data <- read.csv(input$file_upload$datapath)
data$Date_Time <- as.POSIXct(data$Date_Time, format = "%Y-%m-%d %H:%M:%S")
valid_data <- data[is.finite(data$CGM_Value), ]
last_date <- max(valid_data$Date_Time, na.rm = TRUE)
start_date <- last_date - 42460*60 # 4 days in seconds
filtered_data <- subset(valid_data, Date_Time >= start_date & Date_Time <= last_date)
result_data <- filtered_data %>%
mutate(Date = as.Date(Date_Time), Hour = as.numeric(format(Date_Time, "%H"))) %>%
group_by(Date, Hour)
This is my code ,but in this code mutate() doenot work on filtered_data instead it work on data frame of data.I want to add Date and Hour to already existing filter_data data frame .
in this code mutate() doe not work on filtered_data instead it work on data frame of data
Your code seems to make a result_data from mutating on filtered_data adding a Date and Hour field
what do you mean 'does not work' ? do you mean it errors ? if so what is the error ?
do you mean it gives incorrect results ? if so how is what you get different from what you intend ?
it does not mean any error instead 'filter_data' containing 'date_time' in rage between 4-8-2018 to 8-8-2018 but after using the function mutate() on this 'filter_data' it return the' result_data' containg date from 3-8-2018 to 7-8-2018 ,that means it doe not work on the ' filtered_data' .
I don't see how what you describe is possible. result_data would have the same rows as filter_data as you only mutate and group it; so no rows are added or removed; furthermore it should have the same date_time as no instruction modifies that.
I tested out your code and it worked as I expect.
You should consider making a minimal reproducible example, if you do indeed have an issue that can be investigated
library(tidyverse)
data <- data.frame(Date_Time='2024-06-27 11:29:33',
CGM_Value=1)
data$Date_Time <- as.POSIXct(data$Date_Time, format = "%Y-%m-%d %H:%M:%S")
valid_data <- data[is.finite(data$CGM_Value), ]
last_date <- max(valid_data$Date_Time, na.rm = TRUE)
start_date <- last_date - 42460*60 # 4 days in seconds
filtered_data <- subset(valid_data, Date_Time >= start_date & Date_Time <= last_date)
result_data <- filtered_data %>%
mutate(Date = as.Date(Date_Time), Hour = as.numeric(format(Date_Time, "%H"))) %>%
group_by(Date, Hour)
> filtered_data
Date_Time CGM_Value
1 2024-06-27 11:29:33 1
> result_data
# A tibble: 1 × 4
# Groups: Date, Hour [1]
Date_Time CGM_Value Date Hour
<dttm> <dbl> <date> <dbl>
1 2024-06-27 11:29:33 1 2024-06-27 11
Hi @sql ,
Could you run these two lines:
data <- read.csv(input$file_upload$datapath)
dput(data)
and post the output here, between a pair of triple backticks, like this?
```
paste output of dput() here
```
That might help folks here sort out what might be happening.
this work fine for me but in my result_data date 8-8-2018 is taken as 7-8-2018.
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this is my 'result_data '
It's the original data that would be helpful — the results don't allow folks to follow the chain of events that leads to the results.
We need the output of dput() not just a listing of the data. The dput() function gives us an exact copy of your R data set.
Here is a very simple example of how to do it.
dat <- data.frame(xx = 1:10, yy = letters[1:10])
dput(dat)
This gives us
structure(list(xx = 1:10, yy = c("a", "b", "c", "d", "e", "f",
"g", "h", "i", "j")), class = "data.frame", row.names = c(NA, -10L))
We can then copy it into R
dat <- data.frame(xx = 1:10, yy = letters[1:10])
my_dat <- structure(list(xx = 1:10, yy = c("a", "b", "c", "d", "e", "f",
"g", "h", "i", "j")), class = "data.frame", row.names = c(NA, -10L))
and we have an exact copy of your code and data.
1 Like
sorry ..my file contains around 10 Laks data
If you put row numbers on to your filtered data, you shpuld be able to find a relevant row that is problematic in result. It would only take one row to solve this (probwbly)
Yes, that is a little big to post here. I think @ nirgrahamuk's approach makes sense but to supply some sample data
dput(head(mydata, 500))
probably would give us enough to let us get a feel for the data.
