Waterfall charts are an effective way to display data visually. They are particularly useful if you’re analyzing an income statement and want to see which parts accounted for the bulk of the change in profitability from one period to the next. In this example, I’m going to use Amazon’s first-quarter earnings of 2022, which saw the company’s bottom line fall into the red for the first time since 2015. Using a waterfall chart, we can quickly analyze what were the big drivers behind the drop in profitability — and the results may surprise you.
Step 1: Preparing the data for a waterfall chart
In a waterfall chart, you want to calculate the change in values. To start with, I’ve entered all the main income statement line items from Amazon’s Q1 earnings for 2022 and 2021, side by side:
I’ve grouped some expenses together for the sake of not having too many items. With waterfall charts, there are a couple of dangers. The first is that your descriptions run too long and it’s hard to display the line items. The second is that you have too many items and your chart needs to become excessively wide to accommodate all the changes.
One thing you’ll notice here is that at the bottom I have the net income (loss) line. This is a summation of the above items to ensure that it correctly ties out to the profit or loss that the company reported. This is an important step to make sure that you’ve entered your data correctly. Expenses should be negative (outflows) while income should be positive (inflows).
The next step is to now calculate the difference between the two periods, which can be done in a change column that takes the current value and subtracts from it the prior period’s value:
At the bottom, I’ve summed up all the changes. These figures are in millions, and so this is a significant $11.951 billion change in net income from a profit of $8.1 billion in the prior-year period to a loss of $3.8 billion.
Now that the data looks correct, the next step is to plot these values on a waterfall chart.
Step 2: Plotting the waterfall chart
To create the chart, I’ll select the data in the change column along with the related headers. From there I can either click on the image of a chart in the menu bar or I can go to the Insert menu and select Chart. If it doesn’t detect which chart I want to use, then I can select the image of waterfall chart from the Chart type drop-down option in the Setup tab:
Now it will show this:
The chart looks correct, however there are multiple changes we can make to help this look better.
Step 3: Modifying the waterfall chart
To start with, I’m going to modify the colors. While red makes sense for negatives, I’m going to change the blue to green, to better reflect a positive inflow of cash. This can be done by double-clicking on the chart and in the Chart Editor, going to the Series section, and scrolling to the Positive label. There, I can change the fill color:
This also gives me the option to change the line color and transparency using the opacity percentages. At this point, I’ll remove the legend since the green and red values are sufficient to tell you whether it was a positive or negative change.
The next thing I’ll change is the grey subtotal bar at the end. Ideally, you would have a starting and ending point on the chart to better show where one period started and where the other ended. But by default, the subtotal just adds up the sum of the change. To adjust this, I’m going to add a row to my table above Net Sales, called Q1 2021 Net Income. In the change column, I will simply put the amount, no change. This is what my updated table looks like:
If the chart doesn’t automatically update, you may need to update the range. This can be done by double-clicking on the chart and in the Setup section, modifying the range for the Series and/or the X-axis. But the bar charts for the totals still need adjusting. The first one shows green. To fix this, I’ll double-click on the chart to edit it and under the Series section, select the box to Use first value as a subtotal. Now the first bar chart will turn grey.
In the same section, I’ll also uncheck the box that says Add subtotal after last value in series. That will remove the last bar chart. Then, I’ll click on the option to Add new subtotal. Select to add it after the last item. By doing this, I can now specify the name of that total, as opposed to just showing ‘Subtotal.’ In this space, I’ll enter Q1 2022 Net Loss.
The only thing left now is to adjust the chart and stretch it out sufficiently so that the labels display horizontally. And I’ll also add a title — this can be done in the Customize section and under the Chart & Axis Titles area. Here is my completed waterfall chart in Google Sheets:
Now, from looking at this, you can see that Amazon was still at a profit until it reached the other income and expenses line. This would still require additional digging to see the reason for the loss, but it would point us in the right direction. And Amazon’s breakdown of these other expense items tells us that it incured a $7.6 billion loss on its investment in Rivian Automotive — the key reason its net profit from a year ago turned into a loss. While other expenses increased, they alone weren’t enough to pull the company into a net loss position.
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Did you know that you can easily add checkboxes to Google Sheets? In this post, I’ll show you how you can do that. Plus, I’ll share a google sheets script that can automatically update other cells when you tick and untick checkboxes in Google Sheets.
Adding checkboxes to Google Sheets
In Google Sheets, all you need to to do add a checkbox to your sheet is to go to the Insert tab and click on the Checkbox button:
Clicking the button will add a checkbox to the active cell. By default it is unchecked, and selecting the cell will show a value of FALSE in the formula bar. When the checkbox is ticked, then the value changes to TRUE.
