Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE)Reading datasets: railroad_2012_clean_county, birds, wild_birds_data
hw1
challenge1
Susmita Madineni
railroad_2012_clean_county, birds, wild bird_data
readr, readxl
Describing the railroad_2012_clean_county data
This dataset gives the information about rail roads in different states, along with total number of employees and county information in the year 2012. It has mainly 3 columns - “state”, “county”, “total_employees”. The dataset has 2930 rows and 3 columns.The reader can easily understand the data by looking at the first few rows.
Code
# Reading railroad_2012_clean_county.csv dataset
library(readr)
railroad <- read_csv("_data/railroad_2012_clean_county.csv")Rows: 2930 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): state, county
dbl (1): total_employees
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Code
view(railroad)
# Preview the first few rows of the dataset
head(railroad)# A tibble: 6 × 3
state county total_employees
<chr> <chr> <dbl>
1 AE APO 2
2 AK ANCHORAGE 7
3 AK FAIRBANKS NORTH STAR 2
4 AK JUNEAU 3
5 AK MATANUSKA-SUSITNA 2
6 AK SITKA 1Code
# Understanding the dimensions of the dataset
dim(railroad)[1] 2930 3Code
# Identifying the column names of the dataset
colnames(railroad)[1] "state" "county" "total_employees"Code
#Making a proportional table for state in the dataset
prop.table(table(select(railroad, state)))state
AE AK AL AP AR AZ
0.0003412969 0.0020477816 0.0228668942 0.0003412969 0.0245733788 0.0051194539
CA CO CT DC DE FL
0.0187713311 0.0194539249 0.0027303754 0.0003412969 0.0010238908 0.0228668942
GA HI IA ID IL IN
0.0518771331 0.0010238908 0.0337883959 0.0122866894 0.0351535836 0.0313993174
KS KY LA MA MD ME
0.0324232082 0.0406143345 0.0215017065 0.0040955631 0.0081911263 0.0054607509
MI MN MO MS MT NC
0.0266211604 0.0293515358 0.0392491468 0.0266211604 0.0180887372 0.0320819113
ND NE NH NJ NM NV
0.0167235495 0.0303754266 0.0034129693 0.0071672355 0.0098976109 0.0040955631
NY OH OK OR PA RI
0.0208191126 0.0300341297 0.0249146758 0.0112627986 0.0221843003 0.0017064846
SC SD TN TX UT VA
0.0156996587 0.0177474403 0.0310580205 0.0754266212 0.0085324232 0.0313993174
VT WA WI WV WY
0.0047781570 0.0133105802 0.0235494881 0.0180887372 0.0075085324 Code
#Filter county APO from the dataset
filter(railroad, county == "APO")# A tibble: 2 × 3
state county total_employees
<chr> <chr> <dbl>
1 AE APO 2
2 AP APO 1Code
#Filter the rows that has total_employees below 3 and above 100
filter(railroad, `total_employees` < 3 | `total_employees` > 100)# A tibble: 809 × 3
state county total_employees
<chr> <chr> <dbl>
1 AE APO 2
2 AK FAIRBANKS NORTH STAR 2
3 AK MATANUSKA-SUSITNA 2
4 AK SITKA 1
5 AL AUTAUGA 102
6 AL BALDWIN 143
7 AL BARBOUR 1
8 AL BLOUNT 154
9 AL COLBERT 199
10 AL CULLMAN 129
# … with 799 more rowsCode
#Arranging railroad based on total_employees and selecting state, county columns,grouping them based on state and then slicing out first 10 rows (with piping)
railroad %>%
arrange(desc(total_employees)) %>%
select(state,county)%>%
group_by(state) %>%
slice(1:10)# A tibble: 478 × 2
# Groups: state [53]
state county
<chr> <chr>
1 AE APO
2 AK SKAGWAY MUNICIPALITY
3 AK ANCHORAGE
4 AK JUNEAU
5 AK FAIRBANKS NORTH STAR
6 AK MATANUSKA-SUSITNA
7 AK SITKA
8 AL JEFFERSON
9 AL MOBILE
10 AL COLBERT
# … with 468 more rowsDescribing the birds dataset
This dataset gives the information about value of 1000 heads of the birds like chicken, ducks, turkeys,etc in different countries across the world in different years(from 1961 to 2018). It has 14 columns - “Domain Code”, “Domain”, “Area Code”, “Area”, “Element Code”, “Element”,“Item Code”, “Item”,“Year Code”, “Year”, “Unit”, “Value”, “Flag”, “Flag Description”. The dataset has 30977 rows and 14 columns.The reader can understand the data by looking at the first few rows.
