diff --git a/README.Rmd b/README.Rmd
index 7610c5924..544079a26 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -170,7 +170,7 @@ long_data <- data_to_long(wide_data, rows_to = "Row_ID") # Save row number
 data_to_wide(long_data,
   names_from = "name",
   values_from = "value",
-  id_cols = "Row_ID"
+  by = "Row_ID"
 )
 ```
 
diff --git a/README.md b/README.md
index 411ad4c72..ba33c6086 100644
--- a/README.md
+++ b/README.md
@@ -137,6 +137,9 @@ 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()
@@ -155,6 +158,9 @@ It is also possible to extract one or more variables:
 # 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
 
 # more variables
 head(data_extract(iris, ends_with("Width")))
@@ -215,11 +221,17 @@ 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
@@ -227,32 +239,50 @@ data_merge(x, y, join = "full")
 #> 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
@@ -291,7 +321,7 @@ long_data <- data_to_long(wide_data, rows_to = "Row_ID") # Save row number
 data_to_wide(long_data,
   names_from = "name",
   values_from = "value",
-  id_cols = "Row_ID"
+  by = "Row_ID"
 )
 #>    Row_ID          X1          X2          X3         X4          X5
 #> 1       1 -0.08281164 -1.12490028 -0.70632036 -0.7027895  0.07633326
@@ -323,13 +353,22 @@ 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)
@@ -339,12 +378,18 @@ 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)
@@ -365,6 +410,9 @@ 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))
@@ -398,6 +446,9 @@ 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))
@@ -436,6 +487,9 @@ 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)
@@ -487,6 +541,9 @@ 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))
@@ -519,6 +576,9 @@ 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