Corporate
Commercial Leevin
2023-06-07
#DELIVERABLES
#GRAPHS INFO HERE
#FINDINGS
#Spotahome e Airbnb tem o maior numero de reservas #Mountjoy tem gap entre meados de Agosto e meados de Outubro #Possui 4 meses do ano com 100% de ocupação #Marlborough tem gap entre meados de agosto e setembro #Possui 5 meses do ano com 100% de ocupação #the8mews #Possui gap em agosto e entre outubro e dezembro #Possui 3 meses do ano com 100% de ocupação #Westmoreland possui Gap entre agosto e meados de setembro # 4 meses 100% # bolton julho e meados de setembro tem gap #4 meses 100% #apartamento novo e com 2 reservas para mais de 30 dias # disponivel entre setembro e dezembro
#RECOMMENDATION
#Better work in august and september
Percentual Geral Por Plataforma
p_ocup_per_mon <- merged %>%
mutate(Month=factor(Month, levels = month.name)) %>%
ggplot(mapping = aes(x=Month, y=per, fill=Platform))+
geom_bar(position=position_fill(), stat="identity", color='#D7E1EB')+
scale_fill_manual(values = c('#80ED99',
'#57CC99',
'#38A3A5',
'#4D7298'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*merged$per)),
colour='#16212C', fontface='bold',
position = position_stack(vjust = 0.5), size=3.3)+
theme_modern_rc(base_size = 10, plot_title_size = 14,
base_family = 'Calibri', plot_title_face = "bold")+
xlab('')+ylab('')+
labs(title = 'Ocupação Por Plataforma')
p_ocup_per_mon 
per_plt <- merged_ %>% aggregate(dias ~ Platform, sum)
per_plt$somatotal <- sum(per_plt$dias)
per_plt$percentual <- per_plt$dias/per_plt$somatotal
per_plt## Platform dias somatotal percentual
## 1 Airbnb 329 1057 0.31125828
## 2 Direct 202 1057 0.19110691
## 3 HouseAnywhere 92 1057 0.08703879
## 4 Spotahome 434 1057 0.41059603PERCENTUAL POR PLATAFORMA
p_per_plt <- per_plt %>%
ggplot(mapping = aes(x=Platform, y=percentual,fill=Platform))+
geom_bar(position=position_dodge(), stat="identity", color='#D7E1EB')+
scale_fill_manual(values = c('#80ED99',
'#57CC99',
'#38A3A5',
'#4D7298'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*per_plt$percentual)),
colour='white', fontface='bold',
position = position_nudge(x=0,y=0.02), size=4)+
theme_modern_rc(base_size = 10, plot_title_size = 14,
base_family = 'Calibri', plot_title_face = "bold")+
xlab('')+ylab('')+
labs(title = 'Percentual Por Plataforma')
p_per_plt
#PERCENTUAL POR PROPRIEDADE POR MES
merged_property <- merge(p_ocu_geral, abc)
merged_property <- merged_property %>%
group_by(Platform, Month, Property) %>%
summarise(per_p = mes/soma,.groups = 'drop')
meses <- data.frame(Month=c("March", "April", "May", "June",
"July", "August", "September", "October", "November", "December"),
soma=c(31,30,31,30,31,31,30,31,30,31))
#MERGE PARA GRAFICOS POR ENDEREÇO
p <- merge(p_ocu_geral, meses)
p$percent <- p$mes / p$soma1 MOUNTJOY
mountjoy <- merged_property_MJ <- p %>% filter(str_detect(Property, 'D01 HR99')) %>%
group_by(Month) %>%
mutate(Month=factor(Month, levels = month.name))
ggplot(mountjoy, aes(x=Month, y=percent, fill=Platform))+
geom_bar(position= position_dodge(), stat="identity",color='#D7E1EB')+
scale_fill_manual(values = c('#57CC99',
'#38A3A5',
'#4D7298',
'#80ED99'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*mountjoy$percent)),
colour='white', fontface='bold',
position = position_identity(), size=4)+
theme_modern_rc(base_size = 10, plot_title_size = 14,
base_family = 'Calibri', plot_title_face = "bold")+
xlab('')+ylab('')+
labs(title = 'Ocupação Mensal Mountjoy Square')
48 MARLBOROUGH
merged_property_general <- merge(merged_property, p)
marlborough <- merged_property_general %>% filter(str_detect(Property, 'D01 KN61')) %>%
group_by(Month) %>%
mutate(Month=factor(Month, levels = month.name))
ggplot(marlborough, aes(x=Month, y=percent, fill=Platform))+
geom_bar(position= position_dodge(), stat="identity", color='#D7E1EB')+
scale_fill_manual(values = c('#57CC99',
'#38A3A5',
'#4D7298',
'#80ED99'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*marlborough$percent)),
colour='white', fontface='bold',
position = position_identity(), size=4)+
theme_modern_rc(base_size = 10, plot_title_size = 14)+
xlab('')+ylab('')+
labs(title = 'Ocupação Mensal Marlborough Court')
8 MEWS
the_8mews <- merged_property_general %>% filter(str_detect(Property, 'D01 DW25')) %>%
group_by(Month) %>%
mutate(Month=factor(Month, levels = month.name))
ggplot(the_8mews, mapping = aes(x=Month, y=percent, fill=Platform))+
geom_bar(position= position_dodge(), stat="identity",color='#D7E1EB')+
scale_fill_manual(values = c('#57CC99',
'#38A3A5',
'#4D7298',
'#80ED99'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*the_8mews$percent)),
colour='white', fontface='bold',
position = position_identity(), size=4)+
