There is a story behind every number – data visualization brings such stories to life. Data visualization has grown over the years, and more data points have been brought into the mix. In the past, analysts were content with measuring data sets with one or two variables. But what if there are multiple variables, and every one of these is important? Well, that’s where line graphs come into play. And making line graphs is not as difficult as advertised.
Years ago, data visualization was pretty much simple. Analysts get to check the relationship (or correlation) between variables using visuals. Nowadays, there are lots of twists in the marketplace, and you get to see multiple variables in a single data set. To cater to these new changes, more innovative ways of measuring data were introduced – and making line graphs were one of such ways.
Line graphs don’t just show the relationship between variables, but it’s a reliable tool that showcases how one or more variables changes over time. It shows the progression of a variable over a period.
In this guide, you will discover what line graphs are, and how making line graphs could be the next step towards building a more robust data visualization strategy for your business. Finally, you will discover how to create line graphs using tools like ChartExpo.
Enough said! Here’s your first step in your journey to making line graphs.
Definition: Also known as line charts, line graphs show how the value of a variable changes over time. It can also be used to measure how the value of several variables change over time. Regardless of the number of variables involved, line graphs help you see how they change over time.
If you are working with a data set, and you see the phrase “over time”, that’s a clue to use a line graph. One of the primary benefits of a line graph is that they are efficient, simple, and easy to understand. Aside from using line graphs to measure changes over time, you can use the tool for other analyses like.
On the flip side, line graphs are not ideal.
Now you understand what line graphs are, and the benefits of making line graphs, you’ve got to take a look at the various components of a line graph.
A line graph transforms data into a visual masterpiece, offering clarity and insights to the readers. Let’s dissect the composition of a line graph and understand the significance of each element:
Just as artists have different techniques, there are distinct types of line graphs that convey information with precision. Each type has its personality, telling stories in unique ways.
The basic line graph is the fundamental storyteller in the world of data. It consists of a single line connecting data points, typically representing how one variable changes over time. This simplicity makes it perfect for illustrating trends and variations with clarity.
When the story involves more than one character, we turn to the multiple-line graph. It accommodates two or more lines on the same graph, allowing for direct comparisons between different datasets. The multiple-line graph excels in showcasing relationships and divergences between variables.
Line Graph Examples function like stacking layers of information, akin to building blocks. The stacked line graph accomplishes this precisely. Each line signifies a distinct category, and they layer atop one another to depict the cumulative sum. This visual layering accentuates not just individual values but also the comprehensive structure of the data.
Stepping away from smooth transitions, the step-line graph introduces a unique rhythm. Instead of connecting points with continuous lines, it uses horizontal and vertical steps. This method accentuates individual data points, making it useful for displaying data that changes abruptly.
For a touch of elegance, we introduce the spline line graph. Instead of straight lines, this type uses smooth curves to connect data points. The result is a graph with a more polished and visually appealing look. This graph type is particularly suitable for capturing gradual changes in data.
Line Graph Examples go beyond mere lines, as seen in the area line graph that emphasizes space. By shading the area below the line, it underscores the magnitude of values. This type is particularly effective at depicting volume or the cumulative impact of data, enriching the narrative with depth.
Imagine a stage with different acts. Comparative line graphs compare multiple lines, each with its scale. This type accommodates the complexity of multiple variables, allowing for nuanced analysis and interpretation.
Now you’re done with the basics, it’s time to get into the nitty-gritty of how to create line graphs using ChartExpo.
Are you tired of Excel’s data visualizations looking as exciting as a tax form instruction manual?
We get it; those uninspiring charts can be a buzzkill. But fear not; we have the antidote to Excel’s limitations in data visualization – ChartExpo.
No more settling for dull line charts. With ChartExpo, you can create vibrant, multi-line masterpieces effortlessly.
Benefits of Using ChartExpo
How to Install ChartExpo in Excel?
ChartExpo charts are available both in Google Sheets and Microsoft Excel. Please use the following CTA’s to install the tool of your choice and create beautiful visualizations in a few clicks in your favorite tool.
Suppose you want to analyze the monthly website visitors, advertising spending, & bounce rate data below.
Months | Visitors | Advertising Spending (in $) | Bounce Rate (%) |
Jan | 84551 | 1484 | 36 |
Feb | 103091 | 2096 | 43 |
Mar | 102740 | 1724 | 33 |
Apr | 80207 | 1287 | 49 |
May | 97442 | 1255 | 47 |
Jun | 88406 | 1568 | 46 |
Jul | 60540 | 1286 | 31 |
Aug | 101165 | 1539 | 31 |
Sep | 74958 | 1125 | 31 |
Oct | 78681 | 1811 | 36 |
Nov | 66559 | 1400 | 48 |
Dec | 89007 | 1146 | 39 |
Follow the steps below to learn about Excel line graphs with multiple lines with Chart Expo for analysis.
