I'm trying to count the frequency of multiple columns in a data.frame.
I used the table function on each column and bound them all by cbind, and was going to use the aggregate function after to calculate the means by my identifier.
Example:
df1
V1 V2 V3
George Mary Mary
George Mary Mary
George Mary George
Mary Mary George
Mary George George
Mary
Frequency<- as.data.frame(cbind(table(df1$V1), table(df1$V2), table(df1$V3)))
row.names V1
George 3
Mary 3
1
George 1
Mary 4
1
George 3
Mary 2
The result I get (visually) is a 2 column data frame, but when I check the dimension of Frequency, I get a result implying that the 2nd column only exists.
It's causing me trouble when I try to rename the columns and run the aggregate function, errors I get for rename:
colnames(Frequency) <- c("Name", "Frequency")
Error in names(Frequency) <- c("Name", "Frequency") :
'names' attribute [2] must be the same length as the vector [1]
The Final purpose is to run an aggregate command and get the mean by name:
Name.Mean<- aggregate(Frequency$Frequency, list(Frequency.Name), mean)
Desired output:
Name Mean
George Value
Mary Value
Using mtabulate (data from #user3169080's post)
library(qdapTools)
d1 <- mtabulate(df1)
is.na(d1) <- d1==0
colMeans(d1, na.rm=TRUE)
# Alice George Mary
# 4.0 3.0 2.5
I hope this is what you were looking for:
> df1
V1 V2 V3
1 George George George
2 Mary Mary Alice
3 George George George
4 Mary Mary Alice
5 <NA> George George
6 <NA> Mary Alice
7 <NA> <NA> George
8 <NA> <NA> Alice
> ll=unlist(lapply(df1,table))
> nn=names(ll)
> nn1=sapply(nn,function(x) substr(x,4,nchar(x)))
> mm=data.frame(ll)
> mm$names=nn1
> tapply(mm$ll,mm$names,mean)
> Mean=tapply(mm$ll,mm$names,mean)
> data.frame(Mean)
Mean
Alice 4.0
George 3.0
Mary 2.5
Related
I already asked and got the solution about this topic.
However, additionally, I want to check which data are replaced in the new column. I just tried below,
df$check <- str_match_all(df, "\\d{11}") %% unlist
but, it cannot work. Ultimately, I want to get the below data set.
original edited check
1 010-1234-5678 010-1234-5678
2 John 010-8888-8888 John 010-8888-8888
3 Phone: 010-1111-2222 Phone: 010-1111-2222
4 Peter 018.1111.3333 Peter 018.1111.3333
5 Year(2007,2019,2020) Year(2007,2019,2020)
6 Alice 01077776666 Alice 010-9999-9999 01077776666
Here is my code.
x = c("010-1234-5678",
"John 010-8888-8888",
"Phone: 010-1111-2222",
"Peter 018.1111.3333",
"Year(2007,2019,2020)",
"Alice 01077776666")
df = data.frame(
original = x
)
df$edited <- gsub("\\d{11}", "010-9999-9999", df$original)
df$check <- c("","","","","","01077776666") # I want to know the way here.
Thank you.
In an ifelse using `==` you could test if the columns match, then if not, use gsub to match the first digit and get it and the rest of the string out of "original".
transform(df, check=ifelse(!do.call(`==`, df[c("original", "edited")]),
gsub('(\\D*)(\\d.*)', '\\2', original),
NA))
# original edited check
# 1 010-1234-5678 010-1234-5678 <NA>
# 2 John 010-8888-8888 John 010-8888-8888 <NA>
# 3 Phone: 010-1111-2222 Phone: 010-1111-2222 <NA>
# 4 Peter 018.1111.3333 Peter 018.1111.3333 <NA>
# 5 Year(2007,2019,2020) Year(2007,2019,2020) <NA>
# 6 Alice 01077776666 Alice 010-9999-9999 01077776666
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)
I have a data frame that looks similar to this:
EVENT ID GROUP YEAR X.1 X.2 X.3 Y.1 Y.2 Y.3
1 1 John Smith GROUP1 2015 1 John Smith 5 Adam Smith 12 Mike Smith 20 Sam Smith 7 Luke Smith 3 George Smith
Each row repeats for new logs, but the values in X.1 : Y.3 change often.
