Análise

Resultados

1. Quais bactérias e quantas bactérias estão presentes na UTI do hospital independente da área e da situação de sujo e limpo?

# 
# bac %>% select(micro) %>% 
#   group_by(micro) %>% count() %>% 
#   ungroup %>% 
#   mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>% 
#   arrange(desc(n)) %>% kable()

bac  %>% select(naDNA,id,local,super,micro,momento,gram,diluicao,naDNA) %>% distinct_all() %>%
  group_by(micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>% 
  arrange((desc(n))) %>% datatable(
    .,
    rownames = FALSE,
    filter = "top",
    extensions = c('Buttons', 'Scroller'),
    options = list(
      dom = 'Blfrtip',
      buttons = c('csv', 'excel'),
      deferRender = T,
      #scrollY = '600px',
      scroller = TRUE))

# bacz <- bac %>% select(micro) %>% 
#   group_by(micro) %>% count() %>% 
#   ungroup %>% 
#   mutate(total = sum(n),  prop = paste0(round(n/total*100,1),"%")) %>% 
#   arrange(desc(n))

bacz <- bac  %>% select(naDNA,id,local,super,micro,momento,gram,diluicao,naDNA) %>% distinct_all() %>%
  group_by(micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,1),"%")) %>%
  arrange(desc(n))


  ggplot(bacz) +
  aes(x=reorder(micro,+n),y=n, fill=micro, label=paste0(n," (",round(n/total*100,2),"%)")) + 
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_manual(values = cor) +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.y = element_text(face = "italic"),
        axis.text.x = element_text(angle = 0,
                                   hjust = 0.5,
                                   size=9,
                                   colour = "black")) +
  labs(caption = paste0("Total = ", bacz$total[1]) ) +
  geom_text(vjust=0.5,hjust=-0.01, size=3) + 
  xlab("") + ylab("Frequência") + coord_flip() #+ facet_wrap(~gram*momento, nrow = 2)

2. Qual a frequência de bactérias Gram positivas e Gram negativas na UTI comparando a situação de sujo e limpo?

bac  %>% select(momento,gram,id) %>% distinct_all() %>%
    group_by(momento,gram) %>% count() %>% 
    ungroup %>% 
    mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
    arrange(desc(n)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	37	119	31.09%
Antes da limpeza	Gram negativa	29	119	24.37%
Após a limpeza	Gram positiva	28	119	23.53%
Após a limpeza	Gram negativa	25	119	21.01%

bac  %>% select(momento,gram,id) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>%
  ggplot() +
  ggtitle("Gram positiva") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  labs(caption = paste0("Total = ", bacz$total[1]) ) +
  geom_text(vjust=-0.2, size=3) + 
  xlab("") + ylab("Frequência") + facet_grid(~momento)

bac %>% filter(gram=="Gram positiva") %>% 
  select(momento,gram,id) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	37	65	56.92%
Após a limpeza	Gram positiva	28	65	43.08%

bacz <- bac %>% filter(gram=="Gram positiva") %>% 
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n))

ggplot(bacz) +
  ggtitle("Gram positiva") +
  scale_fill_manual(values = cor) +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  labs(caption = paste0("Total = ", bacz$total[1]) ) +
  geom_text(vjust=-0.2, size=3) + 
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic")) 

bac %>% filter(gram=="Gram positiva" & momento=="Antes da limpeza") %>% 
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% kable()

momento	gram	micro	n	total	prop
Antes da limpeza	Gram positiva	Bastonete Gram positivo	17	37	45.95%
Antes da limpeza	Gram positiva	Enterococcus faecalis	9	37	24.32%
Antes da limpeza	Gram positiva	Staphylococcus aureus	2	37	5.41%
Antes da limpeza	Gram positiva	Staphylococcus epidermidis	2	37	5.41%
Antes da limpeza	Gram positiva	Staphylococcus haemolyticus	2	37	5.41%
Antes da limpeza	Gram positiva	Enterococcus faecium	1	37	2.7%
Antes da limpeza	Gram positiva	Kocuria rhizophila	1	37	2.7%
Antes da limpeza	Gram positiva	Staphylococcus cohnii	1	37	2.7%
Antes da limpeza	Gram positiva	Staphylococcus saprophyticus	1	37	2.7%
Antes da limpeza	Gram positiva	Staphylococcus sciuri	1	37	2.7%

bac %>% filter(gram=="Gram positiva" & momento=="Após a limpeza") %>% 
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% kable()

momento	gram	micro	n	total	prop
Após a limpeza	Gram positiva	Staphylococcus haemolyticus (MRSA + MBL)	4	28	14.29%
Após a limpeza	Gram positiva	Staphylococcus hominis	3	28	10.71%
Após a limpeza	Gram positiva	Staphylococcus hominis (MRSA + MBL)	3	28	10.71%
Após a limpeza	Gram positiva	Staphylococcus lentus	3	28	10.71%
Após a limpeza	Gram positiva	Bastonete Gram positivo	2	28	7.14%
Após a limpeza	Gram positiva	Enterococcus faecium	2	28	7.14%
Após a limpeza	Gram positiva	Staphylococcus hominis (MBL)	2	28	7.14%
Após a limpeza	Gram positiva	Aerococcus viridans	1	28	3.57%
Após a limpeza	Gram positiva	Alloiococcus otitis	1	28	3.57%
Após a limpeza	Gram positiva	Corynebacterium jeikeium	1	28	3.57%
Após a limpeza	Gram positiva	Enterococcus columbae	1	28	3.57%
Após a limpeza	Gram positiva	Enterococcus faecalis	1	28	3.57%
Após a limpeza	Gram positiva	Staphylococcus caprae	1	28	3.57%
Após a limpeza	Gram positiva	Staphylococcus epidermidis	1	28	3.57%
Após a limpeza	Gram positiva	Staphylococcus haemolyticus	1	28	3.57%
Após a limpeza	Gram positiva	Staphylococcus warneri (MRSA + MBL)	1	28	3.57%

a <- bac %>% filter(gram=="Gram positiva" & momento=="Antes da limpeza") %>% 
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram,micro) %>% count() %>% 
  ungroup %>% mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% 
  ggplot() +
  #ggtitle("Gram positiva") +
  labs(subtitle = "Antes da limpeza") +
  scale_fill_manual(values = cor) +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  xlab("") + ylab("Frequência") + theme(axis.text.x = element_text(face = "italic"))

a

b <- bac %>% filter(gram=="Gram positiva" & momento=="Após a limpeza") %>% 
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram,micro) %>% count() %>% 
  ungroup %>% mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% 
  ggplot() +
 # ggtitle("Gram positiva") +
  labs(subtitle = "Após a limpeza") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  xlab("") + ylab("Frequência") + theme(axis.text.x = element_text(face = "italic"))


b

bac %>% filter(gram=="Gram negativa") %>% 
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% kable()

momento	gram	micro	n	total	prop
Antes da limpeza	Gram negativa	Serratia marcescens	12	54	22.22%
Antes da limpeza	Gram negativa	Klebsiella pneumoniae	5	54	9.26%
Após a limpeza	Gram negativa	Pantoea spp	5	54	9.26%
Antes da limpeza	Gram negativa	Acinetobacter baumannii complex	4	54	7.41%
Após a limpeza	Gram negativa	Acinetobacter baumannii	4	54	7.41%
Após a limpeza	Gram negativa	Klebsiella pneumoniae (ESBL)	4	54	7.41%
Após a limpeza	Gram negativa	Acinetobacter baumannii complex	3	54	5.56%
Após a limpeza	Gram negativa	Klebsiella pneumoniae	3	54	5.56%
Antes da limpeza	Gram negativa	Enterobacter cloacae complex	2	54	3.7%
Antes da limpeza	Gram negativa	Klebsiella pneumoniae (ESBL)	2	54	3.7%
Após a limpeza	Gram negativa	Acinetobacter baumannii complex (KPC)	2	54	3.7%
Antes da limpeza	Gram negativa	Aeromonas salmonicida	1	54	1.85%
Antes da limpeza	Gram negativa	Burkholderia mallei	1	54	1.85%
Antes da limpeza	Gram negativa	Escherichia coli	1	54	1.85%
Antes da limpeza	Gram negativa	Pseudomonas stutzeri	1	54	1.85%
Após a limpeza	Gram negativa	Acinetobacter baumannii complex (ESBL)	1	54	1.85%
Após a limpeza	Gram negativa	Aeromonas salmonicida	1	54	1.85%
Após a limpeza	Gram negativa	Enterobacter cloacae (ESBL)	1	54	1.85%
Após a limpeza	Gram negativa	Enterobacter cloacae complex	1	54	1.85%

bacz <- bac %>% filter(gram=="Gram negativa") %>% 
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) 

ggplot(bacz) +
  ggtitle("Gram negativa") +
  scale_fill_manual(values = cor) +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(caption = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

a <- bac %>% filter(gram=="Gram negativa" & momento=="Antes da limpeza") %>% 
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram,micro) %>% count() %>% 
  ungroup %>% mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% 
  ggplot() +
  scale_fill_manual(values = cor) +
  ggtitle("Gram negativa") +
  labs(subtitle = "Antes da limpeza") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  xlab("") + ylab("Frequência") + theme(axis.text.x = element_text(face = "italic"))

a

b <- bac %>% filter(gram=="Gram negativa" & momento=="Após a limpeza") %>% 
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram,micro) %>% count() %>% 
  ungroup %>% mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% 
  ggplot() +
  ggtitle("Gram negativa") +
  labs(subtitle = "Após a limpeza") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  xlab("") + ylab("Frequência") + theme(axis.text.x = element_text(face = "italic"))


b

3. Qual a frequência de bactérias Gram positivas e Gram negativas encontrada por área, comparando sujo e limpo?

LEITO 03

bac %>% filter(local =="Leito 03") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	4	9	44.44%
Após a limpeza	Gram positiva	4	9	44.44%
Após a limpeza	Gram negativa	1	9	11.11%

bacz <- bac %>% filter(local =="Leito 03") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))


ggplot(bacz) +
  ggtitle("Leito 03") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