Using checkboxes to trigger other calculations
Ticking a checkbox or unticking it doesn’t on its own accomplish anything. However, it could trigger another calculation, with the value being used in a formula. For example, suppose you have a checkbox in cell A1. You could create another formula that looks at if the value is TRUE or FALSE (checked vs unchecked):
=if(A1=TRUE,1,0)
In the above formula, if the checkbox is selected, the formula will return a value of 1. Otherwise, it will be 0. This formula could be modified to do a summation or other something more complex.
Using Google Scripts with checkboxes
Another way you can use checkboxes is with a script that runs when they are checked. Suppose for example you had an inventory sheet and wanted to check off when an item was shipped or received. Clicking the checkbox could populate the date when you checked off the box. With a formula, you wouldn’t have that capability since it would always recalculate. But with a script, it could lock in that value every time the checkbox is ticked or unticked.
To create a script in Google Sheets, you need to go to the Extensions menu and select App Script. The following script will look for changes in the 2nd column (Column B) and if a value is set to TRUE, it will populate the date in the 1st column (Column A). If it’s set to FALSE, then it will clear the value in column A:
function onEdit(e) {
let range=e.range;
let activeRow = range.getRow();
let activeColumn = range.getColumn();
let cellValue = range.getValue();
let sheet = SpreadsheetApp.getActiveSheet();
if (activeColumn == 2) {
if (cellValue == false) {
sheet.getRange(activeRow,1).clearContent();
} else {
sheet.getRange(activeRow,1).setValue(new Date());
}
}
}
Copy that code in its entirety as a new function in the app script. Then, click on the Save button. Now you can go into the spreadsheet and try it out. If you want to change any of the columns, you can change either the active column from B (replace the number 2 in the code above) or where the date value gets populated (see the lines of code that reference activeRow,1, which corresponds to the first column, column A).
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The stock market is off to a rough start to 2022, with the S&P 500 falling more than 5% in just one month. Using a spreadsheet, we can analyze historical trends and patterns to identify what normally happens after such bad starts. Below, I’ll use data from Google Sheets to pull in historical values and analyze how the index has performed afterward and whether this year is doomed to be a bad year, or if a recovery is likely and if now is a good time to invest in stocks.
Start with downloading the historical data
The first step is to get the S&P 500’s historical values in Google Sheets. This can be done using the GOOGLEFINANCE function. Using the .INX symbol, I can calculate the S&P 500 values going back to the 70s. Here’s a matrix showing the returns over the past 50 years, after applying some conditional formatting to the values:
Filtering the data
To zero on in just the largest January declines, I can use the Filter by condition option to specify January values where the percent change is less than negative 5%:
That leaves me with the years when the S&P 500 dropped by 5% or more in the first month:
Now that I have a list of the years I’m looking to analyze, I can start creating some charts.
Using charts to summarize the performances
The first visual I’m going to create will look at how the index has performed after January, after those bad starts. To do that, I need to take the year-end values and divide them by the values at the end of January. This tells me how much the index rose or declined in the remaining months. And when grouping those variances, this is what the data shows:
Of the 7 previous times when the S&P 500 dropped 5% in January, 3 times it would continue to drop in the following months and finish even lower. Only two times would the index rise by more than 10%. I can also average the results, comparing the down years versus the overall average:
This tells me that in a year where the S&P 500 typically tanks in the first month, the overall returns from the index are likely to be negative. However, to add a bit more context to this, I’ll look at the individual returns by year and compare them against the 50-year average, which is summarized in this table:
By keeping the average column constant, it creates a straight line for the chart and makes it easy to visualize the individual years’ returns and how they compare against it:
A few of the things that stand out from the data is that in three of the years (2000, 2008, 2009), the markets were either in the midst of a significant crash or recovering from it. It helps put into context some of these returns, suggesting that the other years might indicate more typical returns in a non-crash year. And if that’s the case, investors may expect fairly modest returns this year, possibly negative ones overall. Although it isn’t a large data set, it certainly suggests that the stock market may be facing a down year in 2022.
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Have you ever wondered how a stock has typically performed month over month? Using a spreadsheet, you can calculate monthly returns and identify patterns of which months are traditionally strong for a stock, and which ones aren’t. In this example, I’m going to use Google Sheets to pull in stock prices and calculate historical stock returns by month.
