Summarising and presenting AMR data

Introduction

This course introduces common ways of summarising data visually, reviews the strengths and limitations of each approach, and discusses how you can use visual summaries to enhance the analysis of AMR-related data. The course explores the effectiveness of visual summaries in communicating important information to a wide range of people. You will have the opportunity to reflect on the most appropriate ways to visualise AMR-related data, to look for patterns and trends, and identify errors in this data.

This course is part of a series related to AMR data. It is recommended that you first complete the courses Fundamentals of data for AMR and Processing and analysing AMR data to understand AMR data and data analysis principles.

By the end of this course, you should be able to:

• describe the different ways to represent data visually
• explain why visual summaries of data are an essential part of AMR data analysis
• review the strengths and limitations of each visual presentation
• make a simple graph and map using AMR data.

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• pass the end-of-course quiz
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1 Recap: AMR data and analysis

In the previous courses Fundamentals of data for AMR and Processing and analysing AMR data, you learnt how AMR data is transformed into information and the methods used to analyse data to answer important questions about AMR. This section briefly reviews key concepts from these courses to help you critically interpret visual data summaries and communicate key findings to different audiences.

1.1 Review of data types

Table 1 below reminds you of the definitions of data types previously described in the course Fundamentals of data for AMR.

You need to first understand the types of data you have (i.e. discrete, continuous, nominal or ordinal) before preparing to present it in visual formats. This is because the data type will determine the formats you can use to summarise and display findings.

To understand your dataset, first begin by examining individual records and summarising the data in tables. You may find that summary tables are sufficient for presenting the data, especially if the dataset is small. If the data are more complex, then graphs or maps can help highlight important findings, trends or errors that need to be corrected (such as data entry errors).

1.2 Review of different approaches to data analysis

Data analysis is about identifying and summarising meaningful patterns in data. In the course Processing and analysing AMR data, you were introduced to the basics of descriptive statistics and inferential statistics. It is recommended that you revisit the course to understand statistical concepts.

Here, we will briefly recap data analysis concepts relevant to visualising data.

1.2.1 Basic descriptive statistics

Descriptive analysis is the process of summarising data and generally involves characterising and visualising your dataset. Depending on the nature of the data, you may only need to undertake a descriptive analysis before reporting your findings. The approach to descriptive analysis varies depending on whether the data is categorical or numerical.

1.2.1.1 Categorical variables

Descriptive statistics for categorical variables include counts (frequencies) and proportions (relative frequencies). Percentages are obtained by multiplying proportions by 100.

AMR data is often presented as basic descriptive statistics in sentences in a report, in a frequency table, or in a visual format using a pie chart or a bar chart. An example of using text is in reporting the percentage of isolates that were resistant:

‘A large number of MRSA isolates showed resistance to levofloxacin (83.9%), ciprofloxacin (83%), erythromycin (77.7%), and clindamycin (72.3%).’

(Kot et al., 2020)

Activity 3: Approaches to presenting information

Timing: Allow about 10 minutes

Figure 1 and Table 3 display data from the WHO GLASS dashboard showing AMC data for Daily Defined Dose (DDD) of antibiotics per 1000 inhabitants in African and Asian countries (WHO, n.d.). Table 3 is a frequency table showing the number of DDDs and Figure 1 shows the same data in a bar chart.

Table 3 AMC data for antibiotics expressed as Defined Daily Dose (DDD) per 1,000 inhabitants per day, as collected by the WHO’s Global Antimicrobial Resistance and Use Surveillance System (GLASS) in African and Asian countries (WHO, n.d.).

Country	Defined daily dose
Benin	21.0
Cote d’Ivoire	16.7
Gabon	19.9
Kenya	19.0
Mali	23.4
Uganda	25.3
Brunei Darussalam	4.1
Lao People’s Democratic Republic	20.6
Bhutan	9.6
Maldives	19.6
Nepal	78.9

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Figure 1 AMC data for antibiotics expressed as DDD per 1,000 inhabitants per day, as collected by the WHO’s Global Antimicrobial Resistance and Use Surveillance System (GLASS) in African and Asian countries (WHO, n.d.).

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An ordered list of the DDDs of antibiotics per 1000 inhabitants in selected countries in Africa and Asia.

