Antibiogram
TS.Hoang
2024-04-17
Negative bacteria
Processing data
Gram_neg_SIR_diameter <- read.table("/Users/qcvp/R/Mẫu RIVS/báo20192020/negative.txt", sep = "\t", header = T)
names(Gram_neg_SIR_diameter)## [1] "Sample.ID" "NA." "AN" "AUG" "ATM" "FEP"
## [7] "FOX" "CIP" "SXT" "ETP" "FF" "CN"
## [13] "IPM" "LEV" "PIP" "PTZ" "TIC" "TCC"
## [19] "TOB" "Taxonomy" "Medium" "Transect" "Station" "Substrate"
## [25] "Type"antibio_neg <- Gram_neg_SIR_diameter[, c(1:19)]
antibio_neg_meta <- Gram_neg_SIR_diameter[, c(20:25)]
row.names(antibio_neg_meta) <- Gram_neg_SIR_diameter$Sample.ID##Dealing with the missing values
library(VIM)
library(FactoMineR)
library(missMDA)
library(naniar)
library(ggfortify)
summary(antibio_neg)## Sample.ID NA. AN AUG
## Length:78 Length:78 Length:78 Length:78
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## ATM FEP FOX CIP
## Length:78 Length:78 Length:78 Length:78
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## SXT ETP FF CN
## Length:78 Length:78 Length:78 Length:78
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## IPM LEV PIP PTZ
## Length:78 Length:78 Length:78 Length:78
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## TIC TCC TOB
## Length:78 Length:78 Length:78
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :characterdim(na.omit(antibio_neg)) #How many lines and column do I keep if I remove all the missing values## [1] 35 19p01 <- gg_miss_var(antibio_neg) #plot of missing data
p01
res<-summary(aggr(antibio_neg, sortVar=TRUE))$combinations
##
## Variables sorted by number of missings:
## Variable Count
## AN 0.53846154
## FOX 0.43589744
## PIP 0.42307692
## FF 0.39743590
## TCC 0.38461538
## AUG 0.37179487
## ETP 0.37179487
## NA. 0.33333333
## TOB 0.32051282
## PTZ 0.29487179
## TIC 0.29487179
## SXT 0.20512821
## ATM 0.10256410
## IPM 0.08974359
## CN 0.07692308
## CIP 0.03846154
## FEP 0.02564103
## LEV 0.02564103
## Sample.ID 0.00000000head(res[rev(order(res[,2])),]) # Only 12 % of the dataset has no holes ## Combinations Count Percent
## 1 0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0 35 44.871795
## 9 0:1:1:1:0:0:1:0:1:1:1:0:0:0:1:1:1:1:1 12 15.384615
## 3 0:0:1:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0:0 8 10.256410
## 11 0:1:1:1:1:0:1:0:0:1:1:0:0:0:1:0:0:1:0 6 7.692308
## 8 0:1:1:1:0:0:1:0:0:1:1:1:1:0:1:1:1:1:1 5 6.410256
## 5 0:0:1:0:0:0:1:0:0:0:1:0:0:0:1:0:0:0:1 4 5.128205R-I-S intepreted analysis
Load antibiogram table
Gram_neg_SIR <- read.table("/Users/qcvp/R/Mẫu RIVS/báo20192020/negative.txt", sep = "\t", header = T)
names(Gram_neg_SIR)## [1] "Sample.ID" "NA." "AN" "AUG" "ATM" "FEP"
## [7] "FOX" "CIP" "SXT" "ETP" "FF" "CN"
## [13] "IPM" "LEV" "PIP" "PTZ" "TIC" "TCC"
## [19] "TOB" "Taxonomy" "Medium" "Transect" "Station" "Substrate"
## [25] "Type"row.names(Gram_neg_SIR) <- Gram_neg_SIR$Sample.ID
Gram_neg_SIR2 <- Gram_neg_SIR[,-1]Setup
library(dplyr)
names(Gram_neg_SIR)## [1] "Sample.ID" "NA." "AN" "AUG" "ATM" "FEP"
## [7] "FOX" "CIP" "SXT" "ETP" "FF" "CN"
## [13] "IPM" "LEV" "PIP" "PTZ" "TIC" "TCC"
## [19] "TOB" "Taxonomy" "Medium" "Transect" "Station" "Substrate"
## [25] "Type"#Media
med_neg_res <- data.frame("Aero" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="Aero",1:18], 2, function(x) (length(which(x == "R"))/length(x))),
"MA" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="MA",1:18], 2, function(x) (length(which(x == "R"))/length(x))),
"MAC" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="MAC",1:18], 2, function(x) (length(which(x == "R"))/length(x))),
"SS" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="SS",1:18], 2, function(x) (length(which(x == "R"))/length(x))),
