library(tidyverse)
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## Warning: package 'forcats' was built under R version 4.2.3
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## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
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library(ggpubr)
## Warning: package 'ggpubr' was built under R version 4.2.3
library(haven)
## Warning: package 'haven' was built under R version 4.2.3
DATOS_RESISTENCIA_BACTERIANA <- read_sav("C:/Users/fidel/OneDrive - UNIVERSIDAD AUTONOMA DE SINALOA/INVESTIGACIÓN DGPI/DATOS_RESISTENCIA_BACTERIANA.sav")
db<-DATOS_RESISTENCIA_BACTERIANA
head(db)
## # A tibble: 6 × 35
## FOLIO FECHA NOMBRE SEXO EDAD EDAD1 CULTIVO BACTERIA BACTERIA1
## <dbl> <date> <chr> <dbl+l> <dbl> <dbl+l> <dbl+l> <dbl+lb> <dbl+lbl>
## 1 1 2022-02-22 CLAUDIA MAB… 1 [FEM… 49 3 [> 3… 1 [URO… 1 [ESCH… 1 [GRAM …
## 2 2 2022-02-22 JOSE PATIÑO 2 [MAS… 34 2 [20-… 2 [EXU… 4 [KLEB… 1 [GRAM …
## 3 3 2022-02-22 ANA KAREN D… 1 [FEM… 33 2 [20-… 1 [URO… 9 [LEVA… 3 [LEVAD…
## 4 4 2022-02-22 EVANGELINA … 1 [FEM… 53 3 [> 3… 1 [URO… 1 [ESCH… 1 [GRAM …
## 5 5 2022-02-22 ALEJANDRA R… 1 [FEM… 45 2 [20-… 1 [URO… 1 [ESCH… 1 [GRAM …
## 6 6 2022-02-22 MARIA RAMIR… 1 [FEM… 33 2 [20-… 1 [URO… 6 [S. A… 2 [GRAM …
## # ℹ 26 more variables: LEUCOCITOS <dbl+lbl>, ERITROCITOS <dbl+lbl>, PH <dbl>,
## # NITRITOS <dbl+lbl>, PROTEINAS <dbl+lbl>, GLUCOSA <dbl+lbl>,
## # SANGRE <dbl+lbl>, AK <dbl+lbl>, AM <dbl+lbl>, CB <dbl+lbl>, CF <dbl+lbl>,
## # CFX <dbl+lbl>, CPF <dbl+lbl>, CL <dbl+lbl>, GENT <dbl+lbl>, NET <dbl+lbl>,
## # NF <dbl+lbl>, NOF <dbl+lbl>, SXT <dbl+lbl>, CLM <dbl+lbl>, DC <dbl+lbl>,
## # E <dbl+lbl>, PE <dbl+lbl>, TE <dbl+lbl>, VA <dbl+lbl>,
## # RESISTENCIA <dbl+lbl>
head(db)
## # A tibble: 6 × 35
## FOLIO FECHA NOMBRE SEXO EDAD EDAD1 CULTIVO BACTERIA BACTERIA1
## <dbl> <date> <chr> <dbl+l> <dbl> <dbl+l> <dbl+l> <dbl+lb> <dbl+lbl>
## 1 1 2022-02-22 CLAUDIA MAB… 1 [FEM… 49 3 [> 3… 1 [URO… 1 [ESCH… 1 [GRAM …
## 2 2 2022-02-22 JOSE PATIÑO 2 [MAS… 34 2 [20-… 2 [EXU… 4 [KLEB… 1 [GRAM …
## 3 3 2022-02-22 ANA KAREN D… 1 [FEM… 33 2 [20-… 1 [URO… 9 [LEVA… 3 [LEVAD…
## 4 4 2022-02-22 EVANGELINA … 1 [FEM… 53 3 [> 3… 1 [URO… 1 [ESCH… 1 [GRAM …
## 5 5 2022-02-22 ALEJANDRA R… 1 [FEM… 45 2 [20-… 1 [URO… 1 [ESCH… 1 [GRAM …
## 6 6 2022-02-22 MARIA RAMIR… 1 [FEM… 33 2 [20-… 1 [URO… 6 [S. A… 2 [GRAM …
## # ℹ 26 more variables: LEUCOCITOS <dbl+lbl>, ERITROCITOS <dbl+lbl>, PH <dbl>,
## # NITRITOS <dbl+lbl>, PROTEINAS <dbl+lbl>, GLUCOSA <dbl+lbl>,
## # SANGRE <dbl+lbl>, AK <dbl+lbl>, AM <dbl+lbl>, CB <dbl+lbl>, CF <dbl+lbl>,
## # CFX <dbl+lbl>, CPF <dbl+lbl>, CL <dbl+lbl>, GENT <dbl+lbl>, NET <dbl+lbl>,
## # NF <dbl+lbl>, NOF <dbl+lbl>, SXT <dbl+lbl>, CLM <dbl+lbl>, DC <dbl+lbl>,
## # E <dbl+lbl>, PE <dbl+lbl>, TE <dbl+lbl>, VA <dbl+lbl>,
## # RESISTENCIA <dbl+lbl>
# Recodificar los valores de la variable1 según un diccionario
db$BACTERIA = as.numeric(db$BACTERIA)
db <- db %>%
mutate(BACTERIA = recode(BACTERIA, `1` = "Escherichia coli", `2` = "Pseudomona aeruginosa", `3` = "Shigella", `4` ="klebsiella pneumoniae" , `5` = "klebsiella pneumoniae", `6`= "Staphylococcus aureus", `7` = "Staphylococcus epidermidis", `8` ="Enterobacter", `9`= "Yeast", `10`= "Proteus"))
str(db$BACTERIA)