Figure 1 AMC data for antibiotics expressed as DDD per 1,000 inhabitants per day, as collected by the WHO’s Global Antimicrobial ...

Which type of data display (Table 3 or Figure 1) do you think most effectively conveys information about the country with the highest and lowest DDD of antibiotics per 1000 inhabitants?

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Answer

While tables can summarise and display data to great effect, in this example, the bar chart (Figure 1) demonstrates a superior approach to presenting the data. When looking at the bar chart, it can be rapidly determined that there is a pronounced difference between the highest DDD and lowest DDD by country.

Do you agree that your brain needs to work harder to get the same information from the table?

1.2.2 Plotting variability in data

Data obtained from a sample should be representative of the population of interest. Since it is rarely possible to include the entire population in a sample, there is always some degree of uncertainty or variability in how well the data represent the population (see the Sampling courses and Processing and analysing AMR data course for more information). It is good practice to indicate uncertainty in data either in text, tables or graphs. For graphs, uncertainty in the data is represented by error bars.

Error bars can be used to display the standard deviation, standard error, confidence intervals (e.g. 95% interval), or minimum and maximum values in a dataset. (To revise these terms, revisit the course Processing and analysing AMR data.) When using error bars on graphs, always state the measure used to represent uncertainty in the data as the measures are not the same.

Error bars are markers drawn over data points on a graph and either extend from the centre of a data point or the top edge of a column (such as a bar chart). You can get a sense of how precise the measurements are (which reflects the level of uncertainty in the data collected) by looking at the error bar's length: short error bars indicate less variability in the data, and in contrast, long error bars indicate more variability, that is, the values are more spread out.

You can also use error bars to gauge if there might be a significant difference between groups. Overlapping error bars may suggest there is not a significance difference between groups, while no overlap between error bars may suggest there may be a significance difference. You cannot conclude statistical significance by looking at the graphs alone, so always perform a statistical test when drawing conclusions. (For a recap on methods of testing statistical significance, revisit the course Processing and analysing AMR data.)

You often see error bars plotted on line graphs and bar charts, such as the one in Figure 2.

Activity 4: Plotting variability in data

Timing: Allow about 10 minutes

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Figure 2 Deaths attributable to bacterial antimicrobial resistance by syndrome in 2021 in South Asia (Bangladesh, Bhutan, India, Nepal and Pakistan) in all groups and sexes (IHME, 2025a)

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Bar chart of the number of deaths attributed to bacterial antimicrobial resistance by syndrome in 2021 in South Asia (Bangladesh, Bhutan, India, Nepal and Pakistan) in all groups and sexes. Error bars show the uncertainty range (IHME, 2025a).

Figure 2 Deaths attributable to bacterial antimicrobial resistance by syndrome in 2021 in South Asia (Bangladesh, Bhutan, India, Nepal ...

Figure 2 shows uncertainty intervals for the number of deaths attributed to antibiotic resistance for each syndrome. What can you say about the uncertainty intervals?

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Answer

The uncertainty intervals appear to be wider in some syndromes than others. For example, tuberculosis appear to have wider uncertainty intervals than peritoneal and abdominal syndromes. This may indicate that the data collected on tuberculosis was more variable than in peritoneal and abdominal syndromes.

2 Summarising AMR data visually

Research has shown that our brains understand complex information more easily when it is presented in a visual format (Ware, 2012). That is why data are often best communicated visually, using graphs and maps rather than tables or spreadsheets. Visual summaries can be a great way of communicating your findings, particularly to people who may be unfamiliar with the data.

When choosing the most appropriate visual display, ask yourself the following:

• What is the primary aim of my visual analysis? For example, do I want to demonstrate a difference in prevalence of resistant pathogens by geographic area or between two groups? Do I want to highlight a change in antimicrobial use trends over time?
• What do I want to communicate, and with whom am I communicating? Graphs and maps can help you share public health information to people who may not be familiar with the topic.
• What type of data do I have? What is the data’s measurement scale (nominal, ordinal, discrete or continuous)? The data type will influence the kind of visual representation you can use.

Table 4 summarises the different analysis aims and gives examples of the best visual displays for presenting the analysis, described in more detail as we work through this course.

