Plotting three variables using ggplot

[anas.buhayh]

I'm trying to make a bar chart to show the average speed of shipping per category per year. my data set is like the following

Category | Order.Date | Speed
Stationary | 2010-5-6 | 3
Supplies | 2011-5-6 | 5
Technology | 2014-5-6 | 3
Supplies | 2013-5-6 | 2
Stationary | 2015-5-6 | 1

I want to make a chart that looks like the following where y-axes are the speed, x-axes are the years, and the color is the category.

I've tried the following code but it returns the same values for y

ggplot(westData, aes(
  fill=Category, 
  y=mean(Speed,na.rm = TRUE), 
  x=format(Order.Date,"%Y"
           ))) + 
    geom_bar(position="dodge", stat="identity")

And I get this message after I run it
"Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous."

I tried to change the Y value to the following

group_by(westData,Category) %>% summarise(avgShippingSpeed=mean(Speed,na.rm = TRUE))

But it returns an error of "Error: Aesthetics must be either length 1 or the same as the data (9553): y"

[williaml]

You need to provide a reproducible example:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

Like this:

westData <- tibble::tribble(
     ~Category, ~Order.Date, ~Speed,
  "Stationary",  "2010-05-06",      3,
    "Supplies",  "2011-05-06",      5,
  "Technology",  "2014-05-06",      3,
    "Supplies",  "2013-05-06",      2,
  "Stationary",  "2015-05-06",      1
  )

[DavoWW]

Is this the sort of output you want?

library(tidyverse)
library(lubridate)

westData <- tibble::tribble(
     ~Category, ~Order.Date, ~Speed,
  "Stationary",  "2010-05-06",      3,
    "Supplies",  "2011-05-06",      5,
  "Technology",  "2014-05-06",      3,
    "Supplies",  "2013-05-06",      2,
  "Stationary",  "2015-05-06",      1
  )

# Create a column for just year; calculate means for Category x Year; plot means
westData %>%
  mutate(Year = year(Order.Date)) %>% 
  group_by(Category, Year) %>% 
  summarise(m_speed = mean(Speed, na.rm=TRUE)) %>% 
  ggplot(., aes(x=Year, y=m_speed, fill=Category)) +
    geom_col()

Your bigger dataset will produce a more complete plot.
HTH

[anas.buhayh]

This is exactly what I needed. I didn't know that I should make a column for just the year.
Thank you!