## chr [1:197] "Escherichia coli" "klebsiella pneumoniae" "Yeast" ...
library(gtsummary)
## Warning: package 'gtsummary' was built under R version 4.2.3
db %>% select(BACTERIA) %>% tbl_summary()
| Characteristic |
N = 197 |
| BACTERIA |
|
| Enterobacter |
1 (0.5%) |
| Escherichia coli |
128 (65%) |
| klebsiella pneumoniae |
6 (3.0%) |
| Proteus |
1 (0.5%) |
| Pseudomona aeruginosa |
6 (3.0%) |
| Staphylococcus aureus |
22 (11%) |
| Staphylococcus epidermidis |
28 (14%) |
| Yeast |
5 (2.5%) |
dbresis <- db %>% select(BACTERIA, AK:VA)
dbresis<-dbresis %>% mutate_at(vars(AK:VA), as.character)
dbresisten<- dbresis %>%
mutate_at(vars(AK:VA), ~recode(., `1` = "S", `3` = "R", `NA`= ""))
datos <- dbresisten %>% gather(key= "farmaco", value= "resultado", -BACTERIA)
datos %>% select(BACTERIA, farmaco, resultado
) %>% mutate (farmaco=paste("Farmaco", farmaco)) %>%
tbl_strata(strata = farmaco,
.tbl_fun =
~ .x %>%
tbl_summary(by = resultado, percent = 'row') %>%
add_n(),
.header = "**{strata}**, N = {n}"
)
## 79 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 54 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 79 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 54 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 54 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 79 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 172 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 54 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 172 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 172 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 54 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 79 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 79 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 79 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 172 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 54 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 172 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
## 172 observations missing `resultado` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `resultado` column before passing to `tbl_summary()`.
| Characteristic |
Farmaco AK, N = 197
|
Farmaco AM, N = 197
|
Farmaco CB, N = 197
|
Farmaco CF, N = 197
|
Farmaco CFX, N = 197
|
Farmaco CL, N = 197
|
Farmaco CLM, N = 197
|
Farmaco CPF, N = 197
|
Farmaco DC, N = 197
|
Farmaco E, N = 197
|
Farmaco GENT, N = 197
|
Farmaco NET, N = 197
|
Farmaco NF, N = 197
|
Farmaco NOF, N = 197
|
Farmaco PE, N = 197
|
Farmaco SXT, N = 197
|
Farmaco TE, N = 197
|
Farmaco VA, N = 197
|
| N |
R, N = 13 |
S, N = 105 |
N |
R, N = 130 |
S, N = 13 |
N |
R, N = 79 |
S, N = 39 |
N |
R, N = 77 |
S, N = 66 |
N |
R, N = 38 |
S, N = 105 |
N |
R, N = 21 |
S, N = 97 |
N |
R, N = 5 |
S, N = 20 |
N |
R, N = 52 |
S, N = 91 |
N |
R, N = 24 |
S, N = 1 |
N |
R, N = 11 |
S, N = 14 |
N |
R, N = 40 |
S, N = 103 |
N |
R, N = 17 |
S, N = 101 |
N |
R, N = 12 |
S, N = 106 |
N |
R, N = 48 |
S, N = 70 |
N |
R, N = 24 |
S, N = 1 |
N |
R, N = 65 |
S, N = 78 |
N |
R, N = 2 |
S, N = 23 |
N |
R, N = 3 |
S, N = 22 |
| BACTERIA |
118 |
|
|
143 |
|
|
118 |
|
|
143 |
|
|
143 |
|
|
118 |
|
|
25 |
|
|
143 |
|
|
25 |
|
|
25 |
|
|
143 |
|
|
118 |
|
|
118 |
|
|
118 |
|
|
25 |
|
|
143 |
|
|
25 |
|
|
25 |
|
|
| Escherichia coli |
|
11 (10%) |
95 (90%) |
|
95 (89%) |
12 (11%) |
|
71 (67%) |
35 (33%) |
|
65 (61%) |
42 (39%) |
|
34 (32%) |
73 (68%) |
|
17 (16%) |
89 (84%) |
|
1 (100%) |
0 (0%) |
|
46 (43%) |
61 (57%) |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
33 (31%) |
74 (69%) |
|
14 (13%) |
92 (87%) |
|
9 (8.5%) |
97 (92%) |
|
43 (41%) |
63 (59%) |
|
1 (100%) |
0 (0%) |
|
52 (49%) |
55 (51%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
| klebsiella pneumoniae |
|
0 (0%) |
5 (100%) |
|
5 (100%) |
0 (0%) |
|
2 (40%) |
3 (60%) |
|
2 (40%) |
3 (60%) |
|
1 (20%) |
4 (80%) |
|
0 (0%) |
5 (100%) |
|
|
|
|
1 (20%) |
4 (80%) |
|
|
|
|
|
|
|
0 (0%) |
5 (100%) |
|
0 (0%) |
5 (100%) |
|
0 (0%) |
5 (100%) |
|
2 (40%) |
3 (60%) |
|
|
|
|
2 (40%) |
3 (60%) |
|
|
|
|
|
|
| Proteus |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
|
|
|