Code
# Reading birds.csv dataset
library(readr)
birds_data <- read_csv("_data/birds.csv")Rows: 30977 Columns: 14
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): Domain Code, Domain, Area, Element, Item, Unit, Flag, Flag Description
dbl (6): Area Code, Element Code, Item Code, Year Code, Year, Value
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Code
view(birds_data)
# Preview the first few rows of the dataset
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`Code
# Understanding the dimensions of the dataset
dim(birds_data)[1] 30977 14Code
# Identifying the column names of the dataset
colnames(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"Code
#Making a proportional table for state in the dataset
prop.table(table(select(birds_data, Area)))Area
Afghanistan
0.0018723569
Africa
0.0093617845
Albania
0.0074894276
Algeria
0.0074894276
American Samoa
0.0018723569
Americas
0.0074894276
Angola
0.0018723569
Antigua and Barbuda
0.0018723569
Argentina
0.0074894276
Armenia
0.0017432288
Aruba
0.0009361785
Asia
0.0093617845
Australia
0.0056170707
Australia and New Zealand
0.0074894276
Austria
0.0074894276
Azerbaijan
0.0017432288
Bahamas
0.0018723569
Bahrain
0.0018723569
Bangladesh
0.0037447138
Barbados
0.0037447138
Belarus
0.0026148433
Belgium
0.0024534332
Belgium-Luxembourg
0.0050359944
Belize
0.0056170707
Benin
0.0018723569
Bermuda
0.0035510217
Bhutan
0.0018723569
Bolivia (Plurinational State of)
0.0056170707
Bosnia and Herzegovina
0.0034864577
Botswana
0.0018723569
Brazil
0.0056170707
Brunei Darussalam
0.0037447138
Bulgaria
0.0074894276
Burkina Faso
0.0018723569
Burundi
0.0027762533
Cabo Verde
0.0018723569
Cambodia
0.0037447138
Cameroon
0.0018723569
Canada
0.0074894276
Caribbean
0.0074894276
Cayman Islands
0.0017109468
Central African Republic
0.0037447138
Central America
0.0056170707
Central Asia
0.0034864577
Chad
0.0018723569
Chile
0.0037447138
China, Hong Kong SAR
0.0090066824
China, Macao SAR
0.0018723569
China, mainland
0.0056170707
China, Taiwan Province of
0.0074894276
Colombia
0.0018723569
Comoros
0.0018723569
Congo
0.0018723569
Cook Islands
0.0035187397
Costa Rica
0.0018723569
Côte d'Ivoire
0.0027762533
Croatia
0.0034864577
Cuba
0.0018723569
Cyprus
0.0089421183
Czechia
0.0033573296
Czechoslovakia
0.0041320980
Democratic People's Republic of Korea
0.0037447138
Democratic Republic of the Congo
0.0018723569
Denmark
0.0074894276
Dominica
0.0018723569
Dominican Republic
0.0018723569
Eastern Africa
0.0074894276
Eastern Asia
0.0093617845
Eastern Europe
0.0074894276
Ecuador
0.0074894276
Egypt
0.0093617845
El Salvador
0.0018723569
Equatorial Guinea
0.0037447138
Eritrea
0.0008393324
Estonia
0.0034864577
Eswatini
0.0018723569
Ethiopia
0.0008393324
Ethiopia PDR
0.0010330245
Europe
0.0093617845
Falkland Islands (Malvinas)
0.0018723569
Fiji
0.0056170707
Finland
0.0053588146
France
0.0093617845
French Guyana
0.0037447138
French Polynesia
0.0037447138
Gabon
0.0018723569
Gambia
0.0018723569
Georgia
0.0017432288
Germany