LEITO 04

bac %>% filter(local =="Leito 04") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	4	8	50%
Após a limpeza	Gram positiva	1	8	12.5%
Após a limpeza	Gram negativa	3	8	37.5%

bacz <- bac %>% filter(local =="Leito 04") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Leito 04") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

LEITO 05

bac %>% filter(local =="Leito 05") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	4	12	33.33%
Após a limpeza	Gram positiva	4	12	33.33%
Após a limpeza	Gram negativa	4	12	33.33%

bacz <- bac %>% filter(local =="Leito 05") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Leito 05") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

LEITO 06

bac %>% filter(local =="Leito 06") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	5	18	27.78%
Após a limpeza	Gram positiva	4	18	22.22%
Antes da limpeza	Gram negativa	6	18	33.33%
Após a limpeza	Gram negativa	3	18	16.67%

bacz <- bac %>% filter(local =="Leito 06") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Leito 06") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

LEITO 08

bac %>% filter(local =="Leito 08") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	3	11	27.27%
Após a limpeza	Gram positiva	4	11	36.36%
Antes da limpeza	Gram negativa	1	11	9.09%
Após a limpeza	Gram negativa	3	11	27.27%

bacz <- bac %>% filter(local =="Leito 08") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Leito 08") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

LEITO 12

bac %>% filter(local =="Leito 12") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	5	10	50%
Após a limpeza	Gram positiva	1	10	10%
Após a limpeza	Gram negativa	4	10	40%

bacz <- bac %>% filter(local =="Leito 12") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Leito 12") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

Área administrativa

bac %>% filter(local =="Área administrativa") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Após a limpeza	Gram positiva	2	4	50%
Antes da limpeza	Gram negativa	2	4	50%

bacz <- bac %>% filter(local =="Área administrativa") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Área administrativa") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

Banheiro CTI

bac %>% filter(local =="Banheiro CTI") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	2	7	28.57%
Após a limpeza	Gram positiva	1	7	14.29%
Antes da limpeza	Gram negativa	3	7	42.86%
Após a limpeza	Gram negativa	1	7	14.29%

bacz <- bac %>% filter(local =="Banheiro CTI") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Banheiro CTI") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

Copa CTI

bac %>% filter(local =="Copa CTI") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	7	21	33.33%
Após a limpeza	Gram positiva	2	21	9.52%
Antes da limpeza	Gram negativa	9	21	42.86%
Após a limpeza	Gram negativa	3	21	14.29%

bacz <- bac %>% filter(local =="Copa CTI") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Copa CTI") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

CTI

bac %>% filter(local =="Pia do CTI") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Após a limpeza	Gram positiva	2	6	33.33%
Antes da limpeza	Gram negativa	3	6	50%
Após a limpeza	Gram negativa	1	6	16.67%

bacz <- bac %>% filter(local =="Pia do CTI") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Pia do CTI") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

Farmácia

bac %>% filter(local =="Farmácia") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

momento	gram	n	total	prop
Antes da limpeza	Gram positiva	3	13	23.08%
Após a limpeza	Gram positiva	3	13	23.08%
Antes da limpeza	Gram negativa	5	13	38.46%
Após a limpeza	Gram negativa	2	13	15.38%

bacz <- bac %>% filter(local =="Farmácia") %>%
  select(momento,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento))
ggplot(bacz) +
  ggtitle("Farmácia") +
  aes(x=reorder(gram,-n),y=n, fill=gram, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2)

4. Qual a frequência de cada bactéria encontrado por área, comparando sujo e limpo?

LEITO 03

bac %>% filter(local =="Leito 03") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Bastonete Gram positivo	3	9	33.33%
Gram positiva	Antes da limpeza	Staphylococcus saprophyticus	1	9	11.11%
Gram positiva	Após a limpeza	Enterococcus columbae	1	9	11.11%
Gram positiva	Após a limpeza	Staphylococcus caprae	1	9	11.11%
Gram positiva	Após a limpeza	Staphylococcus epidermidis	1	9	11.11%
Gram positiva	Após a limpeza	Staphylococcus hominis (MRSA + MBL)	1	9	11.11%
Gram negativa	Após a limpeza	Klebsiella pneumoniae (ESBL)	1	9	11.11%

bacz <- bac %>% filter(local =="Leito 03") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>%
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 03") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

LEITO 04

bac %>% filter(local =="Leito 04") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Bastonete Gram positivo	4	8	50%
Gram positiva	Após a limpeza	Staphylococcus haemolyticus	1	8	12.5%
Gram negativa	Após a limpeza	Klebsiella pneumoniae	1	8	12.5%
Gram negativa	Após a limpeza	Pantoea spp	2	8	25%

bacz <- bac %>% filter(local =="Leito 04") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))

ggplot(bacz) +
  ggtitle("Leito 04") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2)+ theme(axis.text.x = element_text(face = "italic"))

LEITO 05

bac %>% filter(local =="Leito 05") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Bastonete Gram positivo	4	12	33.33%
Gram positiva	Após a limpeza	Staphylococcus haemolyticus (MRSA + MBL)	1	12	8.33%
Gram positiva	Após a limpeza	Staphylococcus hominis (MBL)	2	12	16.67%
Gram positiva	Após a limpeza	Staphylococcus hominis (MRSA + MBL)	1	12	8.33%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex (ESBL)	1	12	8.33%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex (KPC)	2	12	16.67%
Gram negativa	Após a limpeza	Klebsiella pneumoniae (ESBL)	1	12	8.33%

bacz <- bac %>% filter(local =="Leito 05") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 05") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

LEITO 06

bac %>% filter(local =="Leito 06") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Enterococcus faecalis	3	18	16.67%
Gram positiva	Antes da limpeza	Staphylococcus aureus	1	18	5.56%
Gram positiva	Antes da limpeza	Staphylococcus epidermidis	1	18	5.56%
Gram positiva	Após a limpeza	Bastonete Gram positivo	1	18	5.56%
Gram positiva	Após a limpeza	Staphylococcus haemolyticus (MRSA + MBL)	1	18	5.56%
Gram positiva	Após a limpeza	Staphylococcus hominis	1	18	5.56%
Gram positiva	Após a limpeza	Staphylococcus lentus	1	18	5.56%
Gram negativa	Antes da limpeza	Acinetobacter baumannii complex	1	18	5.56%
Gram negativa	Antes da limpeza	Pseudomonas stutzeri	1	18	5.56%
Gram negativa	Antes da limpeza	Serratia marcescens	4	18	22.22%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex	1	18	5.56%
Gram negativa	Após a limpeza	Klebsiella pneumoniae (ESBL)	1	18	5.56%
Gram negativa	Após a limpeza	Pantoea spp	1	18	5.56%

bacz <- bac %>% filter(local =="Leito 06") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 06") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2)+ theme(axis.text.x = element_text(face = "italic"))

LEITO 08

bac %>% filter(local =="Leito 08") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Bastonete Gram positivo	3	11	27.27%
Gram positiva	Após a limpeza	Aerococcus viridans	1	11	9.09%
Gram positiva	Após a limpeza	Enterococcus faecium	2	11	18.18%
Gram positiva	Após a limpeza	Staphylococcus hominis	1	11	9.09%
Gram negativa	Antes da limpeza	Enterobacter cloacae complex	1	11	9.09%
Gram negativa	Após a limpeza	Acinetobacter baumannii	1	11	9.09%
Gram negativa	Após a limpeza	Pantoea spp	2	11	18.18%

bacz <- bac %>% filter(local =="Leito 08") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 08") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

LEITO 12

bac %>% filter(local =="Leito 12") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Bastonete Gram positivo	3	10	30%
Gram positiva	Antes da limpeza	Staphylococcus haemolyticus	2	10	20%
Gram positiva	Após a limpeza	Bastonete Gram positivo	1	10	10%
Gram negativa	Após a limpeza	Acinetobacter baumannii	2	10	20%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex	1	10	10%
Gram negativa	Após a limpeza	Klebsiella pneumoniae	1	10	10%

bacz <- bac %>% filter(local =="Leito 12") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 12") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

Área administrativa

bac %>% filter(local =="Área administrativa") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Após a limpeza	Alloiococcus otitis	1	4	25%
Gram positiva	Após a limpeza	Staphylococcus haemolyticus (MRSA + MBL)	1	4	25%
Gram negativa	Antes da limpeza	Acinetobacter baumannii complex	1	4	25%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae	1	4	25%

bacz <- bac %>% filter(local =="Área administrativa") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))

ggplot(bacz) +
  ggtitle("Área administrativa") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

Banheiro CTI

bac %>% filter(local =="Banheiro CTI") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Enterococcus faecalis	1	7	14.29%
Gram positiva	Antes da limpeza	Staphylococcus aureus	1	7	14.29%
Gram positiva	Após a limpeza	Staphylococcus lentus	1	7	14.29%
Gram negativa	Antes da limpeza	Serratia marcescens	3	7	42.86%
Gram negativa	Após a limpeza	Klebsiella pneumoniae (ESBL)	1	7	14.29%

bacz <- bac %>% filter(local =="Banheiro CTI") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))

ggplot(bacz) +
  ggtitle("Banheiro CTI") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

Copa CTI

bac %>% filter(local =="Copa CTI") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Enterococcus faecalis	2	21	9.52%
Gram positiva	Antes da limpeza	Enterococcus faecium	1	21	4.76%
Gram positiva	Antes da limpeza	Kocuria rhizophila	1	21	4.76%
Gram positiva	Antes da limpeza	Staphylococcus cohnii	1	21	4.76%
Gram positiva	Antes da limpeza	Staphylococcus epidermidis	1	21	4.76%
Gram positiva	Antes da limpeza	Staphylococcus sciuri	1	21	4.76%
Gram positiva	Após a limpeza	Staphylococcus haemolyticus (MRSA + MBL)	1	21	4.76%
Gram positiva	Após a limpeza	Staphylococcus hominis	1	21	4.76%
Gram negativa	Antes da limpeza	Aeromonas salmonicida	1	21	4.76%
Gram negativa	Antes da limpeza	Burkholderia mallei	1	21	4.76%
Gram negativa	Antes da limpeza	Escherichia coli	1	21	4.76%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae	2	21	9.52%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae (ESBL)	1	21	4.76%
Gram negativa	Antes da limpeza	Serratia marcescens	3	21	14.29%
Gram negativa	Após a limpeza	Acinetobacter baumannii	1	21	4.76%
Gram negativa	Após a limpeza	Enterobacter cloacae (ESBL)	1	21	4.76%
Gram negativa	Após a limpeza	Klebsiella pneumoniae	1	21	4.76%

bacz <- bac %>% filter(local =="Copa CTI") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))

ggplot(bacz) +
  ggtitle("Copa CTI") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

