Making calculations based on specific values and names in columns

[kash]

Hi,

so I want to make some calculations on a data frame based on specific values and names of different columns:

So for instance for every unique "subjectID", I want to calculate the sum of the "rating" for "Absurd" with "statementID" 1 2 and 3, and the sum of "LureN" with "statementID" 3, and sum of "LureE" with "statementID" 3.

If it is not clear, here is what I want to do:
Absurd 1 + Absurd 2 + Absurd 3 + LureN 3 + LureE

I have tried with this code so far:

sumratings <- aggregate(BADE_WEB$rating, by=list(BADE_WEB$subjectID, BADE_WEB$interpretationType, BADE_WEB$statementID), FUN=sum)

Which only gives separate values for all interpretationType and statementID.

All help is appreciated.

[pieterjanvc]

Hi,

Welcome to the RStudio community!

First of all, I'd like to point you towards our guide for creating a reprex so we can more easily use your data instead of sharing a screenshot. A reprex consists of the minimal code and data needed to recreate the issue/question you're having. You can find instructions how to build and share one here:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

I recreated your dataset (some number are not the same in the rating) and used the dplyr package from the Tidyverse to create your results

library(dplyr)

#Get the data
myData = data.frame(
  subjectID = "0532",
  interpretationType = c("LureN", "LureE", "Absurd", "True"),
  statementID = rep(1:3, 4),
  rating = 0.5
)
myData
#>    subjectID interpretationType statementID rating
#> 1       0532              LureN           1    0.5
#> 2       0532              LureE           2    0.5
#> 3       0532             Absurd           3    0.5
#> 4       0532               True           1    0.5
#> 5       0532              LureN           2    0.5
#> 6       0532              LureE           3    0.5
#> 7       0532             Absurd           1    0.5
#> 8       0532               True           2    0.5
#> 9       0532              LureN           3    0.5
#> 10      0532              LureE           1    0.5
#> 11      0532             Absurd           2    0.5
#> 12      0532               True           3    0.5

#Filter the data only to have the variables of interest
myData = myData %>% 
  filter(statementID == 3 | interpretationType == "Absurd", 
         interpretationType != "True")
myData
#>   subjectID interpretationType statementID rating
#> 1      0532             Absurd           3    0.5
#> 2      0532              LureE           3    0.5
#> 3      0532             Absurd           1    0.5
#> 4      0532              LureN           3    0.5
#> 5      0532             Absurd           2    0.5

#Group the result by subjectID and calculate the sum
myData %>% group_by(subjectID) %>% 
  summarise(sumRatings = sum(rating), .groups = "drop")
#> # A tibble: 1 x 2
#>   subjectID sumRatings
#> * <chr>          <dbl>
#> 1 0532             2.5

^{Created on 2021-01-26 by the reprex package (v0.3.0)}

If you don't know the Tidyverse yet, you can check out the basics of dplyr here. It's really handy once you get to know it!

Hope this helps,
PJ

[kash]

Oh sorry, will do that in future posts.

Thanks a ton, definitely helped.

How would I do if I also wanted to subtract ratings for interpretationType True with statement ID 3?

[pieterjanvc]

Hi,

That would be possible with just a few changes:

library(dplyr)

#Get the data
myData = data.frame(
  subjectID = "0532",
  interpretationType = c("LureN", "LureE", "Absurd", "True"),
  statementID = rep(1:3, 4),
  rating = 0.5
)
myData
#>    subjectID interpretationType statementID rating
#> 1       0532              LureN           1    0.5
#> 2       0532              LureE           2    0.5
#> 3       0532             Absurd           3    0.5
#> 4       0532               True           1    0.5
#> 5       0532              LureN           2    0.5
#> 6       0532              LureE           3    0.5
#> 7       0532             Absurd           1    0.5
#> 8       0532               True           2    0.5
#> 9       0532              LureN           3    0.5
#> 10      0532              LureE           1    0.5
#> 11      0532             Absurd           2    0.5
#> 12      0532               True           3    0.5

#Filter the data only to have the variables of interest
myData = myData %>% 
  filter(statementID == 3 | interpretationType == "Absurd")
myData
#>   subjectID interpretationType statementID rating
#> 1      0532             Absurd           3    0.5
#> 2      0532              LureE           3    0.5
#> 3      0532             Absurd           1    0.5
#> 4      0532              LureN           3    0.5
#> 5      0532             Absurd           2    0.5
#> 6      0532               True           3    0.5

#Group the result by subjectID and calculate the sum
myData %>% group_by(subjectID) %>% 
  summarise(
    sumRatings = sum(rating[interpretationType != "True"]) - 
      sum(rating[interpretationType == "True"]), 
    .groups = "drop")
#> # A tibble: 1 x 2
#>   subjectID sumRatings
#> * <chr>          <dbl>
#> 1 0532               2

^{Created on 2021-01-26 by the reprex package (v0.3.0)}

PJ

[kash]

Thanks a ton for taking your time! This helped a lot.

/K