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How to reshape data from long to wide format
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I am a beginner, confronted with a big task and all the typical long to wide reshaping tools I found using the search function did not really do the job for me. I would be glad if someone could help me.
I try to achieve the following:
I have patientdata in which every patient has a unique patient number but multiple stays in hospital have lead to multiple cases per person. I want to work with these cases. Problem is, I have all the diagnoses per case but not everybody has the same number of diagnosis and I don't know how to tell R to create a new dagnosis (and date of diagnosis) variable each time there is already a diagnosis. Every help is highly appreciated!
So, I have a huge dataset that looks roughly like that:
Patient Case Diagnosis DateOfDiagnosis
1 John Doe 1 A 2010-10-10
2 John Doe 1 B 2010-10-10
3 John Doe 1 C 2010-10-10
4 Peter Griffin 2 D 2010-10-11
5 Peter Griffin 2 E 2010-10-11
6 Homer Simpson 3 F 2010-10-12
7 Homer Simpson 4 G 2010-10-13
I need row by case and I need all the diagnosis and their dates in separate variables. This would be no problem but there is no pattern in the cases or diagnosis so some patients have only one case others 5 and some cases have 1 others 5 diagnoses with respective date.
So what I need looks like this:
Patient Case Diag1 DateOfDiag1 Diag2 DateOfDiag2 Diag3 DateOfDiag3 ....
1 John Doe 1 A 2010-10-10 B 2010-10-10 C 2010-10-10
2 Peter Grif 2 D 2010-10-11 E 2010-10-11 NA NA
3 Homer Simp 3 F 2010-10-12 NA NA NA NA
4 Homer Simp 4 G 2010-10-13 NA NA NA NA
The code for my example is:
Patient <- c('John Doe','John Doe','John Doe', 'Peter Griffin','Peter Griffin', 'Homer Simpson', 'Homer Simpson')
Case <- c(1,1,1,2,2,3,4)
Diagnosis <- c('A','B','C','D','E','F','G')
DateOfDiagnosis <- as.Date(c('2010-10-10','2010-10-10','2010-10-10','2010-10-11','2010-10-11','2010-10-12','2010-10-13'))
df<-data.frame(Patient, Case, Diagnosis, DateOfDiagnosis)
Every help is highly appreciated!
Kind regards,
Jan
You could use pivot_wider, after creating a unique column.
library(dplyr)
library(tidyr)
df %>%
group_by(Patient, Case) %>%
mutate(row = row_number()) %>%
pivot_wider(values_from = c(Diagnosis, DateOfDiagnosis), names_from = row)
# Patient Case Diagnosis_1 Diagnosis_2 Diagnosis_3 DateOfDiagnosis_1 DateOfDiagnosis_2 DateOfDiagnosis_3
# <fct> <dbl> <fct> <fct> <fct> <date> <date> <date>
#1 John Doe 1 A B C 2010-10-10 2010-10-10 2010-10-10
#2 Peter Griffin 2 D E NA 2010-10-11 2010-10-11 NA
#3 Homer Simpson 3 F NA NA 2010-10-12 NA NA
#4 Homer Simpson 4 G NA NA 2010-10-13 NA NA
I have a code where I see which people work in certain groups. When I ask the leader of each group to present those who work for them, in a survey, I get a row of all of the team members. What I need is to clean the data into multiple rows with their group information.
I don't know where to start.
