extract duplicate row based on condition across column in R - r

I'm stuck trying to keep row based on condition in R. I want to keep row of data based on the same condition across a large number of columns. So in the below example I want to keep rows from duplicated rows where hv value '0' at each column.
here is the data frame:
ID A B C
1 001 1 1 1
2 002 0 1 0
3 002 1 0 0
4 003 0 1 1
5 003 1 0 1
6 003 0 0 1
I want get like this:
ID A B C
1 001 1 1 1
2 002 0 0 0
3 003 0 0 1
Any help would be much appreciated, thanks!


Please check this code
# A tibble: 6 × 4
ID A B C
<dbl> <dbl> <dbl> <dbl>
1 1 1 1 1
2 2 0 1 0
3 2 1 0 0
4 3 0 1 1
5 3 1 0 1
6 3 0 0 1
code
data2 <- data %>% group_by(ID) %>%
mutate(across(c('A','B','C'), ~ ifelse(.x==0, 0, NA), .names = 'x{col}')) %>%
fill(xA, xB, xC) %>%
mutate(across(c('xA','xB','xC'), ~ ifelse(is.na(.x), 1, .x))) %>%
ungroup() %>% group_by(ID) %>% slice_tail(n=1)
output
# A tibble: 3 × 7
# Groups: ID [3]
ID A B C xA xB xC
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 1 1 1 1 1
2 2 1 0 0 0 0 0
3 3 0 0 1 0 0 1

Related

complete sequence column names and fill R

I have large data similar to the following:
week_0<-c(5,0,1,0,8,1)
week_4<-c(1,0,1,0,1,1)
week_8<-c(1,0,6,0,0,0)
week_9<-c(2,4,1,7,8,1)
week_10<-c(2,4,1,7,8,1)
Participant<-c("Lion","Cat","Dog","Snake","Tiger","Mouse")
test_data<-data.frame(Participant,week_0,week_4,week_8,week_9,week_10)
> test_data
Participant week_0 week_4 week_8 week_9 week_10
1 Lion 5 1 1 2 2
2 Cat 0 0 0 4 4
3 Dog 1 1 6 1 1
4 Snake 0 0 0 7 7
5 Tiger 8 1 0 8 8
6 Mouse 1 1 0 1 1
I want to fill out the gap between the columnnames numbers. The end result that I'm looking for is:
test_data
Participant week_0 week_1 week_2 week_3 week_4 week_5 week_6 week_7 week_8 week_9 week_10
1 Lion 5 5 5 5 1 1 1 1 1 2 2
2 Cat 0 0 0 0 0 0 0 0 0 4 4
3 Dog 1 1 1 1 1 1 1 1 6 1 1
4 Snake 0 0 0 0 0 0 0 0 0 7 7
5 Tiger 8 8 8 8 1 1 1 1 0 8 8
6 Mouse 1 1 1 1 1 1 1 1 0 1 1
I have looked at the Fill function in r, but I can't get the result that I want.
Any suggestions on how to do this?

