https://p4css.github.io/R1081/

Rundown

Group up !!!!!
AS#6 Feature extraction of news headline for clickbait detection(Due: 11/20 23:59. Final Submission: 11/24 23:59)
Preview video before 11/18: The links are shared to all participants. If you’re enrolled students, you MUST go to NTU COOL to watch videos. Also, remember to submit your practice to NTU COOL.
- 4_6 Chinese Information Retrieval: Filter words by word frequency, part-of-speech, stop words, and tf-idf.
- ML5.1 PCA Introduction
- ML5.2 PCA on IRIS
- ML5.3 PCA_fb_page_ideology

Midterm
Preview video before 11/11: The links are shared to all participants. If you’re enrolled students, you MUST go to NTU COOL to watch videos. Also, remember to submit your practice to NTU COOL.

In-class slides and code
AS#5: Scraping online news (See more detail on NTU COOL).
Preview video before 10/28: The links are shared to all participants. If you’re enrolled students, you MUST go to NTU COOL to watch videos. Also, remember to submit your practice to NTU COOL.

In-class slides and code
- Reviewing the case paid maternal leave using dplyr by R02_1p_readxl_paid_maternal_leave.Rmd
- Reviewing dplyr and pivot analysis by R02_2p_pivot_on_tp_theft_dplyr.Rmd
- Reviewing joining data with PTT posts and comments by InClass04_join_ptt_blank.Rmd
AS#4: Scraping ubike data and combining them for discovering usage trends of ubike site
- Previewing AS#4 AS04_scraping_json.html
- Download the repo and answer questions in AS04_scraping_json.Rmd
Preview video instruction before 10/21: The links are shared to all participants. If you’re enrolled students, you MUST go to NTU COOL to watch videos. Also, remember to submit your practice to NTU COOL.

In-class slides and code
- Practice: Joining demographic and referendum data (for AS#3) open
AS#3: Combining more data with referendum data (Due: 10/16 WED 23:59. Closed:10/20 SUN 23:59)
- Previewing AS#3 AS03_join_edu_data.html
- Download the repo and answer questions in AS03_join_edu_data.Rmd
Preview video instruction before 10/14: (The links are shared to all participants. If you’re enrolled students, you MUST go to NTU COOL to watch videos)
Practice Week5 (At-Home) - Read JSON and Crawler Design
- Follow the instructions in the video and fill in the blank rmd files. R02_4p_read_json.Rmd and R03_1p_crawl_104.Rmd
- Submitting these two .rmd files, and knitted .html files
- Submitting before the start of the next week class (10/14 09:00). No delay.

Preview Datacamp before 10/07
- String Manipulation in R with stringr: Detecting, matching, splitting, replacing string with Regular expression.
- Data Visualization with ggplot2: You can also learn by case with the 2nd unit of Exploratory Data Analysis in R: Case Study.

In-class slides and code
- Reviewing slide R01_2 R Basic for Assignment#1.
  Code: R01_2_vector.html, R01_3_dataframe_import_export.html
- Practice: Comparing base and dplyr:
  Slide R02_1 Using R to analyze maternal leave.
  Code: R02_1p_readxl_paid_maternal_leave.Rmd
  (HTML: R02_1_readxl_paid_maternal_leave.html)
Assignment#2 AS02_dplyr_ptt_result.html: Following the assignment to detect questionable users or just big fans of the Kaohsiung Mayor Han. Answer the question directly in the AS#2 markdown file AS02_prac01_dplyr_ptt_blank.Rmd in the repo.
Preview Datacamp before 09/30
- Cleanning data in R: tidyr::gather(), tidyr::spread(), tidyr::separate(), Dealing with missing values
- Joining Data with dplyr in R: left_joing(), right_join(), full_join(), inner_join()

In-class slides and code
- (Option) Slide R01_1 Start R: Installing R and RStudio, using RStudio, and editing R Markdown. Code: R01_1_loading_data.html
Assignment#1 R Basic(How to upload your first assignment)
Preview Datacamp before 09/23
- Importing Data in R (Part 1): read.csv(), options(stringAsFactors = F), read_csv() with args: skip and n_max, and library(readxl).
- Data Manipulation with dplyr in R: glimpse(), select(), filter(), arrange(), mutate(), count(), group_by(), summarize(), transmnute()

AS#5
- (Focus on crawling html files)
- For referendum data, joining town-level number of 7-11 store to referendum data.