I am using the code below to group by month to sum or count. However, the SLARespond column seems like it sums for the whole data set, not for each month.
Any way that I can fix the problem?
also, instead of sum function, can I do count function with SLAIncident$IsSlaRespondByViolated == 1
Appreciate for helps!
SLAIncident <- SLAIncident %>%
mutate(month = format(SLAIncident$CreatedDateLocal, "%m"), year = format(SLAIncident$CreatedDateLocal, "%Y")) %>%
group_by(year, month) %>%
summarise(SLARespond = sum(SLAIncident$IsSlaRespondByViolated))
If you could provide a small bit of the dataset to illustrate your example that would be great. I would first make sure that your months/years are characters or factors so that dplyr can grab them. An ifelse function wrapped in a sum should also fit your criteria for the second part of the question. I am using your code here to convert the dates into month and year but I recommend lubridate
SLAIncident <- SLAIncident %>%
mutate(month = as.character(format(SLAIncident$CreatedDateLocal, "%m")),
year = as.character(format(SLAIncident$CreatedDateLocal, "%Y"))) %>%
group_by(year, month) %>%
summarise(SLARespond = sum(IsSlaRespondByViolated),
sla_1 = sum(ifelse(isSlaRespondByViolated == 1, 1, 0)))
Also as hinted to in the comments, these column names are really long and could use some tidying
Related
In a dataset where each patient had multiple test administrations and a score on each test date, I have to identify the earliest & latest test dates, then subtract the difference of the scores of those dates. I think I've identified the first & last dates through dplyr, creating new columns for those:
SplitDates <- SortedDates %>%
group_by(PatientID) %>%
mutate(EarliestTestDate = min(AdministrationDate),
LatestTestDate = max(AdministrationDate)) %>%
arrange(desc(PatientID))
Score column is TotalScore
Now how do I extract the scores from these 2 dates (for each patient) to create new columns of earliest & latest scores? Haven't been able to figure out a mutate with case_when or if_else to create a score based on a record with a certain date.
Have you tried to use one combine verb, like left_join, for example?
SplitDates <- SortedDates %>%
group_by(PatientID) %>%
mutate(EarliestTestDate = min(AdministrationDate),
LatestTestDate = max(AdministrationDate)) %>%
ungroup() %>%
left_join(SortedDates,
by = c(“PatientID” = “PatientID”, “AdministrationDate” = “EarliestTestDate”)) %>% # picking the score of EarliestTestDate
left_join(SortedDates,
by = c(“PatientID” = “PatientID”, “AdministrationDate” = “LatestTestDate”)) %>% # picking the score of EarliestTestDate
arrange(desc(PatientID)) # now you can make the mutante task that you need.
I suggest to you see the dplyr cheatsheet.
I would like to do some computation on several rows in a table.
I created an exemple below:
library(dplyr)
set.seed(123)
year_week <- c(200045:200053, 200145:200152, 200245:200252)
input <- as.vector(sample(1:10,25,TRUE))
partial_sum <- c( 20,12,13,18,12,13,4,15,9,13,10,20,11,9,9,5,13,13,,8,13,11,15,14,7,14)
df <- data.frame(year_week, input, partial_sum)
Given are the columns input and year_week. The later represents dates but the values are numerical in my case with the first 4 digits as years and the last two as the working weeks for that year.
What I need, is to iterate over each week in each year and to sum up the values from the same weeks in the other years and save the results into a column called here partial_sum. The current value is excluded from the sum.
The week 53 in the lap year 2000 will get the same treatment but in this case I have only one lap year therefore its value 3 doesn't change.
Any idea on how to make it?
Thank you
I would expect something like this would work, though as pointed out in comments your example isn't exactly reproducible.
library(dplyr)
df %>%
mutate(week = substr(year_week, 5, 6)) %>%
group_by(week) %>%
mutate(result = sum(input))
Perhaps this helps - grouped by 'week' by taking the substring, get the difference between the sum of 'input' and the 'input'
library(dplyr)
df %>%
group_by(week = substring(year_week, 5)) %>%
mutate(partial_sum2 = sum(input) - input)
There are three columns: website, Date ("%Y %m"), click_tracking (T/F). I would like to add a variable describing the number of websites whose click tracking = T in each month / the number of all website in that month.
I thought the steps would be something like:
aggregate(sum(df$click_tracking = TRUE), by=list(Category=df$Date), FUN = sum)
as.data.frame(table(Date))
Then somehow loop through Date and divide the two variables above which would have been already grouped by Date. How can I achieve this? Many thanks!
If we are creating a column, then do a group by 'Date' and get the sum of 'click_tracking' (assuming it is a logical column - TRUE/FALSE) iin mutate
library(dplyr)
df %>%
group_by(Date) %>%
mutate(countTRUE = sum(click_tracking))
If the column is factor, convert to logical with as.logical
df %>%
group_by(Date) %>%
mutate(countTRUE = sum(as.logical(click_tracking)))
If it is to create a summarised output
df %>%
group_by(Date) %>%
summarise(countTRUE = sum(click_tracking))
In the OP's code, = (assignment) is used instead of == in sum(df$click_tracking = TRUE) and there is no need to do a comparison on a logical column
aggregate(cbind(click_tracking = as.logical(click_tracking)) ~ Date, FUN = sum)
This will create the proportion of websites with click tracking (out of all websites) per month.
aggregate(data=df, click_tracking ~ Date, mean)
This is my tibble:
date;temp
1953-1-1;-0.2
1953-1-2;-0.2
1953-1-3;-0.2
1953-1-4;-0.1
...
1954-1-1;2
1954-1-2;3
1954-1-3;4
1954-1-4;5
...
1955-1-1;6
1955-1-2;7
1955-1-3;8
1955-1-4;9
I would now like to calculate the mean temperature per year. That means I want to calculate all values of the column temp for each year. However, I have no idea how I can work in R with the year numbers. Can someone tell me how to solve the problem?
tb <- tb %>%
mutate(year = substr(date, start=1, stop=4)) %>%
group_by(year) %>%
summarise(mean_temp = mean(temp, na.rm=TRUE))
Otherwise, lubridate is a nice library to work with Dates.
Upfront apology if this has been asked, I have been searching all day and have not found an answer I can apply to my problem.
I am trying to solve this issue using dplyr (and co.) because my previous method (for loops) was too inefficient. I have a dataset of event times, at sites, that are in groups. I want to summarize the number (and proportion) of events that occur in a moving window along a sequence.
# Example data
set.seed(1)
sites = rep(letters[1:10],10)
groups = c('red','blue','green','yellow')
times = round(runif(length(sites),1,100))
timePeriod = seq(1,100)
# Example dataframe
df = data.frame(site = sites,
group = rep(groups,length(sites)/length(groups)),
time = times)
This is my attempt to summarize the number of sites from each group that contain a time (event) within a given moving window of time.
The goal is to move through each element of the vector timePeriod and summarize how many events in each group occurred at timePeriod[i] +/- half-window. Ultimately storing them in, e.g., a dataframe with a column for each group, and a row for each time step, is ideal.
df %>%
filter(time > timePeriod[i]-25 & time < timePeriod[i]+25) %>%
group_by(group) %>%
summarise(count = n())
How can I do this without looping through my sequence of time and storing the summary table for each group individually? Thanks!
Combining lapply and dplyr, you can do the following, which is close to what you had worked so far.
lapply(timePeriod, function(i){
df %>%
filter(time > (i - 25) & time < ( i + 25 ) ) %>%
group_by(group) %>%
summarise(count = n()) %>%
mutate(step = i)
}) %>%
bind_rows()