So basically, I would like to use ggplot function geom_line + geom_point to create the same plots but with fancier graphics.
> a
V1 V2 V3
1 0.8224887 0.7882316 0.7596440
2 0.7892779 0.7604186 0.7409430
3 0.8254516 0.8257800 0.8014778
4 0.8268519 0.7887464 0.7887322
5 0.8226651 0.7981079 0.7934783
plot(6:10, a$V1, type="l", xlab="Folds", ylab="Accuracy", col="Blue",ylim=c(0.7,0.9))
par(new=TRUE)
plot(6:10, a$V2, type="l", xlab="Folds", ylab="Accuracy", col="Orange",ylim=c(0.7,0.9))
par(new=TRUE)
plot(6:10, a$V3, type="l", xlab="Folds", ylab="Accuracy", col="Green",ylim=c(0.7,0.9))
My main goal is to get a legend that helps to distinguish each variable.
I tried to plot just the first line:
ggplot(data = a)+
theme_classic()+
geom_line(aes(x=6:10, y = a$V1, color = "blue"))
The problem is that i don't even get the color I want.
Thanks for reading and helping!
library (dplyr)
library (ggplot2)
a <- data.frame(
V1=rnorm(5),
V2=rnorm(5),
V3=rnorm(5),
Folds = 6:10) # make some example data
a %>%
tidyr::gather(key,value,-Folds) %>% #get data in long format for ggplot
ggplot(.,aes(x = Folds,y = value,y,col = key))+
geom_line() + # add line
geom_point() + # add points
scale_color_manual("My Variables",values = c("blue","orange","green")) + #change colours
theme_classic()
library(tidyverse)
originalData <- tibble(
V1=c(0.8224887, 0.7892779, 0.8254516, 0.8268519, 0.8226651),
V2=c(0.7882316, 0.7604186, 0.8257800, 0.7887464, 0.7981079),
V3=c(0.7596440, 0.7409430, 0.8014778, 0.7887322, 0.7934783)
)
# ggplot works best if your data is 'tidy'
tidyData <- originalData %>%
pivot_longer(cols=c(V1, V2, V3), names_to="Variable") %>%
add_column(X=rep(6:10, each=3))
tidyData
tidyData %>%
ggplot(aes(x=X, y=value, colour=Variable)) +
geom_line() +
theme_classic()
Giving
You can customise your plot from here as you like.
Related
I made an upset plot using the ggupset package and added a break to the y axis with scale_y_break from the ggbreakpackage.
However, when I add scale_y_break, the combination matrix under the bar plot disappears.
Is there a way to combine the combination matrix of the plot made without scale_y_break with the bar plot portion of a plot made with scale_y_break? I can't seem to be able to access the grobs of these plots or use any other workaround. If anyone could help, I would greatly appreciate it!
Example with scale_x_upset and scale_y_break:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)+ scale_y_break(breaks = c(750,1000))
I would like to combine the barplot portion of the plot created with:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)+ scale_y_break(breaks = c(750,1000))
with the combination matrix portion of the plot made with:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)
Thanks!
This question already has answers here:
Plotting two variables as lines using ggplot2 on the same graph
(5 answers)
Closed 8 months ago.
I am new to R and have the following example code that I wish to apply for every column in my data.
data(economics, package="ggplot2")
economics$index <- 1:nrow(economics)
loessMod10 <- loess(uempmed ~ index, data=economics, span=0.10)
smoothed10 <- predict(loessMod10)
plot(economics$uempmed, x=economics$date, type="l", main="Loess Smoothing and Prediction", xlab="Date", ylab="Unemployment (Median)")
lines(smoothed10, x=economics$date, col="red")
Could someone please suggest how this would be possible?
