R ggplotly reverts legend name changes in ggplot2-CodePudding

Based on the data and code below, ggplotly changes the legend labels back to the column values.

I did find a solution, but that requires adding a new column. Is there a way to do this without modifying the data?

The ggplotly() ignores legend labels editing despite using scale_fill_manual()

Data (pop_gen_df):

structure(list(age_group = c("<  5 years", "5 - 14", "15  -  24", 
"25  -  34", "35  -  44", "45  -  54", "55  -  64", "65  -  74", 
"75  -  84", "85  ", "<  5 years", "5 - 14", "15  -  24", "25  -  34", 
"35  -  44", "45  -  54", "55  -  64", "65  -  74", "75  -  84", 
"85  "), Type = c("males", "males", "males", "males", "males", 
"males", "males", "males", "males", "males", "females", "females", 
"females", "females", "females", "females", "females", "females", 
"females", "females"), Value = c(-6, -13, -13, -15, -17, -15, 
-11, -6, -3, -1, 6, 12, 12, 14, 16, 15, 12, 7, 4, 2)), row.names = c(NA, 
-20L), class = c("tbl_df", "tbl", "data.frame"))

Code:

# Plot
gg_pop_hisp = ggplot(pop_hisp_df, aes( x = forcats::as_factor(age_group), y = Value, fill = Type))  
  geom_bar(data = subset(pop_hisp_df, Type == "females"), stat = "identity")   
  geom_bar(data = subset(pop_hisp_df, Type == "males"), stat = "identity")   
  scale_y_continuous(labels = function(z) paste0(abs(z), "%"))            # CHANGE
  scale_fill_manual(name = "", values = c("females"="#FC921F", "males"="#149ECE"), labels = c("Females", "Males"))  
  ggtitle("HISPANIC POPULATION BY GENDER AND AGE GROUP")  
  labs(x = "PERCENTAGE POPULATION", y = "AGE GROUPS", fill = "Gender")  
  theme_minimal()  
  theme(legend.position="bottom")  
  coord_flip()

# Interactive and place legend at the bottom
ggplotly(gg_pop_hisp) %>% 
  layout(
    legend = list(
      orientation = 'h', x = 0.3, y = -0.1, 
      title = list(text = '')
      )
    )

CodePudding user response：

I'm not sure if this falls under "not modifying the data", but it instead modifies the data being used defining the aesthetic.

Pass Type as a factor when defining the fill aesthetic and define your labels there. You no longer need to explicitly add the labels in scale_fill_manual and plotly will pick up on them, but you need to update the color mappings to use the capitalized version of the labels.

gg_pop_hisp = ggplot(
  pop_hisp_df, 
  aes( 
    x = forcats::as_factor(age_group), 
    y = Value, 
    # explicitly define fill using a factor to define the labels
    fill = factor(
      Type, levels = c("females", "males"), labels = c("Females", "Males")
    )
  )
)  
  geom_bar(data = subset(pop_hisp_df, Type == "females"), stat = "identity")   
  geom_bar(data = subset(pop_hisp_df, Type == "males"), stat = "identity")   
  scale_y_continuous(labels = function(z) paste0(abs(z), "%"))            
  
  # Update the color mappings to use the new labels
  # No need for the labels arguement because it's taken from the factor labels
  scale_fill_manual(name = "", values = c("Females"="#FC921F", "Males"="#149ECE"))  
  
  ggtitle("HISPANIC POPULATION BY GENDER AND AGE GROUP")  
  labs(x = "PERCENTAGE POPULATION", y = "AGE GROUPS", fill = "Gender")  
  theme_minimal()  
  theme(legend.position="bottom")  
  coord_flip()