Final answer:
To visualize items sold in 2017 with R, a dataset containing sales information is filtered to include only 2017 sales data, and then a bar graph or other visual representation is created to illustrate the sales patterns for that year.
Step-by-step explanation:
In order to identify and visualize items sold in the year 2017 using R, we must perform several steps. First, we assume you have a dataset which contains information about the items sold, including a date or year column indicating when each item was sold. Here’s how to filter for 2017 sales and create a visualization:
- Load the dataset into R.
- Use the subset or dplyr package to filter rows that correspond to the year 2017.
- After filtering the data, use ggplot2 or base R graphics to create a visualization such as a bar graph or line graph to represent the number of items sold in 2017.
From the data outcome, you may learn patterns about which items were popular, seasonal trends, or significant spikes or drops in sales. The goal is to extract meaningful information that helps in decision-making for business strategies.
Example Code:
# Assume sales_data is your dataframe and 'year' is the column with the year of sale
library(dplyr)
sales_2017 <- sales_data %>% filter(year == 2017)
library(ggplot2)
ggplot(sales_2017, aes(x=items)) + geom_bar()