"Antibiotic" = names(Gram_neg_SIR2[,1:18]))
#Sample types
sampletypes_neg_res <- data.frame(
"Environment" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Type =="Environment",1:18], 2, function(x) (length(which(x == "R"))/length(which(x %in% c("R", "S", "I"))))),
"Antibiotic" = names(Gram_neg_SIR2[,1:18]))antibiotic_class <- rep("Fluoroquinolones", 18)
antibiotic_class[med_neg_res$Antibiotic %in% c("AUG", "PIP", "TCC", "TIC", "PTZ")] <- "Penicillins"
antibiotic_class[med_neg_res$Antibiotic %in% c("FEP", "FOX")] <- "Cephalosporins"
antibiotic_class[med_neg_res$Antibiotic %in% c("AN", "TOB", "CN")] <- "Aminoglycosides"
antibiotic_class[med_neg_res$Antibiotic %in% c("ATM")] <- "Monobactams"
antibiotic_class[med_neg_res$Antibiotic %in% c("ETP", "IPM")] <- "Carbapenems"
antibiotic_class[med_neg_res$Antibiotic %in% c("CIP", "LEV" ,"NA.")] <- "Fluoroquinolones"
antibiotic_class[med_neg_res$Antibiotic %in% c("FF","SXT")] <- "Others"
med_neg_res$antibiotic_class <- antibiotic_class
sampletypes_neg_res$antibiotic_class <- antibiotic_class
rm(antibiotic_class)Barplot resistant percentage
library(tidyr)
med_neg_res_mac <- gather(med_neg_res, Medium, Resistant, MAC)
med_neg_res_ae <- gather(med_neg_res, Medium, Resistant, Aero)
med_neg_res_ma <- gather(med_neg_res, Medium, Resistant, MA)
med_neg_res_ss <- gather(med_neg_res, Medium, Resistant, SS)library(ggplot2)
p1 <- ggplot(med_neg_res_mac, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Mac-Conkey isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p1
p2 <- ggplot(med_neg_res_ma, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Marine Agar isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p2
p3 <- ggplot(med_neg_res_ae, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Aeromonas isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p3
p4 <- ggplot(med_neg_res_ss, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Salmonella Shigella isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p4
Combine
library(tidyr)
med_neg_res_long <- gather(med_neg_res, Medium, Resistant, c(Aero, MA, MAC, SS))
#plot
p5 <- ggplot(med_neg_res_long, aes(x = Antibiotic, y = Resistant, fill= Medium)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13)) +
facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x")
p5
MAR index bar plot
Total
Gram_neg_SIR3 <- Gram_neg_SIR2 %>% separate(Taxonomy, c("Genus", "Species"))
# Adding color for plotting
custom_colors <- c( "#CBD588", "#5F7FC7","#DA5724", "#508578", "#CD9BCD",
"#AD6F3B", "#673770","#D14285", "#652926", "#C84248",
"#8569D5", "#5E738F","#D1A33D", "#8A7C64", "#599861", "black", "red", "green")
# MAR calculate
Gram_neg_SIR3$MAR <- apply(Gram_neg_SIR3[,1:18], 1, function(x) (length(which(x == "R"))/length(which(x %in% c("R", "S", "I")))))
# Plotting Transect
box3 = ggplot(Gram_neg_SIR3, aes(x = Transect , y = MAR, color = Transect)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Transect), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=13),
axis.text.x = element_blank()) +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + geom_hline(yintercept = 0.2, linetype="dashed")
#facet_wrap(~Transect, nrow=1, scales = "free_x")
box3
# Plotting face wrap with Sample type
box4 = ggplot(Gram_neg_SIR3, aes(x = Transect , y = MAR, color = Transect)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Transect), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=13),
axis.text.x = element_blank()) +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + geom_hline(yintercept = 0.2, linetype="dashed") +
facet_wrap(~Substrate, nrow=1, scales = "free_x")
box4
Plotting with Genus in each Transect
