For the letter F – filters! Filters are incredibly useful, especially when combined with the main pipe %>%. I frequently use filters along with ggplot functions, to chart a specific subgroup or remove missing cases or outliers. As one example, I could use a filter to chart only fiction books from my reading dataset.

library(tidyverse)

## -- Attaching packages ------------------------------------------- tidyverse 1.3.0 --

## <U+2713> ggplot2 3.2.1     <U+2713> purrr   0.3.3
## <U+2713> tibble  2.1.3     <U+2713> dplyr   0.8.3
## <U+2713> tidyr   1.0.0     <U+2713> stringr 1.4.0
## <U+2713> readr   1.3.1     <U+2713> forcats 0.4.0

## -- Conflicts ---------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

reads2019 <- read_csv("~/Downloads/Blogging A to Z/SarasReads2019_allrated.csv", col_names = TRUE)

## Parsed with column specification:
## cols(
##   Title = col_character(),
##   Pages = col_double(),
##   date_started = col_character(),
##   date_read = col_character(),
##   Book.ID = col_double(),
##   Author = col_character(),
##   AdditionalAuthors = col_character(),
##   AverageRating = col_double(),
##   OriginalPublicationYear = col_double(),
##   read_time = col_double(),
##   MyRating = col_double(),
##   Gender = col_double(),
##   Fiction = col_double(),
##   Childrens = col_double(),
##   Fantasy = col_double(),
##   SciFi = col_double(),
##   Mystery = col_double(),
##   SelfHelp = col_double()
## )

reads2019 %>%
  filter(Fiction == 1) %>%
  ggplot(aes(Pages)) +
  geom_histogram() +
  scale_y_continuous(breaks = seq(0,16,1)) +
  scale_x_continuous(breaks = seq(0,1200,100)) +
  ylab("Frequency") +
  theme_classic()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

I could also use filters to create a new dataset – perhaps one of my top books I read during 2019.

library(magrittr)

## 
## Attaching package: 'magrittr'

## The following object is masked from 'package:purrr':
## 
##     set_names

## The following object is masked from 'package:tidyr':
## 
##     extract

top_books <- reads2019 %>%
  filter(MyRating == 5)

top_books %$%
  list(Title)

## [[1]]
##  [1] "1Q84"                                                       
##  [2] "Alas, Babylon"                                              
##  [3] "Elevation"                                                  
##  [4] "Guards! Guards! (Discworld, #8; City Watch #1)"             
##  [5] "How Music Works"                                            
##  [6] "Lords and Ladies (Discworld, #14; Witches #4)"              
##  [7] "Moving Pictures (Discworld, #10; Industrial Revolution, #1)"
##  [8] "Redshirts"                                                  
##  [9] "Swarm Theory"                                               
## [10] "The Android's Dream (The Android's Dream #1)"               
## [11] "The Dutch House"                                            
## [12] "The Emerald City of Oz (Oz #6)"                             
## [13] "The End of Mr. Y"                                           
## [14] "The Human Division (Old Man's War, #5)"                     
## [15] "The Last Colony (Old Man's War, #3)"                        
## [16] "The Long Utopia (The Long Earth #4)"                        
## [17] "The Marvelous Land of Oz (Oz, #2)"                          
## [18] "The Miraculous Journey of Edward Tulane"                    
## [19] "The Night Circus"                                           
## [20] "The Patchwork Girl of Oz (Oz, #7)"                          
## [21] "The Patron Saint of Liars"                                  
## [22] "The Wonderful Wizard of Oz (Oz, #1)"                        
## [23] "The Year of the Flood (MaddAddam, #2)"                      
## [24] "Witches Abroad (Discworld, #12; Witches #3)"                
## [25] "Wyrd Sisters (Discworld, #6; Witches #2)"

Or I could create one of the 10 longest books I read:

long_books <- reads2019 %>%
  arrange(desc(Pages)) %>%
  filter(between(row_number(), 1, 10)) %>%
  select(Title, Pages)

library(expss)

## 
## Use 'expss_output_viewer()' to display tables in the RStudio Viewer.
##  To return to the console output, use 'expss_output_default()'.

## 
## Attaching package: 'expss'

## The following objects are masked from 'package:magrittr':
## 
##     and, equals, or

## The following objects are masked from 'package:stringr':
## 
##     fixed, regex

## The following objects are masked from 'package:dplyr':
## 
##     between, compute, contains, first, last, na_if, recode, vars

## The following objects are masked from 'package:purrr':
## 
##     keep, modify, modify_if, transpose

## The following objects are masked from 'package:tidyr':
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##     contains, nest

## The following object is masked from 'package:ggplot2':
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##     vars

as.etable(long_books, rownames_as_row_labels = FALSE)

Title	Pages
It	1156
1Q84	925
Insomnia	890
The Institute	576
The Robber Bride	528
Life of Pi	460
Cell	449
Cujo	432
The Human Division (Old Man’s War, #5)	431
The Year of the Flood (MaddAddam, #2)	431