Challenge 3

challenge_3

Jaswanth Reddy Kommuru

eggs_tidy

Tidy Data: Pivoting

Author

Jaswanth Reddy Kommuru

Published

May 9, 2023

Code

library(tidyverse)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to:

read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
identify what needs to be done to tidy the current data
anticipate the shape of pivoted data
pivot the data into tidy format using pivot_longer

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

animal_weights.csv ⭐
eggs_tidy.csv ⭐⭐ or organiceggpoultry.xls ⭐⭐⭐
australian_marriage*.xls ⭐⭐⭐
USA Households*.xlsx ⭐⭐⭐⭐
sce_labor_chart_data_public.xlsx 🌟🌟🌟🌟🌟

Code

eggsdata<-read_csv("~/Documents/601/601_Spring_2023/posts/_data/eggs_tidy.csv")
view(eggsdata)

Code

head(eggsdata)

# A tibble: 6 × 6
  month     year large_half_dozen large_dozen extra_large_half_dozen
  <chr>    <dbl>            <dbl>       <dbl>                  <dbl>
1 January   2004             126         230                    132 
2 February  2004             128.        226.                   134.
3 March     2004             131         225                    137 
4 April     2004             131         225                    137 
5 May       2004             131         225                    137 
6 June      2004             134.        231.                   137 
# ℹ 1 more variable: extra_large_dozen <dbl>

Code

str(eggsdata)

spc_tbl_ [120 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ month                 : chr [1:120] "January" "February" "March" "April" ...
 $ year                  : num [1:120] 2004 2004 2004 2004 2004 ...
 $ large_half_dozen      : num [1:120] 126 128 131 131 131 ...
 $ large_dozen           : num [1:120] 230 226 225 225 225 ...
 $ extra_large_half_dozen: num [1:120] 132 134 137 137 137 ...
 $ extra_large_dozen     : num [1:120] 230 230 230 234 236 ...
 - attr(*, "spec")=
  .. cols(
  ..   month = col_character(),
  ..   year = col_double(),
  ..   large_half_dozen = col_double(),
  ..   large_dozen = col_double(),
  ..   extra_large_half_dozen = col_double(),
  ..   extra_large_dozen = col_double()
  .. )
 - attr(*, "problems")=<externalptr>

Code

dim(eggsdata)

[1] 120   6

Code

nrow(eggsdata)

[1] 120

Code

ncol(eggsdata)

[1] 6

Code

summary(eggsdata)

    month                year      large_half_dozen  large_dozen   
 Length:120         Min.   :2004   Min.   :126.0    Min.   :225.0  
 Class :character   1st Qu.:2006   1st Qu.:129.4    1st Qu.:233.5  
 Mode  :character   Median :2008   Median :174.5    Median :267.5  
                    Mean   :2008   Mean   :155.2    Mean   :254.2  
                    3rd Qu.:2011   3rd Qu.:174.5    3rd Qu.:268.0  
                    Max.   :2013   Max.   :178.0    Max.   :277.5  
 extra_large_half_dozen extra_large_dozen
 Min.   :132.0          Min.   :230.0    
 1st Qu.:135.8          1st Qu.:241.5    
 Median :185.5          Median :285.5    
 Mean   :164.2          Mean   :266.8    
 3rd Qu.:185.5          3rd Qu.:285.5    
 Max.   :188.1          Max.   :290.0

Briefly describe the data

Describe the data, and be sure to comment on why you are planning to pivot it to make it “tidy”

The dataset consists of egg prices observed on a monthly basis between 2004 and 2013. It comprises 120 rows and 6 columns. Each row contains the prices of eggs of different sizes (large/extra_large) and quantities (half_dozen/dozen) in a particular month and year. However, the current format of the dataset violates the “tidy” data principle that requires each observation to be represented in a separate row. To rectify this, we can pivot the columns for large_half_dozen, large_dozen, extra_large_half_dozen, and extra_large_dozen, creating two new columns - “type” with values “large” and “extra_large” and “quantity” with values “dozen” and “half_dozen”. This will transform the dataset into a tidy format, adhering to the principle of tidy data.

Anticipate the End Result

The first step in pivoting the data is to try to come up with a concrete vision of what the end product should look like - that way you will know whether or not your pivoting was successful.

One easy way to do this is to think about the dimensions of your current data (tibble, dataframe, or matrix), and then calculate what the dimensions of the pivoted data should be.

