<jupyter_start><jupyter_text>Week 1 Activity  Instructions***If you have any issues, please visit the help section of the course.******To reset all of the outputs, use the menu to go to Cell -> All Output -> Clear*****For this assignment, make sure to start from the top and work down** 1) First, import the studentinfo.csv dataset into R as a data frame named `D1`. To do so, click on the cell below and press Shift + Enter.You will not see any output, but you will see a number to the left in the brackets (see screenshot below). Note that if you re-run the cell, the number will change; as long as there's a number in the brackets, though, the cell has been run successfully.![Code Cell Bracket](http://www.learninganalytics.net/wp-content/uploads/2018/04/Code-Cell-Bracket.png)<jupyter_code>D1 <- read.csv("studentInfo.csv", header=TRUE)<jupyter_output><empty_output><jupyter_text>2) Next, view the data by running the following cell. (As before, click the cell and press Shift + Enter).<jupyter_code>D1<jupyter_output><empty_output><jupyter_text>3) What does the `code_presentation` variable (column) represent?It represents the code name of the presentation. It consists of the year and “B” for the presentation starting in February and “J” for the presentation starting in October. 4) There are a lot of rows in this dataset, and Jupyter does not display them all. When you scroll through the output above, what do you think indicates that some rows aren't being shown? (Hint: look for a different-looking row about halfway through the output)5) How many people from Wales enrolled in the presentation starting in October in 2014? When you run the next cell, you will see a red background with some output starting with "Attaching package: 'dplyr'" (see screenshot below). You can ignore this. The answer to the question will be at the bottom of the output.![Red Text](http://www.learninganalytics.net/wp-content/uploads/2018/04/Red-Text.png)<jupyter_code>install.packages("dplyr")
library(dplyr)
D1b <- filter(D1, code_presentation == "2013J", region == "Wales")
nrow(D1b)<jupyter_output><empty_output><jupyter_text>6) How many people enrolled from Scotland and had between 60 and 100 credits?<jupyter_code>COL <- filter(D1, region == "Scotland", studied_credits >= 60, studied_credits <= 100)
nrow(COL)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0897052541648868, 'fraction_numerical': 0.018795386586928663, 'mean_word_length': 5.416438356164384, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 20, 'https://': 0, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '545499', 'license': 'MIT', 'n_tokens_mistral': 705, 'n_tokens_neox': 651, 'n_words': 303, 'path': '01_LearningAnalyticsFundamentals/Week 1.ipynb', 'repo_name': 'seokyeongheo/learninganalytics-study', 'size': '2341'}