Bar Chart: Data Visualization in Python, R, Tableau and Excel

bar charts are one of the most popular way to visualize your data you’ll be hard pushed not to find a bar chart during corporate business meetings science seminars or even during news broadcasts the reason for that is that bar charts are among the clearest and most straightforward visual representations of information as such they are also easily interpreted by an audience irrelevant of their technical qualifications or background that’s why it is likely that you will also be using this particular form of visualization regularly therefore mastering the bar chart will be one of the most fundamental skills you gain throughout this course we’ll create a bar chart using data about used cars we have a data set containing two columns the first represents the car brand while the second the number of car advertisements of that brand this data set is rather simplistic but extremely fitting for an introduction into charts rest assured that even if you’re a beginner in the field you’ll quickly get used to creating and styling charts we’ll also discover the excel tableau python and r built-in functionalities that help you create bar charts with ease exciting stuff can’t wait to kick off here we’ll discover how to create and style our charts so they look professional and ready for any presentation without further ado let’s get started we’ll go ahead and open our file called bar chart data our data contains the following two columns brand and car listings all right we’ll invest a bit of time in formatting our excel sheet to achieve a professional look we’ll keep the raw data in this sheet and instead work on our graphic on a separate excel sheet let’s start by selecting any cell containing data then by pressing ctrl and a we can grab all cells from the table let’s copy them with ctrl and c and place them onto a new sheet we won’t be placing the data directly into the first cells as they will be reserved for formatting instead let’s select the cell b3 and right click on it from the drop down menu we choose the first paste option alternatively you can use the shortcut control and v to paste the data so far not much of a difference but we’re only just beginning let’s remove the grid lines on this sheet by selecting view from the top ribbon in excel and deselect grid lines from the show panel excellent let’s also change the font and font size of our sheet we’ll click on a1 or any empty cell and then press ctrl and a this time all cells in our sheet have been selected now from the home section we can change the font to arial and reduce the font size okay the next step is to make the column names more prominent compared to the rest of the table let’s select cells b3 and c3 and bold them we’ll also click on the grid lines icon and select a thick bottom border cool let’s reduce the width of the a column as we don’t require all that empty space so let’s make it around 30 pixels wide in cell b1 we’ll type in a title for our sheet which will be used cars since this is a title it should stand out from the other elements we can increase its font size to 12 and bold it lastly let’s rename the sheet itself to bar chart great now we can confidently begin creating the actual bar chart let’s select both columns then go to the top ribbon in excel and click on insert the charts panel has a variety of charts we can use to display our data not only that but there is also the recommended charts section through it excel suggests different visualizations we can utilize based on our data let’s try it out all right perhaps unsurprisingly the first option we are presented with is a bar chart in excel this type of chart is referred to as a clustered column the next option

is a horizontal bar chart where the bars are shown horizontally on the y-axis in excel it’s clustered bar clustered signifies that we can display multiple data series as columns stacked next to one another note that with a simplified data set such as ours the output of several different types of charts will be identical either way for this lecture we’ll settle on the basic one clustered column then we click ok and just with a few clicks we have our chart it really couldn’t be simpler on the x-axis we have car brands and on the y-axis the number of cars sold we’ve got seven columns in total representing the number of used cars sold by each one of the seven car companies so far so good now although this chart is displaying the correct information we can still improve its appearance data visualization is not only about creating a chart but also styling it in a compelling way therefore let’s spend some time on that too with each visualization we create in this course we’ll aim to reach a professional graph with some widely accepted dimensions and colors first we’ll change the chart size a chart of this size is optimal for this sheet the second item on the agenda is to change the font we’ll click on the home button and choose the arial font let’s also reduce the font size to eight what’s next now we’d like to remove the fill of the chart and its borderline let’s left-click on our chart and select format chart area be careful not to select an element of the charts such as the plot area or the axis okay from this newly appeared ribbon we can control the fill for the background color or the border for the chart’s border for a professional presentation clean design works best therefore our chart will have no fill and no border line all right we’re making great progress we’re now ready to continue with styling specific chart elements it is only logical we start by focusing on the star of the show the bars one idea is to alter their bin size if we click on one of the bins on our chart it doesn’t matter which one we see that all are automatically selected alternatively if we wish to select only one of the bins in excel we need to double click on it okay if we right click on any bin and choose format data series we’ll get a variety of formatting options let’s reduce the gap between each bar and make it around 95 percent great what’s next generally data visualizations convey insights through shape and color while the shape is somewhat restricted by the nature of the data the color is up to us choosing a color for our visualizations is of utmost importance and should be thought through carefully let’s go ahead and pick a nice color for our chart by selecting the first icon in the menu displaying a bucket we can alter the color to a dark blue it is a very universal color that fits nicely in any presentation what else currently we can’t determine the exact value of each column to remedy that we’ll insert data labels we need to right click on a series element and opt for add data labels let’s bold them as well cool we now have the exact amount each company has sold while we’re at it we should address the y-axis and add a label there to do so we select chart design from the top excel ribbon here we can select different chart elements the one we’re looking for is a y-axis label which we add by selecting axis title and then primary vertical horizontal is for the x-axis excellent let’s name it number of listings and bold it make sure the color is actual black

