challenge_1
tidyverse
birds.csv
hw2
Author

Adithya Parupudi

Published

August 16, 2022

Code 
library(tidyverse)
library(readr)
library(dplyr)

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

Challenge Overview

Today’s challenge is to

  1. read in a dataset, and

  2. describe the dataset using both words and any supporting information (e.g., tables, etc)

Read in the Data

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

  • railroad_2012_clean_county.csv ⭐
  • birds.csv ⭐⭐
  • FAOstat*.csv ⭐⭐
  • wild_bird_data.xlsx ⭐⭐⭐
  • StateCounty2012.xlsx ⭐⭐⭐⭐

Find the _data folder, located inside the posts folder. Then you can read in the data, using either one of the readr standard tidy read commands, or a specialized package such as readxl.

Code 
birds_data <- read_csv("_data/birds.csv",show_col_types = FALSE)
#spec(birds_data) -> full column specification

After importing the csv file, and I notice that out of the 14 columns, 8 of them are of character type and 6 columns are double. Total rows -> 30977!

Describe the data

Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).

Code 
names(birds_data)
 [1] "Domain Code"      "Domain"           "Area Code"        "Area"            
 [5] "Element Code"     "Element"          "Item Code"        "Item"            
 [9] "Year Code"        "Year"             "Unit"             "Value"           
[13] "Flag"             "Flag Description"

Column names at a glance

Code 
head(birds_data)
# A tibble: 6 × 14
  Domai…¹ Domain Area …² Area  Eleme…³ Element Item …⁴ Item  Year …⁵  Year Unit 
  <chr>   <chr>    <dbl> <chr>   <dbl> <chr>     <dbl> <chr>   <dbl> <dbl> <chr>
1 QA      Live …       2 Afgh…    5112 Stocks     1057 Chic…    1961  1961 1000…
2 QA      Live …       2 Afgh…    5112 Stocks     1057 Chic…    1962  1962 1000…
3 QA      Live …       2 Afgh…    5112 Stocks     1057 Chic…    1963  1963 1000…
4 QA      Live …       2 Afgh…    5112 Stocks     1057 Chic…    1964  1964 1000…
5 QA      Live …       2 Afgh…    5112 Stocks     1057 Chic…    1965  1965 1000…
6 QA      Live …       2 Afgh…    5112 Stocks     1057 Chic…    1966  1966 1000…
# … with 3 more variables: Value <dbl>, Flag <chr>, `Flag Description` <chr>,
#   and abbreviated variable names ¹​`Domain Code`, ²​`Area Code`,
#   ³​`Element Code`, ⁴​`Item Code`, ⁵​`Year Code`
# ℹ Use `colnames()` to see all variable names
Code 
str(birds_data)
spec_tbl_df [30,977 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ Domain Code     : chr [1:30977] "QA" "QA" "QA" "QA" ...
 $ Domain          : chr [1:30977] "Live Animals" "Live Animals" "Live Animals" "Live Animals" ...
 $ Area Code       : num [1:30977] 2 2 2 2 2 2 2 2 2 2 ...
 $ Area            : chr [1:30977] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ Element Code    : num [1:30977] 5112 5112 5112 5112 5112 ...
 $ Element         : chr [1:30977] "Stocks" "Stocks" "Stocks" "Stocks" ...
 $ Item Code       : num [1:30977] 1057 1057 1057 1057 1057 ...
 $ Item            : chr [1:30977] "Chickens" "Chickens" "Chickens" "Chickens" ...
 $ Year Code       : num [1:30977] 1961 1962 1963 1964 1965 ...
 $ Year            : num [1:30977] 1961 1962 1963 1964 1965 ...
 $ Unit            : chr [1:30977] "1000 Head" "1000 Head" "1000 Head" "1000 Head" ...
 $ Value           : num [1:30977] 4700 4900 5000 5300 5500 5800 6600 6290 6300 6000 ...
 $ Flag            : chr [1:30977] "F" "F" "F" "F" ...
 $ Flag Description: chr [1:30977] "FAO estimate" "FAO estimate" "FAO estimate" "FAO estimate" ...
 - attr(*, "spec")=
  .. cols(
  ..   `Domain Code` = col_character(),
  ..   Domain = col_character(),
  ..   `Area Code` = col_double(),
  ..   Area = col_character(),
  ..   `Element Code` = col_double(),
  ..   Element = col_character(),
  ..   `Item Code` = col_double(),
  ..   Item = col_character(),
  ..   `Year Code` = col_double(),
  ..   Year = col_double(),
  ..   Unit = col_character(),
  ..   Value = col_double(),
  ..   Flag = col_character(),
  ..   `Flag Description` = col_character()
  .. )
 - attr(*, "problems")=<externalptr>

We get to see a get a high level view of the column names and its entries.

Code 
hist(birds_data$`Item Code`)

Code 
hist(birds_data$`Area Code`)

Using the histogram functions, observed that the frequency for item code and area codes respectively.