Using base R - extract the numeric suffix part from the 'week' column names, then get a sequence between the min/max values ('i2'), replicate the columns based on matching the indexes and rename the column names with i2
i1 <- as.integer(sub("week_", "", names(test_data)[-1]))
i2 <- Reduce(`:`, as.list(range(i1)))
test_data <- cbind(test_data[1], test_data[-1][cumsum(!is.na(match(i2, i1)))])
names(test_data)[-1] <- paste0("week_", i2)
-output
> test_data
Participant week_0 week_1 week_2 week_3 week_4 week_5 week_6 week_7 week_8 week_9 week_10
1 Lion 5 5 5 5 1 1 1 1 1 2 2
2 Cat 0 0 0 0 0 0 0 0 0 4 4
3 Dog 1 1 1 1 1 1 1 1 6 1 1
4 Snake 0 0 0 0 0 0 0 0 0 7 7
5 Tiger 8 8 8 8 1 1 1 1 0 8 8
6 Mouse 1 1 1 1 1 1 1 1 0 1 1
With tidyverse, an option is to reshape to 'long' with pivot_longer, use complete to expand the data, fill the missing values with previous non-NA, and reshape back to 'wide' with pivot_wider
library(dplyr)
library(tidyr)
test_data %>%
pivot_longer(cols = starts_with('week_'),
names_prefix = "week_", names_transform = as.integer) %>%
complete(Participant, name = full_seq(name, period = 1)) %>%
fill(value, .direction = "downup") %>%
pivot_wider(names_from = name, values_from = value,
names_prefix = "week_") %>%
arrange(match(Participant, test_data$Participant))
-output
# A tibble: 6 × 12
Participant week_0 week_1 week_2 week_3 week_4 week_5 week_6 week_7 week_8 week_9 week_10
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Lion 5 5 5 5 1 1 1 1 1 2 2
2 Cat 0 0 0 0 0 0 0 0 0 4 4
3 Dog 1 1 1 1 1 1 1 1 6 1 1
4 Snake 0 0 0 0 0 0 0 0 0 7 7
5 Tiger 8 8 8 8 1 1 1 1 0 8 8
6 Mouse 1 1 1 1 1 1 1 1 0 1 1

Please check the below code
test_data<-data.frame(Participant,week_0,week_4,week_8,week_9,week_10) %>%
pivot_longer(starts_with('week'), names_to = 'name', values_to = 'value') %>%
mutate(seq=as.numeric(str_replace_all(name,'\\w*\\_',''))) %>% arrange(Participant)
seq <- data.frame(Participant=rep(unique(Participant),11)) %>% group_by(Participant) %>%
mutate(seq=row_number(), seq=seq-1) %>%
arrange(Participant)
test_data2 <- test_data %>% right_join(seq, by=c('Participant','seq')) %>%
arrange(Participant) %>%
mutate(name=ifelse(is.na(name),paste0('week_',seq),name)) %>% arrange(Participant,seq) %>%
group_by(Participant) %>%
fill(value) %>%
pivot_wider(Participant, names_from = name, values_from = value)
Created on 2023-01-28 with reprex v2.0.2
# A tibble: 6 × 11
# Groups: Participant [6]
Participant week_0 week_2 week_3 week_4 week_5 week_6 week_7 week_8 week_9 week_10
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Cat 0 0 0 0 0 0 0 0 4 4
2 Dog 1 1 1 1 1 1 1 6 1 1
3 Lion 5 5 5 1 1 1 1 1 2 2
4 Mouse 1 1 1 1 1 1 1 0 1 1
5 Snake 0 0 0 0 0 0 0 0 7 7
6 Tiger 8 8 8 1 1 1 1 0 8 8

how to cumulative sum variable by unique values and input back in

I'm looking to do the following -- cumulative sum the indicator values and remove the indicators after those days
original:
transaction
day
indicator
1
1
0
1
2
0
1
3
0
1
4
1
1
5
1
1
6
1
2
1
0
2
2
0
2
3
0
2
4
0
2
5
1
2
6
1
and make the new table like this --
transaction
day
indicator
1
1
0
1
2
0
1
3
0
1
4
3
2
1
0
2
2
0
2
3
0
2
4
0
2
5
2

Change all day with indicator == 1 to the first day with indicator == 1
df%>%
group_by(transaction)%>%
mutate(day=case_when(indicator==0~day,
T~head(day[indicator==1],1)))%>%
group_by(transaction,day)%>%
summarise(indicator=sum(indicator))%>%
ungroup
transaction day indicator
<int> <int> <int>
1 1 1 0
2 1 2 0
3 1 3 0
4 1 4 3
5 2 1 0
6 2 2 0
7 2 3 0
8 2 4 0
9 2 5 2