This is what my data frame looks like,
LeaderName <- c('John','Jane','Louis','Carl')
Group <- c('3','1','4','2')
Member1 <- c('Lucy','Stephanie','Chris','Leslie')
Member1ID <- c('1','2','3','4')
Member2 <- c('Earl','Carlos','Devon','Francis')
Member2ID <- c('5','6','7','8')
Member3 <- c('Luther','Peter','','Severus')
Member3ID <- c('9','10','','11')
GroupInfo <- data.frame(LeaderName, Group, Member1, Member1ID, Member2 ,Member2ID, Member3, Member3ID)
This is what I would like it to show with a certain code
LeaderName_ <- c('John','Jane','Louis','Carl','John','Jane','Louis','Carl','John','Jane','','Carl')
Group_ <- c('3','1','4','2','3','1','4','2','3','1','','2')
Member <- c('Lucy','Stephanie','Chris','Leslie','Earl','Carlos','Devon','Francis','Luther','Peter','','Severus')
MemberID <- c('1','2','3','4','5','6','7','8','9','10','','11')
ActualGroupInfor <- data.frame(LeaderName_,Group_,Member,MemberID)
An option would be melt from data.table and specify the column name patterns in the measure parameter
library(data.table)
melt(setDT(GroupInfo), measure = patterns("^Member\\d+$",
"^Member\\d+ID$"), value.name = c("Member", "MemberID"))[, variable := NULL][]
# LeaderName Group Member MemberID
# 1: John 3 Lucy 1
# 2: Jane 1 Stephanie 2
# 3: Louis 4 Chris 3
# 4: Carl 2 Leslie 4
# 5: John 3 Earl 5
# 6: Jane 1 Carlos 6
# 7: Louis 4 Devon 7
# 8: Carl 2 Francis 8
# 9: John 3 Luther 9
#10: Jane 1 Peter 10
#11: Louis 4
#12: Carl 2 Severus 11
Here is a solution in base r:
reshape(
data=GroupInfo,
idvar=c("LeaderName", "Group"),
varying=list(
Member=which(names(GroupInfo) %in% grep("^Member[0-9]$",names(GroupInfo),value=TRUE)),
MemberID=which(names(GroupInfo) %in% grep("^Member[0-9]ID",names(GroupInfo),value=TRUE))),
direction="long",
v.names = c("Member","MemberID"),
sep="_")[,-3]
#> LeaderName Group Member MemberID
#> John.3.1 John 3 Lucy 1
#> Jane.1.1 Jane 1 Stephanie 2
#> Louis.4.1 Louis 4 Chris 3
#> Carl.2.1 Carl 2 Leslie 4
#> John.3.2 John 3 Earl 5
#> Jane.1.2 Jane 1 Carlos 6
#> Louis.4.2 Louis 4 Devon 7
#> Carl.2.2 Carl 2 Francis 8
#> John.3.3 John 3 Luther 9
#> Jane.1.3 Jane 1 Peter 10
#> Louis.4.3 Louis 4
#> Carl.2.3 Carl 2 Severus 11
Created on 2019-05-23 by the reprex package (v0.2.1)
We have a daily meeting when participants nominate each other to speak. The first person is chosen randomly.
I have a dataframe that consists of names and the order of speech every day.
I have a day1, a day2 ,a day3 , etc. in the columns.
The data in the rows are numbers, meaning the order of speech on that particular day.
NA means that the person did not participate on that day.
Name day1 day2 day3 day4 ...
Albert 1 3 1 ...
Josh 2 2 NA
Veronica 3 5 3
Tim 4 1 2
Stew 5 4 4
...
I want to create two analysis, first, I want to create a dataframe who has chosen who the most times. (I know that the result depends on if a participant was nominated before and therefore on that day that participant cannot be nominated again, I will handle it later, but for now this is enough)
It should look like this:
Name Favorite
Albert Stew
Josh Veronica
Veronica Tim
Tim Stew
...
My questions (feel free to answer only one if you can):
1. What code shall I use for it without having to manunally put the names in a different dataframe?
2. How shall I handle a tie, for example Josh chose Veronica and Tim first the same number of times? Later I want to visualise it and I have no idea how to handle ties.
I also would like to analyse the results to visualise strong connections.
Like to show that there are people who usually chose each other, etc.
Is there a good package that is specialised for these? Or how should I get to it?
I do not need DNA sequences, only this simple ones, but I have not found a suitable one yet.
Thanks for your help!
