Remove "lifecycle" badge, following easystats/easystats#432

Author: etiennebacher

README.Rmd

@@ -19,7 +19,7 @@ library(datawizard)
 
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.04684/status.svg)](https://doi.org/10.21105/joss.04684)
 [![downloads](http://cranlogs.r-pkg.org/badges/datawizard)](https://cran.r-project.org/package=datawizard)
-[![total](https://cranlogs.r-pkg.org/badges/grand-total/datawizard)](https://cranlogs.r-pkg.org/) [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)
+[![total](https://cranlogs.r-pkg.org/badges/grand-total/datawizard)](https://cranlogs.r-pkg.org/) 
 
 <!-- ***:sparkles: Hockety pockety wockety wack, prepare this data forth and back*** -->
 

README.md

@@ -4,7 +4,6 @@
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.04684/status.svg)](https://doi.org/10.21105/joss.04684)
 [![downloads](http://cranlogs.r-pkg.org/badges/datawizard)](https://cran.r-project.org/package=datawizard)
 [![total](https://cranlogs.r-pkg.org/badges/grand-total/datawizard)](https://cranlogs.r-pkg.org/)
-[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)
 
 <!-- ***:sparkles: Hockety pockety wockety wack, prepare this data forth and back*** -->
 <!-- ***Hockety pockety wockety wock, messy data is in shock*** -->
@@ -50,11 +49,11 @@ It covers two aspects of data preparation:
 badge](https://easystats.r-universe.dev/badges/datawizard)](https://easystats.r-universe.dev)
 [![R-CMD-check](https://github.com/easystats/datawizard/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/easystats/datawizard/actions)
 
-| Type | Source | Command |
-|----|----|----|
-| Release | CRAN | `install.packages("datawizard")` |
+| Type        | Source     | Command                                                                      |
+|-------------|------------|------------------------------------------------------------------------------|
+| Release     | CRAN       | `install.packages("datawizard")`                                             |
 | Development | r-universe | `install.packages("datawizard", repos = "https://easystats.r-universe.dev")` |
-| Development | GitHub | `remotes::install_github("easystats/datawizard")` |
+| Development | GitHub     | `remotes::install_github("easystats/datawizard")`                            |
 
 > **Tip**
 >
@@ -71,9 +70,10 @@ To cite the package, run the following command:
 citation("datawizard")
 To cite package 'datawizard' in publications use:
 
-  Patil et al., (2022). datawizard: An R Package for Easy Data
-  Preparation and Statistical Transformations. Journal of Open Source
-  Software, 7(78), 4684, https://doi.org/10.21105/joss.04684
+  Patil et al., (2022). datawizard: An R Package for Easy
+  Data Preparation and Statistical Transformations. Journal
+  of Open Source Software, 7(78), 4684,
+  https://doi.org/10.21105/joss.04684
 
 A BibTeX entry for LaTeX users is
 
@@ -136,9 +136,6 @@ columns, can be achieved using `extract_column_names()` or
 # find column names matching a pattern
 extract_column_names(iris, starts_with("Sepal"))
 #> [1] "Sepal.Length" "Sepal.Width"
-```
-
-``` r
 
