I have rows of EBITDA, EBIT, etc, they are Calculated Items.
For example, EBITDA = Revenue-Expense
It is relevant for numeric columns, but not for percentages columns. The percentage column should keep the formula which is row operation : Achv(EBITDA) = Actual/Target , not Achv(EBITDA) = Revenue-Expense
I've tried using grand total but it's only one usage per view. Is there any suggestion?
Thanks
Related
I have 3 columns like target, sales, percentage (sales/target *100). When i have shown these three columns in the devexperss xtrareport pivot grid the grand total percentage is just taking the sum of the columns. i want to show the percentage of the sum of sales / sum of target in both row and column grand total.
Check the attached excel sheet
I achieved this with the following steps.
Create a new unbounded column
Set the Summary type as "Custom"
Set the Unbound Expression formula as "Sales/Target*100"
Set the Unbound Expression Mode as "Use Summary Value"
Set the Unbound Type as Decimal
Add that new column into the data area.
I am practicing my R programming skills using Kaggle data sets, and I could use some help. I am working on the Ghosts, Ghouls, and Goblins data set and the goal is to predict which type of monster each row represents based on a set of descriptive stats. I trained a multinomial logistic regression model using a training data set to get probability values for each of the 3 types, and now I just want to put the name of the monster in the last cell of each row in the test data set based on on the max probability from 3 columns in that row. Here is the head of my table: predProbs Table
What I have currently tried seems to populate every cell in the type column with the same value. How can I calculate the max probability within the columns "Ghost", "Ghoul", and "Goblin", get the column name of the column containing the max value, and then populate the last cell in every row (column name: type) with the name? I want to do this for every row in the test data set. This is what I am currently trying to do and then just cbind typesList with the whole list called predProbs.
for (i in nrow(predProbs)) {typesList = append(typesList, which.max(apply(predProbs[i,7:9], MARGIN = 2, max)))}
But this doesn't seem to be creating the vector that I need. Any thoughts?
This is similar to this post: find max value in a row and update new column with the max column name
But, unfortunately, I'm not very fluent in SQL yet so I'm not able to translate it to R.
Any help would be greatly appreciated. Thanks!
-Wes
You should think of something like this:
t(apply(predProbs,1,function(i)append(i,names(predProbs)[which.max(i)],length(i))))
I have a dataframe with multiple columns and I want to apply different functions on each column.
An example of my dataset -
I want to calculate the count of column pq110a for each country mentioned in qcountry2 column(me-mexico,br-brazil,ar-argentina). The problem I face here is that I have to use filter on these columns for example for sample patients I want-
Count of pq110 when the values are 1 and 2 (for some patients)
Count of pq110 when the value is 3 (for another patients)
Similarly when the value is 6.
For total patient I want-total count of pq110.
Output I am expecting is-Output
Similalry for each country I want this output.
Please suggest how can I do this for other columns also,countrywise.
Thanks !!
I guess what you want to do is count the number of columns of 'pq110' which have the same value within different 'qcountry2'.
So I'll try to use 'tapply' to divide data into several subsets and then use 'table' to count column number for each different value.
tapply(my_data[,"pq110"], INDEX = as.factor(my_data[,"qcountry2"]), function(x)table(x))
I have a large data frame with around 190000 rows. The data frame has a label column storing 12 nominal categories. I want to change the weight column value of each row based on the label value of that row. For example, if the label of a row is "Res", I want to change its weight field value to 0.5 and if it is "Condo", I want to change its weight value to 2.
I know it is easy to implement this by if else statement but given the number of rows, the processing time takes so much long. I wanted to use cut() but it seems that cut categorizes based on intervals not nominal categories. I would appreciate any suggestion that can decrease the processing time.
In a data frame (in R), I have two columns - the first is a list of species names (species), the second is the number of occurrence records I have for that species (number). There is a large variation in the number column with most values being <100 but a few being very high values (>100,000), and there are many rows (~4000). Here is a simplified example:
x<-data.frame(species=c("a","b","c","d","e","f","g","h","i","j"),number=c(53,17,67,989,135,67,13,786,100400,28))
Basically what I want to do is reduce the maximum number of records (the value in the number column) until the mean of all the values in this column stabilises.
To do this, I need to set a maximum limit for values in the number column so that any value > this limit is reduced to this maximum limit, and record the mean. I want to repeat this multiple times, each time reducing the maximum limit by 100.
I've not been able to find any similar questions online and am not really sure where to start with this! Any help, even just a point in the right direction, would be much appreciated! Cheers
you should use the pmin value :
pmin(x$number, 1e3)
# to test multiple limits :
mns <- sapply(c(1e6, 1e4, 1e2), function(u) mean(pmin(x$number, u)))