I'm working on some Covid-19 related questions in R Studio.
I have a data frame containing the columns of the date, cases (newly infected people on this date), deaths on this date, country, population, and indicator 14, which is the Number of cases per 100,000 residents over the last 14 days including the current date.
Now I want to create a new indicator, which is looking at the cases per 100,000 over the last 7 days.
The way to calculate it would of course be: 7 days indicator = (sum from k= i-6 to i of cases_k/population) * 100,000
So I wanted to code a function incidence <- function(cases, population) {} performing the formula on the data but I'm struggling:
How can I always address the last 7 days?
I know that I can e.g. compute a sum vom 0 to 5 with the following: i <- 0:5; sum(i^2) but how do I define from k= i-6 to i in this case?
Do I have to use a loop inside the function?
Thank you!
Related
I have (df) has (ID), (Adm_Date), (ICD_10), (points). and it has 1,000,000 rows.
(Points) represent value for (ICD_10)
(ID): each one has many rows
(Adm_Date) from 2010-01-01 to 2018-01-01.
I want the sum (points) without duplicate for filter rows starting from (Adm_date) to 2 years previous back from (Adm_Date) by (ID).
The periods like these:
01-01-2010 to 31-01-2012,
01-02-2010 to 29-02-2012,
01-03-2010 to 31-03-2012,...... so on to the last date 01-12-2016 to 31-12-2018.
my problem is with the filter of the dates. It does not filter the rows based on period date. It does sum (points) for each (ID) without duplicates for all data from the 2010 to 2018 period instead of summing them per period date for each (ID).
I used these codes
start.date= seq(as.Date (df$Adm_Date))
end.date = seq(as.Date (df$Adm_Date+ years(-2)))
Sum_df<- df %>% dplyr::filter(Adm_Date >=start.date & Adm_Date<=end.date) %>%
group_by(ID) %>%
mutate(sum_points = sum(points*!duplicated(ICD_10)))
but the filiter did not work, because it does sum (points) for each (ID) from all dates from the 2010 to 2018 instead of summing them per period date for each (ID).
sum_points will start from 01-01-2012, any Adm_Date >= 01-01-2012 I need to get their sum.
If I looked at the patient has ID=11. I will sum points from row 3 to row 23, Also I need to ignore repeat ICD_10 (e.g. G81, and I69 have repeated in this period). so results show like this
ID(11), Adm_Date(07-05-2012), sum_points(17), while the sum points for the same patient at Adm_Date(13-06-2013) I will sum from row 11 to row 27 because look back for 2 years from Adm_Date. So,
ID(11), Adm_Date(13-06-2013), sum_points(14.9)
I have about a half million of ID and more than a million rows.
I hope I explained it well. Thank you
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Guess this is pretty basic but I'm struggling to find a way and find a answer online either. I'm trying to create a dataframe with future dates but those dates should be duplicated per combinations of other 2 variables
so I should have
Dates | Channel | Product
Channel can take 4 values and product 7 values and I need to create dates for future 45 days after my last day in current df. Therefore I have 28 combinations per day and my new df should be 1260 rows (45 * 7 *4)
as the sample below
I know about this function
Dates =seq(max(train$Date), by="day", length.out=45)
However this will create a vector not duplicating dates for each combination. Anyway I can adapt this?
I am struggling with a dataframe of exchange-rate observations taken 3 times a day for approximately 30 days. This means that currently the dataframe is formed by 90 observations. For the purpose of my research I need to reduce the observations to 1 per day (30 observations), possibly by making the mean every 3 observations. In sum, I need a code that takes the observations 3 by 3 and outputs one observation every 3. I have tried some different codes but my attempts have all completely failed. I was wondering if someone had to do something similar and managed.
Thanks!
Use group_by and summarise like this:
library(tidyverse)
df=tibble(
day = rep(1:30, each=3),
rate = rnorm(90)
)
df %>%
group_by(day) %>%
summarise(mrate = mean(rate))
P.S.
Attach data. It will be easier to help out on specific data.
