essay about graph

How to Describe a Graph in Writing [+ 22 Examples]

Graphs are a powerful way to convey data visually, but describing them effectively is crucial.

I’ll guide you through the types of graphs and practical ways to write about them, whether in essays, reports, or presentations. Let’s make data storytelling seamless and comprehensive.

Types of Graphs

Table of Contents

The first thing you need to know are the major types of graphs–and there are several.

Read on to learn the most important points about each one.

Line Graphs

Line graphs are essential for displaying changes over time.

Each data point is plotted and connected by a line, making it perfect for tracking trends or progressions.

For instance, it’s often used in business to show quarterly sales or yearly revenue growth.

The slope of the line reveals trends – a steep rise means rapid growth, while a dip signals a decline.

Multiple lines can be included to compare trends across different categories, products, or demographics.

Always label your axes clearly, with time on the x-axis and the measured variable on the y-axis for straightforward comprehension.

Bar graphs excel in comparing different categories or groups.

Each bar represents a category’s value, offering a visual side-by-side comparison.

Their versatility makes them ideal for financial data, demographic information, or survey responses.

Vertical bars emphasize magnitude differences, while horizontal bars are useful when categories are long or numerous.

Each bar should be uniformly spaced to prevent misleading interpretations.

Use different colors or patterns for various groups to enhance readability and clarify distinctions.

Always start the y-axis at zero to accurately represent data differences and avoid exaggerating trends or disparities.

Pie charts visually convey how individual parts contribute to a whole.

Each slice represents a category’s proportion, making it clear how each segment relates to others. Use them when showing relative percentages, like budget allocations or market shares.

The sum of all slices should always equal 100%, so they are not suitable for continuous data or comparisons across time.

Limit the number of slices to 5-7 for clarity.

Highlight critical slices with distinct colors or labels, and consider combining smaller segments into an “Other” category for better visualization.

Scatter Plots

Scatter plots are crucial when examining relationships between two variables.

Each point represents a pair of data, plotted on the x and y axes.

This method is particularly valuable in scientific research, economics, and marketing, helping to identify trends, correlations, or clusters.

A positive correlation shows that as one variable increases, so does the other, while a negative correlation indicates an inverse relationship.

Clusters suggest groups with shared characteristics.

A trend line can be drawn to illustrate the relationship between variables.

Scatter plots are excellent for identifying outliers that may warrant further investigation.

Histograms look like bar graphs but represent frequency distributions for continuous data.

Data is grouped into bins, where each bar’s height shows the frequency of data points falling within that range.

For instance, histograms can reveal customer age distribution or test scores.

The bins should be of equal size, and the data continuous.

Adjust the number of bins according to data spread: too many create a confusing graph, while too few obscure patterns. Unlike bar graphs, histograms shouldn’t have gaps between bars unless there are no data points in that range.

11 Ways to Describe a Graph in Writing

Now let’s go through 11 ways that I’ve found to clearly and cleverly describe graphs in all your writing.

Describing the graph’s shape provides immediate visual insight into trends and patterns.

A linear trend suggests consistent data, while a curved line indicates shifts in growth rate.

Peaks represent rapid growth periods, whereas valleys highlight declines. Flat sections may signal stabilization.

If describing a bar graph, look for patterns like pyramid shapes or skewed distributions.

Pie charts often have distinct shapes when grouped segments stand out.

Highlighting these forms helps readers understand data dynamics quickly.

• “The line graph’s shape is linear, showing a consistent rise in revenue.”
• “The bar graph forms a pyramid, indicating balanced age distribution.”

Quantify the graph’s data range by focusing on its extremes.

Determine the minimum and maximum values to illustrate fluctuations, whether significant or moderate.

Emphasize the overall range to offer perspective on the extent of the trends.

For instance, large differences between bars or peaks highlight strong growth, while small variations suggest stability.

Providing size context gives your audience a sense of proportion, making the data’s impact more meaningful.

• “The graph’s highest point is $12 million, contrasting sharply with its lowest at $1 million.”
• “Temperature shifts varied by 40°C, from -20°C in winter to 20°C in summer.”

When describing line graphs, analyze the lines for clues about trends.

Is the line smooth or jagged?

A smooth line suggests stable growth, while jagged lines imply sudden changes.

Pay attention to the slope: a steep incline or decline represents rapid shifts, while a shallow slope signals gradual changes.

Highlight sections where lines intersect, converge, or diverge, indicating crucial turning points or contrasts between data sets.

• “The line sharply inclines in Q2, then plateaus in Q3 due to seasonal trends.”
• “The two lines cross each other in September, revealing a pivotal shift.”

Axis Labels

Accurate axis labeling clarifies the data categories and units used.

For line graphs, the x-axis typically denotes time or specific groups, while the y-axis indicates the measured value.

Bar graphs require clear labels to identify the represented categories.

Scatter plots often show correlations, so it’s essential to label both variables accurately.

Incorrect labeling can lead to confusion, so ensure your axes directly correspond to the data presented.

• “On the bar graph, the x-axis shows months, and the y-axis tracks monthly revenue.”
• “The scatter plot’s x-axis measures advertising expenses, while the y-axis records sales.”

Identifying and describing trends helps readers grasp the graph’s narrative.

Is there a general upward or downward trajectory? Are trends consistent, fluctuating, or mixed?

An upward trend signifies growth, while downward trends indicate decline.

If trends fluctuate significantly, highlight potential causes like seasonality or market changes.

Trends can also differ between data sets in the same graph, so compare trends side by side.

• “The graph reveals an upward trend in quarterly revenue, suggesting steady business growth.”
• “Despite fluctuations, the overall trend is downward, indicating reduced consumer interest.”

Comparisons

When comparing multiple data sets in the same graph, focus on similarities and differences.

Highlight which sets lead or lag, noting by how much.

Compare growth rates, peak times, or relative sizes between bars or lines. Emphasize differences that are statistically significant or exceed expectations.

Highlight where data sets converge or diverge, offering insights into critical market trends or strategic opportunities.

• “Product A outsold Product B, particularly in Q2 and Q3.”
• “While Company X’s revenue plateaued, Company Y showed remarkable growth.”

Identifying anomalies brings attention to unexpected data points that break the pattern.

Outliers, significant spikes, or dips can indicate errors, market disruptions, or seasonal effects.

Comparing these to broader trends can help interpret their significance.

Outliers might need verification, especially if they contradict the general pattern. Recognizing anomalies also guides corrective actions or further analysis.

• “A sudden spike in Q1 2023 was due to a successful marketing campaign.”
• “The outlier data point in November appears inconsistent with historical trends.”

Gaps and Inconsistencies

Gaps or inconsistencies highlight where data is incomplete or missing, complicating analysis.

Gaps might result from system errors, data unavailability, or incomplete data sets.

Label these gaps clearly to prevent misinterpretation. Inconsistencies may also arise from misaligned data categories, differing collection periods, or varied data sources.

Understanding and explaining these gaps or inconsistencies is crucial for accurate reporting.

• “Data gaps between Q3 and Q4 make trend analysis challenging.”
• “Sales data for Europe is inconsistent, possibly due to reporting delays.”

Understanding and conveying the unit of measurement is essential for accurate interpretation.

Whether it’s dollars, percentages, or thousands of items, units contextualize data and allow accurate comparisons.

Inconsistent units across multiple graphs can lead to confusion, so ensure they’re uniformly labeled.

For continuous data, specify the increments used along the y-axis.

• “Profits are measured in millions of dollars along the y-axis.”
• “Population size is recorded in thousands, revealing a significant growth trend.”

Colors and Patterns

Effective color and pattern use make graphs more readable.

Use contrasting colors to distinguish between data sets or groups. Patterns can help differentiate data if colors are not an option.

Avoid overusing color, which can cause confusion or distract from key insights.

Ensure that color choices align with industry standards or audience preferences, and label colors/patterns clearly.

• “Blue represents the North region, while green highlights the West.”
• “The dotted line marks international sales, while the solid line shows domestic trends.”

Clarify the graph’s primary purpose – whether it’s to compare, analyze trends, or identify correlations.

Knowing the graph’s goal helps focus on the most relevant insights.

For comparisons, emphasize differences and similarities.

For trends, focus on direction and consistency. Correlation graphs should highlight relationships between variables.

Clearly stating the graph’s purpose enables the audience to understand the intended takeaway.

• “The bar graph compares the revenue of different departments over five years.”
• “The scatter plot correlates customer satisfaction scores with net promoter scores.”

Here is a good video about how to describe a graph:

Paragraph Examples of Describing a Graph

Here are three examples of how to describe a graph in a paragraph.

Line Graph Analysis

The line graph displays quarterly revenue growth from 2019 to 2023. We see a steady increase from Q1 2019 to Q4 2020, followed by a sudden decline due to the pandemic. Revenue rebounded quickly in Q1 2021 and remained on an upward trajectory since, suggesting economic resilience despite setbacks.

Bar Graph Analysis

The bar graph compares monthly sales of three products over a year. Product A consistently outperformed the others, with notable peaks in summer months. Product B showed more stable growth, while Product C had fluctuating sales, possibly due to seasonal demand.

Pie Chart Analysis

The pie chart illustrates the market share of five smartphone brands. Brand X holds the largest share at 40%, followed by Brand Y at 25%. The remaining three brands together capture 35%, highlighting intense competition.

Final Thoughts: How to Describe a Graph in Writing

Describing graphs is crucial for clear data communication.

Remember to identify the graph type, focus on key features, and adapt language for the audience. Let your graph’s story shine.

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Figures and Charts

What this handout is about.

This handout will describe how to use figures and tables to present complicated information in a way that is accessible and understandable to your reader.

Do I need a figure/table?

When planning your writing, it is important to consider the best way to communicate information to your audience, especially if you plan to use data in the form of numbers, words, or images that will help you construct and support your argument.  Generally speaking, data summaries may take the form of text, tables or figures. Most writers are familiar with textual data summaries and this is often the best way to communicate simple results. A good rule of thumb is to see if you can present your results clearly in a sentence or two. If so, a table or figure is probably unnecessary. If your data are too numerous or complicated to be described adequately in this amount of space, figures and tables can be effective ways of conveying lots of information without cluttering up your text. Additionally, they serve as quick references for your reader and can reveal trends, patterns, or relationships that might otherwise be difficult to grasp.

So what’s the difference between a table and a figure anyway?

Tables present lists of numbers or text in columns and can be used to synthesize existing literature, to explain variables, or to present the wording of survey questions. They are also used to make a paper or article more readable by removing numeric or listed data from the text. Tables are typically used to present raw data, not when you want to show a relationship between variables.

Figures are visual presentations of results. They come in the form of graphs, charts, drawings, photos, or maps.  Figures provide visual impact and can effectively communicate your primary finding. Traditionally, they are used to display trends and patterns of relationship, but they can also be used to communicate processes or display complicated data simply.  Figures should not duplicate the same information found in tables and vice versa.