Start with pulling in historical stock prices
To get started, I’ll need to extract a stock’s price history. This can be done using Google Sheets’ GOOGLEFINANCE function. The key is in setting a start date that goes far enough back to ensure you get enough historical data to use in your calculations. A good function to use within that is the TODAY() function which ensures you will always be counting backward from today’s date and don’t need to hardcode a date. If I want to go back to 2008, for example, I can set my formula to deduct about 5,000 days.
To pull Amazon’s stock price going back that far, this is what my formula would look like in Google Sheets:
The one problem here is that the date values contain the time, 16:00:00, which represents the 4 pm closing time of the stock market. I only want the actual date since I’m going to be doing a lookup and don’t want to include time. To extract just the date, I can use the DATE() function and use the YEAR(), MONTH() and DAY() functions to refer back to the values in column A. For example:
=DATE(YEAR(A2),MONTH(A2),DAY(A2))
The above formula would give me the date in column A without the time. I’ll add an IF statement at the start so that in case the value in column A is blank, my formula simply won’t compute anything:
=if(A2="","",DATE(YEAR(A2),MONTH(A2),DAY(A2)))
Now I have a table that has just date values without any time:
Creating a date matrix
Next, what I’m going to do is create a matrix that has years going vertically and months going across:
I’m going to fill in these values with the stock’s returns in each of those months. The key to making this work is by using the DATEVALUE() function which allows me to enter a date. For example, if I entered the following formula:
=DATEVALUE("Jan 1, 2022")
It would result in the following output:
1/1/2022
In the first cell of my matrix, for the JAN 2021, I’ll combine the month abbreviation (JAN) with the year (2021) and the first day (1). Here’s how that would work if the month name is in cell F1 and the year is in E2:
=DATEVALUE(F$1&" 1, "&$E2)
However, let’s assume I don’t want to pull the first day of the month and instead want to pull the ending month’s value. For that, I can use the EOMONTH() function. And then I would enclose the current formula within that:
=EOMONTH(DATEVALUE(F$1&" 1, "&$E2),0)
The 0 value at the end indicates how many months I want to jump by. And since I just want the end of the current month, I don’t need to jump by any months, which is why I set it to 0. At this point, I have a date, and now I can use this inside of a MATCH() function to find the row that matches this date. Assuming the date values in are column C, here is the formula:
Now the formula will pull in the price for a given month. But if I want the month-over-month return, I need to take the month-end price and divide it by the previous month’s ending value. To get the previous month’s price, I use the same formula except instead of a 0, I’ll enter -1 for the number of months:
I’ll combine the two formulas now, taking the current month-end price and dividing it by the previous month’s value and also deduct -1 at the end to adjust for it being a percent-change calculation:
Now, copying this formula across the entire matrix, I can see the stock’s historical returns by month. I’ve added some conditional formatting to contrast the good months from the bad ones:
Besides relying on colors, I can also add a win rate % where I can count the times where the return was more than 0% (i.e. a ‘win’) and divide this by the total number of values. In column F, for January, the formula looks like this:
=COUNTIF(F2:F12,">0")/COUNTA(F2:F12)
I’ll also average the returns so that it’s easier to see the best and worst-performing months:
Judging from this, April looks to be the best time to own Amazon’s stock. It normally returns a positive return and its average over the past 11 years has been a gain of just under 8.5%. To re-create this analysis for other stocks, simply change the ticker symbol.
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In this post, I’ll show you how you can calculate stock returns using Google Sheets. However, you can use a similar approach in Excel by using the STOCKHISTORYFUNCTION.
First thing’s first — let’s pull in the historical data
For this example, I’m going to pull in the S&P 500’s historical values to see how the index has performed both in the past 12 months and over the course of several years.