CTI

bac %>% filter(local =="Pia do CTI") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Após a limpeza	Corynebacterium jeikeium	1	6	16.67%
Gram positiva	Após a limpeza	Enterococcus faecalis	1	6	16.67%
Gram negativa	Antes da limpeza	Acinetobacter baumannii complex	1	6	16.67%
Gram negativa	Antes da limpeza	Serratia marcescens	2	6	33.33%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex	1	6	16.67%

bacz <- bac %>% filter(local =="Pia do CTI") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Pia do CTI") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

Farmácia

bac %>% filter(local =="Farmácia") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Enterococcus faecalis	3	13	23.08%
Gram positiva	Após a limpeza	Staphylococcus hominis (MRSA + MBL)	1	13	7.69%
Gram positiva	Após a limpeza	Staphylococcus lentus	1	13	7.69%
Gram positiva	Após a limpeza	Staphylococcus warneri (MRSA + MBL)	1	13	7.69%
Gram negativa	Antes da limpeza	Acinetobacter baumannii complex	1	13	7.69%
Gram negativa	Antes da limpeza	Enterobacter cloacae complex	1	13	7.69%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae	2	13	15.38%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae (ESBL)	1	13	7.69%
Gram negativa	Após a limpeza	Aeromonas salmonicida	1	13	7.69%
Gram negativa	Após a limpeza	Enterobacter cloacae complex	1	13	7.69%

bacz <- bac %>% filter(local =="Farmácia") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Farmácia") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

5. Qual a frequência de bactérias resistentes por área, comparando sujo e limpo?

LEITO 03

bac %>% filter(local =="Leito 03", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Staphylococcus saprophyticus	1	4	25%
Gram positiva	Após a limpeza	Staphylococcus epidermidis	1	4	25%
Gram positiva	Após a limpeza	Staphylococcus hominis (MRSA + MBL)	1	4	25%
Gram negativa	Após a limpeza	Klebsiella pneumoniae (ESBL)	1	4	25%

bacz <- bac %>% filter(local =="Leito 03", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))

ggplot(bacz) +
  ggtitle("Leito 03") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

LEITO 04

bac %>% filter(local =="Leito 04", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Após a limpeza	Staphylococcus haemolyticus	1	2	50%
Gram negativa	Após a limpeza	Klebsiella pneumoniae	1	2	50%

bacz <- bac %>% filter(local =="Leito 04", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 04") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

LEITO 05

bac %>% filter(local =="Leito 05", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Após a limpeza	Staphylococcus haemolyticus (MRSA + MBL)	1	8	12.5%
Gram positiva	Após a limpeza	Staphylococcus hominis (MBL)	2	8	25%
Gram positiva	Após a limpeza	Staphylococcus hominis (MRSA + MBL)	1	8	12.5%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex (ESBL)	1	8	12.5%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex (KPC)	2	8	25%
Gram negativa	Após a limpeza	Klebsiella pneumoniae (ESBL)	1	8	12.5%

bacz <- bac %>% filter(local =="Leito 05", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 05") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

LEITO 06

bac %>% filter(local =="Leito 06", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Staphylococcus epidermidis	1	12	8.33%
Gram positiva	Após a limpeza	Staphylococcus haemolyticus (MRSA + MBL)	1	12	8.33%
Gram positiva	Após a limpeza	Staphylococcus hominis	1	12	8.33%
Gram positiva	Após a limpeza	Staphylococcus lentus	1	12	8.33%
Gram negativa	Antes da limpeza	Acinetobacter baumannii complex	1	12	8.33%
Gram negativa	Antes da limpeza	Pseudomonas stutzeri	1	12	8.33%
Gram negativa	Antes da limpeza	Serratia marcescens	4	12	33.33%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex	1	12	8.33%
Gram negativa	Após a limpeza	Klebsiella pneumoniae (ESBL)	1	12	8.33%

bacz <- bac %>% filter(local =="Leito 06", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 06") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

LEITO 08

bac %>% filter(local =="Leito 08", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Após a limpeza	Enterococcus faecium	2	5	40%
Gram positiva	Após a limpeza	Staphylococcus hominis	1	5	20%
Gram negativa	Antes da limpeza	Enterobacter cloacae complex	1	5	20%
Gram negativa	Após a limpeza	Acinetobacter baumannii	1	5	20%

bacz <- bac %>% filter(local =="Leito 08", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 08") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

LEITO 12

bac %>% filter(local =="Leito 12", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Staphylococcus haemolyticus	2	6	33.33%
Gram negativa	Após a limpeza	Acinetobacter baumannii	2	6	33.33%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex	1	6	16.67%
Gram negativa	Após a limpeza	Klebsiella pneumoniae	1	6	16.67%

bacz <- bac %>% filter(local =="Leito 12", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Leito 12") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2)

Área administrativa

bac %>% filter(local =="Área administrativa", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Após a limpeza	Staphylococcus haemolyticus (MRSA + MBL)	1	3	33.33%
Gram negativa	Antes da limpeza	Acinetobacter baumannii complex	1	3	33.33%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae	1	3	33.33%

bacz <- bac %>% filter(local =="Área administrativa", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Área administrativa") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

Banheiro CTI

bac %>% filter(local =="Banheiro CTI", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Após a limpeza	Staphylococcus lentus	1	5	20%
Gram negativa	Antes da limpeza	Serratia marcescens	3	5	60%
Gram negativa	Após a limpeza	Klebsiella pneumoniae (ESBL)	1	5	20%

bacz <- bac %>% filter(local =="Banheiro CTI", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Banheiro CTI") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2)+ theme(axis.text.x = element_text(face = "italic"))

Copa CTI

bac %>% filter(local =="Copa CTI", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Staphylococcus cohnii	1	13	7.69%
Gram positiva	Antes da limpeza	Staphylococcus epidermidis	1	13	7.69%
Gram positiva	Após a limpeza	Staphylococcus haemolyticus (MRSA + MBL)	1	13	7.69%
Gram positiva	Após a limpeza	Staphylococcus hominis	1	13	7.69%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae	2	13	15.38%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae (ESBL)	1	13	7.69%
Gram negativa	Antes da limpeza	Serratia marcescens	3	13	23.08%
Gram negativa	Após a limpeza	Acinetobacter baumannii	1	13	7.69%
Gram negativa	Após a limpeza	Enterobacter cloacae (ESBL)	1	13	7.69%
Gram negativa	Após a limpeza	Klebsiella pneumoniae	1	13	7.69%

bacz <- bac %>% filter(local =="Copa CTI", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Copa CTI") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2)+ theme(axis.text.x = element_text(face = "italic"))

CTI

bac %>% filter(local =="Pia do CTI", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Após a limpeza	Enterococcus faecalis	1	5	20%
Gram negativa	Antes da limpeza	Acinetobacter baumannii complex	1	5	20%
Gram negativa	Antes da limpeza	Serratia marcescens	2	5	40%
Gram negativa	Após a limpeza	Acinetobacter baumannii complex	1	5	20%

bacz <- bac %>% filter(local =="Pia do CTI", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Pia do CTI") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2)+ theme(axis.text.x = element_text(face = "italic"))

Farmácia

bac %>% filter(local =="Farmácia", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(gram,momento)) %>% kable()

gram	momento	micro	n	total	prop
Gram positiva	Antes da limpeza	Enterococcus faecalis	2	11	18.18%
Gram positiva	Após a limpeza	Staphylococcus hominis (MRSA + MBL)	1	11	9.09%
Gram positiva	Após a limpeza	Staphylococcus lentus	1	11	9.09%
Gram positiva	Após a limpeza	Staphylococcus warneri (MRSA + MBL)	1	11	9.09%
Gram negativa	Antes da limpeza	Acinetobacter baumannii complex	1	11	9.09%
Gram negativa	Antes da limpeza	Enterobacter cloacae complex	1	11	9.09%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae	2	11	18.18%
Gram negativa	Antes da limpeza	Klebsiella pneumoniae (ESBL)	1	11	9.09%
Gram negativa	Após a limpeza	Enterobacter cloacae complex	1	11	9.09%

bacz <- bac %>% filter(local =="Farmácia", inter=="R") %>%
  select(gram, momento,micro,id) %>% distinct_all() %>%
  group_by(gram, momento,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(micro, gram,momento))
ggplot(bacz) +
  ggtitle("Farmácia") +
  aes(x=reorder(micro,-n),y=n, fill=micro, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3) + 
  labs(subtitle = paste0("Total = ", bacz$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, nrow = 2) + theme(axis.text.x = element_text(face = "italic"))

6. Qual a correlação das bactérias Gram positivas e Gram negativas mais encontradas e com perfil de resistência entre as duas situações (limpo e sujo)?

library(reshape2)

Correlação

dad <- bac %>% filter(inter=="R") %>%
  select(micro, gram, momento, local,id) %>% distinct_all() %>%
  group_by(micro, gram,momento,local) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n))

dad2 <- dcast(dad, local+momento ~gram)
names(dad2) <- c("local","momento","gnegativa","gpositiva")

ggscatter(dad2,
          x = "gnegativa",
          y = "gpositiva",
          add = "reg.line",#adicnionar linha de regressao
          conf.int = TRUE,
          cor.coef = TRUE,
          cor.method = "spearman",
          xlab = "Gram negativa",
          ylab = "Gram positiva",
          facet.by=c("momento"),
          font.label = c(5, "plain"))+
  labs(tag = "")

dad2 %>% kable

local	momento	gnegativa	gpositiva
Área administrativa	Antes da limpeza	2	0
Área administrativa	Após a limpeza	0	1
Banheiro CTI	Antes da limpeza	1	0
Banheiro CTI	Após a limpeza	1	1
Copa CTI	Antes da limpeza	3	2
Copa CTI	Após a limpeza	3	2
Farmácia	Antes da limpeza	4	1
Farmácia	Após a limpeza	1	3
Leito 03	Antes da limpeza	0	1
Leito 03	Após a limpeza	1	2
Leito 04	Após a limpeza	1	1
Leito 05	Após a limpeza	3	3
Leito 06	Antes da limpeza	3	1
Leito 06	Após a limpeza	2	3
Leito 08	Antes da limpeza	1	0
Leito 08	Após a limpeza	1	2
Leito 12	Antes da limpeza	0	1
Leito 12	Após a limpeza	3	0
Pia do CTI	Antes da limpeza	2	0
Pia do CTI	Após a limpeza	1	1