Using tables

Tables are easily constructed using your word processor’s table function or a spread sheet program such as Excel. Elements of a table include the Legend or Title, Column Titles, and the Table Body (quantitative or qualitative data). They may also include subheadings and footnotes. Remember that it is just as important to think about the organization of tables as it is to think about the organization of paragraphs. A well-organized table allows readers to grasp the meaning of the data presented with ease, while a disorganized one will leave the reader confused about the data itself, or the significance of the data.

Title: Tables are headed by a number followed by a clear, descriptive title or caption. Conventions regarding title length and content vary by discipline. In the hard sciences, a lengthy explanation of table contents may be acceptable. In other disciplines, titles should be descriptive but short, and any explanation or interpretation of data should take place in the text. Be sure to look up examples from published papers within your discipline that you can use as a model. It may also help to think of the title as the “topic sentence” of the table—it tells the reader what the table is about and how it’s organized. Tables are read from the top down, so titles go above the body of the table and are left-justified.

Column titles: The goal of column headings is to simplify and clarify the table, allowing the reader to understand the components of the table quickly. Therefore, column titles should be brief and descriptive and should include units of analysis.

Table body: This is where your data are located, whether they are numerical or textual. Again, organize your table in a way that helps the reader understand the significance of the data. Be sure to think about what you want your readers to compare, and put that information in the column (up and down) rather than in the row (across). In other words, construct your table so that like elements read down, not across. When using numerical data with decimals, make sure that the decimal points line up. Whole numbers should line up on the right.

Other table elements

Tables should be labeled with a number preceding the table title; tables and figures are labeled independently of one another. Tables should also have lines demarcating different parts of the table (title, column headers, data, and footnotes if present). Gridlines or boxes should not be included in printed versions. Tables may or may not include other elements, such as subheadings or footnotes.

Quick reference for tables

Tables should be:

• Centered on the page.
• Numbered in the order they appear in the text.
• Referenced in the order they appear in the text.
• Labeled with the table number and descriptive title above the table.
• Labeled with column and/or row labels that describe the data, including units of measurement.
• Set apart from the text itself; text does not flow around the table.

Table 1. Physical characteristics of the Doctor in the new series of Doctor Who

Table 2. Physical characteristics of the Doctor in the new series of Doctor Who

Using figures

Figures can take many forms. They may be graphs, diagrams, photos, drawings, or maps. Think deliberately about your purpose and use common sense to choose the most effective figure for communicating the main point. If you want your reader to understand spatial relationships, a map or photograph may be the best choice. If you want to illustrate proportions, experiment with a pie chart or bar graph. If you want to illustrate the relationship between two variables, try a line graph or a scatterplot (more on various types of graphs below). Although there are many types of figures, like tables, they share some typical features: captions, the image itself, and any necessary contextual information (which will vary depending on the type of figure you use).

Figure captions

Figures should be labeled with a number followed by a descriptive caption or title. Captions should be concise but comprehensive. They should describe the data shown, draw attention to important features contained within the figure, and may sometimes also include interpretations of the data. Figures are typically read from the bottom up, so captions go below the figure and are left-justified.

The most important consideration for figures is simplicity. Choose images the viewer can grasp and interpret clearly and quickly. Consider size, resolution, color, and prominence of important features. Figures should be large enough and of sufficient resolution for the viewer to make out details without straining their eyes. Also consider the format your paper will ultimately take. Journals typically publish figures in black and white, so any information coded by color will be lost to the reader.  On the other hand, color might be a good choice for papers published to the web or for PowerPoint presentations. In any case, use figure elements like color, line, and pattern for effect, not for flash.

Additional information

Figures should be labeled with a number preceding the table title; tables and figures are numbered independently of one another. Also be sure to include any additional contextual information your viewer needs to understand the figure. For graphs, this may include labels, a legend explaining symbols, and vertical or horizontal tick marks. For maps, you’ll need to include a scale and north arrow. If you’re unsure about contextual information, check out several types of figures that are commonly used in your discipline.

Quick reference for figures

Figures should be:

• Labeled (under the figure) with the figure number and appropriate descriptive title (“Figure” can be spelled out [“Figure 1.”] or abbreviated [“Fig. 1.”] as long as you are consistent).
• Referenced in the order they appear in the text (i.e. Figure 1 is referenced in the text before Figure 2 and so forth).
• Set apart from the text; text should not flow around figures.

Every graph is a figure but not every figure is a graph. Graphs are a particular set of figures that display quantitative relationships between variables. Some of the most common graphs include bar charts, frequency histograms, pie charts, scatter plots, and line graphs, each of which displays trends or relationships within and among datasets in a different way. You’ll need to carefully choose the best graph for your data and the relationship that you want to show. More details about some common graph types are provided below. Some good advice regarding the construction of graphs is to keep it simple. Remember that the main objective of your graph is communication. If your viewer is unable to visually decode your graph, then you have failed to communicate the information contained within it.

Pie charts are used to show relative proportions, specifically the relationship of a number of parts to the whole. Use pie charts only when the parts of the pie are mutually exclusive categories and the sum of parts adds up to a meaningful whole (100% of something). Pie charts are good at showing “big picture” relationships (i.e. some categories make up “a lot” or “a little” of the whole thing). However, if you want your reader to discern fine distinctions within your data, the pie chart is not for you. Humans are not very good at making comparisons based on angles. We are much better at comparing length, so try a bar chart as an alternative way to show relative proportions. Additionally, pie charts with lots of little slices or slices of very different sizes are difficult to read, so limit yours to 5-7 categories.

The chart shows the relative proportion of fifteen elements in Martian soil, listed in order from “most” to “least”: oxygen, silicon, iron, magnesium, calcium, sulfur, aluminum, sodium, potassium, chlorine, helium, nitrogen, phosphorus, beryllium, and other. Oxygen makes up about ⅓ of the composition, while silicon and iron together make up about ¼. The remaining slices make up smaller proportions, but the percentages aren’t listed in the key and are difficult to estimate. It is also hard to distinguish fifteen colors when comparing the pie chart to the color coded key.

The chart shows the relative proportion of five leisure activities of Venusian teenagers (tanning, trips to Mars, reading, messing with satellites, and stealing Earth cable). Although each of the five slices are about the same size (roughly 20% of the total), the percentage of Venusian teenagers engaging in each activity varies widely (tanning: 80%, trips to Mars: 40%, reading: 12%, messing with satellites: 30%, stealing Earth cable: 77%). Therefore, there is a mismatch between the labels and the actual proportion represented by each activity (in other words, if reading represents 12% of the total, its slice should take up 12% of the pie chart area), which makes the representation inaccurate. In addition, the labels for the five slices add up to 239% (rather than 100%), which makes it impossible to accurately represent this dataset using a pie chart.

Bar graphs are also used to display proportions. In particular, they are useful for showing the relationship between independent and dependent variables, where the independent variables are discrete (often nominal) categories. Some examples are occupation, gender, and species. Bar graphs can be vertical or horizontal. In a vertical bar graph the independent variable is shown on the x axis (left to right) and the dependent variable on the y axis (up and down). In a horizontal one, the dependent variable will be shown on the horizontal (x) axis, the independent on the vertical (y) axis. The scale and origin of the graph should be meaningful. If the dependent (numeric) variable has a natural zero point, it is commonly used as a point of origin for the bar chart. However, zero is not always the best choice. You should experiment with both origin and scale to best show the relevant trends in your data without misleading the viewer in terms of the strength or extent of those trends.

The graph shows the number of male and female spaceship crew members for five different popular television series: Star Trek (1965), Battlestar (1978), Star Trek: TNG (1987), Stargate SG-1 (1997), and Firefly (2002). Because the television series are arranged chronologically on the x-axis, the graph can also be used to look for trends in these numbers over time.

Although the number of crew members for each show is similar (ranging from 9 to 11), the proportion of female and male crew members varies. Star Trek has half as many female crew members as male crew members (3 and 6, respectively), Battlestar has fewer than one-fourth as many female crew members as male crew members (2 and 9, respectively), Star Trek: TNG has four female crew members and six male crew members, Stargate SG-1 has less than one-half as many female crew members as male crew members (3 and 7, respectively), and Firefly has four female and five male crew members.

Frequency histograms/distributions

Frequency histograms are a special type of bar graph that show the relationship between independent and dependent variables, where the independent variable is continuous, rather than discrete. This means that each bar represents a range of values, rather than a single observation. The dependent variables in a histogram are always numeric, but may be absolute (counts) or relative (percentages). Frequency histograms are good for describing populations—examples include the distribution of exam scores for students in a class or the age distribution of the people living in Chapel Hill. You can experiment with bar ranges (also known as “bins”) to achieve the best level of detail, but each range or bin should be of uniform width and clearly labeled.

XY scatter plots

Scatter plots are another way to illustrate the relationship between two variables. In this case, data are displayed as points in an x,y coordinate system, where each point represents one observation along two axes of variation. Often, scatter plots are used to illustrate correlation between two variables—as one variable increases, the other increases (positive correlation) or decreases (negative correlation). However, correlation does not necessarily imply that changes in one variable cause changes in the other. For instance, a third, unplotted variable may be causing both. In other words, scatter plots can be used to graph one independent and one dependent variable, or they can be used to plot two independent variables. In cases where one variable is dependent on another (for example, height depends partly on age), plot the independent variable on the horizontal (x) axis, and the dependent variable on the vertical (y) axis. In addition to correlation (a linear relationship), scatter plots can be used to plot non-linear relationships between variables.

The scatter plot shows the relationship between temperature (x-axis, independent variable) and the number of UFO sightings (y-axis, dependent variable) for 53 separate data points. The temperature ranges from about 0°F and 120°F, and the number of UFO sightings ranges from 1 to 10. The plot shows a low number of UFO sightings (ranging from 1 to 4) at temperatures below 80°F and a much wider range of the number of sightings (from 1 to 10) at temperatures above 80°F. It appears that the number of sightings tends to increase as temperature increases, though there are many cases where only a few sightings occur at high temperatures.

XY line graphs

Line graphs are similar to scatter plots in that they display data along two axes of variation. Line graphs, however, plot a series of related values that depict a change in one variable as a function of another, for example, world population (dependent) over time (independent). Individual data points are joined by a line, drawing the viewer’s attention to local change between adjacent points, as well as to larger trends in the data. Line graphs are similar to bar graphs, but are better at showing the rate of change between two points. Line graphs can also be used to compare multiple dependent variables by plotting multiple lines on the same graph.

Example of an XY line graph:

The line graph shows the age (in years) of the actor of each Doctor Who regeneration for the first through the eleventh regeneration. The ages range from a maximum of about 55 in the first regeneration to a minimum of about 25 in the eleventh regeneration. There is a downward trend in the age of the actors over the course of the eleven regenerations.

