Discrete value supplied to continuous scale error if variables as called in a loop

I'd like to create a ggplot2 barplot with error bars for each variable, but I'm having trouble with Error: Discrete value supplied to continuous scale. I've tried to use the following code:

# Packages
library(dplyr)
library(ggplot2)
# My data set:
ds.1.1 <- read.csv("https://raw.githubusercontent.com/Leprechault/trash/main/exp_ivan_1.csv")
ds.1.2 <- ds.1.1%>%dplyr::select(!EM)
ds.1.2.wide <- ds.1.2|> summarize(across(everything(), list(mean = ~mean(.x, na.rm = TRUE), sd = ~sd(.x, na.rm = TRUE)/sqrt(3))), .by = Tratamentos)
str(ds.1.2.wide)
# 'data.frame':	4 obs. of  17 variables:
#  $ Tratamentos: chr  "T-0" "T-2" "T-3" "T-5"
#  $ GE_mean    : num  0.78 0.695 0.71 0.685
#  $ GE_sd      : num  0.00667 0.02887 0.01633 0.01106
#  $ PC_mean    : num  0.56 0.49 0.455 0.435
#  $ PC_sd      : num  0.00943 0.00667 0.0318 0.01732
#  $ IVE_mean   : num  5.61 4.92 4.88 4.78
#  $ IVE_sd     : num  0.0893 0.2382 0.2879 0.3859
#  $ CE_mean    : num  50.5 59.1 53.7 62.1
#  $ CE_sd      : num  0.355 0.993 0.251 0.721
#  $ AM_mean    : num  0.707 0.583 0.601 0.604
#  $ AM_sd      : num  0.01371 0.00947 0.0226 0.02371
#  $ EE_mean    : num  0.0201 0.0249 0.0233 0.0216
#  $ EE_sd      : num  0.000347 0.000416 0.000347 0.001319
#  $ FB_mean    : num  0.291 0.283 0.296 0.307
#  $ FB_sd      : num  0.002886 0.005818 0.000816 0.005005
#  $ PB_mean    : num  0.105 0.492 0.114 0.111
#  $ PB_sd      : num  0.00042 0.31233 0.00333 0.00565
plots_pull <- list()
target.columns <- c(2,4,6,8,10,12,14,16)
for (j in 1:length(target.columns)){
ds.1.2.wide.sub<-ds.1.2.wide|>dplyr::select(Tratamentos,colnames(ds.1.2.wide)[target.columns[j]],colnames(ds.1.2.wide)[target.columns[j]+1])   
# The errorbars overlapped, so use position_dodge to move them horizontally
pd <- position_dodge(0.1)#position_jitter(height = 0.001) # move them .05 to the left and right
# Use 95% confidence interval instead of SEM
plots_pull[[j]] <-ggplot(data=ds.1.2.wide.sub, aes(x=Tratamentos, y=colnames(ds.1.2.wide.sub)[2])) +
geom_errorbar(data=ds.1.2.wide.sub,mapping=aes(ymin=ds.1.2.wide.sub[2]-ds.1.2.wide.sub[3], ymax=ds.1.2.wide.sub[2]-ds.1.2.wide.sub[3]),position=pd, width=0.2) +
geom_line(aes(linetype = Tratamentos), position = pd) +
scale_colour_grey(start = 0, end = .5) +
theme_bw()  + theme(
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
axis.text.x=element_text(angle = 0, hjust = 0),
axis.text.y=element_text(angle = 0, hjust = 0),
text=element_text(size=18,  family="serif"),legend.position="none")
}
plots_pull    
#[[1]]
#Don't know how to automatically pick scale for object of type ##<data.frame>.
#Defaulting to continuous.
#Error: Discrete value supplied to continuous scale

I maked a partial inspection and the error means to be in:

pd <- position_dodge(0.1)#position_jitter(height = 0.001) # move them .05 to the left and right
ggplot(data=ds.1.2.wide.sub, aes(x=Tratamentos, y=colnames(ds.1.2.wide.sub)[2])) +
geom_errorbar(data=ds.1.2.wide.sub,mapping=aes(ymin=ds.1.2.wide.sub[2]-ds.1.2.wide.sub[3], ymax=ds.1.2.wide.sub[2]-ds.1.2.wide.sub[3]),position=pd, width=0.2) +
geom_line(aes(linetype = Tratamentos), position = pd) +
scale_colour_grey(start = 0, end = .5) +
theme_bw() 
# Don't know how to automatically pick scale for object of type <data.frame>. Defaulting to continuous.
# ERROR while rich displaying an object: Error: Discrete value supplied to continuous scale

Please, any help will be welcome.