The ID's and the ID's present in X.1 : Y.3 have a numeric value and then the name ID, i.e., "1 John Smith" or "20 Sam Smith" will be the string.
I have an issue where in certain instances, the ID will remain as "1 John Smith" but in X.1 : Y.3 the number may change preceding "John Smith", so for example it might be "14 John Smith". The names will always be correct, it's just the number that sometimes gets mixed up.
I have a list of 200+ ID's that are impacted by this mismatch - what is the most efficient way to replace the values in X.1 : Y.3 so that they match the correct ID in column ID?
I won't know which column "14 John Smith" shows up in, it could be X.1, or Y.2, or Y.3 depending on the row.
I can use a replace function in a dplyr line of code, or gsub for each 200+ ID's and for each column effected, but it seems very inefficient. Is there a quicker way than repeated something like the below x times?
df%>%mutate(X.1=replace(X.1, grepl('John Smith', X.1), "1 John Smith"))%>%as.data.frame()
Sometimes it helps to temporarily reshape the data. That way we can operate on all the X and Y values without iterating over them.
library(stringr)
library(tidyr)
## some data to work with
exd <- read.csv(text = "EVENT,ID,GROUP,YEAR,X.1,X.2,X.3,Y.1,Y.2,Y.3
1,1 John Smith,GROUP1,2015,19 John Smith,11 Adam Smith,9 Sam Smith,5 George Smith,13 Mike Smith,12 Luke Smith
2,2 John Smith,GROUP1,2015,1 George Smith,9 Luke Smith,19 Adam Smith,7 Sam Smith,17 Mike Smith,11 John Smith
3,3 John Smith,GROUP1,2015,5 George Smith,18 John Smith,12 Sam Smith,6 Luke Smith,2 Mike Smith,4 Adam Smith",
stringsAsFactors = FALSE)
## re-arrange to put X and Y columns into a single column
exd <- gather(exd, key = "var", value = "value", X.1, X.2, X.3, Y.1, Y.2, Y.3)
## find the X and Y values that contain the ID name
matches <- str_detect(exd$value, str_replace_all(exd$ID, "^\\d+ *", ""))
## replace X and Y values with the matching ID
exd[matches, "value"] <- exd$ID[matches]
## put it back in the original shape
exd <- spread(exd, key = "var", value = value)
exd
## EVENT ID GROUP YEAR X.1 X.2 X.3 Y.1 Y.2 Y.3
## 1 1 1 John Smith GROUP1 2015 1 John Smith 11 Adam Smith 9 Sam Smith 5 George Smith 13 Mike Smith 12 Luke Smith
## 2 2 2 John Smith GROUP1 2015 1 George Smith 9 Luke Smith 19 Adam Smith 7 Sam Smith 17 Mike Smith 2 John Smith
## 3 3 3 John Smith GROUP1 2015 5 George Smith 3 John Smith 12 Sam Smith 6 Luke Smith 2 Mike Smith 4 Adam Smith
Not sure if you're set on dplyr and piping, but I think this is a plyr solution that does what you need. Given this example dataset:
> df
EVENT ID GROUP YEAR X.1 X.2 X.3 Y.1 Y.2 Y.3
1 1 1 John Smith GROUP1 2015 19 John Smith 11 Adam Smith 9 Sam Smith 5 George Smith 13 Mike Smith 12 Luke Smith
2 2 2 John Smith GROUP1 2015 1 George Smith 9 Luke Smith 19 Adam Smith 7 Sam Smith 17 Mike Smith 11 John Smith
3 3 3 John Smith GROUP1 2015 5 George Smith 18 John Smith 12 Sam Smith 6 Luke Smith 2 Mike Smith 4 Adam Smith
This adply function goes row by row and replaces any matching X:Y column values with the one from the ID column:
library(plyr)
adply(df, .margins = 1, function(x) {
idcol <- as.character(x$ID)
searchname <- trimws(gsub('[[:digit:]]+', "", idcol))
sapply(x[5:10], function(y) {
ifelse(grepl(searchname, y), idcol, as.character(y))
})
})
Output:
EVENT ID GROUP YEAR X.1 X.2 X.3 Y.1 Y.2 Y.3
1 1 1 John Smith GROUP1 2015 1 John Smith 11 Adam Smith 9 Sam Smith 5 George Smith 13 Mike Smith 12 Luke Smith