one minor change regarding the y-axis the y-axis ticks are a bit crowded at the moment ticks if you’re wondering refers to the numbers on the axis to address the issue let’s right-click on the axis and select format axis here under units we can select major to be 200 instead of 100 thereby having the number of ticks all right it’s time to format the graph’s title let’s double-click on the title area and choose a meaningful title for our chart cars listings by brand again since this is the title we want it to stand out on that account we’ll increase the font size to 12 and also have it in bold for this particular chart aligning the title on the left hand side of the visual is a good option great now our chart is ready it not only displays the relevant information but also looks professional the last remaining step is to save our work currently our workbook is a csv file which is intended to contain files of data and not graphics or charts if we close excel right now and reopen it our chart will disappear and will not be saved this is extremely important so i’ll repeat that if we close excel right now and reopen it our chart will disappear and will not be saved therefore it is crucial to save our file under a different extension let’s go to file and save as in the first box we’ll type in the name of the file in our case it will be used cars bar chart when we select the second box a drop-down menu appears showing us the different extension files which excel supports we’ll opt for an excel workbook under the extension xlsx and hit the save button good job everyone we’ve successfully created our first bar chart in excel here we’ll get familiar with the tableau interface and use its capabilities to make and style our very own bar chart let’s get started the first thing we’ll need to do is connect to our data source in our case we have our car data in a text file with the extension csv all right let’s select the connect to a text file option and choose the file bar chart data csv ok in this area of tableau we can explore the data set we can join columns together or filter the data but the status that is really small so we won’t spend a lot of time here yet tableau is quite intuitive and beginner friendly therefore we might as well follow its instructions in this pop-up suggesting we go to a worksheet let’s click on sheet1 alright we blindly followed tableau’s lead and ended up in sheet1 now before we go any further let’s spend a second to discuss how tableau is organized and why it makes sense to be here tableau uses a structure with workbooks and sheets this whole area is known as a workbook a workbook can contain three different types of sheets the first type of sheet is a worksheet a single view containing shelves cards legends data and analytics panels the second type is a dashboard a collection of views from multiple worksheets and lastly a sheet can be a story which is a sequence of worksheets or dashboards working together to convey information in tableau we have easy access to all three through the ribbon at the bottom part of the workbook for now we’ll be creating with a worksheet but as we progress through the course we’ll also learn how to create dashboards cool what else the panel you see here on the left is the data panel in tableau data is categorized as either a dimension or a measure so each column in our data will be placed in one of the two categories a dimension contains qualitative information for instance names dates or geographical data a measure on the other hand represents

