Statistics Sunday: My 2019 Reading
This article is originally published at http://www.deeplytrivial.com/I've spent the month of April blogging my way through the tidyverse, while using my reading dataset from 2019 as the example. Today, I thought I'd bring many of those analyses and data manipulation techniques together to do a post about my reading habits for the year.
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/SaraReads2019_allchanges.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 %>%
summarise(Books = n(),
Authors = n_distinct(Author))
## # A tibble: 1 x 2
## Books Authors
## <int> <int>
## 1 87 42
authors <- reads2019 %>%
group_by(Author) %>%
summarise(Books = n(),
Pages = sum(Pages),
AvgRating = mean(MyRating),
Oldest = min(OriginalPublicationYear),
Newest = max(OriginalPublicationYear),
AvgRT = mean(read_time),
Gender = first(Gender),
Fiction = sum(Fiction),
Childrens = sum(Childrens),
Fantasy = sum(Fantasy),
Sci = sum(SciFi),
Mystery = sum(Mystery))
authors %>%
ggplot(aes(reorder(Author, desc(Books)), Books)) +
geom_col() +
theme(axis.text.x = element_text(angle = 90)) +
xlab("Author")
authors %>%
mutate(Author = fct_reorder(Author, desc(Author))) %>%
filter(Books > 1) %>%
ggplot(aes(reorder(Author, Books), Books)) +
geom_col() +
coord_flip() +
xlab("Author")
We can also plot average rating by author, which can tell me a little more about how much I like particular authors. Let's plot those for any author who contributed at least 2 books to my dataset.
authors %>%
filter(Books > 1) %>%
ggplot(aes(Author, AvgRating)) +
geom_col() +
scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
ylab("Average Rating")
We can also look at how genre affected ratings. Using the genre labels I generated before, let's plot average rating.
genre <- reads2019 %>%
group_by(Fiction, Childrens, Fantasy, SciFi, Mystery) %>%
summarise(Books = n(),
AvgRating = mean(MyRating)) %>%
bind_cols(Genre = c("Non-Fiction",
"General Fiction",
"Mystery",
"Science Fiction",
"Fantasy",
"Fantasy Sci-Fi",
"Children's Fiction",
"Children's Fantasy"))
genre %>%
ggplot(aes(reorder(Genre, desc(AvgRating)), AvgRating)) +
geom_col() +
scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
xlab("Genre") +
ylab("Average Rating")
lowratings <- reads2019 %>%
filter(MyRating <= 3) %>%
mutate(Rating = case_when(MyRating == 2 ~ "Hated",
MyRating == 3 ~ "Disliked")) %>%
arrange(desc(MyRating), Author) %>%
select(Title, Author, Rating)
library(expss)
##
## Attaching package: 'expss'
## 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':
##
## contains, nest
## The following object is masked from 'package:ggplot2':
##
## vars
as.etable(lowratings, rownames_as_row_labels = FALSE)
| Title | Author | Rating |
|---|---|---|
| The Scarecrow of Oz (Oz, #9) | Baum, L. Frank | Disliked |
| The Tin Woodman of Oz (Oz, #12) | Baum, L. Frank | Disliked |
| Herself Surprised | Cary, Joyce | Disliked |
| The 5 Love Languages: The Secret to Love That Lasts | Chapman, Gary | Disliked |
| Boundaries: When to Say Yes, How to Say No to Take Control of Your Life | Cloud, Henry | Disliked |
| Summerdale | Collins, David Jay | Disliked |
| When We Were Orphans | Ishiguro, Kazuo | Disliked |
| Bird Box (Bird Box, #1) | Malerman, Josh | Disliked |
| Oz in Perspective: Magic and Myth in the L. Frank Baum Books | Tuerk, Richard | Disliked |
| Cujo | King, Stephen | Hated |
| Just Evil (Evil Secrets Trilogy, #1) | McKeehan, Vickie | Hated |
reads2019 <- reads2019 %>%
mutate(MyRating = replace(MyRating,
MyRating == 2, 1),
MyRating = replace(MyRating,
Title == "Herself Surprised", 2))
lowratings <- reads2019 %>%
filter(MyRating <= 2) %>%
mutate(Rating = case_when(MyRating == 1 ~ "Hated",
MyRating == 2 ~ "Disliked")) %>%
arrange(desc(MyRating), Author) %>%
select(Title, Author, Rating)
library(expss)
as.etable(lowratings, rownames_as_row_labels = FALSE)
| Title | Author | Rating |
|---|---|---|
| Herself Surprised | Cary, Joyce | Disliked |
| Cujo | King, Stephen | Hated |
| Just Evil (Evil Secrets Trilogy, #1) | McKeehan, Vickie | Hated |
Let's look again at my top books, but this time, classify them by long genre descriptions from above. I can get that information into my full reading dataset with a join, using the genre flags. Then I can plot the results from that dataset without having to summarize first.
topbygenre <- reads2019 %>%
left_join(genre, by = c("Fiction","Childrens","Fantasy","SciFi","Mystery")) %>%
select(-Books, -AvgRating) %>%
filter(MyRating == 5)
topbygenre %>%
ggplot(aes(fct_infreq(Genre))) +
geom_bar() +
scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
xlab("Genre") +
ylab("Books")
genre %>%
ggplot(aes(reorder(Genre, desc(AvgRating)), AvgRating, label = Books)) +
geom_col() +
scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
xlab("Genre") +
ylab("Average Rating") +
geom_text(aes(x = Genre, y = AvgRating-0.25), size = 5,
color = "white")
genre %>%
filter(Books > 2) %>%
ggplot(aes(reorder(Genre, desc(AvgRating)), AvgRating, label = Books)) +
geom_col() +
scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
xlab("Genre") +
ylab("Average Rating") +
geom_text(aes(x = Genre, y = AvgRating-0.25), size = 5,
color = "white")
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This article is originally published at http://www.deeplytrivial.com/
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