- The goal of dtables is to quickly and intelligently generate useful and presentable descriptive frequencies and statistics tables with minimal input.
- The purpose of dtables is really just to make my life, and hopefully yours, a little easier.
- If you have any questions or want to help out feel free to send me an email or a pull request! My email can be found in the DESCRIPTION.
- See the latest changes since the CRAN version in the NEWS
# Install CRAN version:
install.packages("dtables")
# Or install Github (more recent, less stable, more fun) version:
devtools::install_github("gitronald/dtables")
# Load the package and data
library(dtables)
data(iris2)
- Essentially a shortcut to
data.frame(table(x)), but better, much better:
> dft(iris2$Species, prop = TRUE, perc = TRUE)
group n prop perc
1 setosa 52 0.3466667 34.7%
2 versicolor 49 0.3266667 32.7%
3 virginica 49 0.3266667 32.7%
- To add descriptive statistics to your dft, simply add a
byargument with the variable to describe:
> dft(iris2$Species, by = iris2$Sepal.Length, neat = FALSE)
group n prop perc mean sd median trimmed mad min max range ...
11 setosa 52 0.3466667 34.7% 4.955769 0.3460780 5.0 4.945238 0.29652 4.3 5.7 1.4
12 versicolor 49 0.3266667 32.7% 5.963265 0.4893422 6.0 5.978049 0.59304 4.9 6.9 2.0
13 virginica 49 0.3266667 32.7% 6.751020 0.6636774 6.8 6.773171 0.74130 4.9 7.7 2.8
- Some like it neat:
> dft(iris2[, c("Species", "Color")], by = iris2$Sepal.Width, neat = TRUE)
Species Color n prop mean sd se
11 setosa blue 15 0.1 3.3 0.4 0.1
12 versicolor blue 16 0.1 2.8 0.3 0.1
13 virginica blue 16 0.1 3.1 0.3 0.1
14 setosa orange 11 0.1 3.3 0.3 0.1
15 versicolor orange 8 0.1 3.0 0.3 0.1
16 virginica orange 11 0.1 3.0 0.3 0.1
17 setosa red 13 0.1 3.4 0.4 0.1
18 versicolor red 16 0.1 2.7 0.4 0.1
19 virginica red 11 0.1 2.9 0.3 0.1
110 setosa yellow 13 0.1 3.7 0.4 0.1
111 versicolor yellow 9 0.1 2.7 0.3 0.1
112 virginica yellow 11 0.1 2.9 0.3 0.1
- Generate variable details for an entire data.frame or for individual variables.
- Uses variable details to tag variables as:
- Variables which would make sense in a descriptive frequencies table -
frequencies - Variables which would make sense in a descriptive statistics table -
statistics
- Variables which would make sense in a descriptive frequencies table -
- Will flag variables that it can't classify as frequencies or statistics (e.g. an empty variable or a complex variable such as date).
> dvariable(iris2)
Note: 'Approval' was not classified.
Note: 'Date' was not classified.
variable class mode type levels frequencies statistics
1 Sepal.Length numeric numeric double 32 0 1
2 Sepal.Width numeric numeric double 21 0 1
3 Petal.Length numeric numeric double 39 0 1
4 Petal.Width numeric numeric double 21 0 1
5 Species factor numeric integer 3 1 0
6 Color character character character 4 1 0
7 Attractiveness integer numeric integer 5 1 1
8 LikelyToBuy integer numeric integer 11 1 1
9 Sold logical logical logical 2 1 0
10 Review numeric numeric double 5 1 1
11 Approval logical logical logical 0 0 0
12 Date Date numeric double 99 0 0
- Classification matters because these feed into
dtable...
- Uses
dvariableto decide whether to produce descriptive frequencies and/or statistics tables for each variable and returns output with or without rounding and presentation formatting.
> dtable(iris2)
$Frequencies
dataset demographic group n perc
1 iris2 Species setosa 52 34.7%
2 versicolor 49 32.7%
3 virginica 49 32.7%
4 iris2 Color blue 47 31.3%
5 orange 30 20.0%
6 red 40 26.7%
7 yellow 33 22.0%
8 iris2 Attractiveness 1 27 18.0%
9 2 30 20.0%
10 3 31 20.7%
11 4 32 21.3%
12 5 30 20.0%
13 iris2 LikelyToBuy -5 16 10.7%
14 -4 13 8.7%
15 -3 12 8.0%
16 -2 13 8.7%
17 -1 21 14.0%
18 0 10 6.7%
19 1 13 8.7%
20 2 14 9.3%
21 3 15 10.0%
22 4 8 5.3%
23 5 15 10.0%
24 iris2 Sold FALSE 79 52.7%
25 TRUE 71 47.3%
26 iris2 Review 1 31 20.7%
27 2 27 18.0%
28 3 34 22.7%
29 4 27 18.0%
30 5 31 20.7%
$Statistics
dataset variable vars n mean sd median trimmed mad min max range skew kurtosis se
1 iris2 Sepal.Length 1 150 5.9 0.9 5.8 5.8 1.1 4.3 7.7 3.4 0.3 -0.8 0.1
2 iris2 Sepal.Width 1 150 3.1 0.4 3.0 3.1 0.3 2.2 4.4 2.2 0.3 0.0 0.0
3 iris2 Petal.Length 1 150 3.8 1.8 4.4 3.8 2.1 1.0 6.9 5.9 -0.2 -1.4 0.2
4 iris2 Petal.Width 1 150 1.2 0.8 1.3 1.2 1.3 0.1 2.5 2.4 -0.1 -1.5 0.1
5 iris2 Attractiveness 1 150 3.1 1.4 3.0 3.1 1.5 1.0 5.0 4.0 -0.1 -1.3 0.1
6 iris2 LikelyToBuy 1 150 -0.1 3.2 -0.5 -0.2 3.7 -5.0 5.0 10.0 0.1 -1.2 0.3
7 iris2 Review 1 150 3.0 1.4 3.0 3.0 1.5 1.0 5.0 4.0 0.0 -1.3 0.1
- Will use all variables by default and a subset of variables can be specified by setting a
varsargument.
-
Select variables:
dtable(iris2, vars = c("Species", "Sold", "Sepal.Length")) -
Specify frequencies and statistics tables:
dtable(iris2, frequencies = "Species", statistics = "LikelyToBuy") -
Generate raw output:
dtable(iris2, neat = FALSE) -
Generate list outputs:
dtable(iris2, as.list = T)
- Generate descriptive statistics with dynamic input
# Single variable
dnum(iris2$Sepal.Length)
dnum(iris2["Sepal.Length"])
dnum(iris2[, "Sepal.Length"])
data var n mean sd median trimmed mad min max range skew kurtosis se
1 iris2 Sepal.Length 150 5.871333 0.9 5.8 5.8 1.1 4.3 7.7 3.4 0.3 -0.8 0.1
# Multiple variables
dnum(iris2[, c("Sepal.Length", "Sepal.Width", "Sold")])
data var n mean sd median trimmed mad min max range skew kurtosis se
1 iris2 Sepal.Length 150 5.8713333 0.9 5.8 5.8 1.1 4.3 7.7 3.4 0.3 -0.8 0.1
2 iris2 Sepal.Width 150 3.0780000 0.4 3.0 3.1 0.3 2.2 4.4 2.2 0.3 0.0 0.0
3 iris2 Sold 150 0.4733333 0.5 0.0 0.5 0.0 0.0 1.0 1.0 0.1 -2.0 0.0