0.0074894276
Ghana
0.0018723569
Greece
0.0093617845
Grenada
0.0018723569
Guadeloupe
0.0049714304
Guam
0.0018723569
Guatemala
0.0018723569
Guinea
0.0018723569
Guinea-Bissau
0.0018723569
Guyana
0.0018723569
Haiti
0.0074894276
Honduras
0.0018723569
Hungary
0.0074894276
Iceland
0.0018723569
India
0.0037447138
Indonesia
0.0037447138
Iran (Islamic Republic of)
0.0074894276
Iraq
0.0018723569
Ireland
0.0074894276
Israel
0.0068437873
Italy
0.0037447138
Jamaica
0.0018723569
Japan
0.0037447138
Jordan
0.0080059399
Kazakhstan
0.0017432288
Kenya
0.0018723569
Kiribati
0.0018723569
Kuwait
0.0018723569
Kyrgyzstan
0.0034864577
Lao People's Democratic Republic
0.0056170707
Latvia
0.0017432288
Lebanon
0.0029699454
Lesotho
0.0018723569
Liberia
0.0037447138
Libya
0.0018723569
Liechtenstein
0.0018723569
Lithuania
0.0034864577
Luxembourg
0.0006133583
Madagascar
0.0074894276
Malawi
0.0018723569
Malaysia
0.0037447138
Mali
0.0018723569
Malta
0.0052942506
Martinique
0.0051651225
Mauritania
0.0018723569
Mauritius
0.0074894276
Melanesia
0.0056170707
Mexico
0.0056170707
Micronesia
0.0037447138
Micronesia (Federated States of)
0.0018077929
Middle Africa
0.0056170707
Mongolia
0.0018723569
Montenegro
0.0004196662
Montserrat
0.0018723569
Morocco
0.0037447138
Mozambique
0.0074894276
Myanmar
0.0093617845
Namibia
0.0037447138
Nauru
0.0018723569
Nepal
0.0037447138
Netherlands
0.0053588146
Netherlands Antilles (former)
0.0018723569
New Caledonia
0.0018723569
New Zealand
0.0074894276
Nicaragua
0.0018723569
Niger
0.0018723569
Nigeria
0.0018723569
Niue
0.0018723569
North Macedonia
0.0008716144
Northern Africa
0.0093617845
Northern America
0.0074894276
Northern Europe
0.0074894276
Norway
0.0056170707
Oceania
0.0074894276
Oman
0.0027762533
Pacific Islands Trust Territory
0.0019369209
Pakistan
0.0037447138
Palestine
0.0018723569
Panama
0.0056170707
Papua New Guinea
0.0056170707
Paraguay
0.0074894276
Peru
0.0018723569
Philippines
0.0074894276
Poland
0.0074894276
Polynesia
0.0037447138
Portugal
0.0042289441
Puerto Rico
0.0018723569
Qatar
0.0018723569
Republic of Korea
0.0073602996
Republic of Moldova
0.0017432288
Réunion
0.0050682765
Romania
0.0074894276
Russian Federation
0.0034864577
Rwanda
0.0041320980
Saint Helena, Ascension and Tristan da Cunha
0.0018723569
Saint Kitts and Nevis
0.0018723569
Saint Lucia
0.0018723569
Saint Pierre and Miquelon
0.0029699454
Saint Vincent and the Grenadines
0.0018723569
Samoa
0.0018723569
Sao Tome and Principe
0.0056170707
Saudi Arabia
0.0028730994
Senegal
0.0018723569
Serbia
0.0016786648
Serbia and Montenegro
0.0018077929
Seychelles
0.0037447138
Sierra Leone
0.0037447138
Singapore
0.0037447138
Slovakia
0.0033573296
Slovenia
0.0034864577
Solomon Islands
0.0018723569
Somalia
0.0018723569
South-eastern Asia
0.0093617845
South Africa
0.0074894276
South America
0.0074894276
South Sudan
0.0002259741
Southern Africa
0.0093617845
Southern Asia
0.0074894276
Southern Europe
0.0093617845
Spain
0.0056816348
Sri Lanka
0.0037447138
Sudan
0.0002259741