|
1 (100%) |
0 (0%) |
|
|
|
|
|
|
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
|
|
|
|
1 (100%) |
0 (0%) |
|
|
|
|
|
|
| Pseudomona aeruginosa |
|
1 (20%) |
4 (80%) |
|
5 (100%) |
0 (0%) |
|
4 (80%) |
1 (20%) |
|
5 (100%) |
0 (0%) |
|
2 (40%) |
3 (60%) |
|
4 (80%) |
1 (20%) |
|
|
|
|
2 (40%) |
3 (60%) |
|
|
|
|
|
|
|
2 (40%) |
3 (60%) |
|
2 (40%) |
3 (60%) |
|
3 (60%) |
2 (40%) |
|
2 (40%) |
3 (60%) |
|
|
|
|
5 (100%) |
0 (0%) |
|
|
|
|
|
|
| Staphylococcus epidermidis |
|
0 (0%) |
1 (100%) |
|
15 (94%) |
1 (6.2%) |
|
1 (100%) |
0 (0%) |
|
2 (12%) |
14 (88%) |
|
1 (6.2%) |
15 (94%) |
|
0 (0%) |
1 (100%) |
|
1 (6.7%) |
14 (93%) |
|
2 (12%) |
14 (88%) |
|
14 (93%) |
1 (6.7%) |
|
6 (40%) |
9 (60%) |
|
3 (19%) |
13 (81%) |
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
1 (100%) |
0 (0%) |
|
14 (93%) |
1 (6.7%) |
|
4 (25%) |
12 (75%) |
|
2 (13%) |
13 (87%) |
|
0 (0%) |
15 (100%) |
| Enterobacter |
|
|
|
|
1 (100%) |
0 (0%) |
|
|
|
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
|
|
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
|
|
|
|
|
|
|
|
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
|
1 (100%) |
0 (0%) |
| Staphylococcus aureus |
|
|
|
|
8 (100%) |
0 (0%) |
|
|
|
|
1 (12%) |
7 (88%) |
|
0 (0%) |
8 (100%) |
|
|
|
|
2 (25%) |
6 (75%) |
|
0 (0%) |
8 (100%) |
|
8 (100%) |
0 (0%) |
|
3 (38%) |
5 (62%) |
|
1 (12%) |
7 (88%) |
|
|
|
|
|
|
|
|
|
|
8 (100%) |
0 (0%) |
|
1 (12%) |
7 (88%) |
|
0 (0%) |
8 (100%) |
|
2 (25%) |
6 (75%) |
# Continue from your code...
datos <- dbresisten %>%
gather(key= "farmaco", value= "resultado", -BACTERIA) %>%
mutate(farmaco=paste("Farmaco", farmaco))
# Convert 'resultado' to factor for better handling in ggplot
datos$resultado <- as.factor(datos$resultado)
# Create a table with counts of each combination of 'BACTERIA', 'farmaco', and 'resultado'
heatmap_data <- datos %>%
group_by(BACTERIA, farmaco, resultado) %>%
summarise(count=n()) %>%
ungroup()
## `summarise()` has grouped output by 'BACTERIA', 'farmaco'. You can override
## using the `.groups` argument.
# Generate proportions by dividing count by sum of counts for each 'BACTERIA'-'farmaco' combination
heatmap_data <- heatmap_data %>%
group_by(BACTERIA, farmaco) %>%
mutate(proportion = count / sum(count)) %>%
ungroup()
# Filter out blank values
heatmap_data <- heatmap_data %>% filter(resultado != "")
# Plot the heatmap
ggplot(heatmap_data, aes(x=resultado, y=BACTERIA, fill=proportion)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x="", y="", fill="Proportion") +
facet_wrap(~farmaco)+theme_test()+
theme(legend.position='right',text = element_text(size = 12, face="bold"), axis.text.x = element_text(size=10, colour = "black"),axis.text.y = element_text(size=10, colour = "black"),
legend.text = element_text(size = 12, color = "black"),strip.text.x = element_text(size = 12,face="bold"))
ggplot(heatmap_data, aes(x=resultado, y=farmaco, fill=proportion)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x="", y="", fill="Proportion") +
facet_wrap(~BACTERIA)+theme_test()+
theme(legend.position='right',text = element_text(size = 12, face="bold"), axis.text.x = element_text(size=10, colour = "black"),axis.text.y = element_text(size=10, colour = "black"),
legend.text = element_text(size = 12, color = "black"),strip.text.x = element_text(size = 12,face="bold"))
library(ggpubr)
datosplot <- datos %>% drop_na()
ggplot(datosplot, aes(x = BACTERIA, fill = resultado)) +
stat_count(geom = "bar",
position = "fill",stat="identity")+
facet_wrap(. ~ farmaco)+theme_pubr()+
coord_flip() + theme(legend.text = element_text(size = 8, color = "black"))
## Warning in stat_count(geom = "bar", position = "fill", stat = "identity"):
## Ignoring unknown parameters: `stat`
#+ theme(text = element_text(size = 5))+theme(axis.text.x = element_text(angle = 90))+theme(axis.text.x = element_text(hjust = 1, vjust = .5))+ scale_fill_brewer(palette="Pastel1")+ylab("")+xlab("")+theme(panel.spacing.x = unit(1, "lines"))+scale_x_discrete(limits = rev)+ scale_y_continuous(expand=expansion(mult=c(0,0.01)))+
datosplot %>% select(BACTERIA, farmaco, resultado
