1 Pacotes

library(tidyverse)
library(kableExtra)
library(data.table)
library(gridExtra)

2 Marcas mais relacionadas com o Rugby

2.1 Ajustando o conjunto de dados

ResMul <- data.frame(Freq = colSums(Marcas_ASS[2:21]),
                     Pct_Respostas = (colSums(Marcas_ASS[2:21]) / sum(Marcas_ASS[2:21]))*100,
                     Pct_Casos = (colSums(Marcas_ASS[2:21]) / nrow(Marcas_ASS[2:21]))*100)

2.2 Tabela

ResMul <- ResMul[order(-ResMul$Freq), ] 
ResMul %>% kbl %>% kable_classic(full_width = F, html_font = "Cambria")
Freq Pct_Respostas Pct_Casos
Adidas 9 18.367347 69.230769
Kipsta 5 10.204082 38.461539
Topper 4 8.163265 30.769231
Gilbert 4 8.163265 30.769231
Canterbury 4 8.163265 30.769231
Nike 4 8.163265 30.769231
Under.Armor 3 6.122449 23.076923
Penalty 2 4.081633 15.384615
Kevingston 2 4.081633 15.384615
Sulback 2 4.081633 15.384615
Kappa 1 2.040816 7.692308
Blk 1 2.040816 7.692308
Flash 1 2.040816 7.692308
Offload 1 2.040816 7.692308
Fila 1 2.040816 7.692308
Umbro 1 2.040816 7.692308
Asics 1 2.040816 7.692308
Soul.Rugby 1 2.040816 7.692308
Puma 1 2.040816 7.692308
Mikasa 1 2.040816 7.692308

2.2.1 Reordenando

setDT(ResMul, keep.rownames = T)

colnames(ResMul)[1] <- 'Marca'

ResMul2 <- transform(ResMul, Marca = reorder(Marca, Freq))

2.3 Gráfico

g1 <- ggplot(ResMul2, aes(y = Marca, weight = Freq, fill = Freq)) +
  geom_bar(show.legend = F) +
  scale_fill_continuous(low = "#87b5c5", high = "#3c525a") +
  scale_x_continuous(breaks = c(0,3,6,9)) +
  theme_minimal(base_size = 10) +
  theme(text=element_text(family= "Times New Roman", face="bold"),
        plot.title = element_text(hjust = 0.5))+
  labs(title = "Marcas mais associadas ao Rugby") + xlab("Contagem") + ylab("Marca")

g1

3 Marcas menos assocadas com o Rugby

3.1 Ajustando o conjunto de dados

ResMul2 <- data.frame(Freq = colSums(Marcas_N_ASS[2:16]),
                     Pct_Respostas = (colSums(Marcas_N_ASS[2:16]) / sum(Marcas_N_ASS[2:16]))*100,
                     Pct_Casos = (colSums(Marcas_N_ASS[2:16]) / nrow(Marcas_N_ASS[2:16]))*100)

3.2 Tabela

ResMul2 <- ResMul2[order(-ResMul2$Freq), ] 
ResMul2 %>% kbl %>% kable_classic(full_width = F, html_font = "Cambria")
Freq Pct_Respostas Pct_Casos
Puma 6 18.750 46.153846
Nike 5 15.625 38.461539
Reebok 3 9.375 23.076923
Adidas 2 6.250 15.384615
Fila 2 6.250 15.384615
Under.Armor 2 6.250 15.384615
Umbro 2 6.250 15.384615
Kappa 2 6.250 15.384615
Mizuno 2 6.250 15.384615
Olympikus 1 3.125 7.692308
Peak 1 3.125 7.692308
Skcetchers 1 3.125 7.692308
Diadora 1 3.125 7.692308
Penalty 1 3.125 7.692308
Asics 1 3.125 7.692308

3.2.1 Reordenando

setDT(ResMul2, keep.rownames = T)

colnames(ResMul2)[1] <- 'Marca'

ResMul2 <- transform(ResMul2, Marca = reorder(Marca, Freq))

3.3 Gráfico

g2 <- ggplot(ResMul2, aes(y = Marca, weight = Freq, fill = Freq)) +
  geom_bar(show.legend = F) +
  scale_fill_continuous(low = "#87b5c5", high = "#3c525a") +
  scale_x_continuous(breaks = c(0,3,6,9)) +
  theme_minimal(base_size = 10) +
  theme(text=element_text(family= "Times New Roman", face="bold"),
        plot.title = element_text(hjust = 0.5))+
  labs(title = "Marcas menos associadas ao Rugby") + xlab("Contagem") + ylab("Marca")

g2

grid.arrange(g1, g2, ncol = 2, nrow = 1)