Please try the below code
code
df <- bind_rows(df1, df2) %>% group_by(transaction) %>%
mutate(cumsum=cumsum(indicator), cumsum2=ifelse(cumsum==1, day, NA)) %>%
fill(cumsum2) %>%
mutate(day=ifelse(!is.na(cumsum2), cumsum2, day)) %>%
group_by(transaction, day) %>% slice_tail(n=1) %>% select(-cumsum2)
Created on 2023-01-19 with reprex v2.0.2
output
# A tibble: 8 × 4
# Groups: transaction, day [8]
transaction day indicator cumsum
<dbl> <int> <dbl> <dbl>
1 1 1 0 0
2 1 2 0 0
3 1 3 0 0
4 1 4 1 3
5 2 1 0 0
6 2 2 0 0
7 2 3 0 0
8 2 4 1 2

Another approach to try. After grouping by transaction, change indicator to either 0 (same) or the sum of indicator. Finally, keep or filter previous rows where cumall (cumulative all) values for indicator are 0. Using lag will provide the last row containing the sum.
library(tidyverse)
df %>%
group_by(transaction) %>%
mutate(indicator = ifelse(indicator == 0, 0, sum(indicator))) %>%
filter(cumall(lag(indicator, default = 0) == 0))
Output
transaction day indicator
<int> <int> <dbl>
1 1 1 0
2 1 2 0
3 1 3 0
4 1 4 3
5 2 1 0
6 2 2 0
7 2 3 0
8 2 4 0
9 2 5 2

Is there a R function for preparing datasets for survival analysis like stset in Stata?

Datasets look like this
id start end failure x1
1 0 1 0 0
1 1 3 0 0
1 3 6 1 0
2 0 1 1 1
2 1 3 1 1
2 3 4 0 1
2 4 6 0 1
2 6 7 1 1
As you see, when id = 1, it's just the data input to coxph in survival package. However, when id = 2, at the beginning and end, failure occurs, but in the middle, failure disappears.
Is there a general function to extract data from id = 2 and get the result like id = 1?
I think when id = 2, the result should look like below.
id start end failure x1
1 0 1 0 0
1 1 3 0 0
1 3 6 1 0
2 3 4 0 1
2 4 6 0 1
2 6 7 1 1

A bit hacky, but should get the job done.
Data:
# Load data
library(tidyverse)
df <- read_table("
id start end failure x1
1 0 1 0 0
1 1 3 0 0
1 3 6 1 0
2 0 1 1 1
2 1 3 1 1
2 3 4 0 1
2 4 6 0 1
2 6 7 1 1
")
Data wrangling:
# Check for sub-groups within IDs and remove all but the last one
df <- df %>%
# Group by ID
group_by(
id
) %>%
mutate(
# Check if a new sub-group is starting (after a failure)
new_group = case_when(
# First row is always group 0
row_number() == 1 ~ 0,
# If previous row was a failure, then a new sub-group starts here
lag(failure) == 1 ~ 1,
# Otherwise not
TRUE ~ 0
),
# Assign sub-group number by calculating cumulative sums
group = cumsum(new_group)
) %>%
# Keep only last sub-group for each ID
filter(
group == max(group)
) %>%
ungroup() %>%
# Remove working columns
select(
-new_group, -group
)
Result:
> df
# A tibble: 6 × 5
id start end failure x1
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 0 1 0 0
2 1 1 3 0 0
3 1 3 6 1 0
4 2 3 4 0 1
5 2 4 6 0 1
6 2 6 7 1 1