quantitative numeric information tableau automatically sorts the variables into the bins and is usually very good at doing so if we double click on car listings and then on brand we see that we get a bar chart straight away obviously tableau has figured out a way to visually represent our data and this is what is so great about tableau it is extremely good at guessing our visualization intentions that could be the end of the lecture but things are not always that perfect that’s why tableau also allows us to manually select chart types there are two ways to do this the first one is to choose an option from the marks panel if we click on automatic a drop down menu with options appears let’s select bar alternatively on the right hand side of the worksheet we have a show me field with different visualization options for example we can select a pie chart or a tree map some of the options are unavailable at the moment because of the nature of our data not all data could be represented with all charts for instance if we hover over the map we see that we need a geographical dimension which we do not have or if we go to histogram tableau prompts that a histogram requires precisely one measure okay let’s stick to a bar chart for now and try out some of tableau’s styling capabilities to improve its appearance the first thing we’ll do is to enlarge the plot to make all the labels on the x-axis visible we’ll just extend it to the right until we can read the mercedes-benz label what’s next generally data visualizations convey insights through shape and color while the shape is somewhat restricted by the nature of the data the color is up to us nonetheless choosing a color for our visualizations is of utmost importance and should be thought through carefully in tableau color can be altered through the color mark we click on it and a panel appears allowing us to choose the color for our bars and modify the opacity level in case you are wondering opacity controls the transparency of the color elements through the more colors option we can select a custom color for our chart so far so good the color element is a component of the marks panel which in turn is an integral part of creating and styling charts marks allow us to change and add details to our charts but there is another neat application of marks let’s check it out from the data pane we’ll select brand and drag it into the color square see tableau colors are plot by the individual brand straight away great now when we left click on color and then on edit colors instead of individual colors we are given the option of different color palettes selecting automatic we observe 20 or so options let’s check out hue circle for instance once we’ve landed on a palette we click on assign palette and then unapply this sets it as the new color palette for the graph feel free to explore the other options on your own after the lecture great there are tons of styling options available with the marks panel we’ll be exploring those throughout the course for our graph though let’s revert to a single color with control z to make sure our graph is professional we can choose a dark blue color it is a very universal color and would fit nicely in any presentation brilliant we’re well on our way to making an awesome chart now let’s direct our attention to the bars and their height although we have a scale on the x-axis it isn’t very precise that makes gauging the accurate value for each bar difficult if we were comparing the adds between audi and renault for instance we have to squint to find out that renault is ahead therefore to display the number of car listings of each brand we’ll add labels yet another instance where we’ll make use of the marks panel we’ll drag the cars measure into the labels field and voila labels for each bar have appeared

superb next we’ll focus on the text in our graph we’ll start by changing the font tableau allows for font formatting on different levels we can format a particular axis or individual label or generally format the appearance of the entire workbook in a professional presentation we want our fonts to be uniform across the whole worksheet therefore we’ll opt for the last option from the top menu page we’ll select format and then workbook let’s go for a pretty standard font like ariel once we change it here we’ll effectively change the font for the entire workbook excellent what else the majority of the work is done so just a few minor details left first we don’t require a brand label to make the visualizations less crowded we can right click on it and select hide field labels for columns looks much better ok let’s also rename the chart then double-click on the field increase the font size to 16 and bold it for better readability it’s time to type in our new title which will be cars listings by brand remember having an informative title is an integral part of creating any chart we’ll click apply and we have a new title excellent now this looks like a presentation worthy chart all that’s left is to save our work i’m using tableau public which only allows me to save my work on their server if you already have an account you can just enter your name and password and save your file on the tableau server alternatively you will have to complete their registration process finally depending on the size of the file you’re uploading saving it might take a little while all right good job everyone we’ve successfully created and saved our first bar chart in tableau creating a bar chart with python let’s get right to it the first order of business is to import the libraries we’ll need to create our bar chart the two essential packages for us are pandas and matplotlib let’s import pandas as pd pandas is a widely used library for data analysis and is what we’ll rely on for handling our data then we will also import matplotlib.pyplot as plt matplotlib is the library we’ll be using for visualization okay now that we’re all set we can proceed with loading our data set we’ll name our first variable df used cars and use the pandas read csv method to load our csv file our file is called bar chart data dot csv if your data set isn’t in the same directory as your jupyter notebook you’ll need to specify the exact file location for instance if i were to use the exact path my code would look like this here it is important to indicate the path with forward slashes between the folder names this is counter-intuitive as the path locations in your windows or mac are always specified with backward slashes however in python a backward slash has a different meaning it is an escape character and can be used in conjunction with other symbols therefore always remember to use only forward slashes when specifying a path to a file good after we’ve loaded the csv file we can check what the data frame of used cars contains it looks identical to the data set we have already examined now let’s see how we can translate this information in the form of a bar chart luckily the pie plot module from matplotlib has a readily available bar plot function we’ll need to type plt dot bar before we go any further let’s hold shift and press the tab button a window appears allowing us to explore the different parameters of the function unsurprisingly the first parameter is the x-axis the second called height is the y-axis and so on we can expand the window with the plus sign and scroll through alternatively we can access