1 Like
I agree, and to get started, even the output of dput(head(data))would help folks get a sense of things, and you could always share a larger table later, @sql .
as far as I can tell, the code works as expected, I've added some code that generates some fake data that shows that the codes does what it should do.
I've also added a line that shows how to generate a sample of your dataset that migt give us some idea of what your data looks like.
library(magrittr)
library(dplyr)
data = data.frame(
Date_Time = seq(
as.POSIXct("2018-04-08, 00:00:00", tz = "UTC"),
as.POSIXct("2018-08-08, 11:59:59", tz = "UTC"),
by = 3600),
CGM_Value = sample(c(runif(5, 0, 100), Inf, -Inf), 2929, replace = TRUE)
)
# give me 20 random rows of data
dput(data[sample(nrow(data), 20), ])
# the following seems to work as expected
valid_data <- data[is.finite(data$CGM_Value), ]
last_date <- max(valid_data$Date_Time, na.rm = TRUE)
print(last_date)
start_date <- last_date - 4*24*60*60 # 4 days in seconds
print(start_date)
filtered_data <- subset(valid_data, Date_Time >= start_date & Date_Time <= last_date)
print(range(filtered_data$Date_Time))
result_data <- filtered_data %>%
mutate(Date = as.Date(Date_Time), Hour = as.numeric(format(Date_Time, "%H"))) %>%
group_by(Date, Hour)
structure(list(SID = c("DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
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"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
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"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
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"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
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"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
"DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001", "DCLP1-001-001",
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), CGM_Value = c(108L, 118L, 124L, 125L, 123L, 121L, 120L,
119L, 120L, 122L, 127L, 132L, 139L, 145L, 152L, 157L, 162L,
165L, 169L, 173L, 177L, 182L, 187L, 190L, 191L, 192L, 193L,
198L, 204L, 207L, 202L, 203L, 208L, 210L, 214L, 215L, 217L,
218L, 219L, 218L, 220L, 220L, 218L, 218L, 216L, 215L, 213L,
211L, 210L, 208L, 206L, 204L, 202L, 199L, 195L, 192L, 188L,
185L, 180L, 175L, 172L, 170L, 168L, 166L, 164L, 162L, 161L,
162L, 163L, 163L, 163L, 160L, 159L, 159L, 153L, 155L, 159L,
160L, 163L, 160L, 156L, 155L, 155L, 162L, 161L, 160L, 160L,
158L, 170L, 166L, 179L, 192L, 198L, 192L, 180L, 167L, 156L,
147L, 141L, 138L, 139L, 141L, 143L, 144L, 147L, 146L, 144L,
143L, 142L, 139L, 134L, 130L, 126L, 122L, 119L, 117L, 118L,
122L, 130L, 138L, 140L, 142L, 144L, 145L, 147L, 145L, 144L,
143L, 140L, 137L, 137L, 134L, 134L, 130L, 128L, 126L, 123L,
120L, 118L, 116L, 113L, 109L, 108L, 105L, 102L, 98L, 95L,
93L, 90L, 88L, 87L, 86L, 83L, 103L, 100L, 104L, 102L, 101L,
99L, 98L, 99L, 100L, 104L, 104L, 104L, 103L, 102L, 102L,
103L, 104L, 105L, 106L, 107L, 106L, 106L, 103L, 98L, 94L,
91L, 88L, 87L, 87L, 86L, 85L, 84L, 86L, 89L, 91L, 97L, 104L,
112L, 118L, 123L, 129L, 137L, 141L, 146L, 152L, 159L, 164L,
167L, 174L, 181L, 185L, 189L, 195L, 206L, 213L, 219L, 225L,
