I'm working in R and trying to count the total number of each phenological stage in each cameraID. This is a screenshot of my data frame:
[Data frame - Phenology][1]
I have tried a lot of different R-codes, the last one was:
my_summary <- PHENOLOGY %>%
count(CameraID, DOY, Bud, sort = TRUE)
This R-codes doesn't add and summarize new observations of buds per day in each CameraID.
[1]: https://i.stack.imgur.com/bP56D.png
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I have a large dataset with COVID-19 cases, with number of cases per for each date.This data is in the dat dataframe. I am trying to summarize these data by a variable which contains ID of all districts and the date variable (Meldedatum), for some reason the output in new data frame is just 1 row with total cases for the entire period and it is not grouped by ID and date variable. I dot know why that is. I am adding screen shot of the dataset to show what it looks like. Can someone help?
sample of data. There are more than 100,000 observations in total for 44 districts, I am just including sample with 2 different districts and dates.
dat<-data.frame(Landkreis=c("Sk Stuttgart", "Sk Stuttgart", "Lk Freiburg","Lk Freiburg"),
Anzahlfall=c(1,1,1,1),AnzahlTodesfall=c(0,1,2,1),
Meldedatum=c("09-03-2020","18-03-2020","09-03-2020","20-03-2020"),IdLandkreis=c(8111, 8111,8116,8116))
datAggMelde <- dat %>% group_by(IdLandkreis, Meldedatum) %>%
summarize(sumCount = sum(AnzahlFall, na.rm = TRUE),
sumDeath = sum(AnzahlTodesfall, na.rm = TRUE),
Landkreis = first(Landkreis) )
I have a set of longitudinal data, which is a number of patients followed up over several years at irregular time points, I am unable to post it due to confidentiality issues,
Essentially, each row represents a single patient encounter, with admission date, discharge date, patient identifier and various demographic (e.g. ethnicity) and other variables,
e.g:
Patient
Admission Date
Ethnicity
1
26-01-2007
White
1
28-08-2008
White
2
12-02-2001
Black
2
01-12-2015
Black
2
03-12-2018
Black
I've tried using various packages such as brolgar and tsibble, but am unable to get simple summary statistics like number of individual patients, number of encounters per patient, time from first to last attendance per patient in each ethnic category (this one probably deserves another question as it's probably a lot more difficult) for example,
In a standard dataset you could use dplyr to do something like:
df %>%
group_by(Ethnicity) %>%
summarise(n=n)
to count the number of patients per group,
But I'm not sure how to do it for this dataset despite having gone through packages like brolgar/tsibble,
Would be grateful for any advice
Thanks a lot
I've done this :
df <- data.frame("Patient" = c(rep(1, 2), rep(2, 3)),
"Admission Date" = c("26-01-2007", "28-08-2008", "12-02-2001", "01-12-2015", "03-12-2018"),
"Ethnicity" = c(rep("White", 2), rep("Black", 3)),
stringsAsFactors = FALSE)
individual_patient <- n_distinct(df$Patient)
df2 <- df %>% group_by(Patient) %>% summarise(Encounter_number = n())
Are they other things you need to compute ?
I have a dataset with repeated measures: measurements nested within participants (ID) nested in groups. A variable G (with range 0-100) was measured on the group-level. I want to create a new column that shows:
The first day on which the maximum value of G was reached in a group coded as zero.
How many days each measurement (in this same group) occurred before or after the day on which the maximum was reached. For example: a measurement taken 2 days before the maximum is then coded -2, and a measurement 5 days after the maximum is coded as 5.
Here is an example of what I'm aiming for: Example
I highlighted the days on which the maximum value of G was reached in the different groups. The column 'New' is what I'm trying to get.
I've been trying with dplyr and I managed to get for each group the maximum with group_by, arrange(desc), slice. I then recoded those maxima into zero and joined this dataframe with my original dataframe. However, I cannot manage to do the 'sequence' of days leading up to/ days from the maximum.
EDIT: sorry I didn't include a reprex. I used this code so far:
To find the maximum value: First order by date
data <- data[with(data, order(G, Date)),]
Find maximum and join with original data:
data2 <- data %>%
dplyr::group_by(Group) %>%
arrange(desc(c(G)), .by_group=TRUE) %>%
slice(1) %>%
ungroup()
data2$New <- data2$G
data2 <- data2 %>%
dplyr::select(c("ID", "New", "Date"))
data3 <- full_join(data, data2, by=c("ID", "Date"))
data3$New[!is.na(data3$New)] <- 0
This gives me the maxima coded as zero and all the other measurements in column New as NA but not yet the number of days leading up to this, and the number of days since. I have no idea how to get to this.
It would help if you would be able to provide the data using dput() in your question, as opposed to using an image.
It looked like you wanted to group_by(Group) in your example to compute number of days before and after the maximum date in a Group. However, you have ID of 3 and Group of A that suggests otherwise, and maybe could be clarified.
