Im trying to make a barplot that, on the y axis, plots proportion (0-1) and lengths on the x axis. So for example, if 20% of the lengths are between 2-2.5 cm the barplot would show this part as 0.20 and so on.
dput of the data:
structure(list(lakeID = c("WE1", "WE1", "WE1", "WE1", "WE1",
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"WE1", "WE1", "WE1", "WE1", "WE1", "WE1", "WE1", "WE1", "WE1",
"WE1", "WE1", "WE1", "WE1"), length_mm = c(3.29578034252511,
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2.34623359489188, 2.9461923111907, 4.19818117295273, 3.11437886634752
), date = structure(c(1502236800, 1502236800, 1502236800, 1502236800,
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2017L, 2017L, 2017L)), row.names = c(NA, -225L), class = c("tbl_df",
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Ive tried this code but have not had any success
props = benthosBx %>%
group_by(length_mm) %>%
summarise(length = n()) %>%
mutate(freq = length_mm / sum(length_mm))
barplot(prop.table(table(props$freq,props$length_mm),margin=1), beside=F)
Any ideas on how to setup the data to make this work?
CodePudding user response：
What you are describing is really just a histogram, except the counts are divided by the total number of observations:
library(ggplot2)
ggplot(benthosBx, aes(x = length_mm))
geom_histogram(aes(y = ..count../nrow(benthosBx)), breaks = seq(0, 7, 0.5),
fill = 'deepskyblue3', color = 'black')
theme_light()
labs(x = "Length (mm)", y = "Proportion", title = "Length distribution")
Or, in base R:
h <- hist(benthosBx$length_mm, breaks = seq(0, 7, 0.5), plot = FALSE)
h$counts <- h$counts / nrow(benthosBx)
plot(h, xlab = 'Length(mm)', ylab = 'Proportion', main = 'Length distribution',
col = 'gray75')
CodePudding user response：
Try these options ...
library(tidyverse)
library(scales)
#>
#> Attaching package: 'scales'
#> The following object is masked from 'package:purrr':
#>
#> discard
#> The following object is masked from 'package:readr':
#>
#> col_factor
data_df <- structure(list(lakeID = c("WE1", "WE1", "WE1", "WE1", "WE1",
"WE1", "WE1", "WE1", "WE1", "WE1", "WE1", "WE1", "WE1", "WE1",
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"WE1", "WE1", "WE1", "WE1"), length_mm = c(3.29578034252511,
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data_df |>
ggplot(aes(length_mm))
geom_density()
scale_y_continuous(labels = label_percent())
data_df |>
mutate(length_mm = cut_width(length_mm, 1)) |>
count(length_mm) |>
mutate(pct = n / sum(n)) |>
ggplot(aes(length_mm, pct))
geom_col()
scale_y_continuous(labels = label_percent())
Created on 2022-04-26 by the 