) %>% mutate (farmaco=paste("Farmaco", farmaco)) %>%
tbl_strata(strata = farmaco,
.tbl_fun =
~ .x %>%
tbl_summary(by = resultado, percent = 'row') %>%
add_n(),
.header = "**{strata}**, N = {n}"
)
| Characteristic |
Farmaco Farmaco AK, N = 118
|
Farmaco Farmaco AM, N = 143
|
Farmaco Farmaco CB, N = 118
|
Farmaco Farmaco CF, N = 143
|
Farmaco Farmaco CFX, N = 143
|
Farmaco Farmaco CL, N = 118
|
Farmaco Farmaco CLM, N = 25
|
Farmaco Farmaco CPF, N = 143
|
Farmaco Farmaco DC, N = 25
|
Farmaco Farmaco E, N = 25
|
Farmaco Farmaco GENT, N = 143
|
Farmaco Farmaco NET, N = 118
|
Farmaco Farmaco NF, N = 118
|
Farmaco Farmaco NOF, N = 118
|
Farmaco Farmaco PE, N = 25
|
Farmaco Farmaco SXT, N = 143
|
Farmaco Farmaco TE, N = 25
|
Farmaco Farmaco VA, N = 25
|
| N |
R, N = 13 |
S, N = 105 |
N |
R, N = 130 |
S, N = 13 |
N |
R, N = 79 |
S, N = 39 |
N |
R, N = 77 |
S, N = 66 |
N |
R, N = 38 |
S, N = 105 |
N |
R, N = 21 |
S, N = 97 |
N |
R, N = 5 |
S, N = 20 |
N |
R, N = 52 |
S, N = 91 |
N |
R, N = 24 |
S, N = 1 |
N |
R, N = 11 |
S, N = 14 |
N |
R, N = 40 |
S, N = 103 |
N |
R, N = 17 |
S, N = 101 |
N |
R, N = 12 |
S, N = 106 |
N |
R, N = 48 |
S, N = 70 |
N |
R, N = 24 |
S, N = 1 |
N |
R, N = 65 |
S, N = 78 |
N |
R, N = 2 |
S, N = 23 |
N |
R, N = 3 |
S, N = 22 |
| BACTERIA |
118 |
|
|
143 |
|
|
118 |
|
|
143 |
|
|
143 |
|
|
118 |
|
|
25 |
|
|
143 |
|
|
25 |
|
|
25 |
|
|
143 |
|
|
118 |
|
|
118 |
|
|
118 |
|
|
25 |
|
|
143 |
|
|
25 |
|
|
25 |
|
|
| Escherichia coli |
|
11 (10%) |
95 (90%) |
|
95 (89%) |
12 (11%) |
|
71 (67%) |
35 (33%) |
|
65 (61%) |
42 (39%) |
|
34 (32%) |
73 (68%) |
|
17 (16%) |
89 (84%) |
|
1 (100%) |
0 (0%) |
|
46 (43%) |
61 (57%) |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
33 (31%) |
74 (69%) |
|
14 (13%) |
92 (87%) |
|
9 (8.5%) |
97 (92%) |
|
43 (41%) |
63 (59%) |
|
1 (100%) |
0 (0%) |
|
52 (49%) |
55 (51%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
| klebsiella pneumoniae |
|
0 (0%) |
5 (100%) |
|
5 (100%) |
0 (0%) |
|
2 (40%) |
3 (60%) |
|
2 (40%) |
3 (60%) |
|
1 (20%) |
4 (80%) |
|
0 (0%) |
5 (100%) |
|
|
|
|
1 (20%) |
4 (80%) |
|
|
|
|
|
|
|
0 (0%) |
5 (100%) |
|
0 (0%) |
5 (100%) |
|
0 (0%) |
5 (100%) |
|
2 (40%) |
3 (60%) |
|
|
|
|
2 (40%) |
3 (60%) |
|
|
|
|
|
|
| Proteus |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
|
|
|
|
1 (100%) |
0 (0%) |
|
|
|
|
|
|
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
|
|
|
|
1 (100%) |
0 (0%) |
|
|
|
|
|
|
| Pseudomona aeruginosa |
|
1 (20%) |
4 (80%) |
|
5 (100%) |
0 (0%) |
|
4 (80%) |
1 (20%) |
|
5 (100%) |
0 (0%) |
|
2 (40%) |
3 (60%) |
|
4 (80%) |
1 (20%) |
|
|
|
|
2 (40%) |
3 (60%) |
|
|
|
|
|
|
|
2 (40%) |
3 (60%) |
|
2 (40%) |
3 (60%) |
|
3 (60%) |
2 (40%) |
|
2 (40%) |
3 (60%) |
|
|
|
|
5 (100%) |
0 (0%) |
|
|
|
|
|
|
| Staphylococcus epidermidis |
|
0 (0%) |
1 (100%) |
|
15 (94%) |
1 (6.2%) |
|
1 (100%) |
0 (0%) |
|
2 (12%) |
14 (88%) |
|
1 (6.2%) |
15 (94%) |
|
0 (0%) |
1 (100%) |
|
1 (6.7%) |
14 (93%) |
|
2 (12%) |
14 (88%) |
|
14 (93%) |
1 (6.7%) |
|
6 (40%) |
9 (60%) |
|
3 (19%) |
13 (81%) |
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
1 (100%) |
0 (0%) |
|
14 (93%) |
1 (6.7%) |
|
4 (25%) |
12 (75%) |
|
2 (13%) |
13 (87%) |
|
0 (0%) |
15 (100%) |
| Enterobacter |
|
|
|
|
1 (100%) |
0 (0%) |
|
|
|
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
|
|
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
1 (100%) |
0 (0%) |
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
|
|
|
|
|
|
|
|
|
1 (100%) |
0 (0%) |
|
0 (0%) |
1 (100%) |
|
0 (0%) |
1 (100%) |
|
1 (100%) |
0 (0%) |
| Staphylococcus aureus |
|
|
|
|
8 (100%) |
0 (0%) |
|
|
|
|
1 (12%) |
7 (88%) |
|
0 (0%) |
8 (100%) |
|
|
|
|
2 (25%) |
6 (75%) |
|
0 (0%) |
8 (100%) |
|
8 (100%) |
0 (0%) |
|
3 (38%) |
5 (62%) |
|
1 (12%) |
7 (88%) |
|
|
|
|
|
|
|
|
|
|
8 (100%) |
0 (0%) |
|
1 (12%) |
7 (88%) |
|
0 (0%) |
8 (100%) |
|
2 (25%) |
6 (75%) |
library(AMR)
## Warning: package 'AMR' was built under R version 4.2.3
dbresistence <- dbresisten
#CLEAN DATA with AMR
#Now we can thus clean our data:
dbresistence$BACTERIA <- as.mo(dbresistence$BACTERIA, info = TRUE)