General tips for graphs

Strive for simplicity. Your data will be complex. Don’t be tempted to convey the complexity of your data in graphical form. Your job (and the job of your graph) is to communicate the most important thing about the data. Think of graphs like you think of paragraphs—if you have several important things to say about your data, make several graphs, each of which highlights one important point you want to make.

Strive for clarity. Make sure that your data are portrayed in a way that is visually clear. Make sure that you have explained the elements of the graph clearly. Consider your audience. Will your reader be familiar with the type of figure you are using (such as a boxplot)? If not, or if you’re not sure, you may need to explain boxplot conventions in the text. Avoid “chartjunk.” Superfluous elements just make graphs visually confusing. Your reader does not want to spend 15 minutes figuring out the point of your graph.

Strive for accuracy. Carefully check your graph for errors. Even a simple graphical error can change the meaning and interpretation of the data. Use graphs responsibly. Don’t manipulate the data so that it looks like it’s saying something it’s not—savvy viewers will see through this ruse, and you will come off as incompetent at best and dishonest at worst.

How should tables and figures interact with text?

Placement of figures and tables within the text is discipline-specific. In manuscripts (such as lab reports and drafts) it is conventional to put tables and figures on separate pages from the text, as near as possible to the place where you first refer to it. You can also put all the figures and tables at the end of the paper to avoid breaking up the text. Figures and tables may also be embedded in the text, as long as the text itself isn’t broken up into small chunks. Complex raw data is conventionally presented in an appendix. Be sure to check on conventions for the placement of figures and tables in your discipline.

You can use text to guide the reader in interpreting the information included in a figure, table, or graph—tell the reader what the figure or table conveys and why it was important to include it.

When referring to tables and graphs from within the text, you can use:

• Clauses beginning with “as”: “As shown in Table 1, …”
• Passive voice: “Results are shown in Table 1.”
• Active voice (if appropriate for your discipline): “Table 1 shows that …”
• Parentheses: “Each sample tested positive for three nutrients (Table 1).”

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

American Psychological Association. 2010. Publication Manual of the American Psychological Association . 6th ed. Washington, DC: American Psychological Association.

Bates College. 2012. “ Almost everything you wanted to know about making tables and figures.” How to Write a Paper in Scientific Journal Style and Format , January 11, 2012. http://abacus.bates.edu/~ganderso/biology/resources/writing/HTWtablefigs.html.

Cleveland, William S. 1994. The Elements of Graphing Data , 2nd ed. Summit, NJ: Hobart Press..

Council of Science Editors. 2014. Scientific Style and Format: The CSE Manual for Authors, Editors, and Publishers , 8th ed. Chicago & London: University of Chicago Press.

University of Chicago Press. 2017. The Chicago Manual of Style , 17th ed. Chicago & London: University of Chicago Press.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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How to Describe an IELTS Academic Writing Task 1 Graph

On the following pages there are lessons to teach you how to write an academic IELTS writing task 1 but in this first lesson you’ll get an overview of how to answer a task 1.

You can also view a video of this lesson:

Once you have studied the general structure, you can view other examples by looking at the model graphs that are on this site. 

Alternatively, follow on with these lessons to a variety of strategies and tips to achieve the writing score you need.

Steps to Respond to a Task 1

To analyse this, we’ll look at a line graph. Look at the following question and the graph.

You should spend about 20 minutes on this task.

The line graph below shows changes in the amount and type of fast food consumed by Australian teenagers from 1975 to 2000.

Summarize the information by selecting and reporting the main features and make comparisons where relevant.

Write at least 150 words.

There are three basic things you need to structure an IELTS writing task 1.

• Introduce the graph
• Give an overview
• Give the detail

We’ll look at each of these in turn.

Introduce the Graph

You need to begin with one or two sentences that state what the IELTS writing task 1 shows. To do this, paraphrase the title of the graph, making sure you put in a time frame if there is one.

Here is an example for the above line graph:

The line graph compares the fast food consumption of teenagers in Australia between 1975 and 2000, a period of 25 years.

You can see this says the same thing as the title, but in a different way.

Give an Overview

You also need to state what the main trend or trends in the graph are. Don’t give detail such as data here – you are just looking for something that describes what is happening overall.

One thing that stands out in this graph is that one type of fast food fell over the period, whilst the other two increased, so this would be a good overview.

Here is an example:

Overall, the consumption of fish and chips declined over the period, whereas the amount of pizza and hamburgers that were eaten increased.

This covers the main changes that took place over the whole period.

You may sometimes see this overview as a conclusion. It does not matter if you put it in the conclusion or the introduction when you do an IELTS writing task 1, but you should provide an overview in one of these places.

Give the Detail

You can now give more specific detail in the body paragraphs.

When you give the detail in your body paragraphs in your IELTS writing task 1, you must make reference to the data.

The key to organizing your body paragraphs for an IELTS writing task 1 is to group data together where there are patterns . To do this you need to identify any similarities and differences .

Look at the graph – what things are similar and what things are different? As we have already identified in the overview, the consumption of fish and chips declined over the period, whereas the amount of pizza and hamburgers that were eaten increased.

So it is clear that pizza and hamburgers were following a similar pattern, but fish and chips were different. On this basis, you can use these as your ‘groups’, and focus one paragraph on fish and chip and the other one on pizza and hamburgers.

Here is an example of the first paragraph:

In 1975, the most popular fast food with Australian teenagers was fish and chips, being eaten 100 times a year. This was far higher than Pizza and hamburgers, which were consumed approximately 5 times a year. However, apart from a brief rise again from 1980 to 1985, the consumption of fish and chips gradually declined over the 25 year timescale to finish at just under 40.

As you can see, the focus is on fish and chips. This does not mean you should not mention the other two foods, as you should still make comparisons of the data as the questions asks.

The second body then focuses on the other foods:

In sharp contrast to this, teenagers ate the other two fast foods at much higher levels. Pizza consumption increased gradually until it overtook the consumption of fish and chips in 1990. It then levelled off from 1995 to 2000. The biggest rise was seen in hamburgers as the occasions they were eaten increased sharply throughout the 1970’s and 1980’s, exceeding that of fish and chips in 1985. It finished at the same level that fish and chips began, with consumption at 100 times a year.

Full Model Answer:

The line graph compares the fast food consumption of teenagers in Australia between 1975 and 2000, a period of 25 years. Overall, the consumption of fish and chips declined over the period, whereas the amount of pizza and hamburgers that were eaten increased.

(194 words)

Now you've been through this first introductory lesson, you can go to the next lesson or start viewing some model answers. 

More Task 1 Academic Lessons:

IELTS Table: Tips and techniques for a high score.

IELTS Table advice for a high score. Learn how to describe an IELTS table, which is just another way to present data.

IELTS Process Diagram Strategies and Tips

IELTS Process Diagram: In task 1 of IELTS writing you usually have to describe some kind of graph or chart. But sometimes you get a process. It is therefore crucial that you know how to do this. This easy to follow lesson explains how.

IELTS Task 1 Line Graph Structure Using Groups

For an IELTS Task 1 Line Graph there are different ways to organise your answer. Grouping information is a good way to get a logically structured response.

Describing IELTS Graphs: Tips to avoid a common mistake

IELTS Graphs: A common mistake In IELTS graphs is to get the subject of the graph wrong. This lesson explains how this mistake is made and show you what you need to do to avoid it. There is a also a practice exercise.

Tips for Organising an IELTS Line Graph

Organising an IELTS Line Graph - This lesson shows you have to improve the coherency of your graph in order to achieve a high band score.

Which Tenses for IELTS are the Most Important?

Candidates often ask which tenses for IELTS are needed in order to do well in the exam. This lesson goes through the grammar tenses and how they apply to the test.

Useful Language for IELTS Graphs

This useful language for IELTS graphs looks at phrases for introducing graphs and describing changes

Describing Graph Trends Using the Language of Change

Describing graph trends: In IELTS you must know how to describe the trends that you see in the graph you are given. This lesson provides practice with some common language used to describe trends.

Writing Tips for a Graph in the Future in IELTS Academic

Graph in the future: Sometimes graphs in IELTS refer to a future time. You must know the language to write about these. In this lesson, learn how to write about an IELTS graph in the future. Getting the tenses right is an important part of the IELTS writing task 1.

Learn Compare and Contrast Language for IELTS Graphs

Compare and Contrast Language: In the academic IELTS task 1, you have to know the right language if you want to get a band 7 or higher. Practice your IELTS language for bar charts in this task 1 writing lesson.

Describing an IELTS task 1 graph over time

This lesson shows you how to write an IELTS task 1 graph or chart that is over time.

IELTS Bar and Line Graph: How to describe two graphs together

This Bar and Line Graph example shows you how you can write about two charts together in the IELTS test for task 1, with strategies and techniques.

IELTS Pie Chart Strategies and Tips for a Band 7, 8 or 9

This IELTS pie chart lesson provides you with tips and advice on how to describe an IELTS Pie Chart in order to get a Band 7, 8 or 9.

Take an IELTS Quiz to test your IELTS knowledge

IELTS Quizzes to test and train you on the writing task and task 2 of the IELTS test. Gap fills and multiple choice.

Prepositions in Graphs Quiz: Between; from; to; at; of; in; with; by

Prepositions in Graphs: Practice using prepositions in the IELTS test. View a model answer and practice using a gap fill.

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How To Write a Line Graph Essay: Step By Step Guide

• Post author By admin
• March 13, 2024

Have you ever stared at a line graph in a textbook, wondering how to turn that squiggly line into a compelling essay? Fear not, fellow learners! This guide will equip you with the knowledge and skills to write a stellar line graph essay, even if you’re new to data analysis or academic writing. So, let’s start with a guide on how to write a line graph essay?

Table of Contents

Why Line Graphs Matter

Data surrounds us, from weather patterns to social media trends. Line graphs are a fantastic tool to visualize changes over time, making them crucial in various fields like science, economics, and even social media marketing. Understanding how to interpret and write about line graphs is a valuable skill for academic success and beyond.

How To Analyze the Graph?

Before putting pen to paper (or fingers to keyboard), take a close look at your line graph. It’s like deciphering a secret code! Here’s what you need to identify:

• The Big Picture: What’s the title? Does it give you a clue about the subject matter?
• Axes in Action: Identify the X-axis (horizontal) and Y-axis (vertical). What information do they represent? Are there units of measurement (e.g., years, dollars)?
• Labels for Clarity: Look for labels for each line on the graph. What do they represent? Are there any legends or keys explaining the lines?
• Time Traveler: What time frame does the graph cover? Is it a few months, years, or even decades?
• The Ups and Downs: This is where the magic happens! Analyze the trends. Does the line generally increase, decrease, or fluctuate? Are there any specific peaks or drops?