2.1 Graphs of ‘AMR data’

Graphs display data in a visual form to tell a story. They can be used to display patterns, trends, comparisons and differences in data that may not be otherwise evident. The goal of graphs should be to help people to understand patterns and relationships in data quickly.

2.1.1 Designing good graphs

Graphs are only as good as they are clear and understandable. A checklist of best practice to ensure a graph is conveying your message most effectively is given in Table 5 below:

In the following sections, you will learn about common graph types: histograms, boxplots, scatter plots, bar charts and line graphs. Not all of these graphs will be appropriate for the data and message that you want to convey. Understanding the strengths and limitations of each graph will help guide your decision on which graph to choose.

2.1.2 Histograms

Histograms show the distribution of values for a quantitative (continuous) variable. Histograms are useful when there are many observations and you want to understand the overall shape and spread of your data.

The x-axis is marked in the units of measurement for the independent variable (e.g. age, time, MIC, zone diameter). The y-axis is the scale that shows you the number of times (frequency, proportion) the value in an interval occurred.

To create a histogram, you need to first group data from the independent variable into class intervals (bins) of equal width and then count the values in each interval (class frequency). Class frequencies are represented by bars on a histogram. The height of each bar corresponds to its class frequency. An example of how to construct a histogram from raw data is shown below:

Example

In this example, a series of observations have been recorded in a variable called ‘Age’. To construct the histogram, ‘Age’ is split into five class intervals. Each interval contains the count of occurrences.

Table 6 Variable name: Age

39	41	22	38	46	55	65	78	83	18
28	54	53	61	10	16	29	58	55	66

Table 7 Frequency of the variable: Age

Class interval	Class frequency	Observations
0–20	3	10, 16, 18
21–40	5	22, 28, 29, 38, 39
41–60	7	41, 46, 53, 54, 55, 55, 58
61–80	4	61, 65, 66, 78
81–100	1	83

The histogram is then constructed based on the number of class intervals which are plotted on the x-axis with the y-axis showing the frequency (number) of occurrences in each class interval.

Figure 3 Example histogram of the age groups of people

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A histogram showing the number of people in a sample in each of five equal age groups (from 0–20 to 81–100).

Figure 3 Example histogram of the age groups of people

Traditionally, a histogram is drawn with no space between classes to indicate all values of the variable are represented. (This is different from a bar chart, which has space between the classes.) However, sometimes histograms may be drawn with spaces between the classes for greater visual impact. You can see this in Figure 4.

Figure 4 Histogram showing the distribution of zone diameters (mm) in Amoxicillin/Bacillus anthracis as measured by the EUCAST disk distribution method (MIC EUCAST, 2025).

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Histogram showing a distribution of zone diameters obtained from testing of Amoxicillin against Bacillus anthracis (black bars showing a bimodal distribution). This is a typical distribution for AMR data.

Figure 4 Histogram showing the distribution of zone diameters (mm) in Amoxicillin/Bacillus anthracis as measured by the EUCAST disk ...

The purpose of the histogram (or, indeed, of any graph) is to help understand the data. When viewing a histogram, look for important features, including the shape and spread of the data and whether there are any deviations (outliers). Outliers are data points that lie a long way from the general pattern in the data.

A histogram can have different shapes: it can be unimodal (a single peak representing the interval with the most values e.g. a normal distribution), bimodal (two peaks) or multimodal (more than two peaks). A histogram can also be symmetrical (when the right and left sides of the midpoint are similar) or skewed, where the intervals are grouped to the right (positively skewed) or left side (negatively skewed).

AMR data often has a bimodal shape because there are often two separate populations of isolates – those that are susceptible and those resistant. Also, AMR data is often skewed. Depending on the population of interest, there might be more isolates that are susceptible than resistant, or vice versa. For example, in secondary care, there may be more isolates tested that are resistant to an antimicrobial agent than isolates that are susceptible because the people who are sampled are more likely to have been treated with antimicrobials and suffered treatment failure. Figure 4 demonstrates a bimodal distribution of zone diameter measurements obtained by testing the susceptibility of Aeromonas salmoncida to oxolinic acid. (Note, the purpose of including Figure 4 in this course is to demonstrate a typical distribution of AMR data. Therefore, it is not necessary to interpret the breakpoints depicted on the graph.)