To do that in Google Sheets, I’m going to use the GOOGLEFINANCE function which allows me to pull in historical prices. To get the values from the S&P 500, the ticker symbol I’m going to use is ‘.INX’ and to get the last year of data, I’m going to set my start date equal to TODAY()-365 and my end date will be TODAY(). Here’s the full formula:
Once you’ve got the data loaded, then what you’ll want to do is enter the dates that you need values for. In this example, I’m going to use the last day of every month. For this, I can use the EOMONTH function. It takes two arguments: the start_date and the number of months. If I want the current month-end date, then I just set the second argument (months) to zero. As for start date, that can just be any date that falls within the month, which I can enclose within a DATE function. Here’s how the formula would look if I want the last day of September 2021:
=EOMONTH(date(2021,9,1),0)
But since I need to adjust this so that I can copy the formula down and have it automatically adjust, I am going to use the ROW function, which will return the current row number. Since I want the values to be increasingly negative as I copy down the formula (e.g. the current month should be 0, the following one -1, then -2, and so on), I will multiply this by a factor of negative 1 and add 1 to the total (to ensure the first value start at zero):
ROW(A1)*-1+1
That replaces the zero value from the earlier formula:
=EOMONTH(date(2021,9,1),ROW(A1)*-1+1)
And now, I can easily copy this formula down and my month-end dates will populate without requiring me to make any manual adjustments along the way:
Next, I’ll do a lookup to get the values. And that’s as simple as a VLOOKUP on my dates, which are in column A with the corresponding values in column B. If you use weekly dates, then be careful not to set the last argument in the VLOOKUP function to false because you’ll end up with errors as the weekly values won’t always fall neatly on the end of the month. Instead, leave the last argument blank or set it to TRUE so that it finds the closest match. Here’s what that looks like:
All that’s left at this point is to now just calculate the change in value. I can take the new value, divide it by the previous period’s value, and subtract one from it. This will give me a percent change:
If I wanted to determine the cumulative % change since my first month-end date, then the old value would always remain the same — it would be the first date in the series. By freezing that cell, I can calculate the cumulative % change:
If you wanted to pull in the returns by year, you can do the same thing. All that changes is that instead of pulling in the month-end dates you will use the year-end dates. The main difference here is in calculating the different dates. Rather than multiplying by a factor of -1, you’ll need to use -12. And the starting date should be Dec. 1. Here’s how my formula looks like:
=EOMONTH(date(2020,12,1),ROW(A1)*-12+12)
And when I copy that down, it will automatically adjust for each previous year:
The one thing you may notice in Google Sheets is that the GOOGLEFINANCE function returns a timestamp for the date. Each day ends at 16:00:00. This can create some unintended results. For example, using the VLOOKUP function, if I use the date 12/31/2020, because it looks for an approximate match, it will actually return the value from 12/30/2020. Unless you add the timestamp, an exact match won’t work. And since a date with no time will by default by 0:00, the lookup of 12/31/2020 16:00:00 won’t be a match. One way to get around this is just to use a different date. Rather than using the EMONTH function, I can just adjust the date by reducing the year by 1. This is the formula I can use if instead I want to get the first day of the year:
=DATE(2021-ROW(A1)+1,1,1)
Using the ROW function again can allow me to automatically adjust the year. Here is the updated table:
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Want to create a dashboard to track the stock market and the latest business-related news? Below, I’ll show you how you can create a stock market dashboard using Excel and Google Sheets to pull in all the data you’ll need. If you’d prefer to just download the file, you can do so here.
Step 1: Compiling the data
You can get stock prices into Excel using the STOCKHISTORY function. However, that isn’t available on older versions of Excel and it also doesn’t pull in the current day’s prices. Using Google Sheets can be more effective for this purpose. Plus, on there, I can pull in business-related news as well.
To start, I’m going to pull in values for the Dow Jones, Nasdaq, and S&P 500. I’ll also download the values of a couple of exchange-traded funds (ETFs) that track healthcare and tech stocks. To get the latest price, you can use the built-in GOOGLEFINANCE function that’s only available on Google Sheets. To get the latest value of the Dow Jones, the following formula will work:
=GOOGLEFINANCE(“.DJI”,”price”)
And to calculate the percentage change:
=GOOGLEFINANCE(“.DJI”,”changepct”)/100
For the Nasdaq, you’ll use “.IXIC” and for the S&P 500 the ticker is “.INX”
For the ETFs, since they aren’t indexes, there is no period beforehand and I reference XLK for tech and XLV for healthcare. In my Google Sheets file, I have a simple layout for the values and their changes that I will later pull into Power Query:
Next, I’ll also download the latest business-related news. Google Sheets has another unique function for this: IMPORTFEED. All you need to do is find an rss feed from a website that you want to pull information from. Not every website has an rss feed but what you can do is just do a Google search for the name of a source and ‘rss’ to see if you can find a link. There are three sources I’m going to use for this dashboard:
In Google Sheets, the top articles from each of those rss feeds will show up, including the title, URL, date created, and even a brief summary:
Now, it’s time to pull all this data into Excel.