2 2 2 John Smith GROUP1 2015 1 George Smith 9 Luke Smith 19 Adam Smith 7 Sam Smith 17 Mike Smith 2 John Smith
3 3 3 John Smith GROUP1 2015 5 George Smith 3 John Smith 12 Sam Smith 6 Luke Smith 2 Mike Smith 4 Adam Smith
Data:
names <- c("EVENT","ID",'GROUP','YEAR', paste(rep(c("X.", "Y."), each = 3), 1:3, sep = ""))
first <- c("John", "Sam", "Adam", "Mike", "Luke", "George")
set.seed(2017)
randvals <- t(sapply(1:3, function(x) paste(sample(1:20, size = 6),
paste(sample(first, replace = FALSE, size = 6), "Smith"))))
df <- cbind(data.frame(1:3, paste(1:3, "John Smith"), "GROUP1", 2015), randvals)
names(df) <- names
I think that the most efficient way to accomplish this is by building a loop. The reason is that you will have to repeat the function to replace the names for every name in your ID list. With a loop, you can automate this.
I will make some assumptions first:
The ID list can be read as a character vector
You don't have any typos in the ID list or in your data.frame, including
different lowercase and uppercase letters in the names.
Your ID list does not contain the numbers. In case that it does contain numbers, you have to use gsub to erase them.
The example can work with a data.frame (DF) with the same structure that
you put in your question.
>
ID <- c("John Smith", "Adam Smith", "George Smith")
for(i in 1:length(ID)) {
DF[, 5:10][grep(ID[i], DF[, 5:10])] <- ID[i]
}
With each round this loop will:
Identify the positions in the columns X.1:Y.3 (columns 5 to 10 in your question) where the name "i" appears.
Then, it will change all those values to the one in the "i" position of the ID vector.
So, the first iteration will do: 1) Search for every position where the name "John Smith" appears in the data frame. 2) Replace all those "# John Smith" with "John Smith".
Note: If you simply want to delete the numbers, you can use gsub to replace them. Take into account that you probably want to erase the first space between the number and the name too. One way to do this is using gsub and a regular expression:
DF[, 5:10] <- gsub("[0-9]+ ", "", DF[, 5:10])
This question already has answers here:
reshape wide to long with character suffixes instead of numeric suffixes
(3 answers)
Closed 5 years ago.
I have data in this format:
boss employee1 employee2
1 wil james andy
2 james dean bert
3 billy herb collin
4 tony mike david
and I would like it in this format:
boss employee
1 wil james
2 wil andy
3 james dean
4 james bert
5 billy herb
6 billy collin
7 tony mike
8 tony david
I have searched the forums, but I have not yet found anything that helps. I have tried using dplyr and some others, but I am still pretty new to R.
If this question has been answered and you could give me a link that would be greatly appreciated.
Thanks,
Wil
Here is a solution that uses tidyr. Specifically, the gather function is used to combine the two employee columns. This also generates a column bsaed on the column headers (employee1 and employee2) which is called key. We remove that with select from dplyr.
library(tidyr)
library(dplyr)
df <- read.table(
text = "boss employee1 employee2
1 wil james andy
2 james dean bert
3 billy herb collin
4 tony mike david",
header = TRUE,
stringsAsFactors = FALSE
)
df2 <- df %>%
gather(key, employee, -boss) %>%
select(-key)
> df2
boss employee
1 wil james
2 james dean
3 billy herb
4 tony mike
5 wil andy
6 james bert
7 billy collin
8 tony david
I would be shocked if there isn't a slicker, base solution but this should work for you.
