Despite having managed to plot a multiple slope plot with fake data (See reproducible example below), I am having troubles managing to adapt the code to my real data, and keep facing errors due to key redundancy.
First, some context: I have a dataset with numerous "_x" and "_y" variables, which are measures at time 1 and 2 -recorded in a column, since each entry has a time1 and a time2- and I would like to plot my slopes for each individual, making a plot for each variable (pair of variables).
I have managed -with some help- to do it for one set of variables in the following reproducible example with no "_x" or "_y" colnames. Yet when I try to adapt this code with selects -in order to just take those columns instead of all the dataset-, changing the colnames to mimic the example, changing the regex etc. etc. I keep facing errors of keys redundancy.
"Error in
spread():
! Each row of output must be identified by a unique combination of keys.
Keys are shared for 195 rows:"
I suspect that this is cause I do have some values in my data that are the same, but with the column ID it shouldn't be a problem and I don't quite grasp what can I do to solve it.
The foo example:
library(tidyverse)
Id <- rep(1:10)
a = c(5,10,15,12,13,25,12,13,11,9)
b = c(8,14,20,13,19,29,15,19,20,11)
c = c(10,14,20,1.5,9,21,13,21,11,10)
d = c(15,9,20,14,12,5,12,13,12,30)
group = as.factor( rep(1:2,each=5) )
data = data.frame(Id,a,b,c,d,group)
case_mapping <- data.frame(
key = c("a", "b", "c", "d"),
key2 = c("x1", "x2", "y1", "y2")
)
data %>%
gather(key, val, c(a:d)) %>%
left_join(case_mapping, by = "key") %>%
select(-key) %>%
extract(key2, into = c("key", "order"), "([a-z])([0-9])") %>%
spread(key, val) %>%
ggplot() +
aes(x, y, group = Id, color = group) + xlab("Age")+ #ggtitle(paste("Variable")+
geom_point() +
geom_line()
And now a example of my data.
library(tidyverse)
Id <- rep(1:10)
var1_x = c(5,10,15,12,13,25,12,13,11,9)
var2_x = c(8,14,20,13,19,29,NA,19,20,11) # just adding some nas.
var3_x = c(10,14,20,1.5,9,21,13,21,11,10)
var1_y = var1_x+3
var2_y = var2_x*2
var3_y = c(10,14,20,1.5,9,21,13,21,11,10) #same, just to see.
age1 = c(15,9,20,14,12,5,12,13,12,30)
age2 = c(18,19,24,16,15,9,16,19,14,37)
group = as.factor( rep(1:2,each=5) )
data = data.frame(Id,var1_x,var2_x,var3_x, var1_y,var2_y,var3_y,age1,age2,group)
Now, should I create a for loop, so I can pair the variables correctly. First we create two strings with the colnames _x and _y
sub_x = colnames(data)[2:4] # sub x
sub_y = colnames(data)[5:7] # suby
And now we should be able to implement the for loop.
for( i in 1:length(sub_x)) {
# We define the matching keys.
case_mapping <- data.frame(
key = c(sub_x[i],sub_y[i], "age1", "age2"),
key2 = c("x1", "x2", "y1", "y2")
)
# And now we should be able to plot this.
data %>%
gather(key, val, c(!!sym(sub_x[i]), !!sym(sub_y[i]), age1,age2 )) %>%
left_join(case_mapping, by = "key") %>%
select(-key) %>%
extract(key2, into = c("key", "order"), "([a-z])([0-9])") %>%
spread(key, val) %>%
ggplot() +
aes(x, y, group = Id, color = group) +
xlab("Age")+
geom_point() +
geom_line()
}
Yet this doesn't give me any results and when I try to tweak it it throws errors due to the gather. I hope you can enlighten me in order to understand what I am doing wrong.
PD: Sorry if I'm not fully grammatically correct, but English is my second language.
Edit to clarify:
I intend to plot something like this for every variable -and if there is a way to indicate the ID to each slope that would be really nice so I don't have to look it up from the data to see to which they correspond)
EDIT 2 With the help of Tjebo I somewhat "solve it" but I still need to automatize via dplyr the construction of this data_long2 from the data_long1 provided.
data_long2 <- data.frame( Id = rep(data_long$Id,2), Group = rep(data_long$group,2), Var= rep(data_long$var,2) , Valueage= c(data_long$age1,data_long$age2), Valuevar= c(data_long$x,data_long$y) )
ggplot(data_long2) +
## I've removed the grouping by ID, because there was only one observation per ID
aes(Valueage, Valuevar, color=Id) +
geom_point() +
geom_line(aes(group = Id))+
# geom_line() +
## you can for example facet by your new variable column
facet_grid(~Var)
#> Warning: Removed 1 rows containing missing values (geom_point).
And changing color to group 
