O is for order_by
This article is originally published at http://www.deeplytrivial.com/This will be a quick post on another tidyverse function, order_by. I'll admit, I don't use this one as often as arrange. It can be useful, though, if you don't want to permanently change the order of your dataset but want to use functions that require ordering the data. One example is the cumulative sum function (cumsum).
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_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()
## )
library(lubridate)
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
reads2019 <- reads2019 %>%
mutate(date_started = as.Date(reads2019$date_started, format = '%m/%d/%Y'),
date_read = as.Date(date_read, format = '%m/%d/%Y'),
days_reading = order_by(date_read, cumsum(read_time)),
DOY = yday(date_read))
reads2019 <- reads2019 %>%
mutate(read_time = ifelse(read_time > DOY, DOY, read_time),
days_reading = order_by(date_read, cumsum(read_time)))
reads2019 %>%
ggplot(aes(DOY, days_reading)) +
geom_point() +
scale_x_continuous(breaks = seq(0, 380, 20)) +
scale_y_continuous(breaks = seq(0, 360, 20)) +
xlab("Day of Year") +
ylab("Days Reading")
Another potential use for order_by is with the fill function, which fills missing values from the next or previous column entry. If you were doing a study where people receive repeated measures and miss one of the measurements, or if your dataset only notes a new value when the previous one has changed, you could use fill to copy previous values into the empty cells.
Tomorrow, we'll talk more about the scales package, to enhance your ggplots!
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This article is originally published at http://www.deeplytrivial.com/
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