0701平均価格課題
tidyverse
2024-07-01
#5 分析環境
# 変数の消去
rm(list = ls())
# パッケージ `pacman`を使って必要なパッケージをインストール
if(!require("pacman")) install.packages("pacman")## 要求されたパッケージ pacman をロード中ですpacman::p_load("tidyverse",
"gt",
"skimr",
"showtext")
# 表示を科学表示から変更
options(scipen = 999)
# 日本語フォントの追加
# フォントの追加と設定（適宜日本語フォントを指定してください）
font_add_google("Noto Sans JP", "noto")
showtext::showtext_auto()
#6 データの読み込み
# CSVファイルを読み込む
dat_hirame <- read_csv("qfr_2024_hirame_2.csv")## Rows: 3000 Columns: 9## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Date, Market, Gear_Type, Species, Highest_Price, Lowest_Price
## dbl (3): Number_of_Vessels, Landing_Amount, Average_Price
##
## 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.#7 データの概要
#7.1 str
# データの構造を確認
dat_hirame |> str()## spc_tbl_ [3,000 × 9] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Date : chr [1:3000] "2018-12-17" "2008-8-28" "1997-9-1" "2008-11-27" ...
## $ Market : chr [1:3000] "宮古" "大船渡" "大船渡" "大船渡" ...
## $ Gear_Type : chr [1:3000] "その他" "底刺網" "その他" "底刺網" ...
## $ Number_of_Vessels: num [1:3000] 1 7 12 5 11 2 1 1 2 8 ...
## $ Species : chr [1:3000] "ヒラメ" "ヒラメ" "ヒラメ" "ヒラメ" ...
## $ Landing_Amount : num [1:3000] 3 48 88 41 65 14 9 1 3 16 ...
## $ Highest_Price : chr [1:3000] "1000" "2800" "5500" "2000" ...
## $ Average_Price : num [1:3000] 1000 1368 1922 1190 1506 ...
## $ Lowest_Price : chr [1:3000] "1000" "200" "1200" "500" ...
## - attr(*, "spec")=
## .. cols(
## .. Date = col_character(),
## .. Market = col_character(),
## .. Gear_Type = col_character(),
## .. Number_of_Vessels = col_double(),
## .. Species = col_character(),
## .. Landing_Amount = col_double(),
## .. Highest_Price = col_character(),
## .. Average_Price = col_double(),
## .. Lowest_Price = col_character()
## .. )
## - attr(*, "problems")=<externalptr>#7.2 summary
# データの記述統計量を確認
dat_hirame |> summary()## Date Market Gear_Type Number_of_Vessels
## Length:3000 Length:3000 Length:3000 Min. : 1.000
## Class :character Class :character Class :character 1st Qu.: 2.000
## Mode :character Mode :character Mode :character Median : 4.000
## Mean : 4.603
## 3rd Qu.: 6.000
## Max. :32.000
##
## Species Landing_Amount Highest_Price Average_Price
## Length:3000 Min. : -100.00 Length:3000 Min. :-30000
## Class :character 1st Qu.: 5.00 Class :character 1st Qu.: 924
## Mode :character Median : 14.00 Mode :character Median : 1345
## Mean : 33.44 Mean : 1554
## 3rd Qu.: 31.00 3rd Qu.: 1932
## Max. :20000.00 Max. : 9313
## NA's :39 NA's :31
## Lowest_Price
## Length:3000
## Class :character
## Mode :character
##
##
##
## #7.3 skim
# データの概要
dat_hirame |> skimr::skim()| Name | dat_hirame |
| Number of rows | 3000 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| character | 6 |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Date | 0 | 1.00 | 8 | 10 | 0 | 2634 | 0 |
| Market | 0 | 1.00 | 2 | 3 | 0 | 3 | 0 |
| Gear_Type | 0 | 1.00 | 3 | 3 | 0 | 3 | 0 |
| Species | 0 | 1.00 | 2 | 6 | 0 | 4 | 0 |
| Highest_Price | 31 | 0.99 | 1 | 5 | 0 | 301 | 0 |
| Lowest_Price | 31 | 0.99 | 1 | 4 | 0 | 309 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Number_of_Vessels | 0 | 1.00 | 4.60 | 3.75 | 1 | 2 | 4 | 6 | 32 | ▇▂▁▁▁ |
| Landing_Amount | 39 | 0.99 | 33.44 | 377.45 | -100 | 5 | 14 | 31 | 20000 | ▇▁▁▁▁ |
| Average_Price | 31 | 0.99 | 1554.21 | 1121.89 | -30000 | 924 | 1345 | 1932 | 9313 | ▁▁▁▇▆ |
#8 データの変換
dat_hirame <- dat_hirame |>
dplyr::mutate(Date = as.Date(Date, format = "%Y-%m-%d"), # 日付型変換
Highest_Price = as.numeric(Highest_Price), #最高価格の数値型への変換
Lowest_Price = as.numeric(Lowest_Price)) # 最低価格の数値型への変換## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `Highest_Price = as.numeric(Highest_Price)`.
