I am able to generate some plots based on a list of data frames:
df1 <- mtcars
df2 <- mtcars
combined_mtcars <- list(first_df = df1, second_df = df2)
# make the plots
imap(.x = combined_mtcars, ~ggplot(.x, aes(x = hp, y = mpg, group = cyl)) +
geom_line() +
ggtitle(.y))
I wanted to then save each plot to a directory called /plots. So I tried adding ggsave like so:
imap(.x = combined_mtcars, ~ggplot(.x, aes(x = hp, y = mpg, group = cyl)) +
geom_line() +
ggtitle(.y)) %>%
imap(~ggsave(plot = .y, file = paste0("/plots/", .y, ".png")))
This resulted in error "Saving 6.62 x 5.57 in image
Error in UseMethod("grid.draw") :
no applicable method for 'grid.draw' applied to an object of class "character"".
How can I save each iteration where the filename is the same as the title .y?
We need to make sure the ggplot object is being passed as the first argument, using the tag argument in the labs() function allows us to assign the plot to a "variable".
imap(.x = combined_mtcars, ~ggplot(.x, aes(x = hp, y = mpg, group = cyl)) +
geom_line() +
labs(title = .y, tag="Plot")%>%
imap(~ggsave(plot = Plot, file = paste0("/plots/", .y, ".png")))
If that does not work, try this since ggsave may default to the correct plot.
imap(.x = combined_mtcars, ~ggplot(.x, aes(x = hp, y = mpg, group = cyl)) +
geom_line() +
ggtitle(.y)) %>%
imap(~ggsave(file = paste0("/plots/", .y, ".png")))
Related
I'm trying to plot the line graph for X=date Y=column which is XS1-XS10.
I use the lapply with a next function
plot_data_column = function (column) {
ggplot(data= excess_return, aes(y=column,x=date)) +
geom_line()+
geom_hline(yintercept = mean(excess_return$column), color="red")+
ggtitle(column)+
theme_minimal()
}
and then use lapply to plug in the columns of the dataset into the ggplot.
ggplots = lapply(excess_return[,1:10], plot_data_column)
My problem occurs with geom_hline which doesn't recognize the column and ggtitle(column).
P.s I have tried also like this
ggplots = lapply(colnames(excess_return[,1:10]), plot_data_column)
I wonder why R doesn't accept the XS1 as it were written manually by me?
because this code perfectly works.
ggplot(data= excess_return, aes(y=XS1,x=date)) +
geom_line()+
geom_hline(yintercept = mean(excess_return$XS1), color="red")+
theme_minimal()
The only thing what i want to is to iterate the XS1 to XS10.
Thank you for support
you can adapt this to your data strucutre:
plot_data_column = function (.data, .column) {
ggplot2::ggplot(data= .data, ggplot2::aes(y=!!dplyr::sym(.column),x = Petal.Width)) +
ggplot2::geom_line() +
ggplot2::geom_hline(yintercept = .data %>%
dplyr::pull(!!dplyr::sym(.column)) %>%
mean(),
color="red")+
ggplot2::ggtitle(.column) +
ggplot2::theme_minimal()
}
plots <- names(iris)[1:3] %>%
purrr::map(~plot_data_column(.data = iris, .column = .x))
You need to change the names(iris)[1:3] to your names names(excess_return)[1:10] and x = Petal.Width to x = date.
Another option:
library(tidyverse)
custom_ggplot_function <- function(var, var_name) {
ggplot(mtcars, aes(x = disp, y = var)) +
geom_line() +
geom_hline(yintercept = mean(var), color = "red") +
labs(y = var_name) +
ggtitle(var_name) +
theme_minimal()
}
mtcars %>%
select(mpg, cyl, disp) %>%
map2(.y = names(.), ~ custom_ggplot_function(.x, .y))
I would like to create shorthand notations or functions that combines multiple geoms for ggplot.
For example, instead of
mtcars %>%
ggplot(aes(x = cyl, y = mpg)) +
geom_point() +
geom_smooth(method = "lm") +
ggpubr::stat_cor()
I would like to be able to create a function to combine the geoms like so
lm_and_cor <- function() {
geom_smooth(method = "lm", se = FALSE) +
stat_cor()
}
mtcars %>%
ggplot(aes(x = cyl, y = mpg)) +
geom_point() +
lm_and_cor()
I am aware that I can create functions that does all of the plotting, basically
plot_data <- function(x) {
x %>%
ggplot(aes(x = cyl, y = mpg)) +
geom_point() +
geom_smooth(method = "lm") +
ggpubr::stat_cor()
}
which to be fair does what I want, to some degree. However, I would instead like to combine multiple geoms in a single function, as the underlying geom (e.g. point, lines, etc.) will not always be the same. Is this doable, and is it feasible?
With ggplot2 you can use list of elements:
lm_and_cor <- function()
list(geom_smooth(method = "lm", se = FALSE),
ggpubr::stat_cor()
)
mtcars %>%
ggplot(aes(x = cyl, y = mpg)) +
geom_point() +
lm_and_cor()
Output:
Do you mean something like this?
You can store multiple geom in a list object.
Edit: I misunderstand the question. This should meet the expectation.
data(iris)
library(ggplot2)
x <- list(geom_point(), geom_line())
ggplot(iris, aes(Sepal.Length, Sepal.Width)) + x
Or if you want to make a function to plot by column use this {{variable}}.
library(dplyr)
plotting <- function(data, x, y){
data %>%
ggplot(aes({{x}}, {{y}})) +
geom_point() +
geom_smooth(method = "lm")}
plotting(iris, Sepal.Length, Sepal.Width)
df1 <- mtcars
df2 <- mtcars
combined_mtcars <- list(first_df = df1, second_df = df2)
# make the plots
map(.x = combined_mtcars, .f = function(i) {
ggplot(i, aes(x = hp, y = mpg, group = cyl)) +
geom_line()
})
This generates two charts, one for each df in combined_mtcars.