Step 2: Loading the data into Excel using Power Query
To import data from Google Sheets into Excel, you need to first share the sheet. While in Google Sheets, go into File -> Share -> Publish to web. Then, you’ll be prompted to select what you want to share. I’ll start with the Markets tab I created and then the News tab:
Copy this URL as you’ll need it to load the data into Power Query. While you’re back in Excel, go under the Data tab and click on the From Web button under the Get & Transform Data section. You’ll be prompted to enter a URL. This is where you’ll paste the link that you copied from Google Sheets:
On the next page, select Table 0 as where you want to extract data from. And if you want to do some cleanup (getting rid of extra columns), you can do so by clicking on the Transform Data button:
To remove any unneeded columns in Power Query, just right-click on a column header and click Remove:
Once you’re done, click on the button to Close & Load if you want the data to be loaded on a new sheet. If you want to control where it gets pasted, then use the drop down and select Close & Load To.
Repeat these steps for the other Google Sheets tab.
In addition, I’m also going to load data from a few other sources:
Top 100 Gainers on Yahoo Finance: https://finance.yahoo.com/gainers/?offset=0&count=100
Top 100 Losers on Yahoo Finance: https://finance.yahoo.com/losers?offset=0&count=100
Upcoming IPOs from IPOScoop: https://www.iposcoop.com/ipo-calendar/
The process for importing these links into the dashboard is the same as for Google Sheets. Go through Power Query, import from web, and paste in the URL plus make any formatting changes necessary. The next step involves putting all this data together in a dashboard.
Step 3: Creating the dashboard
In my spreadsheet, I’ve created two tabs: one that hold all my Power Query downloads (the ‘Data’ tab) and a ‘Dashboard’ tab for where all the information will be displayed.
To make the set up of the dashboard easy to manage, I’m going to change the column width to 10 for everything. To do that, press CTRL+A to select all the cells on the Dashboard tab, then right-click on any of the headers, and there you’ll be able to select column width.
First up, I’m going to get the indexes and market indicators as a starting point. To do this, all I need to do is link to the values and the percentages for the S&P 500, Dow Jones, Nasdaq, Tech, and Healthcare tickers I imported from Google Sheets. By default, I’ll set the formatting for all the cells to be green:
To make this more dynamic, I will add some conditional formatting so that if the percentage change is negative, the corresponding cells will highlight in red. For this, I can select all the cells in green above and create a conditional formatting rule the starts with where the first percentage is (in my spreadsheet, it is cell E6):
=E$6<0
This is a simple rule but by not freezing the column (E) and freezing only the row (6), it can be applied to all the cells above. I can apply a red background color so that if any of the percentages are negative, the cells will highlight accordingly:
For the next part of the dashboard, I will copy over the news stories that were also downloaded from Google Sheets. This time, I’m going to use the HYPERLINK function so that I can not just link to the title but also create a clickable link that will allow me to open the story should I want to open it in my default browser. The function itself is simple and involves just two arguments, one for the actual URL and another for what the text should show up. Since it’s shorter, I’m going with the title. After applying some formatting and copying all three sources, this is what my dashboard looks like:
For the last part of the dashboard, I’m going to pull in the tables from the other data sources (top 100 gainers, losers, and upcoming IPOs). If these are on the Data tab, you can just cut and paste them onto the Dashboard tab. And for each one of the tables, I’m going to create a chart based on the symbol and the percent change.
To do this, select the Symbol column and the % Change columns. Then under the Insert tab in Excel, open up the charts and select Treemap. If you selected too many columns or didn’t specify which ones you wanted, you might get a different look. But if you only selected those two, you should see something like this:
Since the chart includes the symbols, the legend can be deleted. Also, I’m going to change the color scheme so that it goes from dark green to light green. This change can be made by clicking the Change Colors button next to the chart:
To add the percentage to each of the boxes, right-click on one of the ticker symbols and click Format Labels. Then, check off the box for value so that the percentages will also show up next to the symbols:
These steps can be repeated for the other charts. However, for the losers table, since the percentage change is negative, it needs to be flipped to positive first. To do that, that query needs to be edited. If you click on Queries & Connections section under the Data tab, you’ll see a list of all your queries. Click on the one that takes you to the top losers query. Right-click edit and Power Query will open up.
Once in Power Query, select the % Change column and under the Transform column at the top, click on the Standard drop down, which will show you all the different calculations you can apply:
Click on Multiply and then for the value in the next box, enter -1. Pressing OK will then flip all the values to negatives.
Now, you can create the same Treemap chart for this table. For the IPOScoop download, the field I’m going to use is Est. $ Volume. This query will also need to be edited in order to use that field since it is text. Although it is a bit more complex since this field contains text and dollar signs, there’s a relatively easy way to parse out what you need.
