Visualizing Multiple Dimensions

Code

library(tidyverse)
library(ggplot2)
library(lubridate)

knitr::opts_chunk$set(echo = TRUE)

Analysing eggs_tidy data

Code

library(readr)

#reading eggs_tidy csv data
eggs_data <- read_csv("_data/eggs_tidy.csv")

Rows: 120 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): month
dbl (5): year, large_half_dozen, large_dozen, extra_large_half_dozen, extra_...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Code

#extracting all the column names
colnames(eggs_data)

[1] "month"                  "year"                   "large_half_dozen"      
[4] "large_dozen"            "extra_large_half_dozen" "extra_large_dozen"     

Code

#the dataset represents the price of particular quantity of eggs sold during specific time
#The quantity type has it's own column, Converting it to a single column below as they all represent the quantity/ carton size of eggs
eggs_tidy_data <- eggs_data%>%
  pivot_longer(col = c(large_half_dozen, large_dozen, extra_large_half_dozen, extra_large_dozen),
                         names_to= "carton_size",
                         values_to= "price_per_pound")
eggs_tidy_data

# A tibble: 480 × 4
   month     year carton_size            price_per_pound
   <chr>    <dbl> <chr>                            <dbl>
 1 January   2004 large_half_dozen                  126 
 2 January   2004 large_dozen                       230 
 3 January   2004 extra_large_half_dozen            132 
 4 January   2004 extra_large_dozen                 230 
 5 February  2004 large_half_dozen                  128.
 6 February  2004 large_dozen                       226.
 7 February  2004 extra_large_half_dozen            134.
 8 February  2004 extra_large_dozen                 230 
 9 March     2004 large_half_dozen                  131 
10 March     2004 large_dozen                       225 
# … with 470 more rows

Code

#combining month and year to form a new column of data
#Please note - As no day is mentioned, it assumes the day to be 1st of the month specified
eggs_lubricate_data <- eggs_tidy_data%>%
  mutate( date = str_c(month, year , sep = "-"),
          date = my(date))
eggs_lubricate_data

# A tibble: 480 × 5
   month     year carton_size            price_per_pound date      
   <chr>    <dbl> <chr>                            <dbl> <date>    
 1 January   2004 large_half_dozen                  126  2004-01-01
 2 January   2004 large_dozen                       230  2004-01-01
 3 January   2004 extra_large_half_dozen            132  2004-01-01
 4 January   2004 extra_large_dozen                 230  2004-01-01
 5 February  2004 large_half_dozen                  128. 2004-02-01
 6 February  2004 large_dozen                       226. 2004-02-01
 7 February  2004 extra_large_half_dozen            134. 2004-02-01
 8 February  2004 extra_large_dozen                 230  2004-02-01
 9 March     2004 large_half_dozen                  131  2004-03-01
10 March     2004 large_dozen                       225  2004-03-01
# … with 470 more rows

Code

# Visualization with Multiple Dimensions
#Histogram
eggs_lubricate_data%>%
  ggplot(aes(x= price_per_pound,color = carton_size)) + geom_histogram() + labs(title = "Quantity of carton sold for a specific price", x = "Price Per Pound", y = "Amount")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Code

#Stacked Bar Graph
eggs_lubricate_data%>%
  ggplot(aes(x = year, y = price_per_pound, fill = carton_size)) + geom_bar(position = "stack", stat = "identity") + labs(title = "Total amount/price sold per year", x = "Year", y = "Price per pound")