NOTE: This answer doesn't solve your question, but I believe it solves the underlying problem that leads to your question. I hope it's useful!

I tried reproducing your plot, and it seems the problem lies in how messy your data is. The main problem is that each column of your dataframe says two things about their values: both if they are mean or sd, and their category (GE, PC, etc). This makes it difficult to pinpoint the value we need for our plots, causing issues further: you had to solve this through an intricate system to select which columns you wanted for each plot.

Let's clean up the data first:

library(dplyr)
library(stringr)
library(tidyr)
ds_2 <- ds.1.2.wide |> 
as_tibble() |> 
#we first convert to long, so that we have one row per data point
tidyr::pivot_longer(2:17) |>
#we extract the data that describes each data point from the column name into its own columns
mutate(
category = stringr::str_extract(name, ".*_") |> stringr::str_remove("_"), 
type = stringr::str_extract(name, "_.*") |> stringr::str_remove("_")) |> 
select(-name) |> #we no longer need the name column, as now we have two columns that separate it
#finally, we transform our data agan so that each combination of treatment and category is in its own row, and the data points asociated with them are in their own columns
tidyr::pivot_wider(names_from = "type", values_from = "value")

Now, we can plot each category of data simply by filtering the "category" column:

ds_2 |> 
filter(category == "GE") |> 
ggplot(aes(Tratamentos, mean)) +
geom_line(position = position_dodge(0.1)) +
geom_errorbar(mapping = aes(ymin=mean-sd, 
ymax=mean+sd),
width=0.2) +
scale_colour_grey(start = 0, end = .5) +
theme_bw() +
theme(
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
axis.text.x=element_text(angle = 0, hjust = 0),
axis.text.y=element_text(angle = 0, hjust = 0),
text=element_text(size=18,  family="serif"),legend.position="none")

Therefore, the loop becomes way simpler:

categories <- unique(ds_2$category)
plots_new <- list()
for (j in categories){
plots_new[[j]] <- ds_2 |> 
#filter data
filter(category == j) |> 
#plot
ggplot(aes(Tratamentos, mean)) +
geom_line(position = position_dodge(0.1)) +
geom_errorbar(mapping = aes(ymin=mean-sd, 
ymax=mean+sd),
width=0.2) +
scale_colour_grey(start = 0, end = .5) +
theme_bw() +
theme(
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
axis.text.x=element_text(angle = 0, hjust = 0),
axis.text.y=element_text(angle = 0, hjust = 0),
text=element_text(size=18,  family="serif"),legend.position="none") +
labs(subtitle = j)
}
plots_new[[6]]

The main issue is that using y=colnames(ds.1.2.wide.sub)[2] you are mapping a character string aka a discrete variable on the y aesthetic. If you pass a column name as a character string then you have to use the .data pronoun or !!sym() to tell ggplot2 that the character string is the name of a column in your data.

Besides that you can simplify your code considerably by switching to e.g. purrr::map2 instead of using a for loop, by putting your plotting code in a function and by looping directly over column names instead of positions:

library(ggplot2)
library(dplyr)

target.columns <- c(2, 4, 6, 8, 10, 12, 14, 16)

# Loop directly over the column names
mean_cols <- names(ds.1.2.wide)[target.columns]
sd_cols <- names(ds.1.2.wide)[target.columns + 1]

plot_fun <- function(x, y) {
  ggplot(
    data = ds.1.2.wide,
    aes(
      x = Tratamentos,
      y = .data[[x]]
    )
  ) +
    geom_errorbar(
      aes(
        ymin = .data[[x]] - .data[[y]],
        ymax = .data[[x]] + .data[[y]]
      ),
      width = 0.2
    ) +
    geom_line(aes(group = 1)) +
    scale_colour_grey(start = 0, end = .5) +
    theme_bw() +
    theme(
      panel.border = element_blank(),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      axis.line = element_line(colour = "black"),
      axis.text.x = element_text(angle = 0, hjust = 0),
      axis.text.y = element_text(angle = 0, hjust = 0),
      text = element_text(size = 18, family = "serif"),
      legend.position = "none"
    )
}

plots_pull <- purrr::map2(mean_cols, sd_cols, plot_fun)