Sudan (former)
0.0016463828
Suriname
0.0037447138
Sweden
0.0037447138
Switzerland
0.0074894276
Syrian Arab Republic
0.0093617845
Tajikistan
0.0008716144
Thailand
0.0056170707
Timor-Leste
0.0018723569
Togo
0.0018723569
Tokelau
0.0018723569
Tonga
0.0018723569
Trinidad and Tobago
0.0018723569
Tunisia
0.0030022275
Turkey
0.0074894276
Turkmenistan
0.0017432288
Tuvalu
0.0018723569
Uganda
0.0018723569
Ukraine
0.0034864577
United Arab Emirates
0.0018723569
United Kingdom of Great Britain and Northern Ireland
0.0074894276
United Republic of Tanzania
0.0037447138
United States of America
0.0056170707
United States Virgin Islands
0.0018723569
Uruguay
0.0074894276
USSR
0.0020014850
Uzbekistan
0.0017432288
Vanuatu
0.0018723569
Venezuela (Bolivarian Republic of)
0.0018723569
Viet Nam
0.0037447138
Wallis and Futuna Islands
0.0018723569
Western Africa
0.0037447138
Western Asia
0.0093617845
Western Europe
0.0093617845
World
0.0093617845
Yemen
0.0018723569
Yugoslav SFR
0.0040029699
Zambia
0.0018723569
Zimbabwe
0.0056170707 Code
#Filter Area Code = 3 from the dataset
filter(birds_data, `Area Code` == 3)# A tibble: 232 × 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 … 3 Alba… 5112 Stocks 1057 Chic… 1961 1961
2 QA Live … 3 Alba… 5112 Stocks 1057 Chic… 1962 1962
3 QA Live … 3 Alba… 5112 Stocks 1057 Chic… 1963 1963
4 QA Live … 3 Alba… 5112 Stocks 1057 Chic… 1964 1964
5 QA Live … 3 Alba… 5112 Stocks 1057 Chic… 1965 1965
6 QA Live … 3 Alba… 5112 Stocks 1057 Chic… 1966 1966
7 QA Live … 3 Alba… 5112 Stocks 1057 Chic… 1967 1967
8 QA Live … 3 Alba… 5112 Stocks 1057 Chic… 1968 1968
9 QA Live … 3 Alba… 5112 Stocks 1057 Chic… 1969 1969
10 QA Live … 3 Alba… 5112 Stocks 1057 Chic… 1970 1970
# … with 222 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`Code
#Filter the rows that has Item Code as 1057 and Item as Chickens
filter(birds_data, `Item Code` == 1057 & `Item` == "Chickens")# A tibble: 13,074 × 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 13,064 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`Code
#Arranging data based on Value and selecting Area, Item, Value columns, grouping them based on Area and then slicing out first 10 rows (with piping)
birds_data %>%
arrange(desc(Value)) %>%
select(Area, Item, Value)%>%
group_by(Area) %>%
slice(1:10)# A tibble: 2,474 × 3
# Groups: Area [248]
Area Item Value
<chr> <chr> <dbl>
1 Afghanistan Chickens 14414
2 Afghanistan Chickens 14152
3 Afghanistan Chickens 13573
4 Afghanistan Chickens 13378
5 Afghanistan Chickens 13212
6 Afghanistan Chickens 13022
7 Afghanistan Chickens 12888
8 Afghanistan Chickens 12402
9 Afghanistan Chickens 12156
10 Afghanistan Chickens 12053
# … with 2,464 more rowsDescribing the wild_bird_data dataset
This dataset gives the information wet body weight and population size of wild birds. It has 2 columns - “Reference”, “Taken from Figure 1 of Nee et al.”. The dataset has 147 rows and 2 columns.The reader can understand the data by looking at the first few rows.
