I have a data frame that follows the below long Pattern:
Name MedName Name1 atenolol 25mg Name1 aspirin 81mg Name1 sildenafil 100mg Name2 atenolol 50mg Name2 enalapril 20mg
And would like to get below (I do not care if I can get the columns to be named this way, just want the data in this format):
Name medication1 medication2 medication3 Name1 atenolol 25mg aspirin 81mg sildenafil 100mg Name2 atenolol 50mg enalapril 20mg NA
Through this very site I have become familiarish with the reshape/reshape2 package, and have went through several attempts to try to get this to work but have thus far failed.
When I try dcast(dataframe, Name ~ MedName, value.var='MedName')
I just get a bunch of columns that are flags of the medication names (values that get transposed are 1 or 0) example:
Name atenolol 25mg aspirin 81mg Name1 1 1 Name2 0 0
I also tried a dcast(dataset, Name ~ variable)
after I melted the dataset, however this just spits out the following (just counts how many meds each person has):
Name MedName Name1 3 name2 2
Finally, I tried to melt the data and then reshape using idvar="Name"
timevar="variable"
(of which all just are Mednames), however this does not seem built for my issue since if there are multiple matches to the idvar, the reshape just takes the first MedName and ignores the rest.
Does anyone know how to do this using reshape or another R function? I realize that there probably is a way to do this in a more messy manner with some for loops and conditionals to basically split and re-paste the data, but I was hoping there was a more simple solution. Thank you so much!
To summarize, if you need to reshape a Pandas dataframe from long to wide, use pd. pivot() . If you need to reshape a Pandas dataframe from wide to long, use pd. melt() .
To convert long data back into a wide format, we can use the cast function. There are many cast functions, but we will use the dcast function because it is used for data frames.
With the data.table package, this could easily be solved with the new rowid
function:
library(data.table) dcast(setDT(d1), Name ~ rowid(Name, prefix = "medication"), value.var = "MedName")
which gives:
Name medication1 medication2 medication3 1 Name1 atenolol 25mg aspirin 81mg sildenafil 100mg 2 Name2 atenolol 50mg enalapril 20mg <NA>
Another method (commonly used before version 1.9.7):
dcast(setDT(d1)[, rn := 1:.N, by = Name], Name ~ paste0("medication",rn), value.var = "MedName")
giving the same result.
A similar approach, but now using the dplyr and tidyr packages:
library(dplyr) library(tidyr) d1 %>% group_by(Name) %>% mutate(rn = paste0("medication",row_number())) %>% spread(rn, MedName)
which gives:
Source: local data frame [2 x 4] Groups: Name [2] Name medication1 medication2 medication3 (fctr) (chr) (chr) (chr) 1 Name1 atenolol 25mg aspirin 81mg sildenafil 100mg 2 Name2 atenolol 50mg enalapril 20mg NA
Assuming your data is in the object dataset
:
library(plyr) ## Add a medication index data_with_index <- ddply(dataset, .(Name), mutate, index = paste0('medication', 1:length(Name))) dcast(data_with_index, Name ~ index, value.var = 'MedName') ## Name medication1 medication2 medication3 ## 1 Name1 atenolol 25mg aspirin 81mg sildenafil 100mg ## 2 Name2 atenolol 50mg enalapril 20mg <NA>
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