In Power Query, select the column, and under the Add Column tab, click on the Column From Examples button (choose the option for From Selection):
That will create a new column:
In Column1, I can enter the value that I want Power Query to extract. If I just enter a few values to show what I want (in this case, I only need to enter 300), Power Query fills in the rest, figuring out what I am trying to do. It’s an easy way to parse data in Power Query.
After creating the new column, I can change the format from text to currency by clicking on the ‘abc’ letters in the title:
Now that I have the column created, I can remove the original one and load the data back into Excel and proceed with making a Treemap for this chart using the symbol and the newly created column.
The last thing I’m going to do is create a new column to show the change in volume to determine how much more (or less) trading there was for each stock on the day compared to the average. This will compare the average three-month volume with the current day’s volume. The one complication is that some of the values contain letters:
To convert these values, it’s important to first parse out the letters. If a value doesn’t contain a letter, then it is in thousands. I’m going to set everything to millions. So if the value doesn’t contain a letter, it will be multiplied by 0.000001 to convert it into a fraction of a million. And if it contains a ‘B’, it will multiply by a factor of 1,000. Otherwise, the value will remain as is. Here’s how the first part of the formula will look like, which involves determining the multiplication factor:
Since the letter is always at the end of the string, just using the RIGHT function (which looks at the right-most string) will suffice. This result needs to be multiplied by the remaining value. That value can be extracted by using the SUBSTITUTE function which will replace one value with another:
SUBSTITUTE([@Volume],”B”,””)
In the above formula, the value of B will be replaced with an empty string. This is the same as simply removing the value. To ensure that any ‘M’s are also removed, I will embed this formula within another one that will substitute out those values:
SUBSTITUTE(SUBSTITUTE([@Volume],”B”,””),”M”,””)
I multiply this by the first part of the formula, and my numerator is as follows:
For the denominator, I’m going to use the exact same formula, except instead of the current volume, I’m going to use the field for the three-month average:
The -1 at the end is to put the change in a percentage of less than 100%.
Another step you might consider at this point to help identify these changes is to format these numbers so they are easier to read. You can use conditional formatting (color scales) to easily highlight the highs and lows. And if you want to format the percentages so that they show commas and negative percentages show up red, use the following in the custom number format:
#,##0%;[Red]#,##0%
The semi-colon before the [Red] separates out what the percentages should look like when they are positive (the part before the semi-colon) and what they should like when negative (the part that comes afterward). The [Red] text indicates the value should be in red text.
Here’s how this section looks as part of my dashboard:
And here’s a snapshot of the dashboard as a whole.
One thing to remember: if you want to update the queries and the dashboard, make sure you go under the Data tab and click the Refresh All button. Otherwise, your data may not be up to date.
Also, to prevent your tables from stretching out when updating the queries, select each one of them and under the Table Design tab, click the Properties button (under the External Table Data section), where you should see this:
Make sure the Adjust column width checkbox is unticked. This will prevent your columns from stretching out and disrupting your layout.
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A big advantage of using Google Sheets is that the data is readily accessible online and you don’t need to worry about if people are running different versions of it like you would with Excel. One of the areas where it may be lacking is in creating dashboards. Although you can incorporate slicers, they’re not as user-friendly or nice looking as what you would get in Excel. But in this post, I’ll go over how to make dashboards in Google Sheets quickly and easily.
Here is a sample of what my data set looks like. If you want to view the data plus the dashboard I created here, you can check out the Google Sheets file here.
Step one is to create some pivot tables. Like with Excel, I prefer to create a pivot table for each view that I want. I will set up four pivot tables, categorizing sales by:
Store
Salesperson
Product
Date
To keep things simple, you can put each one of those fields in the ROW section while the sales can be in the VALUES section:
When creating the pivot tables, be sure to un-check the option to Show totals (this is so that they don’t show up in the charts):
What you may want to do is create one pivot table and then copy and paste others, and just change the rows. One additional step you will need to do for the pivot table that contains the dates is to also group them by month. To do that, right-click on any of the dates and select Create Pivot Date Group:
Then, from the following menu, select Year-Month:
This is how your pivot tables might look like once you are done:
Where you put these pivot tables isn’t important. The key is leaving enough space between them so that they don’t potentially overlap should your data get bigger. Otherwise, you will run into errors and have difficulty updating your data. Since my pivot tables won’t get any wider based on the selections I’ll make, there doesn’t need to be any extra columns between any of them.
