I am an aspiring data scientist, and this will be my first ever question on StackOF.
I have this line of code to help wrangle me data. My date filter is static. I would prefer not to have to go in an change this hardcoded value every year. What is the best alternative for my date filter to make it more dynamic? The date column is also difficult to work with because it is not a
"date", it is a "dbl"
library(dplyr)
library(lubridate)
# create a sample dataframe
df <- data.frame(
DATE = c(20191230, 20191231, 20200122)
)
Tried so far:
df %>%
filter(DATE >= 20191231)
# load packages (lubridate for dates)
library(dplyr)
library(lubridate)
# create a sample dataframe
df <- data.frame(
DATE = c(20191230, 20191231, 20200122)
)
This looks like this:
DATE
1 20191230
2 20191231
3 20200122
# and now...
df %>% # take the dataframe
mutate(DATE = ymd(DATE)) %>% # turn the DATE column actually into a date
filter(DATE >= floor_date(Sys.Date(), "year") - days(1))
...and filter rows where DATE is >= to one day before the first day of this year (floor_date(Sys.Date(), "year"))
DATE
1 2019-12-31
2 2020-01-22
Related
My objective is to check if a patient is using two drugs at the same date.
In the example, patient 1 is using drug A and drug B at the same date, but I want to extract it with code.
df <- data.frame(id = c(1,1,1,2,2,2),
date = c("2020-02-01","2020-02-01","2020-03-02","2019-10-02","2019-10-18","2019-10-26"),
drug_type = c("A","B","A","A","A","B"))
df$date <- as.factor(df$date)
df$drug_type <- as.factor(df$drug_type)
In order to do this, I firstly made date and drug type factor variables.
Next I used following code:
df %>%
mutate(lev_actdate = as.factor(actdate))%>%
filter(nlevels(drug_type)>1 & nlevels(date) < nrow(date))
But I failed. I assumed that if a patient is using two drugs at the same date, the number of levels in the date column will be less than its row number. However, now I don't know how to make it with code.
Additionally, I feel weird about following:
if I use nlevels(df$date), right result will be returned, but when I use df %>% nlevels(date), the error will be return with showing
"Error in nlevels(., df$date) : unused argument (df$date)"
Could you please tell me why this occurred and how can I fix it?
Thank you for your time.
You could use something like
library(dplyr)
df %>%
group_by(id, date) %>%
filter(n_distinct(drug_type) >= 2)
df %>% nlevels(date) is the same as nlevels(df, date) which is not the same as nlevels(df$date). Instead of the latter youcould try df %>% nlevels(.$date) or perhaps df %>% {nlevels(.$date)}.
Do you need something like this?
library(dplyr)
df %>%
group_by(date) %>%
distinct() %>%
summarise(drug_type_sum = toString(drug_type))
date drug_type_sum
<fct> <chr>
1 2019-10-02 A
2 2019-10-18 A
3 2019-10-26 B
4 2020-02-01 A, B
5 2020-03-02 A
I'm trying to create a new variable which equals the latest month's value minus the previous month's (or 3 months prior, etc.).
A quick df:
country <- c("XYZ", "XYZ", "XYZ")
my_dates <- c("2021-10-01", "2021-09-01", "2021-08-01")
var1 <- c(1, 2, 3)
df1 <- country %>% cbind(my_dates) %>% cbind(var1) %>% as.data.frame()
df1$my_dates <- as.Date(df1$my_dates)
df1$var1 <- as.numeric(df1$var1)
For example, I've tried (partially from: How to subtract months from a date in R?)
library(tidyverse)
df2 <- df1 %>%
mutate(dif_1month = var1[my_dates==max(my_dates)] -var1[my_dates==max(my_dates) %m-% months(1)]
I've also tried different variations of using lag():
df2 <- df1 %>%
mutate(dif_1month = var1[my_dates==max(my_dates)] - var1[my_dates==max(my_dates)-lag(max(my_dates), n=1L)])
Any suggestions on how to grab the value of a variable when dates equal the second latest observation?
Thanks for help, and apologies for not including any data. Can edit if necessary.
Edited with a few potential answers:
#this gives me the value of var1 of the latest date
df2 <- df1 %>%
mutate(value_1month = var1[my_dates==max(my_dates)])
#this gives me the date of the second latest date
df2 <- df1 %>%
mutate(month1 = max(my_dates) %m-%months(1))
#This gives me the second to latest value
df2 <- df1 %>%
mutate(var1_1month = var1[my_dates==max(my_dates) %m-%months(1)])
#This gives me the difference of the latest value and the second to last of var1
df2 <- df1 %>%
mutate(diff_1month = var1[my_dates==max(my_dates)] - var1[my_dates==max(my_dates) %m-%months(1)])
mutate requires the output to be of the same length as the number of rows of the original data. When we do the subsetting, the length is different. We may need ifelse or case_when
library(dplyr)
library(lubridate)
df1 %>%
mutate(diff_1month = case_when(my_dates==max(my_dates) ~
my_dates %m-% months(1)))
NOTE: Without a reproducible example, it is not clear about the column types and values
Based on the OP's update, we may do an arrange first, grab the last two 'val' and get the difference
df1 %>%
arrange(my_dates) %>%
mutate(dif_1month = diff(tail(var1, 2)))
. my_dates var1 dif_1month
1 XYZ 2021-08-01 3 -1
2 XYZ 2021-09-01 2 -1
3 XYZ 2021-10-01 1 -1
I'm trying to replace vba code with R code. Currently in vba I use sumif in a range to find the total value of an ID depending on some dates. In R I'm using mutate an summarize but there's always an error. I don´t know how to fix it.
