The code in this section is evaluated.
library(tidyverse)
new_penguins <- palmerpenguins::penguins %>%
drop_na() %>%
mutate(bill_length_mean1 = mean(bill_length_mm)) %>%
group_by(species, island) %>%
mutate(bill_length_mean2 = mean(bill_length_mm)) %>%
ungroup() %>%
select(species, island, contains("bill_length"))
new_penguins## # A tibble: 333 x 5
## species island bill_length_mm bill_length_mean1 bill_length_mean2
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 Adelie Torgersen 39.1 44.0 39.0
## 2 Adelie Torgersen 39.5 44.0 39.0
## 3 Adelie Torgersen 40.3 44.0 39.0
## 4 Adelie Torgersen 36.7 44.0 39.0
## 5 Adelie Torgersen 39.3 44.0 39.0
## 6 Adelie Torgersen 38.9 44.0 39.0
## 7 Adelie Torgersen 39.2 44.0 39.0
## 8 Adelie Torgersen 41.1 44.0 39.0
## 9 Adelie Torgersen 38.6 44.0 39.0
## 10 Adelie Torgersen 34.6 44.0 39.0
## # ... with 323 more rowsThe code in this section is not evaluated.
The production and sales of different types of greenhouse flowers and plants has been collected annually since 2007 for both Canada as a whole and its individual provinces (no territories).
More information on this record can be found here.
The data and accompanying metadata can be downloaded together as a .zip file from here.
library(tidyverse)
library(gghighlight)
theme_set(theme_bw())For these demos, I will be storing all data files in a folder called
data with subfolders for each topic.
if (!dir.exists("./data/flowers")) {
dir.create("./data/flowers", recursive=TRUE)
}
download.file(
"https://www150.statcan.gc.ca/n1/tbl/csv/32100246-eng.zip",
destfile = "./data/flowers/flowers.zip"
)
unzip(
"./data/flowers/flowers.zip",
exdir = "./data/flowers"
)list.files("./data/flowers", pattern="csv")flowers <- read_csv("./data/flowers/32100246.csv")
glimpse(flowers)The variables of interest are:
| Variable | Description |
|---|---|
REF_DATE |
Year of record |
GEO |
Country or province |
VALUE |
Number of plants produced or monetary value of plants sold |
Output |
Whether VALUE corresponds to production (number) or
sales (dollars) |
Flowers and plants |
Flower and plant types |
flowers <- flowers %>%
select(REF_DATE, GEO, VALUE, Output, `Flowers and plants`) %>%
rename(
year = REF_DATE,
location = GEO,
value = VALUE,
output = Output,
type = `Flowers and plants`
)flower_sales_can <- flowers %>%
filter(location == "Canada", output == "Sales")
ggplot(flower_sales_can, aes(x=year, y=value, colour=type))+
geom_line()We can overwrite the existing flowers data with these
changes since we'll want to use the cleaned data again later. We'll
remove the word Total with the space that comes after, the
space before the square brackets, and the square brackets with all of
its contents. Finally, we will capitalise the first letter of each
flower type.
flowers <- flowers %>%
mutate(
type = str_remove(type, pattern="Total\\s"),
type = str_remove(type, pattern="\\s\\[.*\\]"),
type = str_to_sentence(type)
)We can check our work using:
flowers %>%
distinct(type)Now, to re-obtain the data we originally had:
flower_sales_can <- flowers %>%
filter(location == "Canada", output == "Sales")We can avoid the scientific notation on the y-axis by simply scaling the values.
flower_sales_can <- flower_sales_can %>%
mutate(value = value / 1e6)Now our y-axis will have the unit of millions of dollars rather than single dollars.
We can remove the legend entirely and just label the lines directly
on the plot using gghighlight(). gghighlight()
highlights five levels by default. Coincidentally, we have exactly five
lines!
ggplot(flower_sales_can, aes(x=year, y=value, colour=type))+
geom_line()+
gghighlight(label_params = list(alpha=0.6))+
labs(
x="Year", y="Value of plants sold (million dollars)",
title="Total value of plants sold in Canada",
subtitle="2007 to 2020"
)flower_prod_can <- flowers %>%
filter(year == 2020, location == "Canada", output == "Production (number)")
ggplot(flower_prod_can, aes(x=type, y=value))+
geom_col()flower_prod_can <- flower_prod_can %>%
mutate(type = fct_reorder(type, value, .desc=TRUE))We can avoid the scientific notation on the y-axis by once again scaling the values.
flower_prod_can <- flower_prod_can %>%
mutate(value = value / 1e6)Now our y-axis will have the unit of millions of units rather than single units.
ggplot(flower_prod_can, aes(x=type, y=value))+
geom_col()+
labs(
x="Plant type", y="Number of plants produced (millions)",
title="Number of plants produced in Canada in 2020"
)When making plots for webpages, we can make them as wide as we want. For print, we may be limited to the printing margins. However, in reducing the width of the plot,
The solution here is to flip the axes — put the plant types on the y-axis and their counts on the x-axis.
If we are making a horizontal bar plot and we want the values to be
sorted from largest to smallest, in fct_reorder(), we would
use the default .desc=FALSE.
flower_prod_can <- flower_prod_can %>%
mutate(type = fct_reorder(type, value))Since ggplot2 v3.3.0, we no longer need to build a
vertical bar plot and add coord_flip() to it. We can now
build a horizontal bar plot by supplying the values to the x-aesthetic
and the flower types to the y-aesthetic.
ggplot(flower_prod_can, aes(x=value, y=type))+
geom_col()+
labs(
x="Number of plants produced (millions)", y="Plant type",
title="Number of plants produced in Canada in 2020"
)# This is a comment
## Documentation