challenge_2
faostat
faostat_livestock
Prachiti Parkar
Data wrangling and Data Sumarization to find statistics in data
Author

Prachiti Parkar

Published

March 8, 2023

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

Read in the Data

I have selected faostat_livestock dataset to do my analysis on.

Code 
# Importing the faostat_livestock dataset and reading them
library(readr)
data <-read_csv("_data/FAOSTAT_livestock.csv")
#Lets see the first 3 rows of the dataset
head(data, 3)
# A tibble: 3 × 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…    5111 Stocks     1107 Asses    1961  1961 Head 
2 QA      Live …       2 Afgh…    5111 Stocks     1107 Asses    1962  1962 Head 
3 QA      Live …       2 Afgh…    5111 Stocks     1107 Asses    1963  1963 Head 
# … 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`

Describe the data

Using R commands I am exploring the data and coming up with the description from the data.

There are total 82117 rows and 14 columns The data ranges from year 1961 to 2018

Checking the variation in data for each column

Code 
# Check the variation in data for each column
summary(data)
 Domain Code           Domain            Area Code          Area          
 Length:82116       Length:82116       Min.   :   1.0   Length:82116      
 Class :character   Class :character   1st Qu.:  73.0   Class :character  
 Mode  :character   Mode  :character   Median : 146.0   Mode  :character  
                                       Mean   : 912.7                     
                                       3rd Qu.: 221.0                     
                                       Max.   :5504.0                     
                                                                          
  Element Code    Element            Item Code        Item          
 Min.   :5111   Length:82116       Min.   : 866   Length:82116      
 1st Qu.:5111   Class :character   1st Qu.: 976   Class :character  
 Median :5111   Mode  :character   Median :1034   Mode  :character  
 Mean   :5111                      Mean   :1018                     
 3rd Qu.:5111                      3rd Qu.:1096                     
 Max.   :5111                      Max.   :1126                     
                                                                    
   Year Code         Year          Unit               Value          
 Min.   :1961   Min.   :1961   Length:82116       Min.   :0.000e+00  
 1st Qu.:1976   1st Qu.:1976   Class :character   1st Qu.:1.250e+04  
 Median :1991   Median :1991   Mode  :character   Median :2.247e+05  
 Mean   :1990   Mean   :1990                      Mean   :1.163e+07  
 3rd Qu.:2005   3rd Qu.:2005                      3rd Qu.:2.377e+06  
 Max.   :2018   Max.   :2018                      Max.   :1.490e+09  
                                                  NA's   :1301       
     Flag           Flag Description  
 Length:82116       Length:82116      
 Class :character   Class :character  
 Mode  :character   Mode  :character

Provide Grouped Summary Statistics

Conducting exploratory data analysis, using dplyr commands such as group_by(), select(), filter(), and summarise().

Code 
#install.packages("dplyr")
library(dplyr)

Grouping by the Area and finding the mean_value

Code 
data %>% 
  group_by(Area) %>% 
  summarise(mean_value = mean(Value))
# A tibble: 253 × 2
   Area                mean_value
   <chr>                    <dbl>
 1 Afghanistan           3597216.
 2 Africa               78159910.
 3 Albania                    NA 
 4 Algeria               2575444.
 5 American Samoa           5399.
 6 Americas             95795716.
 7 Angola                1123117.
 8 Antigua and Barbuda      7877.
 9 Argentina            12879607.
10 Armenia                167587.
# … with 243 more rows

Table output by selecting Columns “Area”, “Year”, and “Value”.

Code 
data %>% 
  select(Area, Year, Value)
# A tibble: 82,116 × 3
   Area         Year   Value
   <chr>       <dbl>   <dbl>
 1 Afghanistan  1961 1300000
 2 Afghanistan  1962  851850
 3 Afghanistan  1963 1001112
 4 Afghanistan  1964 1150000
 5 Afghanistan  1965 1300000
 6 Afghanistan  1966 1200000
 7 Afghanistan  1967 1200000
 8 Afghanistan  1968 1328000
 9 Afghanistan  1969 1250000
10 Afghanistan  1970 1300000
# … with 82,106 more rows

I wanted to check out how any data rows / points are available for Afghanistan and Ireland in the year of 1962 for the item Asses. There are 2 datapoints available.

Code 
data %>% 
  filter(Area == "Afghanistan" | Area == "Ireland",Item == "Asses", Year == 1962)
# A tibble: 2 × 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…    5111 Stocks     1107 Asses    1962  1962 Head 
2 QA      Live …     104 Irel…    5111 Stocks     1107 Asses    1962  1962 Head 
# … 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`

Table Value Statistics

Find the central tendency (mean, median, mode) and dispersion (standard deviation, mix/max/quantile) for Value within the data set.

Code 
#deleting the rows with Null values
data2 <- na.omit(data)
# Find the central tendency (mean, median, mode) and dispersion (standard deviation, mix/max/quantile) for different subgroups within the data set.
data2%>%
  group_by(Year)%>%
  summarize(mean.Value = mean(Value, na.rm = TRUE), median.Value = median(Value, na.rm = TRUE), min.Value = min(Value, na.rm = TRUE), max.Value = max(Value, na.rm = TRUE), sd.Value = sd(Value, na.rm = TRUE), var.Value = var(Value, na.rm = TRUE), IQR.Value = IQR(Value, na.rm = TRUE))
# A tibble: 58 × 8
    Year mean.Value median.Value min.Value  max.Value  sd.Value var.Va…¹ IQR.V…²
   <dbl>      <dbl>        <dbl>     <dbl>      <dbl>     <dbl>    <dbl>   <dbl>
 1  1961  14080955.      129000         10  994268736 66803024.  4.46e15  2.14e6
 2  1962  14085663.      130000         10  997193122 66008462.  4.36e15  2.01e6
 3  1963  14568714.      154000         10  999696719 67268611.  4.53e15  2.49e6
 4  1964  15101322.      160000         10 1013486149 68974136.  4.76e15  2.61e6
 5  1965  15558689.      140000         10 1030878735 70820667.  5.02e15  2.75e6
 6  1966  15481029.      155000         10 1040688491 72204592.  5.21e15  2.42e6
 7  1967  15804831.      148000         10 1059154631 72456707.  5.25e15  2.54e6
 8  1968  15526705.      105850         10 1074022618 72343610.  5.23e15  2.25e6
 9  1969  15203756.      116412.        12 1080720926 71629580.  5.13e15  2.15e6
10  1970  15475993.      103000         12 1081641464 72217288.  5.22e15  2.24e6
# … with 48 more rows, and abbreviated variable names ¹​var.Value, ²​IQR.Value

Some Conclusion from seeing the data summary

  1. The Data is Collected from the year 1961 - 2018
  2. The Element column just has one value Stocks and the Element Code also has only 1 value - 5111
  3. The Area Code varies from 1 - 5504
  4. The Value Column has a lot of variance

Some insights: There is no need of both Year Code and Year, either can be used to save space