Challenge 2: Data Wrangling

challenge_2

railroads

faostat

hotel_bookings

Author

Christa Bonney

Published

August 16, 2022

Code

library(tidyverse)
library(modeest)

Error in library(modeest): there is no package called 'modeest'

Code

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

Code

#read in birds.csv data
birds_data <- (read.csv("~/GIT/601_Fall_2022/posts/_data/birds.csv"))

Error in file(file, "rt"): cannot open the connection

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#get data dimensions
dim(birds_data)

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

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#get column names (variables)
colnames(birds_data)

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

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#preview data
head(birds_data)

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

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#view birds_data data set
view(birds_data)

Error in view(birds_data): object 'birds_data' not found

Based on the current data provided it can be surmised that what is being catalogued is agricultural data, specifically livestock - poultry (chickens, ducks, geese, etc.) and their FAO estimated monetary value per 1000 birds, for various countries/areas, from the years 1961 to 2018. Since all of the observations fall under the “Live Animal” ‘Domain’, I believe that the ‘element code’ pertains to the category that the associated ‘item code’ would fall under (ex. ‘stocks’ of ‘chickens’). Altogether we are looking at 30977 observations across 14 variables. Of the 14 variables there are:

“Domain.Code”: consisting of qualitative ordinal values

“Domain”: consisting of qualitative ordinal values

“Area.Code”: consisting of qualitative ordinal values

“Area”: consisting of qualitative ordinal values

“Element.Code”: consisting of qualitative ordinal values

“Element”: consisting of qualitative ordinal values

“Item.Code”: consisting of qualitative ordinal values

“Item”: consisting of qualitative ordinal values

“Year.Code”: consisting of qualitative ordinal values

“Year”: consisting of qualitative ordinal values

“Unit”: consisting of qualitative ordinal values

“Value”: consisting of quantitative continuous values

“Flag”: consisting of qualitative ordinal values

“Flag.Description”:consisting of qualitative ordinal values

The various variables are considered ordinal assuming the organization tracking this information has a curated selection of options for data input per each category.

For this assignment the focus will be on the “Area”, “Item”, “Year” and “Value” variables to generate information to examine global changes in the value per 1000 head of chickens in the years 2008 and 2018.

Code

#get table of values in 'Area' column to get variety of countries featured in data set
#tried to pipe, but got error that could not combine unrelated 'Domain' (a character vector) and 'Area.Code' (an integer vector) - using select function instead worked.

table(select(birds_data, "Area"))

Error in select(birds_data, "Area"): object 'birds_data' not found

Code

#get table of values in 'Item' column to get variety of poultry species featured in dataset
#tried to pipe, but got error that could not combine unrelated 'Domain' (a character vector) and 'Area.Code' (an integer vector) - using select function instead worked.

table(select(birds_data, "Item"))

Error in select(birds_data, "Item"): object 'birds_data' not found

Code

#get table of values in 'Year' column to get range of years featured in data set
#tried to pipe, but got error that could not combine unrelated 'Domain' (a character vector) and 'Area.Code' (an integer vector) - using select function instead worked.

table(select(birds_data, "Year"))

Error in select(birds_data, "Year"): object 'birds_data' not found

From these tables we can see that a large variety of countries and general geographical areas such as Yemen, Mainland China, Eastern Europe, and Saint Lucia, etc., have contributed agricultural data pertaining to their poultry production fields with data gathered from 1961 to 2018. The categorical selection options range from ‘chickens’ to ‘ducks’, ‘geese’, ‘turkeys’, and ‘pigeons (and related fowl)’.

Code

#select out desired columns to create a condensed data set

select_bird_data <- as_tibble(select(birds_data, "Item", "Value", "Area", "Year"))

Error in select(birds_data, "Item", "Value", "Area", "Year"): object 'birds_data' not found

Code

#rename "Value" column to 'Value per 1000 Head Chickens' to reflect units of measurement for poultry
select_bird_data <- rename(select_bird_data, 'Value per 1000 Head Chickens' = 'Value')

Error in rename(select_bird_data, `Value per 1000 Head Chickens` = "Value"): object 'select_bird_data' not found

Code

#filter for 'chickens' and year '2018' so can examine global agricultural poultry value data for just chickens for year 2018

#arrange to get max, use slice 1:10 to get top 10 countries where chickens were of highest value in 2018

#arrange with desc() to get min, slice 1:10 to get top 10 countries where chickens were of lowest value in 2018

#summarize data to get info for highest, lowest, average price, median, mode price, quantiles, and max/min price of chicken across globe in 2018

select_bird_data %>%
  filter(Item == 'Chickens' & Year == '2018')

Error in filter(., Item == "Chickens" & Year == "2018"): object 'select_bird_data' not found

Code

#filter for 'chickens' and year '2018' so can examine global agricultural poultry value data for just chickens for year 2018

