Object not found even though column exists

Question

Based on the data and code below, I am getting this error even though the column does exist in the dataframe.

What could be causing this issue?

Purpose: Use dummy data for plotting.

Error in count(., Attrition) : object 'Attrition' not found

Data (hr_people_analytics_char_tbl):

       structure(list(Attrition = c("Yes", "No", "Yes", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", 
"No", "No", "No", "No", "No", "No", "Yes", "No", "No", "Yes", 
"No", "Yes", "No", "No", "No", "No", "No", "No", "Yes", "Yes", 
"No", "Yes", "No", "No", "No", "No", "No", "Yes", "No", "No", 
"Yes", "No", "No", "No", "No", "Yes", "Yes", "No", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "Yes", "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "No", "Yes", "No", "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "Yes", "No", "Yes", "No", "No", "No", "No", 
"Yes", "No", "No", "No", "Yes", "No", "No", "No", "No", "No", 
"No", "No", "No", "No", "No", "Yes", "No", "Yes", "No", "Yes", 
"Yes", "No", "No", "No", "No", "Yes", "No", "No", "No", "Yes", 
"No", "No", "No", "Yes", "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "No", "Yes", "No", "No", "No", "No", "No", "Yes", "No", 
"No", "No", "No", "Yes", "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "Yes", "No", "No", "No", "No", "No", "No", 
"No"), BusinessTravel = c("Travel_Rarely", "Travel_Frequently", 
"Travel_Rarely", "Travel_Frequently", "Travel_Rarely", "Travel_Frequently", 
"Travel_Rarely", "Travel_Rarely", "Travel_Frequently", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Non-Travel", 
"Travel_Rarely", "Travel_Rarely", "Non-Travel", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Frequently", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Frequently", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Frequently", "Travel_Frequently", "Travel_Rarely", 
"Non-Travel", "Travel_Rarely", "Travel_Frequently", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Non-Travel", 
"Travel_Rarely", "Travel_Frequently", "Travel_Frequently", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Frequently", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Frequently", "Travel_Rarely", "Travel_Frequently", "Travel_Rarely", 
"Travel_Frequently", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Non-Travel", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Frequently", "Travel_Rarely", "Travel_Rarely", "Travel_Frequently", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Non-Travel", 
"Travel_Rarely", "Non-Travel", "Travel_Rarely", "Travel_Rarely", 
"Travel_Frequently", "Travel_Rarely", "Non-Travel", "Non-Travel", 
"Travel_Frequently", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Frequently", "Travel_Frequently", "Non-Travel", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Frequently", 
"Travel_Rarely", "Travel_Frequently", "Travel_Frequently", "Non-Travel", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Frequently", "Travel_Frequently", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Frequently", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Frequently", "Travel_Rarely", 
"Travel_Rarely", "Travel_Frequently", "Travel_Rarely", "Travel_Rarely", 
"Travel_Frequently", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Frequently", "Non-Travel", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Frequently", "Travel_Rarely", "Non-Travel", 
"Travel_Rarely", "Travel_Rarely", "Non-Travel", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Frequently", "Travel_Frequently", "Non-Travel", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Frequently", "Travel_Rarely", 
"Travel_Rarely", "Travel_Rarely", "Travel_Rarely", "Non-Travel", 
"Non-Travel", "Travel_Rarely", "Travel_Frequently", "Non-Travel", 
"Travel_Rarely", "Travel_Rarely"), Department = c("Sales", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Sales", "Research & Development", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Research & Development", "Sales", "Sales", "Research & Development", 
"Sales", "Research & Development", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Sales", "Research & Development", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Sales", "Research & Development", "Sales", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Sales", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Sales", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Sales", "Research & Development", "Research & Development", 
"Human Resources", "Research & Development", "Research & Development", 
