Using as.numeric and lenght

[Jolien]

Hello there!

I needed to merge different datasets in R. During this data has been lost. I need to know where / when / in which step(s) this happened, so I need to count the number of values for a specific column (in this case, Calcium and Ketosis), over the different merged / new datasets.

I now am using length for calcium and that works, but lit up a complete new error too, that's for another post.

When I try using
length(which(DfKetosisUterusRaw$Ketosis > 0 ))

for the DfKetosisUterusRaw$Ketosis I get the following:

When I then try to change the $ketosis into numeric i get the following

So at this point, I am stuck. Apparently Ketosis is seen as a factor and I cannot seem to get the column as numeric. Maybe some of you have an idea what I am doing wrong?

Any explanation would be very much appreciated. Thanks in advance.

[pieterjanvc]

Hi,

Here is a way of testing the different columns for missing or 0 values

#Dummy data
myData = data.frame(x = 1:10, 
                    y = c(1,5,0.2,0,0,8,NA,5,NA,6.3),
                    z = c(1,NA,0.2,7,0,8,4.3,5,0,6.3))

#Number of values in columns that are missing or 0
nMissing = apply(myData, 2, function(x){
  sum(is.na(x) | x == 0)
})
nMissing
#> x y z 
#> 0 4 3

#Number of 'correct' values in each column 
nrow(myData) - nMissing
#>  x  y  z 
#> 10  6  7

^{Created on 2021-08-26 by the reprex package (v2.0.1)}

The apply function runs a function over each column (option 2 as second argument), in this case a check of NA or 0

Since I could not work with your data or code, I came up with a dummy example. Next time consider creating a reprex. 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.…

Hope this helps,
PJ

[Jolien]

Dear PieterJan,

Thank you for your answer. I will go and test this right away for the ketosis dataframe! I will try to make a reprex. I have looked into creating reprex before and to be honest, for me it isn't that simple as it seems... but I ll give it some more hours.

Thanks so much