What I'm currently stuck on is trying to plot each column of my dataframe as its own histogram in ggplot. I attached a screenshot below:
Ideally I would be able to compare the values in every 'Esteem' column side-by-side by plotting multiple histograms.
I tried using the melt() function to reshape my dataframe, and then feed into ggplot() but somewhere along the way I'm going wrong...
You could pivot to long, then facet by column:
library(tidyr)
library(ggplot2)
esteem81_long <- esteem81 %>%
pivot_longer(
Esteem81_1:Esteem81_10,
names_to = "Column",
values_to = "Value"
)
ggplot(esteem81_long, aes(Value)) +
geom_bar() +
facet_wrap(vars(Column))
Or for a list of separate plots, just loop over the column names:
plots <- list()
for (col in names(esteem81)[-1]) {
plots[[col]] <- ggplot(esteem81) +
geom_bar(aes(.data[[col]]))
}
plots[["Esteem81_4"]]
Example data:
set.seed(13)
esteem81 <- data.frame(Subject = c(2,6,7,8,9))
for (i in 1:10) {
esteem81[[paste0("Esteem81_", i)]] <- sample(1:4, 5, replace = TRUE)
}
esteem_long <- esteem81 %>% pivot_longer(cols = -c(Subject))
plot <- ggplot(esteem_long, aes(x = value)) +
geom_histogram(binwidth = 1) +
facet_wrap(vars(name))
plot
I'm using pivot_longer() from tidyr and ggplot2 for the plotting.
The line pivot_longer(cols = -c(Subject)) reads as "apart from the "Subject" column, all the others should be pivoted into long form data." I've left the default new column names ("name" and "value") - if you rename them then be sure to change the downstream code.
geom_histogram automates the binning and tallying of the data into histogram format - change the binwidth parameter to suit your desired outcome.
facet_wrap() allows you to specify a grouping variable (here name) and will replicate the plot for each group.
Related
I created a data frame called "Pivot_long" by using the pivot_longer function to combine 3 variables from another dataset ("Leaves") into one column. Now i need to create a a figure with multiple boxplots to display this "Pivot_long" data. How would i go about doing that?
Formula for the new data frame:
Pivot_long<- pivot_longer(data = Leaves, names_to = "Type", values_to = "Values", cols = -X)
Do you mean how to visualize 4 plots next to eachother?
The setting par() allows for combining multiple plots.
par(mfrow=c(2,2))
Run this before your plot() function, and this will create a 2x2 matrix with 4 seperate plots.
Here is a example with the iris data set:
library(tidyverse)
iris_long <- iris %>%
select(-4, -5) %>%
pivot_longer(
cols = everything()
)
ggplot(iris_long, aes(x = name, y=value)) +
geom_boxplot()
when using the simple R boxplot function, I can easily place my dataframe directly into the parenthesis and a perfect boxplot emerges, eg:
baseline <- c(0,0,0,0,1)
post_cap <- c(1,5,5,6,11)
qx314 <- c(0,0,0,3,7)
naive_capqx <- data.frame(baseline, post_cap, qx314)
boxplot(naive_capqx)
this is an image of the boxplot made with the simple R boxplot function
However, I need to make this boxplot slightly more aesthetic and so I need to use ggplot. When I place the dataframe itself in, the boxplot cannot form as I need to specify x, y and fill coordinates, which I don't have. My y coordinates are the values for each vector in the dataframe and my x coordinates are just the name of the vector. How can I do this using ggplot? Is there a way to reform my dataframe so I can split it into coordinates, or is there a way ggplot can read my data?
geom_boxplot expects tidy data. Your data isn't tidy because the column names contain information. So the first thing to do is to tidy your data by using pivot_longer...
library(tidyverse)
naive_capqx %>%
pivot_longer(everything(), values_to="Value", names_to="Variable") %>%
ggplot() +
geom_boxplot(aes(x=Variable, y=Value))
giving
Turn the df into a long format df. Below, I use gather() to lengthen the df; I use group_by() to ensure boxplot calculation by key (formerly column name).
pacman::p_load(ggplot2, tidyverse)
baseline <- c(0,0,0,0,1)
post_cap <- c(1,5,5,6,11)
qx314 <- c(0,0,0,3,7)
naive_capqx <- data.frame(baseline, post_cap, qx314) %>%
gather("key", "value")) %>%
group_by(key)
ggplot(naive_capqx, mapping = aes(x = key, y = value)) +
geom_boxplot()
I've tried about every iteration I can find on Stack Exchange of for loops and lapply loops to create ggplots and this code has worked well for me. My only problem is that I can't assign unique titles and labels. From what I can tell in the function i takes the values of my response variable so I can't index the title I want as the ith entry in a character string of titles.
