Atividades
Kimberly Silva
ATIVIDADE 1
Gráfico e/ou estatística que incomoda.
O gráfico representado acima foi feito pelo INPE (Instituto Nacional de Pesquisas Espaciais), ainda em agosto de 2019, onde as queimadas da região amazônica teve seu maior número já registrado e visto nos útimos anos, veículos de informação sensacionalistas e que usam de fake news, utilizou de tal gráfico para representar que o respectivo ano é o que obteve menor registro de queimadas no Brasil. Porém, alguns pontos: o ano ainda não acabou, o que contradiz com a média de focos utilizadas com os outros anos (período de 1 ano), as informações então estão incompletas, invalidando esse comparativo. Outro foco que vale a pena comparar é sobre o que lidera ser no governo Lula, porém estamos no primeiro ano de governo Bolsonaro, onde o ano não acabou e em agosto esse número de queimadas estava representando uns 8 mil quase 9 contra os 14 mil aproximadamente de 2005. Então fica o questionamento do verdadeiro por quê esse gráfico foi lançado.
Atividade 2
Gráfico do Banco de Dados Pokémon
library(readxl)
pokemon<-load("/Users/kimberly/df_pokemon (1).rdata")
pie(table(df$egg_group_2), main= "Gráfico Pizza Pokemon", col="hotpink1")
Foi realizado um gráfico de pizza a partir do egg group 2 da base de dado pokemon, o que não foi muito fácil por ser uma base de dados com muitas variáveis, deixando um tanto quanto confuso. Plant, Dragon e Ground lideram, enquanto Bug e Flying são os menores no quesito estudado.
Atividade 3
Histograma
O histograma abaixo demonstra a relação de quantidades de estados brasileiros que se enquadram em determinado PIB (Produto Interno Bruto). É notório que a maioria dos estados obtem um PIB abaixo, quase na linha em que se começa o gráfico e apenas um, ficando como outliner, está acima de todos os outros, detendo do maior PIB do país.
options(scipen = 999)
library(readxl)
BasesEstados <- read_excel("/Users/kimberly/BasesEstados.xlsx")
library(ggplot2)
ggplot(BasesEstados) +
aes(x = Estado, weight = PIB) +
geom_bar(fill = "#f781bf") +
labs(x = "Estado", y = "PIB (Produto Interno Bruto)", title = "Relação PIB por Estado") +
theme_classic()
Atividade 4
Tabela Cruzada
Passo 1: Importação base de Dados Titanic
library(readxl)
load("/Users/kimberly/Titanic (1).rdata")Cruzamento dos Dados
# Quali X Quali
Titanic$Sexo<-as.factor(Titanic$Sexo)
Titanic$Sobreviveu<-as.factor(Titanic$Sobreviveu)
# Tabela Cruzada (duas variáveis qualitativas)
tabela1<-table(Titanic$Sexo, Titanic$Sobreviveu)
# Prop
prop.table(table(Titanic$Sexo, Titanic$Sobreviveu),1)*100##
## Não sobreviveu Sobreviveu
## Feminino 26.80851 73.19149
## Masculino 78.84393 21.15607Conclusão
Resulta-se então que, como dito no filme “mulheres e crianças primeiro”, realmente, mais mulheres sobreviveram, contrapondo o número de não sobreviventes masculinos. Basicamente o números de não sobreviventes mulheres (26.80851) é o mesmo de sobreviventes homens (2115607).
