I have data that looks kinda like this:
id = rep(1:33,3)
year = rep(1:3,33)
group = sample(c(1:3),99, replace=T)
test_result = sample(c(TRUE,FALSE), size=99, replace = T)
df = data.frame(id, year, group, test_result)
df$year = as.factor(year)
df$group = as.factor(group)
My goal is to visualize it so that I can see how group number and year relate to test_result.
df %>%
group_by(id,year) %>%
summarize(x=sum(test_result)) %>%
ggplot() +
geom_histogram(aes(fill = year,
x = x),
binwidth = 1,
position='dodge') +
theme_minimal()
gets me almost all the way there. What I want is to be able to add something like facet_wrap(group~.) to the end of this to show how these change by group but obviously group is not part of the aggregated dataframe.
Right now my best solution is just to show multiple plots like
df %>% filter(group==1) # Replace group number here
group_by(id,year) %>%
summarize(x=sum(test_result)) %>%
ggplot() +
geom_histogram(aes(fill = year,
x = x),
binwidth = 1,
position='dodge') +
theme_minimal()
but I'd love to figure out how to put them all in one figure and I'm wondering if maybe the way to do that is to put more of the grouping logic into ggplot?
Related
I have a dataframe of Lots, Time, Value with the same structure as the sample data below.
df <- tibble(Lot = c(rep(123,4),rep(265,5),rep(132,3),rep(455,4)),
time = c(seq(4), seq(5), seq(3), seq(4)), Value = runif(16))
I'd like to split the dataframe by every N Lots and plot them. The Lots are different sizes so I can't subset the data by every n rows!
I've been using an approach like this but it's not scalable for a large dataset.
df %>% filter(Lot == c(123, 265)) %>% ggplot(., aes(x = time, y = Value)) +
geom_point() + stat_smooth()
How can I do this?
Create a lot number column and create a list of plots for every n unique lot values.
This would give you list of plots.
library(tidyverse)
lot_n <- 2
df %>%
mutate(Lot_number = match(Lot, unique(Lot)),
group = ceiling(Lot_number/lot_n)) %>%
group_split(group) %>%
map(~ggplot(.x, aes(x = time, y = Value)) +
geom_point() + stat_smooth()) -> list_plots
list_plots
Individual plots can be accessed via list_plots[[1]], list_plots[[2]] etc.
You can also plot the data with facets.
df %>%
mutate(Lot_number = match(Lot, unique(Lot)),
group = ceiling(Lot_number/lot_n)) %>%
ggplot(aes(x = time, y = Value)) +
geom_point() + stat_smooth() +
facet_wrap(~group, scales = 'free')
I have a dataset with 4000 categoric variables which are city names arranged by date. I can do a plot of the entire dataset with an overall count.
What I need to do is be able to plot aggregates of specific cities by specific date ranges. I cannot use by quarter or anything like that because the required date ranges every year are different. I need to be able to, say, select 2016/4/1 to 2016/6/23 to get a count of how many are Denver.
How can I do this?
library(ggplot2)
library(ggpubr)
theme_set(theme_classic())
df <- log %>%
group_by(Location) %>%
summarise(counts = n())
df
ggplot(df, aes(x = Location, y = counts)) +
geom_bar(fill = "#0073C2FF", stat = "identity",width = .65) +
geom_text(aes(label = counts), vjust = -0.3) +
theme(axis.text.x = element_text(angle=65, vjust=0.6)) +
labs(title="Locations of Library Instruction",
subtitle="2016-2020")
I'm trying to plot proportions with geom_bar() combining fill and facet_grid.
library(tidyverse)
set.seed(123)
df <- data_frame(val_num = c(rep(1, 60), rep(2, 40), rep(1, 30), rep(2, 70)),
val_cat = ifelse(val_num == 1, "cat", "mouse"),
val_fill = sample(c("black", "white", "gray"), 200, replace = TRUE),
group = rep(c("A", "B"), each = 100))
ggplot(df) +
stat_count(mapping = aes(x = val_cat, y = ..count../tapply(..count.., ..x.. , sum)[..x..],
fill = val_fill),
position = position_dodge2(preserve = "single")) +
facet_grid(.~ group)
However, it seems that proportions are calculated for all cats (or all mices) in categories A and B together. In other words, sum of proportions in the first three columns is not 1.
It should be solved with adding group = group into the mapping. However:
ggplot(df) +
stat_count(mapping = aes(x = val_cat, y = ..count../tapply(..count.., ..x.. , sum)[..x..],
fill = val_fill, group = group),
position = position_dodge2(preserve = "single")) +
facet_grid(.~ group)
plot ignores fill argument (and moreover does not solve the issue). I tried to specify group with different choices including interaction() but without any real success.
I would like to solve problem within ggplot and I would like to avoid data manipulation before plotting.
So it wasn't as easy as I thought because I don't tend to use the stat_xxx() functions. Although you seem persistent in not manipulating the data before hand, here is an approach you can use.
grouped.df <- df %>%
group_by( group, val_fill ) %>%
count( val_cat ) %>%
ungroup() %>%
group_by( group, val_cat ) %>%
mutate( prop=n/sum(n) ) %>%
ungroup()
grouped.df %>%
ggplot() +
geom_col( aes(x=val_cat,y=prop,fill=val_fill), position="dodge" ) +
facet_wrap( ~ group )
to produce
But getting back to your "no data manipulation approach", I think your error is within your y variable. For example, consider the following code and output.
df2 %>%
ggplot() +
stat_count( aes(x=val_cat,y=..count..,color=val_fill,label=tapply(..count.., ..x.. , sum)[..x..]),
geom="text" ) +
facet_wrap( ~ group )
In the plot above, the y value is the numerator of your attempted proportion and the label value is the denominator of your attempted proportion. I think all you need to do is mess around some more with your tapply() function calls until you have the right combination of y and label.
