Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)Challenge 1
challenge_1
railroads
faostat
wildbirds
Reading in data and creating a post
Read in the Data
- birds.csv ⭐⭐
Code
birds <- read_csv("_data/birds.csv")
birds# A tibble: 30,977 × 14
Domain Cod…¹ Domain Area …² Area Eleme…³ Element Item …⁴ Item Year …⁵ Year
<chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
1 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1961 1961
2 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1962 1962
3 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1963 1963
4 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1964 1964
5 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1965 1965
6 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1966 1966
7 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1967 1967
8 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1968 1968
9 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1969 1969
10 QA Live … 2 Afgh… 5112 Stocks 1057 Chic… 1970 1970
# … with 30,967 more rows, 4 more variables: Unit <chr>, 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
This dataset has 30977 rows and 14 columns. The different type of birds in the dataset are (Chickens, Ducks, Geese and Guinea Fowls, Pigeons other birds, Turkeys). The dataset shows the different area of the birds along with a code designated to the area. The dataset shows the Year, with the amount of units of that bird and the value of the bird as well. The years range from 1961-2018. Each point of data is marked with where the data might have come from such as Official sources or unofficial sources.
Code
head(birds)# 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`Code
dim(birds)[1] 30977 14Code
library(summarytools)
dfSummary(birds)Data Frame Summary
birds
Dimensions: 30977 x 14
Duplicates: 0
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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%)
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