a detailed description of functions and parameters through matplotlib’s online documentation for our bar chart we’d like to plot the number of car listings by brand therefore for the x-axis we should select the brand column from the car’s listing variable on the y-axis which is the height we’ll need the number of cars sold hence from our used cars data frame we’ll take the second column cars listings also as the last line we ought to include to ensure we’re able to see our plot no matter which environment we use all right let’s run the cell and check the result look at that only a line or so of code is all that’s required to create a bar chart good job now although this chart is displaying the correct information we can still improve its appearance data visualization is not only about creating a chart but also styling it in a compelling way therefore let’s spend some time on that too with each visualization we create in this course our final goal would be to reach a professional graph with some widely accepted dimensions and colors so let’s get straight to it first things first we want to be able to read all the labels on our x-axis and at the moment they are overlapping we can resolve the issue by increasing the size of the plot the default size is 6.4 by 4.8 inches we could increase it by specifying the size through the fig size parameter from my experience a 9×6 figure will be appropriate for most visualizations excellent but there is another way as well we can avoid overlapping labels by rotating them let’s try it out shall we we’ll just introduce an additional line of code plt dot x ticks with a rotation angle of 45 degrees and here’s the result rotating labels is an easy way to utilize space without changing anything else in the plot now that that’s out of the way let’s enhance our chart a bit more so far we’ve been working with matplotlib which as a visualization library has a default setting for chart formatting including font font size background themes etc sadly this particular look isn’t everyone’s cup of tea not long ago a new visualization library called seaborn has emerged and has become the preferred choice for many programmers especially in the field of data science seaborn is actually built on top of matplotlib as such the two libraries can be seamlessly integrated and work alongside each other which is great news for us to be more precise we can import seaborn and set its look to overwrite the default one in matplotlib let’s scroll back to the top of our code and try it out first we’ll import seaborn as sns and secondly we’ll overwrite the matplotlib look with sns.set to take advantage of the seaborn styling in essence this will allow us to code the graphs in matplotlib but they will be displayed with what some call the much superior seaborne look alright it’s time to put it to test by re-running the last cell of code tremendous moving forward we’ll be sticking with the seaborne visuals in the majority of cases what’s next on the agenda generally data visualizations convey insights through shape and color while the shape is somewhat restricted by the nature of the data the color is up to us nonetheless choosing a color for our visualizations is of utmost importance and should be thought through carefully in matplotlib we can change the colors of our plot by adding a color argument furthermore we can choose a color name from a predefined color list available in the seaborne library seaborne recognizes over a hundred color names starting from the basic ones such as red green or blue which we can refer to by their initials rgb respectively if you’re feeling more adventurous you can also choose colors such as linen honeydew or dark orchid in fact you can assign an individual

color for each of the bars let’s see how we have seven columns and we’ll assign each their own color we can type in a string with the following seven letters r g b w y m c each of these letters is an abbreviation for a frequently used color here’s what we get cool each column has been assigned an individual color we have r for red g for green b for blue w for white y for yellow m for magenta and lastly c is for cyan this shows how versatile plotting in python is we can specify a color for as many bars as we have in this manner just bear in mind the color abbreviations aren’t limitless therefore at some point you might have repeats however in a professional presentation we want our color to be uniform across the whole worksheet therefore let’s choose dark blue or midnight blue as the only color of our bar chart you can find the full list of colors in this lecture’s resources or check the python documentation online okay what’s left well every plot needs a title to integrate a title we type plt dot title and in brackets specify the desired title cars listings by brand has a nice ring to it note that you need to put that in quotation marks so that python knows that it is a string let’s examine the result while we’ve successfully placed a title on top it is a bit small compared to the rest of the graph but no worries everything in python is customizable we can increase the font size by adding an extra argument and then specify a font size of 16 we could also add a font-weight argument and set it to bold here we could also do with a y-axis label to add one we just write plt dot y label and in the bracket specify the name as a string i know for a fact the axis label appear a bit smaller so let’s increase their size to a 13 with the font size parameter in fact let’s also format the other text elements a little bit so they look more pronounced increase the font size to 13 for the x and y-axis labels and set the y-tix font size to be equal to 13 as well marvelous our bar chart looks presentation ready a quick side note not all people are comfortable with coding in python in fact you may often need to use your plot outside a jupiter notebook to achieve that you can export your plot as an image this is easily achieved with the save fig method in the brackets we need to specify the name and file format in our case it will be a png so we’ll have usedcarsbar.png and that’s it great job everyone creating a bar chart with r so let’s jump straight into our studio and start writing some quality code the first thing we need to do is open up a new script it is a good idea to begin by saving it under an indicative name such as used cars bar r script so far so good we’ll be using ggplot2 it’s one of the most popular visualization libraries in r and a favorite among the community this is the library we’ll be using for the majority of our visuals so it’s worthwhile to take a second and see what ggplot is all about we’re ready to start talking about ggplot2 ggplot2 was developed in 2005 by hadley wickham it relies on a concept known as the grammar of graphics which is a set of rules for dividing each plot into components or layers it is also the basis of how each ggplot is created which is why it pays off to explore the grammar of graphics in more detail without further ado introducing the grammar of graphics you can think of it as a way of dividing each plot into layers where each layer is responsible for a