The example I've supplied creates plots with the correct values but the 2nd and 3rd plots in the plot lists don't have the correct titles or labels.
Mock dataset:
library(ggplot2)
nms=c("SampleA","SampleB","SampleC")
measr1=c(0.6,0.6,10)
measr2=c(0.6,10,0.8)
measr3=c(0.7,10,10)
qual1=c("U","U","")
qual2=c("U","","J")
qual3=c("J","","")
df=data.frame(nms,measr1,qual1,measr2,qual2,measr3,qual3,stringsAsFactors = FALSE)
identify columns in dataset that contain response variable
measrsindex=c(2,4,6)
Create list of plots that show all samples for each measurement
plotlist=list()
plotlist=lapply(df[,measrsindex], function(i) ggplot(df,aes_string(x="nms",y=i))+
geom_col()+
ggtitle("measr1")+
geom_text(aes(label=df$qual1)))
Create list of plots that show all measurements for each sample
plotlist2=list()
plotlist2=lapply(df[,measrsindex],function(i)ggplot(df,aes_string(x=measrsindex, y=i))+
geom_col()+
ggtitle("SampleA")+
geom_text(aes(label=df$qual1)))
The problem is that I cant create unique title for each plot. (All plots in the example have the title "measr1" or "SampleA)
Additionally I cant apply unique labels (from qual columns) for each bar. (ex. the letter for qual 2 should appear on top of the column for measr2 for each sample)
Additionally in the second plot list the x-values aren't "measr1","measr2","measr3" they're the index values for those columns which isn't ideal.
I'm relatively new to R and have never posted on Stack Overflow before so any feedback about my problem or posting questions is welcomed.
I've found lots of questions and answers about this sort of topic but none that have a data structure or desired plot quite like mine. I apologize if this is a redundant question but I have tried to find the solution in previous answers and have been unable.
This is where I got the original code to make my loops, however this example doesn't include titles or labels:
Looping over ggplot2 with columns
You could loop over the names of the columns instead of the column itself and then use some non-standard evaluation to get column values from the names. Also, I have included label in aes.
library(ggplot2)
library(rlang)
plotlist3 <- purrr::map(names(df)[measrsindex],
~ggplot(df, aes(nms, !!sym(.x), label = qual1)) +
geom_col() + ggtitle(.x) + geom_text(vjust = -1))
plotlist3[[1]]
plotlist3[[2]]
The same can be achieved with lapply as well
plotlist4 <- lapply(names(df)[measrsindex], function(x)
ggplot(df, aes(nms, !!sym(x), label = qual1)) +
geom_col() + ggtitle(x) + geom_text(vjust = -1))
I would recommend putting your data in long format prior to using ggplot2, it makes plotting a much simpler task. I also recoded some variables to facilitate constructing the plot. Here is the code to construct the plots with lapply.
library(tidyverse)
#Change from wide to long format
df1<-df %>%
pivot_longer(cols = -nms,
names_to = c(".value", "obs"),
names_sep = c("r","l")) %>%
#Separate Sample column into letters
separate(col = nms,
sep = "Sample",
into = c("fill","Sample"))
#Change measures index to 1-3
measrsindex=c(1,2,3)
plotlist=list()
plotlist=lapply(measrsindex, function(i){
#Subset by measrsindex (numbers) and plot
df1 %>%
filter(obs == i) %>%
ggplot(aes_string(x="Sample", y="meas", label="qua"))+
geom_col()+
labs(x = "Sample") +
ggtitle(paste("Measure",i, collapse = " "))+
geom_text()})
#Get the letters A : C
samplesvec<-unique(df1$Sample)
plotlist2=list()
plotlist2=lapply(samplesvec, function(i){
#Subset by samplesvec (letters) and plot
df1 %>%
filter(Sample == i) %>%
ggplot(aes_string(x="obs", y = "meas",label="qua"))+
geom_col()+
labs(x = "Measure") +
ggtitle(paste("Sample",i,collapse = ", "))+
geom_text()})
Watching the final plots, I think it might be useful to use facet_wrap to make these plots. I added the code to use it with your plots.
