00 0.12 0.00 0.50 0.00 0.12
## 8.82 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00
## 8.89 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00
## 8.9 0.00 0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.00 0.00 0.00 0.00
## 8.94 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.96 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.40 0.00
## 9.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00
## 9.1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00
## 9.2 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.50 0.00 0.00
## 9.29 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00
## 9.3 0.00 0.00 0.00 0.50 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00
## 9.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00
## 9.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.00 0.00
## 9.39 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.4 0.00 0.00 0.00 0.00 0.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.45 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.7 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## Horas_estudo
## Desempenho 34 35 36 40 44 59 60
## 5.82 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 5.9 0.00 0.00 1.00 0.00 0.00 0.00 0.00
## 6 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 6.5 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 6.6 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 7 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 7.5 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 7.7 0.00 0.00 1.00 0.00 0.00 0.00 0.00
## 7.8 0.00 0.00 0.00 1.00 0.00 0.00 0.00
## 8 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.1 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.3 0.00 0.00 0.00 0.00 0.00 0.50 0.00
## 8.4 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.5 0.00 0.00 0.00 0.00 0.07 0.00 0.00
## 8.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.6 0.00 0.25 0.00 0.00 0.00 0.00 0.00
## 8.64 0.00 0.00 0.00 1.00 0.00 0.00 0.00
## 8.7 0.00 0.00 0.00 0.50 0.00 0.00 0.00
## 8.79 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.8 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.82 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 8.89 0.00 0.00 0.00 0.50 0.00 0.00 0.00
## 8.9 0.00 0.00 0.00 0.00 0.33 0.00 0.00
## 8.94 0.00 0.00 1.00 0.00 0.00 0.00 0.00
## 8.96 0.00 1.00 0.00 0.00 0.00 0.00 0.00
## 9 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.03 0.00 1.00 0.00 0.00 0.00 0.00 0.00
## 9.07 0.00 0.00 0.00 1.00 0.00 0.00 0.00
## 9.08 0.00 1.00 0.00 0.00 0.00 0.00 0.00
## 9.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.1 0.00 1.00 0.00 0.00 0.00 0.00 0.00
## 9.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.2 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.29 0.00 0.00 0.00 0.50 0.00 0.00 0.00
## 9.3 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.36 0.00 0.00 0.00 0.00 0.00 0.00 0.50
## 9.39 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.4 0.00 0.00 0.33 0.00 0.33 0.00 0.00
## 9.44 0.00 0.00 1.00 0.00 0.00 0.00 0.00
## 9.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 9.5 0.00 0.00 0.00 1.00 0.00 0.00 0.00
## 9.6 0.50 0.00 0.50 0.00 0.00 0.00 0.00
## 9.7 0.00 1.00 0.00 0.00 0.00 0.00 0.00tabela_desempenho %>% barplot(beside=T,
main= "Gráfico 3",
legend = rownames(tabela_desempenho),
args.legend = list(x = "top"))