How To Write a Line Graph Essay?

Now that you’ve unlocked the secrets of the graph, let’s craft your essay.

Introduction: Setting the Scene (100-120 words)

• Hook: Start with a captivating sentence that grabs the reader’s attention. This could be a surprising fact related to the graph’s topic or a thought-provoking question.
• Keyword Introduction: Mention the importance of line graphs and how they help us understand data.
• Thesis Statement: Briefly state that you’ll be analyzing the line graph and explaining the trends you observe.

Did you know that smartphone usage has skyrocketed in the last decade? This essay will analyze a line graph depicting this trend, exploring the reasons behind the increase and its potential impact on society.

Overview Paragraph: The Big Picture (80-100 words)

• Briefly paraphrase the title of the graph and describe what it depicts.
• Highlight the main trends you observed – is there a steady increase, a sharp decline, or something else?
• Mention any interesting comparisons between the lines on the graph (if applicable).

The line graph titled “Global Smartphone Usage 2010-2020” reveals a significant rise in smartphone users over this decade. The data shows a steady upward trend, with a slight acceleration in recent years. Notably, the graph also shows a difference in the growth rates between developed and developing countries.

Body Paragraphs: Diving Deeper (300-400 words each)

This is where you showcase your detective skills and explain the trends you identified. Here’s what to focus on in each body paragraph:

• Pick a Specific Trend: Choose a key trend you observed in the graph (e.g., a constant increase, a sudden drop).
• Support Your Claims: Use evidence from the graph! Mention specific years, numbers, or percentages to illustrate your points.
• Explain Why: Don’t just describe, explain! What factors might be contributing to the trend? Consider possible causes related to the topic of the graph.

One significant trend is the steady rise in smartphone users globally. According to the graph, the number of users doubled between 2010 and 2020, from 1 billion to 2 billion.  This growth can be attributed to several factors, including the increasing affordability of smartphones, the development of user-friendly apps, and the expansion of internet access in many regions.

Conclusion: Wrapping it Up (80-100 words)

• You can optionally add a final thought or prediction about the future based on the graph’s data.

In conclusion, the line graph clearly demonstrates the explosive growth in smartphone usage over the past decade. This trend is likely to continue as technology advances and internet access becomes more widespread. Understanding these changes is crucial for businesses, educators, and policymakers to adapt and shape the future of technology and its impact on society.

Key Points To Enhance Reader Experience

• Visuals: Consider including an image of the line graph you’re analyzing (with proper attribution if necessary). Annotating specific trends on the image can further enhance understanding.
• Real-world Examples: Where relevant, connect the data in the line graph to real-world examples to add context and make your essay more relatable.

Common Mistakes to Avoid: How To Write a Line Graph Essay

• Just Describing, Not Explaining: Don’t simply describe the trends in the graph. Analyze them and explain why they might be happening.
• Ignoring the Title and Labels: The title, axes labels, and any legends are crucial for interpreting the data. Make sure you use the information they provide in your essay.
• Making Up Data: Don’t invent information or draw conclusions that aren’t supported by the graph. Stick to the facts!

Bonus Tips: How To Write a Line Graph Essay

• Transition Words for a Smooth Flow: Transition words like “however,” “furthermore,” and “in addition” will help connect your ideas and create a logical flow in your essay.
• Vary Your Sentence Structure: Don’t be afraid to mix up short and long sentences to keep your writing engaging.
• Proofread and Edit: Once you’ve finished writing, take the time to proofread your essay for any grammatical errors or typos. Double-check that your data and interpretations align with the graph.

Additional Tips for Advanced Line Graph Essay Writing

• Compare and Contrast: If your line graph has multiple lines, delve deeper into the comparisons between them. Analyze how they rise, fall, or interact with each other.
• Consider External Factors: Are there any external events or developments that might explain the trends in the graph? Think outside the box and explore these connections.
• Acknowledge Limitations: No data source is perfect. Briefly mention any limitations of the data presented in the graph, such as the timeframe or sample size.

By following these steps and incorporating these tips for How To Write a Line Graph Essay, you’ll be well on your way to writing compelling line graph essays that demonstrate your analytical and communication skills.

Remember, practice makes perfect! The more you analyze line graphs and write about them, the more confident you’ll become. So grab your data, get ready to explore trends, and write those essays like a pro!

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Writing about Charts, Graphs, and Diagrams

by Nicholas Walker , under IELTS and TOEFL

Make sure to organize your IELTS Task 1 essay so that the reader can navigate it easily.

Step 1: Give a clear overview of what the figure is about, showing that you understand the main message it conveys.

Step 2: Describe the data in a systematic way (left to right, top to bottom, biggest to smallest) using numbers and words from the figure.

Step 3: Compare significant elements of the figure, mentioning any trends or changes in the past, extrapolating for the future if you can.

Useful chart, graph, and diagram vocabulary

Some words are likely to appear in a discussion of a chart, graph, or diagram. Use the target structure checker with the following list of common chart, graph, and diagram vocabulary to see if you are using the vocabulary your readers (the examiners) are expecting to see.

above, according to, apparent, are compared, are presented, bar graph, below, can be seen, chart, climbed, comparable, compares, considerable, considerably, decline, decrease, demonstrates, detailed, details, difference, dipped, diving sharply, downward, dramatic, dropped off, evident, falls, fell, fluctuation, fluctuations, gap, gradual, graph, greater, grew, grows, high, higher, highlighted, highlights, highs, histogram, illustrated, illustrates, increase, indicates, jump, leads, led, level, leveled, levelled, leveling, levelling, levels, lists, low, lower, lows, moderate, narrowed, note, number, observe, peak, peaks, period, photo, pie chart, pinpoints, plummeted, presents, proves, rapid climb, rates of, reports, reveals, remained, remains, rise, rises, rose, shown, shows, significant, slid, slight, steady, summarizes, results, value of, to every, to the left, to the right, total, trend, upward, we can see, widened, widening, widens

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Useful Vocabulary for Writing an IELTS Graph Essay

When it comes to IELTS writing task 1, 25% of your marks are for the range of words you use. That means IELTS graph vocabulary is a very important component to review as you prepare for the Writing Task 1. You can start by checking out this IELTS writing task 1 vocabulary guide . And below, I’ll provide an overview of words and useful phrases to incorporate into your writing so that you can get top marks on the lexical resource category and a high band score overall. Basically, the better your IELTS writing chart vocabulary, the higher score you’ll get. It’s not hard, but there is a clear formula to doing well.

How to Use IELTS Graph Vocabulary in Writing Task 1

Because IELTS writing task 1 involves describing a graph or chart of some type, it will help to have a handle on IELTS writing chart vocabulary — words and phrases that help you write about the information on the chart or graph.

How are graphs described in IELTS? Let’s walk through the best vocabulary for the task, step by step.

1. Start With Introduction Phrases

Often ESL students start their essay with ‘The graph shows…’. While this is fine, the verb ‘shows’ could be replaced by a more exciting and high-level vocabulary word. Here are four different prompts to start your essay:

• The graph illustrates the trends in…
• The graph reveals information about the changes in…
• The graph provides the differences between…
• The graph presents how X has changed over a period of…
• DO NOT write the word below or above in your introduction. i.e. The graph above/below shows…

2. Add Suitable Adverbs

Adverbs help express a relation of place, time, circumstance, manner, cause, and degree, and can greatly add some color and interest to your writing as well as show off your range of vocabulary. Unlike adjectives (which describe nouns), adverbs describe verbs, or actions. Here’s a great list of adverbs to use:

3. Use Appropriate Synonyms

Again using a variety of nouns and verbs for words like rise and fall will help increase your overall score. Here are some suggestions:

4. Add Time Phrases

Below are some excellent time phrases with sentence examples:

Using IELTS Graph Vocabulary in a Model Essay

Look at the sample IELTS writing Task 1 graphs on the British Council website . Below is my model answer with useful words in bold:

The bar charts illustrate the trends in computer ownership, with a further classification by level of education, from 2002 to 2010.

Over the period, it can be observed that there was a significant surge in the percentage of the population that owned a computer. In the year 2002, only about 58% of the population owned a computer, whereas by 2010 , this gradually increased to where over three-quarters of individuals had a home computer.

Looking at the information by level of education reveals that higher levels of education correspond to higher levels of computer ownership in both of those years. In 2002, a significantly low percentage of the population who did not finish high school had a computer, but this figure skyrocketed by 2010, going from 15% to over 40%. There were also dramatic climbs , of approximately 30 percentage points, for those with a high school diploma or an unfinished college education (reaching 65% and 85%, respectively, in 2010).

To conclude, during the last decade, there has been a substantial growth in computer ownership across all educational levels.

Other IELTS Graph Vocabulary Resources

Keep in mind that IELTS writing task 1 may contain one of several different types of infographic: a bar chart, pie chart, line graph, diagram, etc. Regardless of the type, you’ll want to have a good handle on IELTS writing chart vocabulary.

For more specific guides to the different kinds of graphs, charts, and graphics you may find on IELTS writing task 1, check out the following resources:

• How to Describe a Bar Chart
• How to Describe a Pie Chart
• How to Describe a Map
• How to Describe a Process Diagram

You can also check out Magoosh’s IELTS linking words PDF for transitions between ideas. Hopefully you’ll start to incorporate some of these key words and phrases, as well as the above suggestions, in your IELTS Task 1 Writing. If you still don’t feel comfortable doing so, consider dedicating more time to your IELTS studies with Magoosh’s fun, engaging IELTS prep for extra practice.

Eliot Friesen-Meyers is the Senior Curriculum Manager for Magoosh IELTS and TOEFL. He attended Goshen College (B.A.), New York University (M.A.), and Harvard University (M.T.S.), gaining experience and skills in curriculum development, ESOL instruction, online teaching and learning, and IELTS and TOEFL test prep education. Eliot’s teaching career started with Literacy Americorps in Pittsburgh, Pennsylvania, and later, taught ESL programs at Northeastern University, University of California-Irvine, and Harold Washington College. Eliot was also a speaker at the 2019 TESOL International Conference . With over 10 years of experience, he understands the challenges students face and loves helping them overcome those challenges. Come join Eliot on Youtube , Facebook , and Instagram . Recent blog posts Complete Guide to IELTS Writing Task 1 Complete Guide to IELTS Writing Task 2

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11 responses to “Useful Vocabulary for Writing an IELTS Graph Essay”

I would like to get sample of all types of graph eassy

IELTS Liz offers a pretty good range of graphs and charts for IELTS Writing Task 1 (Academic) . You can also get a nice selection of these on the official IELTS websites . And last but not least, Magoosh offers a good selection of these types of questions with a Magoosh IELTS Premium subscription. 🙂

Thank you Magoosh for the comprehensive guide. I’m a subscriber to you GMAT course and is now checking out IELTS.