Here is one approach using tidyverse I hope will be helpful. After grouping and arranging by Date, you can look at the difference in dates comparing to the Date where G is maximum (the first maximum detected in date order).
Also note, as.numeric is included to provide a number, as the result for New is a difftime (e.g., "7 days").
library(tidyverse)
data %>%
group_by(Group) %>%
arrange(Date) %>%
mutate(New = as.numeric(Date - Date[which.max(G)]))
I have a large df with around 40.000.000 rows , covering in total a time period of 2 years and more than 400k unique users.
The time variable is formatted as POSIXct and I have a unique user_id per user. I observe each user over several points in time.
Each row is therefore a unqiue combination of user_id, time and a set of variables.
Based on a set of dummy variables (df$v1, df$v2), a category variable(df$category_var) and the time variable (df$time_var) I now want to calculate 3 new variables on a user_id level on a rolling time window over the previous 30 days.
So in each row, the new variable should be calculated over the values of the previous 30 days of the input variables.
I do not observe all users over the same time period, some enter later some leave earlier, also the distances between times are not equal, therefore I can not calculate the variables just by number of rows.
So far I only managed to calculate my new variables per user_id over the whole observation period, but I couldn’t achieve to calculate the variables for the previous 30 days rolling window per user.
After checking and trying all the related posts here, I assume a data.table solution is the most suitable, but since I have so far mainly worked with dplyr the attempt of calculating these variables on the rolling time window on a groupey_by user_id level has taken more than a week without any results. I would be so grateful for your support!
My df basically looks like :
user_id <- c(1,1,1,1,1,2,2,2,2,3,3,3,3,3)
time_var <- c(“,2,3,4,5, 1.5, 2, 3, 4.5, 1,2.5,3,4,5)
category_var <- c(“A”, “A”, “B”, “B”, “A”, “A”, “C”, “C”, “A”, …)
v1 <- c(0,1,0,0,1,0,1,1,1,0,1,…)
v2 <- c(1,1,0,1,0,1,1,0,...)
My first needed new variable (new_x1) is basically a cumulative sum based on a condition in dummy variable v1. What I achieved so far:
df <- df %>% group_by(user_id) %>% mutate(new_x1=cumsum(v1==1))
What I need: That variables only counting over the previoues 30 days per user
Needed new variable (new_x2): Basically cumulative count of v1 if v2 has a (so far) unique value. So for each new value in v2 given v1==1, count.
What I achieved so far:
df <- df %>%
group_by(user_id, category_var) %>%
mutate(new_x2 = cumsum(!duplicated(v2 )& v1==1))
I also need this based on the previous 30 days and not the whole observation period per user.
My third variable of interest (new__x3):
The time between two observations given a certain condition (v1==1)
#Interevent Time
df2 <- df%>% group_by(user_id) %>% filter(v1==1) %>% mutate(time_between_events=time-lag(time))
I would also need this on the previoues 30 days.
Thank you so much!
Edit after John Springs Post:
My potential solution would then be
setDT(df)[, `:=`(new_x1= cumsum(df$v1==1[df$user_id == user_id][between(df$time[df$user_id == user_id], time-30, time, incbounds = TRUE)]),
new_x2= cumsum(!duplicated(df$v1==1[df$user_id == user_id][between(df$time[df$user_id == user_id], time-30, time, incbounds = TRUE)]))),
by = eval(c("user_id", "time"))]
I really not familiar with data.table and not sure, if I can nest my conditions on cumsum in on data.table like that.
Any suggestions?
I'm new to R; have a simple stumbling block for which I've been searching for an answer for too long.
Dateframe includes a list of individuals with their performance over a five year period. The analysis needs to include only those individuals that participated in the most recent year, so I need to identify those individuals and then select all records from the original data frame for those individuals with all columns (there's 50 or more other columns).
Original data frame is performance_fiveyr; variables I'm working with are person_id and year. I have tried any number of possible ways to get what I need; I'm listing one of those ways here...
First step is to create the list of individuals that participated this past year
person_current <- subset (x = performance_fiveyr,
subset = year==2015, # keep only records from 2015
select = person_id # keep only the person_id variable
)
Next step then is to select from performance_fiveyr all rows that have a person_id that exists in person_current and return all other columns (more than 50 columns total).
performance_current <- performance_fiveyr[performance_fiveyr$person_id
%in% person_current, ]
I've tried more than a few variations of this and end up with either all columns and no rows or all rows and no variables.
Here is some example data:
set.seed(0)
p5 <- data.frame(id = sample(5, 20, replace=TRUE), year = sample(2010:2015, 20, replace=TRUE))
p5 <- p5[order(p5$id, p5$year), ]
I think you were on the right track. I think the below does what you are after:
current <- unique(p5[p5$year==2015, 'id'])
p_current <- p5[p5$id %in% current, ]
p_current