Gram_neg_SIR3_farm <- Gram_neg_SIR3[Gram_neg_SIR2$Transect == "Farm", ]
Gram_neg_SIR3_urban <- Gram_neg_SIR3[Gram_neg_SIR2$Transect == "Urban", ]
Gram_neg_SIR3_recovery <- Gram_neg_SIR3[Gram_neg_SIR2$Transect == "Recovery", ]
box5 = ggplot(Gram_neg_SIR3_farm, aes(x = Genus , y = MAR, color = Genus)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Genus), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 20),
axis.text.y = element_text(size=20),
axis.text.x = element_blank()) +
ggtitle("Farm MAR") +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + #theme(legend.position="bottom") +
geom_hline(yintercept = 0.2, linetype="dashed") +
facet_wrap(~Type, nrow=1, scales = "free_x")
box5
box6 = ggplot(Gram_neg_SIR3_urban, aes(x = Genus , y = MAR, color = Genus)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Genus), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 20),
axis.text.y = element_text(size=20),
axis.text.x = element_blank()) +
ggtitle("Urban MAR") +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + #theme(legend.position="bottom") +
geom_hline(yintercept = 0.2, linetype="dashed") +
facet_wrap(~Type, nrow=1, scales = "free_x")
box6
box5 = ggplot(Gram_neg_SIR3_recovery, aes(x = Genus , y = MAR, color = Genus)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Genus), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 20),
axis.text.y = element_text(size=20),
axis.text.x = element_blank()) +
ggtitle("Recovery MAR") +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + #theme(legend.position="bottom") +
geom_hline(yintercept = 0.2, linetype="dashed") +
facet_wrap(~Type, nrow=1, scales = "free_x")
box5
# Positive bacteria Processing data
Gram_neg_SIR_diameter <- read.table("/Users/qcvp/R/Mẫu RIVS/báo20192020/positive.txt", sep = "\t", header = T)
names(Gram_neg_SIR_diameter)## [1] "Samples_ID" "CIP" "SXT" "ETP" "FF"
## [6] "CN" "IMP" "K" "LEV" "R"
## [11] "T" "TOB" "Taxonomy" "Medium" "Transect"
## [16] "Station" "Type" "Substrate"antibio_neg <- Gram_neg_SIR_diameter[, c(1:12)]##Dealing with the missing values
library(VIM)
library(FactoMineR)
library(missMDA)
library(naniar)
library(ggfortify)
summary(antibio_neg)## Samples_ID CIP SXT ETP
## Length:61 Length:61 Length:61 Length:61
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## FF CN IMP K
## Length:61 Length:61 Length:61 Length:61
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## LEV R T TOB
## Length:61 Length:61 Length:61 Length:61
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :characterdim(na.omit(antibio_neg)) #How many lines and column do I keep if I remove all the missing values## [1] 0 12p01 <- gg_miss_var(antibio_neg) #plot of missing data
p01
res<-summary(aggr(antibio_neg, sortVar=TRUE))$combinations
##
## Variables sorted by number of missings:
## Variable Count
## IMP 0.4262295
## TOB 0.4098361
## CIP 0.3770492
## LEV 0.3770492
## ETP 0.2131148
## K 0.2131148
## R 0.2131148
## Samples_ID 0.0000000
## SXT 0.0000000
## FF 0.0000000
## CN 0.0000000
## T 0.0000000head(res[rev(order(res[,2])),]) # Only 12 % of the dataset has no holes ## Combinations Count Percent
## 4 0:1:0:0:0:0:1:0:1:0:0:0 23 37.704918
## 1 0:0:0:0:0:0:0:0:0:0:0:1 22 36.065574
## 3 0:0:0:1:0:0:0:1:0:1:0:0 13 21.311475
## 2 0:0:0:0:0:0:1:0:0:0:0:1 3 4.918033R-I-S intepreted analysis
Load antibiogram table
Gram_neg_SIR <- read.table("/Users/qcvp/R/Mẫu RIVS/báo20192020/positive.txt", sep = "\t", header = T)
names(Gram_neg_SIR)## [1] "Samples_ID" "CIP" "SXT" "ETP" "FF"
## [6] "CN" "IMP" "K" "LEV" "R"
## [11] "T" "TOB" "Taxonomy" "Medium" "Transect"
## [16] "Station" "Type" "Substrate"row.names(Gram_neg_SIR) <- Gram_neg_SIR$Sample.ID
Gram_neg_SIR2 <- Gram_neg_SIR[,-1]Setup