It's possible to perform loess smoothing within ggplot.
library(data.table)
library(ggplot2)
df <- economics
##
#
gg.melt <- setDT(df) |> melt(id='date', variable.name = 'KPI')
ggplot(gg.melt, aes(x=date, y=value))+
geom_line()+
stat_smooth(method=loess, color='red', size=0.5, se=FALSE, method.args = list(span=0.1))+
facet_wrap(~KPI, scales = 'free_y')
Regarding combining everything on one plot I'm not seeing how you would do that as the y-scales are so different. If the point is to see how the peaks line up, etc. you could do this:
ggplot(gg.melt, aes(x=date, y=value))+
geom_line()+
stat_smooth(method=loess, color='red', size=0.5, se=FALSE, method.args = list(span=0.1))+
facet_grid(KPI~., scales = 'free_y')
There is also the dygraphs package which allows creation of dynamic graphics that can be saved to html:
gg.melt[, scaled:=scale(value, center = FALSE, scale=diff(range(value))), by=.(KPI)]
gg.melt[, pred:=predict(loess(scaled~as.integer(date), .SD, span=0.1)), by=.(KPI)]
gg.dt <- dcast(gg.melt, date~KPI, value.var = list('scaled', 'pred'))
library(dygraphs)
dygraph(gg.dt) |>
dyCrosshair(direction = 'vertical') |>
dyRangeSelector()
It's possible to create a dygraph(...) version of the second plot, where the different KPI are in different facets, but you have to use RMarkdown for that.
You can make your data from wide to long by the date and use facet_wrap. Maybe you want something like this:
library(ggplot2)
library(reshape2)
library(dplyr)
economics %>%
melt(., "date") %>%
ggplot(., aes(date, value)) +
geom_line() +
facet_wrap(~variable, scales = "free")
Output:
Comment: All plots in one graph
If you mean all plots in one graph, you can give the variables a color like this:
economics %>%
melt(., "date") %>%
ggplot(., aes(date, value, color = variable)) +
geom_line() +
scale_y_log10()
Output:
I'm hoping to recreate the gridExtra output below with ggplot's facet_grid, but I'm unsure of what variable ggplot identifies with the layers in the plot. In this example, there are two geoms...
require(tidyverse)
a <- ggplot(mpg)
b <- geom_point(aes(displ, cyl, color = drv))
c <- geom_smooth(aes(displ, cyl, color = drv))
d <- a + b + c
# output below
gridExtra::grid.arrange(
a + b,
a + c,
ncol = 2
)
# Equivalent with gg's facet_grid
# needs a categorical var to iter over...
d$layers
#d + facet_grid(. ~ d$layers??)
The gridExtra output that I'm hoping to recreate is:
A hacky way of doing this is to take the existing data frame and create two, three, as many copies of the data frame you need with a value linked to it to be used for the facet and filtering later on. Union (or rbind) the data frames together into one data frame. Then set up the ggplot and geoms and filter each geom for the desired attribute. Also for the facet use the existing attribute to split the plots.
This can be seen below:
df1 <- data.frame(
graph = "point_plot",
mpg
)
df2 <- data.frame(
graph = "spline_plot",
mpg
)
df <- rbind(df1, df2)
ggplot(df, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point(data = filter(df, graph == "point_plot")) +
geom_smooth(data = filter(df, graph == "spline_plot"), se=FALSE) +
facet_grid(. ~ graph)
If you really want to show different plots on different facets, one hacky way would be to make separate copies of the data and subset those...
mpg2 <- mpg %>% mutate(facet = 1) %>%
bind_rows(mpg %>% mutate(facet = 2))
ggplot(mpg2, aes(displ, cyl, color = drv)) +
geom_point(data = subset(mpg2, facet == 1)) +
geom_smooth(data = subset(mpg2, facet == 2)) +
facet_wrap(~facet)
This question already has an answer here:
Setting individual y axis limits with facet wrap NOT with scales free_y
(1 answer)
Closed 4 years ago.
I'm trying to create a facet_wrap() where the unit of measure remains identical across the different plots, while allowing to slide across the y axis.
To clearify with I mean, I have created a dataset df:
library(tidyverse)
df <- tibble(
Year = c(2010,2011,2012,2010,2011,2012),
Category=c("A","A","A","B","B","B"),
Value=c(1.50, 1.70, 1.60, 4.50, 4.60, 4.55)
)
with df, we can create the following plot using facet_wrap:
ggplot(data = df, aes(x=Year, y=Value)) + geom_line() + facet_wrap(.~ Category)
Plot 1
To clarify the differences between both plots, one can use scale = "free_y":
ggplot(data = df, aes(x=Year, y=Value)) + geom_line()
+ facet_wrap(.~ Category, scale="free_y")
Plot 2
Although it's more clear, the scale on the y-axis in plot A isequal to 0.025, while being 0.0125 in B. This could be misleading to someone who's comparing A & B next to each other.