 # return data columns matching a pattern
 data_select(iris, starts_with("Sepal")) |> head()
@@ -156,10 +153,8 @@ It is also possible to extract one or more variables:
 ``` r
 # single variable
 data_extract(mtcars, "gear")
-#>  [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4
-```
-
-``` r
+#>  [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5
+#> [32] 4
 
 # more variables
 head(data_extract(iris, ends_with("Width")))
@@ -220,68 +215,44 @@ x
 #> 1 1 a 5  1
 #> 2 2 b 6  2
 #> 3 3 c 7  3
-```
-
-``` r
 y
 #>   c d   e id
 #> 1 6 f 100  2
 #> 2 7 g 101  3
 #> 3 8 h 102  4
-```
-
-``` r
 
 data_merge(x, y, join = "full")
 #>    a    b c id    d   e
 #> 3  1    a 5  1 <NA>  NA
 #> 1  2    b 6  2    f 100
 #> 2  3    c 7  3    g 101
 #> 4 NA <NA> 8  4    h 102
-```
-
-``` r
 
 data_merge(x, y, join = "left")
 #>   a b c id    d   e
 #> 3 1 a 5  1 <NA>  NA
 #> 1 2 b 6  2    f 100
 #> 2 3 c 7  3    g 101
-```
-
-``` r
 
 data_merge(x, y, join = "right")
 #>    a    b c id d   e
 #> 1  2    b 6  2 f 100
 #> 2  3    c 7  3 g 101
 #> 3 NA <NA> 8  4 h 102
-```
-
-``` r
 
 data_merge(x, y, join = "semi", by = "c")
 #>   a b c id
 #> 2 2 b 6  2
 #> 3 3 c 7  3
-```
-
-``` r
 
 data_merge(x, y, join = "anti", by = "c")
 #>   a b c id
 #> 1 1 a 5  1
-```
-
-``` r
 
 data_merge(x, y, join = "inner")
 #>   a b c id d   e
 #> 1 2 b 6  2 f 100
 #> 2 3 c 7  3 g 101
-```
-
-``` r
 
 data_merge(x, y, join = "bind")
 #>    a    b c id    d   e
@@ -322,17 +293,28 @@ data_to_wide(long_data,
   values_from = "value",
   id_cols = "Row_ID"
 )
-#>    Row_ID          X1          X2          X3         X4          X5
-#> 1       1 -0.08281164 -1.12490028 -0.70632036 -0.7027895  0.07633326
-#> 2       2  1.93468099 -0.87430362  0.96687656  0.2998642 -0.23035595
-#> 3       3 -2.05128979  0.04386162 -0.71016648  1.1494697  0.31746484
-#> 4       4  0.27773897 -0.58397514 -0.05917365 -0.3016415 -1.59268440
-#> 5       5 -1.52596060 -0.82329858 -0.23094342 -0.5473394 -0.18194062
-#> 6       6 -0.26916362  0.11059280  0.69200045 -0.3854041  1.75614174
-#> 7       7  1.23305388  0.36472778  1.35682290  0.2763720  0.11394932
-#> 8       8  0.63360774  0.05370100  1.78872284  0.1518608 -0.29216508
-#> 9       9  0.35271746  1.36867235  0.41071582 -0.4313808  1.75409316
-#> 10     10 -0.56048248 -0.38045724 -2.18785470 -1.8705001  1.80958455
+#>    Row_ID          X1          X2          X3         X4
+#> 1       1 -0.08281164 -1.12490028 -0.70632036 -0.7027895
+#> 2       2  1.93468099 -0.87430362  0.96687656  0.2998642
+#> 3       3 -2.05128979  0.04386162 -0.71016648  1.1494697
+#> 4       4  0.27773897 -0.58397514 -0.05917365 -0.3016415
+#> 5       5 -1.52596060 -0.82329858 -0.23094342 -0.5473394
+#> 6       6 -0.26916362  0.11059280  0.69200045 -0.3854041
+#> 7       7  1.23305388  0.36472778  1.35682290  0.2763720
+#> 8       8  0.63360774  0.05370100  1.78872284  0.1518608
+#> 9       9  0.35271746  1.36867235  0.41071582 -0.4313808
+#> 10     10 -0.56048248 -0.38045724 -2.18785470 -1.8705001
+#>             X5
+#> 1   0.07633326
+#> 2  -0.23035595
+#> 3   0.31746484
+#> 4  -1.59268440
+#> 5  -0.18194062
+#> 6   1.75614174
+#> 7   0.11394932
+#> 8  -0.29216508
+#> 9   1.75409316
+#> 10  1.80958455
 ```
 
 ### Empty rows and columns
@@ -352,22 +334,13 @@ tmp
 #> 3  3  3 NA  3
 #> 4 NA NA NA NA
 #> 5  5  5 NA  5
-```
-
-``` r
 
 # indices of empty columns or rows
 empty_columns(tmp)
 #> c 
 #> 3
-```
-
-``` r
 empty_rows(tmp)
 #> [1] 4
-```
-
-``` r
 
 # remove empty columns or rows
 remove_empty_columns(tmp)
@@ -377,18 +350,12 @@ remove_empty_columns(tmp)
 #> 3  3  3  3
 #> 4 NA NA NA
 #> 5  5  5  5
-```
-
-``` r
 remove_empty_rows(tmp)
 #>   a  b  c  d
 #> 1 1  1 NA  1
 #> 2 2 NA NA NA
 #> 3 3  3 NA  3
 #> 5 5  5 NA  5
-```
-
-``` r
 