"Sales", "Research & Development", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Sales", "Sales", "Research & Development", "Sales", "Research & Development", 
"Sales", "Sales", "Sales", "Research & Development", "Human Resources", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Human Resources", "Research & Development", 
"Sales", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Human Resources", 
"Research & Development", "Research & Development", "Sales", 
"Research & Development", "Sales", "Research & Development", 
"Sales", "Research & Development", "Sales", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Research & Development", "Research & Development", "Sales", 
"Sales", "Sales", "Human Resources", "Research & Development", 
"Research & Development", "Sales", "Sales", "Human Resources", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Sales", 
"Sales", "Research & Development", "Sales", "Research & Development", 
"Research & Development", "Research & Development", "Sales", 
"Sales", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Sales", 
"Sales", "Research & Development", "Research & Development", 
"Sales", "Research & Development", "Research & Development", 
"Sales", "Research & Development", "Research & Development", 
"Research & Development", "Sales", "Research & Development", 
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"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development", "Research & Development", 
"Research & Development", "Research & Development"), EducationField = c("Life Sciences", 
"Life Sciences", "Other", "Life Sciences", "Medical", "Life Sciences", 
"Medical", "Life Sciences", "Life Sciences", "Medical", "Medical", 
"Life Sciences", "Life Sciences", "Medical", "Life Sciences", 
"Life Sciences", "Life Sciences", "Medical", "Life Sciences", 
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"Medical", "Other", "Life Sciences", "Marketing", "Medical", 
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"Medical", "Medical", "Marketing", "Marketing", "Life Sciences", 
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"Marketing", "Medical", "Marketing", "Life Sciences", "Life Sciences", 
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"Medical", "Life Sciences", "Medical", "Medical", "Life Sciences", 
"Technical Degree", "Life Sciences", "Life Sciences", "Medical", 
"Life Sciences", "Marketing", "Medical", "Medical", "Medical", 
"Technical Degree", "Other", "Medical", "Medical", "Medical", 
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"Life Sciences", "Human Resources", "Life Sciences", "Marketing", 
"Medical", "Medical", "Medical", "Life Sciences", "Human Resources", 
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"Other", "Medical", "Marketing", "Technical Degree", "Medical", 
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"Medical", "Life Sciences", "Life Sciences", "Life Sciences", 
"Human Resources", "Medical", "Medical", "Technical Degree", 
"Life Sciences", "Medical", "Technical Degree", "Medical", "Life Sciences", 
"Life Sciences", "Medical", "Medical", "Marketing", "Marketing", 
"Life Sciences", "Marketing", "Technical Degree", "Medical", 
"Medical", "Marketing", "Marketing", "Medical", "Medical", "Medical", 
"Life Sciences", "Medical", "Life Sciences", "Life Sciences", 
"Life Sciences", "Life Sciences", "Life Sciences", "Technical Degree", 
"Technical Degree", "Medical", "Medical", "Life Sciences", "Life Sciences", 
"Life Sciences", "Life Sciences", "Marketing", "Life Sciences", 
"Medical", "Medical", "Marketing", "Medical", "Medical", "Medical", 
"Medical", "Medical", "Life Sciences", "Medical", "Life Sciences", 
"Medical", "Life Sciences", "Medical", "Medical", "Life Sciences", 
"Medical", "Medical", "Life Sciences", "Technical Degree"), Gender = c("Female", 
"Male", "Male", "Female", "Male", "Male", "Female", "Male", "Male", 
"Male", "Male", "Female", "Male", "Male", "Male", "Female", "Male", 
"Male", "Female", "Male", "Female", "Male", "Female", "Male", 
"Male", "Female", "Female", "Male", "Female", "Female", "Male", 
"Male", "Male", "Male", "Male", "Female", "Male", "Female", "Female", 
"Female", "Male", "Female", "Male", "Male", "Female", "Female", 
"Male", "Male", "Male", "Male", "Male", "Male", "Female", "Male", 
"Female", "Female", "Male", "Female", "Male", "Male", "Male", 
"Female", "Female", "Female", "Female", "Female", "Male", "Male", 
"Male", "Male", "Female", "Male", "Male", "Male", "Female", "Female", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Female", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Female", "Male", "Male", "Female", "Female", "Male", "Male", 