The strengths and limitations of histograms are listed below:

2.1.3 Box-and-whisker plot

Another way of displaying information about the spread of data is the box-and-whisker plot (also referred to as the boxplot). Boxplots are graphs that summarise the five-number summary of continuous or discrete data. They consist of:

• a central box that spans the quartiles Q1 (lower quartile, 25th percentile) and Q3 (upper quartile, 75th percentile) – (note that the range from Q1 to Q3 is known as the interquartile range (IQR))
• a line in the box that marks the median (50th percentile)
• lines (whiskers) that extend from the box out to either the smallest (minimum) and largest (maximum) observations, excluding outliers, or 1.5 times the interquartile range on each side, also excluding outliers (if it is ambiguous which method is used, it is normally mentioned in the Figure’s legend)
• outliers, which are data values that are far away from other data values. On a boxplot, outliers are identified by a symbol such as a dot or an asterisk.

Boxplots are most beneficial when used for a side-by-side comparison of more than one distribution.

Activity 5: Understanding box-and-whisker plots

Timing: Allow about 15 minutes

Look at the box plot in Figure 5. Can you annotate one of the boxplots to show the five-number summary for a distribution?

Figure 5 Biofilm formation by Stenotrophomonas maltophilia isolated from patient samples according to patient and sample types. Biofilm biomass, assessed by spectrophotometric assay after crystal violet assay, was stratified according to patients with or without cystic fibrosis (CF, non-CF) and (B) sample type. (Significance level from Mann-Whitney test: * p<0.05; **p<0.01; *** p<0.001) (Pompilio et al., 2020)

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Box-and-whisker plots showing the biomass of biofilm formed by S. maltophilia isolated from samples from patients with and without cystic fibrosis (CF). The left-hand plot (A) shows two plots obtained from CF and non-CF patients; the right-hand plot (B) shows three plots obtained from CF patients, respiratory samples from non-CF patients and blood samples.

Figure 5 Biofilm formation by Stenotrophomonas maltophilia isolated from patient samples according to patient and sample types. Biofilm ...

Answer

Figure 6 Annotated Figure 5

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Box-and-whisker plots showing the biomass of biofilm formed by S. maltophilia isolated from samples from patients with and without cystic fibrosis (CF). The left-hand plot (A) shows two plots obtained from CF and non-CF patients; the right-hand plot (B) shows three plots obtained from CF patients, respiratory samples from non-CF patients and blood samples. Labels have been added to the left-hand plot (A) to show where the Maximum, Median, Minimum, Q1 and Q3 are.

Figure 6 Annotated Figure 5

The box extends from the first (Q1) and third (Q3) quartiles. The line in the middle of the box is plotted at the median, while the ends of the whiskers represent the minima and the maxima of all the data. The lines extending from the interquartile range are called whiskers. The whiskers extend to the maximum and minimum values in the dataset, excluding outliers. Note, the asterisks in Figure 5 and Figure 6 are not outliers, rather they are indicating the statistical significance of the results.

The strengths and limitations of boxplots are listed below:

2.1.4 Scatter plots

Scatter plots are used when we are interested in the relationship between two different variables. Each point on the graph represents the values of a pair of variables. The value of one variable is plotted on the x-axis, and the value of the other variable is plotted on the y-axis. The variables generally have to be numeric and are commonly continuous, although they may also be discrete.

A scatter plot gives us a good idea of the correlation between the two variables and the nature of that correlation. You can plot several combinations to explore correlations and then investigate these further with more complex statistical analyses. As well as making it easy to identify any general pattern in the relationship between the variables, a scatter plot can help to identify outliers.

The scatter plot in Figure 7 shows the relationship between DDD and resistance to antibiotics (y-axis) in Kenya between 2016 and 2018.

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Figure 7 Relationship between antibiotic resistance and DDD in millions by in Kenya between 2016 and 2018. Note that DDD/million inhabitants is a technical unit of measure of antimicrobial consumption in non-hospital settings. The assumed average maintenance dose per day for a drug used for its main indication in adults (AfricaCDC, 2023).

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Scatter plot showing the relationship between percentage antibiotic resistance (plotted on the y-axis) and DDD in millions (x-axis) in Kenya between 2016 and 2018. The distribution of dots shows a low positive correlation between the prevalence of resistance and antibiotic use.