Using base R:
df1 <- df[, 1:2]
df2 <- df[, c(1, 3)]
names(df1)[2] <- names(df2)[2] <- "employee"
rbind(df1, df2)
# boss employee
# 1 wil james
# 2 james dean
# 3 billy herb
# 4 tony mike
# 11 wil andy
# 21 james bert
# 31 billy collin
# 41 tony david
Using dplyr:
df %>%
select(boss, employee1) %>%
rename(employee = employee1) %>%
bind_rows(df %>%
select(boss, employee2) %>%
rename(employee = employee2))
# boss employee
# 1 wil james
# 2 james dean
# 3 billy herb
# 4 tony mike
# 5 wil andy
# 6 james bert
# 7 billy collin
# 8 tony david
Data:
df <- read.table(text = "
boss employee1 employee2
1 wil james andy
2 james dean bert
3 billy herb collin
4 tony mike david
", header = TRUE, stringsAsFactors = FALSE)
I'm trying to track user actions but I'd like to see what they do AFTER a specific event. How do I get the next n amount of lines?
For example below, I'd like to know what the user is doing after they "Get mushroom" to see if they are eating it. I'd want to reference the "Get mushroom" for each User and see the next few lines after that.
User Action
Bob Enter chapter 1
Bob Attack
Bob Jump
Bob Get mushroom
Bob Open inventory
Bob Eat mushroom
Bob Close inventory
Bob Run
Mary Enter chapter 1
Mary Get mushroom
Mary Attack
Mary Jump
Mary Attack
Mary Open inventory
Mary Close inventory
I'm not sure how to approach this after grouping by users. Expected results would be something like the below if i wanted 3 lines below
User Action
Bob Get mushroom # Action I want to find and the next 3 lines below it
Bob Open inventory
Bob Eat mushroom
Bob Close inventory
Mary Get mushroom # Action I want to find and the next 3 lines below it
Mary Attack
Mary Jump
Mary Attack
Thank you.
Two alternatives with dplyr and data.table:
library(dplyr)
df1 %>%
group_by(User) %>%
slice(rep(which(Action == 'Get-mushroom'), each=4) + 0:3)
library(data.table)
setDT(df1)[df1[, rep(.I[Action == 'Get-mushroom'], each=4) + 0:3, User]$V1]
both result in:
User Action
1: Bob Get-mushroom
2: Bob Open-inventory
3: Bob Eat-mushroom
4: Bob Close-inventory
5: Mary Get-mushroom
6: Mary Attack
7: Mary Jump
8: Mary Attack
Try this:
df
User Action
1 Bob Enterchapter1
2 Bob Attack
3 Bob Jump
4 Bob Getmushroom
5 Bob Openinventory
6 Bob Eatmushroom
7 Bob Closeinventory
8 Bob Run
9 Mary Enterchapter1
10 Mary Getmushroom
11 Mary Attack
12 Mary Jump
13 Mary Attack
14 Mary Openinventory
15 Mary Closeinventory
indices <- which(df$Action == 'Getmushroom')
n <- 3
# ensure that x + n does not go beyond the #rows of df
do.call(rbind, lapply(indices, function(x)df[x:min(x+n, nrow(df)),]))
User Action
4 Bob Getmushroom
5 Bob Openinventory
6 Bob Eatmushroom
7 Bob Closeinventory
10 Mary Getmushroom
11 Mary Attack
12 Mary Jump
13 Mary Attack
First find out the indices which has the term Get mushroom using which
You can use lapply on every indices and get the next 3 indices using seq.
args <- which(df$Action == "Get mushroom")
df[unlist(lapply(args, function(x) seq(x, x+3))), ]
# User Action
#4 Bob Get mushroom
#5 Bob Open inventory
#6 Bob Eat mushroom
#7 Bob Close inventory
#10 Mary Get mushroom
#11 Mary Attack
#12 Mary Jump
#13 Mary Attack
Or a similar approach (as suggested by #Sotos in comments)
df[sapply(args, function(x) seq(x, x+3)), ]
This sapply solution would work on dataframe and not on data.table as it does not accept 2-column matrix.
For it to work on data.table, you can unlist it using c
df[c(sapply(args, function(x) seq(x, x+3))), ]