Challenge 3 Xiaoyan Hu

challenge_3

animal_weights

eggs

australian_marriage

usa_households

sce_labor

Author

Xiaoyan Hu

Published

September 27, 2022

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 and describe the data

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

# read the data and preview
setwd("/Users/cassie199/Desktop/22fall/DACSS601/601_Fall_2022/posts/_data")

Error in setwd("/Users/cassie199/Desktop/22fall/DACSS601/601_Fall_2022/posts/_data"): cannot change working directory

Code

data1<-read_csv("eggs_tidy.csv")

Error: 'eggs_tidy.csv' does not exist in current working directory ('C:/Users/srika/OneDrive/Desktop/601_Fall_2022/posts').

Code

#preview
head(data1)

Error in head(data1): object 'data1' not found

Briefly describe the data.

Describe the data, and be sure to comment on why you are planning to pivot it to make it “tidy” – in eggs tidy file, there are a lot different animals including:

some of them are not necessary such as cattles - dairy and non dairy since they are both cattles. Similar as swine, chicken. They can be combined together to show less columns.

In this file there is 120 varibles and 6 observations.This data including 10 years data from each month and recorded four different sizes of egg (sold or produced).

some of the varibles seems unnecessary such as repeated in years or month, they can be combine to one column. Therefore, this data is great for pivot. My expected result is to see in each month(by colomn), how many eggs in each size were produced/or sold

Code

#dimension of data
dim(data1)

Error in eval(expr, envir, enclos): object 'data1' not found

Code

#column names
colnames(data1)

Error in is.data.frame(x): object 'data1' not found

Anticipate the End Result and examples

Instructions

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!

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     677.     462.
2 USA      1990 NAFTA     835.    -453.
3 France   1980 EU        215.    1392.
4 Mexico   1990 NAFTA    1317.     957.
5 USA      1980 NAFTA    1373.     407.
6 France   1990 EU        116.    1478.

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

#current dimension
dim(data1)

Error in eval(expr, envir, enclos): object 'data1' not found

Code

# existing rows
nrow(data1)

Error in nrow(data1): object 'data1' not found

Code

# existing columns
ncol(data1)

Error in ncol(data1): object 'data1' not found

Code

#expected rows from first pivot
nrow(data1) * (ncol(data1)-2)

Error in nrow(data1): object 'data1' not found

Code

# expected column from first pivot
6-2

[1] 4

Code

#expected rows from second pivot
(nrow(data1) * (ncol(data1)-2)) /12

Error in nrow(data1): object 'data1' not found

Code

# expected column from first pivot
2+12

[1] 14

Any additional comments? I dont quiet understand how did you get 3 in here since income and outcome considered as 2.

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               677.
 2 Mexico   1980 NAFTA incoming               462.
 3 USA      1990 NAFTA outgoing               835.
 4 USA      1990 NAFTA incoming              -453.
 5 France   1980 EU    outgoing               215.
 6 France   1980 EU    incoming              1392.
 7 Mexico   1990 NAFTA outgoing              1317.
 8 Mexico   1990 NAFTA incoming               957.
 9 USA      1980 NAFTA outgoing              1373.
10 USA      1980 NAFTA incoming               407.
11 France   1990 EU    outgoing               116.
12 France   1990 EU    incoming              1478.

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

#pivot rows
data1<-pivot_longer(data1, col = c(large_half_dozen, large_dozen, extra_large_half_dozen, extra_large_dozen), names_to = "size", values_to = "count")

Error in pivot_longer(data1, col = c(large_half_dozen, large_dozen, extra_large_half_dozen, : object 'data1' not found

Code

data1

Error in eval(expr, envir, enclos): object 'data1' not found

Code

data1<-pivot_wider(data1,  names_from = "month", values_from = "count")

Error in pivot_wider(data1, names_from = "month", values_from = "count"): object 'data1' not found

Code

data1

Error in eval(expr, envir, enclos): object 'data1' not found

Any additional comments??