Publications by Leo Tzang
Homework1
# 3.6.1 to 3.6.6. library(ISLR2) data("Boston") attach(Boston) lm.fit <- lm(medv ~ lstat, data = Boston) lm.fit ## ## Call: ## lm(formula = medv ~ lstat, data = Boston) ## ## Coefficients: ## (Intercept) lstat ## 34.55 -0.95 summary(lm.fit) ## ## Call: ## lm(formula = medv ~ lstat, data = Boston) ## ## Residuals: ## M...
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Document
# chapter5 cfa1 prob_equities <- 0.6 prob_tbills <- 0.4 expected_return_equities <- 50000 expected_return_tbills <- -30000 expected_risk_premium <- expected_return_equities - expected_return_tbills print(paste("Expected risk premium:", expected_risk_premium)) ## [1] "Expected risk premium: 80000" # chapter6 problem set 10 # Given data weights_bi...
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Publish Document 110035145 羅暐傑
library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(tidyr) library(readr) unique_categories <- read_csv("bike folder/bike_orderlines.csv") ## Rows: 15644 Column...
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Final
#1. Count the usefulness by learning platform library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1 ## �...
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HW5重做
library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(tidyr) library(readr) unique_categories <- read_csv("bike folder/bike_orderlines.csv") ## Rows: 15644 Column...
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HW4 重做
reshape it from wide to long format (stock_df_long) library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ ggplot2 3.4.4 ✔ tibble 3.2....
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Hw3 重做
Which product categories have the most sales? Use ‘bike_orderline_tbl’. Group and summarize the data calling the new column as ‘Sales’. Format the sales as ‘dollars()’. Rename ‘category_1’ to ‘Prime category’, ‘category_2’ to ‘Secondary category’, ‘frame_material’ to ‘Frame Material’. library(dplyr) ## ## Atta...
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HW2 重做 NETFLIX
You want to learn which six actors have the most appearances in TV show. The multiple actors are listed in the cast column. Transform the data so that for each TV show, every actor has a row. Rename the column name “cast” to “actor”. library(tidyr) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'pa...
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DocumentFinalExam
install.packages("nycflights13") ## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3' ## (as 'lib' is unspecified) library(nycflights13) data(flights) # Question 1: How many flights arrived late each month? late_arrivals <- flights[flights$arr_delay > 5, ] late_arrivals_by_month <- table(format(late_arrivals$time_hour, "%Y-%m")...
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Publish DocumentFinalExamm
install.packages("nycflights13") ## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3' ## (as 'lib' is unspecified) library(nycflights13) data(flights) # Question 1: How many flights arrived late each month? late_arrivals <- flights[flights$arr_delay > 5, ] late_arrivals_by_month <- table(format(late_arrivals$time_hour, "%Y-%m")...
24 sym R (9811 sym/20 pcs) 1 img