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.

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

The strengths and limitations of line graphs are listed below:

Table 12 Strengths and limitations of line graphs
Strengths Limitations
Simple to read
Show clear patterns in data (visibly show how one variable is affected by another as it changes) Cannot use a line graph to compare different categories of data (use a bar chart instead)
Compare multiple continuous data sets The scale can change the appearance of the data which can be misleading. Always use the most appropriate scale for both axes when plotting line graphs

2.2 Mapping AMR data