Wanna ask, I read and watched many other sources that says we should not write a conclusion. However, yours did.

So, is it permissible or not permissible?

The concluding sentence is optional–if you have time to write a concluding sentence after writing and reviewing your essay, then it looks good to have a concluding sentence. If you don’t have time to write a concluding essay or you’d rather focus on other parts of your essay, then it’s totally fine to leave it out. You can read more about this in our Complete Guide to IELTS Academic Writing Task 1.

“Growth” is not an noun? Because in the board it’s saying that it is a verb

Thanks for pointing this out! It seems like a mistake on our part. We should probably change that to “grow”. I’ll make a note for our writing team to make this change 🙂

I appreciate you very much. Your blog on Useful Words for Writing an IELTS Graph Essay was the outstanding blog ever. You have given so much good information about the new english words & grammar in your post, which will help me in future. Always keep data like this on your website

I have two significant questions. The first one is related to the unit of measurement in over view. Is it academic? And the second one is of conclusion. Do we need to write conclusion?

Hi Aakash, I’m afraid I don’t understand your first question. Can you please provide some more information? For your second question: a conclusion is not necessary. You can add one if you’d like, but it’s more important to spend time analyzing the graph.

This is one of the best among the essay I’ve read recently.

Thanks for the feedback! 🙂

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When Graphs Are a Matter of Life and Death

John Carter has only an hour to decide. The most important auto race of the season is looming; it will be broadcast live on national television and could bring major prize money. If his team wins, it will get a sponsorship deal and a chance to start making some real profits for a change.

There’s just one problem. In seven of the past twenty-four races, the engine in the Carter Racing car has blown out. An engine failure live on TV will jeopardize sponsorships—and the driver’s life. But withdrawing has consequences, too. The wasted entry fee means finishing the season in debt, and the team won’t be happy about the missed opportunity for glory. As Burns’s First Law of Racing says, “Nobody ever won a race sitting in the pits.”

One of the engine mechanics has a hunch about what’s causing the blowouts. He thinks that the engine’s head gasket might be breaking in cooler weather. To help Carter decide what to do, a graph is devised that shows the conditions during each of the blowouts: the outdoor temperature at the time of the race plotted against the number of breaks in the head gasket. The dots are scattered into a sort of crooked smile across a range of temperatures from about fifty-five degrees to seventy-five degrees.

The upcoming race is forecast to be especially cold, just forty degrees, well below anything the cars have experienced before. So: race or withdraw?

This case study, based on real data, and devised by a pair of clever business professors, has been shown to students around the world for more than three decades. Most groups presented with the Carter Racing story look at the scattered dots on the graph and decide that the relationship between temperature and engine failure is inconclusive. Almost everyone chooses to race. Almost no one looks at that chart and asks to see the seventeen missing data points—the data from those races which did not end in engine failure.

As soon as those points are added, however, the terrible risk of a cold race becomes clear. Every race in which the engine behaved properly was conducted when the temperature was higher than sixty-five degrees; every single attempt that occurred in temperatures at or below sixty-five degrees resulted in engine failure. Tomorrow’s race would almost certainly end in catastrophe.

One more twist: the points on the graph are real but have nothing to do with auto racing. The first graph contains data compiled the evening before the disastrous launch of the space shuttle Challenger, in 1986. As Diane Vaughn relates in her account of the tragedy, “ The Challenger Launch Decision ” (1996), the data were presented at an emergency NASA teleconference, scribbled by hand in a simple table format and hurriedly faxed to the Kennedy Space Center. Some engineers used the chart to argue that the shuttle’s O-rings had malfunctioned in the cold before, and might again. But most of the experts were unconvinced. The chart implicitly defined the scope of relevance—and nobody seems to have asked for additional data points, the ones they couldn’t see. This is why the managers made the tragic decision to go ahead despite the weather. Soon after takeoff, the rubber O-rings leaked, a joint in the solid rocket boosters failed, and the space shuttle broke apart , killing all seven crew members. A decade later, Edward Tufte, the great maven of data visualization, used the Challenger teleconference as a potent example of the wrong way to display quantitative evidence. The right graph, he pointed out, would have shown the truth at a glance.

In “ A History of Data Visualization and Graphic Communication ” (Harvard), Michael Friendly and Howard Wainer, a psychologist and a statistician, argue that visual thinking, by revealing what would otherwise remain invisible, has had a profound effect on the way we approach problems. The book begins with what might be the first statistical graph in history, devised by the Dutch cartographer Michael Florent van Langren in the sixteen-twenties. This was well into the Age of Discovery, and Europeans were concerned with the measurement of time, distance, and location. Such measurements were particularly important at sea, where accurate navigation presented a considerable challenge. Mariners had to rely on error-prone charts and faulty compasses; they made celestial observations while standing on the decks of rocking boats, and—if all else failed—threw rope overboard in an attempt to work out how far from the seabed they were. If establishing a north-south position was notoriously difficult, the spin of the Earth made it nearly impossible to accurately calculate a ship’s east-west position.

In 1628, van Langren wrote a letter to the Spanish court, in an effort to demonstrate the importance of improving the way longitude was calculated (and of giving him the funding to do so). To make his case, he drew a simple one-dimensional graph. On the left, he drew a tick mark, representing the ancient city of Toledo, in Spain. From this point, he drew a single horizontal line on the page, marking across its length twelve historical calculations of the longitudinal distance from Toledo to Rome. The estimates were wildly different, scattered all across the line. There was a cluster of estimates at around twenty degrees, including those made by the great astronomer Tycho Brahe and the pioneering cartographer Gerardus Mercator; others, including the celebrated mathematician Ptolemy, put the distance between the two cities closer to thirty degrees. All the estimates were too large—we now know that the correct distance is sixteen and a half degrees. But the graph was meant to show just how divergent the estimates were. Depending on which one was used, a traveller from Toledo could end up anywhere between sixty miles outside Rome and more than six hundred miles away, on the plains of eastern Bulgaria.

Van Langren could have put these values in a table, as would have been typical for the time, but, as Friendly and Wainer observe, “only a graph speaks directly to the eyes.” Once the numbers were visualized, the enormous differences among them—and the stakes dependent on those differences—became impossible to ignore. Van Langren wrote, “If the Longitude between Toledo and Rome is not known with certainty, consider, Your Highness, what it will be for the Western and Oriental Indies, that in comparison the former distance is almost nothing.”

Van Langren’s image marked an extraordinary conceptual leap. He was a skilled cartographer from a long line of cartographers, so he would have been familiar with depicting distances on a page. But, as Tufte puts it, in his classic study “ Visual Explanations ” (1997), “Maps resemble miniature pictorial representations of the physical world.” Here was something entirely new: encoding the estimate of a distance by its position along a line. Scientists were well versed in handling a range of values for a single property, but until then science had only ever been concerned with how to get rid of error—how to take a collection of wrong answers and reduce its dimension to give a single, best answer. Van Langren was the first person to realize that a story lay in that dimension, one that could be physically seen on a page by abstracting it along a thin inked line.

The originality of van Langren’s graph attests to a long history of missed opportunities to arrive at the same idea. Friendly and Wainer offer an example from the banks of the Nile, which, before the Aswan Dam was built, in the nineteen-sixties, flooded each year. “Egyptians, who knew that their prosperity depended on the river’s annual overflow, had been keeping the Nile’s high-water mark for more than three millennia,” they write. The records helped farmers track the level of flooding in the recent past and decide when and where to plant crops. But, over thousands of years, nobody realized the significance of the data in aggregate—until the nineteen-fifties, when William Popper used it to chart the Nile’s flood levels in the course of thirteen centuries. Friendly and Wainer write, “No one thought to make a graph of the high-water level over time or compare the average water level in the last decade to what might occur in the next.” Popper’s work showed, for the first time, the surprisingly wide variation in silting across different periods—and silting was an important factor in fertilizing crops. Fat years and lean years didn’t just happen.

It was another hundred and fifty years after van Langren’s letter before the next significant advances in visualizing data arrived, courtesy of a 1786 book by the Scottish engineer William Playfair, “The Commercial and Political Atlas.” Despite the title, it didn’t contain a single conventional geographical map. Instead, it displayed Playfair’s great ability to chart out the shape of an object that existed only in his mind, cementing his place in the history of data graphics: he gave us the line graph of a time series, the bar chart, and, eventually, the pie chart—practically the entire suite of Excel charting options.

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Playfair explained his approach using a graph that showed the expenditure of the Royal Navy over the preceding decades. Time is on the horizontal x-axis, money is on the vertical y-axis; the line wiggles up and down from left to right. With the advantage of a few centuries’ worth of perspective, it’s hard to believe that this kind of image would be anything other than intuitive to grasp. But Playfair, introducing the time-series graph to the world for the first time, had to work hard to get people to understand what they were seeing. He asked his readers to imagine that he had taken the money spent by the Navy in a single year and laid it out neatly, in guineas, in a straight column on a table. To the right, he would create another column of guineas, to correspond to the amount paid out in the following year. If he continued doing this, creating a column of guineas for each year, “they would make a shape, the dimensions of which would agree exactly with the amount of the sums.”

Where van Langren had abstracted the range of longitudinal estimates into a line, Playfair had gone further. He discovered that you could encode time by its position on the page. This idea may have come naturally to him. Friendly and Wainer describe how, when Playfair was younger, his brother had explained one way to record the daily high temperatures over an extended period: he should imagine a bunch of thermometers in a row and record his temperature readings as if he were tracing the different mercury levels; from there, it was only a small step to let the image of the thermometer fade into the background, use a dot to represent the top of the column of mercury, and line up the dots from left to right on the page. By visualizing time on the x-axis, Playfair had created a tool for making pictures from numbers which offered a portal to a much deeper connection with time and distance. As the industrial age emerged, this proved to be a life-saving insight.

Back when long-distance travel was provided by horse-drawn stagecoaches, departure timetables were suggestive rather than definitive. Where schedules did exist, they would often be listed alongside caveats, such as “barring accidents!” or “God permitting!” Once passenger railways started to open up, in the eighteen-twenties and thirties, train times would be advertised, but, without nationally agreed-on time and time zones, their punctuality fell well shy of modern standards. When George Hudson, the English tycoon known as the Railway King, was confronted with data showing how often his trains ran late, he countered with the data on how often his trains were early, and insisted that, in net terms, his railway ran roughly on time.

As train travel became increasingly popular, patience was no longer the only casualty of this system: head-on collisions started to occur. With more lines and stations being added, rail operators needed a way to avoid accidents. A big breakthrough came from France, in an elegant new style of graph first demonstrated by the railway engineer Charles Ibry.