Foi utilizado metodo de Correlação de Spearman, já que os dados não seguem uma distribuição normal

7. Qual a frequência total de bactérias gram positivas e gram negativas com perfilde resistência por área e considerando situação de sujo/limpo

Resposta

bac %>% filter(inter == "R") %>%
  select(momento,local,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,local,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% kable

momento	local	gram	n	total	prop
Antes da limpeza	Copa CTI	Gram negativa	6	74	8.11%
Antes da limpeza	Leito 06	Gram negativa	6	74	8.11%
Antes da limpeza	Farmácia	Gram negativa	5	74	6.76%
Após a limpeza	Leito 05	Gram negativa	4	74	5.41%
Após a limpeza	Leito 05	Gram positiva	4	74	5.41%
Após a limpeza	Leito 12	Gram negativa	4	74	5.41%
Antes da limpeza	Banheiro CTI	Gram negativa	3	74	4.05%
Antes da limpeza	Pia do CTI	Gram negativa	3	74	4.05%
Após a limpeza	Copa CTI	Gram negativa	3	74	4.05%
Após a limpeza	Farmácia	Gram positiva	3	74	4.05%
Após a limpeza	Leito 06	Gram positiva	3	74	4.05%
Após a limpeza	Leito 08	Gram positiva	3	74	4.05%
Antes da limpeza	Área administrativa	Gram negativa	2	74	2.7%
Antes da limpeza	Copa CTI	Gram positiva	2	74	2.7%
Antes da limpeza	Farmácia	Gram positiva	2	74	2.7%
Antes da limpeza	Leito 12	Gram positiva	2	74	2.7%
Após a limpeza	Copa CTI	Gram positiva	2	74	2.7%
Após a limpeza	Leito 03	Gram positiva	2	74	2.7%
Após a limpeza	Leito 06	Gram negativa	2	74	2.7%
Antes da limpeza	Leito 03	Gram positiva	1	74	1.35%
Antes da limpeza	Leito 06	Gram positiva	1	74	1.35%
Antes da limpeza	Leito 08	Gram negativa	1	74	1.35%
Após a limpeza	Área administrativa	Gram positiva	1	74	1.35%
Após a limpeza	Banheiro CTI	Gram negativa	1	74	1.35%
Após a limpeza	Banheiro CTI	Gram positiva	1	74	1.35%
Após a limpeza	Farmácia	Gram negativa	1	74	1.35%
Após a limpeza	Leito 03	Gram negativa	1	74	1.35%
Após a limpeza	Leito 04	Gram negativa	1	74	1.35%
Após a limpeza	Leito 04	Gram positiva	1	74	1.35%
Após a limpeza	Leito 08	Gram negativa	1	74	1.35%
Após a limpeza	Pia do CTI	Gram negativa	1	74	1.35%
Após a limpeza	Pia do CTI	Gram positiva	1	74	1.35%

b <- bac %>% filter(inter == "R") %>%
  select(momento,local,gram,id,micro) %>% distinct_all() %>%
  group_by(momento,local,gram) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) 
  
ggplot(b) +
  ggtitle("Gram Negativa e Positiva") +
  aes(x=reorder(local,-n),y=n, fill=local, label=paste0(n," \n ",round(n/total*100,2),"%")) + 
  geom_bar(stat = "identity", position = position_dodge(1)) +
  theme_pubr() +
  theme(legend.key = element_blank(),
        legend.position = "none",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   colour = "black")) +
  geom_text(vjust=-0.2, size=3, position = position_dodge(1)) + 
  labs(subtitle = paste0("Total = ", b$total[1]) ) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento*gram, ncol = 2)

10. Qual bactéria é mais frequente nos leitos da UTI por superfície?

LEITOS

bac %>% filter(local %in% c("Leito 03","Leito 04","Leito 05","Leito 06","Leito 08","Leito 12")) %>%
  select(momento,super,id,micro) %>% distinct_all() %>%
  group_by(momento,super,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) %>% kable()

momento	super	micro	n	total	prop
Antes da limpeza	Mesinha	Bastonete Gram positivo	5	68	7.35%
Antes da limpeza	Cama	Bastonete Gram positivo	4	68	5.88%
Antes da limpeza	Chão	Bastonete Gram positivo	4	68	5.88%
Antes da limpeza	Monitor cardíaco	Bastonete Gram positivo	4	68	5.88%
Após a limpeza	Chão	Pantoea spp	4	68	5.88%
Antes da limpeza	Monitor cardíaco	Serratia marcescens	3	68	4.41%
Antes da limpeza	Cama	Staphylococcus haemolyticus	2	68	2.94%
Antes da limpeza	Mesinha	Enterococcus faecalis	2	68	2.94%
Após a limpeza	Chão	Acinetobacter baumannii	2	68	2.94%
Após a limpeza	Mesinha	Klebsiella pneumoniae	2	68	2.94%
Após a limpeza	Monitor cardíaco	Klebsiella pneumoniae (ESBL)	2	68	2.94%
Após a limpeza	Monitor cardíaco	Staphylococcus haemolyticus (MRSA + MBL)	2	68	2.94%
Antes da limpeza	Cama	Acinetobacter baumannii complex	1	68	1.47%
Antes da limpeza	Cama	Enterococcus faecalis	1	68	1.47%
Antes da limpeza	Cama	Serratia marcescens	1	68	1.47%
Antes da limpeza	Chão	Enterobacter cloacae complex	1	68	1.47%
Antes da limpeza	Chão	Staphylococcus aureus	1	68	1.47%
Antes da limpeza	Chão	Staphylococcus epidermidis	1	68	1.47%
Antes da limpeza	Mesinha	Pseudomonas stutzeri	1	68	1.47%
Antes da limpeza	Monitor cardíaco	Staphylococcus saprophyticus	1	68	1.47%
Após a limpeza	Cama	Acinetobacter baumannii complex	1	68	1.47%
Após a limpeza	Cama	Enterococcus faecium	1	68	1.47%
Após a limpeza	Cama	Staphylococcus epidermidis	1	68	1.47%
Após a limpeza	Cama	Staphylococcus haemolyticus	1	68	1.47%
Após a limpeza	Cama	Staphylococcus hominis	1	68	1.47%
Após a limpeza	Cama	Staphylococcus hominis (MBL)	1	68	1.47%
Após a limpeza	Chão	Acinetobacter baumannii complex (ESBL)	1	68	1.47%
Após a limpeza	Chão	Acinetobacter baumannii complex (KPC)	1	68	1.47%
Após a limpeza	Chão	Klebsiella pneumoniae (ESBL)	1	68	1.47%
Após a limpeza	Chão	Staphylococcus hominis (MBL)	1	68	1.47%
Após a limpeza	Chão	Staphylococcus hominis (MRSA + MBL)	1	68	1.47%
Após a limpeza	Chão	Staphylococcus lentus	1	68	1.47%
Após a limpeza	Mesinha	Acinetobacter baumannii	1	68	1.47%
Após a limpeza	Mesinha	Acinetobacter baumannii complex (KPC)	1	68	1.47%
Após a limpeza	Mesinha	Bastonete Gram positivo	1	68	1.47%
Após a limpeza	Mesinha	Enterococcus faecium	1	68	1.47%
Após a limpeza	Mesinha	Staphylococcus caprae	1	68	1.47%
Após a limpeza	Monitor cardíaco	Acinetobacter baumannii complex	1	68	1.47%
Após a limpeza	Monitor cardíaco	Aerococcus viridans	1	68	1.47%
Após a limpeza	Monitor cardíaco	Bastonete Gram positivo	1	68	1.47%
Após a limpeza	Monitor cardíaco	Enterococcus columbae	1	68	1.47%
Após a limpeza	Monitor cardíaco	Pantoea spp	1	68	1.47%
Após a limpeza	Monitor cardíaco	Staphylococcus hominis	1	68	1.47%
Após a limpeza	Monitor cardíaco	Staphylococcus hominis (MRSA + MBL)	1	68	1.47%

a <- bac %>% filter(local %in% c("Leito 03","Leito 04","Leito 05","Leito 06","Leito 08","Leito 12")) %>%
  select(momento,super,id,micro) %>% distinct_all() %>%
  group_by(momento,super,micro) %>% count() %>% 
  ungroup %>% 
  mutate(total = sum(n),  prop = paste0(round(n/total*100,2),"%")) %>%
  arrange(desc(n)) 

ggplot(a) +
  ggtitle("Leitos 03, 04, 05, 06, 08 e 12") +
  aes(x=reorder(micro,+n),y=n, fill=super, label=n) + 
  geom_bar(stat = "identity", position = "stack") +
  theme_pubr() +
  scale_fill_discrete(name="Superficie")+
  theme(legend.position = "right",
        axis.text.x = element_text(angle = 0,
                                   hjust = 0,
                                   colour = "black")) +
  geom_text(size=3,hjust=+2, vjust=0.5, position = "stack") + 
  labs(subtitle = paste0("Total = ", a$total[1])) +
  xlab("") + ylab("Frequência") + facet_wrap(~momento, ncol = 2) + coord_flip() + theme(axis.text.y = element_text(face = "italic"))

11. Quais os padrões de susceptibilidade antimicrobiana de cepas de bactérias isoladas (Microrganismos x medicamentos x suceptibilidade de antimicrobianos S (%) I (%) R (%))

Resposta

library(summarytools)
library(tidyr)


