How to melt a dataframe into multiple factors - r

Question

I have been trying to plot a line plot with ggplot.
My data looks something like this:
I04 F04 I05 F05 I06 F06
CAT 3 12 2 6 6 20
DOG 0 0 0 0 0 0
BIEBER 1 0 0 1 0 0
and can be found here.
Basically, we have a certain number of CATs (or other creatures) initially in a year (this is I04), and a certain number of CATs at the end of the year (this is F04). This goes on for some time.
I can plot something like this fairly simply using the code below, and get this:
This is fantastic, but doesn't work very well for me. After all, I have these staring and ending inventory for each year. So I am interested in seeing how the initial values (I04, I05, I06) change over time. So, for each animal, I would like to create two different lines, one for initial quantity and one for final quantity (F01, F05, F06). This seems to me like now I have to consider two factors.
This is really difficult given the way my data is set up. I'm not sure how to tell ggplot that all the I prefixed years are one factor, and all the F prefixed years are another factor. When the dataframe gets melted, it's too late. I'm not sure how to control this situation.
Any advice on how I can separate these values or perhaps another, better way to tackle this situation?
Here is the code I have:
library(ggplot2)
library(reshape2)
DF <- read.csv("mydata.csv", stringsAsFactors=FALSE)
## cleaning up, converting factors to numeric, etc
text_names <- data.frame(as.character(DF$animals))
names(text_names) <- c("animals")
numeric_cols <- DF[, -c(1)]
numeric_cols <- sapply(numeric_cols, as.numeric)
plot_me <- data.frame(cbind(text_names, numeric_cols))
plot_me$animals <- as.factor(plot_me$animals)
meltedDF <- melt(plot_me)
p <- ggplot()
p <- p + geom_line(aes(seq(1:36), meltedDF$value, group=meltedDF$animals, color=meltedDF$animals))
p

Answer 1

Using your original data from the link:
nd <- reshape(mydata, idvar = "animals", direction = "long", varying = names(mydata)[-1], sep = "")
ggplot(nd, aes(x = time, y = I, group = animals, colour = animals)) + geom_line() + ggtitle("Development of initial inventories")
ggplot(nd, aes(x = time, y = F, group = animals, colour = animals)) + geom_line() + ggtitle("Development of final inventories")

Answer 2

I think from a data analyst perspective the following approach might provide better insight.
For each animal we visualize the initial and the final quantity in a separate panel. Moreover, each subplot has its own y scale because the values of the different animal types are radically different. Like this, differences within and across animal types are easier to spot.
Given the current structure of your data, we do not need two different factors. After the gather call the indicator column includes data like I04, F04, etc. We just need to separate the first character from the rest resulting in two columns type and time. We can use type as the argument for color in the ggplot call. time provides a unified x-axis across all animal types.
library(tidyr)
library(dplyr)
library(ggplot2)
data %>% gather(indicator, value, -animals) %>%
separate(indicator, c('type', 'time'), sep = 1) %>%
mutate(
time = as.numeric(time)
) %>% ggplot(aes(time, value, color = type)) +
geom_line() +
facet_grid(animals ~ ., scales = "free_y")
Of course, you might also do it the other way round, namely using a subplot for the initial and the final quantities like this:
data %>% gather(indicator, value, -animals) %>%
separate(indicator, c('type', 'time'), sep=1) %>%
mutate(
time = as.numeric(time)
) %>% ggplot(aes(time, value, color = animals)) +
geom_line() +
facet_grid(type ~ ., scales = "free_y")
But as described above, I would not recommend that because the y scale varies too much across animal types.