In a presentation to the French Minister of Public Works in 1847, Ibry displayed a chart that could show simultaneously the locations of all the trains between Paris and Le Havre in a twenty-four-hour period. Like Playfair, Ibry used the horizontal axis to denote the passing of time. Every millimetre across represented two minutes. In the top left corner was a mark to denote the Paris railway station, and then, down the vertical axis, each station was marked out along the route to Le Havre. They were positioned precisely according to distance, with one kilometre in the physical world corresponding to two and a half millimetres on the graph.

With the axes set up in this way, the trains appeared on the graph as simple diagonal lines, sweeping from left to right as they travelled across distance and time. In the simplest sections of the rail network, with no junctions or crossings or stops, you could choose where to place the diagonal line of each train to insure that there was sufficient spacing around it. Things got complicated, however, if the trains weren’t moving at the same speed. The faster the train, the steeper the line, so a passenger express train crossed quickly from top to bottom, while slower freight trains appeared as thin lines with a far shallower angle. The problem of scheduling became a matter of spacing a series of differently angled lines in a box so that they never unintentionally crossed on the page, and hence never met on the track.

These train graphs weren’t meant to be illustrations—they weren’t designed to persuade or to provide conceptual insight. They were created as an instrument for solving the intricate complexities of timetabling, almost akin to a slide rule. Yet they also constituted a map of an abstract conceptual space, a place where, to paraphrase the statistician John Tukey, you were forced to notice what you otherwise wouldn’t see.

Within a decade, the graphs were being used to create train schedules across the world. Until recently, some transit departments still preferred to work by hand, rather than by computer, using lined paper and a pencil, angling the ruler more sharply to denote faster trains on the line. And contemporary train-planning software relies heavily on these very graphs, essentially unchanged since Ibry’s day. In 2016, a team of data scientists was able to work out that a series of unexplained disruptions on Singapore’s MRT Circle Line were caused by a single rogue train. Onboard, the train appeared to be operating normally, but as it passed other trains in the tunnels it would trigger their emergency brakes. The pattern could not be seen by sorting the data by trains, or by times, or by locations. Only when a version of Ibry’s graph was used did the problem reveal itself.

Until the nineteenth century, Friendly and Wainer tell us, most modern forms of data graphics—pie charts, line graphs, and bar charts—tended to have a one-dimensional view of their data. Playfair’s line graph of Navy expenditures, for instance, was concerned only with how that one variable changed over time. But, as the nineteenth century progressed, graphs began to break free of their one-dimensional roots. The scatter plot, which some trace back to the English scientist John Herschel, and which Tufte heralds as “the greatest of all graphical designs,” allowed statistical graphs to take on the form of two continuous variables at once—temperature, or money, or unemployment rates, or wine consumption—whether it had a real-world physical presence or not. Rather than featuring a single line joining single values as they move over time, these graphs could present clouds of points, each plotted according to two variables.

Their appearance is instantly familiar. As Alberto Cairo puts it in his recent book, “ How Charts Lie ,” scatter plots got their name for a reason: “They are intended to show the relative scattering of the dots, their dispersion or concentration in different regions of the chart.” Glancing at a scatter allows you to judge whether the data is trending in one direction or another, and to spot if there are clusters of similar dots that are hiding in the numbers.

A famous example comes from around 1911, when the astronomers Ejnar Hertzsprung and Henry Norris Russell independently produced a scatter of a series of stars, plotting their luminosity against their color, moving across the spectrum from blue to red. (A star’s color is determined by its surface temperature; its luminosity, or intrinsic brightness, is determined both by its surface temperature and by its size.) The result, as Friendly and Wainer concede, is “not a graph of great beauty,” but it did revolutionize astrophysics. The scatter plot showed that the stars were distributed not at random but concentrated in groups, huddled together by type. These clusters would prove to be home to the blue and red giants, and also the red and white dwarfs.

In graphs like these, the distance between any two given dots on the page took on an entirely abstract meaning. It was no longer related to physical proximity; it now meant something more akin to similarity. Closeness within the conceptual space of the graph meant that two stars were alike in their characteristics. A surprising number of stars were, say, reddish and dim, because the red dwarf turned out to be a significant category of star; the way stars in this category clustered on the scatter plot showed that they were conceptually proximate, not that they were physically so.

But if you could find clusters of dots in two dimensions, why not three? Friendly and Wainer discuss a three-dimensional scatter plot that improved our understanding of Type 2 diabetes. In 1979, two scientists, Gerald M. Reaven and R. G. Miller, plotted blood-glucose levels against the production of insulin in the pancreas for a series of patients. Along a third axis, they added a metric for how efficiently insulin is used by the body. What emerged was a three-dimensional structure that looks a little like an egg with floppy wings. It allowed Reaven and Miller to split participants into three groups—those with overt diabetes, those with latent diabetes, and those who were unaffected—and to understand how patients might transition from one state to another. Previously it had been thought that overt diabetes was preceded by the latent stage, but the graph showed that the only “path” from one to the other was through the region occupied by those classified as normal. Because of this and evidence from other studies, they are now considered two separate disease classes.

If three dimensions are possible, though, why not four? Or four hundred? Today, much of data science is founded on precisely these high-dimensional spaces. They’re dizzying to contemplate, but the fundamental principles are the same as those of their nineteenth-century scatter-plot predecessors. The axes could be the range of possible answers to a questionnaire on a dating Web site, with individuals floating as dots in a vast high-dimensional space, their positions fixed by the responses they gave when they signed up. In 2012, Chris McKinlay, a grad student in applied mathematics, worked out how to scrape data from OkCupid and used this strategy—hunting for dots in a similar region, in the hope that proximity translated into romantic compatibility. (He says the eighty-eighth time was the charm.) Or the axes could relate to your reaction to a film on a streaming service, or the amount of time you spend looking at a particular post on a social-media site. Or they could relate to something physical, like the DNA in your cells: the genetic analysis used to infer our ancestry looks for variability and clusters within these abstract, conceptual spaces. There are subtle shifts in the codes for proteins sprinkled throughout our DNA; often they have no noticeable effect on our development, but they can leave clues to where our ancestors came from. Geneticists have found millions of these little variations, which can be shared with particular frequency among groups of people who have common ancestors. The only way to reveal the groups is by examining the variation in a high-dimensional space.

These are scatter plots that no one ever needs to see. They exist in vast number arrays on the hard drives of powerful computers, turned and manipulated as though the distances between the imagined dots were real. Data visualization has progressed from a means of making things tractable and comprehensible on the page to an automated hunt for clusters and connections, with trained machines that do the searching. Patterns still emerge and drive our understanding of the world forward, even if they are no longer visible to the human eye. But these modern innovations exist only because of the original insight that it was possible to think of numbers visually. The invention of graphs and charts was a much quieter affair than that of the telescope, but these tools have done just as much to change how and what we see. ♦

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Sample of Chart or Graph Essay

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A bar chart is a type of visual technique that enables its readers to easily recognize patterns or trends in the data set they are checking (Statistics Canada, 2009). A bar chart consists of rectangular bars whose length is proportional to the value it represents. A bar chart is used to compare two or more values that represent different conditions or options.

Bar charts are usually used when the data set is small. A bar chart can be horizontal or vertical. On looking at a bar chart, one can easily find the tallest or the shortest bar, compare two bars, check the change in one bar over time, etc. (SkyMark Corporation, 2009).

For the purposes of this assignment, the following bar chart is used which is a stacked bar chart, a special type of bar chart. This is one of the many charts used in the survey conducted by Infosys on Enterprise Architecture (Infosys, 2005).

The stacked bar graph can be especially helpful when it is required to show segments of totals (Brio Software, Inc., 2002) which in this case it represents how many respondents voted the focus area as critical and how many voted it as high.

Thus, using a single chart, one can convey a lot of information using stacked bar charts. However, if there were more segments, then it would have become difficult to analyze the items in each stack. With more items, it can become more complicated to analyze stack bar charts and hence are not frequently used.

The chart represents the most important focus areas of Enterprise Architecture. As can be seen from the chart, the bars are sorted according to the total number of votes for a focus area with the focus area that has received the highest total votes at the top.

Upon careful inspection of the chart, one can understand that although the focus area “Enterprise data architecture” is ranked as the second highest most important focus area, it is critical by less number of people as compared to “Regulatory compliance (e.g. SOX) and Risk” which is 7 th highest most important focus area. This is also highlighted in the text and justified that this is considered critical by more people as it is being externally imposed (Infosys, 2005).

Again, the focus area “Infrastructure renewal / consolidation” was considered as highly important focus area by maximum people as compared to the other focus areas.

The chart also brings out very clearly that none of the respondents considered “XML technologies” and “Grid computing” as critical focus areas of Enterprise Architecture.

While the stacked bar chart is very helpful in understanding the overall votes by respondents as well how many considered each focus area as critical or high, it does not give an idea of the total votes, it needs to be calculated (Klass, 2001). Also it does not tell how many respondents were there and what percentage of respondents voted for each focus area. The text provides information that there were 45 respondents in all.

Considering this, “Integration and Service Oriented Architecture” was voted by 32 respondents or 71% in all. Without the text providing that information, one could be misled into thinking that there may be 35 respondents only or perhaps much more. Also, this means that the respondents could choose more than one focus area and that there was no restriction on the number of focus areas that one could choose.

Brio Software, Inc. (2002). “Stacked Bar Charts “, Brio Software, Inc. Web.

Infosys Technologies Limited (2005). “Infosys Enterprise Architecture Survey 2005”, Infosys Technologies Limited. Web.

Klass, G. (2001). “How to Construct Bad Charts and Graphs “, Illinois State University. Web.

Sky Mark Corporation (2009). “ Bar Charts “, SkyMark Corporation. Web.

Statistics Canada (2009). “ Bar graphs “. Web.

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IvyPanda. (2018, June 24). Sample of Chart or Graph. https://ivypanda.com/essays/sample-of-chart-or-graph/

"Sample of Chart or Graph." IvyPanda , 24 June 2018, ivypanda.com/essays/sample-of-chart-or-graph/.

IvyPanda . (2018) 'Sample of Chart or Graph'. 24 June.

IvyPanda . 2018. "Sample of Chart or Graph." June 24, 2018. https://ivypanda.com/essays/sample-of-chart-or-graph/.

1. IvyPanda . "Sample of Chart or Graph." June 24, 2018. https://ivypanda.com/essays/sample-of-chart-or-graph/.

Bibliography

IvyPanda . "Sample of Chart or Graph." June 24, 2018. https://ivypanda.com/essays/sample-of-chart-or-graph/.

How To Write a Graph Essay

Julia barrus.

Graph essays present a written way for math students to interpret and explain information on a graph. There are many types of graphs, so it's important to understand the graph format and how to read the graph. After determining a way to read the graph and to interpret its information, you need to write an essay that presents the information. You will need expository writing skills and mathematical skills.

