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Compute some basic descriptive statistics.

num_stat.Rd

Values of type factor, character and logical are treated as categorical. For logicals, the two categories are given the labels `Yes` for TRUE, and `No` for FALSE. Factor levels with zero counts are retained.

Usage

num_stat(x, digits = 3, digits_pct = 1, rounding_fn = signif_pad)

cat_stat(x, digits_pct = 1)

Arguments

x

A vector or numeric, factor, character or logical values.

digits

An integer specifying the number of significant digits to keep for numerical results. See signif_pad.

digits_pct

An integer specifying the number of significant digits to keep for percentage.

rounding_fn

The function to use to do the rounding. Defaults to signif_pad.

Value

A list. For numeric x, the list contains the numeric elements:

  • N: the number of non-missing values

  • NMISS: the number of missing values

  • SUM: the sum of the non-missing values

  • MEAN: the mean of the non-missing values

  • SD: the standard deviation of the non-missing values

  • MIN: the minimum of the non-missing values

  • MEDIAN: the median of the non-missing values

  • CV: the percent coefficient of variation of the non-missing values

  • GMEAN: the geometric mean of the non-missing values if non-negative, or NA

  • GCV: the percent geometric coefficient of variation of the non-missing values if non-negative, or NA

  • GSD: the geometric standard deviation of the non-missing values if non-negative, or NA

  • Q1: the first quartile of the non-missing values (alias q25)

  • Q2: the second quartile of the non-missing values (alias q50 or Median)

  • Q3: the third quartile of the non-missing values (alias q75)

  • IQR: the inter-quartile range of the non-missing values (i.e., Q3 - Q1)

If x is categorical (i.e. factor, character or logical), the list contains a sublist for each category, where each sublist contains the numeric elements:

  • FREQ: the frequency count

  • PCT: the percent relative frequency, including NA in the denominator

  • PCTnoNA: the percent relative frequency, excluding NA from the denominator

  • Nall: total count, including NA from the denominator

  • N: total count, excluding NA from the denominator

See also

signif_pad round_pad

Examples

x <- exp(rnorm(100, 1, 1))
num_stat(x)
#> $N
#> [1] "100"
#> 
#> $NMISS
#> [1] "0"
#> 
#> $SUM
#> [1] "494"
#> 
#> $MEAN
#> [1] "4.94"
#> 
#> $SD
#> [1] "6.55"
#> 
#> $CV
#> [1] "132.7%"
#> 
#> $GMEAN
#> [1] "2.92"
#> 
#> $GCV
#> [1] "142.0%"
#> 
#> $GSD
#> [1] "2.86"
#> 
#> $MEDIAN
#> [1] "2.98"
#> 
#> $MIN
#> [1] "0.199"
#> 
#> $Q1
#>    25% 
#> "1.90" 
#> 
#> $Q2
#>    50% 
#> "2.98" 
#> 
#> $Q3
#>    75% 
#> "5.07" 
#> 
#> $IQR
#>    75% 
#> "3.17" 
#> 
#> $MAX
#> [1] "42.8"
#> 

y <- factor(sample(0:1, 99, replace = TRUE), labels = c("Female", "Male"))
y[1:10] <- NA
cat_stat(y)
#> $Female
#> $Female$FREQ
#> [1] 45
#> 
#> $Female$PCT
#> [1] "45.5%"
#> 
#> $Female$PCTnoNA
#> [1] "50.6%"
#> 
#> $Female$Nall
#> [1] 99
#> 
#> $Female$N
#> [1] 89
#> 
#> 
#> $Male
#> $Male$FREQ
#> [1] 44
#> 
#> $Male$PCT
#> [1] "44.4%"
#> 
#> $Male$PCTnoNA
#> [1] "49.4%"
#> 
#> $Male$Nall
#> [1] 99
#> 
#> $Male$N
#> [1] 89
#> 
#> 
cat_stat(is.na(y))
#> $Yes
#> $Yes$FREQ
#> [1] 10
#> 
#> $Yes$PCT
#> [1] "10.1%"
#> 
#> $Yes$PCTnoNA
#> [1] "10.1%"
#> 
#> $Yes$Nall
#> [1] 99
#> 
#> $Yes$N
#> [1] 99
#> 
#> 
#> $No
#> $No$FREQ
#> [1] 89
#> 
#> $No$PCT
#> [1] "89.9%"
#> 
#> $No$PCTnoNA
#> [1] "89.9%"
#> 
#> $No$Nall
#> [1] 99
#> 
#> $No$N
#> [1] 99
#> 
#>