theme_modern_rc(base_size = 10, plot_title_size = 14)+
xlab('')+ylab('')+
labs(title = 'Ocupação Mensal The 8 Mews')
103 WESTMORELAND
westmoreland <- merged_property_general %>% filter(str_detect(Property, 'D04 KT95')) %>%
group_by(Month) %>%
mutate(Month=factor(Month, levels = month.name))
ggplot(westmoreland, mapping = aes(x=Month, y=percent, fill=Platform))+
geom_bar(position= position_dodge(), stat="identity",color='#D7E1EB')+
scale_fill_manual(values = c('#57CC99',
'#38A3A5',
'#4D7298',
'#80ED99'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*westmoreland$percent)),
colour='white', fontface='bold',
position = position_identity(), size=4)+
theme_modern_rc(base_size = 10, plot_title_size = 14,
base_family = 'Calibri', plot_title_face = "bold")+
xlab('')+ylab('')+
labs(title = 'Ocupação Mensal Westmoreland')
8 BOLTON
bolton<- merged_property_general %>% filter(str_detect(Property, 'D01 TN82')) %>%
group_by(Month) %>%
mutate(Month=factor(Month, levels = month.name))
ggplot(bolton, mapping = aes(x=Month, y=percent, fill=Platform))+
geom_bar(position= position_dodge(), stat="identity",color='#D7E1EB')+
scale_fill_manual(values = c('#57CC99',
'#38A3A5',
'#4D7298',
'#80ED99'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*bolton$percent)),
colour='white', fontface='bold',
position = position_identity(), size=4)+
theme_modern_rc(base_size = 10, plot_title_size = 14,
base_family = 'Calibri', plot_title_face = "bold")+
xlab('')+ylab('')+
labs(title = 'Ocupação Mensal Bolton')
4 MEWS
the_4mews <- merged_property_general %>% filter(str_detect(Property, 'D01 FV12')) %>%
group_by(Month) %>%
mutate(Month=factor(Month, levels = month.name))
ggplot(the_4mews, mapping = aes(x=Month, y=percent, fill=Platform))+
geom_bar(position= position_dodge(), stat="identity",color='#D7E1EB')+
scale_fill_manual(values = c('#57CC99',
'#38A3A5',
'#4D7298',
'#80ED99'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*the_4mews$percent)),
colour='white', fontface='bold',
position = position_identity(), size=4)+
theme_modern_rc(base_size = 10, plot_title_size = 14,
base_family = 'Calibri', plot_title_face = "bold")+
xlab('')+ylab('')+
labs(title = 'Ocupação Mensal The 4 Mews')
#Percentual Mensal Por Propriedade
p_ocu_MENSAL <- corporate %>% group_by(Month, Property) %>%
summarise(mes=sum(Value),.groups = 'drop')
p_ocu_mensal <- merge(p_ocu_MENSAL, meses)
a <-p_ocu_mensal$mes / p_ocu_mensal$soma
p_ocu_mensal$percent <- a
p_ocu_MENSAL$per_property <- p_ocu_mensal$per_property
view(p_ocu_MENSAL)
p_ocu_MENSAL$per_property## NULLsprintf("%1.2f%%", 100*p_ocu_MENSAL$per_property)## character(0)Ocupação Mensal Por Apartamento
perc_propertie <- p_ocu_mensal %>%
mutate(Month=factor(Month, levels = month.name)) %>%
ggplot(aes(y=Month, x=percent, fill=Property))+
geom_bar(position=position_fill(), stat="identity", color='#D7E1EB')+
scale_fill_manual(values = c('#7CAB5F',
'#B3D89C',
'#9DC3C2',
'#77A6B6',
'#4D7298',
'#3C5268',
'#358F80',
'#248277',
'#14746F',
'#036666'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*p_ocu_mensal$percent)),
colour = '#F8F6F4',
position = position_fill(vjust = 0.5), size=2.4)+
theme(legend.position="top")+
theme_modern_rc(base_size = 10, plot_title_size = 14,
base_family = 'Calibri', plot_title_face = "bold")+
xlab('')+ylab('')+
labs(title = 'Ocupação Mensal Por Apartamento')+
coord_flip()
perc_propertie
Total Geral de Ocupação
percentage <- data.frame(Month=c("March", "April", "May", "June",
"July", "August", "September", "October", "November", "December"),
properties = c(5,5,6,6,6,6,6,6,6,6))
geral <- aggregate(Value ~ Month, data=corporate, sum)
geral <- merge(geral, meses)
geral <- merge(geral, percentage)
geral$geral_percentual <- geral$Value / geral$soma / geral$properties
geral_graph <- geral %>%
mutate(Month=factor(Month, levels = month.name)) %>%
ggplot(aes(x=Month, y=geral_percentual, fill=Month))+
geom_bar(position='dodge', stat="identity", color='#99E2B4')+
scale_fill_manual(values = c('#99E2B4',
'#88D4AB',
'#78C6A3',
'#67B99A',
'#56AB91',
'#469D89',
'#358F80',
'#248277',
'#14746F',
'#036666'))+
geom_text(aes(label = sprintf("%1.2f%%", 100*geral$geral_percentual)),
colour='#e9f5db', fontface='bold',
position = position_nudge(x=0,y=.05), size=3.3)+
geom_hline(yintercept = mean(geral$geral_percentual),
linetype = "dashed", size = 1, color='#99E2B4', show.legend = TRUE)+
annotate("text", x=.68, y=0.62, color='#99E2B4', size=2.7,
label=sprintf("%1.2f%%", 100*mean(geral$geral_percentual)))+
annotate("text", x=.68, y=0.565, color='#99E2B4', size=2.7,
label='Average')+
theme_modern_rc(base_size = 10, plot_title_size = 14)+
xlab('')+ylab('')+
labs(title = 'Ocupação Geral Mensal')
geral_graph