If I am not misunderstanding your problem, here is some code to get the number of occurences of who choose who as next speaker. I added a fourth day to have some count that is not 1. There are ties in the result, choosing the first couple of each group by speaker ('who') may be a solution :
df <- read.table(textConnection(
"Name,day1,day2,day3,day4
Albert,1,3,1,3
Josh,2,2,,2
Veronica,3,5,3,1
Tim,4,1,2,4
Stew,5,4,4,5"),header=TRUE,sep=",",stringsAsFactors=FALSE)
purrr::map(colnames(df)[-1],
function (x) {
who <- df$Name[order(df[x],na.last=NA)]
data.frame(who,lead(who),stringsAsFactors=FALSE)
}
) %>%
replyr::replyr_bind_rows() %>%
filter(!is.na(lead.who.)) %>%
group_by(who,lead.who.) %>% summarise(n=n()) %>%
arrange(who,desc(n))
Input:
Name day1 day2 day3 day4
1 Albert 1 3 1 3
2 Josh 2 2 NA 2
3 Veronica 3 5 3 1
4 Tim 4 1 2 4
5 Stew 5 4 4 5
Result:
# A tibble: 12 x 3
# Groups: who [5]
who lead.who. n
<chr> <chr> <int>
1 Albert Tim 2
2 Albert Josh 1
3 Albert Stew 1
4 Josh Albert 2
5 Josh Veronica 1
6 Stew Veronica 1
7 Tim Stew 2
8 Tim Josh 1
9 Tim Veronica 1
10 Veronica Josh 1
11 Veronica Stew 1
12 Veronica Tim 1
I have a data frame like this:
names <- c('Mike','Mike','Mike','John','John','John','David','David','David','David')
dates <- c('04-26','04-26','04-27','04-28','04-27','04-26','04-01','04-02','04-02','04-03')
values <- c(NA,1,2,4,5,6,1,2,NA,NA)
test <- data.frame(names,dates,values)
Which is:
names dates values
1 Mike 04-26 NA
2 Mike 04-26 1
3 Mike 04-27 2
4 John 04-28 4
5 John 04-27 5
6 John 04-26 6
7 David 04-01 1
8 David 04-02 2
9 David 04-02 NA
10 David 04-03 NA
I'd like to get rid of duplicates with NA values. So, in this case, I have a valid observation from Mike on 04-26 and also have a valid observation from David on 04-02, so rows 1 and 9 should be erased and I will end up with:
names dates values
1 Mike 04-26 1
2 Mike 04-27 2
3 John 04-28 4
4 John 04-27 5
5 John 04-26 6
6 David 04-01 1
7 David 04-02 2
8 David 04-03 NA
I tried to use duplicated function, something like this:
test[!duplicated(test[,c('names','dates')]),]
But that does not work since some NA values come before the valid value. Do you have any suggestions without trying things like merge or making another data frame?
Update: I'd like to keep rows with NA that are not duplicates.
What about this way?
library(dplyr)
test %>% group_by(names, dates) %>% filter((n()>=2 & !is.na(values)) | n()==1)
Source: local data frame [8 x 3]
Groups: names, dates [8]
names dates values
(fctr) (fctr) (dbl)
1 Mike 04-26 1
2 Mike 04-27 2
3 John 04-28 4
4 John 04-27 5
5 John 04-26 6
6 David 04-01 1
7 David 04-02 2
8 David 04-03 NA
Here is an attempt in data.table:
# set up
libary(data.table)
setDT(test)
# construct condition
test[, dupes := max(duplicated(.SD)), .SDcols=c("names", "dates"), by=c("names", "dates")]
# print out result
test[dupes == 0 | !is.na(values),]
Here is a similar method using base R, except that the dupes variable is kept separately from the data.frame:
dupes <- duplicated(test[c("names", "dates")])
# this generates warnings, but works nonetheless
dupes <- ave(dupes, test$names, test$dates, FUN=max)
# print out result
test[dupes == 0 | !is.na(test$values),]
If there are duplicated rows where the values variable is NA, and these duplicates add nothing to the data, then you can drop them prior to running the code above:
testNoNADupes <- test[!(duplicated(test) & is.na(test$values)),]
This should work based on your sample.
test <- test[order(test$values),]
test <- test[!(duplicated(test$names) & duplicated(test$dates) & is.na(test$values)),]
Supose I have a data frame with 3 columns (name, y, sex) where name is character, y is a numeric value and sex is a factor.
sex<-c("M","M","F","M","F","M","M","M","F")
x<-c("MARK","TOM","SUSAN","LARRY","EMMA","LEONARD","TIM","MATT","VIOLET")
name<-as.character(x)
y<-rnorm(9,8,1)
score<-data.frame(x,y,sex)
score
name y sex
1 MARK 6.767086 M
2 TOM 7.613928 M
3 SUSAN 7.447405 F
4 LARRY 8.040069 M
5 EMMA 8.306875 F
6 LEONARD 8.697268 M
7 TIM 10.385221 M
8 MATT 7.497702 M
9 VIOLET 10.177969 F
If I wanted to order it by y I would use:
score[order(score$y),]
x y sex
1 MARK 6.767086 M
3 SUSAN 7.447405 F
8 MATT 7.497702 M
2 TOM 7.613928 M
4 LARRY 8.040069 M
5 EMMA 8.306875 F
6 LEONARD 8.697268 M
9 VIOLET 10.177969 F
7 TIM 10.385221 M
So far, so good... The names keep the correct score BUT how could I reorder it to have M and F levels not mixed. I need to order and at the same time keep factor levels separated.
Finally I would like to take a step further to involve character, the example doesn't help, but what if there were tied y values and I would have to order again within factor (e.g. TIM and TOM got 8.4 and I have to assign alphabetical order).
I was thinking about by function but it creates a list and doesn't help really. I think there must be some function like it to apply on data frames and get data frames as return.