232L, 236L, 242L, 245L, 245L, 246L, 249L, 251L, 251L, 249L,
243L, 246L, 238L, 227L, 215L, 204L, 195L, 186L, 167L, 171L,
168L, 169L, 173L, 179L, 182L, 180L, 175L, 169L, 163L, 159L,
156L, 147L, 143L, 139L, 135L, 133L, 129L, 126L, 123L, 119L,
115L, 112L, 108L, 106L, 103L, 102L, 100L, 99L, 102L, 104L,
106L, 107L, 108L, 108L, 109L, 109L, 110L, 109L, 109L, 109L,
109L, 108L, 105L, 105L, 104L, 100L, 99L, 98L, 97L, 96L, 97L,
96L, 95L, 98L, 100L, 102L, 103L, 104L, 106L, 109L, 112L,
114L, 117L, 120L, 123L, 126L, 128L, 130L, 129L, 130L, 136L,
136L, 135L, 135L, 136L, 141L, 136L, 137L, 138L, 139L, 139L,
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94L, 96L, 97L, 97L, 98L, 100L, 101L, 102L, 103L, 104L, 113L,
121L, 127L, 130L, 135L, 138L, 139L, 140L, 142L, 138L, 138L,
136L, 133L, 131L, 130L, 128L, 128L, 127L, 127L, 127L, 124L,
119L, 118L, 116L, 112L, 107L, 103L, 100L, 99L, 97L, 97L,
100L, 103L, 104L, 111L, 122L, 114L, 119L, 122L, 125L, 129L,
132L, 129L, 127L, 126L, 121L, 116L, 113L, 113L, 113L, 113L,
112L, 114L, 118L, 123L, 127L, 134L, 133L, 140L, 144L, 142L,
142L, 141L, 139L, 135L, 134L, 131L, 133L, 134L, 133L, 129L,
125L, 129L, 128L, 130L, 132L, 132L, 131L, 128L, 124L, 124L,
122L, 120L, 119L, 120L, 122L, 122L, 125L, 126L, 126L, 127L,
127L, 126L, 126L, 126L, 127L, 125L, 124L, 126L, 122L, 121L,
118L, 114L, 109L, 109L, 105L, 102L, 103L, 106L, 108L, 105L,
106L, 106L, 107L, 110L, 112L, 113L, 116L, 120L, 120L, 120L,
118L, 122L, 121L, 125L, 199L, 210L)), row.names = c(NA, 500L
), class = "data.frame")
Maybe this is part of your problem. The as.Date() function defaults to calculating in the UTC timezone, but the data are in the local timezone after as.POSIXct(). I'm in the US Eastern timezone. The data data frame in my code is the result of the dput() code in your last post.
data$Date_Time <- as.POSIXct(data$Date_Time, format = "%Y-%m-%d %H:%M:%S")
> data$Date_Time[500]
[1] "2017-11-04 21:13:00 EDT"
> as.Date(data$Date_Time[500]) #Date is shifted to UTC!!!
[1] "2017-11-05"
> as.Date(data$Date_Time[500], tz = "US/Eastern") #Specify the timezone
[1] "2017-11-04"
3 Likes
This may do what you want but I used {data.table} rather than {dplyr} so the syntax will look strange.
I called the data.frame you supplied dat1 .
suppressMessages(library(data.table)); suppressMessages(library(tidyverse))
DT <- as.data.table(dat1)
DT[, Date_Time := (ymd_hms(Date_Time))]
last <- DT[, max(Date_Time)]
start <- last - 42460*60 # 4 days in seconds
DT2 <- subset(DT, Date_Time <= last & Date_Time >= start)
DT2[, hhr := hour(Date_Time)]
setkey(DT2, Date_Time, hhr)
A couple of links that may help you see what I am doing.
data.table in R – The Complete Beginners Guide
OOPS, I forgot you were filtering the data. See new code below.
suppressMessages(library(data.table)); suppressMessages(library(tidyverse))
DT <- as.data.table(dat1)
DT <- na.omit(DT)
DT[, Date_Time := (ymd_hms(Date_Time))]
last <- DT[, max(Date_Time)]
start <- last - 42460*60 # 4 days in seconds
DT2 <- subset(DT, Date_Time <= last & Date_Time >= start)
DT2[, hhr := hour(Date_Time)]
setkey(DT2, Date_Time, hhr)
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