Fish Market Short Analysis

Brief Analysis

This is a brief analysis of fish market dataset. First we start by loading the necessary libraries.

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.4     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.1     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(ggplot2)

Second, we read the dataset with the read_csv command. Don’t forget that our delimiter is “;”, so we have to specify it. Also, our dataset contains Turkish characters, thus we also have to specify an encoding argument.

fish <- read.csv("C:/Users/mctas/Desktop/MEF_BDA/R/balik_hal_fiyatlari.csv", sep=';', encoding = 'UTF-8')

Then, we pipe our dataset into a view.

fish %>% View()

If we get a glimpse of our dataframe, fish:

glimpse(fish)

## Rows: 18,596
## Columns: 6
## $ TARIH        <chr> "2021-01-02 00:00:00", "2021-01-02 00:00:00", "2021-01-02~
## $ MAL_TURU     <chr> "BALIK", "BALIK", "BALIK", "BALIK", "BALIK", "BALIK", "BA~
## $ MAL_ADI      <chr> "TIRSI (DENIZ)", "KIRLANGIÇ (DENIZ)", "ÇIMÇIM (DENIZ)", "~
## $ BIRIM        <chr> "KG", "KG", "KG", "KG", "KG", "KG", "KG", "KG", "KG", "KG~
## $ ASGARI_UCRET <dbl> 5.83, 3.00, 3.50, 2.50, 45.00, 130.00, 38.00, 25.00, 10.0~
## $ AZAMI_UCRET  <dbl> 12.50, 80.00, 8.00, 5.00, 45.00, 130.00, 38.00, 55.00, 10~

As you can see, our date column is type character. We have to convert it into datetime by using the command below.

fish$TARIH <- ymd_hms(fish$TARIH)

Let’s check the minimum and maximum dates in the dataframe.

min(fish$TARIH)

## [1] "2021-01-02 UTC"

max(fish$TARIH)

## [1] "2021-10-21 UTC"

Also, let’s have a general idea about the minimum and maximum mean prices of fish overall. This table shows us the overall mean prices of different products, including regular fish, imported fish etc.

ortalama_ucretler <- fish %>% group_by(MAL_TURU) %>% summarise(ASGARI_UCRET_ORTALAMA=mean(ASGARI_UCRET), AZAMI_UCRET_ORTALAMA=mean(AZAMI_UCRET))
ortalama_ucretler

## # A tibble: 4 x 3
##   MAL_TURU      ASGARI_UCRET_ORTALAMA AZAMI_UCRET_ORTALAMA
##   <chr>                         <dbl>                <dbl>
## 1 BALIK                          30.9                 68.4
## 2 ITHAL (DONUK)                  24.4                 30.8
## 3 KÜLTÜR                         22.2                 64.6
## 4 TATLI SU                       29.6                 35.8

If we want to check out daily minimum and maximum mean prices of all fish products, we get the following dataframe.

historic_fish_prices <- fish %>% group_by(TARIH) %>% summarise(ASGARI_gunluk_ortalama=mean(ASGARI_UCRET), AZAMI_gunluk_ortalama=mean(AZAMI_UCRET))
historic_fish_prices

## # A tibble: 284 x 3
##    TARIH               ASGARI_gunluk_ortalama AZAMI_gunluk_ortalama
##    <dttm>                               <dbl>                 <dbl>
##  1 2021-01-02 00:00:00                   21.1                  36.7
##  2 2021-01-03 00:00:00                   19.5                  51.1
##  3 2021-01-04 00:00:00                   23.3                  48.4
##  4 2021-01-05 00:00:00                   26.2                  49.7
##  5 2021-01-06 00:00:00                   21.8                  46.7
##  6 2021-01-07 00:00:00                   23.8                  60.3
##  7 2021-01-08 00:00:00                   21.1                  51.8
##  8 2021-01-09 00:00:00                   21.9                  43.8
##  9 2021-01-10 00:00:00                   22.3                  44.9
## 10 2021-01-11 00:00:00                   20.0                  49.6
## # ... with 274 more rows

Let’s add two more columns to this dataframe. These columns should represent the overall minimum and maximum mean prices of all fish.

historic_fish_prices$ASGARI_tum_zamanlar_ortalama <- mean(historic_fish_prices$ASGARI_gunluk_ortalama)
historic_fish_prices$AZAMI_tum_zamanlar_ortalama <- mean(historic_fish_prices$AZAMI_gunluk_ortalama)

mean(historic_fish_prices$ASGARI_gunluk_ortalama)

## [1] 30.74063

mean(historic_fish_prices$AZAMI_gunluk_ortalama)

## [1] 65.42487

Let’s plot the historic data of fish prices using the library reshape2.

library(reshape2)

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

historic_fish_prices.long <- melt(historic_fish_prices, id = "TARIH", 
                                  measure = c("ASGARI_tum_zamanlar_ortalama", 
                                                      "AZAMI_gunluk_ortalama", 
                                                      "ASGARI_gunluk_ortalama",
                                                      "AZAMI_tum_zamanlar_ortalama"))

ggplot(historic_fish_prices.long, aes(TARIH, value, colour = variable)) + geom_line(size=1)

Fish Market Short Analysis

Murat Can Taşar

10/25/2021

Brief Analysis

Results