Figure 7 Relationship between antibiotic resistance and DDD in millions by in Kenya between 2016 and 2018. Note that DDD/million ...

The relationship between two numeric variables is called correlation. There are three types of correlation (the strength and significance of which should be tested using formal statistical tests):

Positive correlation: as the values of one variable increases, so do the second variable's values. In Figure 7, as antimicrobial consumption increases at the national level, so does resistance to aminopenicillins. Note that this correlation is weak – although the trend is there, there are plenty of countries where it is not the case.

Negative correlation: as the values of one variable increases, the values of the other variable decreases. For example, as antimicrobial consumption increases, antimicrobial susceptibility (the opposite of resistance) decreases.

No correlation: there is no apparent relationship between the variables. For example, some studies have found that patient age is not correlated with AMR for specific antimicrobial classes and/or bacterial species.

The strengths and limitations of scatter plots are listed below:

In summary, scatter plots should be used when there are many different data points and you want to highlight similarities in the dataset. This is useful when looking for outliers or for understanding the distribution of your data.

2.1.5 Bar charts

Bar charts show relationships between different data series that are independent of each other. The most common data displayed in bar charts includes nominal, ordinal, discrete data or variables that can be treated as discrete.

The difference between histograms and bar charts is that histograms show the frequency of a continuous variable, whereas bar charts show the frequency of categorical or discrete data.

There are many bar charts: column (vertical), horizontal (left to right), grouped and stacked.

2.1.5.1 Horizontal and column bar charts

Horizontal and column bar charts are usually interchangeable. However, there are some reasons to choose one over the other. Horizontal charts work better when the labels are long and for data that needs to show ranking from largest to smallest. An example of a horizontal graph can be seen in Figure 8.

Figure 8 Percentage of specimen type sent for bacterial culture, Health Facility X, Quarter 3, 2015

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Horizontal bar chart (essentially, a typical bar chart rotated by 90^o) showing the percentage of patients with different types of specimens sent for bacterial culture.

Figure 8 Percentage of specimen type sent for bacterial culture, Health Facility X, Quarter 3, 2015

Column charts work better for grouped data and time-series data. Simple column charts compare categories of one variable, while grouped (or clustered) column charts compare categories of two or more variables as shown in Figure 9. The bars within a group adjoin, but there is a space between groups. It is important that the bars are distinctive and described in the legend.

Figure 9 Percentage of female and male patients infected by type of bacteria, Health Facility X, Quarter 3, 2015

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Grouped (or clustered) conventional bar chart showing the percentages of female patients (LH bar of each pair, blue) and male patients (RH bar, green) infected with different bacteria.

Figure 9 Percentage of female and male patients infected by type of bacteria, Health Facility X, Quarter 3, 2015

Activity 6: Interpreting bar charts

Timing: Allow about 20 minutes

Review Figure 10 and describe what you see, what you think are the key findings and how might you improve the graph.

Figure 10 Number of countries reporting to GLASS by economic status in 2017–2018 (WHO, 2019)

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Grouped bar chart showing the numbers of countries reporting both information on surveillance systems (LH bar of each pair, blue) and AMR rates (RH bar, green) to GLASS by both year (2017, 2018) and economic status.

Figure 10 Number of countries reporting to GLASS by economic status in 2017–2018 (WHO, 2019)

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Answer

In Figure 10, groups of countries are categorised by economic status (low, low-middle, upper-middle, high – this wasn’t made clear in the caption) and year (2017, 2018) on the x-axis, and the number of countries in each category participating in GLASS on the y-axis. Within each group, there are two bars. One bar represents the frequency of countries that report information on their surveillance system, and the other bar represents the frequency of countries that report information on AMR rates in bacteria of interest to GLASS.

The graph shows that, overall, more high-income countries participate in GLASS compared to lower-income countries. Reporting to GLASS increased from 2017 to 2018 for all types of countries. When looking at the bar lengths, it appears there is an approximate doubling in reporting on surveillance systems from 2017 to 2018 for most categories. While the number of countries reporting AMR rates also increased, the increase was not as large when compared to countries reporting on their surveillance systems, other than in high-income countries.

The graph could be improved by reporting the exact frequencies in each bar so that the actual change in reporting rates in each category can be determined. The graph could also be improved by defining the country group acronyms (LIC, LMC, UMC, HIC). Another way to present the data is demonstrate proportional representation in each economic category which can be done by plotting percent on the y-axis rather than the count.

