paper
Large variation in the association between seasonal antibiotic use and resistance across multiple bacterial species and antibiotic classes
Data code link
https://github.com/orgs/gradlab/repositories
Today's tweet repeats Figure S3 in the paper, a line chart with dual Y axes
After running the code provided by the paper, the mapping data set is obtained
regressions
ci
dat01$deviates_table[[1]]
Save these three datasets as csv files
library(tidyverse)
library(readr)
regressions %>%
write_csv(file = "regressions.csv")
ci %>%
write_csv(file = "ci.csv")
dat01$deviates_table[[1]] %>%
write_csv(file = "deviates_table.csv")The first step in mapping is to read the data set
regressions<-read_csv("regressions.csv")
head(regressions)
ci<-read_csv("ci.csv")
head(ci)
deviates_table<-read_csv("deviates_table.csv")
head(deviates_table)mapping code
library(ggplot2)
col<-"#359023"
title<-"Ampicillin *"
ratio<-27.79891
ggplot()+
geom_point(data=deviates_table,
aes(x=month,y=seasonal_deviate))+
geom_errorbar(data = deviates_table,
aes(x = month,
ymin = seasonal_deviate - sem,
ymax = seasonal_deviate + sem),
width = 0.5,
color = col)+
geom_line(data=regressions,
aes(x = month, y = value,
color = leg, linetype = leg),
size = 0.7) +
geom_ribbon(data = ci,
aes(x = month, ymin = r_lower,
ymax = r_upper),
fill = col,
alpha = 0.3) +
geom_ribbon(data = ci,
aes(x = month,
ymin = u_upper/ratio,
ymax = u_lower/ratio),
fill = "grey20", alpha = 0.3) +
scale_color_manual(values = c(col, "grey20")) +
scale_y_continuous(sec.axis = sec_axis(~. * ratio),
limits = c(-.165, .165)) +
scale_x_continuous(breaks=c(1, 3, 5, 7, 9, 11)) +
ggtitle(title) +
xlab("Month") +
theme_classic() +
guides(color = guide_legend(nrow = 2, byrow = TRUE)) +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(size = 9),
plot.title = element_text(size = 11, hjust = 0.5, face = "bold"),
axis.text = element_text(size = 10),
axis.title.y = element_blank()
) -> f3splot
print(f3splot)Add titles to both axes
library(ggpubr)
f3splot %>%
annotate_figure(left = text_grob(expression("Seasonal deviates in resistance ("*log["2"]*"(MIC))"), size = 10, rot = 90)) %>%
annotate_figure(right = text_grob("Seasonal deviates in use\n(mean daily claims/10,000 people)", size = 10, rot = 270))
The sample data and code of today's tweet can be obtained by leaving a message in the background of the official account 20220413
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Xiao Ming's data analysis notebook
Xiaoming’s data analysis notebook public account mainly shares: 1. Simple examples of R language and python for data analysis and data visualization; 2. Reading notes on horticultural plants related transcriptomics, genomics, and population genetics literature; 3. Bioinformatics Learn introductory study materials and your own study notes!