The pop-up menu appears when you right-click a Visual Discovery control in the analytical dashboard. These options do not appear when you are developing in HTML Composer. HorizontalIs the orientation of the graph. If Horizontal is checked, the orientation of the graph is horizontal. If it is not checked, the orientation is vertical. Show unselectedShows unselected data in gray. If Show Unselected is not checked, unselected data is omitted from the display. To add unselected data, either turn on Show Unselected in the Properties dialog box, or use the right mouse button menu in the view and choose Select All. Show Goal LinesShows goal lines on the graph. See Using Goal Lines. AnimateSelect this option to put a single component or a perspective into a mode where its glyphs are automatically colored and not colored in sequential order. See Animating Data. Primary OrderDisplays the items in one of the following orders: Original Order, Label, Size, Total Selected, or Percent Selected. See Selecting Primary and Secondary Order. Label ModeSets the label mode. See Displaying Labels. Label 'name'Adds or removes the label from the selected data item. Create Goal LinePlaces a goal line on the graph. See Using Goal Lines. Full SizeShows text in a normal, readable size. Fit AllShows the entire table in the current window. If there are fewer table rows than will fit in the window at full size, then the lines are shown in a larger font with more spacing. If there are more lines than will fit, then they are reduced to a smaller size, with a minimum of 1 pixel high. If all table rows does not fit at 1 pixel high, then lines are overplotted to allow all to fit. When values are overplotted, the line that is the longest or with the hottest color is drawn on the top, obscuring all others under it. One Line/PixelDraws text as one-pixel-high lines. This is helpful when you need to reduce the size of the text to see more of the data. Depending on how many lines you have in your table, this may still require paging to see all rows. UndoReverses the previous action. You may repeatedly undo actions retained in the history file for your current session by selecting Undo over and over again. A description of the previous action appears on the pop-up menu. If you have performed no action, Undo is not available for selection and no action appears to the right of the word Undo. RedoRestores the previous undo action. If you have performed no action, Redo is not available for selection and no action appears to the right of the word Redo. Select AllSelects all of the items in the graph. When you choose Select All, any previous selections are ignored. Selection state returns to the original setting. Unselect AllWhen selected, all of the items become unselected. All items appear in the unselected color (gray, by default) or are hidden in the graph (if hide unselected is active). Reverses the selection state of items. Selected items become unselected and unselected items become selected. Exclude UnselectedExcludes (temporarily removes) items from the graph. Restore ExcludedRestores the items you excluded. If you accidentally excluded the unselected, this menu option restores those excluded items. Save ImageEnables you to save the graph to a GIF or JPEG file. Copy ImageEnables you to copy the selected component and paste it to another file. PropertiesTakes you to the collection of tabs available for the respective visualization component. Common tabs include Data, Selecting, and Colors. A typical line graph will have continuous data along both the vertical (y-axis) and horizontal (x-axis) dimensions. The y-axis usually shows the value of whatever variable we are measuring; the x-axis is most often used to show when we measured it, either chronologically or based on some independent variable (e.g., as we rev our old car’s engine, we measure the decibel level at different RPM). While some line graphs do not use continuous data on the x-axis (particularly slopegraphs and parallel coordinates diagrams, which are specialized variants of line graphs), what we absolutely can’t use on our x-axis is data that doesn’t have any meaningful relationship among the categories shown. Let’s say we have a list of the first six months of 2020: January, February, March, April, May, June. It would feel wrong to list them in any other order, because they are continuous and have an intrinsic order. January 2020 leads to February 2020, which leads to March 2020, and so on. Let’s also say we have a list of types of produce: apples, pears, limes, lemons, dates, grapes. Unlike our list of months, one kind of produce doesn’t necessarily lead to the next. We could order them alphabetically, by size, by color, or randomly, and it wouldn’t feel unusual, because they have no intrinsic order, and are not continuous—they are categorical.
A line graph—also known as a line plot or a line chart—is a graph that uses lines to connect individual data points. A line graph displays quantitative values over a specified time interval. In finance, line graphs are commonly used to depict the historical price action of an asset or security. Line graphs can be compared with other visualizations of data including bar charts, pie charts, and (in trading) candlestick charts, among others.
Line graphs use data point "markers," which are connected by straight lines. These data points, connected by straight lines, aid in visualization. While line graphs are used across many different fields for different purposes, they are especially helpful when it is necessary to create a graphical depiction of changes in values over time. Line graphs are often used in finance to create visual representations of values over time, including changes in the prices of securities, company revenue sheets, and histories of major stock indexes. They are also useful for comparing different securities. In investing, specifically with respect to the field of technical analysis, line graphs are used by investors to visualize trends, which can greatly aid them in their analyses. There are some limitations to line graphs. For example, line graphs often lose clarity when there are too many data points. It is also easy to manipulate them visually in order to achieve certain effects. For example, the apparent degree of change can be visually manipulated by adjusting the range of data points on the axes.