#Plot for Measures
ggplot(df1, aes(x = Sample,
y = meas,
label = qua)) +
geom_col()+
facet_wrap(~ obs) +
ggtitle("Measures")+
labs(x="Samples")+
geom_text()
#Plot for Samples
ggplot(df1, aes(x = obs,
y = meas,
label = qua)) +
geom_col()+
facet_wrap(~ Sample) +
ggtitle("Samples")+
labs(x="Measures")+
geom_text()
Here is a sample of the plots using facet_wrap.
I have a dataset in which I have one numeric variable and many categorical variables. I would like to make a grid of density plots, each showing the distribution of the numeric variable for different categorical variables, with the fill corresponding to subgroups of each categorical variable. For example:
library(tidyverse)
library(nycflights13)
dat <- flights %>%
select(carrier, origin, distance) %>%
mutate(origin = origin %>% as.factor,
carrier = carrier %>% as.factor)
plot_1 <- dat %>%
ggplot(aes(x = distance, fill = carrier)) +
geom_density()
plot_1
plot_2 <- dat %>%
ggplot(aes(x = distance, fill = origin)) +
geom_density()
plot_2
I would like to find a way to quickly make these two plots. Right now, the only way I know how to do this is to create each plot individually, and then use grid_arrange to put them together. However, my real dataset has something like 15 categorical variables, so this would be very time intensive!
Is there a quicker and easier way to do this? I believe that the hardest part about this is that each plot has its own legend, so I'm not sure how to get around that stumbling block.
This solutions gives all the plots in a list. Here we make a single function that accepts a variable that you want to plot, and then use lapply with a vector of all the variables you want to plot.
fill_variables <- vars(carrier, origin)
func_plot <- function(fill_variable) {
dat %>%
ggplot(aes(x = distance, fill = !!fill_variable)) +
geom_density()
}
plotlist <- lapply(fill_variables, func_plot)
If you have no idea of what those !! mean, I recommend watching this 5 minute video that introduces the key concepts of tidy evaluation. This is what you want to use when you want to create this sorts of wrapper functions to do stuff programmatically. I hope this helps!
Edit: If you want to feed an array of strings instead of a quosure, you can change !!fill_variable for !!sym(fill_variable) as follows:
fill_variables <- c('carrier', 'origin')
func_plot <- function(fill_variable) {
dat %>%
ggplot(aes(x = distance, fill = !!sym(fill_variable))) +
geom_density()
}
plotlist <- lapply(fill_variables, func_plot)
Alternative solution
As #djc wrote in the comments, I'm having trouble passing the column names into 'fill_variables'. Right now I am extracting column names using the following code...
You can separate the categorical and numerical variables like; cat_vars <- flights[, sapply(flights, is.character)] for categorical variables and cat_vars <- flights[, sapply(flights, !is.character)] for continuous variables and then pass these vectors into the wrapper function given by mgiormenti
Full code is given below;
library(tidyverse)
library(nycflights13)
cat_vars <- flights[, sapply(flights, is.character)]
cont_vars<- flights[, !sapply(flights, is.character)]
dat <- flights %>%
select(carrier, origin, distance) %>%
mutate(origin = origin %>% as.factor,
carrier = carrier %>% as.factor)
func_plot_cat <- function(cat_vars) {
dat %>%
ggplot(aes(x = distance, fill = !!cat_vars)) +
geom_density()
}
func_plot_cont <- function(cont_vars) {
dat %>%
ggplot(aes(x = distance, fill = !!cont_vars)) +
geom_point()
}
plotlist_cat_vars <- lapply(cat_vars, func_plot_cat)
plotlist_cont_vars<- lapply(cont_vars, func_plot_cont)
print(plotlist_cat_vars)
print(plotlist_cont_vars)
I am having a 2d matrix which i wish to plot column wise against a first column,I am able to plot it using matplot, is there any way to do a similar thing using plotly, I tried using by adding them as traces but it was overwriting the traces not adding them.
mat <- array(rnorm(10*10*10), dim=c(10, 10, 10))
df <- data.frame( 500*(1:10))
for(i in 3:5){
for(j in 4:5){
df <- cbind(df,mat[1:10,i,j])
}
}
matplot(x= df[,1], y= as.matrix(df[-1]), type='l')
# plot_ly(y= as.matrix(df[-1]), x= df[,1] ,type="scatter", mode="markers+lines")
You need to reshape to long format. Using readable columns names would help.
library(tidyr)
df2 <- df %>%
setNames(paste0('V', 1:7)) %>%
gather(key, value, -V1)
p <- ggplot(df2, aes(V1, value, color = key)) + geom_line()
plotly::ggplotly(p)