I can't quite figure this out. A CSV of 200+ rows assigned to data like so:
gid,bh,p1_id,p1_x,p1_y
90467,R,543333,80.184,98.824
90467,L,408045,74.086,90.923
90467,R,543333,57.629,103.797
90467,L,408045,58.589,95.937
Trying to group by p1_id and plot the mean values for p1_x and p1_y:
grp <- data %>% group_by(p1_id)
Trying to plot geom_point objects like so:
geom_point(aes(mean(grp$p1_x), mean(grp$p1_y), color=grp$p1_id))
But that isn't showing unique plot points per distinct p1_id values.
What's the missing step here?
Why not calculate the mean first?
library(dplyr)
grp <- data %>%
group_by(p1_id) %>%
summarise(mean_p1x = mean(p1_x),
mean_p1y = mean(p1_y))
Then plot:
library(ggplot2)
ggplot(grp, aes(x = mean_p1x, y = mean_p1y)) +
geom_point(aes(color = as.factor(p1_id)))
Edit: As per #eipi10, you can also pipe directly into ggplot
data %>%
group_by(p1_id) %>%
summarise(mean_p1x = mean(p1_x),
mean_p1y = mean(p1_y)) %>%
ggplot(aes(x = mean_p1x, y = mean_p1y)) +
geom_point(aes(color = as.factor(p1_id)))
Related
everyone!
How can I arrange weekdays, starting on Sunday, in R? I got the weekdays using lubridate's function weekdays(), but the days appears randomly (image attached) and I can't seem to find a way to sort it. I tried the arrange function, but I guess it only works with numeric values. A bar chart looks very weird starting on Friday. This is what the code looks like:
my_dataset <- my_dataset %>%
mutate(weekDay = weekdays(Date))
my_dataset %>%
group_by(weekDay) %>%
summarise(mean_steps = mean(TotalSteps)) %>%
ggplot(aes(x = weekDay, y = steps))+
geom_bar(stat = "identity")
Thanks!
I tried the arrange function, but I guess it only works with numeric values.
Your weekDay-vector probably is of the class character. This will be arranged in alphabetical order by ggplot. The solution to this is to convert this character-vector into a factor-class.
There are several ways to get the x-axis in the order you would like to see. All of them mean to convert weekDays into a factor.
In order to come close to your example I have at first created a data frame with weekdays and some data. As those are both created randomly a seed was set to make the code reproducible.
One method is to create the data.frame with summaries and then to define in this DF weekdays as a factor with defined levels.
This can also be done within the ggplot-call when creating the aesthetics.
library(tidyverse)
set.seed(111)
myData <- data.frame(
weekDay = sample(weekdays(Sys.Date() + 0:6), 100, replace = TRUE),
TotalSteps = sample(1000:8000, 100)
)
myData %>%
group_by(weekDay) %>%
summarise(mean_steps = mean(TotalSteps)) -> DF # new data.frame
# the following defines weekDay as a factor and also sets
# the sequence of factor levels. This sequence is then taken
# by ggplot to construct the x-axis.
DF$weekDay <- factor(DF$weekDay, levels = c(
"Sonntag", "Montag",
"Dienstag", "Mittwoch",
"Donnerstag", "Freitag",
"Samstag"
))
ggplot(DF, aes(x = weekDay, y = mean_steps)) +
geom_bar(stat = "identity") +
labs(x="")
# the factor can also be defined within the ggplot-call
myData %>%
group_by(weekDay) %>%
summarise(mean_steps = mean(TotalSteps)) %>%
ggplot(aes(x = factor(weekDay, levels = c(
"Sonntag", "Montag",
"Dienstag", "Mittwoch",
"Donnerstag", "Freitag",
"Samstag"
)), y = mean_steps)) +
geom_bar(stat = "identity") +
labs(x="")
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')
Usually when i need a subset on geom_label() i use ifelse() and i specify a number as below:
library(tidyverse)
data = starwars %>% filter(mass < 500)
data %>%
ggplot(aes(x = mass, y = height, label = ifelse(birth_year > 100, name, NA))) +
geom_point() +
geom_label()
#> Warning: Removed 54 rows containing missing values (geom_label).
Created on 2020-05-31 by the reprex package (v0.3.0)
But with the dataset i'm working on, i need a dynamic solution, something like ifelse("birth_year is in top n", name, NA).
Thoughts?
For your method, I think using rank should work fine, e.g.,
ifelse(rank(birth_year) < 10, name, NA))
You can use rank(-birth_year) if you want it sorted the other way (or, if you're using dplyr, rank(desc(birth_year)), which will work on non-numeric columns too). You may want to read up on tie methods at ?rank.
