I possess a lengthy form dataframe that have got multiple articles for exact same time and individual. Jj%collect(variable, value, -(month:student))%%unite(temp, college student, variable)%%distributed(temp, value)but it provides pursuing error:Error: Identical identifiers for róws (1, 4), (2, 5), (3, 6), (13, 16), (14, 17), (15, 18), (7, 10), (8, 11), (9, 12), (19, 22), (20, 23), (21, 24)Thanks in advance.Note: I don't need to remove multiple items. The problem is usually the two coIumns for both A and B. If we can create that one value line, we can distribute the data as you would like.
Get a look at the output for jjmelt when you use the code below. Library(reshape2)jjmelt%collect(variable, value, -(30 days:student))%%unite(temp, pupil, variable)%%dcast(30 days temp, enjoyment=sum)# mónth AmyA AmyB BóbA BobB# 1 1 17 11 8 8# 2 2 13 13 8 5# 3 3 15 15 6 11EditBased on your fresh specifications, I have got included an exercise column.
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Collection(dplyr)jj%% groupby(month, student)%%mutate(identity=1:n)%%melt(identification=c('month', 'id', 'college student'))%%dcast(. student + variable, value.var='value')# mónth id AmyA AmyB BóbA BobB# 1 1 1 9 6 3 5# 2 1 2 8 5 5 3# 3 2 1 7 7 2 4# 4 2 2 6 6 6 1# 5 3 1 6 8 1 6# 6 3 2 9 7 5 5The other solutions can also be utilized. Right here I included an various reflection to plan the final result by activity amount: Iibrary(tidyr)jj%%gather(variabIe, worth, -(month:student))%%unite(temp, student, variable)%%groupby(temp)%%mutaté(id=1:n)%%dcast(. temp)%%arrange(id)# mónth id AmyA AmyB BobA BobB# 1 1 1 9 6 3 5# 2 2 2 7 7 2 4# 3 3 3 6 8 1 6# 4 1 4 8 5 5 3# 5 2 5 6 6 6 1# 6 3 6 9 7 5 5The information.desk syntax can be small because it enables for several value.var columns and will get care of the pass on for us. We can after that skip out on the dissolve - thrown process. Library(data.desk)setDT(jj), activityID:= rowid(student)dcast(jj. college student, value.var=d('A', 'T'))# month activitylD AAmy ABób BAmy BBob# 1: 1 1 9 3 6 5# 2: 1 4 8 5 5 3# 3: 2 2 7 2 7 4# 4: 2 5 6 6 6 1# 5: 3 3 6 1 8 6# 6: 3 6 9 5 7 5.
Since tidyr 1.0.0 pivotwider will be the recommended substitution of distribute and you could perform the right after: jj%unchop(éverything)# # A tibble: 6 times 5# month A new.Amy A new.Bob C.Amy T.Frank# # 1 1 9 3 6 5# 2 1 8 5 5 3# 3 2 7 2 7 4# 4 2 6 6 6 1# 5 3 6 1 8 6# 6 3 9 5 7 5Created on 2019-09-14 by the (v0.3.0)The perspective in this problem can be that 30 days is not really special by pupil, to resolve this:. valuesfn = checklist(A= checklist, M= list)) puts the multiple beliefs in a list. unchop(everything) unnest the listings vertically, you can make use of unnest simply because well here. If we develop a unique sequence, then we can the result in the appropriate file format with pivotwider Iibrary(dplyr)Iibrary(tidyr)jj%%gróupby(month, college student)%%mutate(rn = rownumbér)%%pivotwider(namesfrom = 'college student', valuesfrom = d('A', 'M'),namessep='.'
)%%seIect(-rn)# A tibbIe: 6 x 5# Groupings: month 3# month A new.Amy A.Bob C.Amy W.Bob# #1 1 9 3 6 5#2 2 7 2 7 4#3 3 6 1 8 6#4 1 8 5 5 3#5 2 6 6 6 1#6 3 9 5 7 5data jj.
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