# bac %>% mutate(id = factor(id), inter = factor(inter)) %>% 
#   #  filter(inter=="R") %>% 
#   select(anti,id,inter) %>% 
#   group_by(anti,inter) %>%  count() %>% 
#   descr(., stats = c("common"), transpose = T) %>% 
#   tb() %>% arrange(desc(mean)) %>% as.data.frame() #%>% summarise(sum(mean))
bac2 <- bac

bac2 %>% mutate(id = factor(id), inter = factor(inter)) %>% 
  select(anti,inter,micro, id, inter) %>% 
  group_by(anti,inter) %>%  count() %>% arrange(anti,desc(n)) %>% 
  ungroup %>%
  as.data.frame() %>% pivot_wider(names_from = inter, values_from = n, values_fill = 0) %>% 
  mutate(TS = sum(S),  PS = paste0(round(S/TS*100,2),"%"),
         TI = sum(I),  PI = paste0(round(I/TI*100,2),"%"),
         TR = sum(R),  PR = paste0(round(R/TR*100,2),"%"),
         TNEG = sum(NEG),  PNEG = paste0(round(NEG/TR*100,2),"%"),
         TPOS = sum(POS),  PPOS = paste0(round(POS/TR*100,2),"%"),
         TNA = sum(`NA`),  PNA = paste0(round(`NA`/TR*100,2),"%")) %>% as.data.frame() %>%  kable("pandoc")

anti	S	I	R	NEG	POS	NA	TS	PS	TI	PI	TR	PR	TNEG	PNEG	TPOS	PPOS	TNA	PNA
Ácido Nalidíxico	8	0	0	0	0	0	845	0.95%	35	0%	334	0%	37	0%	19	0%	47	0%
Amicacina	40	2	0	0	0	0	845	4.73%	35	5.71%	334	0%	37	0%	19	0%	47	0%
Amoxicilina/Ácido clavulânico	2	0	6	0	0	0	845	0.24%	35	0%	334	1.8%	37	0%	19	0%	47	0%
Ampicilina	13	1	23	0	0	0	845	1.54%	35	2.86%	334	6.89%	37	0%	19	0%	47	0%
Ampicilina/Sulbactam	16	0	7	0	0	0	845	1.89%	35	0%	334	2.1%	37	0%	19	0%	47	0%
Benzilpenicilina	8	0	18	0	0	0	845	0.95%	35	0%	334	5.39%	37	0%	19	0%	47	0%
BLSE	0	0	0	8	4	1	845	0%	35	0%	334	0%	37	2.4%	19	1.2%	47	0.3%
Cefalotina	2	0	2	0	0	0	845	0.24%	35	0%	334	0.6%	37	0%	19	0%	47	0%
Cefepima	31	1	14	0	0	0	845	3.67%	35	2.86%	334	4.19%	37	0%	19	0%	47	0%
Cefoxitina	14	1	23	0	0	0	845	1.66%	35	2.86%	334	6.89%	37	0%	19	0%	47	0%
Ceftarolina	7	0	0	0	0	2	845	0.83%	35	0%	334	0%	37	0%	19	0%	47	0.6%
Ceftazidima	23	1	14	0	0	0	845	2.72%	35	2.86%	334	4.19%	37	0%	19	0%	47	0%
Ceftriaxona	24	6	16	0	0	0	845	2.84%	35	17.14%	334	4.79%	37	0%	19	0%	47	0%
Cefuroxima	8	0	33	0	0	0	845	0.95%	35	0%	334	9.88%	37	0%	19	0%	47	0%
Cefuroxima Axetil	8	0	38	0	0	0	845	0.95%	35	0%	334	11.38%	37	0%	19	0%	47	0%
Ciprofloxacina	39	0	7	0	0	0	845	4.62%	35	0%	334	2.1%	37	0%	19	0%	47	0%
Clindamicina	8	0	21	0	0	0	845	0.95%	35	0%	334	6.29%	37	0%	19	0%	47	0%
Colistina	28	0	11	0	0	0	845	3.31%	35	0%	334	3.29%	37	0%	19	0%	47	0%
Daptomicina	32	0	1	0	0	4	845	3.79%	35	0%	334	0.3%	37	0%	19	0%	47	1.2%
Eritromicina	9	9	22	0	0	0	845	1.07%	35	25.71%	334	6.59%	37	0%	19	0%	47	0%
Ertapenem	25	1	5	0	0	0	845	2.96%	35	2.86%	334	1.5%	37	0%	19	0%	47	0%
Estreptomicina Alto Nível (Sinergia)	13	0	0	0	0	0	845	1.54%	35	0%	334	0%	37	0%	19	0%	47	0%
Gentamicina	62	4	7	0	0	0	845	7.34%	35	11.43%	334	2.1%	37	0%	19	0%	47	0%
Gentamicina Alto Nível (Sinergia)	13	0	0	0	0	0	845	1.54%	35	0%	334	0%	37	0%	19	0%	47	0%
Imipenem	25	0	2	0	0	0	845	2.96%	35	0%	334	0.6%	37	0%	19	0%	47	0%
Levofloxacina	29	1	10	0	0	0	845	3.43%	35	2.86%	334	2.99%	37	0%	19	0%	47	0%
Linezolid	37	0	2	0	0	0	845	4.38%	35	0%	334	0.6%	37	0%	19	0%	47	0%
Meropenem	40	0	6	0	0	0	845	4.73%	35	0%	334	1.8%	37	0%	19	0%	47	0%
Nitrofurantoína	41	2	5	0	0	0	845	4.85%	35	5.71%	334	1.5%	37	0%	19	0%	47	0%
Norfloxacina	8	0	0	0	0	0	845	0.95%	35	0%	334	0%	37	0%	19	0%	47	0%
Oxacilina	12	0	16	0	0	0	845	1.42%	35	0%	334	4.79%	37	0%	19	0%	47	0%
Piperacilina/Tazobactam	31	1	2	0	0	0	845	3.67%	35	2.86%	334	0.6%	37	0%	19	0%	47	0%
Resistência Induzida a Clindamicina	1	0	0	21	1	3	845	0.12%	35	0%	334	0%	37	6.29%	19	0.3%	47	0.9%
Rifampicina	21	1	4	0	0	0	845	2.49%	35	2.86%	334	1.2%	37	0%	19	0%	47	0%
Sem dados	0	0	0	0	0	33	845	0%	35	0%	334	0%	37	0%	19	0%	47	9.88%
Teicoplanina	36	0	3	0	0	0	845	4.26%	35	0%	334	0.9%	37	0%	19	0%	47	0%
Teste de screening de cefoxitina	0	0	0	8	14	4	845	0%	35	0%	334	0%	37	2.4%	19	4.19%	47	1.2%
Tigeciclina	71	3	0	0	0	0	845	8.4%	35	8.57%	334	0%	37	0%	19	0%	47	0%
Trimetoprim/Sulfametoxazol	25	0	13	0	0	0	845	2.96%	35	0%	334	3.89%	37	0%	19	0%	47	0%
Vancomicina	35	1	3	0	0	0	845	4.14%	35	2.86%	334	0.9%	37	0%	19	0%	47	0%

bac2$inter[is.na(bac2$inter)] <- "NAava"

bac2 %>% mutate(id = factor(id), inter = factor(inter)) %>% 
  select(momento,anti,inter,micro, id, inter) %>% 
  group_by(momento,anti,inter,micro) %>%  count() %>% arrange(anti,desc(n)) %>% 
  pivot_wider(names_from = micro, values_from = n) %>% replace(is.na(.), 0) %>% as.data.frame() %>%   mutate(sum = rowSums(.[4:38])) %>% select(momento,anti,inter,sum) %>% arrange(desc(sum)) %>% pivot_wider(names_from = inter, values_from = sum, values_fill = 0) %>% as.data.frame() -> antitab


bac2 %>% mutate(id = factor(id), inter = factor(inter)) %>% #filter(inter!="NA") %>% 
  select(momento,anti,inter,micro, id, inter) %>% 
  group_by(momento,anti,inter,micro) %>%  count() %>% arrange(anti,desc(n)) %>% 
  pivot_wider(names_from = micro, values_from = n) %>% replace(is.na(.), 0) %>% as.data.frame() %>%   mutate(sum = rowSums(.[4:38])) %>% select(anti,momento,inter,sum) %>% arrange(desc(sum)) -> anti

levels(anti$inter) <- c("Intermediário","Não Avaliado","Negativo","Positivo","Resistente","Sensível")

anti %>% filter(inter %in% c("Intermediário", "Resistente","Sensível")) %>% 
  ggplot() +
  # ggtitle("S, R, I, NEG, POS") +
  aes(x=reorder(anti,-sum),y=sum, fill=inter) + 
  geom_bar(stat = "identity", position = "stack") +
  theme_pubr() +
  theme(legend.title = element_blank(),
        legend.position = "right",
        axis.text.x = element_text(angle = 90,
                                   hjust = 1,
                                   vjust = 0.3,
                                   colour = "black")) +
  xlab("") + ylab("Frequência") 

anti %>% filter(inter %in% c("Intermediário", "Resistente","Sensível")) %>% 
ggbarplot(., x = "anti", 
                   y = "sum",
                   fill = "inter", palette = "lancet",
                   label = F,
                   lab.pos = c("in"),
                   facet.by = "momento",
                   xlab = "",
                   repel=T,
                   ylab= "Frequência",
                   lab.size = 3,
                   position = position_stack()
) + 
  theme_pubr(legend="right") +
  theme(
    legend.title = element_blank(),
    text = element_text(size = 10),
    axis.text.y = element_text(
      angle = 0,
      hjust = 0,
      colour = "black"),
    axis.text.x = element_text(
      angle = 90,
      hjust = 1,
      vjust = 0.3,
      colour = "black")) #+ coord_flip()

bac2 %>% mutate(id = factor(id), inter = factor(inter)) %>% #filter(inter!="NA") %>% 
  select(anti,inter,micro, id, inter) %>% 
  group_by(anti,inter,micro) %>%  count() %>% arrange(anti,desc(n)) %>% 
  pivot_wider(names_from = micro, values_from = n) %>% replace(is.na(.), 0) %>% as.data.frame() %>%   mutate(sum = rowSums(.[4:37])) %>% select(anti,inter,sum) %>% arrange(desc(sum)) -> anti2