# Just for illustration purposes
library(patchwork)

plots_pull |> 
  wrap_plots()

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Multiple things that are confusing in your code:

1. Are you trying to plot all the "column pairs" in the same graph? You have the position dodge for that but you also try to plot a separate graph.
2. It is unclear what you want to achieve by the geom_line. I guess you are trying to connect the error bars from T0 to T5? Or do you just want to get the mean?

Generally, your input tibble/data frame for ggplot is not clean enough or in a structure that is not ideal for handling by ggplot. Here are some suggestions:

Firstly, (especially if you want to get everything in one graph), it is best to turn the data frame into one observation per line (long format).

df <- ds.1.2.wide %>% 
gather(test, value, -Tratamentos) %>% 
separate(test, into = c("test", "stat")) %>% 
spread(stat, value)

Next, although it may not be advisable to use a for loop, here is the way to minimally correct your original code (I also try to correct for the indents):

target.tests <- unique(df$test)
plots_pull <- list()
for (j in 1:length(target.tests)){
# Use 95% confidence interval instead of SEM # note that +- SD is not the same as CI...
plots_pull[[j]] <- df %>% 
filter(test == !!target.tests[j]) %>%
ggplot() +
geom_errorbar(mapping = aes(x = Tratamentos, ymin = mean - sd, ymax = mean + sd), width = 0.2) +
geom_line(aes(x = Tratamentos, y = mean, linetype = test, group = test)) +
scale_colour_grey(start = 0, end = .5) + # not sure why you need this
theme_bw()  + 
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
axis.text.x=element_text(angle = 0, hjust = 0),
axis.text.y=element_text(angle = 0, hjust = 0),
text=element_text(size=18,  family="serif"),
legend.position="none")
}
plots_pull

In case, you mean to plot everything into one graph (then you need to dodge):

pd <- position_dodge(0.1)
# target.tests <- unique(df$test) # in this case you do not need this and no loop is needed
df %>% 
ggplot() +
geom_errorbar(mapping = aes(x = Tratamentos, ymin = mean - sd, ymax = mean + sd, group = test), position=pd, width = 0.2) +
geom_line(aes(x = Tratamentos, y = mean, linetype = test, group = test), position=pd) +
scale_colour_grey(start = 0, end = .5) + # not sure why you need this
theme_bw()  + 
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
axis.text.x=element_text(angle = 0, hjust = 0),
axis.text.y=element_text(angle = 0, hjust = 0),
text=element_text(size=18,  family="serif"),
legend.position="none")

In case you meant to plot the mean instead of jointing the error bars, then you should use another geom_errorbar but not geom_line:

pd <- position_dodge(0.1)
df %>% 
ggplot() +
geom_errorbar(mapping = aes(x = Tratamentos, ymin = mean - sd, ymax = mean + sd, group = test), position=pd, width = 0.2) +
geom_errorbar(aes(x = Tratamentos, ymin = mean, ymax = mean, group = test), position=pd, width = 0.3) + # then not linetype is needed?
scale_colour_grey(start = 0, end = .5) + # not sure why you need this
theme_bw()  + 
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
axis.text.x=element_text(angle = 0, hjust = 0),
axis.text.y=element_text(angle = 0, hjust = 0),
text=element_text(size=18,  family="serif"),
legend.position="none")

If you insist on using your data frame structure, then you will need something like this (adding !!rlang::sym() to all the column names for ggplot, otherwise ggplot will think the column name as a string rather than a variable):

ggplot() +
geom_errorbar(data=ds.1.2.wide.sub,
mapping=aes(x=Tratamentos, 
ymin=!!rlang::sym(colnames(ds.1.2.wide.sub)[2]) - !!rlang::sym(colnames(ds.1.2.wide.sub)[3]),
ymax=!!rlang::sym(colnames(ds.1.2.wide.sub)[2]) + !!rlang::sym(colnames(ds.1.2.wide.sub)[3])),
width=0.2) +
theme_bw()