2.1.5.2 Stacked bar chart

In a stacked bar chart, each bar represents a total amount broken down into subsections. The same colour is used for the equivalent subsection in each bar. This allows you to compare both the whole picture and the components of each bar. A 100% component bar chart is a variation of the stacked bar chart, and shows all of the bars at the same height (100%) and the components as percentages of the total. This type of chart is useful for comparing the contribution made by different subgroups within the categories of the main variable.

Figure 11 consolidates data into one stacked bar chart making it much clearer to compare the distribution of resistance of Campylobacter jejuni found in chickens, cattle and people in Denmark between 2016 and 2019. The subsections have been ordered so that the largest changes are first and last. This choice of order highlights changes that may be of specific interest when interpreting the graph. The most noticeable feature to compare between different hosts’ susceptibility is the proportion of C. jejuni isolates with resistance to both ciprofloxacin and tetracycline from people returning from travel overseas compared to domestic cases in people, chickens and cattle. Also, unlike what was observed in cattle, resistance to ciprofloxacin and tetracycline in C. jejuni isolated from poultry and from local cases of campylobacteriosis in people appears to have risen over time. However, statistical significance should be tested before making any firm conclusions from this graph.

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Figure 11 Distribution (%) of AMR profiles in Campylobacter jejuni from broilers, cattle and human cases in Denmark (Korsgaard et al., 2020). Note: the number of isolates included each year is shown in parentheses, where broilers include isolates from Danish broiler meat. CIP: all isolates with ciprofloxacin resistance but not tetracycline resistance, TET: all isolates with tetracycline resistance but not ciprofloxacin resistance, CIP TET: all isolates with both ciprofloxacin and tetracycline resistance, Other resistance: all isolates without both ciprofloxacin and tetracycline resistance. CIP TET, CIP and TET isolates may be resistant to erythromycin, nalidixic acid or streptomycin

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Stacked bar chart, showing the distribution of AMR profiles in C. jejuni samples extracted from broiler chickens, cattle, people with no recent travel history and people returning from overseas travel from 2015 to 2019.

Figure 11 Distribution (%) of AMR profiles in Campylobacter jejuni from broilers, cattle and human cases in Denmark (Korsgaard et al., ...

The strengths and limitations of bar charts are listed below:

Table 11 Strengths and limitations of bar charts

Strengths	Limitations
Represent three or more variables on a single chart (unlike a histogram or scatterplot)	May require an additional explanation in text if many groups are compared or using stacked graphs
Understood by a wide audience, commonly used, and the scales and bars are easily read	Data can be manipulated or misrepresented – care must be taken when presenting data. For example, if the scale is exaggerated or minimised it can change the impression of the magnitude of differences between categories
Useful for showing time trends	Doesn’t show relationships between variables

2.1.6 Line graphs

Line graphs show trends in data and help explore relationships with time-related variables (such as age or calendar year). They help to determine the relationship between two sets of values, with one variable dependent on the other. The independent time-related variable is plotted on the x-axis and the dependent variable on the y-axis. Several dependent variables can be plotted against one independent variable on the same graph if they have the same units and range.

Line graphs are particularly useful when there are many data points and when you want to compare several trend lines on a single graph.

Figure 12 shows data from Kenya which reports on trends in resistance patterns to aminoglycosides (AG), fluoroquinolones (FQ) and penicillin (PCN) between 1990 and 2020. The graph shows that resistance rates to these antimicrobials have changed in different ways over this time period. Resistance to all three antimicrobials was fairly stable between 1990 and 2006/2008. Resistance to aminoglycosides and penicillin (PCN) has declined from this time onwards. However, there was an increase in resistance to fluoroquinolones between 2004 and 2012, before resistance levels also started to reduce. This graph raises several questions, one of which is: what explains the increase in fluoroquinolones resistance levels between 2004 and 2012, and why did it start to decline after this time? What might explain why there has been a reduction in antibiotic resistance to the three antibiotics in recent years? Time series data presented in line graphs prompts important questions about the data that allow you to undertake exploratory analyses to look for associations in the data.

