Visualizing Sales of Eggs: Part 2
For this analysis I’m chosing dataset poultry_tidy.csv from our classroom. I begin by loading in the dataset using the read_csv() function in the tidyverse.
library(knitr)
library(tidyverse)
data <- read_csv("/Users/shrutishelke1999/Downloads/poultry_tidy - poultry_tidy.csv")
head(data)
# A tibble: 6 × 4
Product Year Month Price_Dollar
<chr> <dbl> <chr> <dbl>
1 Whole 2013 January 2.38
2 Whole 2013 February 2.38
3 Whole 2013 March 2.38
4 Whole 2013 April 2.38
5 Whole 2013 May 2.38
6 Whole 2013 June 2.38
After running the code from my last blog post, I’m curious to see how the different products were sold accross different years. To visualize that efficiently, facet_wrap seems like a good function to explore within the ggplot library.
ggplot(data, aes(Year,Price_Dollar, color = Product)) +
geom_col() +
labs(x = "Year of Production", y = "Price of eggs in Dollars", title = "Selling of Eggs by Year")+
facet_wrap(~Product)
B/S Breasts seem to have the highest selling in 2012. This makes me wonder if there’s a particular month in which the selling is higher than usual. Let’s plot the selling for B/S breasts by month.
# A tibble: 6 × 4
Product Year Month Price_Dollar
<chr> <dbl> <chr> <dbl>
1 B/S Breast 2013 January 7.04
2 B/S Breast 2013 February 7.04
3 B/S Breast 2013 March 7.04
4 B/S Breast 2013 April 7.04
5 B/S Breast 2013 May 7.04
6 B/S Breast 2013 June 7.04
ggplot(filter,aes(Year,Price_Dollar)) +
geom_col() +
facet_wrap(~Month)
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Text and figures are licensed under Creative Commons Attribution CC BY-NC 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Shelke (2022, May 19). Data Analytics and Computational Social Science: Blog Post 5. Retrieved from https://github.com/DACSS/dacss_course_website/posts/httpsrpubscomsshelke901517/
BibTeX citation
@misc{shelke2022blog, author = {Shelke, Shruti}, title = {Data Analytics and Computational Social Science: Blog Post 5}, url = {https://github.com/DACSS/dacss_course_website/posts/httpsrpubscomsshelke901517/}, year = {2022} }