Suppose you have a dataset with \(n\) rows and \(k\) variables. In our example, 3 of the variables are used to identify a case, so you will be pivoting \(k-3\) variables into a longer format where the \(k-3\) variable names will move into the names_to variable and the current values in each of those columns will move into the values_to variable. Therefore, we would expect \(n * (k-3)\) rows in the pivoted dataframe!

The desired output would showcase the egg sizes as “large” and “extra” under the “type” column, with the quantities “half” and “dozen” included in the “quantity” column. The resulting table should have a new column called “price” that displays the corresponding values for each combination of size and quantity.

Example: find current and future data dimensions

Lets see if this works with a simple example.

Code

df<-tibble(country = rep(c("Mexico", "USA", "France"),2),
           year = rep(c(1980,1990), 3), 
           trade = rep(c("NAFTA", "NAFTA", "EU"),2),
           outgoing = rnorm(6, mean=1000, sd=500),
           incoming = rlogis(6, location=1000, 
                             scale = 400))
df

# A tibble: 6 × 5
  country  year trade outgoing incoming
  <chr>   <dbl> <chr>    <dbl>    <dbl>
1 Mexico   1980 NAFTA     410.    1526.
2 USA      1990 NAFTA     152.     571.
3 France   1980 EU       1499.     792.
4 Mexico   1990 NAFTA     989.     991.
5 USA      1980 NAFTA     382.     423.
6 France   1990 EU       1050.     312.

Code

#existing rows/cases
nrow(df)

[1] 6

Code

#existing columns/cases
ncol(df)

[1] 5

Code

#expected rows/cases
nrow(df) * (ncol(df)-3)

[1] 12

Code

# expected columns 
3 + 2

[1] 5

Or simple example has \(n = 6\) rows and \(k - 3 = 2\) variables being pivoted, so we expect a new dataframe to have \(n * 2 = 12\) rows x \(3 + 2 = 5\) columns.

Challenge: Describe the final dimensions

Document your work here.

Code

dimentions=dim(eggsdata)
dimentions

[1] 120   6

The number of rows we are expecting are

Code

dimentions[1] * (dimentions[2]-2)

[1] 480

The number of columns we are expecting are

Code

dimentions[2] - 4 + 3

[1] 5

Any additional comments?

The current dataset has 120 rows and 6 columns. However, as we plan to convert 4 columns into rows, the number of rows will increase to 480 (120 rows x 4 columns). Conversely, the expected number of columns will be reduced to 4.

Pivot the Data

Now we will pivot the data, and compare our pivoted data dimensions to the dimensions calculated above as a “sanity” check.

Example

Code

df<-pivot_longer(df, col = c(outgoing, incoming),
                 names_to="trade_direction",
                 values_to = "trade_value")
df

# A tibble: 12 × 5
   country  year trade trade_direction trade_value
   <chr>   <dbl> <chr> <chr>                 <dbl>
 1 Mexico   1980 NAFTA outgoing               410.
 2 Mexico   1980 NAFTA incoming              1526.
 3 USA      1990 NAFTA outgoing               152.
 4 USA      1990 NAFTA incoming               571.
 5 France   1980 EU    outgoing              1499.
 6 France   1980 EU    incoming               792.
 7 Mexico   1990 NAFTA outgoing               989.
 8 Mexico   1990 NAFTA incoming               991.
 9 USA      1980 NAFTA outgoing               382.
10 USA      1980 NAFTA incoming               423.
11 France   1990 EU    outgoing              1050.
12 France   1990 EU    incoming               312.

Yes, once it is pivoted long, our resulting data are \(12x5\) - exactly what we expected!

Challenge: Pivot the Chosen Data

Document your work here. What will a new “case” be once you have pivoted the data? How does it meet requirements for tidy data?

Code

eggs_pivot <- eggsdata %>%
  pivot_longer(cols = contains("dozen"),
               names_to= c("type", "quantity"),
               names_sep = '_',
               values_to = "price")
eggs_pivot

# A tibble: 480 × 5
   month     year type  quantity price
   <chr>    <dbl> <chr> <chr>    <dbl>
 1 January   2004 large half      126 
 2 January   2004 large dozen     230 
 3 January   2004 extra large     132 
 4 January   2004 extra large     230 
 5 February  2004 large half      128.
 6 February  2004 large dozen     226.
 7 February  2004 extra large     134.
 8 February  2004 extra large     230 
 9 March     2004 large half      131 
10 March     2004 large dozen     225 
# ℹ 470 more rows

Any additional comments?

The resulting dataset from the pivot operation has the expected dimensions of 480 rows and 5 columns. It adheres to the principles of tidy data, as each distinct case is represented by a separate entry or row within the dataset.