Now that the pivot tables are set up, the next step is to set up the different charts for each of them. For the sales by store, I will create a pie chart to show the split among the stores:
The one thing you will want to pay attention to for each chart is the range. Since your pivot table could expand, it’s a good idea to make the range bigger than it needs to be, even if it will contain blank values. For example, changing this:
To this:
This will ensure that additional data gets picked up by the chart should your pivot table get bigger. This is also why it is important to ensure you don’t place any other pivot tables below one another. Ideally, you’ll want to keep them side by side rather one on top of the other.
For the pivot table that shows sales by salesperson, I’ll use a bar chart since the names can be long:
For the product sales, I’ll mix it up and have those as column charts:
And for the sales by date, I will set those up as a line chart:
I will also add a scorecard chart, using any of the pivot tables. For this, I just want to pull the total sales:
Now that these charts all set up, it’s just a matter of organizing how you want to see them on your worksheet:
The one thing missing to make this dynamic: slicers. To add slicers to all these pivot tables, click on any of them and click on the Data tab and select the Add a Slicer button:
Then, select the columns you want to filter by:
As long as you are referencing the correct data range, then the slicer will apply to all the pivot tables correctly. And now, if I add a slicer for the stores and only select stores A and B, my dashboard updates as follows:
One thing to remember when you are applying changes: don’t forget to click on the green OK button on the bottom, otherwise your selections won’t be applied:
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Google Sheets has come a long way in being a formidable alternative to Excel. While it may not have all of the same features as Excel, Google is adding to its functionality. Creating a pivot table with slicers is now a possibility in Google Sheets, and below, I’ll show you how you can do that with the online spreadsheeting program.
The basics: creating a pivot table in Google Sheets
To create a pivot table in Google Sheets involves about the same steps as it does in Excel: compiling and organizing your data set, and then creating the pivot table. Here’s a quick look of my sample data that I have ready to use:
Then, on the Data menu, select the option to create Pivot Table:
The next step is selecting where you to put your pivot table:
The default, a new worksheet, will often work the best. Although the layout looks a little different, the process remains the same with a blank pivot table being your starting point:
On the right-hand side of the page, you’ll see options to put fields into columns and rows, which is what you’re used to with Excel. Again, the main difference is the layout but the logic remains the same:
When clicking on the Add button, you’ll see options as to which fields to add, even having the option for a calculated field as well:
In my example, I’m going to select Total Sales so that I can summarize my data based on sales:
Next up, let’s add fields for both the row and column sections of the pivot table. If you have a lot of dates in your data set, you’ll want to put the field into the Row section. Otherwise, Google Sheets may give you an error where there are too many columns.
Even if you want to use dates in the column section, you’ll better of first putting it under Rows. Then, right-click on one of the dates and select Create pivot date group:
From here, you can group your dates so that you don’t have too many entries. In my example, I’m going to use Month as my breakdown. After that, I can move the Date field back into the Column section:
The problem here is that even if you have multiple years, it’ll group it into the same month. For example, I have one entry for Dec. 31, 2018, and it has not separated that out from the 2019 values. In order to fix this, I need to change the grouping from Month to Year-Month. Then my pivot table looks as follows:
Now the data from December 2018 is broken out. Next up, I’ll add another field for the Row section. Here, I’ll add the Store field. And now my pivot table is looking more like what I’d expect it to:
Next, let’s also add the Salesperson field as well so that we have more of a breakdown:
One of the things that stands out right away is that in Google Sheets the layout of the pivot table is much more intuitive. One of the annoyances of pivot tables in Excel is they’re not in a tabular format by default. With Google Sheets, it’s not something you need to worry about.
It still doesn’t have the repeating rows for the Store field, but that’s a quick fix: simply click the option to Repeat row labels:
And then, voila:
Just like with a regular pivot table you can also drill down into the individual cells to the detail. In Google Sheets, it also gives you a specific name as to what cell you’ve drilled down on, making it easier to refer back to when looking at many different tabs:
Adding slicers to the pivot table
You can also add slicers to your pivot table to make it easier to make changes to it and update it on-the-fly. To add a slicer, just click on the Data tab while you’re on the pivot table and click on Slicer:
Then that will generate the slicer, where you’ll be prompted to select a column to filter by:
Click on the filter icon and then on the right-hand side you’ll see the option to select a field from a drop-down list. In this example, I’m going to select Salesperson:
Then, on the slicer you can filter by the values in the column:
If I hit the clear button and select only Rep A, Rep B, and Rep C, this is what my pivot table now looks like:
The slicer shows the number of items selected and as you can see, it only has the sales reps that I selected in the data. You can add more slicers for other columns but the process remains the same. The big difference you can see from Excel is that your selections are how you’d make the selections in a normal filter; you don’t have buttons for each slicer option the way you do in Excel.