Figure 12 Trends in resistance to aminoglycosides (AG), fluoroquinolones (FQ) and penicillin (PCN) in all pathogens in Kenya between 1990 and 2020 (IHME, 2025b).

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Line graph showing trends in the percentage resistance to three antibiotics in Kenya from 1990 to 2020.

Figure 12 Trends in resistance to aminoglycosides (AG), fluoroquinolones (FQ) and penicillin (PCN) in all pathogens in Kenya between 1990...

The strengths and limitations of line graphs are listed below:

2.2 Mapping AMR data

Geospatial data surround us. Every day many of us turn to maps to help navigate our way or look at different countries in the world. Maps are a great way to analyse geospatial data, which is data with a geographic value such as Global Positioning System (GPS) coordinates, Geographic Information System (GIS) data, address or postcode. With the increasing use of maps (particularly electronic ones) in daily life, they can offer an intuitive data presentation for non-specialist data users.

Maps are used extensively in disease surveillance because they can be used to display information on the spread of diseases across different geographic regions. For example, Figure 13 is a map where different colours are used to represent different categories of the indicator of interest (in this case, the number of countries enrolled in GLASS).

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Figure 13 Number of countries enrolled in GLASS in 2024 (WHO, 2025)

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World map showing countries enrolled in GLASS in 2024 highlighted in various countries. A key indicates what each colour represents.

Figure 13 Number of countries enrolled in GLASS in 2024 (WHO, 2025)

Because maps are so effective at telling a story, they are used by governments, NGOs, non-profits, public health departments, the media and at local levels to understand the situation in different areas.

Maps answer spatial questions about your data and can help you understand trends or patterns. It is a good idea to ask yourself whether a map is the best way to display data. If the answer is yes, then you need to ensure your map represents your data accurately, in sufficient detail and attractively.

The strengths and limitations of maps are listed below:

3 Creating data visualisations using common software

3.1 Basic software tools

Many statistical software packages are available where you can quickly and easily construct different types of graphs to explore the data and determine which type best conveys your key messages. These software packages are especially helpful when larger data sets are involved or complex analyses are required.

The most basic and widely used programme for summarising and presenting data is Microsoft Excel. Advantages of MS Excel include:

• flexible storage, modelling and manipulation of datasets
• a range of automated functions, including data analysis and chart functions
• users can select from a range of automated charts to display the data graphically. This process requires minimal amount of user input.

While MS Excel is familiar to many and easy to use, the programme has several limitations, including:

• fewer functions for data analysis and graphic displays than dedicated statistical programmes
• a limited number of total cells compared to these programmes.

While MS Excel is convenient for data entry, it is generally recommended that MS Excel spreadsheets be exported to a dedicated statistical software programme for more than basic analysis.

Google Maps is an open-source service that provides information about geographical sites around the world. It offers conventional road maps, aerial and satellite views of many places. It can be customised to visualise geographic statistical data, such as economic and population statistics, and can be used to create disease prevalence maps. Google Maps is an obvious tool to use to visualise geospatial data as many people are familiar with the platform.

3.2 Specialist software tools

There are now several freely downloadable statistical programs for conducting more complex analyses. Even for simple analyses, there are many advantages to using statistical software, including the ability to reproduce graphs and maps if data is updated, or if another user needs to conduct analyses. Some software programs have steep learning curves but are very powerful once mastered.

R is an example of a free statistical software programme that can be used to summarise and present data in interesting ways, including producing a range of graphs and maps. You can download R by visiting the R project website.

QGIS is an open-source Geographical Information System (GIS) platform that can be run on all operating systems and can support a number of database formats and functionalities. It allows users to analyse and edit spatial information as well as creating and exporting graphical maps.

Box 1: Steps for generating a graph from the IHME data visualization tool

Step 1: From the VizHub, click on the icon ‘Antimicrobial resistance’ if it is not already selected. (There is a tab at the top left of the interactive display.)

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Figure 18 Screenshot of the IHME ‘Antimicrobial resistance’ tab

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Screenshot of the IHME ‘Antimicrobial resistance’ tab.

