Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 1
challenge_1
my name
dataset
Reading in data and creating a post
Challenge Overview
Today’s challenge is to
read in a dataset, and
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.xls ⭐⭐⭐⭐
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
dataframe <- read_csv('_data/birds.csv', show_col_types = FALSE)
head(dataframe)# 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`Add any comments or documentation as needed. More challenging data sets may require additional code chunks and documentation.
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).
Description : The dataset ‘birds.csv’ contains information about the population of wild birds like chicken, geese etc for a few countries anually from 1961 to 2018.
Code
#| Displaying the first few rows.
head(dataframe)# 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`Displaying the summary of the dataset.
Code
library(summarytools)
dfSummary(dataframe)Data Frame Summary
dataframe
Dimensions: 30977 x 14
Duplicates: 0
----------------------------------------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
---- ------------------ -------------------------------- ----------------------- ---------------------- ---------- ---------
1 Domain Code 1. QA 30977 (100.0%) IIIIIIIIIIIIIIIIIIII 30977 0
[character] (100.0%) (0.0%)
2 Domain 1. Live Animals 30977 (100.0%) IIIIIIIIIIIIIIIIIIII 30977 0
[character] (100.0%) (0.0%)
3 Area Code Mean (sd) : 1201.7 (2099.4) 248 distinct values : 30977 0
[numeric] min < med < max: : (100.0%) (0.0%)
1 < 156 < 5504 :
IQR (CV) : 152 (1.7) : .
: :
4 Area 1. Africa 290 ( 0.9%) 30977 0
[character] 2. Asia 290 ( 0.9%) (100.0%) (0.0%)
3. Eastern Asia 290 ( 0.9%)
4. Egypt 290 ( 0.9%)
5. Europe 290 ( 0.9%)
6. France 290 ( 0.9%)
7. Greece 290 ( 0.9%)
8. Myanmar 290 ( 0.9%)
9. Northern Africa 290 ( 0.9%)
10. South-eastern Asia 290 ( 0.9%)
[ 238 others ] 28077 (90.6%) IIIIIIIIIIIIIIIIII
5 Element Code 1 distinct value 5112 : 30977 (100.0%) IIIIIIIIIIIIIIIIIIII 30977 0
[numeric] (100.0%) (0.0%)
6 Element 1. Stocks 30977 (100.0%) IIIIIIIIIIIIIIIIIIII 30977 0
[character] (100.0%) (0.0%)
7 Item Code Mean (sd) : 1066.5 (9) 1057 : 13074 (42.2%) IIIIIIII 30977 0
[numeric] min < med < max: 1068 : 6909 (22.3%) IIII (100.0%) (0.0%)
1057 < 1068 < 1083 1072 : 4136 (13.4%) II
IQR (CV) : 15 (0) 1079 : 5693 (18.4%) III
1083 : 1165 ( 3.8%)
8 Item 1. Chickens 13074 (42.2%) IIIIIIII 30977 0
[character] 2. Ducks 6909 (22.3%) IIII (100.0%) (0.0%)
3. Geese and guinea fowls 4136 (13.4%) II
4. Pigeons, other birds 1165 ( 3.8%)
5. Turkeys 5693 (18.4%) III
9 Year Code Mean (sd) : 1990.6 (16.7) 58 distinct values . . . . : : : : 30977 0
[numeric] min < med < max: : : : . : : : : : : (100.0%) (0.0%)
1961 < 1992 < 2018 : : : : : : : : : :
IQR (CV) : 29 (0) : : : : : : : : : :
: : : : : : : : : :
10 Year Mean (sd) : 1990.6 (16.7) 58 distinct values . . . . : : : : 30977 0
[numeric] min < med < max: : : : . : : : : : : (100.0%) (0.0%)
1961 < 1992 < 2018 : : : : : : : : : :
IQR (CV) : 29 (0) : : : : : : : : : :
: : : : : : : : : :
11 Unit 1. 1000 Head 30977 (100.0%) IIIIIIIIIIIIIIIIIIII 30977 0
[character] (100.0%) (0.0%)
12 Value Mean (sd) : 99410.6 (720611.4) 11495 distinct values : 29941 1036
[numeric] min < med < max: : (96.7%) (3.3%)
0 < 1800 < 23707134 :
IQR (CV) : 15233 (7.2) :
:
13 Flag 1. * 1494 ( 7.4%) I 20204 10773
[character] 2. A 6488 (32.1%) IIIIII (65.2%) (34.8%)
3. F 10007 (49.5%) IIIIIIIII
4. Im 1213 ( 6.0%) I
5. M 1002 ( 5.0%)
14 Flag Description 1. Aggregate, may include of 6488 (20.9%) IIII 30977 0
[character] 2. Data not available 1002 ( 3.2%) (100.0%) (0.0%)
3. FAO data based on imputat 1213 ( 3.9%)
4. FAO estimate 10007 (32.3%) IIIIII
5. Official data 10773 (34.8%) IIIIII
6. Unofficial figure 1494 ( 4.8%)
----------------------------------------------------------------------------------------------------------------------------Checking the dimensions of the dataset:
Code
dim(dataframe)[1] 30977 14Code
#| It can be observed that there are 30977 rows and 14 columns
Displaying the column names of the dataset :
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colnames(dataframe) [1] "Domain Code" "Domain" "Area Code" "Area"
[5] "Element Code" "Element" "Item Code" "Item"
[9] "Year Code" "Year" "Unit" "Value"
[13] "Flag" "Flag Description"Code
#| The dataset has 14 coulmns describing various fields such as above.Number of unique years :
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unique_years <- dataframe%>% select(Year)%>% n_distinct(.)
unique_years[1] 58It can be observed that the dataset has the data for 58 unique years i.e 1961-2018
Number of unique wildbirds :
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unique_birds <- dataframe%>% select(Item)%>% n_distinct(.)
unique_birds[1] 5It can be observed that the dataset contains information about 5 different types of birds.
Number of unique Areas / countries:
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unique_areas <- dataframe%>% select(Area)%>% n_distinct(.)
unique_areas[1] 248It can be observed that the dataset contains information about 248 different areas / countires