Code
# Reading wild_bird_data.xlsx dataset
library("readxl")
wild_birds_data <- read_xlsx("_data/wild_bird_data.xlsx")
view(wild_birds_data)
# Preview the first few rows of the dataset
head(wild_birds_data)# A tibble: 6 × 2
Reference `Taken from Figure 1 of Nee et al.`
<chr> <chr>
1 Wet body weight [g] Population size
2 5.45887180052624 532194.395145161
3 7.76456810683605 3165107.44544653
4 8.63858738018464 2592996.86778979
5 10.6897349302105 3524193.2266336
6 7.41722577905587 389806.168891807 Code
# Understanding the dimensions of the dataset
dim(wild_birds_data)[1] 147 2Code
# Identifying the column names of the dataset
colnames(wild_birds_data)[1] "Reference" "Taken from Figure 1 of Nee et al."Code
#Making a proportional table for Reference in the dataset
prop.table(table(select(wild_birds_data, Reference)))Reference
10.0837373583453 10.4227948279533 10.6897349302105 10.9430490453536
0.006802721 0.006802721 0.006802721 0.006802721
1003.03939853867 1008.19886351951 102.577037919272 103.351145634621
0.006802721 0.006802721 0.006802721 0.006802721
1042.06074444654 105.251145466066 105.27800534013 1064.32682601983
0.006802721 0.006802721 0.006802721 0.006802721
11.0657951888437 11.3325639394677 11.7501765156051 11.8338885705264
0.006802721 0.006802721 0.006802721 0.006802721
11.911899237195 110.993242131064 1106.07510035687 1137.96479906865
0.006802721 0.006802721 0.006802721 0.006802721
114.163158354383 116.289475709079 12.073867120209 12.9107556641051
0.006802721 0.006802721 0.006802721 0.006802721
128.395335803439 128.575653541364 13.4190149139047 13.7066181013302
0.006802721 0.006802721 0.006802721 0.006802721
135.486491343145 1368.36501582366 138.513955740119 14.5348410462378
0.006802721 0.006802721 0.006802721 0.006802721
14.9327931173702 15.250338580451 15.4756001614342 15.5437260853068
0.006802721 0.006802721 0.006802721 0.006802721
16.173314711739 16.3353638573666 16.713625723242 16.7374033065426
0.006802721 0.006802721 0.006802721 0.006802721
16.8480457069965 16.9541655530351 163.276223677476 17.7715823185348
0.006802721 0.006802721 0.006802721 0.006802721
173.454892979421 175.840624652506 18.4587461659311 18.4717033015697
0.006802721 0.006802721 0.006802721 0.006802721
18.5902522391181 18.7105850428847 18.7218421193228 19.1013268966518
0.006802721 0.006802721 0.006802721 0.006802721
19.3930121740443 19.492233639612 194.303351756182 20.4175008347481
0.006802721 0.006802721 0.006802721 0.006802721
20.556216729787 2053.74863827143 21.2700694334402 212.147017049613
0.006802721 0.006802721 0.006802721 0.006802721
22.2677519174593 22.3057316395665 22.4321177480063 22.689316708056
0.006802721 0.006802721 0.006802721 0.006802721
22.7960529394349 221.693442317827 226.218006871375 23.9762697808197
0.006802721 0.006802721 0.006802721 0.006802721
232.254993447017 2320.09569921356 24.4422047589553 240.78960679855
0.006802721 0.006802721 0.006802721 0.006802721
25.2824793404366 251.746484961854 251.762544720258 255.963705031502
0.006802721 0.006802721 0.006802721 0.006802721