"Male", "Male", "Male", "Female", "Female", "Male", "Female", 
"Female", "Male", "Male", "Female", "Female", "Male", "Female", 
"Male", "Female", "Male", "Female", "Female", "Female", "Male", 
"Male", "Male", "Female", "Male", "Male", "Female", "Female", 
"Male", "Male", "Female", "Female", "Female", "Female", "Male", 
"Female", "Male", "Male", "Female", "Male", "Male", "Female", 
"Male", "Female", "Female", "Male", "Female", "Male", "Male", 
"Male", "Female", "Female", "Male", "Male", "Male", "Female", 
"Male", "Male", "Female", "Male", "Female", "Male", "Male", "Male", 
"Male", "Male", "Female", "Male", "Female", "Female", "Male", 
"Male", "Female", "Male", "Male", "Female", "Female", "Male", 
"Male", "Female", "Female", "Female", "Female", "Female", "Male", 
"Female", "Female", "Female", "Male", "Male", "Female", "Male", 
"Female", "Male", "Male", "Male", "Male", "Female", "Female", 
"Male", "Male"), JobRole = c("Sales Executive", "Research Scientist", 
"Laboratory Technician", "Research Scientist", "Laboratory Technician", 
"Laboratory Technician", "Laboratory Technician", "Laboratory Technician", 
"Manufacturing Director", "Healthcare Representative", "Laboratory Technician", 
"Laboratory Technician", "Research Scientist", "Laboratory Technician", 
"Laboratory Technician", "Manufacturing Director", "Research Scientist", 
"Laboratory Technician", "Manager", "Research Scientist", "Manufacturing Director", 
"Sales Representative", "Research Director", "Research Scientist", 
"Research Scientist", "Manager", "Research Scientist", "Sales Executive", 
"Healthcare Representative", "Manager", "Laboratory Technician", 
"Healthcare Representative", "Laboratory Technician", "Sales Representative", 
"Research Scientist", "Research Scientist", "Sales Representative", 
"Sales Representative", "Research Scientist", "Sales Executive", 
"Laboratory Technician", "Laboratory Technician", "Laboratory Technician", 
"Sales Executive", "Laboratory Technician", "Research Director", 
"Sales Executive", "Research Scientist", "Sales Executive", "Laboratory Technician", 
"Laboratory Technician", "Laboratory Technician", "Sales Executive", 
"Healthcare Representative", "Sales Executive", "Research Director", 
"Sales Executive", "Laboratory Technician", "Laboratory Technician", 
"Manufacturing Director", "Manufacturing Director", "Laboratory Technician", 
"Research Director", "Sales Executive", "Healthcare Representative", 
"Manager", "Manufacturing Director", "Research Scientist", "Research Scientist", 
"Research Scientist", "Sales Executive", "Research Scientist", 
"Research Scientist", "Research Scientist", "Laboratory Technician", 
"Manufacturing Director", "Sales Executive", "Research Director", 
"Research Director", "Human Resources", "Laboratory Technician", 
"Research Scientist", "Sales Executive", "Research Scientist", 
"Manufacturing Director", "Manufacturing Director", "Sales Representative", 
"Laboratory Technician", "Healthcare Representative", "Sales Executive", 
"Healthcare Representative", "Sales Executive", "Sales Executive", 
"Healthcare Representative", "Sales Executive", "Research Director", 
"Sales Executive", "Sales Executive", "Sales Executive", "Laboratory Technician", 
"Human Resources", "Research Scientist", "Laboratory Technician", 
"Research Scientist", "Healthcare Representative", "Manager", 
"Research Director", "Sales Executive", "Research Scientist", 
"Laboratory Technician", "Healthcare Representative", "Laboratory Technician", 
"Manager", "Laboratory Technician", "Research Scientist", "Sales Executive", 
"Manager", "Sales Executive", "Laboratory Technician", "Manager", 
"Research Scientist", "Sales Executive", "Research Scientist", 
"Research Director", "Sales Executive", "Research Scientist", 
"Healthcare Representative", "Sales Representative", "Laboratory Technician", 
"Manufacturing Director", "Research Scientist", "Sales Executive", 
"Sales Executive", "Sales Executive", "Human Resources", "Manufacturing Director", 
"Manufacturing Director", "Sales Executive", "Sales Executive", 
"Human Resources", "Laboratory Technician", "Research Scientist", 
"Research Scientist", "Research Scientist", "Sales Executive", 
"Research Scientist", "Laboratory Technician", "Manager", "Laboratory Technician", 
"Laboratory Technician", "Research Scientist", "Sales Executive", 
"Sales Representative", "Laboratory Technician", "Sales Executive", 
"Manufacturing Director", "Manufacturing Director", "Research Scientist", 
