Create tibble/dataframe based on hour of day

[George_10548]

I want to create two tables, past_90_am, and past_90_pm for observations made before & after noon, respectively.

######## Working With Entire_Health_Database.tab file
######## Set working Directory
setwd("/Users/georgemanning/Documents/datafiles/R_Projects")
######
######
#Get the Tidyverse package
library(tidyverse)
#Get Lubridate package
library(lubridate)
#> 
#> Attaching package: 'lubridate'
#> The following object is masked from 'package:base':
#> 
#>     date
##### Now we have to read the file & assign it to a tibble
#####
library(readxl)
Entire_Health_Database <- read_excel("Entire_Health_Database.xlsx", 
                                     col_types = c("date", "date", "numeric", 
                                                   "numeric", "numeric", "numeric", 
                                                   "numeric", "text"))
#View(Entire_Health_Database)

past_90 <- Entire_Health_Database %>% filter(Date >= today()- days(90))
###
penultimate_90 <- Entire_Health_Database %>% filter(Date >= today()-days(180) & Date <= today()- days(91))


### Next is to extract observations between 0000 & 1159 and those between 1200 & 2359
### I have no idea how to do this yet
## Want to create two new dataframes/tibbles, one containing observations from past_90
## made berfore noon and a second containing observations made after noon.


##  past_90 %>% filter("Time", hour()<12)
## The above doesn't work
## Still trying to figure this out

^{Created on 2019-07-04 by the reprex package (v0.3.0)}

[andresrcs]

Hi!

To help us help you, could you please turn this into a proper reproducible example (reprex) illustrating your issue and including sample data on a copy/paste friendly format? We don't have access to your .xlsx file

Please have a look at this guide, to see how to create one:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[George_10548]

Sorry! I'm new to RStudio & even newer to the Studio Community. I wand to select on time of day as described in the comments. I hope this is sufficient information:

## Goal is to select entries made before and after noon, creating two new
## dataframes: past_90_am and pqst_90_pm

## I can't figure out how to sort on hour.

tibble::tribble(
  ~Date,                 ~Time, ~Blood.Sugar, ~Systolic, ~Diastolic, ~Pulse, ~Weight,                     ~Comments,
  "2019-04-24", "1899-12-31 17:39:33",          104,       120,         68,     60,      NA, "Chose not to inject insulin",
  "2019-06-02", "1899-12-31 07:48:06",           92,       130,         71,     49,      NA,                            NA,
  "2019-04-12", "1899-12-31 18:04:05",          119,       115,         67,     55,      NA, "Injected 5 units of Novolog",
  "2019-04-16", "1899-12-31 17:42:34",          129,       112,         61,     56,      NA, "Injected 5 units of Novolog",
  "2019-06-27", "1899-12-31 05:51:41",          108,       128,         69,     50,      NA,                            NA,
  "2019-05-08", "1899-12-31 17:36:33",           97,       130,         72,     56,      NA,      "Did not inject insulin",
  "2019-04-27", "1899-12-31 17:41:14",          125,       124,         76,     56,      NA,        "\"average\" readings",
  "2019-05-31", "1899-12-31 05:47:15",           93,       125,         70,     55,      NA,                            NA,
  "2019-05-21", "1899-12-31 05:50:46",          132,       125,         67,     59,      NA,                            NA,
  "2019-05-26", "1899-12-31 17:23:11",          106,       134,         83,     59,      NA, "Chose not to inject insulin"
)
#> # A tibble: 10 x 8
#>    Date    Time    Blood.Sugar Systolic Diastolic Pulse Weight Comments    
#>    <chr>   <chr>         <dbl>    <dbl>     <dbl> <dbl> <lgl>  <chr>       
#>  1 2019-0… 1899-1…         104      120        68    60 NA     Chose not t…
#>  2 2019-0… 1899-1…          92      130        71    49 NA     <NA>        
#>  3 2019-0… 1899-1…         119      115        67    55 NA     Injected 5 …
#>  4 2019-0… 1899-1…         129      112        61    56 NA     Injected 5 …
#>  5 2019-0… 1899-1…         108      128        69    50 NA     <NA>        
#>  6 2019-0… 1899-1…          97      130        72    56 NA     Did not inj…
#>  7 2019-0… 1899-1…         125      124        76    56 NA     "\"average\…
#>  8 2019-0… 1899-1…          93      125        70    55 NA     <NA>        
#>  9 2019-0… 1899-1…         132      125        67    59 NA     <NA>        
#> 10 2019-0… 1899-1…         106      134        83    59 NA     Chose not t…

^{Created on 2019-07-04 by the reprex package (v0.3.0)}

[andresrcs]