Atividade 5
Análise de Pokemons
Carregando base de dados
library(readxl)
load("/Users/kimberly/df_pokemon (1).rdata")Gráfico com Cruzamento de Dados
boxplot(df$height~df$type_1, col=c('turquoise2','palegoldenrod','turquoise'), main = 'Pokemon tipo 1 por peso', xlab = 'Tipos 1', ylab = 'Peso')
Tabela de Cruzamento de Dados
tabela<-table(df$height, df$type_1)
tabela##
## bug dark dragon electric fairy fighting fire flying ghost grass
## 1 1 0 0 0 1 0 0 0 0 0
## 2 1 0 0 2 2 0 0 0 0 3
## 3 11 0 1 3 2 0 0 0 1 3
## 4 2 2 0 5 1 0 2 0 3 8
## 5 6 5 0 3 0 1 4 1 1 5
## 6 5 2 2 3 3 3 5 0 2 5
## 7 3 1 1 0 0 2 5 0 1 4
## 8 5 1 1 4 2 2 1 0 1 4
## 9 1 2 0 1 0 1 4 0 2 4
## 10 7 2 1 2 1 2 4 0 2 8
## 11 3 3 2 1 1 0 2 0 1 3
## 12 7 2 0 3 0 2 2 0 1 3
## 13 0 0 0 0 1 2 2 0 1 2
## 14 2 2 2 2 1 5 1 0 0 1
## 15 5 2 1 2 1 2 2 2 2 0
## 16 1 2 1 2 0 1 2 0 2 1
## 17 0 0 0 0 0 0 4 0 1 3
## 18 1 1 2 1 0 0 0 0 0 1
## 19 1 0 1 1 0 0 3 0 0 0
## 20 0 0 2 0 0 0 1 0 0 5
## 21 0 0 0 1 0 1 1 0 0 0
## 22 0 0 1 0 0 0 0 0 1 2
## 23 0 0 0 0 0 1 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 0
## 25 1 0 0 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0
## 29 0 0 1 0 0 0 0 0 0 0
## 30 0 0 1 0 1 0 0 0 0 0
## 32 0 0 1 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0 1
## 35 0 0 0 0 0 0 0 0 0 0
## 37 0 0 0 0 0 0 0 0 0 0
## 38 0 0 0 0 0 0 1 0 0 0
## 40 0 0 1 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0 0 0 0
## 45 0 0 0 0 0 0 0 0 1 0
## 50 0 0 1 0 0 0 0 0 0 0
## 52 0 0 0 0 0 0 0 0 0 0
## 54 0 0 0 0 0 0 0 0 0 0
## 58 0 1 0 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0 0 0 0
## 65 0 0 0 0 0 0 0 0 0 0
## 70 0 0 1 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0
## 145 0 0 0 0 0 0 0 0 0 0
##
## ground ice normal poison psychic rock steel water
## 1 0 0 0 0 0 0 0 0
## 2 1 0 1 0 2 0 1 0
## 3 1 0 11 0 5 1 2 4
## 4 1 3 7 3 5 3 1 10
## 5 2 1 8 2 2 5 1 8
## 6 1 0 10 2 7 1 4 11
## 7 5 1 3 1 2 1 0 3
## 8 1 2 5 3 1 1 2 7
## 9 0 1 2 2 4 2 1 5
## 10 5 1 7 1 3 5 0 7
## 11 3 4 6 0 1 0 0 6
## 12 0 0 10 2 0 3 1 9
## 13 0 2 1 3 3 4 1 5
## 14 0 2 4 1 1 3 0 2
## 15 3 1 6 0 4 2 0 6
## 16 0 0 2 1 3 1 1 3
## 17 0 1 1 1 1 2 2 4
## 18 0 1 3 2 0 1 0 3
## 19 1 0 1 1 0 1 1 0
## 20 3 1 1 1 1 1 0 3
## 21 0 0 1 0 0 0 2 1
## 22 0 0 1 0 0 0 0 1
## 23 0 0 0 0 0 0 0 1
## 24 1 0 0 0 0 0 0 0
## 25 0 1 0 0 0 1 0 1
## 26 0 1 0 0 0 0 0 0
## 27 0 0 0 1 0 1 0 0
## 28 1 0 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0
## 32 0 0 1 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0
## 35 1 0 0 1 0 0 0 0
## 37 0 0 1 0 0 0 0 0
## 38 0 0 0 0 0 0 0 0
## 40 0 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0 1
## 45 0 0 0 0 0 0 0 1
## 50 0 0 0 0 0 0 0 0
## 52 0 0 0 0 1 0 0 0
## 54 0 0 0 0 0 0 1 0
## 58 0 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0 1
## 65 0 0 0 0 0 0 0 1
## 70 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 1 0 0
## 92 0 0 0 0 0 0 1 0
## 145 0 0 0 0 0 0 0 1A conclusão é de que pokemóns por peso por tipo 1 do mesmo pelo boxplot que a espécie “dragon” é mais pesada, enquanto as de “water” possui um número maior de outliers.