Interpret the graph information. For example, on a bar graph that details the quantity of rainfall per month during a 12-month period, read each bar and clearly indicate the method of measurement. Identify any labels such as those that indicate months. Write the information you have interpreted from the graph in clear sentences and in list form, such as "In August, there were 12 inches of rainfall."

Make a hypothesis about the information drawn from the graph. Analyze the graph to see what information you can deduce from the results. For example, "Based on graph information, it is reasonable to hypothesize that reduced snowfall in December translated to increased rainfall in July." If you have outside information that will help explain any patterns or inconsistencies you see, make note of the information so that you can include it in your essay.

Begin with a general introduction. Typically, introductions in expository essays give basic information such as what type of graph you will be writing about and the information it displays. Include five to eight sentences that describe general observations from the graph. Create three sub-topics that you can discuss in your body paragraphs. These sub-topics should explain your hypothesis. End the introductory paragraph with your hypothesis.

Write body paragraphs, taking care to verify that each paragraph supports your hypothesis. For a pie graph that displays information about student populations in inner city schools, the hypothesis might state, "85 percent of the student population receives free and reduced lunch because of poor economic conditions in the community." The body paragraphs would need to discuss student population data, free and reduced lunch statistics and how this information equates to poor economic conditions.

Conclude your essay with a paragraph that explains why your hypothesis is correct. Support your statements with evidence from the graph and your essay. Often, it helps to draw on outside data supported by the particular graph you analyze. Include resources that provide unbiased statistical or mathematical data so that your essay remains as objective as possible.

• 1 IELTSBuddy.com: Free Online Exam Preparation IELTS Writing:
• 2 Purdue OWL: Writing With Statistics

About the Author

Julia Barrus is a writer and teacher who has published with several online sources since 2008. Barrus has a bachelor's degree in English and a master's degree in secondary education curriculum and instruction with an endorsement in English from the University of Phoenix.

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Line graph example

Ielts line graph sample essay.

Home  »  IELTS academic task 1  »  Line Graph Sample Essay

Line graphs come up often in IELTS writing part 1. We will show you how to confidently answer questions about line graphs, helping you practice summarising the data and identifying trends.

For each line graph (or line chart) question, you will need to summarise the information by selecting and reporting the main features and make comparisons where relevant. You should write at least 150 words. To make the best use of your time it’s important to write with a clear structure, focus on the most important trend or trends, choose appropriate vocabulary and avoid common mistakes.

In this tutorial, we have a band range 6.5 / 7  academic task 1  IELTS line graph example essay graded by an  ex-IELTS examiner  (on our team of essay correctors). There are some great examples of useful feedback to help you improve your own line chart answers.

This line graph tutorial will help you: 

• Choose the right information 
• Use the right sentence structures 
• Decide on the perfect tense to use

Each criteria has been considered and r eviewed by one of the ex-IELTS examiners on our team. T he different sentences in the essay have been colour coded to correspond to the four  marking criteria  used by the  British Council / IELTS IDP .     At the IELTS podcast website, we also have an amazing essay correction service if you need help with your IELTS writing, so this is an example of the personalised feedback you could receive.

Make sure you understand what the codes mean:

• TR means task response – have you summarised the main information and made comparisons?
• CC is coherence and cohesion (does your essay ‘flow’ well, does it make sense?)
• LR is lexical resource (or vocabulary)
• GR means grammar

Therefore TR1 is the first comment about Task Response.

Example line graph question:

The line graph shows the average rainfall for three countries in Great Britain over a 12 month period in 2018.    Summarise the information by selecting and reporting the main features, and make comparisons where relevant. Write at least 150 words

The line graph reveals the information of rainfall in three countries: England, Scotland, and Wales for one year starting from January to December in 2018.

In England, the amount of rainfall in January was recorded just above 100ml, which slightly increased in the next month by 5ml, and dipped to 90ml in March. The trend of raising and falling continue for a couple of months and reached its lowest figure in the graph for the month of June at around 70ml. After that, it is predicted that the level of rain will fluctuate and will remain around 120ml in December.

In Scotland, the amount of rain in January was 125ml, dropped unexpectedly to its lowest level to 20ml in February, and rocketed in next month to its peak value of 135ml. In April, the amounts of rain were less than 50ml, then start went up slightly until June. Then after it is predicted to drop until September, the following month it will start climbing up to October, and it will be decreased to 50 ml before reaching to 105 ml in December.

In wales, the rainfall was 50ml in January increased slightly in February and decreased to its lowest point in April around 30ml. In next month went up sharply to 110ml and dipped by 50ml in June. It is predicted that the highest amount of rain will be in July at 125ml and went down just below the month of June’s record. It will remain constant for two months before reaching 105ml in November and it will dip down in December to 52ml.

Overall it can be clearly seen that in each country the amount of rain in January will be approximately same in the month of December.

Let’s look at the essay above with ex-IELTS examiner commentary on each section.

The line graph reveals the information of rainfall (LR1) in three countries: England, Scotland, and Wales for one year starting from January to December in 2018.

TR1 – A good opening paragraph. Effective use of paraphrasing and you have included the key information.

LR1 – This could sound more natural. Perhaps ‘shows information about rainfall’, ‘shows rainfall statistics’ or ‘shows how much rain fell…’?

In England (TR2),   the amount of the rainfall (GR1) in January was recorded just above 100ml, which slightly increased in next month by 5ml, and dipped to 90ml in March. The trend of raising (LR2) and falling continue (GR2) for a couple of months and reached its lowest figure in the graph for the month of June at around 70ml. After that, it is predicted that the level of rain will fluctuate and will remain (TR3) / (GR3) around 120ml in December.

TR2 – Good to adopt a systematic approach to presenting the information

GR1 – the amount of rainfall

LR2 – Check the difference between ‘rise’ and ‘raise’. GR2 – continued

TR3 – Make sure you report the information accurately.

GR3 – Stick to using past tenses here as all the information relates to 2018

In Scotland, the amount of rain in January was 125ml, dropped (CC1) unexpectedly to its lowest level to 20ml (CC2) in February, and rocketed (LR3)   in next month (GR4) to its peak value (LR3) of 135ml. In April, the amounts of rain were less than 50ml, then start went up (GR5) slightly until June. Then after (CC3)   it is predicted to drop until September, the following month it will start climbing up to October, and it will be decreased (TR4) / (GR6) to 50 ml before reaching to 105 ml (LR4) in December.

CC1 – You need to link your ideas together in an appropriate way. Perhaps ‘125ml, and this figure dropped…’?

CC2 – As before. Perhaps ‘… to its lowest level, 20 ml, in February’?

LR3 – Accurate use of some key language to describe graphs.

GR4 – in the next month GR5 – started to go up

CC3 – Take care with your use of linking words.

TR4 – As with the previous paragraph, make sure you report the information correctly.

GR6 – Stick to using past tenses as all the information relates to 2018.

LR4 – Omit the ‘to’ – ‘… reaching 105ml…’

In wales (GR7), the rainfall was 50ml in January increased (CC4) slightly in February and decreased to its lowest point in April (LR5) around 30ml. In next month went up sharply to 110ml and dipped   (LR6) by 50ml in June. It is predicted that the highest amount of rain will be in July (TR5) / (GR8) at 125ml and went down just below the month of June’s record. It will remain constant   (LR7) for two months before reaching 105ml in November and it will dip down in December to 52ml.

GR7 – Take care with your use of punctuation. Use a capital letter for the name of a country.

CC4 – You need to be careful with the way you link your ideas together. Perhaps ‘… in January and this figure increased…’?

LR5 – Very natural use of language.

LR6 – Good to incorporate a wide range of appropriate vocabulary.

TR5 – As before, you need to report the information accurately.

GR8 – As in the previous paragraphs, this is an inappropriate use of tenses.

LR7 – Despite the problems with tenses, this vocabulary is appropriately selected.

Overall it can be clearly seen that (CC5) in each country the amount of rain in January will be (TR6) / (GR9) approximately same in the month of December. (TR7)

CC5 – On the right track to introduce the main features of the graph.

TR6 – As before, there are problems with task achievement.

GR9 – Inappropriate use of tenses.

TR7 – I think this paragraph is intended to be your overview? If so, this would be a useful area to work on as it is one of the key differences between a ‘6’ and a ‘7’ for task achievement.

PROBABLE IELTS SCORE: 6.5 / 7.0

  Some final practical advice for answering line chart questions in your IELTS test:

• Do not start writing before giving yourself enough time to think. First decide the language you will need in your answer. Give yourself 5 minutes to look, think and plan.
• Study the line graph carefully: be clear about the topic and what each line represents.
• The labels on the axes will give you useful information. Look at the horizontal axis to understand the period of time shown, and the vertical or x axis to see how the data is shown (in centimetres, in dollars, in millions?)
• Check the time frames very carefully in the line and plan how time differences will affect your choice of verb tenses.
• What is the main trend or trends?
• The easiest way to make comparisons is by using superlatives in your answer. For example: comparing the largest and smallest amounts of rainfall by country or comparing the least and most expensive products over time.
• Say how two or more lines are related – do they both increase over time? Are any points connected?
• Finally, make sure you have included an overview!

Audio tutorial with transcript

You can download or listen to the audio version here:

| Direct Download Here | Stitcher | iTunes | Spotify | Soundcloud | Transcript |

IELTS task 1 line graph video tutorial

More useful IELTS Academic Task 1 lessons:

• Academic Task 1 Sample Essays
• How to describe a pie chart
• Bar Chart IELTS
• How to describe a map
• Describe an image 
• Describe a natural process
• How to describe a table
• How to paraphrase
• Line graph sample answer
• Marking criteria for Task 1
• Map vocabulary for IELTS Task 1
• How to describe a flow chart
• Essential skills for Task 1
• How to get band 9 for academic task 1
• How to describe a process diagram

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How To Write an IELTS Bar Chart Essay

There are 5 steps to writing   a good IELTS bar chart essay:

1)  Analyse the question

2)  Identify the main features

3)  Write an introduction

4)  Write an overview

5)  Write the details paragraphs

Use this simple planning process as you practice writing IELTS bar chart essays and you’ll have no problem remembering it in the exam.

Steps 1 and 2 of the planning process should take around 5 minutes. It is essential that you don’t miss these out as they are the key to writing a high-scoring essay.

On this page, I’m going to take you through the whole planning process step-by-step as we work on a practice question.

Before we begin, here’s a model essay structure that you can use as a guideline for all IELTS Academic Task 1 questions.

Ideally, your essay should have 4 paragraphs:

Paragraph 1  – Introduction

Paragraph 2  – Overview

Paragraph 3  – 1 st  main feature

Paragraph 4  – 2 nd  main feature

Now that we have all these tools we need, we’re ready to begin planning and writing our IELTS bar chart essay.