## ℹ Microorganism translation was uncertain for "Pseudomona aeruginosa"
## (assumed Pseudomonas aeruginosa). Run mo_uncertainties() to review this,
## or use add_custom_microorganisms() to add custom entries.
mo_uncertainties()
## Matching scores are based on the resemblance between the input and the full
## taxonomic name, and the pathogenicity in humans. See ?mo_matching_score.
##
## --------------------------------------------------------------------------------
## "Pseudomona aeruginosa" -> Pseudomonas aeruginosa (B_PSDMN_AERG, 0.977)
## Also matched: Pseudomonas andropogonis (0.708), Pseudomonas
## antimicrobica (0.680), Pseudomonas alcaligenes (0.674), Pseudomonas
## acidovorans (0.630), Pseudomonas asiatica (0.500), Pseudomonas
## azerbaijanoriens (0.500), Pseudomonas azerbaijanorientalis (0.490),
## Pseudomonas azerbaijanoccidens (0.489), Pseudomonas
## azerbaijanoccidentalis (0.480), and Pseudomonas argentinensis (0.480)
##
## Only the first 10 other matches of each record are shown. Run
## print(mo_uncertainties(), n = ...) to view more entries, or save
## mo_uncertainties() to an object.
library(gt)
bact<-dbresistence %>%
count(mo_name(BACTERIA), sort = TRUE)
bact %>% gt()
| mo_name(BACTERIA) |
n |
| Escherichia coli |
128 |
| Staphylococcus epidermidis |
28 |
| Staphylococcus aureus |
22 |
| Klebsiella pneumoniae |
6 |
| Pseudomonas aeruginosa |
6 |
| (levadura desconocida) |
5 |
| Enterobacter |
1 |
| Proteus |
1 |
#Antibiotic results
# method 1, be explicit about the columns:
#dbresistence <- dbresistence %>% mutate_at(vars(AK:VA), as.sir)
#method 2:
dbresistence <- dbresistence %>%
mutate_if(is_sir_eligible, as.sir)
summary(dbresistence)
## BACTERIA AK AM
## Class :mo Class:sir Class:sir
## <NA> :0 %R :11.0% (n=13) %R :90.9% (n=130)
## Unique:8 %SI :89.0% (n=105) %SI : 9.1% (n=13)
## #1 :B_ESCHR_COLI - %S :89.0% (n=105) - %S : 9.1% (n=13)
## #2 :B_STPHY_EPDR - %I : 0.0% (n=0) - %I : 0.0% (n=0)
## #3 :B_STPHY_AURS
## CB CF CFX
## Class:sir Class:sir Class:sir
## %R :66.9% (n=79) %R :53.8% (n=77) %R :26.6% (n=38)
## %SI :33.1% (n=39) %SI :46.2% (n=66) %SI :73.4% (n=105)
## - %S :33.1% (n=39) - %S :46.2% (n=66) - %S :73.4% (n=105)
## - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0)
##
## CPF CL GENT
## Class:sir Class:sir Class:sir
## %R :36.4% (n=52) %R :17.8% (n=21) %R :28.0% (n=40)
## %SI :63.6% (n=91) %SI :82.2% (n=97) %SI :72.0% (n=103)
## - %S :63.6% (n=91) - %S :82.2% (n=97) - %S :72.0% (n=103)
## - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0)
##
## NET NF NOF
## Class:sir Class:sir Class:sir
## %R :14.4% (n=17) %R :10.2% (n=12) %R :40.7% (n=48)
## %SI :85.6% (n=101) %SI :89.8% (n=106) %SI :59.3% (n=70)
## - %S :85.6% (n=101) - %S :89.8% (n=106) - %S :59.3% (n=70)
## - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0)
##
## SXT CLM DC
## Class:sir Class:sir Class:sir
## %R :45.5% (n=65) %R :20.0% (n=5) %R :96.0% (n=24)
## %SI :54.5% (n=78) %SI :80.0% (n=20) %SI : 4.0% (n=1)
## - %S :54.5% (n=78) - %S :80.0% (n=20) - %S : 4.0% (n=1)
## - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0)
##
## E PE TE
## Class:sir Class:sir Class:sir
## %R :44.0% (n=11) %R :96.0% (n=24) %R : 8.0% (n=2)
## %SI :56.0% (n=14) %SI : 4.0% (n=1) %SI :92.0% (n=23)
## - %S :56.0% (n=14) - %S : 4.0% (n=1) - %S :92.0% (n=23)
## - %I : 0.0% (n=0) - %I : 0.0% (n=0) - %I : 0.0% (n=0)
##
## VA
## Class:sir
## %R :12.0% (n=3)
## %SI :88.0% (n=22)
## - %S :88.0% (n=22)
## - %I : 0.0% (n=0)
##
dbresisten<-dbresisten %>%
mutate_if(is_sir_eligible, as.sir)
ggplot(dbresisten) +
geom_sir() +
scale_y_percent() +
scale_sir_colours() +
labels_sir_count() +
theme_sir()+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "CPF".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "NF".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "NOF".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "DC".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "E".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "PE".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "CPF".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "NF".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "NOF".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "DC".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "E".