I have a dataset with two variables: 1) ID, 2) Infection Status (Binary:1/0).
I would like to use ggplot2 to
Create a stacked percentage bar graph with the various ID on the verticle-axis (arranged alphabetically with A starting on top), and the percent on the horizontal-axis. I can't seem to get a code that will automatically sort the ID alphabetically as my original dataset has quite a number of categories and will be difficult to arrange them manually.
I also hope to have the infected category (1) to be red and towards the left of the blue non-infected category (0). Is it also possible to change the sub-heading of the legend box from "Non_infected" to "Non-infected"?
I hope that the displayed ID in the plot will include the count of the number of times the ID appeared in the dataset. E.g. "A (n=6)", "B (n=3)"
My sample code is as follow:
ID <- c("A","A","A","A","A","A",
"B","B","B",
"C","C","C","C","C","C","C",
"D","D","D","D","D","D","D","D","D")
Infection <- sample(c(1, 0), size = length(ID), replace = T)
df <- data.frame(ID, Infection)
library(ggplot2)
library(dplyr)
library(reshape2)
df.plot <- df %>%
group_by(ID) %>%
summarize(Infected = sum(Infection)/n(),
Non_Infected = 1-Infected)
df.plot %>%
melt() %>%
ggplot(aes(x = ID, y = value, fill = variable)) + geom_bar(stat = "identity", position = "stack") +
xlab("ID") +
ylab("Percent Infection") +
scale_fill_discrete(guide = guide_legend(title = "Infection Status")) +
coord_flip()
Right now I managed to get this output:
I hope to get this:
Thank you so much!
First, we need to add a count to your original data.frame.
df.plot <- df %>%
group_by(ID) %>%
summarize(Infected = sum(Infection)/n(),
Non_Infected = 1-Infected,
count = n())
Then, we augment our ID column, turn the Infection Status into a factor variable, use forcats::fct_rev to reverse the ID ordering, and use scale_fill_manual to control your legend.
df.plot %>%
mutate(ID = paste0(ID, " (n=", count, ")")) %>%
select(-count) %>%
melt() %>%
mutate(variable = factor(variable, levels = c("Non_Infected", "Infected"))) %>%
ggplot(aes(x = forcats::fct_rev(ID), y = value, fill = variable)) +
geom_bar(stat = "identity", position = "stack") +
xlab("ID") +
ylab("Percent Infection") +
scale_fill_manual("Infection Status",
values = c("Infected" = "#F8766D", "Non_Infected" = "#00BFC4"),
labels = c("Non-Infected", "Infected"))+
coord_flip()
I have the following data
set.seed(123)
x = c(rnorm(100, 4, 1), rnorm(100, 6, 1))
gender = rep(c("Male", "Female"), each=100)
mydata = data.frame(x=x, gender=gender)
and I want to plot two cumulative histograms (one for males and the other for females) with ggplot.
I have tried the code below
ggplot(data=mydata, aes(x=x, fill=gender)) + stat_bin(aes(y=cumsum(..count..)), geom="bar", breaks=1:10, colour=I("white")) + facet_grid(gender~.)
but I get this chart
that, obviously, is not correct.
How can I get the correct one, like this:
Thanks!
I would pre-compute the cumsum values per bin per group, and then use geom_histogram to plot.
mydata %>%
mutate(x = cut(x, breaks = 1:10, labels = F)) %>% # Bin x
count(gender, x) %>% # Counts per bin per gender
mutate(x = factor(x, levels = 1:10)) %>% # x as factor
complete(x, gender, fill = list(n = 0)) %>% # Fill missing bins with 0
group_by(gender) %>% # Group by gender ...
mutate(y = cumsum(n)) %>% # ... and calculate cumsum
ggplot(aes(x, y, fill = gender)) + # The rest is (gg)plotting
geom_histogram(stat = "identity", colour = "white") +
facet_grid(gender ~ .)
Like #Edo, I also came here looking for exactly this. #Edo's solution was the key for me. It's great. But I post here a few additions that increase the information density and allow comparisons across different situations.
library(ggplot2)
set.seed(123)
x = c(rnorm(100, 4, 1), rnorm(50, 6, 1))
gender = c(rep("Male", 100), rep("Female", 50))
grade = rep(1:3, 50)
mydata = data.frame(x=x, gender=gender, grade = grade)
ggplot(mydata, aes(x,
y = ave(after_stat(density), group, FUN = cumsum)*after_stat(width),
group = interaction(gender, grade),
color = gender)) +
geom_line(stat = "bin") +
scale_y_continuous(labels = scales::percent_format()) +
facet_wrap(~grade)
I rescale the y so that the cumulative plot always ends at 100%. Otherwise, if the groups are not the same size (like they are in the original example data) then the cumulative plots have different final heights. This obscures their relative distribution.
Secondly, I use geom_line(stat="bin") instead of geom_histogram() so that I can put more than one line on a panel. This way I can compare them easily.
Finally, because I also want to compare across facets, I need to make sure the ggplot group variable uses more than just color=gender. We set it manually with group = interaction(gender, grade).
Answering a million years later....
I was looking for a solution for the same problem and I got here..
Eventually I figured it out by myself, so I'll drop it here in case other people will ever need it.
As required: no pre-work is necessary!
ggplot(mydata) +
geom_histogram(aes(x = x, y = ave(..count.., group, FUN = cumsum),
fill = gender, group = gender),
colour = "gray70", breaks = 1:10) +
facet_grid(rows = "gender")