If i want to find the value for ID=1 that made some value withing 2 days:
#sys.Date() = 2016-01-06
df
DATES ID VALUE
2016/01/01 1 10
2016/01/02 2 15
2016/01/05 1 13
the result must be:
ID Value
1 13
Currently, the code is:
df%>%
group_by(ID) %>%
mutate(Total_op = if (Sys.Date()-as.Date(Dates,format="%YYYY-%mm-
%dd")>=1) Value else 0)))%>%
summarize(SumTotal = sum(Total_op))%>%
collect
But the error showed is:
Error: Column 'sumTotal' must be length X (the group size) or one, not Y
With lubridate we can convert the DATES string to a datetime object and filter accordingly:
library(lubridate)
library(tidyverse)
Dat <- ymd("2016-01-06") #Set a date. Can be done by Sys.Date()
df %>%
mutate_at("DATES",ymd) %>% #convert to datetime
filter(DATES %within% interval(Dat-2,Dat)) %>% #filter entries in the last 2 days
group_by(ID) %>% #group by ID
summarise(SumTotal = sum(VALUE)) #summarise value as Sum
I have a dataset where each individual (id) has an e_date, and since each individual could have more than one e_date, I'm trying to get the earliest date for each individual. So basically I would like to have a dataset with one row per each id showing his earliest e_date value.
I've use the aggregate function to find the minimum values, I've created a new variable combining the date and the id and last I've subset the original dataset based on the one containing the minimums using the new variable created. I've come to this:
new <- aggregate(e_date ~ id, data_full, min)
data_full["comb"] <- NULL
data_full$comb <- paste(data_full$id,data_full$e_date)
new["comb"] <- NULL
new$comb <- paste(new$lopnr,new$EDATUM)
data_fixed <- data_full[which(new$comb %in% data_full$comb),]
The first thing is that the aggregate function doesn't seems to work at all, it reduces the number of rows but viewing the data I can clearly see that some ids appear more than once with different e_date. Plus, the code gives me different results when I use the as.Date format instead of its original format for the date (integer). I think the answer is simple but I'm struck on this one.
We can use data.table. Convert the 'data.frame' to 'data.table' (setDT(data_full)), grouped by 'id', we get the 1st row (head(.SD, 1L)).
library(data.table)
setDT(data_full)[order(e_date), head(.SD, 1L), by = id]
Or using dplyr, after grouping by 'id', arrange the 'e_date' (assuming it is of Date class) and get the first row with slice.
library(dplyr)
data_full %>%
group_by(id) %>%
arrange(e_date) %>%
slice(1L)
If we need a base R option, ave can be used
data_full[with(data_full, ave(e_date, id, FUN = function(x) rank(x)==1)),]
Another answer that uses dplyr's filter command:
dta %>%
group_by(id) %>%
filter(date == min(date))
You may use library(sqldf) to get the minimum date as follows:
data1<-data.frame(id=c("789","123","456","123","123","456","789"),
e_date=c("2016-05-01","2016-07-02","2016-08-25","2015-12-11","2014-03-01","2015-07-08","2015-12-11"))
library(sqldf)
data2 = sqldf("SELECT id,
min(e_date) as 'earliest_date'
FROM data1 GROUP BY 1", method = "name__class")
head(data2)
id earliest_date
123 2014-03-01
456 2015-07-08
789 2015-12-11
I made a reproducible example, supposing that you grouped some dates by which quarter they were in.
library(lubridate)
library(dplyr)
rand_weeks <- now() + weeks(sample(100))
which_quarter <- quarter(rand_weeks)
df <- data.frame(rand_weeks, which_quarter)
df %>%
group_by(which_quarter) %>% summarise(sort(rand_weeks)[1])
# A tibble: 4 x 2
which_quarter sort(rand_weeks)[1]
<dbl> <time>
1 1 2017-01-05 05:46:32
2 2 2017-04-06 05:46:32
3 3 2016-08-18 05:46:32
4 4 2016-10-06 05:46:32
I want use a list of years to filter a database by date
years<-c("2014")
yearsdata <- data.frame(animal=c("cow","pig"),
mydate=c(as.Date("2015-01-01"),
as.Date("2014-01-01")))
yearsdata %>%
mutate(mydate =format(mydate, "%Y") %>%
as.character()) %>%
filter(is.null(years) | mydate %in% years)
The above code works and lets me filter my dataset but it also formats the date column. Is there a way to get my filter results without having the format of the date column change in the finished subset dataframe?
If you're up for using the lubridate package you could do:
library("lubridate")
yearsdata %>%
filter(is.null(years) | year(mydate) %in% years)
Which gives:
# animal mydate
# 1 pig 2014-01-01
All these pipes gives me headache, I would just do
library(data.table)
setDT(yearsdata)[is.null(years) | year(mydate) %in% years]
# animal mydate
# 1: pig 2014-01-01