## Warning: in as.ab(): these values could not be coerced to a valid antimicrobial
## ID: "PE".
## Warning: Removed 12 rows containing missing values (`position_stack()`).
## Warning: Removed 18 rows containing missing values (`position_stack()`).
ab<-antibiogram(dbresisten,)
??antibiogram
## starting httpd help server ... done
ab %>% gt()
| Patógeno (N min-max) |
AK |
AM |
CB |
CF |
CFX |
CL |
CPF |
GENT |
NET |
NF |
NOF |
SXT |
| E. coli (1-107) |
90 |
11 |
33 |
39 |
68 |
84 |
57 |
69 |
87 |
92 |
59 |
51 |
dbresisten %>% group_by(mo_name(BACTERIA)) %>% summarise(amoxicillin = resistance(AM))
## Warning: There were 7 warnings in `summarise()`.
## The first warning was:
## ℹ In argument: `amoxicillin = resistance(AM)`.
## ℹ In group 1: `mo_name(BACTERIA) = "(levadura desconocida)"`.
## Caused by warning:
## ! Introducing NA: no results available for AM in group: mo_name(BACTERIA) =
## "(levadura desconocida)" (minimum = 30).
## ℹ Run ]8;;ide:run:dplyr::last_dplyr_warnings()dplyr::last_dplyr_warnings()]8;; to see the 6 remaining warnings.
## # A tibble: 8 × 2
## `mo_name(BACTERIA)` amoxicillin
## <chr> <dbl>
## 1 (levadura desconocida) NA
## 2 Enterobacter NA
## 3 Escherichia coli 0.888
## 4 Klebsiella pneumoniae NA
## 5 Proteus NA
## 6 Pseudomonas aeruginosa NA
## 7 Staphylococcus aureus NA
## 8 Staphylococcus epidermidis NA
# Continue from your code...
datos <- dbresisten %>%
gather(key= "farmaco", value= "resultado", -BACTERIA)
# Convert 'resultado' to factor for better handling in ggplot
datos$resultado <- as.factor(datos$resultado)
# Create a table with counts of each combination of 'BACTERIA', 'farmaco', and 'resultado'
heatmap_data <- datos %>%
group_by(BACTERIA, farmaco, resultado) %>%
summarise(count=n()) %>%
ungroup()
## `summarise()` has grouped output by 'BACTERIA', 'farmaco'. You can override
## using the `.groups` argument.
# Generate proportions by dividing count by sum of counts for each 'BACTERIA'-'farmaco' combination
heatmap_data <- heatmap_data %>%
group_by(BACTERIA, farmaco) %>%
mutate(proportion = count / sum(count)) %>%
ungroup()
# Filter out blank values
heatmap_data <- heatmap_data %>% filter(resultado != "")
# Plot the heatmap
ggplot(heatmap_data, aes(x=resultado, y=BACTERIA, fill=proportion)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x="", y="", fill="Proportion") +
facet_wrap(~farmaco)+theme_test()+theme(legend.position='right',text = element_text(size = 12, face="bold"), axis.text.x = element_text(size=10, colour = "black"),axis.text.y = element_text(size=10, colour = "black"),
legend.text = element_text(size = 12, color = "black"),strip.text.x = element_text(size = 12,face="bold"))
ggplot(heatmap_data, aes(x=resultado, y=farmaco, fill=proportion)) +
geom_tile() +
scale_fill_gradient(low = "#C0A635", high = "#033195") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(x="", y="", fill="Proportion") +
facet_wrap(~BACTERIA)+theme_test()+
theme(legend.position='right',text = element_text(size = 12, face="bold"), axis.text.x = element_text(size=16, colour = "black"),axis.text.y = element_text(size=10, colour = "black"),
legend.text = element_text(size = 12, color = "black"),strip.text.x = element_text(size = 12,face="bold"))
library(ggpubr)
datosplot <- datos %>% drop_na()
ggplot(datosplot, aes(x = BACTERIA, fill = resultado)) +
stat_count(geom = "bar",
position = "fill",stat="identity",color="black")+
facet_wrap(. ~ farmaco)+theme_classic2()+
coord_flip() + theme(legend.text = element_text(size = 8, color = "black"))+ scale_fill_manual(values = alpha(c( "#C0A635" ,"#033195"), .75))+
theme(legend.position='right',text = element_text(size = 12, face="bold"), axis.text.x = element_text(size=10, colour = "black"),axis.text.y = element_text(size=10, colour = "black"),
legend.text = element_text(size = 12, color = "black"),strip.text.x = element_text(size = 12,face="bold"))
## Warning in stat_count(geom = "bar", position = "fill", stat = "identity", :
## Ignoring unknown parameters: `stat`
#+ theme(text = element_text(size = 5))+theme(axis.text.x = element_text(angle = 90))+theme(axis.text.x = element_text(hjust = 1, vjust = .5))+ scale_fill_brewer(palette="Pastel1")+ylab("")+xlab("")+theme(panel.spacing.x = unit(1, "lines"))+scale_x_discrete(limits = rev)+ scale_y_continuous(expand=expansion(mult=c(0,0.01)))+
dbresisclin <- db %>% select(EDAD, BACTERIA, AK:VA,)
dbresisclin<-dbresisclin %>% mutate_at(vars(AK:VA), as.character)
dbresistencin<- dbresisclin %>%
mutate_at(vars(AK:VA), ~recode(., `1` = "S", `3` = "R", `NA`= ""))
# Gather data to long format
long_df <- dbresistencin %>%
gather(key = "farmaco", value = "resultado", -EDAD, -BACTERIA)
# View the resulting long dataframe
head(long_df)
## # A tibble: 6 × 4
## EDAD BACTERIA farmaco resultado
## <dbl> <chr> <chr> <chr>
## 1 49 Escherichia coli AK S
## 2 34 klebsiella pneumoniae AK <NA>
## 3 33 Yeast AK <NA>
## 4 53 Escherichia coli AK S
## 5 45 Escherichia coli AK S
## 6 33 Staphylococcus aureus AK <NA>
long_df<-long_df %>% drop_na()
long_df %>% tbl_summary(by=resultado) %>% add_p()
## There was an error in 'add_p()/add_difference()' for variable 'BACTERIA', p-value omitted:
## Error in stats::fisher.test(c("Escherichia coli", "Escherichia coli", : FEXACT error 7(location). LDSTP=18360 is too small for this problem,