fill values between interval grouped by ID

I have a data set where subjects have a value of 1 or 0 at different times. I need a function or a piece of code to that feels with 1, the values of 0 between the first and last 1.
I have tried complete() and fill() but not doing what I want
I have the following data:
dat = tibble(ID = c(1,1,1,1,1,1,1,1,1,1,
2,2,2,2,2,2,2,2,2,2,
3,3,3,3,3,3,3,3,3,3),
TIME = c(1,2,3,4,5,6,7,8,9,10,
1,2,3,4,5,6,7,8,9,10,
1,2,3,4,5,6,7,8,9,10),
DV = c(0,0,1,1,0,0,1,0,0,0,
0,1,0,0,0,0,0,0,0,1,
0,1,0,1,0,1,0,1,0,0))
# A tibble: 30 x 3
ID TIME DV
<dbl> <dbl> <dbl>
1 1 1 0
2 1 2 0
3 1 3 1
4 1 4 1
5 1 5 0
6 1 6 0
7 1 7 1
8 1 8 0
9 1 9 0
10 1 10 0
# ... with 20 more rows
I need the following output as shown in DV2:
dat = tibble(ID = c(1,1,1,1,1,1,1,1,1,1,
2,2,2,2,2,2,2,2,2,2,
3,3,3,3,3,3,3,3,3,3),
TIME = c(1,2,3,4,5,6,7,8,9,10,
1,2,3,4,5,6,7,8,9,10,
1,2,3,4,5,6,7,8,9,10),
DV = c(0,0,1,1,0,0,1,0,0,0,
0,1,0,0,0,0,0,0,0,1,
0,1,0,1,0,1,0,1,0,0),
DV2 = c(0,0,1,1,1,1,1,0,0,0,
0,1,1,1,1,1,1,1,1,1,
0,1,1,1,1,1,1,1,0,0))
# A tibble: 30 x 4
ID TIME DV DV2
<dbl> <dbl> <dbl> <dbl>
1 1 1 0 0
2 1 2 0 0
3 1 3 1 1
4 1 4 1 1
5 1 5 0 1
6 1 6 0 1
7 1 7 1 1
8 1 8 0 0
9 1 9 0 0
10 1 10 0 0
# ... with 20 more rows

With dplyr, you can do:
dat %>%
rowid_to_column() %>%
group_by(ID) %>%
mutate(DV2 = if_else(rowid %in% min(rowid[DV == 1]):max(rowid[DV == 1]),
1, 0)) %>%
ungroup() %>%
select(-rowid)
ID TIME DV DV2
<dbl> <dbl> <dbl> <dbl>
1 1 1 0 0
2 1 2 0 0
3 1 3 1 1
4 1 4 1 1
5 1 5 0 1
6 1 6 0 1
7 1 7 1 1
8 1 8 0 0
9 1 9 0 0
10 1 10 0 0

We can create a helper function, and apply it on every group, i.e.
f1 <- function(x) {
v1 <- which(x == 1)
x[v1[1]:v1[length(v1)]] <- 1
return(x)
}
with(dat, ave(DV, ID, FUN = f1))
#[1] 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0

Making a conditional variable based on last observation in temporal data

ID T V1
1 1 1
1 2 1
2 1 0
2 2 0
3 1 1
3 2 1
3 3 1
I need a to make two variables from these data. The first needs to be a 1 on the last observation only when V1 = 1, and then a 1 on the last observation for all cases. Ideal final product:
ID T V1 v2 v3
1 1 1 0 0
1 2 1 1 1
2 1 0 0 0
2 2 0 0 1
3 1 1 0 0
3 2 1 0 0
3 3 1 1 1
Thanks in advance.

in the package dplyr, you can group your data according a variable (according ID in your case) and make operations for each group. As one of your column (T) already counts the rank of each observation (within each group), you can combine with the function n() which returns the number of rows of each group in order to obtain what you want.
Suppose your data are in the dataframe df :
df %>%
group_by(ID) %>%
mutate(
v2 = 1 * (`T` == n()),
v3 = 1 * (`T` == n()) * (V1 == 1)
)
# A tibble: 7 x 5
# Groups: ID [3]
ID T V1 v2 v3
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 1 0 0
2 1 2 1 1 1
3 2 1 0 0 0
4 2 2 0 1 0
5 3 1 1 0 0
6 3 2 1 0 0
7 3 3 1 1 1

Resources