The bounce rate fluctuates across different months of the year. In March and July, the bounce rate was relatively low at 33% and 31%, respectively, indicating higher user engagement during these periods. On the other hand, in April and November, the bounce rate peaked at 49% and 48%, respectively. This suggests potential concerns with the website’s content or user experience during these months.
The number of visitors fluctuates throughout the year, showing a seasonal pattern. February and August experienced the highest visitor volume, with 103,091 and 101,165 visitors, respectively. These peaks could be attributed to targeted marketing campaigns or seasonal factors. Conversely, July sees the lowest number of visitors, with 60,540, which could be influenced by vacation periods.
There is a correlation between advertising expenditure and the number of visitors. The higher spending observed in February and October partially corresponds to the peaks in visitor counts during those months. However, the spending in September and December remains comparatively low.
Data visualization comes with lots of possibilities – and there are countless types of charts you can create with line graphs. Before getting started, you’ve got to do your homework. Yes, identify the right chart that is suitable for your data.
Regardless of how complex or intuitive your data is, there is a data visualization tool to help you create a visually appealing line graph. Making line graphs is tough – but you can skip the tedious process that goes into maneuvering lots of connections by merely using ChartExpo for Google Sheets add-on.
Using ChartExpo to plot line graphs is pretty straightforward – after all, there are various types of built-in line charts, and with simple clicks, you get to create compelling charts within minutes.
One of the importance of a line chart is that you get to see the overall trend with little or no room for error. Let’s say you want to check the performance of a company in the previous decade. First, you’ve got to gather data like the one shown below.
Year | TV | Mobiles | Sound System |
2015 | 700 | 1500 | 1000 |
2016 | 600 | 1400 | 900 |
2017 | 700 | 2000 | 1300 |
2018 | 1200 | 1800 | 800 |
2019 | 980 | 1900 | 1100 |
2020 | 700 | 2500 | 1200 |
Sometimes, the trends in a chart are not always defined, which leads to confusion. But that’s not the case with a line graph. With a multi-series line chart, everything is defined – and you get to closely monitor the trends over a specified time.
Let’s say you’ve got the data below and you want to analyze your online store sales in year 2020, here is what you need to do.
First, compile the data in a table as shown below.
Year | Sales |
Jan | 500 |
Feb | 550 |
Mar | 800 |
Apr | 600 |
May | 850 |
Jun | 1100 |
July | 900 |
Aug | 950 |
Sep | 1150 |
Oct | 1085 |
Nov | 1175 |
Dec | 1300 |
Let’s say you are one of the workers in a healthcare facility, and you’ve got survey data below for analyzing the positive and negative cases of some health issues, here is how to handle it. First, you’ve got your survey data as shown below.
Years | NEGATIVE | POSITIVE |
Jan 2020 | 50 | 50 |
Feb 2020 | 60 | 40 |
Mar 2020 | 10 | 90 |
Apr 2020 | 70 | 30 |
May 2020 | 45 | 55 |
Jun 2020 | 95 | 5 |
July 2020 | 25 | 75 |
Aug 2020 | 20 | 80 |
Sep 2020 | 38 | 62 |
Oct 2020 | 29 | 75 |
Nov 2020 | 30 | 90 |
Dec 2020 | 55 | 35 |
Checking the covid-19 test results is quite easy from the chart. The red part shows the negative test reports, while the green part shows the positive test reports. This way, you can easily study your data with ease. But remember, having negative test is a good sign and having positive COVID-19 is a bad sign so you can flip the colors to show the same chart with more meanings.
Let’s say you’ve been running a PPC marketing campaign for some time, and you need to analyze some metrics like conversions, impressions, and clicks. All you need to do is create compelling visuals using a data visualization tool.
But first, you need to gather data like the one below.