## Caused by warning:
## ! 強制変換により NA が生成されました
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.#8.1 skim
# データの概要
dat_hirame |> skimr::skim()| Name | dat_hirame |
| Number of rows | 3000 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| Date | 1 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Market | 0 | 1 | 2 | 3 | 0 | 3 | 0 |
| Gear_Type | 0 | 1 | 3 | 3 | 0 | 3 | 0 |
| Species | 0 | 1 | 2 | 6 | 0 | 4 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| Date | 0 | 1 | 1994-05-06 | 2021-09-06 | 2012-11-11 | 2634 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Number_of_Vessels | 0 | 1.00 | 4.60 | 3.75 | 1 | 2 | 4.0 | 6 | 32 | ▇▂▁▁▁ |
| Landing_Amount | 39 | 0.99 | 33.44 | 377.45 | -100 | 5 | 14.0 | 31 | 20000 | ▇▁▁▁▁ |
| Highest_Price | 96 | 0.97 | 2504.76 | 1515.82 | 50 | 1500 | 2002.5 | 3000 | 10000 | ▇▆▂▁▁ |
| Average_Price | 31 | 0.99 | 1554.21 | 1121.89 | -30000 | 924 | 1345.0 | 1932 | 9313 | ▁▁▁▇▆ |
| Lowest_Price | 273 | 0.91 | 878.43 | 826.70 | 1 | 300 | 600.0 | 1200 | 8500 | ▇▁▁▁▁ |
# 入港隻数の分布
dat_hirame |>
ggplot(aes(x = Number_of_Vessels)) +
geom_histogram(fill = "lightblue", color = "black") +
labs(title = "入港隻数の分布", x = "入港隻数", y = "頻度") +
theme(text = element_text(family = "noto"))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
print(dat_hirame)## # A tibble: 3,000 × 9
## Date Market Gear_Type Number_of_Vessels Species Landing_Amount
## <date> <chr> <chr> <dbl> <chr> <dbl>
## 1 2018-12-17 宮古 その他 1 ヒラメ 3
## 2 2008-08-28 大船渡 底刺網 7 ヒラメ 48
## 3 1997-09-01 大船渡 その他 12 ヒラメ 88
## 4 2008-11-27 大船渡 底刺網 5 ヒラメ 41
## 5 1995-06-03 大船渡 その他 11 ヒラメ 65
## 6 2017-01-24 大船渡 底刺網 2 ヒラメ 14
## 7 2010-03-15 釜石 底刺網 1 ヒラメ 9
## 8 1994-12-22 大船渡 その他 1 ヒラメ 1
## 9 2020-04-02 大船渡 底刺網 2 ヒラメ 3
## 10 2010-01-25 大船渡 底刺網 8 ヒラメ 16
## # ℹ 2,990 more rows
## # ℹ 3 more variables: Highest_Price <dbl>, Average_Price <dbl>,
## # Lowest_Price <dbl>#平均価格の分布
dat_hirame |>
ggplot(aes(x = Average_Price)) +
geom_histogram(fill = "white", color = "red") +
labs(title = "平均価格", x = "平均価格(円/Kg)", y = "頻度") +
theme(text = element_text(family = "noto"))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 31 rows containing non-finite outside the scale range
## (`stat_bin()`).
#平均価格の分布
dat_hirame |> filter(Average_Price < 1000) |>
ggplot(aes(y = Average_Price)) +
geom_boxplot() +
labs(title = "平均価格", y = "平均価格(円/Kg)") +
theme(text = element_text(family = "noto")) 
#平均価格の分布
dat_hirame |> filter(Average_Price < 100) |>
ggplot(aes(y = Average_Price)) +
geom_boxplot() +
labs(title = "平均価格", y = "平均価格(円/Kg)") +
theme(text = element_text(family = "noto"))
#平均価格の分布
dat_hirame |> filter(Average_Price < 10000) |>
ggplot(aes(y = Average_Price)) +
geom_boxplot() +
labs(title = "平均価格", y = "平均価格(円/Kg)") +
theme(text = element_text(family = "noto")) 
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