I would like to add a title to each chart where the title is the name of the iteration, for "first_df" for the first plot and "second_df" for the second one.
Tried:
imap(.x = combined_mtcars, .f = function(i) {
ggplot(i, aes(x = hp, y = mpg, group = cyl)) +
geom_line() +
ggtitle(.y)
})
Which gave an error "Error in .f(.x[[i]], .y[[i]], ...) : unused argument (.y[[i]])
"
How can I pass the iteration name to ggplot?
How can I set different y axis limits in each plot when using purrr::map2?
I would like to set the y-axis lower limit to half the maximum y-axis value, something like: max(y-axis value/2).
data(mtcars)
library(tidyverse)
mtcars_split <-
mtcars %>%
split(mtcars$cyl)
plots <- map2(
mtcars_split,
names(mtcars_split),
~ggplot(data = .x, mapping = aes(y = mpg, x = wt)) +
geom_jitter() +
ggtitle(.y)+
scale_y_continuous(limits=c(max(.y)/2,NA))
)
plots
Error in max(.y)/2 : non-numeric argument to binary operator
.y is the name of the dataframe, which is why max(.y)/2 is giving you that error. This should give you what you want:
plots <- imap(
mtcars_split,
~ggplot(data = .x, mapping = aes(y = mpg, x = wt)) +
geom_jitter() +
ggtitle(.y) +
scale_y_continuous(limits=c(max(.x$mpg)/2,NA))
)
Note that imap(x, ...) is just shorthand for map2(x, names(x), ...).
This doesn't work based on the y-axis value, but it gets the job done if you don't mind specifying your y-column twice:
plots <- map2(
mtcars_split,
names(mtcars_split),
~ggplot(data = .x, mapping = aes(y = mpg, x = wt)) +
geom_jitter() +
ggtitle(.y)+
scale_y_continuous(limits=c(max(.x$mpg)/2,NA))
)
Or maybe a safer option:
plots <- map2(
mtcars_split,
names(mtcars_split),
~{
ploty <- 'mpg'
plotx <- 'wt'
ggplot(data = .x, mapping = aes_string(y = ploty, x = plotx)) +
geom_jitter() +
ggtitle(.y)+
scale_y_continuous(limits=c(max(.x[[ploty]])/2,NA))
}
)
How would I add a text annotation (eg. sd = sd_value) of the standard deviation in each panel of the following plot using ggplot2 in R?
library(datasets)
data(mtcars)
ggplot(data = mtcars, aes(x = hp)) +
geom_dotplot(binwidth = 1) +
geom_density() +
facet_grid(. ~ cyl) +
theme_bw()
I'd post an image of the plot, but I don't have enough rep.
I think "geom_text" or "annotate" might be useful but I'm not sure quite sure how.
If you want to vary the text label in each facet, you will want to use geom_text. If you want the same text to appear in each facet, you can use annotate.
p <- ggplot(data = mtcars, aes(x = hp)) +
geom_dotplot(binwidth = 1) +
geom_density() +
facet_grid(. ~ cyl)
mylabels <- data.frame(cyl = c(4, 6, 8),
label = c("first label", "seond label different", "and another"))
p + geom_text(x = 200, y = 0.75, aes(label = label), data = my labels)
### compare that to this way with annotate
p + annotate("text", x = 200, y = 0.75, label = "same label everywhere")
Now, if you really want standard deviation by cyl in this example, I'd probably use dplyr to do the calculation first and then complete this with geom_text like so:
library(ggplot2)
library(dplyr)
df.sd.hp <- mtcars %>%
group_by(cyl) %>%
summarise(hp.sd = round(sd(hp), 2))
ggplot(data = mtcars, aes(x = hp)) +
geom_dotplot(binwidth = 1) +
geom_density() +
facet_grid(. ~ cyl) +
geom_text(x = 200, y = 0.75,
aes(label = paste0("SD: ", hp.sd)),
data = df.sd.hp)
I prefer the appearance of the graph when the statistic appears within the facet label itself. I made the following script, which allows the choice of displaying the standard deviation, mean or count. Essentially it calculates the summary statistic then merges this with the name so that you have the format CATEGORY (SUMMARY STAT = VALUE).
#' Function will update the name with the statistic of your choice
AddNameStat <- function(df, category, count_col, stat = c("sd","mean","count"), dp= 0){
# Create temporary data frame for analysis
temp <- data.frame(ref = df[[category]], comp = df[[count_col]])
# Aggregate the variables and calculate statistics
agg_stats <- plyr::ddply(temp, .(ref), summarize,
sd = sd(comp),
mean = mean(comp),
count = length(comp))
# Dictionary used to replace stat name with correct symbol for plot
labelName <- mapvalues(stat, from=c("sd","mean","count"), to=c("\u03C3", "x", "n"))
# Updates the name based on the selected variable
agg_stats$join <- paste0(agg_stats$ref, " \n (", labelName," = ",
round(agg_stats[[stat]], dp), ")")
# Map the names
name_map <- setNames(agg_stats$join, as.factor(agg_stats$ref))
return(name_map[as.character(df[[category]])])
}
Using this script with your original question:
library(datasets)
data(mtcars)
# Update the variable name
mtcars$cyl <- AddNameStat(mtcars, "cyl", "hp", stat = "sd")
ggplot(data = mtcars, aes(x = hp)) +
geom_dotplot(binwidth = 1) +
geom_density() +
facet_grid(. ~ cyl) +
theme_bw()
The script should be easy to alter to include other summary statistics. I am also sure it could be rewritten in parts to make it a bit cleaner!