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))
Related
I have a dataframe that has 23 columns of various parameters defining a patient which I extracted using dplyr from a larger dataframe after pivoting it such that each of the parameters forms the columns of the new dataframe.
Now I am facing an issue. I am getting a lot of rows for the same patient. For each parameter, one of the rows shows the required value and the rest is denoted as NA. So if the same patient is repeated, say 10 times, in every parameter column there is one row with the actual value and the rest is NA.
How do I remove these NAs and gather the information that is scattered in this manner?
I want the 1 and 2 to be on the same row. All the rows seen in this image of dataframe are of the same person.
I am still new to R and I am attempting to solve a seemingly simple problem. I would like to identify all of the unique combinations of values from 4 different rows, and update an additional column in my df to annotate whether or not it is unique.
Giving a df with columns A-Z, I have used the following code to identify unique combinations of column A,B,C,D, and E. I am trying to update column F with this information.
unique(df[ ,c("A", "B","C","D", "E")])
This returns each of the individual rows with unique combinations as expected, but I cannot figure out what the next step I should take in order to update column "F" with a value to indicate that it is a unique row. Thanks in advance for any pointers!
Basically what we have is several columns as follows:
Household ID, restaurantspend, groceryspend, foodtruckspend
We have duplicate household ids because each spend is in its own individual column so an example of our data looks like this:
data example
We want to have the Household ID only have 1 row per id and combine the numerical values of the other column.
aggdata = aggregate(mydata, by=list(mydata$HouseHoldID),Fun=sum)
I have created the above table and saved it as "Mydata". Run the above code. View the output "aggdata", you can see an extra column "Group.1" that's the group based on "HouseHoldID". You can ignore the second column "HouseHoldId" as the same information will be available in the column "Group.1".
Just wondering if there is a way to expand rows which have multiple observations, into rows of unique observations using R? I have data in an excel spreadsheet with the variable headings: Lease, Line, Bay, Date, Predators, Food.Index, DD, MM, YY.
On some dates, there have been multiple predators (from 1 to 4) recorded in the same row. Other days just have 0. On a day where there has been 4 predators recorded, I would like to somehow transform the data to show four unique observations (instead of one row with 4 recorded under "Predators").
I have 1669 rows of data and multiple rows need to be expanded
Example of Data set
Many thanks for your help in advance.
enter image description here
Assuming you have your data in a data.frame, df, one possible solution would be
df.expanded <- df[rep(row.names(df), df$Predators), ]
EDIT: If you also want to keep the rows with 0 predators, you can use pmax to always return at least one:
df.expanded <- df[rep(row.names(df), pmax(df$Predators, 1)),]
Here the pmax(df$Predators, 1) will return the elementwise maximum of df$Predators and 1 so that it returns a new vector where each element is at least 1 but takes the value of df$Predators if that number is greater than 1.
I would like to create a new column in my dataframe that assigns a categorical value based on a condition to the other observations.
In detail, I have a column that contains timestamps for all observations. The columns are ordered ascending according to the timestamp.
Now, I'd like to calculate the difference between each consecutive timestamp and if it exceeds a certain threshold the factor should be increased by 1 (see Desired Output).
Desired Output
I tried solved it with a for loop, however that takes a lot of time because the dataset is huge.
After searching for a bit I found this approach and tried to adapt it: R - How can I check if a value in a row is different from the value in the previous row?
ind <- with(df, c(TRUE, timestamp[-1L] > (timestamp[-length(timestamp)]-7200)))
However, I can not make it work for my dataset.
Thanks for your help!