Date | Impressions | Clicks | Conversions |
05/01/2020 | 4141 | 411 | 0 |
05/02/2020 | 6380 | 601 | 2 |
05/03/2020 | 11541 | 1046 | 7 |
05/04/2020 | 12312 | 1256 | 8 |
05/05/2020 | 16406 | 1504 | 6 |
05/06/2020 | 17765 | 1807 | 5 |
05/07/2020 | 24532 | 2224 | 10 |
05/08/2020 | 29016 | 2345 | 11 |
05/09/2020 | 29122 | 2297 | 8 |
05/10/2020 | 27125 | 2164 | 9 |
05/11/2020 | 26783 | 2280 | 6 |
05/12/2020 | 25942 | 2086 | 16 |
05/13/2020 | 27127 | 2320 | 6 |
05/14/2020 | 24548 | 2111 | 9 |
05/15/2020 | 23448 | 2115 | 38 |
05/16/2020 | 23408 | 2065 | 92 |
05/17/2020 | 21473 | 2075 | 90 |
05/18/2020 | 20959 | 1939 | 82 |
05/19/2020 | 15710 | 1437 | 64 |
05/20/2020 | 21221 | 1964 | 104 |
05/21/2020 | 20317 | 1740 | 80 |
05/22/2020 | 16431 | 1548 | 76 |
05/23/2020 | 16785 | 1376 | 78 |
05/24/2020 | 17247 | 1526 | 82 |
05/25/2020 | 17851 | 1620 | 83 |
05/26/2020 | 6752 | 629 | 32 |
05/27/2020 | 16015 | 1569 | 70 |
05/28/2020 | 13689 | 1369 | 100 |
05/29/2020 | 12416 | 1123 | 100 |
05/30/2020 | 16874 | 1399 | 115 |
05/31/2020 | 18146 | 1324 | 97 |
If you take a close look, you will gain lots of insights from this chart. From this data visualization, you get to analyze lots of metrics and trends by merely looking at the chart. It eliminates the stress that comes with flipping through multiple charts just to gain insight and draw a conclusion from the data trends. Everything you need is showcased on a single screen.
Let’s say you manage a clothing brand, and you want to analyze your total purchase from your visitors. All you’ve got to do is gather some data like the one shown below.
Day | Total Visitors | Converted Visitors |
05/01/2020 | 280 | 2 |
05/02/2020 | 220 | 4 |
05/03/2020 | 210 | 1 |
05/04/2020 | 180 | 5 |
05/05/2020 | 190 | 1 |
05/06/2020 | 230 | 3 |
05/07/2020 | 270 | 5 |
05/08/2020 | 230 | 10 |
05/09/2020 | 210 | 2 |
05/10/2020 | 225 | 4 |
From the chart above, you get to easily compare the number of visitors with the total purchase made. This way, analyzing the shop performance will be quite easy.
Let’s say you opened a new institute in the United States, and you want to monitor the number of students gaining admission, all you’ve got to do is gather some data like the one below.
Months | Admission Count |
Jan | 45 |
Feb | 66 |
Mar | 57 |
Apr | 84 |
May | 64 |
The same chart can be used in combinations and is best suited for small spaces to show some Sparkline trends. E.g if you have detail of admission of each month you can show the data like below:
From the chart above, you’ve got to check the trends for the admission of new students in different months. It’s a great way of analyzing the performance of the institute.
If you manage a restaurant and you want to track the sales of two products (maybe coffee and ice cream) to determine the product that generates the most revenue. All you’ve got to do is use a dual-axis line chart to track the product for the 24-hour period you’re open.
But first, you’ve got to gather data similar to the one displayed below.
Hour of Day | Ice Cream Sale | Coffee Sale |
0 | 22.23 | 9.1 |
1 | 19.87 | 22 |
2 | 18.22 | 7.6 |
3 | 27.67 | 7.3 |
4 | 22.7 | 8.5 |
5 | 15.76 | 8.4 |
6 | 16.85 | 19 |
7 | 17.54 | 9.2 |
8 | 16.14 | 8.6 |
9 | 16.33 | 8.7 |
10 | 16.43 | 8.9 |
11 | 16.34 | 8.5 |
12 | 16.21 | 8.7 |
13 | 16.52 | 8.9 |
14 | 16.42 | 8.9 |
15 | 16.17 | 16 |
16 | 15.57 | 8.6 |
17 | 16.48 | 9 |
18 | 16.34 | 8.8 |
19 | 14.99 | 8.9 |
20 | 17.26 | 9.8 |
21 | 13.67 | 15 |
22 | 12.32 | 9.3 |
23 | 11.28 | 8.6 |
From the chart above, you can take a look at the revenue trends for ice cream and coffee sales. It also helps you to pinpoint the time of the day you are most likely to generate sales in each category.
You can use dual axis line and bar chart, you can also called it a combo chart to show your different data in a single chart
To analyze your data using a dual axis line and bar chart, you’ve got to select your data and provide the information for the dimensions and metrics.
Line Graph Examples can sometimes be as challenging to interpret as deciphering a doctor’s handwriting. But worry not; here’s a guide to mastering the art of reading a line graph with finesse.
Ideally, a making line graph should have one variable plotted on the vertical axis, while a second variable plotted on the horizontal axis – and variable on the horizontal axis has to consist of continuous values. Here is the thing, line graphs are used to measure changes within such plots.
Typically, the measurement on the horizontal axis has to be on regular intervals, and the variables should consist of continuous values. Most times, these variables are temporal, and they generate observations in specific time intervals like every hour, day, week, or month. These interval choices are not inherent in the data set. The analyst, most times, is the person to choose the right time interval for the data set.
The variables on the vertical axis are also defined by the intervals of the horizontal axis, and the analyst has to report the numeric values of the second variable that falls in each interval. These values could be the average value in each interval.