I have been teaching myself R from scratch so please bear with me. I have found multiple ways to count observations, however, I am trying to figure out how to count frequencies using (logical?) expressions. I have a massive set of data approx 1 million observations. The df is set up like so:
Latitude Longitude ID Year Month Day Value
66.16667 -10.16667 CPUELE25399 1979 1 7 0
66.16667 -10.16667 CPUELE25399 1979 1 8 0
66.16667 -10.16667 CPUELE25399 1979 1 9 0
There are 154 unique ID's and similarly 154 unique lat/long. I am focusing in on the top 1% of all values for each unique ID. For each unique ID I have calculated the 99th percentile using their associated values. I went further and calculated each ID's 99th percentile for individual years and months i.e.. for CPUELE25399 for 1979 for month=1 the 99th percentile value is 3 (3 being the floor of the top 1%)
Using these threshold values: For each ID, for each year, for each month- I need to count the amount of times (per month per year) that the value >= that IDs 99th percentile
I have tried at least 100 different approaches to this but I think that I am fundamentally misunderstanding something maybe in the syntax? This is the snippet of code that has gotten me the farthest:
ddply(Total,
c('Latitude','Longitude','ID','Year','Month'),
function(x) c(Threshold=quantile(x$Value,probs=.99,na.rm=TRUE),
Frequency=nrow(x$Value>=quantile(x$Value,probs=.99,na.rm=TRUE))))
R throws a warning message saying that >= is not useful for factors?
If any one out there understands this convoluted message I would be supremely grateful for your help.
Using these threshold values: For each ID, for each year, for each month- I need to count the amount of times (per month per year) that the value >= that IDs 99th percentile
Does this mean you want to
calculate the 99th percentile for each ID (i.e. disregarding month year etc), and THEN
work out the number of times you exceed this value, but now split up by month and year as well as ID?
(note: your example code groups by lat/lon but this is not mentioned in your question, so I am ignoring it. If you wish to add it in, just add it as a grouping variable in the appropriate places).
In that case, you can use ddply to calculate the per-ID percentile first:
# calculate percentile for each ID
Total <- ddply(Total, .(ID), transform, Threshold=quantile(Value, probs=.99, na.rm=T))
And now you can group by (ID, month and year) to see how many times you exceed:
Total <- ddply(Total, .(ID, Month, Year), summarize, Freq=sum(Value >= Threshold))
Note that summarize will return a dataframe with only as many rows as there are columns of .(ID, Month, Year), i.e. will drop all the Latitude/Longitude columns. If you want to keep it use transform instead of summarize, and then the Freq will be repeated for all different (Lat, Lon) for each (ID, Mon, Year) combo.
Notes on ddply:
can do .(ID, Month, Year) rather than c('ID', 'Month', 'Year') as you have done
if you just want to add extra columns, using something like summarize or mutate or transform lets you do it slickly without needing to do all the Total$ in front of the column names.
I am working with a large dataset (10 million + cases) where each case represents a sale's monthly transactions of a given product (there are 17 products). As such, each shop is potentially represented across 204 cases (12 months * 17 Product sales; note, not all stores sell all 17 products throughout the year).
I need to restructure the data so that there is one case for each product transaction. This would result in each shop being represented by only 17 cases.
Ideally, I would like the create the mean value of the transactions over the 12 months.
To be more specific, there dataset currently has 5 variables:
Shop Location — A unique 6 digit sequence
Month — 2013_MM (data is only from 2013)
Number of Units sold Total Profit (£)
Product Type - 17 Different product types (this is a String
Variable)
I am working in R. It would be ideal to save this restructured dataset into a data frame.
I'm thinking an if/for loop could work, but I'm unsure how to get this to work.
Any suggestions or ideas are greatly appreciated. If you need further information, please just ask!
Kind regards,
R
There really wasn't much here to work with, but this is what my interpretation leads to... You're looking to summarise your data set, grouped by shop_location and product_type
# install.packages('dplyr')
library(dplyr)
your_data_set <- xxx
your_data_set %>%
group_by(shop_location, product_type) %>%
summarise(profit = sum(total_profit),
count = n(),
avg_profit = profit/count)