specific element of the chart there are seven layers we can use when creating a gg plot and as we delve into different visualization topics we’ll get a chance to explore each in detail for now let me give you a quick overview of each of these seven layers which constitute the grammar of graphics the first three layers are mandatory while the remaining four are optional let’s start with the first layer also known as the data layer creating a chart naturally means we require some data otherwise our chart would be empty therefore it is only logical that this is the first and most important layer all right what else we must also decide how the data will be visually organized onto different axes this is where the second layer comes in it is called the aesthetics layer here we specify the mapping to the x and y axes good the last required layer is the geometry layer through geometry we specify what shape our data will take in other words will the points take the form of bars circles dots etc these three layers alone are all it takes to create a chart in r the remaining four layers are optional however since we want to be proficient at visualizing data in r we’ll master them too an important distinction between the mandatory and optional layers concerns the order in which they appear for the first three layers we must start with data continue with aesthetics and as a third step determine the geometries the remaining four layers don’t need to appear in strict order in fact we don’t have to include any of them in our plot hence the optional part in this lecture we list the optional layers in a specific order simply for convenience but it is not necessarily the order in which they should appear in a gigi plot with that in mind let’s continue with the fourth layer the facets layer the facets layer enables us to split our visualization into subplots according to a categorical variable or variables subsequently each subplot corresponds to a subset of categories of the variables for instance say we have an engine type variable in our data containing diesel petrol and electric engine cars using the facets layer would allow us to divide our data set into three plots each containing one of the three categories diesel petrol and electric cars so far so good moving on to the fifth layer which is statistics this layer represents the statistical transformations we might perform on our data it can be used for various purposes such as determining the number of bins when plotting a histogram or smoothing lines when drawing a regression line cool the sixth layer is the coordinates layer the coordinates layer as the name suggests is connected to the coordinates or boundaries of our graphs this layer can be used to zoom in or out of a plot apart from this it could be used to perform transformations on the coordinate system for instance switch to polar coordinates finally we arrive at the seventh layer themes the themes layer has a sole purpose to polish the appearance of our plot this is the place that controls the overall style of our graph for instance we can choose from darker or lighter themes not only that with the aid of the themes layer we can use predefined templates or recreate the visuals and style of well-known publications such as the bbc or the economist among others all right phew this is what the grammar of graphics in ggplot2 is all about you can revisit this lecture if you ever need a refresher or check out the course notes or you can just learn by doing speaking of which it’s time to create our bar chart the first step towards a successful visualization is obtaining the necessary data therefore let’s load our data file in a new variable called used cars and apply the read csv function as an argument we need to pass the file to be red our file is called bar chart data.csv if your file isn’t in the default r files library you need to specify the path or directories leading to it this is counter-intuitive as the path locations in your windows or mac

are always specified with backward slashes however in r a backward slash has a different meaning it is an escape character and can be used in conjunction with other symbols therefore always remember to use only forward slashes when specifying a path to a file ok we specify the header is true which means that the first row of our data will be used as columns names lastly we’ll indicate that the separator is a comma after running the cell the new variable appears in the top right of window r studio next to it is a summary indicating used cars contains two variables with seven observations not only that if we click on used cars we’ll be privy to the entire data set a great feature of rstudio granting us easy access to each variable we create and now on to creating our first chart we’ll be relying on the ggplot2 library which means we ought to include the library in our script we achieve it by writing library when in the round brackets include the name of library in our case ggplot2 great now let’s create a new variable which will be a ggplot object now as discussed according to the ggplot philosophy the first thing we’ll need to specify is the data set we’ll be plotting our automobile data hence the first parameter to include is the used cars data frame good the next step is to specify the aesthetics the second mandatory layer of each gg plot here we need to specify which columns will map on the x and y axis we want to display the number of car advertisements by brand therefore on the x-axis we’ll have brand and on the y-axis we’ll select cars listings the last mandatory step for completing the plot is to specify the geometry the geometry shows the shape in which we’ll be displaying our data we’ll be creating a bar chart therefore we’ll opt for the geom bar inside the brackets we’ll need to indicate a y aesthetic we’ll use stat equals identity specifying we want the actual y values plotted the reason here is that geometry bars are frequently used to display histograms which map the distribution of a certain variable that’s why we require the extra parameter okay let’s run the code and examine the result there you have it our first bar plot on the x-axis we have the brand while on the y-axis the number of cars sold and that’s all you need to create a bar chart but don’t go packing up just yet we still have work to do for the remainder of the lecture we’ll concentrate on formatting our chart and improving its appearance it will also give us the chance to get better acquainted with some of the remaining gg plot layers first we can decide on the size of the bars themselves the size is specified using width for the width of each column it can take values from 0 to 1 if we select 1 there will be no space left between the bars making it look like a histogram we’re creating a bar chart right now so let’s select 0.8 leaving us with some space between the individual bars without spacing them out too much okay what comes next generally data visualizations convey insights through shape and color while the shape is somewhat restricted by the nature of the data the color is up to us nonetheless choosing a color for our visualizations is of utmost importance and should be thought through carefully so let’s go ahead and choose a nice color for our bars for this chart we’ll settle on a dark blue color it is a very universal color and would fit nicely in any presentation we’ll include it as a string for both the fill and border of our bars you can find a list of available options with the resources in this lecture or online while colors are very important having a title and easily recognizable labels is arguably even more important for the title we’ll add another element called gigi title as i start typing it out our studio is already suggesting it