library(dplyr)
names(Gram_neg_SIR)## [1] "Samples_ID" "CIP" "SXT" "ETP" "FF"
## [6] "CN" "IMP" "K" "LEV" "R"
## [11] "T" "TOB" "Taxonomy" "Medium" "Transect"
## [16] "Station" "Type" "Substrate"#Media
med_neg_res <- data.frame("Aero" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="Aero",1:11], 2, function(x) (length(which(x == "R"))/length(x))),
"MA" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="MA",1:11], 2, function(x) (length(which(x == "R"))/length(x))),
"MAC" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="MAC",1:11], 2, function(x) (length(which(x == "R"))/length(x))),
"SS" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="SS",1:11], 2, function(x) (length(which(x == "R"))/length(x))),
"TCBS" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="TCBS",1:11], 2, function(x) (length(which(x == "R"))/length(x))),
"Sta" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Medium =="Sta",1:11], 2, function(x) (length(which(x == "R"))/length(x))),
"Antibiotic" = names(Gram_neg_SIR2[,1:11]))
#Sample types
sampletypes_neg_res <- data.frame(
"Environment" = apply(Gram_neg_SIR2[Gram_neg_SIR2$Type =="Environment",1:11], 2, function(x) (length(which(x == "R"))/length(which(x %in% c("R", "S", "I"))))),
"Antibiotic" = names(Gram_neg_SIR2[,1:11]))antibiotic_class <- rep("Fluoroquinolones", 11)
antibiotic_class[med_neg_res$Antibiotic %in% c("R", "E")] <- "Macrolides "
antibiotic_class[med_neg_res$Antibiotic %in% c("TOB", "CN", "K")] <- "Aminoglycosides"
antibiotic_class[med_neg_res$Antibiotic %in% c("IMP")] <- "Carbapenems"
antibiotic_class[med_neg_res$Antibiotic %in% c("CIP", "LEV")] <- "Fluoroquinolones"
antibiotic_class[med_neg_res$Antibiotic %in% c("FF")] <- "Phosphonic antibiotics"
antibiotic_class[med_neg_res$Antibiotic %in% c("SXT", "T")] <- "Others"
med_neg_res$antibiotic_class <- antibiotic_class
sampletypes_neg_res$antibiotic_class <- antibiotic_class
rm(antibiotic_class)Barplot resistant percentage
library(tidyr)
med_neg_res_mac <- gather(med_neg_res, Medium, Resistant, MAC)
med_neg_res_ae <- gather(med_neg_res, Medium, Resistant, Aero)
med_neg_res_ma <- gather(med_neg_res, Medium, Resistant, MA)
med_neg_res_ss <- gather(med_neg_res, Medium, Resistant, SS)
med_neg_res_tcbs <- gather(med_neg_res, Medium, Resistant, TCBS)
med_neg_res_sta <- gather(med_neg_res, Medium, Resistant, Sta)library(ggplot2)
p1 <- ggplot(med_neg_res_mac, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Mac-Conkey isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p1
p2 <- ggplot(med_neg_res_ma, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Marine Agar isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p2
p3 <- ggplot(med_neg_res_ae, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Aeromonas isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p3
p4 <- ggplot(med_neg_res_ss, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Salmonella Shigella isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p4
p5 <- ggplot(med_neg_res_tcbs, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Thiosulfate-Citrate-Bile-Salt Sucrose isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p5
p6 <- ggplot(med_neg_res_sta, aes(x = Antibiotic, y = Resistant)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Streptomycin Thallous Acetate isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() + facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13))
p6
Combine
library(tidyr)
med_neg_res_long <- gather(med_neg_res, Medium, Resistant, c(Aero, MA, MAC, SS, TCBS, Sta))
#plot
p7 <- ggplot(med_neg_res_long, aes(x = Antibiotic, y = Resistant, fill= Medium)) +
