library(tidyverse)
reads2019 <- read_csv("~/Downloads/Blogging A to Z/SarasReads2019_allrated.csv", col_names = TRUE)
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()
library(magrittr)
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)"
long_books <- reads2019 %>% arrange(desc(Pages)) %>% filter(between(row_number(), 1, 10)) %>% select(Title, Pages) library(expss)
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 |
reads2019 %>% filter(read_time > 7 & Pages >= 400) %>% select(Title, Pages, Author, read_time)
## # A tibble: 2 x 4 ## Title Pages Author read_time ## <chr> <dbl> <chr> <dbl> ## 1 The Long War (The Long Earth, #2) 419 Pratchett, Terry 8 ## 2 The Robber Bride 528 Atwood, Margaret 9
Lastly, let's filter with "or", so we select cases that meet one of the two criteria. We create or with | . The first criteria is read time less than 1 day (meaning I started and finished the book in the same day). The second criteria are my long reads/long books criteria from above. Since there's two parts to this side of the |, I enclose them in parentheses so the statement is evaluated together across the data:
reads2019 %>% filter(read_time < 1 | (read_time > 7 & Pages >= 400)) %>% select(Title, Pages, Author, read_time)
## # A tibble: 4 x 4 ## Title Pages Author read_time ## <chr> <dbl> <chr> <dbl> ## 1 Empath: A Complete Guide for Developing Your Gif… 104 Dyer, Judy 0 ## 2 The Long War (The Long Earth, #2) 419 Pratchett, … 8 ## 3 The Robber Bride 528 Atwood, Mar… 9 ## 4 When We Were Orphans 320 Ishiguro, K… 0
You can read more about logical and arithmetic operators that can be used with filter here.
Tomorrow, we'll discuss the group_by function!
Hey, it would be nice, to have access to the data file also, so we could practice in R... where do you get SarasReads2019_allrated.csv ?
ReplyDeleteHey there! I linked to it in the previous post, but here it is again: https://www.dropbox.com/s/y0u7d57gjo4yloz/SaraReads2019_allrated.csv?dl=1
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