TO MAKE CLEAR THE POINT:
sep<-split(score,score$sex)
sep$M<-sep$M[order(sep$M[,2]),]
sep$M
x y sex
1 MARK 6.767086 M
8 MATT 7.497702 M
2 TOM 7.613928 M
4 LARRY 8.040069 M
6 LEONARD 8.697268 M
7 TIM 10.385221 M
sep$F<-sep$F[order(sep$F[,2]),]
sep$F
x y sex
3 SUSAN 7.447405 F
5 EMMA 8.306875 F
9 VIOLET 10.177969 F
merged<-rbind(sep$M,sep$F)
merged
x y sex
1 MARK 6.767086 M
8 MATT 7.497702 M
2 TOM 7.613928 M
4 LARRY 8.040069 M
6 LEONARD 8.697268 M
7 TIM 10.385221 M
3 SUSAN 7.447405 F
5 EMMA 8.306875 F
9 VIOLET 10.177969 F
I know how to do that if I have 2 or 3 factors. But what if I had serious levels of factors, say 20, should I write a for loop?
order takes multiple arguments, and it does just what you want:
with(score, score[order(sex, y, x),])
## x y sex
## 3 SUSAN 6.636370 F
## 5 EMMA 6.873445 F
## 9 VIOLET 8.539329 F
## 6 LEONARD 6.082038 M
## 2 TOM 7.812380 M
## 8 MATT 8.248374 M
## 4 LARRY 8.424665 M
## 7 TIM 8.754023 M
## 1 MARK 8.956372 M
Here is a summary of all methods mentioned in other answers/comments (to serve future searchers). I've added a data.table way of sorting.
# Base R
do.call(rbind, by(score, score$sex, function(x) x[order(x$y),]))
with(score, score[order(sex, y, x),])
score[order(score$sex,score$x),]
# Using plyr
arrange(score, sex,y)
ddply(score, c('sex', 'y'))
# Using `data.table`
library("data.table")
score_dt <- setDT(score)
# setting a key works sorts the data.table
setkey(score_dt,sex,x)
print(score_dt)
Here is Another question that deals with the same
I think there must be some function like it to apply on data frames
and get data frames as return
Yes there is:
library(plyr)
ddply(score, c('y', 'sex'))
It sounds to me like you're trying to order by score within the males and females and return a combined data frame of sorted males and sorted females.
You are right that by(score, score$sex, function(x) x[order(x$y),]) returns a list of sorted data frames, one for male and one for female. You can use do.call with the rbind function to combine these data frames into a single final data frame:
do.call(rbind, by(score, score$sex, function(x) x[order(x$y),]))
# x y sex
# F.5 EMMA 7.526866 F
# F.9 VIOLET 8.182407 F
# F.3 SUSAN 9.677511 F
# M.4 LARRY 6.929395 M
# M.8 MATT 7.970015 M
# M.7 TIM 8.297137 M
# M.6 LEONARD 8.845588 M
# M.2 TOM 9.035948 M
# M.1 MARK 10.082314 M
I believe that the person asked how to sort it by the orders in the case of say 20.
I know how to do that if I have 2 or 3 factors. But what if I had
serious levels of factors, say 20, should I write a for loop?
I have one where there are 9 orders with various counts.
stage_name count
<ord> <int>
1 Closed Lost 957
2 Closed Won 1413
3 Evaluation 1773
4 Meeting Scheduled 4104
5 Nurture 1222
6 Opportunity Disqualified 805
7 Order Submitted 1673
8 Qualifying 5138
9 Quoted 4976
In this case you can see that it is displayed using alphabetical order of stage_name, but stage_name is actually an ordered factor that has a very different order.
This code orders the factor is a much different order:
# Make categoricals ----
check_stage$stage_name = ordered(check_stage$stage_name, levels=c(
'Opportunity Disqualified',
'Qualifying',
'Evaluation',
'Meeting Scheduled',
'Quoted',
'Order Submitted',
'Closed Won',
'Closed Lost',
'Nurture'))
Now we can just apply the factor as the method of ordering this is a dplyr function, but you might need forcats too. I have both libraries installed:
check_stage <- check_stage %>%
arrange(factor(stage_name))
This now gives the output in the factor order as desired:
check_stage
# A tibble: 9 × 2
stage_name count
<ord> <int>
1 Opportunity Disqualified 805
2 Qualifying 5138
3 Evaluation 1773
4 Meeting Scheduled 4104
5 Quoted 4976
6 Order Submitted 1673
7 Closed Won 1413
8 Closed Lost 957
9 Nurture 1222