geom_bar(stat = "identity", position=position_dodge()) +
ylab("Isolates resistance") +
scale_y_continuous(expand = c(0,0), labels = scales::percent_format(scale = 100)) +
theme_bw() +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=15),
axis.text.x = element_text(angle=30, colour = "black", vjust=1, hjust = 1, size=13)) +
facet_grid(~ antibiotic_class, space = "free_x", scales = "free_x")
p7
MAR index bar plot
Total
Gram_neg_SIR3 <- Gram_neg_SIR2 %>% separate(Taxonomy, c("Genus", "Species"))## Warning: Expected 2 pieces. Additional pieces discarded in 16 rows [8, 18, 19, 29, 30,
## 31, 32, 33, 36, 41, 46, 47, 49, 54, 58, 60].# Adding color for plotting
custom_colors <- c( "#CBD588", "#5F7FC7","#DA5724", "#508578", "#CD9BCD",
"#AD6F3B", "#673770","#D14285", "#652926", "#C84248",
"#8569D5", "#5E738F","#D1A33D", "#8A7C64", "#599861", "black", "red", "green")
# MAR calculate
Gram_neg_SIR3$MAR <- apply(Gram_neg_SIR3[,1:11], 1, function(x) (length(which(x == "R"))/length(which(x %in% c("R", "S", "I")))))
# Plotting Transect
box3 = ggplot(Gram_neg_SIR3, aes(x = Transect , y = MAR, color = Transect)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Transect), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=13),
axis.text.x = element_blank()) +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + geom_hline(yintercept = 0.2, linetype="dashed")
#facet_wrap(~Transect, nrow=1, scales = "free_x")
box3
# Plotting face wrap with Sample type
box4 = ggplot(Gram_neg_SIR3, aes(x = Transect , y = MAR, color = Transect)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Transect), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 15),
axis.text.y = element_text(size=13),
axis.text.x = element_blank()) +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + geom_hline(yintercept = 0.2, linetype="dashed") +
facet_wrap(~Substrate, nrow=1, scales = "free_x")
box4
Plotting with Genus in each Transect
Gram_neg_SIR3_farm <- Gram_neg_SIR3[Gram_neg_SIR2$Transect == "Farm", ]
Gram_neg_SIR3_urban <- Gram_neg_SIR3[Gram_neg_SIR2$Transect == "Urban", ]
Gram_neg_SIR3_recovery <- Gram_neg_SIR3[Gram_neg_SIR2$Transect == "Recovery", ]
box5 = ggplot(Gram_neg_SIR3_farm, aes(x = Genus , y = MAR, color = Genus)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Genus), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 20),
axis.text.y = element_text(size=20),
axis.text.x = element_blank()) +
ggtitle("Farm MAR") +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + #theme(legend.position="bottom") +
geom_hline(yintercept = 0.2, linetype="dashed") +
facet_wrap(~Type, nrow=1, scales = "free_x")
box5
box6 = ggplot(Gram_neg_SIR3_urban, aes(x = Genus , y = MAR, color = Genus)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Genus), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 20),
axis.text.y = element_text(size=20),
axis.text.x = element_blank()) +
ggtitle("Urban MAR") +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + #theme(legend.position="bottom") +
geom_hline(yintercept = 0.2, linetype="dashed") +
facet_wrap(~Type, nrow=1, scales = "free_x")
box6
box5 = ggplot(Gram_neg_SIR3_recovery, aes(x = Genus , y = MAR, color = Genus)) +
geom_jitter(position = position_jitter(width = .20), alpha = 0.5, size = 3) + theme_bw() +
geom_boxplot(aes(colour = Genus), alpha=0.1, outlier.colour = NA) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(size = 20),
axis.text.y = element_text(size=20),
axis.text.x = element_blank()) +
ggtitle("Recovery MAR") +
ylab("MAR index") + scale_fill_manual(values = custom_colors) + #theme(legend.position="bottom") +
geom_hline(yintercept = 0.2, linetype="dashed") +
facet_wrap(~Type, nrow=1, scales = "free_x")
box5