This is how you can filter your dataframe but I would recommend keeping all in the same dataframe and add a new variable to differentiate.

library(dplyr)
library(lubridate)

past_90 <- tibble::tribble(
    ~Date,                 ~Time, ~Blood.Sugar, ~Systolic, ~Diastolic, ~Pulse, ~Weight,                     ~Comments,
    "2019-04-24", "1899-12-31 17:39:33",          104,       120,         68,     60,      NA, "Chose not to inject insulin",
    "2019-06-02", "1899-12-31 07:48:06",           92,       130,         71,     49,      NA,                            NA,
    "2019-04-12", "1899-12-31 18:04:05",          119,       115,         67,     55,      NA, "Injected 5 units of Novolog",
    "2019-04-16", "1899-12-31 17:42:34",          129,       112,         61,     56,      NA, "Injected 5 units of Novolog",
    "2019-06-27", "1899-12-31 05:51:41",          108,       128,         69,     50,      NA,                            NA,
    "2019-05-08", "1899-12-31 17:36:33",           97,       130,         72,     56,      NA,      "Did not inject insulin",
    "2019-04-27", "1899-12-31 17:41:14",          125,       124,         76,     56,      NA,        "\"average\" readings",
    "2019-05-31", "1899-12-31 05:47:15",           93,       125,         70,     55,      NA,                            NA,
    "2019-05-21", "1899-12-31 05:50:46",          132,       125,         67,     59,      NA,                            NA,
    "2019-05-26", "1899-12-31 17:23:11",          106,       134,         83,     59,      NA, "Chose not to inject insulin"
)

past_90 %>% 
    filter(hour(Time) < 12)
#> # A tibble: 4 x 8
#>   Date     Time        Blood.Sugar Systolic Diastolic Pulse Weight Comments
#>   <chr>    <chr>             <dbl>    <dbl>     <dbl> <dbl> <lgl>  <chr>   
#> 1 2019-06… 1899-12-31…          92      130        71    49 NA     <NA>    
#> 2 2019-06… 1899-12-31…         108      128        69    50 NA     <NA>    
#> 3 2019-05… 1899-12-31…          93      125        70    55 NA     <NA>    
#> 4 2019-05… 1899-12-31…         132      125        67    59 NA     <NA>

# I recommend this instead
past_90 %>% 
    mutate(noon = case_when(hour(Time) < 12 ~ "before",
                            TRUE ~ "after")) %>% 
    arrange(noon, Time) %>% 
    select(noon, everything())
#> # A tibble: 10 x 9
#>    noon   Date   Time  Blood.Sugar Systolic Diastolic Pulse Weight Comments
#>    <chr>  <chr>  <chr>       <dbl>    <dbl>     <dbl> <dbl> <lgl>  <chr>   
#>  1 after  2019-… 1899…         106      134        83    59 NA     Chose n…
#>  2 after  2019-… 1899…          97      130        72    56 NA     Did not…
#>  3 after  2019-… 1899…         104      120        68    60 NA     Chose n…
#>  4 after  2019-… 1899…         125      124        76    56 NA     "\"aver…
#>  5 after  2019-… 1899…         129      112        61    56 NA     Injecte…
#>  6 after  2019-… 1899…         119      115        67    55 NA     Injecte…
#>  7 before 2019-… 1899…          93      125        70    55 NA     <NA>    
#>  8 before 2019-… 1899…         132      125        67    59 NA     <NA>    
#>  9 before 2019-… 1899…         108      128        69    50 NA     <NA>    
#> 10 before 2019-… 1899…          92      130        71    49 NA     <NA>

[alistaire]

@andresrcs

case_when(hour(Time) < 12 ~ "before", TRUE ~ "after"))

ifelse/if_else gets overused a lot in dplyr because you can't assign to a subset, but this is the perfect case for it:

ifelse(hour(Time) < 12, "before", "after")

[George_10548]

THANK YOU!!

Problem solved!

This was a typical newb error.I thought the dplyr package was loaded as part of tidyverse.

Obviously not.

Now I can proceed with statistical analysis & charting.

[alistaire]

@George_10548

I thought the dplyr package was loaded as part of tidyverse.

...it is. Did you mean lubridate?

[George_10548]

I loaded tidyverse & lubridate at the start.

After looking at the suggested solutions, I specifically loaded dplyr.

Then everything ran properly

[cderv]

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[alistaire]

@George_10548

I loaded tidyverse & lubridate at the start.

After looking at the suggested solutions, I specifically loaded dplyr.

...loading tidyverse loads dplyr, always (it tells you when it does!), so something else must have changed.

[George_10548]

Thank you,

I double checked and my only conclusion is that I must have been making a typo that I didn't notice.

Everything running as expected so once again, thank you.