Line graphs can be constructed manually or by using software such as Microsoft Excel. The latter greatly improves the speed and accuracy of the end product. Line graphs consist of two axes: x-axis (horizontal) and y-axis (vertical). Each axis represents a different data type, and the points at which they intersect is (0,0). The x-axis is the independent axis because its values are not dependent on anything measured. The y-axis is the dependent axis because its values depend on the x-axis's values. Each axis should be labeled according to the data measured along that axis. Then, each axis should be divided in appropriate increments (e.g., day one, day two, etc.). For example, if measuring the changes in a stock's prices for the previous two weeks, the x-axis would represent the time measured (trading days within the period), and the y-axis would represent stock prices. When using line graphs to track the price of a stock, the data point most commonly used is the closing price of the stock. For example, assume that on day one of trading, a given stock's price was $30, resulting in a data point at (1, $30). On day two of trading, the stock's price was $35, resulting in a data point at (2, $35). Each data point is plotted and connected by a line that visually shows the changes in the values over time. If the value of the stock increased daily, the line would slope upward and to the right. Conversely, if the price of the stock was steadily decreasing, then the line would slope downward and to the right. There are three main types of line graphs. Although each type is fundamentally rooted in the same principles, each has its own unique situation where it is best to implement and use. A simple line graph is the most basic type of line graph. In this graph, only one dependent variable is tracked, so there is only a single line connecting all data points on the graph. All points on the graph relate to the same item, and the only purpose of the graph is to track the changes of that variable over time. This graph cannot be used to compare the variable to another variable because only variable is charted. In the example below, the x-axis is time and the y-axis is the year-over-year change in price for all consumer goods in the United States. This graph of the Consumer Price Index shows the annual rate of inflation and, since it is analyzing just one set of data (all items), there is only one line. Consumer Price Index, All Items.Bureau of Labor Statistics In a multiple line graph, more than one dependent variable is charted on the graph and compared over a single independent variable (often time). Different dependent variables are often given different colored lines to distinguish between each data set. Each line relates to only the points in its given data set; lines do not cross between dependent variables. For example, the line graph below shows the Consumer Price Index again. However, this graph shows the change in price for three different categories: medical care (red), commodities (green), and shelter (blue). In this graph, we can see the growth in price for commodities was higher than the other two categories in July 2022. However, shelter or medical expenses were typically the groups that experienced higher inflation over the past decade. Consumer Price Index, Select Categories.Bureau of Labor Statistics A compound line graph uses multiple variables similar to a multiple line graph. However, the variables are often stacked on top of each other to show the total quantity across all variables. This not only informs users of the relationship between each of the variables, but it informs of how the total changes as well. In the example below from the Environmental Protection Agency (EPA), there are five dependent variables that range from abnormally dry land areas to exceptional drought areas. The most extreme drought data was graphed first, and any empty space under that line graph was shaded dark red. Then, subsequent sets of data were plotted after, with the empty area below each of those lines shaded their respective colors. In total, this shows the relationship between drought descriptions as well as the total percent of U.S. land area in these categories by year. EPA Drought Measurements, 2000-2015.Line graphs may vary depending optional features or formatting. The highest-quality, easiest to understand line graphs have the following characteristics:
Line graphs may have a title above the graph to succinctly explain what the graph is depicting. Unless you provide a user with written context, the user will often rely on the title to better understand what data is being pulled in. The title may specifically call out a timeframe or limits to the data (i.e. an appropriate title for the compound line graph could be 'Level of U.S. Dry Land By Year, 2000-2015'). The legend explains what each dependent variable is and how to distinguish different sets of data. In the example above, each dependent variable is marked with its own color. The box that explains what each color means is the legend. Each item of data on a line graph is a reference to a different source that ties the dependent variable to an independent variable. This is the information on your graph; it is the item that creates the dots that get connected to form the lines on your chart. In some examples as seen above, there may be multiple sets of data combined into a single graph. To ensure data is protected and accurate, companies may have specific data integrity analyst or similar positions to monitor database activity. The x-axis is the set of information that runs along the horizontal, flat portion at the bottom of the line graph. In most line graphs, the x-axis will be related to time, whether it is the different months in a year or the number of weeks that have passed since a product launch. The y-axis is the set of information that runs along the vertical, left-side of the graph. Some iterations of line graphs have this set of information on the right. In any case, these numbers count the items being measured. The graph may start at zero, though there are instances where it makes more sense to start at a higher number. Last, we have the line. The line connects all data points within a single dependent variable. This line's movement shows the increase and decrease of information across time. It can also easily be compared against other lines as long as all data sets are being measured over similar periods of time. Though overly simplified, this line can communicate to management what actions should be taken to improve operations or strategic planning.
Want to display multiple sets of data but one set of information is more suitable as a bar chart? Programs such as Excel and Google Sheets can produce combined charts where one dependent variable is shown as a bar graph and another dependent variable is shown as an overlying line graph. You can use a line graph in Excel to display trends over time. In Excel, line graphs are appropriate if you have text labels, dates, or a few numeric labels on the horizontal axis (x-axis). Here are the steps to create a line graph in Excel. (If you are using numeric labels, empty cell A1 before you create the line graph):
Different data visualization tools are best used for specific purposes, and a line graph is no exception. Depending on the underlying data, a line graph is best for:
Line graphs are used to track changes over different periods of time. Line graphs can also be used as a tool for comparison: to compare changes over the same period of time for more than one group.
Line graphs are useful in finance because they are very effective at creating visual representations of trends over time. For this reason, they are often used to depict how a stock is performing over a specific period of time.
A line graph may be a simple line graph, multiple line graph, or compound line graph. Each type of graph has a varying degree of dependent variables and how the user wishes to display the relationship between these variables.
Line graphs can be highly customizable in terms of title, labels, markers, style of line, and other non-essential features. However, all line graphs must have an x-axis (independent variable), a y-axis (quantity of dependent variable), and input data (dependent variables). The data points for each dependent variable are marked on the graph are connected by a line. When analyzing data over time, one of the best graphical depictions of data is the line graph . A line graph often uses time as its x-axis and a numerical quantity on its y-axis. When data points are marked on the chart, all data points within a single dependent variable are connected with a line, making it very useful tool for analyzing changes over time for one or more variables. |