I want you to act as a data scientist using R programming language and code for me. I have a dataset of penguins as follows. If you understand what I instructed. print “OK” and wait for another instruction.
Please write code for the exploratory data analysis (EDA) task and target variable is sex.
Here’s an example of an R script that performs exploratory data analysis (EDA) on a dataset, with the target variable being ‘sex’. The code uses the same dataset (‘penguins.csv’) as the previous answer.
This script includes several EDA techniques such as examining the dataset’s structure, summarizing the dataset, visualizing the target variable distribution, creating pairwise scatterplots, boxplots, and bar charts, and calculating the correlation matrix for the continuous variables.
Make sure to replace the filename and adapt the code to your specific needs.
species island bill_length_mm bill_depth_mm
Adelie :146 Biscoe :163 Min. :32.10 Min. :13.10
Chinstrap: 68 Dream :123 1st Qu.:39.50 1st Qu.:15.60
Gentoo :119 Torgersen: 47 Median :44.50 Median :17.30
Mean :43.99 Mean :17.16
3rd Qu.:48.60 3rd Qu.:18.70
Max. :59.60 Max. :21.50
flipper_length_mm body_mass_g sex year
Min. :172 Min. :2700 female:165 Min. :2007
1st Qu.:190 1st Qu.:3550 male :168 1st Qu.:2007
Median :197 Median :4050 Median :2008
Mean :201 Mean :4207 Mean :2008
3rd Qu.:213 3rd Qu.:4775 3rd Qu.:2009
Max. :231 Max. :6300 Max. :2009
# Pairwise scatterplot of continuous variablespairs_plot<-penguins%>%select_if(is.numeric)%>%ggpairs()print(pairs_plot)
코드
# Boxplots for continuous variables by sexpenguins%>%select(-species, -island)%>%pivot_longer(cols =-sex, names_to ="variable", values_to ="value")%>%ggplot(aes(x =sex, y =value))+geom_boxplot()+facet_wrap(~variable, scales ="free")+ggtitle("Boxplots of Continuous Variables by Sex")
코드
# Visualize categorical variables by sexpenguins%>%ggplot(aes(x =island, fill =sex))+geom_bar(position ="dodge")+ggtitle("Island Distribution by Sex")
---title: "chatGPT"subtitle: "데이터 과학"description: | 데이터 사이언스를 챗GPT로 풀어보자.author: - name: 이광춘 url: https://www.linkedin.com/in/kwangchunlee/ affiliation: 한국 R 사용자회 affiliation-url: https://github.com/bit2rtitle-block-banner: true#title-block-banner: "#562457"format: html: css: css/quarto.css theme: flatly code-fold: true code-overflow: wrap toc: true toc-depth: 3 toc-title: 목차 number-sections: true highlight-style: github self-contained: falsefilters: - lightboxlightbox: autolink-citations: trueknitr: opts_chunk: message: false warning: false collapse: falseeditor_options: chunk_output_type: consoleeditor: markdown: wrap: 72---# autoEDA## `Hmisc::describe()`[`Hmisc`](https://cran.r-project.org/web/packages/Hmisc/index.html) 패키지를 통해 과거 20년전 데이터 분석방법을 음미합니다.```{r}library(tidyverse)penguins <- palmerpenguins::penguins %>%# 영어 변수명 한글 변환set_names(c("종명칭", "섬이름", "부리_길이", "부리_깊이", "물갈퀴_길이","체중", "성별", "연도")) %>%# 결측값 제거# drop_na() %>%# 영어 값 한글 값으로 변환mutate(성별 =ifelse(성별 =="male", "수컷", "암컷"), 섬이름 =case_when( str_detect(섬이름, "Biscoe") ~"비스코",str_detect(섬이름, "Dream") ~"드림",str_detect(섬이름, "Torgersen") ~"토르거센"), 종명칭 =case_when( str_detect(종명칭, "Adelie") ~"아델리",str_detect(종명칭, "Chinstrap") ~"턱끈",str_detect(종명칭, "Gentoo") ~"젠투") ) %>%# 자료형 변환mutate(성별 =factor(성별, levels =c("수컷", "암컷")), 섬이름 =factor(섬이름, levels =c("비스코", "드림", "토르거센")), 종명칭 =factor(종명칭, levels =c("아델리", "턱끈", "젠투")), 연도 =ordered(연도, levels =c(2007, 2008, 2009)))Hmisc::describe(penguins)```## `skimr``skimr` 패키지를 사용하여 분석할 데이터와 친숙해진다.```{r}penguins %>% skimr::skim()```## `dataxray`[`dataxray`](https://github.com/agstn/dataxray) 패키지를 사용해서 데이터에 대한 이해를 더욱 높일 수 있다.:::{.column-page}```{r}library(dataxray)penguins %>%make_xray() %>%view_xray()```:::## `dlookr`[`dlookr`](https://cran.r-project.org/web/packages/dlookr/index.html) 패키지를 사용하여 분석할 데이터와 친숙해진다.```{r}library(kableExtra)penguins %>% dlookr::describe() %>%kable(caption ="요약통계량") %>%kable_styling(bootstrap_options =c("striped", "hover", "condensed", "responsive"), full_width = F) ```## `DataExplorer `[`DataExplorer `](https://cran.r-project.org/web/packages/DataExplorer/) 패키지를 사용하여 분석할 데이터와 친숙해진다.