 # remove empty columns and rows
 remove_empty(tmp)
@@ -409,9 +376,6 @@ table(x)
 #> x
 #>  1  2  3  4  5  6  7  8  9 10 
 #>  2  3  5  3  7  5  5  2 11  7
-```
-
-``` r
 
 # cut into 3 groups, based on distribution (quantiles)
 table(categorize(x, split = "quantile", n_groups = 3))
@@ -445,26 +409,23 @@ summary(swiss)
 #>  Mean   : 41.144   Mean   :19.94   
 #>  3rd Qu.: 93.125   3rd Qu.:21.70   
 #>  Max.   :100.000   Max.   :26.60
-```
-
-``` r
 
 # after
 summary(standardize(swiss))
-#>    Fertility         Agriculture       Examination         Education      
-#>  Min.   :-2.81327   Min.   :-2.1778   Min.   :-1.69084   Min.   :-1.0378  
-#>  1st Qu.:-0.43569   1st Qu.:-0.6499   1st Qu.:-0.56273   1st Qu.:-0.5178  
-#>  Median : 0.02061   Median : 0.1515   Median :-0.06134   Median :-0.3098  
-#>  Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.00000   Mean   : 0.0000  
-#>  3rd Qu.: 0.66504   3rd Qu.: 0.7481   3rd Qu.: 0.69074   3rd Qu.: 0.1062  
-#>  Max.   : 1.78978   Max.   : 1.7190   Max.   : 2.57094   Max.   : 4.3702  
-#>     Catholic       Infant.Mortality  
-#>  Min.   :-0.9350   Min.   :-3.13886  
-#>  1st Qu.:-0.8620   1st Qu.:-0.61543  
-#>  Median :-0.6235   Median : 0.01972  
-#>  Mean   : 0.0000   Mean   : 0.00000  
-#>  3rd Qu.: 1.2464   3rd Qu.: 0.60337  
-#>  Max.   : 1.4113   Max.   : 2.28566
+#>    Fertility         Agriculture       Examination      
+#>  Min.   :-2.81327   Min.   :-2.1778   Min.   :-1.69084  
+#>  1st Qu.:-0.43569   1st Qu.:-0.6499   1st Qu.:-0.56273  
+#>  Median : 0.02061   Median : 0.1515   Median :-0.06134  
+#>  Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.00000  
+#>  3rd Qu.: 0.66504   3rd Qu.: 0.7481   3rd Qu.: 0.69074  
+#>  Max.   : 1.78978   Max.   : 1.7190   Max.   : 2.57094  
+#>    Education          Catholic       Infant.Mortality  
+#>  Min.   :-1.0378   Min.   :-0.9350   Min.   :-3.13886  
+#>  1st Qu.:-0.5178   1st Qu.:-0.8620   1st Qu.:-0.61543  
+#>  Median :-0.3098   Median :-0.6235   Median : 0.01972  
+#>  Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.00000  
+#>  3rd Qu.: 0.1062   3rd Qu.: 1.2464   3rd Qu.: 0.60337  
+#>  Max.   : 4.3702   Max.   : 1.4113   Max.   : 2.28566
 ```
 
 ### Winsorize
@@ -486,9 +447,6 @@ anscombe
 #> 9  12 12 12  8 10.84 9.13  8.15  5.56
 #> 10  7  7  7  8  4.82 7.26  6.42  7.91
 #> 11  5  5  5  8  5.68 4.74  5.73  6.89
-```
-
-``` r
 
 # after
 winsorize(anscombe)
@@ -540,9 +498,6 @@ head(trees)
 #> 4  10.5     72   16.4
 #> 5  10.7     81   18.8
 #> 6  10.8     83   19.7
-```
-
-``` r
 
 # after
 head(ranktransform(trees))
@@ -575,9 +530,6 @@ x
 #> Mazda RX4     21.0   6  160 110
 #> Mazda RX4 Wag 21.0   6  160 110
 #> Datsun 710    22.8   4  108  93
-```
-
-``` r
 
 data_rotate(x)
 #>      Mazda RX4 Mazda RX4 Wag Datsun 710