If you want to compare trends between series, then you’ve got to plot multiple lines in the line chart. These comparisons could be used to observe a breakdown of data across various subgroups.
For emphasis, not every kind of data can be represented using making line graphs. Here are the kinds of data that can be represented on a line graph.
By now, you probably know that a line graph could have continuous data both on the horizontal (x-axis) and on the vertical (y-axis) dimensions. You get to see the value of the measured variable on the y-axis, while the x-axis shows when the variable is being measured – it could be represented chronologically or tied to some independent metrics.
To get the most out of a line graph, there has to be a solid correlation (or relationship) between successive points on the graph. If you manage a store, you could use a line graph to show your product sales data for each month. However, it would be somewhat impossible to use a line graph to show differences in various product sales. To demonstrate the sales of various products, you are better off with a bar chart.
Making line graphs is not all juicy. Yes, there are some benefits as well as disadvantages of using a line graph. Here are some advantages and disadvantages of using line graphs.
Here are some benefits of making line graphs.
Enough of the benefits, here are some disadvantages (or limitations) of a line graph.
Having a good grasp of the benefits and limitations of making line graphs is not enough. If you want to use the chart, you’ve got to understand the best practices for using the chart.
Here are some things you must have in mind while making line graphs.
The interval (or bin size) is arguably the most important piece to consider while making line graphs. If your data is temporal, measurements that are too broad will make it somewhat difficult to trace your data trends. It could also hide useful signals in the chart. However, measurement intervals that are too short could cancel the signals, leading to noise. You see, you should maintain a balance when it comes to choosing measurement intervals.
Technically, it’s possible to plot too many lines on a line graph – but that doesn’t mean you should do it. There is nothing wrong with maintaining a balance when it comes to the number of lines you plot on a line graph.
As a general rule of thumb, you should limit yourself to at most five lines when it comes to using a line graph. Anything more than that could make your line graph somewhat confusing to read.
That’s not to say you can’t plot multiple lines in a line graph.
Yes, multiple lines can be plotted in a line graph if your lines are properly separated. This way, tracking your values will be easy.
An Excel line graph with multiple lines is a dynamic lens through which you can scrutinize and interpret your data. Let’s take a closer look at the reasons why it is useful:
Moving on, here are common ways of misusing a line graph.
Sometimes, the line graph misses information of some bins, and if adequate care is not taken, these gaps would be read as phantom values, especially if the lines are not composed of distinct dots. If the number of points to the plot is scanty, then showing just lines won’t cut it – you’ve got to show all the points as well.
On the flip side, showing all the points could make it somewhat difficult to interpret the plot. To avoid such a situation, you’ve got to include a gap in the line. The gap shows where the values are missing.
If you’ve used online charting tools like histogram and bar charts, you probably know that the vertical axis starts with a zero baseline. With the line graphs, the case is somewhat different – there is no need for a zero-value baseline!
Here’s the thing, a line graph is more focused on identifying changes in value over time. It does not focus on measuring the magnitude of such values. You see, using a zero-value baseline may not be ideal at all times while using a line graph. Therefore, if adjusting the vertical axis range could make it easy to identify changes in your data values, go for it.
Typically, a straight line segment is used to connect one point from the other in a line graph. If you are not disciplined enough, you may fall into the aesthetic temptation of trying to link all points in the line graph using a smooth curve. Yes, using a smooth curve could be aesthetically appealing, but it’s not ideal when it comes to linking points in a line graph.
A line chart displays how information changes over time. Creating it is simple – all you’ve got to do is plot a series of points and connect them with a line. Line charts are arguably the best tools for tracking changes in a data set over time.
Line charts are used for tracking changes over a period of time – it could be a long or short period. If you want to track small changes over time, then line graphs are probably your best shot. Yes, bar charts are not ideal for tracking small changes in any data set. Furthermore, you can use line graphs to compare changes in various data sets over the same time frame.
Creating and analyzing line graphs can be done using ChartExpo. With ChartExpo, you can easily create line graphs within minutes by merely clicking on your computer screen. Yes, no coding skills are required.
Making line graphs is one of the most reliable ways of monitoring the correlation (or relationship) between multiple variables. With simple clicks, you can easily make line graphs in minutes using ChartExpo.
Regardless of how complex or simple your data is, data virtualization is a reliable way of representing it. This way, you get to pass the right message that resonates with your target audience.
Years ago, you had to use lots of spreadsheets to represent multiple data. Well, those days are over – and you get to do that by merely using visualization tools like the line graph.
One more thing…
To get started, you’ve got to generate lots of data by conducting solid surveys. Next, translate the raw data into digestible information using line graphs.
Now you understand what line graphs are, and the importance of making line graphs, what kind of data will you be representing using the tool?
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