```rDataExplorer::create_report(penguins)```:::{.panel-tabset}### 구조```{r}DataExplorer::plot_str(penguins)```### DF 요약```{r}DataExplorer::introduce(penguins)```### DF 요약 시각화```{r}DataExplorer::plot_intro(penguins)```### 결측값```{r}DataExplorer::plot_missing(penguins)```### 분포(범주형)```{r}DataExplorer::plot_bar(penguins)```### 분포(연속형)```{r}DataExplorer::plot_histogram(penguins)```### 상관관계```{r}penguins %>%select_if(is.numeric) %>%# drop_na() %>% DataExplorer::plot_correlation(cor_args =list("use"="pairwise.complete.obs"))```### PCA```{r}penguins_pca <- penguins %>%select_if(is.numeric) %>%drop_na() %>%prcomp(scale =TRUE)summary(penguins_pca)$importance %>%as.data.frame() %>%kable(caption ="PCA 요약") %>%kable_styling(bootstrap_options =c("striped", "hover", "condensed", "responsive"), full_width = F)```:::# 챗GPT EDA[[Model: GPT-4, autoEDA and autoML](https://sharegpt.com/c/1lHtBnV)]{.aside}챗GPT GPT-4 모형을 사용해서 생산성을 크게 향상시킬 수 있다. [@chatGPT2023ds]먼저 역할을 지정하고 데이터셋이 크지 않은 경우 이를 프롬프트에 넣어 GPT-4모형이 작업할 수 있는 환경을 조성한다.:::{.panel-tabset}## 역할부여와 데이터 {.unnumbered}:::{.callout-note collapse="false"}### I want you to act as a data scientist using R programming language and code for me. I have a dataset of penguins as follows. If you understand what I instructed. print "OK" and wait for another instruction.> species,island,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g,sex> Adelie,Torgersen,39.1,18.7,181,3750,MALE> Adelie,Torgersen,39.5,17.4,186,3800,FEMALE> Adelie,Torgersen,40.3,18,195,3250,FEMALE> Adelie,Torgersen,NA,NA,NA,NA,NA> Adelie,Torgersen,36.7,19.3,193,3450,FEMALE> Adelie,Torgersen,39.3,20.6,190,3650,MALE> Adelie,Torgersen,38.9,17.8,181,3625,FEMALE> Adelie,Torgersen,39.2,19.6,195,4675,MALE <br>> ...GPT-4 답변: **OK**:::## EDA 작업지시 {.unnumbered}본격적으로 EDA 작업을 챗GPT에 부여하고 작업을 지시한다.:::{.callout-note collapse="false"}### Please write code for the exploratory data analysis (EDA) task and target variable is sex.Here's an example of an R script that performs exploratory data analysis (EDA) on a dataset, with the target variable being 'sex'. The code uses the same dataset ('penguins.csv') as the previous answer.```# Load librarieslibrary(tidyverse)library(tidymodels)library(ggplot2)library(readr)# Import datasetdata <- read_csv("penguins.csv")# Data preprocessing and cleaningdata_clean <- data %>% drop_na() %>% mutate(sex = as.factor(sex))# Examine the structure of the datasetprint(str(data_clean))# Summary statisticsprint(summary(data_clean))# Target variable distributionprint(table(data_clean$sex))# Visualize target variable distributionggplot(data_clean, aes(x = sex)) + geom_bar() + ggtitle("Sex Distribution")# Pairwise scatterplot of continuous variablespairs_plot <- data_clean %>% select_if(is.numeric) %>% ggpairs()print(pairs_plot)# Boxplots for continuous variables by sexdata_clean %>% gather("variable", "value", -sex) %>% ggplot(aes(x = sex, y = value)) + geom_boxplot() + facet_wrap(~ variable, scales = "free") + ggtitle("Boxplots of Continuous Variables by Sex")# Visualize categorical variables by sexdata_clean %>% ggplot(aes(x = island, fill = sex)) + geom_bar(position = "dodge") + ggtitle("Island Distribution by Sex")# Correlation matrixcor_matrix <- cor(data_clean %>% select_if(is.numeric))print(cor_matrix)```This script includes several EDA techniques such as examining the dataset's structure, summarizing the dataset, visualizing the target variable distribution, creating pairwise scatterplots, boxplots, and bar charts, and calculating the correlation matrix for the continuous variables.Make sure to replace the filename and adapt the code to your specific needs.:::## EDA 수행 {.unnumbered}```{r}# Load librarieslibrary(tidyverse)library(palmerpenguins)library(GGally)# Import datasetpenguins <- palmerpenguins::penguins# Data preprocessing and cleaningpenguins <- penguins %>%drop_na() %>%mutate(sex =as.factor(sex))# Examine the structure of the datasetprint(str(penguins))# Summary statisticsprint(summary(penguins))# Target variable distributionprint(table(penguins$sex))# Visualize target variable distributionggplot(penguins, aes(x = sex)) +geom_bar() +ggtitle("Sex Distribution")# Pairwise scatterplot of continuous variablespairs_plot <- penguins %>%select_if(is.numeric) %>%ggpairs()print(pairs_plot)# Boxplots for continuous variables by sexpenguins %>%select(-species, -island) %>%pivot_longer(cols =-sex, names_to ="variable", values_to ="value") %>%ggplot(aes(x = sex, y = value)) +geom_boxplot() +facet_wrap(~ variable, scales ="free") +ggtitle("Boxplots of Continuous Variables by Sex")# Visualize categorical variables by sexpenguins %>%ggplot(aes(x = island, fill = sex)) +geom_bar(position ="dodge") +ggtitle("Island Distribution by Sex")# Correlation matrixcor_matrix <-cor(penguins %>%select_if(is.numeric))print(cor_matrix)```:::