levels(anti2$inter) <- c("Intermediário","Não Avaliado","Negativo","Positivo","Resistente","Sensível")

bac2 %>% mutate(id = factor(id), inter = factor(inter)) %>% 
  select(anti,inter,micro, id, inter) %>% 
  group_by(anti,inter,micro) %>%  count() %>% arrange(anti,desc(n)) %>% 
  pivot_wider(names_from = micro, values_from = n) %>% replace(is.na(.), 0) %>% as.data.frame() %>%   mutate(sum = rowSums(.[4:37])) %>% select(anti,inter,sum) %>% arrange(desc(sum)) %>% pivot_wider(names_from = inter, values_from = sum, values_fill = 0) %>% as.data.frame() -> antitab2


anti2 %>% filter(inter %in% c("Intermediário", "Resistente","Sensível")) %>% ggbarplot(., x = "anti", 
                    y = "sum",
                    fill = "inter", #palette = "lancet",
                    label = TRUE,
                    lab.pos = c("out"),
                    #facet.by = "momento",
                    xlab = "",
                    repel=T,
                    ylab= "Frequência",
                    lab.size = 3,
                    position = position_stack()
) + 
  theme_pubr(legend="right") +
  theme(
    legend.title = element_blank(),
    text = element_text(size = 10),
    axis.text.y = element_text(
      angle = 0,
      hjust = 1,
      colour = "black"),
    axis.text.x = element_text(
      angle = 90,
      hjust = 1,
      colour = "black")) #+ coord_flip()

12. Comparação entre o numéro de R,S,I,NEG,POS,NA antes e depois da limpeza

Resposta

antitab %>%
  group_by(momento) %>%
  shapiro_test(S) %>% kable("pandoc")

momento	variable	statistic	p
Antes da limpeza	S	0.9160134	0.0057829
Após a limpeza	S	0.9121528	0.0044004

p <- ggboxplot(antitab, x = "momento", y = "S",
               color = "momento", palette = "jco",
               add = "jitter", xlab = "", ylab = "Sensível") + 
  theme_pubr(legend="right") +
  theme(legend.title = element_blank())

p + stat_compare_means(paired = T)

antitab %>%
  group_by(momento) %>%
  shapiro_test(R) %>% kable("pandoc")

momento	variable	statistic	p
Antes da limpeza	R	0.6545600	0.00e+00
Após a limpeza	R	0.8175864	1.61e-05

p <- ggboxplot(antitab, x = "momento", y = "R",
               color = "momento", palette = "jco",
               add = "jitter", xlab = "", ylab = "Resistente") + 
  theme_pubr(legend="right") +
  theme(legend.title = element_blank())

p + stat_compare_means(paired = T)

antitab %>%
  group_by(momento) %>%
  shapiro_test(I) %>% kable("pandoc")

momento	variable	statistic	p
Antes da limpeza	I	0.4110345	0
Após a limpeza	I	0.4899872	0

p <- ggboxplot(antitab, x = "momento", y = "I",
               color = "momento", palette = "jco",
               add = "jitter", xlab = "", ylab = "Intermediário") + 
  theme_pubr(legend="right") +
  theme(legend.title = element_blank())

p + stat_compare_means(paired = T)

antitab %>%
  group_by(momento) %>%
  shapiro_test(NEG) %>% kable("pandoc")

momento	variable	statistic	p
Antes da limpeza	NEG	0.3014146	0
Após a limpeza	NEG	0.2096947	0

p <- ggboxplot(antitab, x = "momento", y = "NEG",
               color = "momento", palette = "jco",
               add = "jitter", xlab = "", ylab = "Negativo") + 
  theme_pubr(legend="right") +
  theme(legend.title = element_blank())

p + stat_compare_means(paired = T)

antitab %>%
  group_by(momento) %>%
  shapiro_test(POS) %>% kable("pandoc")

momento	variable	statistic	p
Antes da limpeza	POS	0.2910119	0
Após a limpeza	POS	0.1822424	0

p <- ggboxplot(antitab, x = "momento", y = "POS",
               color = "momento", palette = "jco",
               add = "jitter", xlab = "", ylab = "Positivo") + 
  theme_pubr(legend="right") +
  theme(legend.title = element_blank())

p + stat_compare_means(paired = T)

antitab %>%
  group_by(momento) %>%
  shapiro_test(NAava) %>% kable("pandoc")

momento	variable	statistic	p
Antes da limpeza	NAava	0.1581813	0
Após a limpeza	NAava	0.3319444	0

p <- ggboxplot(antitab, x = "momento", y = "NAava",
               color = "momento", palette = "jco",
               add = "jitter", xlab = "", ylab = "Não Avaliado") + 
  theme_pubr(legend="right") +
  theme(legend.title = element_blank())

p + stat_compare_means(paired = T)

a<-compare_means(S ~ momento, data = antitab, paired = T)
b<-compare_means(R ~ momento, data = antitab, paired = T)
c<-compare_means(I ~ momento, data = antitab, paired = T)
d<-compare_means(NEG ~ momento, data = antitab, paired = T)
e<-compare_means(POS ~ momento, data = antitab, paired = T)
f<-compare_means(NAava  ~ momento, data = antitab, paired = T)

rbind(a,b,c,d,e,f) %>% kable("pandoc")

.y.	group1	group2	p	p.adj	p.format	p.signif	method
S	Antes da limpeza	Após a limpeza	0.0920815	0.092	0.092	ns	Wilcoxon
R	Antes da limpeza	Após a limpeza	0.0752452	0.075	0.075	ns	Wilcoxon
I	Antes da limpeza	Após a limpeza	0.8595969	0.860	0.86	ns	Wilcoxon
NEG	Antes da limpeza	Após a limpeza	0.8330289	0.830	0.83	ns	Wilcoxon
POS	Antes da limpeza	Após a limpeza	1.0000000	1.000	1	ns	Wilcoxon
NAava	Antes da limpeza	Após a limpeza	0.2614461	0.260	0.26	ns	Wilcoxon

EXTRA

K-means e PCA

Carregamento dos dados

dat <- bac %>% select(micro, local, id) %>% distinct_all() %>%
  group_by(micro, local) %>% count() %>% 
  ungroup 

dat2 <- dcast(dat, micro ~ local)
dat2[is.na(dat2)] <- 0

rownames(dat2) <- dat2$micro

dat2 <- dat2[-1]


dat2

Dimensionamento e padronização

df <- scale(dat2) 

Número ótimo de clusters

# Silhueta média para kmeans
fviz_nbclust(df, kmeans, method = "silhouette")

# Estatística de lacunas
fviz_nbclust(df, kmeans, method = "gap_stat")

# Método Elbow para kmeans
fviz_nbclust(df, kmeans, method = "wss") +
  geom_vline(xintercept = 3, linetype = 2)

nbclust_out <- NbClust(
  data = df,
  distance = "euclidean",
  min.nc = 2,
  max.nc = 5,
  method = "kmeans"
)

*** : The Hubert index is a graphical method of determining the number of clusters.
                In the plot of Hubert index, we seek a significant knee that corresponds to a 
                significant increase of the value of the measure i.e the significant peak in Hubert
                index second differences plot. 
 

*** : The D index is a graphical method of determining the number of clusters. 
                In the plot of D index, we seek a significant knee (the significant peak in Dindex
                second differences plot) that corresponds to a significant increase of the value of
                the measure. 
 
******************************************************************* 
* Among all indices:                                                
* 4 proposed 2 as the best number of clusters 
* 13 proposed 3 as the best number of clusters 
* 5 proposed 4 as the best number of clusters 
* 1 proposed 5 as the best number of clusters 

                   ***** Conclusion *****                            
 
* According to the majority rule, the best number of clusters is  3 
 
 
******************************************************************* 

# create a dataframe of the optimal number of clusters
nbclust_plot <- data.frame(clusters = nbclust_out$Best.nc[1, ])
# select only indices which select between 2 and 5 clusters
nbclust_plot <- subset(nbclust_plot, clusters >= 2 & clusters <= 5)

# create plot
ggplot(nbclust_plot) +
  aes(x = clusters) +
  geom_histogram(bins = 30L, fill = "#0c4c8a") +
  labs(x = "Number of clusters", y = "Frequency among all indices", title = "Optimal number of clusters") +
  theme_pubr()

Clusterização k-means

#set.seed(123)
km.res = kmeans(df, 3, nstart=25)
#km.res <- hkmeans(df, 3,hc.method = "ward.D")
print(km.res)

K-means clustering with 3 clusters of sizes 3, 1, 31

Cluster means:
  Área administrativa Banheiro CTI   Copa CTI   Farmácia   Leito 03   Leito 04
1          0.67858019    1.9402966  1.3141477  1.3157610 -0.4209752 -0.2967516
2         -0.35404184   -0.3424053 -0.7392081 -0.5080661  4.4904016  4.8964014
3         -0.05424835   -0.1767253 -0.1033302 -0.1109425 -0.1041121 -0.1292305
    Leito 05   Leito 06    Leito 08    Leito 12 Pia do CTI
1 -0.4090567  2.7034958 -0.43753614  0.05771805  2.9108445
2  4.3632718  0.5282693  3.73894524  4.50200817 -0.3337274
3 -0.1011646 -0.2786696 -0.07826893 -0.15081169 -0.2709292

