Introduction to fastrep

Overview

The objective of this package is to facilitate producing reports using Rmarkdown, so the functions are of that type that you “you always have to use, but you always have to create”.

The core function : tbl

This is the main function of fastrep, with her you can make tables in HTML or latex format, the main idea is to provide minimal parameters to create their own table, so you just need to provide a data.frame.

df = Orange |> 
  dplyr::group_by(Tree) |> 
  dplyr::count() 
  
  
fastrep::tbl(df)
Tree n
3 7
1 7
5 7
2 7
4 7

As you can see the only parameters is df, you can put a title in the table using title

df |> 
  fastrep::tbl(title = "My Title!", full_page = F)
My Title!
Tree n
3 7
1 7
5 7
2 7
4 7

The value of format will be automatically determined if the function is called within a knitr document, but in your section, the default format is html, if you want to change just use the argument format which the possibles values are html or latex. If you want the code that generates the table you can use the argument code.

df |> 
  fastrep::tbl(title = "My Title!", code = T, format = "latex")

You can choose if you want rows separating the cells in the table (LaTeX version), using tabs = TRUE

freq_tbl

This function creates a frequency table, you only need to supply a data.frame and the variable to make the table.

infert |> 
  fastrep::freq_tbl(education)
#> # A tibble: 4 × 3
#>   education absolute_frequency relative_frequency
#>   <chr>                  <int>              <dbl>
#> 1 0-5yrs                    12             0.0484
#> 2 6-11yrs                  120             0.484 
#> 3 12+ yrs                  116             0.468 
#> 4 Total                    248             1

If you want to generate a table using the output, just combine it with tbl

infert |> 
  fastrep::freq_tbl(education) |> 
  fastrep::tbl()
education absolute_frequency relative_frequency
0-5yrs 12 0.0484
6-11yrs 120 0.4839
12+ yrs 116 0.4677
Total 248 1.0000

You can order the output using sort_by, which has two options: absolute_frequency and relative_frequency.

chickwts |> 
  fastrep::freq_tbl(feed, sort_by = absolute_frequency)
#> # A tibble: 7 × 3
#>   feed      absolute_frequency relative_frequency
#>   <chr>                  <int>              <dbl>
#> 1 horsebean                 10              0.141
#> 2 meatmeal                  11              0.155
#> 3 casein                    12              0.169
#> 4 linseed                   12              0.169
#> 5 sunflower                 12              0.169
#> 6 soybean                   14              0.197
#> 7 Total                     71              1.00

And if you want decreasing order put desc = T

chickwts |> 
  fastrep::freq_tbl(feed, desc = T, sort_by = relative_frequency)
#> # A tibble: 7 × 3
#>   feed      absolute_frequency relative_frequency
#>   <chr>                  <int>              <dbl>
#> 1 soybean                   14              0.197
#> 2 casein                    12              0.169
#> 3 linseed                   12              0.169
#> 4 sunflower                 12              0.169
#> 5 meatmeal                  11              0.155
#> 6 horsebean                 10              0.141
#> 7 Total                     71              1.00

cross_tbl

This function makes cross tables, like all functions from fastrep you need to supply a data.frame, and in this case two variables.

infert |> 
  fastrep::cross_tbl(education, spontaneous)
spontaneous
education 0 1 2 Total
0-5yrs 9 1 2 12
6-11yrs 71 33 16 120
12+ yrs 61 37 18 116
Total 141 71 36 248

The second variable (var2) is on the top of the table, if you want the marginal row table use marg = TRUE.

infert |> 
  fastrep::cross_tbl(education, spontaneous, marg = T)
spontaneous
education 0 1 2
0-5yrs 0.7500000 0.0833333 0.1666667
6-11yrs 0.5916667 0.2750000 0.1333333
12+ yrs 0.5258621 0.3189655 0.1551724

describe

In the base R we have the function summary, but the output is no by default a data.frame, so describe is an enhancement of this function to summarize data frames.

mtcars |> 
  fastrep::describe()
#> # A tibble: 11 × 7
#>    variable    mean median      sd   min    max na_count
#>    <chr>      <dbl>  <dbl>   <dbl> <dbl>  <dbl>    <dbl>
#>  1 mpg       20.1    19.2    6.03  10.4   33.9         0
#>  2 cyl        6.19    6      1.79   4      8           0
#>  3 disp     231.    196.   124.    71.1  472           0
#>  4 hp       147.    123     68.6   52    335           0
#>  5 drat       3.60    3.70   0.535  2.76   4.93        0
#>  6 wt         3.22    3.32   0.978  1.51   5.42        0
#>  7 qsec      17.8    17.7    1.79  14.5   22.9         0
#>  8 vs         0.438   0      0.504  0      1           0
#>  9 am         0.406   0      0.499  0      1           0
#> 10 gear       3.69    4      0.738  3      5           0
#> 11 carb       2.81    2      1.62   1      8           0

As you’ve probably noticed, the output is a tibble, so you can combine with tbl

mtcars |> 
  fastrep::describe() |> 
  fastrep::tbl("Summary of mtcars")
Summary of mtcars
variable mean median sd min max na_count
mpg 20.091 19.200 6.027 10.400 33.900 0
cyl 6.188 6.000 1.786 4.000 8.000 0
disp 230.722 196.300 123.939 71.100 472.000 0
hp 146.688 123.000 68.563 52.000 335.000 0
drat 3.597 3.695 0.535 2.760 4.930 0
wt 3.217 3.325 0.978 1.513 5.424 0
qsec 17.849 17.710 1.787 14.500 22.900 0
vs 0.438 0.000 0.504 0.000 1.000 0
am 0.406 0.000 0.499 0.000 1.000 0
gear 3.688 4.000 0.738 3.000 5.000 0
carb 2.812 2.000 1.615 1.000 8.000 0