my dataframe have this format:
name <- c("Carlos", "Matthew", "Toth", "Mike", "Joseph", "Andrey")
time <- c("79:45","78","74:45","65:30","64","57")
myexample <- cbind.data.frame(name, time)
> myexample
nane time
1 Carlos 79:45
2 Matthew 78
3 Toth 74:45
4 Mike 65:30
5 Joseph 64
6 Andrey 57
How to convert time column with two formats ("79:45" and "78") in seconds?
the time column is in the character format :(
As output:
> myexample
nane time
1 Carlos 79:45
2 Matthew 78:00
3 Toth 74:45
4 Mike 65:30
5 Joseph 64:00
6 Andrey 57:00
Here is one option using sub:
myexample$time <- sub("^(\\d{1,})$", "\\1:00", myexample$time)
myexample
name time
1 Carlos 79:45
2 Matthew 78:00
3 Toth 74:45
4 Mike 65:30
5 Joseph 64:00
6 Andrey 57:00
Demo
Normally the best thing to do here would be to parse your text times into some formal time type. But since you are storing non standard values, where the minutes component can be greater than 60, I chose to leave it as text for the moment.
An option can be using grepl to detect presence of : and appending :00 in values : is not present.
myexample$time <- ifelse(grepl(":",myexample$time), as.character(myexample$time),
paste0(myexample$time, ":00"))
myexample
# name time
# 1 Carlos 79:45
# 2 Matthew 78:00
# 3 Toth 74:45
# 4 Mike 65:30
# 5 Joseph 64:00
# 6 Andrey 57:00
Related
This question already has answers here:
Calculate difference between values in consecutive rows by group
(4 answers)
Closed 2 years ago.
I have a dataframe that looks like this:
Name Date
David 2019-12-23
David 2020-1-10
David 2020-2-13
Kevin 2019-2-12
Kevin 2019-3-19
Kevin 2019-5-1
Kevin 2019-7-23
Basically, I'm trying to calculate the date difference between each instance, specific to each person. I am currently using the following for-loop:
df$daysbetween <- with(df, ave(as.numeric(date) , name,
FUN=function(x) { z=c(NA,NA);
for( i in seq_along(x)[-(1:2)] ){
z <- c(z, (x[i]-x[i-1]))}
return(z) }) )
Currently, it calculates the difference between the second and third, and any following instance, perfectly fine. However, it doesn't calculate the difference between the first and second date and I need it to. Where is the error in my code coming from? Would appreciate any help.
transform(df, diff = ave(Date, Name, FUN = function(x)c(NA,diff(as.Date(x)))))
Name Date diff
1 David 2019-12-23 <NA>
2 David 2020-1-10 18
3 David 2020-2-13 34
4 Kevin 2019-2-12 <NA>
5 Kevin 2019-3-19 35
6 Kevin 2019-5-1 43
7 Kevin 2019-7-23 83
Just use lag from the dplyr package:
Description:
Find the "previous" (lag()) or "next" (lead()) values in a vector. Useful for comparing values behind of or ahead of the current values.
df %>%
group_by(name) %>%
mutate(diff = date - lag(date))
Output:
name date diff
<chr> <date> <drtn>
1 David 2019-12-23 NA days
2 David 2020-01-10 18 days
3 David 2020-02-13 34 days
4 Kevin 2019-02-12 NA days
5 Kevin 2019-03-19 35 days
6 Kevin 2019-05-01 43 days
7 Kevin 2019-07-23 83 days
Say you have
Name August September October November
Bob 5 4 3 2
George 3 2 2 4
Gina 1 4 2 1
And you want to convert into 3 columns like so
Name Month Output
Bob August 5
Bob September 4
.....
I see how to do it in VBA through the following link : https://www.extendoffice.com/documents/excel/2773-excel-convert-matrix-to-list.html
Unsure how to execute in R. All of the searching I've yielded want to simply split the matrix into vectors which isn't correct.
If you have a dataframe, say, df you can define its column names as a column in its own right by using names:
df$Month <- names(df)[2:5]
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 named as Records having 2 vectors Rank and Name
Rank Name
1 Ashish
1 Ashish
2 Ashish
3 Mark
4 Mark
1 Mark
3 Spencer
2 Spencer
1 Spencer
2 Mary
4 Joseph
I want that every name should be placed in either 1, 2 ,3 or 4 tag depending on their occurrence and uniqueness:
I want to create a new vector which will be named as Tagging
So The output should be:
Rank 1 has three unique elements Mark Spencer and Ashish so the tag is 1 for all three.
Rank 2 has one unique records which is Mary as Ashish has already been assigned tag 1 so Mary is tagged as 2.
Rank 3 has no unique records as Spencer and Mark has already been assigned 1 so I cannot tag 3 to anybody.
Rank 4 has one unique record Joseph so he gets tagged as 4.
Let me know which function can help me do this.
I do not want to use looping as this is 1000000 row database
The below solution follows the principle that the highest Rank of a person is going to be that person's tag too.
tbl <- read.table(header=TRUE, text='
Rank Name
1 Ashish
1 Ashish
2 Ashish
3 Mark
4 Mark
1 Mark
3 Spencer
2 Spencer
1 Spencer
2 Mary
4 Joseph
')
Ordering the 'tbl' dataframe by Rank
tbl_ord <- tbl[with(tbl,order(Rank)),]
Removing multiple occurrence of name within same Rank
> name_ord<- tbl_ord[duplicated(tbl_ord$Rank),]
> name_ord
Rank Name
2 1 Ashish
6 1 Mark
9 1 Spencer
8 2 Spencer
10 2 Mary
7 3 Spencer
11 4 Joseph
Displaying unique Names
#name_ord[unique(name_ord$Name),] #this will work too
> name_ord[!duplicated(name_ord$Name),]
Rank Name
2 1 Ashish
6 1 Mark
9 1 Spencer
10 2 Mary
11 4 Joseph
Using the setkey function of data.table package and unique:
library(data.table)
dt<-data.table(Rank=c(1,1,2,3,4,1,3,2,1,2,4), Name=c(rep("Ashish", 3), rep("Mark", 3), rep("Spencer", 3), "Mary", "Joseph"))
setkey(dt, Rank, Name)
dt<-unique(dt)
setkey(dt, Name)
dt<-unique(dt) # works because of the above setkey call which sorted it
setkey(dt, Rank) # if you want to order them by Rank again
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)),]