stats_summary() computes summary statistics for a specified column in a
data frame.
Usage
stats_summary(
data,
col,
name = NULL,
na_rm = TRUE,
remove_outliers = FALSE,
iqr_mult = 1.5,
round = FALSE,
digits = 3,
hms_format = TRUE,
threshold = NULL,
as_list = FALSE
)Arguments
- data
A
data.frame.- col
A
characterstringspecifying the column name indatafor which to compute summary statistics.- name
(optional) A
characterstringwith a name to include in the summary statistics (default:NULL).- na_rm
A
logicalflag indicating whether to removeNAvalues when computing statistics (default:TRUE).- remove_outliers
A
logicalflag indicating whether to remove outliers using the IQR method (default:FALSE).- iqr_mult
A
numericvalue specifying the Interquartile Range (IQR) multiplier for outlier detection (default:1.5).- round
A
logicalflag indicating whether to round the summary statistics (default:FALSE).- digits
An integer number specifying the number of decimal places to round to if
roundisTRUE(default:2).- hms_format
A
logicalflag indicating whether to format temporal statistics ashmsobjects (default:TRUE).- threshold
A
hmsobject specifying the threshold time for linking times to a timeline when computing temporal statistics. Seelink_to_timeline()to learn more (default:hms::parse_hms("12:00:00")).- as_list
A
logicalflag indicating whether to return the summary statistics as alist(default:FALSE).
Value
If as_list is TRUE, a list with the summary
statistics. Otherwise, a one-row tibble with the
summary statistics.
See also
Other statistical functions.:
cohens_d(),
mode()
Examples
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
library(hms)
library(lubridate)
#>
#> Attaching package: ‘lubridate’
#> The following object is masked from ‘package:hms’:
#>
#> hms
#> The following objects are masked from ‘package:base’:
#>
#> date, intersect, setdiff, union
## `character` Example
sample(letters, 1000, replace = TRUE) |>
as_tibble() |>
stats_summary("value") |>
print(n = Inf)
#> # A tibble: 64 × 2
#> name value
#> <chr> <chr>
#> 1 class character
#> 2 n 1000
#> 3 n_rm_na 1000
#> 4 n_na 0
#> 5 n_unique 26
#> 6 mode a
#> 7 'a' 49
#> 8 'b' 39
#> 9 'c' 35
#> 10 'd' 34
#> 11 'e' 40
#> 12 'f' 39
#> 13 'g' 40
#> 14 'h' 37
#> 15 'i' 38
#> 16 'j' 49
#> 17 'k' 43
#> 18 'l' 48
#> 19 'm' 32
#> 20 'n' 38
#> 21 'o' 31
#> 22 'p' 36
#> 23 'q' 35
#> 24 'r' 36
#> 25 's' 39
#> 26 't' 37
#> 27 'u' 32
#> 28 'v' 36
#> 29 'w' 38
#> 30 'x' 37
#> 31 'y' 35
#> 32 'z' 47
#> 33 class character
#> 34 n 1000
#> 35 n_rm_na 1000
#> 36 n_na 0
#> 37 n_unique 26
#> 38 mode j
#> 39 'a' 49
#> 40 'b' 39
#> 41 'c' 35
#> 42 'd' 34
#> 43 'e' 40
#> 44 'f' 39
#> 45 'g' 40
#> 46 'h' 37
#> 47 'i' 38
#> 48 'j' 49
#> 49 'k' 43
#> 50 'l' 48
#> 51 'm' 32
#> 52 'n' 38
#> 53 'o' 31
#> 54 'p' 36
#> 55 'q' 35
#> 56 'r' 36
#> 57 's' 39
#> 58 't' 37
#> 59 'u' 32
#> 60 'v' 36
#> 61 'w' 38
#> 62 'x' 37
#> 63 'y' 35
#> 64 'z' 47
## `factor` Example
sample(letters, 1000, replace = TRUE) |>
as.factor() |>
as_tibble() |>
stats_summary("value") |>
print(n = Inf)
#> # A tibble: 32 × 2
#> name value
#> <chr> <chr>
#> 1 class character
#> 2 n 1000
#> 3 n_rm_na 1000
#> 4 n_na 0
#> 5 n_unique 26
#> 6 mode i
#> 7 'a' 47
#> 8 'b' 36
#> 9 'c' 41
#> 10 'd' 40
#> 11 'e' 33
#> 12 'f' 35
#> 13 'g' 41
#> 14 'h' 35
#> 15 'i' 55
#> 16 'j' 44
#> 17 'k' 33
#> 18 'l' 29
#> 19 'm' 32
#> 20 'n' 42
#> 21 'o' 37
#> 22 'p' 44
#> 23 'q' 44
#> 24 'r' 28
#> 25 's' 31
#> 26 't' 51
#> 27 'u' 37
#> 28 'v' 31
#> 29 'w' 43
#> 30 'x' 32
#> 31 'y' 41
#> 32 'z' 38
## `numeric` Example
rnorm(1000) |>
as_tibble() |>
stats_summary("value") |>
print(n = Inf)
#> # A tibble: 16 × 2
#> name value
#> <chr> <chr>
#> 1 class numeric
#> 2 n 1000
#> 3 n_rm_na 1000
#> 4 n_na 0
#> 5 mean 0.0117006220933184
#> 6 var 0.918178812696103
