This is for the AMR project data analysis at HKU.

• [General info]
• [Clean Routine ]
• [Questionnaire Analysis]
• [Farm Data Analysis]
• [R Functions]
• [Author]

This project is aimed to XXXXXXX

• Summarytable1 & 2. R
  An easy step was craeted for cleaning our Ast result dataset, by running these two file, a summary table1 collecting information about isolates and sample counts from every sources can rendered. Data can be downloaded here
  Also, for summarytable 2, resistance rates calculated from each source in every bacterials are reported in the output excel file. A simple heatmap showing the relative frequency of resistant in E.coli is obtaind in this file.

• Find missing isolates. R
  In this file, we are aimed to find out the missing isolates which finished MULDI-TOF and validated as from the targeted bacteria, but forgot to complete AST test.

• Questionnaire_monthly_summary.R
  Table 1 about our questionnaire answers is constructed, comparisions can be customized to our Gender and Age according to the group variable. Chisq/ fisher test is applied for categorical data analysis, other wise, Wilcoxon-Mann-Whitney test or Kruskal Wallis test is adopted.

• FollowUpandSeq.R
  Help to subset the sequence list. We manunally add columns named: household ID, follw_up, their drug using status etc.

• Resistance_table.R
  this scripts help us to build the resistance table we are interested in. We can simply start by subsetting the data we wanted, and use the bac_restable() to gender a table about resistance% for every antibiotics from each bacterial. Combine_tab() aggregates the results from each bacterial together.

• Antibiotics Classification
  Antibiotics classification based on WHO guideline.

• Hetero-resistance
  Check_homo1.R & Check_heter2.R
  Hetero-resistance condition No.1: Isolates from same samples exhibt different AST result using MIC cut-off method.
  Hetero-resistance condition No.2: Isolates who meet the requirement 1 must also meet the No.2 criteria which is showing high-resistance (>= 8 fold).

• Heatmap
  Plot human ast data, grouped by variables we are interested (ie. Age, Gender, Drug use frequency), it supposed to look like this.

This files focus on generating reports or figures in the Citu U meeting, including farm resistome heatmap in different growing stages, sampling process, and some summary tables as well.

• summary of plot functions

• Count or stat function

• [Conclusion]
• [Author]