"Sales Executive", "Sales Representative", "Research Scientist", 
"Research Scientist", "Research Scientist", "Healthcare Representative", 
"Research Scientist", "Manager", "Research Scientist", "Sales Executive", 
"Sales Executive", "Research Scientist", "Research Scientist", 
"Sales Representative", "Laboratory Technician", "Laboratory Technician", 
"Sales Executive", "Research Scientist", "Research Scientist", 
"Laboratory Technician", "Sales Executive", "Laboratory Technician", 
"Research Scientist", "Research Scientist", "Sales Representative", 
"Laboratory Technician", "Manufacturing Director", "Research Scientist", 
"Manager", "Research Director", "Manufacturing Director", "Healthcare Representative", 
"Manager", "Research Scientist", "Manufacturing Director", "Research Scientist", 
"Manager", "Research Scientist", "Laboratory Technician", "Manufacturing Director", 
"Manufacturing Director", "Manufacturing Director"), MaritalStatus = c("Single", 
"Married", "Single", "Married", "Married", "Single", "Married", 
"Divorced", "Single", "Married", "Married", "Single", "Divorced", 
"Divorced", "Single", "Divorced", "Divorced", "Divorced", "Married", 
"Single", "Divorced", "Single", "Single", "Single", "Single", 
"Divorced", "Single", "Married", "Married", "Single", "Single", 
"Married", "Single", "Married", "Married", "Divorced", "Married", 
"Married", "Married", "Married", "Divorced", "Divorced", "Single", 
"Single", "Single", "Married", "Single", "Married", "Single", 
"Married", "Single", "Single", "Divorced", "Married", "Married", 
"Single", "Married", "Married", "Divorced", "Divorced", "Married", 
"Single", "Divorced", "Single", "Divorced", "Divorced", "Single", 
"Divorced", "Married", "Married", "Single", "Married", "Single", 
"Married", "Married", "Single", "Single", "Married", "Single", 
"Divorced", "Married", "Single", "Married", "Divorced", "Married", 
"Single", "Divorced", "Married", "Married", "Single", "Married", 
"Single", "Divorced", "Married", "Single", "Married", "Married", 
"Married", "Single", "Married", "Divorced", "Single", "Single", 
"Single", "Divorced", "Married", "Married", "Single", "Married", 
"Single", "Single", "Single", "Single", "Married", "Divorced", 
"Single", "Single", "Married", "Divorced", "Married", "Divorced", 
"Married", "Married", "Single", "Married", "Married", "Married", 
"Single", "Married", "Married", "Single", "Single", "Married", 
"Divorced", "Married", "Divorced", "Single", "Married", "Married", 
"Married", "Single", "Single", "Single", "Single", "Divorced", 
"Divorced", "Single", "Divorced", "Married", "Single", "Divorced", 
"Married", "Married", "Divorced", "Single", "Married", "Married", 
"Married", "Married", "Married", "Married", "Divorced", "Married", 
"Divorced", "Divorced", "Single", "Divorced", "Married", "Single", 
"Single", "Married", "Single", "Single", "Divorced", "Divorced", 
"Divorced", "Single", "Single", "Divorced", "Single", "Married", 
"Single", "Single", "Married", "Divorced", "Married", "Married", 
"Single", "Married", "Single", "Married", "Single", "Married", 
"Divorced", "Married", "Married", "Single", "Divorced", "Married", 
"Married"), Over18 = c("Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"), OverTime = c("Yes", 
"No", "Yes", "Yes", "No", "No", "Yes", "No", "No", "No", "No", 
"Yes", "No", "No", "Yes", "No", "Yes", "Yes", "No", "Yes", "No", 
"No", "No", "No", "No", "No", "Yes", "No", "No", "No", "No", 
"Yes", "No", "No", "Yes", "No", "Yes", "No", "Yes", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "Yes", "No", "Yes", 
"Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "No", "No", "Yes", 
"No", "Yes", "No", "No", "Yes", "No", "No", "No", "Yes", "No", 
"No", "No", "No", "No", "No", "No", "Yes", "No", "Yes", "Yes", 
"No", "No", "Yes", "No", "No", "No", "No", "No", "No", "No", 
"Yes", "Yes", "Yes", "No", "No", "No", "No", "No", "No", "Yes", 
"No", "Yes", "No", "No", "No", "Yes", "Yes", "No", "No", "No", 
"Yes", "Yes", "No", "No", "No", "No", "No", "No", "Yes", "No", 
"No", "Yes", "No", "Yes", "No", "No", "Yes", "No", "No", "No", 
"No", "Yes", "Yes", "No", "No", "No", "No", "No", "Yes", "No", 
"No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "Yes", "No", "No", "No", "No", "Yes", "No", "No", "No", 
"No", "No", "Yes", "Yes", "No", "No", "No", "No", "No", "Yes", 
"No", "No", "No", "Yes", "Yes", "No", "No", "No", "No", "Yes", 
"No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "Yes", "No", "No", "Yes", "No", "No", "No", "No")), class = "data.frame", row.names = c(NA, 
-200L))