## (pastp=69.7532, ipn_0:=ipoin[itp=564]=8010, stp[ipn_0]=69.4681).
## Increase workspace or consider using 'simulate.p.value=TRUE'
| Characteristic |
R, N = 634 |
S, N = 1,034 |
p-value |
| EDAD DEL PACIENTE |
38 (26, 53) |
37 (23, 52) |
0.029 |
| BACTERIA |
|
|
|
| Enterobacter |
7 (1.1%) |
5 (0.5%) |
|
| Escherichia coli |
477 (75%) |
783 (76%) |
|
| klebsiella pneumoniae |
15 (2.4%) |
45 (4.4%) |
|
| Proteus |
7 (1.1%) |
5 (0.5%) |
|
| Pseudomona aeruginosa |
37 (5.8%) |
23 (2.2%) |
|
| Staphylococcus aureus |
31 (4.9%) |
53 (5.1%) |
|
| Staphylococcus epidermidis |
60 (9.5%) |
120 (12%) |
|
| farmaco |
|
|
<0.001 |
| AK |
13 (2.1%) |
103 (10.0%) |
|
| AM |
126 (20%) |
13 (1.3%) |
|
| CB |
77 (12%) |
39 (3.8%) |
|
| CF |
75 (12%) |
64 (6.2%) |
|
| CFX |
36 (5.7%) |
103 (10.0%) |
|
| CL |
20 (3.2%) |
96 (9.3%) |
|
| CLM |
4 (0.6%) |
19 (1.8%) |
|
| CPF |
50 (7.9%) |
89 (8.6%) |
|
| DC |
22 (3.5%) |
1 (<0.1%) |
|
| E |
10 (1.6%) |
13 (1.3%) |
|
| GENT |
39 (6.2%) |
100 (9.7%) |
|
| NET |
15 (2.4%) |
101 (9.8%) |
|
| NF |
12 (1.9%) |
104 (10%) |
|
| NOF |
46 (7.3%) |
70 (6.8%) |
|
| PE |
22 (3.5%) |
1 (<0.1%) |
|
| SXT |
62 (9.8%) |
77 (7.4%) |
|
| TE |
2 (0.3%) |
21 (2.0%) |
|
| VA |
3 (0.5%) |
20 (1.9%) |
|
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)
library(purrr)
library(broom)
## Warning: package 'broom' was built under R version 4.2.3
# Your data preparation process here
dbresisclin <- db %>% select(EDAD, BACTERIA, AK:VA)
dbresisclin <- dbresisclin %>% mutate_at(vars(AK:VA), as.character)
dbresistencin <- dbresisclin %>%
mutate_at(vars(AK:VA), ~recode(., `1` = "S", `3` = "R", `NA`= ""))
# Gather data to long format
long_df <- dbresistencin %>%
gather(key = "farmaco", value = "resultado", -EDAD, -BACTERIA)
# Filter out blank values
long_df <- long_df %>% filter(resultado != "")
# Calculate the count for each bacteria and result
df_counts <- long_df %>%
group_by(BACTERIA, resultado) %>%
summarise(n = n(), .groups = "drop")
# Calculate the percentage for each bacteria and result
df_counts <- df_counts %>%
group_by(BACTERIA) %>%
mutate(percent = n / sum(n) * 100)
# Run chi square test for each bacteria and add p-value to data frame
df_counts <- df_counts %>%
nest(data = -BACTERIA) %>%
mutate(chi_test = map(data, ~ chisq.test(.x$n)$p.value)) %>%
unnest(data, chi_test)
