## Plot every column in a data frame as a histogram on one page using ggplot

Here you go:

`library(reshape2)`

library(ggplot2)

d <- melt(diamonds[,-c(2:4)])

ggplot(d,aes(x = value)) +

facet_wrap(~variable,scales = "free_x") +

geom_histogram()

`melt`

ing allows us to use the resulting grouping variables (called `variable`

) to split the data into groups and plot a histogram for each one. Note the use of `scales = "free_x"`

because each of the variables has a markedly different range and scale.

## How do I generate a histogram for each column of my table?

If you combine the `tidyr`

and `ggplot2`

packages, you can use `facet_wrap`

to make a quick set of histograms of each variable in your data.frame.

You need to reshape your data to long form with `tidyr::gather`

, so you have `key`

and `value`

columns like such:

`library(tidyr)`

library(ggplot2)

# or `library(tidyverse)`

mtcars %>% gather() %>% head()

#> key value

#> 1 mpg 21.0

#> 2 mpg 21.0

#> 3 mpg 22.8

#> 4 mpg 21.4

#> 5 mpg 18.7

#> 6 mpg 18.1

Using this as our data, we can map `value`

as our x variable, and use `facet_wrap`

to separate by the `key`

column:

`ggplot(gather(mtcars), aes(value)) + `

geom_histogram(bins = 10) +

facet_wrap(~key, scales = 'free_x')

The `scales = 'free_x'`

is necessary unless your data is all of a similar scale.

You can replace `bins = 10`

with anything that evaluates to a number, which may allow you to set them somewhat individually with some creativity. Alternatively, you can set `binwidth`

, which may be more practical, depending on what your data looks like. Regardless, binning will take some finesse.

## How to plot an histogram for every row of a data frame (with the first column as a character value)

If `plot2`

is the name of the dataframe try -

`library(tidyverse)`

plot2 %>%

pivot_longer(cols = -meta1) %>%

ggplot(aes(value)) +

facet_wrap(~ meta1, scales = "free") +

geom_histogram()

## How to create a histogram for every column in a data set on a separate page

Simply call the `pdf()`

function then use a for loop to iterate over each column:

`pdf('plots.pdf')`

for(i in 1:length(df)){

ggplot(data = df) +

geom_histogram(mapping = aes(x = df[,i]), bins = 4)

}

dev.off()

## Plot multiple histograms based on dataframe in R

Try this:

`library(dplyr)`

library(tidyr)

library(ggplot2)

#Code

df %>%

pivot_longer(-Client) %>%

ggplot(aes(x=name,y=value))+

geom_bar(stat = 'identity',aes(fill=factor(Client)))+

facet_wrap(.~Client,scales = 'free')

Output:

In this case you would need a bar plot. Or this for histogram:

`#Code 2`

df %>%

pivot_longer(-Client) %>%

ggplot(aes(x=value))+

geom_histogram(aes(fill=factor(Client)))+

facet_wrap(.~Client,scales = 'free')

Output:

Some data used:

`#Data`

df <- structure(list(Client = 1:7, Model_1 = c(10.34, 0.97, 2.01, 0.57,

0.68, 0.55, 10.68), Model_2 = c(0.22, 0.6, 0.15, 0.94, 0.65,

3.59, 1.08), Model_3 = c(0.62, 0.04, 0.27, 0.11, 0.26, 0.06,

0.07), Model_4 = c(0.47, 0.78, 0.49, 0.66, 0.41, 0.01, 0.16),

Model_5 = c(1.96, 0.19, 0, 0, 0.5, 5.5, 0.2)), class = "data.frame", row.names = c(NA,

-7L))

## ggplot: Generate a sequence of histograms

Try:

`ggplot(mydf, aes(x=length, y=NOBS))+geom_bar(stat='identity')+facet_grid(~year)`

## How to plot all the columns of a data frame in R

The `ggplot2`

package takes a little bit of learning, but the results look really nice, you get nice legends, plus many other nice features, all without having to write much code.

`require(ggplot2)`

require(reshape2)

df <- data.frame(time = 1:10,

a = cumsum(rnorm(10)),

b = cumsum(rnorm(10)),

c = cumsum(rnorm(10)))

df <- melt(df , id.vars = 'time', variable.name = 'series')

# plot on same grid, each series colored differently --

# good if the series have same scale

ggplot(df, aes(time,value)) + geom_line(aes(colour = series))

# or plot on different plots

ggplot(df, aes(time,value)) + geom_line() + facet_grid(series ~ .)

## Is it possible to plot multiple histograms of the same variable with different scales on one page?

Here you go, using an example of mtcars data:

`attach(mtcars)`

par(mfrow=c(2,2)) #to create plots in 2x2 matrix

hist(mpg) #default histogram

hist(mpg,breaks=24,main="Breaks=24")

hist(mpg,breaks=seq(10,35,by=5),main="Breaks by 5")

hist(mpg,breaks=seq(10,35,by=2),main="Breaks by 2")

Output is shown below. Hope this is what you are looking for..

## R ggpot: Arranging on one page several ggplots created with a loop / name each plot differenly

Try this using `patchwork`

. Your loop is well defined. You only need to create a list to store the plots and then use `wrap_plots()`

from `patchwork`

to arrange the plots in one page. Here the code:

`library(dplyr)`

library(ggplot2)

library(patchwork)

#Create list

List <- list()

cylinder<-unique(mtcars$cyl)

#Loop

for (value in seq_along(cylinder)) {

m<-mtcars%>%

filter(cyl==cylinder[value])%>%

group_by (gear)%>%

summarise(number=n(), average=mean(mpg), se=sd(mpg))

print(m) # reporting the numbers

a<-m%>%

mutate(gear=factor(gear, levels=unique(gear)))%>%

ggplot()+

geom_bar(aes(x=gear, y=average), stat = 'identity', fill ='red') +

geom_errorbar( aes(x= gear, ymin=average-se, ymax=average+se), width=0.2, colour="black", alpha=1, size=1) +

xlab("gears") + ylab("average mpg") +

ggtitle (paste( "cyliner:", value ))+

theme(axis.ticks.x=element_blank())

print(a)

List[[value]]<-a}

#Wrap plots

wrap_plots(List,nrow = 1)

Output:

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