Clustering vector:
                 Acinetobacter baumannii 
                                       3 
         Acinetobacter baumannii complex 
                                       1 
  Acinetobacter baumannii complex (ESBL) 
                                       3 
   Acinetobacter baumannii complex (KPC) 
                                       3 
                     Aerococcus viridans 
                                       3 
                   Aeromonas salmonicida 
                                       3 
                     Alloiococcus otitis 
                                       3 
                 Bastonete Gram positivo 
                                       2 
                     Burkholderia mallei 
                                       3 
                Corynebacterium jeikeium 
                                       3 
             Enterobacter cloacae (ESBL) 
                                       3 
            Enterobacter cloacae complex 
                                       3 
                   Enterococcus columbae 
                                       3 
                   Enterococcus faecalis 
                                       1 
                    Enterococcus faecium 
                                       3 
                        Escherichia coli 
                                       3 
                   Klebsiella pneumoniae 
                                       3 
            Klebsiella pneumoniae (ESBL) 
                                       3 
                      Kocuria rhizophila 
                                       3 
                             Pantoea spp 
                                       3 
                    Pseudomonas stutzeri 
                                       3 
                     Serratia marcescens 
                                       1 
                   Staphylococcus aureus 
                                       3 
                   Staphylococcus caprae 
                                       3 
                   Staphylococcus cohnii 
                                       3 
              Staphylococcus epidermidis 
                                       3 
             Staphylococcus haemolyticus 
                                       3 
Staphylococcus haemolyticus (MRSA + MBL) 
                                       3 
                  Staphylococcus hominis 
                                       3 
            Staphylococcus hominis (MBL) 
                                       3 
     Staphylococcus hominis (MRSA + MBL) 
                                       3 
                   Staphylococcus lentus 
                                       3 
            Staphylococcus saprophyticus 
                                       3 
                   Staphylococcus sciuri 
                                       3 
     Staphylococcus warneri (MRSA + MBL) 
                                       3 

Within cluster sum of squares by cluster:
[1]  41.76277   0.00000 152.57633
 (between_SS / total_SS =  48.0 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"      

# fviz_cluster(km.res, palette = "Dark2", repel = TRUE,
#              ggtheme = theme_classic())
# 
# fviz_dend(km.res, cex = 0.6, palette = "Dark2", 
#           rect = TRUE, rect_border = "Dark2", rect_fill = TRUE)

Criando novo banco de dados com cluster

aggregate(dat2, by=list(cluster=km.res$cluster), mean)

dd <- cbind(dat2, cluster = km.res$cluster)


km.res$cluster

                 Acinetobacter baumannii 
                                       3 
         Acinetobacter baumannii complex 
                                       1 
  Acinetobacter baumannii complex (ESBL) 
                                       3 
   Acinetobacter baumannii complex (KPC) 
                                       3 
                     Aerococcus viridans 
                                       3 
                   Aeromonas salmonicida 
                                       3 
                     Alloiococcus otitis 
                                       3 
                 Bastonete Gram positivo 
                                       2 
                     Burkholderia mallei 
                                       3 
                Corynebacterium jeikeium 
                                       3 
             Enterobacter cloacae (ESBL) 
                                       3 
            Enterobacter cloacae complex 
                                       3 
                   Enterococcus columbae 
                                       3 
                   Enterococcus faecalis 
                                       1 
                    Enterococcus faecium 
                                       3 
                        Escherichia coli 
                                       3 
                   Klebsiella pneumoniae 
                                       3 
            Klebsiella pneumoniae (ESBL) 
                                       3 
                      Kocuria rhizophila 
                                       3 
                             Pantoea spp 
                                       3 
                    Pseudomonas stutzeri 
                                       3 
                     Serratia marcescens 
                                       1 
                   Staphylococcus aureus 
                                       3 
                   Staphylococcus caprae 
                                       3 
                   Staphylococcus cohnii 
                                       3 
              Staphylococcus epidermidis 
                                       3 
             Staphylococcus haemolyticus 
                                       3 
Staphylococcus haemolyticus (MRSA + MBL) 
                                       3 
                  Staphylococcus hominis 
                                       3 
            Staphylococcus hominis (MBL) 
                                       3 
     Staphylococcus hominis (MRSA + MBL) 
                                       3 
                   Staphylococcus lentus 
                                       3 
            Staphylococcus saprophyticus 
                                       3 
                   Staphylococcus sciuri 
                                       3 
     Staphylococcus warneri (MRSA + MBL) 
                                       3 

km.res$size

[1]  3  1 31

km.res$centers

  Área administrativa Banheiro CTI   Copa CTI   Farmácia   Leito 03   Leito 04
1          0.67858019    1.9402966  1.3141477  1.3157610 -0.4209752 -0.2967516
2         -0.35404184   -0.3424053 -0.7392081 -0.5080661  4.4904016  4.8964014
3         -0.05424835   -0.1767253 -0.1033302 -0.1109425 -0.1041121 -0.1292305
    Leito 05   Leito 06    Leito 08    Leito 12 Pia do CTI
1 -0.4090567  2.7034958 -0.43753614  0.05771805  2.9108445
2  4.3632718  0.5282693  3.73894524  4.50200817 -0.3337274
3 -0.1011646 -0.2786696 -0.07826893 -0.15081169 -0.2709292

Vizualizando os clusters

fviz_cluster(km.res, data=df,
             geom.ind = c("text"),
             ellipse.type="euclid",
             star.plot=T,
             palette = "Dark2",
             repel=TRUE,
             font.family = "italic",
             ggtheme=theme_pubr())

Dendrograma

dista=dist(df, method = "euclid")

dista.hc=hclust(d=dista, method = "ward.D")

fviz_dend(dista.hc,cex =0.5, k = 3, color_labels_by_k = TRUE, type = "rectangle")

fviz_dend(dista.hc,cex =0.8, k = 3, color_labels_by_k = TRUE, type = "phylogenic")

fviz_dend(dista.hc,cex =0.5, k = 3, color_labels_by_k = TRUE, type = "circular", lwd = 1)

fviz_dist(dista, gradient = list(low = "#00AFBB", mid = "white", high = "#FC4E07"))+ 
theme(axis.text.y = element_text(face = "italic"),
      axis.text.x = element_text(face = "italic"))

PCA

km.pca <- PCA(
  df,
  graph = F,
  scale.unit = TRUE)

eig.val <- get_eigenvalue(km.pca)
eig.val

       eigenvalue variance.percent cumulative.variance.percent
Dim.1   3.5580000        32.345455                    32.34545
Dim.2   2.6793918        24.358108                    56.70356
Dim.3   1.2426359        11.296690                    68.00025
Dim.4   0.8629244         7.844768                    75.84502
Dim.5   0.7553600         6.866909                    82.71193
Dim.6   0.6331413         5.755830                    88.46776
Dim.7   0.4044704         3.677004                    92.14476
Dim.8   0.3433866         3.121697                    95.26646
Dim.9   0.2529191         2.299265                    97.56573
Dim.10  0.1543937         1.403579                    98.96930
Dim.11  0.1133765         1.030696                   100.00000

fviz_eig(km.pca, addlabels=TRUE)

var <- get_pca_var(km.pca)
var

Principal Component Analysis Results for variables
 ===================================================
  Name       Description                                    
1 "$coord"   "Coordinates for the variables"                
2 "$cor"     "Correlations between variables and dimensions"
3 "$cos2"    "Cos2 for the variables"                       
4 "$contrib" "contributions of the variables"               

$coord

$cor

$cos2

$contrib

Coordinates for the variables

Correlations between variables and dimensions

Cos2 for the variables

contributions of the variables

#  Coordenadas
head(var$coord)

                         Dim.1     Dim.2      Dim.3        Dim.4       Dim.5
Área administrativa -0.1515598 0.1524005  0.8535430 -0.381525622 -0.03773429
Banheiro CTI        -0.4474423 0.6997840 -0.4232991 -0.005728760  0.03385333
Copa CTI            -0.4376934 0.5095557  0.1551532  0.360015354 -0.33402932
Farmácia            -0.2832577 0.3361338  0.4359862  0.607448899  0.41855202
Leito 03             0.7340294 0.2896805 -0.1389382  0.005183907  0.35311513
Leito 04             0.8134368 0.4270941  0.1088101  0.072830818 -0.15418335

# Cos2: qualidade no mapa do fator
head(var$cos2)

                         Dim.1      Dim.2      Dim.3        Dim.4       Dim.5
Área administrativa 0.02297038 0.02322591 0.72853573 1.455618e-01 0.001423877
Banheiro CTI        0.20020460 0.48969771 0.17918211 3.281870e-05 0.001146048
Copa CTI            0.19157555 0.25964698 0.02407253 1.296111e-01 0.111575587
Farmácia            0.08023495 0.11298590 0.19008393 3.689942e-01 0.175185794
Leito 03            0.53879910 0.08391480 0.01930383 2.687289e-05 0.124690297
Leito 04            0.66167947 0.18240934 0.01183964 5.304328e-03 0.023772506

# Contribuições para os componentes principais
head(var$contrib)

                        Dim.1     Dim.2      Dim.3        Dim.4      Dim.5
Área administrativa  0.645598  0.866835 58.6282515 16.868429264  0.1885031
Banheiro CTI         5.626886 18.276450 14.4195178  0.003803195  0.1517221
Copa CTI             5.384361  9.690519  1.9372146 15.019977122 14.7711800
Farmácia             2.255058  4.216849 15.2968321 42.760888886 23.1923574
Leito 03            15.143314  3.131860  1.5534584  0.003114165 16.5073998
Leito 04            18.596949  6.807864  0.9527847  0.614692051  3.1471756

fviz_cos2(km.pca, choice = "var", axes = 1:2)

df %>% cor(method = "spearman") %>% corrplot(.,
                                             method = "number",
                                             type = "upper",
                                             tl.pos = "td")

summary(km.pca)

Call:
PCA(X = df, scale.unit = TRUE, graph = F) 


Eigenvalues
                       Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6   Dim.7
Variance               3.558   2.679   1.243   0.863   0.755   0.633   0.404
% of var.             32.345  24.358  11.297   7.845   6.867   5.756   3.677
Cumulative % of var.  32.345  56.704  68.000  75.845  82.712  88.468  92.145
                       Dim.8   Dim.9  Dim.10  Dim.11
Variance               0.343   0.253   0.154   0.113
% of var.              3.122   2.299   1.404   1.031
Cumulative % of var.  95.266  97.566  98.969 100.000