what would be a good title cars listings by brand has a nice ring to it doesn’t it good job alright what’s next well since we changed the color of our bars the gray background isn’t as fitting any longer no worries we can fix this by assigning a different theme recall that theme was one of the optional layers in a gg plot though optional we’ll be using it often because the appearance and style of our charts matter so let’s add a theme element to our chart and select a classic theme which has a white background very nice the next item on the list is formatting the axis currently the labels on the x-axis are overlapping in parts which of course isn’t ideal however we can amend it and increase space by rotating the labels as with any styling choice this is part of the theme department let’s add a theme element where we’ll include a text parameter for the x-axis we’ll give it the text labels with element text and in brackets specify a rotation angle of 45 lastly we also add a height justification of one just so the labels don’t shift after the rotation i’m excited to see the result superb all labels can be clearly distinguished lastly we don’t need the label on the x-axis for this graph as it is evident from our labels that we are displaying car brands we’ll add a labs function short for labels and say that x is null which will remove the label on the x-axis as for y let’s set the label to number of listing okay amazing we have successfully made our first bar plot in r a great feature of rstudio allows you to export the generated image you just click on export and select image or pdf or you could directly copy this image to your clipboard and paste it in a different document like a powerpoint presentation for instance all right this concludes our lecture on bar charts in r moving forward we’ll apply a lot of the techniques we saw here in this introductory lecture but we’ll also learn some new tricks for making quality charts and i for one can’t wait we’ve successfully crafted our first bar chart and are confident it would serve us well during a presentation after all we’ve done all the necessary steps to ensure it is as professional as possible on top of that your audience can easily tell the most widespread brand for used cars is volkswagen there is a total of 875 units from volkswagen followed closely by mercedes with 820 mitsubishi was the least popular with approximately a third of the listings of the front runner excellent we’ve achieved what we set out to do tell the story of this data set with the aid of a bar chart there isn’t much left for us to comment on this visualization that is one of the greatest strengths of a bar chart it is extremely intuitive for all users regardless of their professional background but not all bar charts are created equal so it’s worth taking a second to explore what can go wrong when making a bar chart let’s look at the following chart depicting the average house prices in pounds for the years 1998 and 1999 visually it appears from the year 1998 to 1999 there has been a three-fold increase in house prices however there is a critical error in this graph the y-axis doesn’t start from the minimum number for price or zero if we show the same information but this time with a scale from zero it tells quite a different story there is an increase in house prices however it isn’t as drastic as the first chart would have us believe by altering the scale we can easily manipulate a graph to misrepresent information of course this can happen by accident that’s why it’s always important to make sure our charts aren’t misleading such misinformation can also be intentional this is especially common in political races where candidates aren’t shy to mislead the audience as a side note we can of course present the first chart with an altered axis

scale if we wanted to show the exact increase in dollar price between the two years however we need to communicate clearly that this was our intention be mindful that even then it’s impossible to be sure that our audience interpreted the information as we intended alright tremendous job everyone we’ve reached the end of our first chapter on data visualization and we’re already able to create as well as style charts in a professional manner well it seems we’re equipped with all the tools required for creating amazing charts and can confidently proceed with other fundamental topics stay tuned and thanks for watching