Code:

library(tidyverse)
library(cowplot)
library(ggsci)

scale_color = pal_jco()(10)

    hr_people_analytics_char_tbl %>% 
        count(Attrition) %>% 
        mutate(prop = n/sum(n)) %>% 
        ggplot(aes(x = fct_reorder(Attrition, prop),
                   y = prop,
                   color = Attrition)) +
        scale_color_manual(values = scale_color) +
        geom_segment(aes(xend = Attrition, yend = 0),
                     show.legend = F) +
        geom_point(aes(size = prop),
                   show.legend = F) +
        geom_label(aes(label = prop, size = prop*10), 
                   fill = "white", 
                   hjust = "inward",
                   show.legend = F) +
        labs(
            x = "Attrition"
        ) +
        coord_flip() +
        theme_minimal()

What does `environment(count)` return for you? – MrFlick Jan 10 '23 at 20:12 — MrFlick, Jan 10 '23 at 20:12
`` just like akrun suggested. – Ed_Gravy Jan 10 '23 at 20:35 — Ed_Gravy, Jan 10 '23 at 20:35

akrun · Accepted Answer · 2023-01-10T20:40:21.997

It could be that the count function in dplyr got masked by the plyr::count when plyr is loaded after dplyr. We may need to use dplyr::count or do this in a fresh session without loading plyr

> hr_people_analytics_char_tbl %>% 
         plyr::count(Attrition)
Error in plyr::count(., Attrition) : object 'Attrition' not found

The reason is that vars in plyr::count expects column name as a string

> hr_people_analytics_char_tbl %>% 
          plyr::count("Attrition")
  Attrition freq
1        No  170
2       Yes   30

whereas

 > hr_people_analytics_char_tbl %>% 
          dplyr::count(Attrition)
  Attrition   n
1        No 170
2       Yes  30

Or in case we want the plyr library as well, use exclude while loading

library(plyr, exclude = "count")
> hr_people_analytics_char_tbl %>% 
     count(Attrition)
  Attrition   n
1        No 170
2       Yes  30

-full code

hr_people_analytics_char_tbl %>% 
        dplyr::count(Attrition) %>% 
        mutate(prop = n/sum(n)) %>% 
        ggplot(aes(x = fct_reorder(Attrition, prop),
                   y = prop,
                   color = Attrition)) +
        scale_color_manual(values = scale_color) +
        geom_segment(aes(xend = Attrition, yend = 0),
                     show.legend = FALSE) +
        geom_point(aes(size = prop),
                   show.legend = FALSE) +
        geom_label(aes(label = prop, size = prop*10), 
                   fill = "white", 
                   hjust = "inward",
                   show.legend = FALSE) +
        labs(
            x = "Attrition"
        ) +
        coord_flip() +
        theme_minimal()

-output

Thank you, that worked but now `mutate` is returning an error `Error in mutate(): ! Problem while computing prop = n/sum(n). Caused by error in `sum()`: ! invalid 'type' (closure) of argument` — Ed_Gravy, Jan 10 '23 at 20:38
@Ed_Gravy I couldn't reproduce that error. Can you try the updated code in my post — akrun, Jan 10 '23 at 20:40
Yes, the updated code worked, thank you and happy new year :) — Ed_Gravy, Jan 10 '23 at 20:41

Object not found even though column exists

1 Answers1