Individuals (the 10 first)
                                           Dist    Dim.1    ctr   cos2    Dim.2
Acinetobacter baumannii                |  2.651 |  0.970  0.755  0.134 | -0.099
Acinetobacter baumannii complex        |  5.132 | -1.512  1.835  0.087 |  2.367
Acinetobacter baumannii complex (ESBL) |  1.656 |  0.251  0.051  0.023 | -1.147
Acinetobacter baumannii complex (KPC)  |  2.476 |  0.718  0.415  0.084 | -0.950
Aerococcus viridans                    |  1.738 |  0.315  0.079  0.033 | -1.122
Aeromonas salmonicida                  |  1.569 | -0.715  0.411  0.208 | -0.670
Alloiococcus otitis                    |  3.147 | -0.469  0.177  0.022 | -1.051
Bastonete Gram positivo                | 10.088 |  9.154 67.285  0.823 |  4.215
Burkholderia mallei                    |  1.403 | -0.507  0.206  0.130 | -0.955
Corynebacterium jeikeium               |  2.201 | -0.675  0.365  0.094 | -0.517
                                          ctr   cos2    Dim.3    ctr   cos2  
Acinetobacter baumannii                 0.011  0.001 |  0.229  0.121  0.007 |
Acinetobacter baumannii complex         5.972  0.213 |  2.546 14.902  0.246 |
Acinetobacter baumannii complex (ESBL)  1.403  0.480 | -0.418  0.401  0.064 |
Acinetobacter baumannii complex (KPC)   0.962  0.147 | -0.459  0.485  0.034 |
Aerococcus viridans                     1.342  0.417 | -0.460  0.486  0.070 |
Aeromonas salmonicida                   0.478  0.182 |  0.341  0.267  0.047 |
Alloiococcus otitis                     1.178  0.112 |  2.031  9.482  0.416 |
Bastonete Gram positivo                18.941  0.175 | -0.040  0.004  0.000 |
Burkholderia mallei                     0.972  0.463 | -0.202  0.094  0.021 |
Corynebacterium jeikeium                0.285  0.055 | -0.350  0.281  0.025 |

Variables (the 10 first)
                                          Dim.1    ctr   cos2    Dim.2    ctr
Área administrativa                    | -0.152  0.646  0.023 |  0.152  0.867
Banheiro CTI                           | -0.447  5.627  0.200 |  0.700 18.276
Copa CTI                               | -0.438  5.384  0.192 |  0.510  9.691
Farmácia                               | -0.283  2.255  0.080 |  0.336  4.217
Leito 03                               |  0.734 15.143  0.539 |  0.290  3.132
Leito 04                               |  0.813 18.597  0.662 |  0.427  6.808
Leito 05                               |  0.729 14.917  0.531 |  0.266  2.647
Leito 06                               | -0.334  3.139  0.112 |  0.862 27.731
Leito 08                               |  0.709 14.144  0.503 |  0.257  2.470
Leito 12                               |  0.725 14.773  0.526 |  0.422  6.633
                                         cos2    Dim.3    ctr   cos2  
Área administrativa                     0.023 |  0.854 58.628  0.729 |
Banheiro CTI                            0.490 | -0.423 14.420  0.179 |
Copa CTI                                0.260 |  0.155  1.937  0.024 |
Farmácia                                0.113 |  0.436 15.297  0.190 |
Leito 03                                0.084 | -0.139  1.553  0.019 |
Leito 04                                0.182 |  0.109  0.953  0.012 |
Leito 05                                0.071 | -0.038  0.119  0.001 |
Leito 06                                0.743 | -0.170  2.339  0.029 |
Leito 08                                0.066 | -0.066  0.352  0.004 |
Leito 12                                0.178 |  0.233  4.384  0.054 |

# Contribuições de variáveis para PC1
fviz_contrib(km.pca, choice = "var", axes = 1, top = 10)

# Contribuições de variáveis para PC2
fviz_contrib(km.pca, choice = "var", axes = 2, top = 10)

# contribuição total para PC1 e PC2 

fviz_contrib(km.pca, choice = "var", axes = 1:2)

fviz_pca_biplot(
  km.pca,
  geom.ind = "text",
  col.var = "contrib",
  gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
  legend.title = "Contribuição",
  palette = "Dark2",
  repel = F
)

fviz_pca_ind(
  km.pca,
  geom = "text",
  habillage = as.factor(dd$cluster),
  addEllipses = TRUE,
  repel = T,
  palette = "Dark2"
)

fviz_pca_ind(km.pca,
             geom.ind = "text",
             col.ind = as.factor(dd$cluster),
             addEllipses = TRUE, 
             legend.title = "Grupos",
             repel = T,
             palette = "Dark2"
)

df %>% pairs.panels(., 
                    show.points=TRUE, 
                    method = "spearman",
                    gap=0, 
                    stars=TRUE,
                    ci=FALSE,
                    alpha=0.05,
                    cex.cor=1,
                    cex=1.0,
                    breaks="Sturges",
                    rug=FALSE,
                    density=F,
                    hist.col="darkgreen",
                    factor=5,
                    digits=2,
                    ellipses=FALSE,
                    scale=FALSE,
                    smooth=TRUE,
                    lm=T,
                    cor=T
) 

dd.pca = prcomp(df, scale = T)


ggbiplot2(
  dd.pca,
  obs.scale = 1,
  var.scale = 1,
  ellipse = T,
  circle = T,
  varname.abbrev = T,
  grupos = as.factor(dd$cluster)
) + theme_pubr() + scale_color_brewer( palette = 'Dark2')

ind <- get_pca_ind(km.pca)
ind

Principal Component Analysis Results for individuals
 ===================================================
  Name       Description                       
1 "$coord"   "Coordinates for the individuals" 
2 "$cos2"    "Cos2 for the individuals"        
3 "$contrib" "contributions of the individuals"

$coord

$cos2

$contrib

Coordinates for the individuals

Cos2 for the individuals

contributions of the individuals

# Coordenadas de indivíduos
head(ind$coord)

                                            Dim.1       Dim.2      Dim.3
Acinetobacter baumannii                 0.9699156 -0.09926623  0.2292384
Acinetobacter baumannii complex        -1.5117696  2.36656852  2.5457906
Acinetobacter baumannii complex (ESBL)  0.2509344 -1.14688822 -0.4175689
Acinetobacter baumannii complex (KPC)   0.7184552 -0.94993678 -0.4592452
Aerococcus viridans                     0.3146240 -1.12183389 -0.4597433
Aeromonas salmonicida                  -0.7150491 -0.66972713  0.3408877
                                             Dim.4      Dim.5
Acinetobacter baumannii                 0.35054455 -1.5204586
Acinetobacter baumannii complex        -2.61861198  0.5402088
Acinetobacter baumannii complex (ESBL) -0.49573957  0.4806389
Acinetobacter baumannii complex (KPC)  -0.67743349  0.9705285
Aerococcus viridans                    -0.01818709 -0.6938637
Aeromonas salmonicida                   1.07793342  0.1786954

# Qualidade dos indivíduos
head(ind$cos2)

                                            Dim.1       Dim.2       Dim.3
Acinetobacter baumannii                0.13381644 0.001401666 0.007475089
Acinetobacter baumannii complex        0.08676500 0.212623616 0.246047354
Acinetobacter baumannii complex (ESBL) 0.02297227 0.479872364 0.063612113
Acinetobacter baumannii complex (KPC)  0.08417506 0.147154464 0.034393251
Aerococcus viridans                    0.03276062 0.416510052 0.069951852
Aeromonas salmonicida                  0.20756486 0.182086549 0.047174225
                                              Dim.4      Dim.5
Acinetobacter baumannii                0.0174794598 0.32884465
Acinetobacter baumannii complex        0.2603248545 0.01107890
Acinetobacter baumannii complex (ESBL) 0.0896583476 0.08427939
Acinetobacter baumannii complex (KPC)  0.0748371720 0.15360334
Aerococcus viridans                    0.0001094699 0.15933701
Aeromonas salmonicida                  0.4717000891 0.01296310

# Contribuições de indivíduos
head(ind$contrib)

                                            Dim.1      Dim.2      Dim.3
Acinetobacter baumannii                0.75542947 0.01050749  0.1208267
Acinetobacter baumannii complex        1.83525850 5.97219379 14.9016174
Acinetobacter baumannii complex (ESBL) 0.05056458 1.40261316  0.4009076
Acinetobacter baumannii complex (KPC)  0.41450085 0.96224382  0.4849280
Aerococcus viridans                    0.07948949 1.34200099  0.4859805
Aeromonas salmonicida                  0.41057996 0.47829023  0.2671842
                                             Dim.4     Dim.5
Acinetobacter baumannii                 0.40686060 8.7443431
Acinetobacter baumannii complex        22.70395318 1.1038279
Acinetobacter baumannii complex (ESBL)  0.81370382 0.8738076
Acinetobacter baumannii complex (KPC)   1.51947134 3.5628258
Aerococcus viridans                     0.00109518 1.8210684
Aeromonas salmonicida                   3.84718489 0.1207827

fviz_pca_ind(km.pca, col.ind = "cos2", 
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE
)

fviz_contrib(km.pca, choice = "ind", axes = 1:2)

set.seed(123)
my.cont.var <- rnorm(35)

fviz_pca_ind(km.pca, col.ind = my.cont.var,
             gradient.cols = c("blue", "yellow", "red"),
             legend.title = "Cont.Var")

fviz_pca_ind(km.pca,
             geom.ind = "point",
             col.ind = as.factor(dd$cluster),
             palette = "Dark2",
             addEllipses = TRUE, 
             legend.title = "Grupos"
)

fviz_pca_biplot(km.pca, 
                geom.ind = c("point","text"),
                fill.ind = as.factor(dd$cluster), 
                col.ind = "black",
                pointshape = 21, 
                pointsize = 2,
                palette = "Dark2",
                repel=F,
                labelsize=3,
                addEllipses = F,
                alpha.var ="contrib", 
                col.var = "contrib",
                gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
                legend.title = list(fill = "Cluster",
                                    color = "Contrib",
                                    alpha = "Contrib"))

Referência

Os procedimentos estatísticos utilizados neste estudo foram realizados no programa R (R Core Team 2020). Pacote ‘stats’: Coeficiente de correlação de Spearman (R Core Team 2020). Pacote ‘ggpot2’: elementos gráficos (Wickham 2016).

antitab

antitab2

write.xlsx(antitab, "anti.xlsx")
write.xlsx(antitab2, "anti2.xlsx")

R Core Team. 2020. “R: A Language and Environment for Statistical Computing.” https://www.R-project.org/.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.