Figure 18 Screenshot of the IHME ‘Antimicrobial resistance’ tab

Step 2: On the ‘Antimicrobial Resistance by Year’ page, make the following selections:

1. Select ‘Pathogens’ on the left-hand side.
2. Select the ‘By resistance’ tab.
3. Then use the drop-down menu showing as ‘Infectious Syndrome’ to select ‘Endocarditis’.
4. Underneath, use the drop-down menu to select Kenya. You can either type the name in the box or scroll down until you reach Kenya.
5. Finally, click on the ‘Attributable’ tab, which selects those deaths directly caused by resistance to the pathogen.

Check that the Measure is set to ‘Deaths’, and the Metric is ‘Number’.

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Figure 19 Screenshot of part of the ‘Antimicrobial resistance’ page in IHME

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Screenshot of part of the ‘Antimicrobial resistance’ page in IHME, highlighting options to select pathogens, by resistance, bloodstream infections, Kenya and attributable.

Figure 19 Screenshot of part of the ‘Antimicrobial resistance’ page in IHME

Box 2: Steps for generating a map from the IHME data visualization tool

Step 1: From the VizHub, click on the ‘Map’ icon (at the top left of the interactive display).

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Figure 20 Screenshot of the IHME ‘Map’ option

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Screenshot of the ‘Map’ option on the IHME webpage.

Figure 20 Screenshot of the IHME ‘Map’ option

Step 2: On the ‘Map’ page, make the following selections:

1. Select ‘Resistance’, then select a Pathogen – from the drop-down menu, click on ‘Salmonella enterica serovar Typhi’.
2. Select an antibiotic class – select ‘fluoroquinolones’.

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Figure 21 Screenshot showing the options available on the IHME ‘Map’ page

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Screenshot showing the options available on the ‘Map’ page in IHME. Options highlighted are: resistance, Salmonella enterica serovar Typhi and fluoroquinolones.

Figure 21 Screenshot showing the options available on the IHME ‘Map’ page

4 End-of-course quiz

Well done – you have reached the end of this course and can now do the quiz to test your learning.

This quiz is an opportunity for you to reflect on what you have learned rather than a test, and you can revisit it as many times as you like.

• End-of-course quiz

Open the quiz in a new tab or window by holding down ‘Ctrl’ (or ‘Cmd’ on a Mac) when you click on the link.

5 Summary

In this course, you have learnt about the most common visual ways to display data. You reviewed the strengths and limitations of each and looked at examples of when a particular visual display is better than another. If you are trying to decide whether to use a table or a graph (and the type of graph), ask yourself how the data will be used, consider your target audience and decide the best way to map out your information. Think about the utility of your visual and let that help drive your decision-making.

You should now be able to:

• describe the different ways to visually represent data
• explain why visual summaries of data are an important part of AMR data analysis
• review the strengths and limitations of each visual presentation
• make a simple graph and map using AMR data.

Now that you have completed this course, consider the following questions:

• What is the single most important lesson that you have taken away from this course?
• How relevant is it to your work?
• Can you suggest ways in which this new knowledge can benefit your practice?

When you have reflected on these, go to your reflective blog  and note down your thoughts.

When you have reflected on your answers and your progress on this course, go to your reflective blog and note down your thoughts.

6 Your experience of this course

You’ve now reached the end of this course. If you’ve enrolled on a pathway, please go back to the pathway page and tick the box to confirm that you’ve completed this course. On the pathway page you’ll see both your progress so far as well as the other courses you need to complete in order to achieve your Certificate of Completion for that pathway.

Now that you have completed this course, take a few moments to reflect on your experience of working through it. Please complete a survey to tell us about your reflections. Your responses will allow us to gauge how useful you have found this course and how effectively you have engaged with the content. We will also use your feedback on this pathway to better inform the design of future online experiences for our learners.

Many thanks for your help.

Now go to the survey.

References

Acknowledgements

This free course was collaboratively written by Melanie Bannister-Tyrrell, Skye Badger and Clare Sansom, and reviewed by Siddharth Mookerjee, Claire Gordon, Natalie Moyen, Peter Taylor and Hilary MacQueen. The course was reviewed and updated by Alana Dowling, Vikki Haley and Rachel McMullan in 2025.

Except for third party materials and otherwise stated (see terms and conditions), this content is made available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Licence.

The material acknowledged below is Proprietary and used under licence (not subject to Creative Commons Licence). Grateful acknowledgement is made to the following sources for permission to reproduce material in this free course: