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I would like to plot each column of a data.frame using a histogram on one page. Here is an example using the sample "diamonds" data set which comes with R:
p = list()
for (i in 1:ncol(diamonds)) p[[i]] <- qplot(diamonds[,i], xlab=names(diamonds)[[i]])
do.call(grid.arrange, p)
This does plot all the columns, but the data looks the same in each one. So, something is clearly wrong.
Is this the right approach for this task? I'm sure I have some silly syntax somewhere that is assigning the same column data set to each element in the list, but I'm not sure what it is.
Thank you
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To create histogram of all columns in an R data frame, we can use hist. data. frame function of Hmisc package. For example, if we have a data frame df that contains five columns then the histogram for all the columns can be created by using a single line code as hist.
Plot two histograms Using plot() will simply plot the histogram as if you'd typed hist() from the start. However, you can now use add = TRUE as a parameter, which allows a second histogram to be plotted on the same chart/axis.
You can also make histograms by using ggplot2 , “a plotting system for R, based on the grammar of graphics” that was created by Hadley Wickham. This post will focus on making a Histogram With ggplot2.
Basic histogram with geom_histogram It is relatively straightforward to build a histogram with ggplot2 thanks to the geom_histogram() function. Only one numeric variable is needed in the input.
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()
melting 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.
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