#arrange to get max, use slice 1:10 to get top 10 countries where chickens were of highest value in 2018

#arrange with desc() to get min, slice 1:10 to get top 10 countries where chickens were of lowest value in 2018

#summarize data to get info for highest, lowest, average price, median, mode price, quantiles, and max/min price of chicken across globe in 2018

#repeat steps to generate comparable data for 2008

chickens_2018 <- (filter(select_bird_data, Item == 'Chickens' & Year == '2018'))

Error in filter(select_bird_data, Item == "Chickens" & Year == "2018"): object 'select_bird_data' not found

Code

#could not generate ordered results with arrange() and 'sort', values stayed out of order regardless of the way filtering and grouping functions were ordered in the script

#several attempts were made to generate centralizing data with summarize(): filtering, na.omit(), na.rm = TRUE, etc

#resorted to finding alternate function, summary(), to get desired results

summary(chickens_2018, 'Value per 1000 Head Chickens')

Error in summary(chickens_2018, "Value per 1000 Head Chickens"): object 'chickens_2018' not found

Code

# Create function to calculate mode
getmode <- function(v) {
   uniqv <- unique(v)
   uniqv[which.max(tabulate(match(v, uniqv)))]
}
# Create 2018 chickens value vector from column
v <- select(chickens_2018, 'Value per 1000 Head Chickens')

Error in select(chickens_2018, "Value per 1000 Head Chickens"): object 'chickens_2018' not found

Code

# Calculate the mode using mode function.
result <- getmode(v)

Error in unique(v): object 'v' not found

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print(result)

Error in print(result): object 'result' not found

Code

#the printed results return the values in the whole column

#not a successful effort to calculate the mode

Code

#could not get method of creating a mode function to work

#installed 'modeest' package, and used mlv(Mysamples, method = "mfv") to attempt to calculate mode for value of chickens per 1000 head in 2018 (global)

#initially did not work

#had to convert column data 'Value per 1000 Head Chickens' into a matrix, then into a vector, to be able to apply the mlv() function

#got return of NA  - since data included a missing/NA value - so also used na.rm = TRUE to get numerical values 

chix_2018V <- as.vector(as.matrix(select(chickens_2018, 'Value per 1000 Head Chickens')))

Error in select(chickens_2018, "Value per 1000 Head Chickens"): object 'chickens_2018' not found

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mlv(chix_2018V, method = "mfv", na.rm = TRUE)

Error in mlv(chix_2018V, method = "mfv", na.rm = TRUE): could not find function "mlv"

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#calculate Standard Deviation
sd(chix_2018V, na.rm = TRUE)

Error in is.data.frame(x): object 'chix_2018V' not found

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#filter for global chicken value data for only year 2008
chickens_2008 <- (filter(select_bird_data, Item == 'Chickens' & Year == '2008'))

Error in filter(select_bird_data, Item == "Chickens" & Year == "2008"): object 'select_bird_data' not found

Code

#use 'summary' function to generate centralizing and other data
summary(chickens_2008, 'Value per 1000 Head Chickens')

Error in summary(chickens_2008, "Value per 1000 Head Chickens"): object 'chickens_2008' not found

Code

#calculate mode for 2008 global chicken value data
chix_2008V <- as.vector(as.matrix(select(chickens_2008, 'Value per 1000 Head Chickens')))

Error in select(chickens_2008, "Value per 1000 Head Chickens"): object 'chickens_2008' not found

Code

mlv(chix_2008V, method = "mfv", na.rm = TRUE)

Error in mlv(chix_2008V, method = "mfv", na.rm = TRUE): could not find function "mlv"

Code

#calculate Standard Deviation
sd(chix_2008V, na.rm = TRUE)

Error in is.data.frame(x): object 'chix_2008V' not found

Value per 1000 Head Chickens in 2008:

mean: 318818 median: 16025 mode: 5, 18, 878

standard deviation: 1910971 min: 4 max: 18969655 1st quantile: 2282 3rd quantile: 107146

Value per 1000 Head Chickens in 2018:

mean: 396772 median: 19374 mode: 40

standard deviation: 1533739 min: 4 max: 23707134 1st quantile: 2900 3rd quantile: 123910

The desire was to compare the reported global values per 1000 head chickens between 2008 and 2018 to see if the overall value had increased with the 10yr interlude, or had decreased. From this information we can see that the ranges in reported global values per 1000 head chickens for both the 2008 and 2018 data sets is quite substantial, evidenced by the minimum, maximum, and SD values for each. The minimum reported global values differ little, being 4 for each. While the maximum values (2008: 18,969,655; 2018: 23,707,134) and mean values (2008: 318,818; 2018: 396,772) indicate a significant increase in the global values for 1000 head chickens in the 10 year period.