CODEWITHSUNDEEP
rdataframedplyrtidyverse
Sophia Wilson
Sophia Wilson

Reputation: 477

How to convert value of difftime from hours to days?

I have dataframe which looks like.

structure(list(format_date = c("Feb-21 (W5)", "Mar-21 (W1)", 
"Mar-21 (W2)"), Total_count = c(53L, 1079L, 1288L), Count_Diff_1 = c(29L, 
508L, 519L), Count_Diff_2 = c(22L, 505L, 624L), Count_Diff_3 = c(2L, 
66L, 145L), Average_Diff_1 = structure(c(10.3464846743295, 6.03846784776903, 
2.75151412973667), class = "difftime", units = "hours"), Average_Diff_2 = structure(c(6.46958333333333, 
7.78101430143014, 2.58788906695157), class = "difftime", units = "hours"), 
    Average_Diff_3 = structure(c(162.45419129418, 133.452629846369, 
    54.3061989570153), class = "difftime", units = "hours"), 
    Per_Diff_1 = c(0.547169811320755, 0.470806302131603, 0.402950310559006
    ), Per_Diff_2 = c(0.415094339622642, 0.468025949953661, 0.484472049689441
    ), Per_Diff_3 = c(0.0377358490566038, 0.0611677479147359, 
    0.112577639751553)), row.names = c(NA, -3L), groups = structure(list(
    format_date = c("Feb-21 (W5)", "Mar-21 (W1)", "Mar-21 (W2)"
    ), .rows = structure(list(1L, 2L, 3L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, 3L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

I want to convert the values in days if it is >24 hours in Average_1,Average_2 and Average_3 column and covert the Per_Diff_1,Per_Diff_2 and Per_Diff_3 with percentage 2 decimal point with % sign.

Also, it would be great if somehow I can mange to order the format_date in descending order. Right now it is coming as Feb-21 (W5), Mar-21 (W1) and Mar-21 (W2) it should be Mar-21 (W2),Mar-21 (W1) and Feb-21 (W5).

Required df<-

format_date   Total_count Count_Diff_1 Count_Diff_2 Count_Diff_3 Average_1    Average_2    Average_3 Per_Diff_1 Per_Diff_2 Per_Diff_3
Feb-21 (W5)    53         29           22           2            10.34 hours  6.46 hours   6.7 Days  54.71%     41.50%     3.77%

Upvotes: 3

Views: 184

Answers (2)

jay.sf
jay.sf

Reputation: 73352

You may simply change units of the difftime class to days in a case handling using if. For the percentages try formatC.

d[6:8] <- lapply(d[6:8], function(x) {if (all(x > 24)) units(x) <- "days";round(x, 2)})
d[9:11] <- lapply(d[9:11], function(x) paste0(formatC(x*100, 1, format="f"), "%"))
d <- d[order(d$format_date, decreasing=T), ]

Result

d
#   format_date Total_count Count_Diff_1 Count_Diff_2 Count_Diff_3 Average_Diff_1
# 3 Mar-21 (W2)        1288          519          624          145     2.75 hours
# 2 Mar-21 (W1)        1079          508          505           66     6.04 hours
# 1 Feb-21 (W5)          53           29           22            2    10.35 hours
#   Average_Diff_2 Average_Diff_3 Per_Diff_1 Per_Diff_2 Per_Diff_3
# 3     2.59 hours      2.26 days      40.3%      48.4%      11.3%
# 2     7.78 hours      5.56 days      47.1%      46.8%       6.1%
# 1     6.47 hours      6.77 days      54.7%      41.5%       3.8%

Upvotes: 1

akrun
akrun

Reputation: 887541

We can use across on the columns that starts_with 'Average' to convert the values if it is greater than 24 then divide by 24, append 'Days' as suffix, or else return by appending 'hours' as suffix. Similarly, the columns that starts_with 'Per' can be formatted to percentage with label_percent from scales. Then, we arrange the 'format_date' in descending order after conversion to yearmon class (from zoo::as.yearmon)

library(dplyr)
library(stringr)
library(scales)
df1 %>%
   ungroup %>%
   mutate(across(starts_with('Average'), 
     ~ case_when(as.numeric(.) > 24 ~ str_c(round(as.numeric(.)/24, 2),
    ' Days'),
      TRUE ~ str_c(as.character(round(., 2)), ' hours'))),
      across(starts_with('Per'), label_percent())) %>%
   arrange(desc(zoo::as.yearmon(str_remove(format_date, '\\s+.*'), '%b-%y')),
          desc(readr::parse_number(str_remove_all(format_date, '.*\\(|\\)'))))

-output

# A tibble: 3 x 11
#  format_date Total_count Count_Diff_1 Count_Diff_2 Count_Diff_3 Average_Diff_1 Average_Diff_2 Average_Diff_3 Per_Diff_1 Per_Diff_2 Per_Diff_3
#  <chr>             <int>        <int>        <int>        <int> <chr>          <chr>          <chr>          <chr>      <chr>      <chr>     
#1 Mar-21 (W2)        1288          519          624          145 2.75 hours     2.59 hours     2.26 Days      40.3%      48.4%      11.3%     
#2 Mar-21 (W1)        1079          508          505           66 6.04 hours     7.78 hours     5.56 Days      47.1%      46.8%      6.1%      
#3 Feb-21 (W5)          53           29           22            2 10.35 hours    6.47 hours     6.77 Days      54.7%      41.5%      3.8%

Upvotes: 4

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