So my question right now is to know whether there exist an elegant way of plotting something like the graph below (with y-scale = 0.025) without having to plot two seperate plots into a grid?
Thanks
Desired result:
Code for the grid:
# Grid
## Plot A
df_A <- df %>%
filter(Category == "A")
plot_A <- ggplot(data = df_A, aes(x=Year, y=Value)) + geom_line() + coord_cartesian(ylim = c(1.5,1.7)) + ggtitle("A")
## Plot B
df_B <- df %>%
filter(Category == "B")
plot_B <- ggplot(data = df_B, aes(x=Year, y=Value)) + geom_line() + coord_cartesian(ylim = c(4.4,4.6)) + ggtitle("B")
grid.arrange(plot_A, plot_B, nrow=1)
Based on the info at Setting individual y axis limits with facet wrap NOT with scales free_y you can you use geom_blank() and manually specified y-limits by Category:
# df from above code
df2 <- tibble(
Category = c("A", "B"),
y_min = c(1.5, 4.4),
y_max = c(1.7, 4.6)
)
df <- full_join(df, df2, by = "Category")
ggplot(data = df, aes(x=Year, y=Value)) + geom_line() +
facet_wrap(.~ Category, scales = "free_y") +
geom_blank(aes(y = y_min)) +
geom_blank(aes(y = y_max))
I am wondering if there is a better way to produce 4 barcharts of different outcome variables arranged in a grid:
This is the code I used:
library(cowplot)
bar1 <- ggplot(data = subset(data, !is.na(MHQ_Heading_Male_Quartile))) +
geom_bar(mapping = aes(x = MHQ_Heading_Male_Quartile))
bar2 <- ggplot(data = subset(data, !is.na(AHQ_Heading_Male_Quartile))) +
geom_bar(mapping = aes(x = AHQ_Heading_Male_Quartile))
bar3 <- ggplot(data = subset(data, !is.na(MHQ_Heading_Female_Quartile))) +
geom_bar(mapping = aes(x = MHQ_Heading_Female_Quartile))
bar4 <- ggplot(data = subset(data, !is.na(AHQ_Heading_Female_Quartile))) +
geom_bar(mapping = aes(x = AHQ_Heading_Female_Quartile))
plot_grid(bar1, bar2, bar3, bar4, ncol = 2)
However, there is a lot of repeated code- is there some function or way to create the same plot with ggplot2 in fewer lines?
I would convert relevant columns from wide to long (the ones ending in "_Quartile") and then use facet_wrap to show the 4 plots in a 2x2 grid with scales = "free".
Something like this:
data %>%
gather(key, value, ends_with("Quartile")) %>%
filter(!is.na(value)) %>%
ggplot(aes(value)) +
geom_bar() +
facet_wrap(~ key, scales = "free", ncol = 2, nrow = 2)
As mentioned you need to make it a long format using dplyr gather (or reshape package) and then facet over this.
`data %>%
select( MHQ_Heading_Male_Quartile, AHQ_Heading_Male_Quartile, MHQ_Heading_Female_Quartile, AHQ_Heading_Female_Quartile) %>%
gather("Type", "Range", MHQ_Heading_Male_Quartile:AHQ_Heading_Female_Quartile) %>%
filter(!is.na(Range)) %>%
ggplot(aes(x=Range)) +
geom_bar() +
facet_wrap(~Type, scales="free")`
I'll leave it to you to clean the graphs up but that's the basic premise.
Extract the column names to be shown into nms and then for each one use qplot to create a ggplot object so that bars is a list of such objects. Then run plot_grid on that.
nms <- grep("Quartile", names(data), value = TRUE)
bars <- lapply(nms, function(nm) qplot(na.omit(data[[nm]]), xlab = nm))
do.call("plot_grid", bars)