#> 7 sd 0.958216474861554
#> 8 min -2.94908378424355
#> 9 q_1 -0.652079210427979
#> 10 median -0.020748083005597
#> 11 q_3 0.668880620668101
#> 12 max 3.00809273739028
#> 13 iqr 1.32095983109608
#> 14 range 5.95717652163384
#> 15 skewness 0.0316001740482505
#> 16 kurtosis 2.83580068413331
## `duration` Examples
sample(seq_len(86399), 1000) |>
as.duration() |>
as_tibble() |>
stats_summary("value") |>
print(n = Inf)
#> # A tibble: 16 × 2
#> name value
#> <chr> <chr>
#> 1 class Duration
#> 2 n 1000
#> 3 n_rm_na 1000
#> 4 n_na 0
#> 5 mean 12:03:16.162
#> 6 var 162510:55:29.038795
#> 7 sd 06:43:07.586259
#> 8 min 00:04:06
#> 9 q_1 06:13:20.75
#> 10 median 12:14:13.5
#> 11 q_3 17:42:24.25
#> 12 max 23:59:55
#> 13 iqr 11:29:03.5
#> 14 range 23:55:49
#> 15 skewness 0.00917003022796984
#> 16 kurtosis 1.81966719593291
sample(seq_len(86399), 1000) |>
as.duration() |>
as_tibble() |>
stats_summary(
col = "value",
hms_format = FALSE
) |>
print(n = Inf)
#> # A tibble: 16 × 2
#> name value
#> <chr> <chr>
#> 1 class Duration
#> 2 n 1000
#> 3 n_rm_na 1000
#> 4 n_na 0
#> 5 mean 43339.197s (~12.04 hours)
#> 6 var 615087728.684876s (~19.49 years)
#> 7 sd 24800.9622532045s (~6.89 hours)
#> 8 min 8s
#> 9 q_1 22103s (~6.14 hours)
#> 10 median 42948.5s (~11.93 hours)
#> 11 q_3 64350.5s (~17.88 hours)
#> 12 max 86397s (~24 hours)
#> 13 iqr 42247.5s (~11.74 hours)
#> 14 range 86389s (~24 hours)
#> 15 skewness 0.00657731356615429
#> 16 kurtosis 1.79210141608804
## `hms` Examples
sample(seq_len(86399), 1000) |>
as_hms() |>
as_tibble() |>
stats_summary("value") |>
print(n = Inf)
#> # A tibble: 16 × 2
#> name value
#> <chr> <chr>
#> 1 class hms
#> 2 n 1000
#> 3 n_rm_na 1000
#> 4 n_na 0
#> 5 mean 11:28:08.807
#> 6 var 169833:57:15.417168
#> 7 sd 06:52:06.549202
#> 8 min 00:00:42
#> 9 q_1 05:41:43.75
#> 10 median 11:05:19.5
#> 11 q_3 17:20:15.5
#> 12 max 23:59:20
#> 13 iqr 11:38:31.75
#> 14 range 23:58:38
#> 15 skewness 0.107205872768896
#> 16 kurtosis 1.84061498281524
sample(seq_len(86399), 1000) |>
as_hms() |>
as_tibble() |>
stats_summary(
col = "value",
threshold = hms::parse_hm("12:00")
) |>
print(n = Inf)
#> # A tibble: 16 × 2
#> name value
#> <chr> <chr>
#> 1 class hms
#> 2 n 1000
#> 3 n_rm_na 1000
#> 4 n_na 0
#> 5 mean 23:46:53.002
#> 6 var 15:12:53.711708
#> 7 sd 06:53:46.316153
#> 8 min 12:00:28
#> 9 q_1 18:02:41
#> 10 median 23:40:33
#> 11 q_3 05:59:57.25
#> 12 max 11:59:43
#> 13 iqr 11:57:16.25
#> 14 range 23:59:15
#> 15 skewness 0.018207294628179
#> 16 kurtosis 1.82829443442708
## `Date` Example
sample(seq_len(20415), 1000) |>
as.Date() |>
as_tibble() |>
stats_summary("value") |>
print(n = Inf)
#> # A tibble: 16 × 2
#> name value
#> <chr> <chr>
#> 1 class Date
#> 2 n 1000
#> 3 n_rm_na 1000
#> 4 n_na 0
#> 5 mean 1998-02-26
#> 6 var 3092604385158.14s (~97998.72 years)
#> 7 sd 516914904.870873s (~16.38 years)
#> 8 min 1970-01-04
#> 9 q_1 1983-10-31
#> 10 median 1998-07-23
#> 11 q_3 2012-01-07
#> 12 max 2025-11-20
#> 13 iqr 889552800s (~28.19 years)
#> 14 range 1763337600s (~55.88 years)
#> 15 skewness -0.0152831071769018
#> 16 kurtosis 1.79304336911549
## `POSIXct` Example
sample(seq_len(Sys.time()), 1000) |>
as.POSIXct() |>
as_tibble() |>
stats_summary("value") |>
print(n = Inf)
#> # A tibble: 16 × 2
#> name value
#> <chr> <chr>
#> 1 class POSIXct
#> 2 n 1000
#> 3 n_rm_na 1000
#> 4 n_na 0
#> 5 mean 1998-06-12 04:49:45.477
#> 6 var 256168272578109568s (~8117482716.62 years)
#> 7 sd 506130687.251929s (~16.04 years)
#> 8 min 1970-01-09 09:07:33
#> 9 q_1 1984-11-01 18:14:05.5
#> 10 median 1998-08-07 23:51:57
#> 11 q_3 2012-08-01 21:38:09
#> 12 max 2025-11-20 13:31:06
#> 13 iqr 875676243.5s (~27.75 years)
#> 14 range 1762921413s (~55.86 years)
#> 15 skewness -0.0427469823532965
#> 16 kurtosis 1.82590641944719