In matthewhirschey/bespokelearnr: Bespoke data science learning

(Re)Introducing %>% {.build}

The %>% operator is a way of "chaining" together strings of commands that make reading your code easy.
The following code chunk illustrates how %>% works:

`r dataframe_name %>%select(r colnames(df_character_select), r colnames(df_numeric_select)) %>%filter(r colnames(df_character_select) == "r df_character_filter") %>%head()`

df_input %>%
  select(colnames(df_character_select), colnames(df_numeric_select)) %>% 
  filter(!!rlang::sym(colnames(df_character_select)) == as.character(df_character_filter)) %>% 
  head()

(Re)Introducing %>% {.build}

The previous code chunk does the following - it takes your dataset and then "pipes" it into select(), and then applies a filter() to the data.

`r dataframe_name %>%select(r colnames(df_character_select), r colnames(df_numeric_select)) %>%filter(r colnames(df_character_select) == "r df_character_filter") %>%head()`

the head function lists only the top n results -- convenient for long variables

When you see %>%, think "and then"

(Re)Introducing %>% {.build}

The alternative to using %>% is running the following code

filter(select(r dataframe_name, r colnames(df_character_select), r colnames(df_numeric_select)), r colnames(df_character_select) == "r df_character_filter")

Although this is only one line as opposed to three, it's both more difficult to write and more difficult to read

matthewhirschey/bespokelearnr documentation built on Oct. 11, 2020, 12:57 a.m.