## Warning: `unnest()` has a new interface. See `?unnest` for details.
## ℹ Try `df %>% unnest(c(data, chi_test))`, with `mutate()` if needed.
# Add significance labels
df_counts <- df_counts %>%
mutate(signif_label = case_when(
chi_test > 0.05 ~ "NS",
chi_test <= 0.001 ~ "<0.001",
chi_test >= 0.004 ~ "0.004",
TRUE ~ as.character(chi_test)
))
# Create the bar plot
ggplot(df_counts, aes(x = resultado, y = percent, fill = resultado)) +
geom_bar(stat = "identity") +
facet_wrap(~BACTERIA, scales = "free_y") +
geom_text(aes(label = paste0("p = ", signif_label)), vjust = -1) +
labs(title = "Bacteria Resistance and Sensitivity",
x = "Result",
y = "Percentage",
fill = "")+theme_sir()
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)
# Your data preparation process here
dbresisclin <- db %>% select(EDAD, BACTERIA, AK:VA)
dbresisclin <- dbresisclin %>% mutate_at(vars(AK:VA), as.character)
dbresistencin <- dbresisclin %>%
mutate_at(vars(AK:VA), ~recode(., `1` = "S", `3` = "R", `NA`= ""))
# Gather data to long format
long_df <- dbresistencin %>%
gather(key = "farmaco", value = "resultado", -EDAD, -BACTERIA)
# Filter out blank values and select only 'R' results
long_df <- long_df %>% filter(resultado == "R")
# Calculate the count for each bacteria and result
df_counts <- long_df %>%
group_by(BACTERIA, farmaco) %>%
summarise(n = n(), .groups = "drop")
# Calculate the total count for each bacteria
df_totals <- long_df %>%
group_by(BACTERIA) %>%
summarise(total = n(), .groups = "drop")
# Join the data frames and calculate proportions and confidence intervals
df_final <- inner_join(df_counts, df_totals, by = "BACTERIA") %>%
mutate(proportion = n / total,
conf_low = proportion - 1.96 * sqrt((proportion * (1 - proportion)) / total),
conf_high = proportion + 1.96 * sqrt((proportion * (1 - proportion)) / total))
# Create the bar plot
ggplot(df_final, aes(x = farmaco, y = proportion, fill = BACTERIA)) +
geom_bar(stat = "identity", position = position_dodge()) +
labs(title = "Bacteria Resistance Frequency",
x = "Drug",
y = "Resistance Frequency",
fill = "Bacteria")
#filter enterobacter
filtentero <- df_final %>% filter(BACTERIA == "Enterobacter")
enterores<-ggplot(filtentero, aes(x = farmaco, y = proportion, fill = BACTERIA))+
geom_errorbar(aes(ymin = conf_low, ymax = conf_high), width = 0.2, position = position_dodge(0.9))+
geom_bar(stat = "identity", position = position_dodge()) +
labs(title = "",
x = "Drug",
y = "Resistance Frequency",
fill = "")
enterores
#filter e colI
filtecoli<-df_final %>% filter(BACTERIA == "Escherichia coli")
ecolires<-ggplot(filtecoli, aes(x = farmaco, y = proportion, fill = BACTERIA))+
geom_errorbar(aes(ymin = conf_low, ymax = conf_high), width = 0.2, position = position_dodge(0.9))+
geom_bar(stat = "identity", position = position_dodge()) +
labs(title = "",
x = "Drug",
y = "Resistance Frequency",
fill = "")
ecolires
#Pseudomona aeruginosa
filtepseudo<-df_final %>% filter(BACTERIA == "Pseudomona aeruginosa")
pseudores<-ggplot(filtepseudo, aes(x = farmaco, y = proportion, fill = BACTERIA))+
geom_errorbar(aes(ymin = conf_low, ymax = conf_high), width = 0.2, position = position_dodge(0.9))+
geom_bar(stat = "identity", position = position_dodge()) +
labs(title = "",
x = "Drug",
y = "Resistance Frequency",
fill = "")
pseudores
# Load AMR package
library(AMR)
# Calculate multidrug resistance
mdr_results <- dbresistencin %>%
select(BACTERIA, AK:VA) %>%
mutate(across(AK:VA, as.sir)) %>%
mutate(mdr = mdro(.))
## (54 isolates had no test results)
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `mdr = mdro(.)`.
## Caused by warning:
## ! in mdro(): NA introduced for isolates where the available percentage of
## antimicrobial classes was below 50% (set with pct_required_classes)
# View the results
head(mdr_results)
## # A tibble: 6 × 20
## BACTERIA AK AM CB CF CFX CPF CL GENT NET NF NOF
## <chr> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir>
## 1 Escherichia… S R S R S R S R S R S
## 2 klebsiella … NA NA NA NA NA NA NA NA NA NA NA
## 3 Yeast NA NA NA NA NA NA NA NA NA NA NA
## 4 Escherichia… S R R R S R R S S S S
## 5 Escherichia… S R R R S S S S R S R
## 6 Staphylococ… NA NA NA NA NA NA NA NA NA NA NA
## # ℹ 8 more variables: SXT <sir>, CLM <sir>, DC <sir>, E <sir>, PE <sir>,
## # TE <sir>, VA <sir>, mdr <ord>
# Chi-squared test
# Fisher's Exact Test
fisher_test <- fisher.test(table(mdr_results$BACTERIA, mdr_results$mdr))
print(fisher_test)
##
## Fisher's Exact Test for Count Data
##
## data: table(mdr_results$BACTERIA, mdr_results$mdr)
## p-value = 9.115e-15
## alternative hypothesis: two.sided
fisher_test$p.value
## [1] 9.114706e-15
# Bar plot of multidrug resistance
ggplot(mdr_results, aes(x = BACTERIA, fill = mdr, na.rm = TRUE)) +
geom_bar(position = "fill") +
labs(x = "Bacteria", y = "Proportion", fill = "MDR Status") +
scale_fill_discrete(labels = c("Non-MDR", "MDR")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Load necessary libraries
library(dplyr)
# Define start and end dates
start_date <- as.Date("2020-01-01")
end_date <- as.Date("2023-12-31")
# Add date column
dbresistencin <- dbresistencin %>%
mutate(FECHA = sample(seq(start_date, end_date, by = "day"), size = nrow(dbresistencin), replace = TRUE))
# Load necessary libraries
library(ggplot2)
library(lubridate)
# Convert date to numeric
dbresistencin <- dbresistencin %>%
mutate(FECHA = as.numeric(FECHA))
# Remove rows with NA in columns AK to VA
dbresistencin <- dbresistencin %>% drop_na(AK:VA)
# Fit linear regression model
#fit <- lm(AK ~ FECHA, data = dbresistencin, na.rm = TRUE)
# Make predictions
#dbresistencin$pred <- predict(fit, newdata = dbresistencin)
# Plot actual vs predicted
#ggplot(dbresistencin, aes(x = as.Date(FECHA), y = AK)) +
# geom_point() +
#geom_line(aes(y = pred), color = "red")