I'd also propose a more general solution: filtering data for the geom_label layer. For more complex conditions (e.g., where a group_by would come in handy) it will be more straightforward:
data %>%
ggplot(aes(x = mass, y = height, label = name)) +
geom_point() +
geom_label(
data = data %>%
group_by(species) %>%
top_n(n = 1, wt = desc(birth_year)) # youngest of each species
)
Something like this? To get top 4 values.
library(ggplot2)
data %>%
ggplot(aes(x = mass, y = height, label = ifelse(birth_year >= sort(birth_year, decreasing = TRUE)[4], name, NA))) +
geom_point() +
geom_label()
This is a more explicit approach. I assume you want to count the number of characters per birth year, per your example. In this case, we handle the ranking first, then add a column to the original dataset, then plot. The new 'label' field is either blank/NA or has members of the top set. I suppress the pesky missing data warning in the geom_label arguments.
data = starwars %>% filter(mass < 500)
# counts names per birthyear, returns vector of top 4
top4 <- data %>%
drop_na(birth_year) %>%
count(birth_year, sort = TRUE) %>%
top_n(4) %>%
pull(birth_year)
# adds column to data with the names from the top 4 birth years
data <- data %>%
mutate(label = ifelse(birth_year %in% top4, name, NA))
# plots data with label, dropping NAs
data %>%
ggplot(aes(x = mass, y = height, label = label)) +
geom_point() +
geom_label(na.rm = TRUE)
I'm trying to produce an animation for a simulation model, and I want to show how the distribution of results changes as the simulation runs.
I've seen gganimate used for scatter plots but not for boxplots (or ideally violin plots). Here I've provided a reprex.
When I use sim_category (which is a bucket for a certain number of simulation runs) I want the result to be cumulative of all previous runs to show the total distribution.
In this example (and my actual code), cumulative = TRUE does not do this. Why is this?
library(gganimate)
library(animation)
library(ggplot2)
df = as.data.frame(structure(list(ID = c(1,1,2,2,1,1,2,2,1,1,2,2),
value = c(10,15,5,10,7,17,4,12,9,20,6,17),
sim_category = c(1,1,1,1,2,2,2,2,3,3,3,3))))
df$ID <- factor(df$ID, levels = (unique(df$ID)))
df$sim_category <- factor(df$sim_category, levels = (unique(df$sim_category)))
ani.options(convert = shQuote('C:/Program Files/ImageMagick-7.0.5-Q16/magick.exe'))
p <- ggplot(df, aes(ID, value, frame= sim_category, cumulative = TRUE)) + geom_boxplot(position = "identity")
gganimate(p)
gganimate's cumulative doesn't accumulate the data, it just keeps gif frames in subsequent frames as they appear. To achieve what you want, you have to do the accumulation before building the plot, something along the following lines:
library(tidyverse)
library(gganimate)
df <- data_frame(
ID = factor(c(1,1,2,2,1,1,2,2,1,1,2,2), levels = 1:2),
value = c(10,15,5,10,7,17,4,12,9,20,6,17),
sim_category = factor(c(1,1,1,1,2,2,2,2,3,3,3,3), levels = 1:3)
)
p <- df %>%
pull(sim_category) %>%
levels() %>%
as.integer() %>%
map_df(~ df %>% filter(sim_category %in% 1:.x) %>% mutate(sim_category = .x)) %>%
ggplot(aes(ID, value, frame = factor(sim_category))) +
geom_boxplot(position = "identity")
gganimate(p)
data = 110 columns( x1: x100) (numeric)
I am trying to plot all the columns using the following code,
x2=melt(x1)
ggplot(x2,aes(x = value)) +
facet_wrap(~variable,scales = "free_x") +
geom_histogram(aes=(density))
+title(sub = s, line = 5.5)
s<-summary(x1)
I'm also want to append summary data to every column in the plot for some analysis . Is there any alternate for this?
Thanks.
To my knowledge, you cannot add a subtitles to subplots of a facet_wrap. You could change the title to accomodate what you want though. Here is an example with min, max, mean and median :
data = data.frame(x1=rnorm(100),
x2=rnorm(100),
x3=rnorm(100),
x4=rnorm(100),
x5=rnorm(100),
x6=rnorm(100))
library(dplyr)
library(tidyr)
library(ggplot2)
x <- data %>%
gather(variable,value) %>%
group_by(variable) %>%
mutate(mean=mean(value),
median=median(value),
min=min(value),
max=max(value),
lab=paste(first(variable),"\nMin:",round(min,2),"Mean:",round(mean,2),"Median:",round(median,2),"Max:",round(max,2)))
ggplot(x,aes(x = value)) +
facet_wrap(~lab,scales = "free_x") +
geom_histogram(aes=(density)) +
geom_vline(aes(x=mean))