I am new to Tableau. I have a dataset made up of year, has a measure that can be one of many values, and a corresponding value for that measure. Example:
YEAR MEASURE Value
1988 Number of Cars 10
1989 Number of Cars 15
1988 Number of Peds 5
1989 Number of Peds 6
This is just an example data set. But, I want to create bar char for Number of Cars and a line graph for Number of Peds. How can this be done? I was told I can do this without reorganizing the dataset (into a wide data set).
thanks
jason
You can create two calculated fields to separate Cars from Peds counts as follows:
if [measure name] = 'cars' then value end
Repeat for Peds. Then follow these instructions for dual axis.
https://onlinehelp.tableau.com/current/pro/desktop/en-us/multiplemeasures_dualaxes.html
Related
I am trying to divide values from the same pill in Tableau. Per my screenshot, the pill is named "Animals" & the grouped values within that pill are dog, cat, hamster & horse.
How can I divide dog by cat? How can I divide hamster by horse? How I divide cat by hamster? etc.
How would I create a calculation to show the desired values?
Without the option of pivoting rows to columns available in Tableau Desktop, you have to manually create each of the desired ratio calculated field.
I have created a sample data by sampling 200 rows in a Programming language
Your created view is like this
Now create a calculated field for each of the desired ratio. say cat/dog like this
ZN(SUM(IF [Animals] = "cat" then 1 else 0 END))/
ZN(SUM(IF [Animals] = "dog" then 1 else 0 END))
Better use this field as discreet
Add this field to view you'll get desired ratios
I am using a Tableau worksheet having 10 factories (entries) and 10 measure values for each factory/entry. There are a total of 100 columns in my Tableau worksheet.
I want to hide all columns whose value is zero and only show non zero values. But since each column is a different measure value, I can't filter or sort them. How do I go about this?
If you have tableau prep, then melt your data in pivoting. In prep you can easily remove nil or zero values easily.
Ideally you should have 11 columns, 1 for factory_id and other 10 for measures.
Alternatively, break all 10 factories in 10 groups of data and Union them after adding an id field for each group.
This link will help https://www.tableau.com/about/blog/2018/4/how-perform-coordinated-pivots-tableau-prep-86661
I am trying to calculate average of 2 columns in another column in Tableau but difficult part is grand total is not getting average instead it is the sum of 3rd calculated field.
A B Calculated field
10 5 2
6 3 2
T 16 8 4 (Here I should get 2 instead it is taking sum of column)
Here I am unable to write separate formula for row totals and grand totals, Only one formula (Calculated Field) is allowed and when I am dragging on sheet it is by default aggregating to sum.
Note: I am expert in Crystal and BO but beginner in Tableau.
Update
Code used for LoD
{FIXED [Product Category]: AVG([Sales])}
Below image is what I got after implementation I have tried with 2 columns but the result is same if I use only one column (I am trying to get the average of sales)
You are almost there - the Grand Total by default does a SUM function you just have use the Total All Using --> Average option.
Output : Level wise SUM(Profit) later averaged across columns and rows. (Show Column Grand Total & Show Row Grand Total active)
Update: Answering the question below. To get the Row-wise avg (which is Cat1-vag in this case) you could just drop the measure and change it to AVG(). Since you needed in a Calculated Field you could use a Simple FIXED LOD. You can also uncheck aggregated measures from Analysis dropdown and have no Dimension in column or row like unlike what this example shows and still get three different averages. Cheers.
{FIXED [Cat1]:AVG([Profit])}
Check out this very smart work around from Joe Mako.
https://community.tableau.com/thread/112791
create a calc field like:
IF FIRST()==0 THEN
WINDOW_AVG(SUM([Sales]),0,IIF(FIRST()==0,LAST(),0))
END
duplicate your Category field
place "Category (copy)" on the level of detail
set the compute using for the calc field pill to use "Category (copy)"
The window function in the calculated field only takes into account what's in the view, and aggregate based on those number.
I am working with a data set of 10,000s of variables which have been repeatedly measured since the 1980s. The first meassurements for each variable are not on the same date and the variables are irregularly measured - sometimes measurements are only a month apart, in a small number of cases they are decades apart.
I want to get the change in each variable per month.
So far I have a cell of dates of measurements,and interpolated rates of change between measurements (each cell represents a single variable in either, and I've only posted the first 5 cells in each array)
DateNumss= {[736614;736641;736669] [736636;736666] 736672 [736631;736659;736685] 736686}
LinearInterpss={[17.7777777777778;20.7142857142857;0] [0.200000000000000;0] 0 [2.57142857142857;2.80769230769231;0]}
How do I get monthly sums of the interpolated change in variable?
i.e.
If the first measurement for a variable is made on the January 1st, and the linearly interpolated change between that an the next measurement is 1 per day; and the next measurement is on Febuary the 5th and the corresponding linearly interpolated change is 2; then January has a total change of 1*31 (31 days at 1) and febuary has a total change of 1*5+2*23 (5 days at 1, 23 days at 2).
You would need the points in the serial dates that correspond with the change of a month.
mat(:,1)=sort(repmat(1980:1989,[1,12]));
mat(:,2)=repmat(1:12,[1,size(mat,1)/12]);
mat(:,3)=1;
monthseps=datenum(mat);
This gives you a list of all 120 changes of months in the eighties.
Now you want, for each month, the change per day, and sum it. If you take the original data it is easier, since you can just interpolate each day's value using matlab. If you only have the "LinearInterpss" you need to map it on the days using interp1 with the method 'previous'.
for ct = 2:length(monthseps)
days = monthseps(ct-1):(monthseps(ct)-1); %days in the month
%now we need each day assigned a certain change. This value depends on your "LinearInterpss". interp1 with method 'previous' searches LineairInterpss for the last value.
vals = interp1(DateNumss,LinearInterpss,days,'previous');
sum(vals); %the sum over the change in each day is the total change in a month
end
I have daily time series data and I want to calculate 5-day averages of that data while also retrieving the corresponding start date for each of the 5-day averages. For example:
x = [732099 732100 732101 732102 732103 732104 732105 732106 732107 732108];
y= [1 5 3 4 6 2 3 5 6 8];
Where x and y are actually size 92x1.
Firstly, how do I compute the 5-day mean when this time series data is not divisible by 5? Ultimately, I want to compute the 'jumping mean', where the average is not computed continuously (e.g., June 1-5, June 6-10, and so on).
I've tried doing the following:
Pentad_avg = mean(reshape(y(1:90),5,[]))'; %manually adjusted to be divisible by 5
Pentad_dt = x(1:5:90); %select every 5th day for time
However, Pentad_dt gives me dates 01-Jun-2004 and 06-Jun-2004 as output. And, that brings me to my second point.
I am looking to find 5-day averages for x and y that correspond to 5-day averages of another time series. This second time series has 5-day averaged data starting from 15-Jun-2004 until 29-Aug-2004 (instead of starting at 01-Jun-2004). Ultimately, how do I align the dates and 5-day averages between these two time series?
Synchronization between two time series can be accomplished using the timeseries object. Placing your data into an object allows Matlab to intelligently process it. The most useful thing is adds for your usage is the synchronize method.
You'll want to make sure to properly set the time vector on each of the timeseries objects.
An example of what this might look like is as follows:
ts1 = timeseries(y,datestr(x));
ts2 = timeseries(OtherData,OtherTimes);
[ts1 ts2] = synchronize(ts1,ts2,'Uniform','Interval',5);
This should return to you each timeseries aligned to be with the same times. You could also specify a specific time vector to align a timeseries to using the resample method.