Here’s our practice question:

The bar chart below shows the sector contributions to India’s gross domestic product from 1960 to 2000.

Summarise the information by selecting and reporting the main features, and make comparisons where relevant.

Write at least 150 words.

Contribution as % of India's GDP

Source: EPW Research Foundation

Step 1 –  Analyse the question

The format of every Academic Task 1 question is the same. Here is our practice question again with the words that will be included in all questions highlighted .

The  bar chart  below shows  the sector contributions to India’s gross domestic product from 1960 to 2000.

Every question consists of:

• Sentence 1 – A brief description of the graphic
• Sentence 2 – The instructions
• The graphic – chart, graph, table, etc.

Sentence 2 tells you what you have to do.

You must do 3 things:

1.     Select the main features.

2.     Write about the main features.

3.     Compare the main features.

All three tasks refer to the ‘ main features ’ of the graphic. You  do not  have to write about everything. Just pick out 2 or 3 key features and you’ll have plenty to write about.

Our practice graphic is a dynamic bar chart. That is, it includes a timeline giving data from several different points in time.

So, for this question, we need to identify the main trends (that is, the general developments or changes in situation) in the three key sectors of the Indian economy – agriculture, industry and service – between 1960 and 2000.

Alternatively, a bar chart may be static with the data coming from one point in time, as in the example below. For this graphic, we would need to compare the different variables, that is, the different leisure activities favoured by Canadian boys and girls.

Step 2 – Identify the Main Features

The graphic in IELTS bar chart questions should not be difficult to interpret. Each question has been created to  test your language skills, not your mathematics ability.

All you are looking for are the main features. These will usually be the easiest things to spot. As we’ve just seen, the type of key features will depend on whether the bar chart is dynamic or static.

There will be lots of information in the graphic to help you identify them. Here are some useful questions to ask?

• What information do the 2 axes give?
• Is it dynamic or static?
• What are the units of measurements?
• What are the time periods?
• What can you learn from the title and any labels?
• What is the most obvious trend?
• Are there any notable similarities?

(I give more detail on how to use these questions, plus downloadable checklists for identifying the main features of all 7 different types of IELTS Academic Writing Task 1 questions, in the lesson on  How To Understand & Analyse Task 1 Questions .)

So, what main features stand out in our practice graphic?

Here's our practice IELTS bar chart again.

There are 3 main features/trends in this IELTS bar chart:

Main feature 1:  The contribution of the agricultural sector dropped steadily.

Main feature 2:  The contribution of the service sector increased each decade.

Main feature 3:  Industry remained static from 1980 to 2000.

The general trends you select will be the starting point for your essay. You will then go on to add more detail.

With just 20 minutes allowed for Task 1, and a requirement of only 150 words, you won't be able to include many details.

We’re now ready to begin writing our essay. Here’s a reminder of the 4 part structure we’re going to use.

Step 3 – Write an Introduction 

In the introduction, you should simply paraphrase the question, that is, say the same thing in a different way. You can do this by using synonyms and changing the sentence structure. For example:

Introduction (Paragraph 1): 

The bar graph illustrates the relative percentage contributions made by the agricultural, industrial and service sectors to the Indian economy between 1960 and 2000.

This is all you need to do for the introduction.

Ideally, key words such as ‘sector’ and ‘contributions’ should be replaced by synonyms but there aren’t any obvious words that could be used instead so it’s fine to repeat them. It’s important that your language sounds natural so never try to force in synonyms that don’t quite fit.

Step 4 – Write an Overview (Paragraph 2)

In the second paragraph, you should report the main features you can see in the graph, giving only general information. The detail comes later in the essay. You should also make any clear comparisons you spot.

This is where we write about the general trends. Here are the ones we picked out above.

Now form these ideas into two or three sentences with a total of around 40 words. State the information simply using synonyms where possible. No elaborate vocabulary or grammar structures are required, just the appropriate words and correct verb tenses.

For example:

Overview  (Paragraph 2) : 

Over the whole time period, the significance of agriculture declined steadily while services grew in importance decade by decade. A different patterned emerged for industry, which initially showed a slowly increasing percentage but then plateaued from 1980 onwards.

Step 5  – Write the 1st Detail Paragraph

Paragraphs 3 and 4 of your IELTS bar chart essay are where you include more detailed information about the data in the graphic. In paragraph 3, you should give evidence to support your first 1 or 2 key features. Don’t forget to make comparisons when relevant.

Here are our first 2 main features again:

And this is an example of what you could write:

Paragraph 3 :

In 1960, agriculture contributed by far the highest percentage of GDP, peaking at 62%, but it then dropped in steady increments to a low of 12% in 2000. The service sector, on the other hand, had a relatively minor impact on the economy in 1960. This situation changed gradually at first, then its percentage contribution jumped from 28% to 43% between 1980 and 1990. By 2000 it matched the high point reached by agriculture in 1960, showing a reversal in the overall trend.

Step 6  – Write the 2nd Detail Paragraph

For the fourth and final paragraph, you do the same thing for your remaining feature/s. We have one main feature left to write about.

Here’s an example of what you could write:

Paragraph 4 :

Industry remained a steady contributor to India’s wealth throughout the period. As a sector, it grew marginally from 16% in 1960 to exactly a quarter in 1980 then remained static for the next two decades, maintaining a constant share of the overall GDP.

Here are the four paragraphs brought together to create our finished essay.

Finished IELTS Bar Chart Essay

(188 words)

This sample IELTS bar chart essay is well over the minimum word limit so you can see that you don’t have space to include very much detail at all. That’s why it is essential to select just a couple of main features to write about.

Now use what you’ve learnt in this lesson to practice answering other IELTS bar chart questions. Start slowly at first and keep practicing until you can plan and write a complete essay in around 20 minutes.

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Ielts academic writing task 1 – all lessons.

IELTS Academic Writing  –  A summary of the test including important facts, test format & assessment.

Academic Writing Task 1  – The format, the 7 question types & sample questions, assessment & marking criteria.  All the key information you need to know.

Understanding Task 1 Questions  – How to quickly and easily analyse and understand IELTS Writing Task 2 questions.

How To Plan a Task 1 Essay  –  Discover  3 reasons why you must plan, the 4 simple steps of essay planning and learn a simple 4 part essay structure.

Vocabulary for Task 1 Essays  –  Learn key vocabulary for a high-scoring essay. Word lists & a downloadable PDF.

Grammar for Task 1 Essays   – Essential grammar for Task 1 Academic essays including, verb tenses, key sentence structures, articles & prepositions.

The 7 Question Types:

Click the links below for a step-by-step lesson on each type of Task 1 question.

• Table Chart
• Process Diagram
• Multiple Graphs
• IELTS Writing
• IELTS Bar Chart
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IELTS Bar Chart Sample Essay

Static or Dynamic?

Before writing an IELTS task 1 bar chart or line graph answer it is important that we analyse the question correctly. Taking a few minutes to do this will help us write a clear answer that fully responds to the question. Just what the examiner wants us to do.

The first thing we need to do is decide if the bar chart is static or dynamic. Static means that the data comes from one point in time. Dynamic means the data comes from more than one point in time.

Whether a chart is static or dynamic will affect the information we choose to include in our answer and the kind of language (tense, grammar etc.) we use.

If it is dynamic we will have to compare the different times and comment on the general trends over the time period.

If it is static we will have to compare the different variables, in this case countries, car price, GDP and time it takes for one person to buy a car.

Main Features

Every IELTS academic task 1 question asks us to ‘select and report the main features’.

This means that we have to not only pick the most significant information from the graph and include it in our essay, but also decide which information is not important and should therefore not be included in our essay. One of the biggest mistakes you can make in task 1 is including all the information you see.

So which information should you choose?

You should look for:

• highest/lowest values
• biggest differences
• similarities
• significant exceptions
• anything else that really stands out

There are 3 main features in this graph

1) It takes over 26 years for a Vietnamese person to buy a car.

2) Vietnam has the second highest average costs but the second lowest wages.

3) Cost of a car in Singapore is nearly 3 times the next most expensive.

I advise my students to follow a basic four paragraph structure for these kinds of questions.

Paragraph 1 

Paraphrase the question using synonyms.

Paragraph 2 

Provide an overview of the main features. No need to include any data in this paragraph, just tell the examiner what is happening in general terms. If you had to describe the main features in two sentences, what would you say?

Paragraph 3

This is where we get more specific and use data. Take 2 of the main features (from your overview) and describe them in detail using data from the chart.

Paragraph 4 

Simply do the same thing as you did in paragraph 3, but with two other main features (from your overview).

Sample Answer 

The graph compares the GDP per capita, cost of a Toyota Camry and approximate length of time it takes for 1 citizen to purchase that mode of transport in eight Asian countries.

Despite having the second lowest average yearly income, it costs more to buy this car in Vietnam than in all but one other Asian nation. It also takes significantly longer for a standard person to buy an automobile in Vietnam than in any other state in Asia. On the other end of the scale, Singaporeans have to pay nearly three times more for their cars than the Vietnamese and it takes them the least amount of time to afford a motor vehicle.

It costs $49,944 to buy a Toyota Camry in Vietnam, but this dwarfs the average yearly income per person at just $1,910. It would therefore take a normal man or woman 26.1 years to save up for that particular car.

This is in contrast to Singapore where it costs $126,245 for that model of motorcar, however the average salary is much greater at $55,182. This means that it generally takes just over 2 years for a typical individual from Singapore to acquire this vehicle.

(200 words) Band 9.

It should be noted that this is not a real IELTS task 1 question. This is just a chart that I saw on the internet, but it allowed me to make a very important point- you don’t have to mention everything on the graph. I only talked about 2 out of the 8 countries and I still wrote 200 words and answer the question fully. The key is finding the most significant data and not talking about anything else. Don’t worry, you won’t lose marks for not talking about everything, quite the opposite.

This graph is also good for demonstrating how important it is to vary your vocabulary. There were four words that could have been overused in this essay- car, average, country and people. Instead of repeating them over and over again I used synonyms to show the examiner I have a wide vocabulary and gain extra marks. Here are the synonyms:

Car- Toyota Camry- automobile- vehicle- motor vehicle- motorcar

Average- approximate- normal- typical- standard

Country- countries- nation- state

People- citizen- man or woman- individual

Next time you see a chart or graph in a newspaper, in a textbook or on the internet, think about what the main features are and what common words would you have to vary with synonyms.

I hope you have found these tips useful. If you have any questions, let me know below.

For more band 9 sample essays check out our task 1 sample essay page.

About Christopher Pell

My name is Christopher Pell and I'm the Managing Director of IELTS Advantage.

I started IELTS Advantage as a simple blog to help 16 students in my class. Several years later, I am very humbled that my VIP Course has been able to help thousands of people around the world to score a Band 7+ in their IELTS tests.

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