Atividade 6 e 7
Gerando TABELA da Base de Dados Pokemon:
1.Importando a base de dados pokemon:
library(readxl)
load("/Users/kimberly/df_pokemon (1).rdata")2.Gerando a Tabela:
tabela<- table(df$speed, df$height)
tabela##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## 5 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 1 2 2 0 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 20 0 1 4 1 2 0 1 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 24 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 25 0 0 2 1 0 0 0 2 1 2 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 29 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 2 1 9 5 1 3 0 3 0 2 1 0 0 1 1 0 0 1 1 0 0 0 0 0
## 31 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 32 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 34 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 35 0 0 1 5 1 7 2 2 0 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 36 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 38 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 39 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 0
## 40 0 0 2 6 3 3 1 3 1 3 1 5 0 1 0 0 0 0 2 0 0 0 0 1 0 0
## 41 0 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
## 42 1 0 1 0 2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 43 0 0 1 0 1 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 44 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 45 0 1 2 3 3 1 1 1 2 3 2 0 1 4 3 0 0 0 0 0 0 1 0 0 0 0
## 46 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 48 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 49 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 2 5 3 5 4 3 0 1 5 4 0 0 0 0 2 2 1 1 2 0 1 0 0 1
## 51 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 52 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## 55 0 1 2 1 4 1 1 2 1 5 1 0 1 1 1 2 1 1 0 2 0 0 0 0 0 0
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## 57 0 0 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 58 0 0 0 0 0 0 0 0 1 1 2 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0
## 59 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 60 0 1 2 4 4 3 3 4 3 5 2 3 1 0 1 0 2 0 0 1 0 2 0 0 1 0
## 61 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 62 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 64 0 0 0 0 1 3 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 65 1 0 1 1 2 8 2 3 3 5 1 3 0 2 1 1 0 1 0 0 0 0 0 0 0 0
## 66 0 0 1 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 67 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 68 0 0 2 0 0 0 0 0 0 0 0 0 2 0 1 0 1 0 0 0 0 0 0 0 0 0
## 69 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 70 0 1 3 1 4 0 0 0 1 3 4 2 1 2 3 3 3 0 0 0 1 0 0 0 0 0
## 71 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 72 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 75 0 1 0 1 0 1 1 0 1 1 1 2 1 1 1 0 0 0 1 0 0 0 0 0 0 0
## 76 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## 77 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0
## 79 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 80 0 0 1 1 0 3 0 0 3 1 2 3 4 0 3 2 0 1 1 2 0 1 0 0 1 0
## 81 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 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 83 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 84 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 85 0 0 1 1 3 2 3 2 0 0 1 1 1 4 4 0 2 1 0 1 0 0 0 0 0 0
## 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 90 0 0 0 2 0 3 0 0 3 2 1 1 2 1 1 1 0 1 0 1 0 1 0 0 0 0
## 91 0 0 1 0 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 95 0 1 1 3 0 1 0 0 1 2 0 1 2 2 1 1 0 2 3 1 0 0 0 0 0 0
## 97 0 0 0 0 0 2 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
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## 100 0 1 2 2 2 1 0 0 0 0 1 3 0 0 1 3 2 1 0 2 1 0 0 0 0 0
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## 103 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 108 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0
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## 111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0
## 112 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
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## 115 0 0 1 0 1 0 0 0 1 1 2 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## 116 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 118 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 120 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0
## 122 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 125 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 126 0 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
## 130 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0
## 140 0 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
## 145 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 150 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 0
## 160 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 27 28 29 30 32 33 35 37 38 40 42 45 50 52 54 58 62 65 70 88 92 145
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 122 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 125 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 130 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 145 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 160 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03.Gerando o coeficiente de relação entre as váriaveis quali e quantitativas de Pokemon
rho <- cor(df$speed, df$height)
rho## [1] 0.22494394.Diagrama de dispersão entre as variáveis:
par(mfrow=c(1,1))
plot(df$height,df$speedt, col = "palegreen", pch=20, xlab = "velocidade", ylab = "altura", main = "Diagrama de dispersão de Altura por Velocidade")## Warning: Unknown or uninitialised column: 'speedt'.abline(lm(df$height~df$speed), col = "seagreen")
5.Conclusão:
Observando o gráfico é possível notar que quanto menor o pokemon, maior sua velocidade, tirando alguns outliers presentes. O gráfico é disperso na linha x, de velocidade apenas, fazendo sua altura variar pouco, enquanto a velocidade varia muito. Há poucos casos de pokemons grandes com boas velocidades sendo um que tem a velocidade marcada no 500 ser maior que 50. O mesmo ocorre com dois outliers presentes após a faixa que marca a velocidade de 700.
6.Gráfico Inédito
ggplot(df) +
aes(x = speed, y = height) +
geom_line(size = 1L, colour = "#fcfbfd") +
labs(title = "Relação velocidade por altura de Pokemon") +
theme_dark()
6.1.Conclusão do gráfico inédito
Ficou explícito o mesmo que pode ser observado no gráfico anterior, pokemons menores tendem a dispersar e ter maiores velocidade, exceçãp de alguns outliers, sendo um caso de altura máxima no gráfico e velocidade na casa dos 350.