There are changes that you can make to the font and color of the slicer but other than that, visually, there aren’t many changes to make. So if you’re looking to replicate a similar Excel-type dashboard in Google Sheets with many options available for how slicers look then you may be disappointed here. However, in terms of functionality, the slicers work in much the same way that they do in Excel.
A good start, but Google Sheets is still lacking
Google Sheets still has a ways to go in being a real replacement for Excel. While it does have some unique functions that Excel doesn’t, adding pivot tables and slicers is a significant step forward.
However, one area that still needs more improvement is charting. For instance, creating a chart from the pivot table is not an easy task and doesn’t look like Google Sheets is designed yet to create easy-to-use pivot charts. And until that happens, there’s still going to be a big gap between the type of dashboard you can create with Google Sheets and what you can make in Excel.
The good news is that Google Sheets has made a lot of progress and it’ll likely be even better in the future.
If you liked this post on How to Make a Pivot Table in Google Sheets with Slicers, please give this site a like on Facebook and also be sure to check out some of the many templates that we have available for download. You can also follow us on Twitter and YouTube.
One of the advantages of using Google Sheets over Excel is that it is easier to access live, dynamic data that you can access from any device that can install the app.
Pulling Stock Quotes
A great feature of Google Sheets is that you can easily pull stock prices (delayed) from Google Finance. There is a unique function called GOOGLEFINANCE that can pull any of the following stock details including price (including open, high, low), volume, even the last time it traded. If I wanted to pull Alphabet’s stock price I could use the following formula:
=GOOGLEFINANCE(“GOOG”,”price”)
That will pull me the most recent stock price. If I wanted to see the percent change since the last day’s close I would just change price to changepct:
=GOOGLEFINANCE(“GOOG”,”changepct”)
If you access the help you will see a list of more options: But you can go even further than that, pulling multiple dates at a time. For example, if I wanted all the closing prices since the start of the year I would enter the following formula:
In Google Sheets it automatically creates a table of values for you and you don’t have to worry about making an array like you would in Excel. The result of the above formula looks like this:
I only entered the formula in cell A1 and it produced the list of results. You can also select an interval if you don’t want every day in the range to show a total.
Getting News Feeds Using RSS
Another unique function of Google Sheets is you can pull news feeds from your favorite news site using the IMPORTFEED function. The key thing is you need to find the rss feed of the news site you want. Finding this is as easy as typing the name of the news feed you want and rss after it. For example, the the list of all of CBC’s rss feeds are found on http://www.cbc.ca/rss/index.html. I can use the top stories rss feed of http://rss.cbc.ca/lineup/topstories.xml for my feed. My formula in looks as follows:
By using “items title” it will only pull the title of the story, which is a bit neater and easier to look at as the titles do not take up as much space as the descriptions as well. If I selected “items” then I would get five columns of data – title, author, link, date, and the description. Instead, what I can do is in the next column over enter the same formula and select “items url” which will now have the story and the related link next to each other. This way I can pick and choose what I want. This is how it would look:
I have shrunk down column A since I didn’t want the whole url to show. I now have all the pieces to make a start page using nothing more than a spreadsheet:
Perhaps it doesn’t rival MSN or Google’s home page but it works for me. I’ve made the formulas for the stock calculations relevant to the cells in column B so I can change the ticker symbol as I want to. The main benefit with using this is 1) you don’t need to open a browser to get stock quotes or news, and 2) you can easily access this information from your phone, all you need is the Google Sheets app installed.
But wait, that’s not all!
Translating Text
I’m not sure why Google felt the need to, but you can even use their translator function as well inside of Google Sheets using the GOOGLETRANSLATE function.
What I could do is translate these news articles. You need to know the two character code for the language, to get that you can find it on this website:
So what I am going to do is translate the news headlines I pulled earlier and translate them into Chinese. Google has two language codes for Chinese – Simplified, and Traditional. I’ll go with simplified, which is zh-CN. My formula looks like this:
=GOOGLETRANSLATE(B1,”en”,”zh-CN”)
My output looks like this:
So now you can pull news stories from your favorite news site (just figure out the rss link) and you can translate it into whatever language you want. Unfortunately I can’t tell the accuracy of the translation, ‘Simplified’ Chinese didn’t make it any simpler for me. I still can only make out CSIS from all of that translated text.