263.89604533577 265.463867650509 27.1737255667256 27.6790862634083
0.006802721 0.006802721 0.006802721 0.006802721
27.8958488030849 275.131029910782 28.6687569833571 282.288057014247
0.006802721 0.006802721 0.006802721 0.006802721
287.527400278505 293.799587810237 301.332380779176 303.545552195872
0.006802721 0.006802721 0.006802721 0.006802721
311.918910823616 32.2163205913993 33.6261656866144 345.112605430166
0.006802721 0.006802721 0.006802721 0.006802721
35.1758377124708 35.4203082644676 352.281226176494 36.9646386454904
0.006802721 0.006802721 0.006802721 0.006802721
37.5910580962782 371.889868196688 380.296824961942 39.0641452782309
0.006802721 0.006802721 0.006802721 0.006802721
393.854923117015 394.224786249175 42.8545510609481 4223.72945322751
0.006802721 0.006802721 0.006802721 0.006802721
428.549354432903 43.1315511306876 44.8797938229394 4450.50815600577
0.006802721 0.006802721 0.006802721 0.006802721
462.782070038066 47.9477535298889 479.722220970486 480.965400920035
0.006802721 0.006802721 0.006802721 0.006802721
486.856962904678 49.9139486401791 5.45887180052624 52.312838051431
0.006802721 0.006802721 0.006802721 0.006802721
527.513423559442 530.357342971806 603.577402390915 64.74167603758
0.006802721 0.006802721 0.006802721 0.006802721
645.449083461034 66.4645646879698 67.0724165442755 672.284528540897
0.006802721 0.006802721 0.006802721 0.006802721
684.506509216141 7.41722577905587 7.76456810683605 71.3921665737811
0.006802721 0.006802721 0.006802721 0.006802721
72.4618812159043 757.205197645473 765.921951081732 79.3893544290883
0.006802721 0.006802721 0.006802721 0.006802721
798.020716987072 8.03684333000353 8.63858738018464 8.70473119796067
0.006802721 0.006802721 0.006802721 0.006802721
8.89032317828959 82.8877457373263 820.520151625127 887.34848570896
0.006802721 0.006802721 0.006802721 0.006802721
9.1169347252776 9.51590845877281 923.172177028805 95.6625004989711
0.006802721 0.006802721 0.006802721 0.006802721
954.839393219695 9639.84540069595 Wet body weight [g]
0.006802721 0.006802721 0.006802721 Code
#Filter the rows that has Reference > 10 and Reference < 12
filter(wild_birds_data, `Reference` > 10.0 & `Reference` < 12.0)# A tibble: 22 × 2
Reference `Taken from Figure 1 of Nee et al.`
<chr> <chr>
1 10.6897349302105 3524193.2266336
2 10.9430490453536 864.665387886239
3 10.0837373583453 74386.4256983317
4 10.4227948279533 131929.739792131
5 11.0657951888437 164390.10921714
6 11.7501765156051 143943.923703849
7 11.911899237195 62266.8051981741
8 11.8338885705264 49948.4692178362
9 11.3325639394677 15.2869871955882
10 116.289475709079 32206.460433963
# … with 12 more rowsCode
#Arranging data based on Reference and selecting Reference and then slicing out first 10 rows (with piping)
wild_birds_data %>%
arrange(desc(Reference)) %>%
select(Reference)%>%
slice(1:10)# A tibble: 10 × 1
Reference
<chr>
1 Wet body weight [g]
2 9639.84540069595
3 954.839393219695
4 95.6625004989711
5 923.172177028805
6 9.51590845877281
7 9.1169347252776
8 887.34848570896
9 820.520151625127
10 82.8877457373263