Challenge 3 Instructions

Code

library(tidyverse)

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

Challenge Overview

Code

data<-read.csv("_data\\animal_weight.csv")
view(data)
nrow(data)

[1] 9

Code

ncol(data)

[1] 17

Code

list(colnames(data))

[[1]]
 [1] "IPCC.Area"          "Cattle...dairy"     "Cattle...non.dairy"
 [4] "Buffaloes"          "Swine...market"     "Swine...breeding"  
 [7] "Chicken...Broilers" "Chicken...Layers"   "Ducks"             
[10] "Turkeys"            "Sheep"              "Goats"             
[13] "Horses"             "Asses"              "Mules"             
[16] "Camels"             "Llamas"            

describe the data

The data set contains 9 observations with 17 variables from different continents.

Code

nrow(data) * (ncol(data)-3)

[1] 126

Any additional comments? Expected column is 126 ## Pivot the Data

Code

data<-pivot_longer(data, col = c(Turkeys,Ducks),
                 names_to="Animal_name",
                 values_to = "animal_value")

Code

data

# A tibble: 18 × 17
   IPCC.Area Cattl…¹ Cattl…² Buffa…³ Swine…⁴ Swine…⁵ Chick…⁶ Chick…⁷ Sheep Goats
   <chr>       <int>   <int>   <int>   <int>   <int>   <dbl>   <dbl> <dbl> <dbl>
 1 Indian S…     275     110     295      28      28     0.9     1.8  28    30  
 2 Indian S…     275     110     295      28      28     0.9     1.8  28    30  
 3 Eastern …     550     391     380      50     180     0.9     1.8  48.5  38.5
 4 Eastern …     550     391     380      50     180     0.9     1.8  48.5  38.5
 5 Africa        275     173     380      28      28     0.9     1.8  28    30  
 6 Africa        275     173     380      28      28     0.9     1.8  28    30  
 7 Oceania       500     330     380      45     180     0.9     1.8  48.5  38.5
 8 Oceania       500     330     380      45     180     0.9     1.8  48.5  38.5
 9 Western …     600     420     380      50     198     0.9     1.8  48.5  38.5
10 Western …     600     420     380      50     198     0.9     1.8  48.5  38.5
11 Latin Am…     400     305     380      28      28     0.9     1.8  28    30  
12 Latin Am…     400     305     380      28      28     0.9     1.8  28    30  
13 Asia          350     391     380      50     180     0.9     1.8  48.5  38.5
14 Asia          350     391     380      50     180     0.9     1.8  48.5  38.5
15 Middle e…     275     173     380      28      28     0.9     1.8  28    30  
16 Middle e…     275     173     380      28      28     0.9     1.8  28    30  
17 Northern…     604     389     380      46     198     0.9     1.8  48.5  38.5
18 Northern…     604     389     380      46     198     0.9     1.8  48.5  38.5
# … with 7 more variables: Horses <int>, Asses <int>, Mules <int>,
#   Camels <int>, Llamas <int>, Animal_name <chr>, animal_value <dbl>, and
#   abbreviated variable names ¹​Cattle...dairy, ²​Cattle...non.dairy,
#   ³​Buffaloes, ⁴​Swine...market, ⁵​Swine...breeding, ⁶​Chicken...Broilers,
#   ⁷​Chicken...Layers

The number of row has increased to 18

Code

nrow(data)

[1] 18

Code

ncol(data)

[1] 17