Publications by Name

Apply8FIN

26.03.2023

# Load packages # Core library(tidyverse) library(tidyquant) Goal Visualize and examine changes in the underlying trend in the downside risk of your portfolio in terms of kurtosis. Choose your stocks. from 2012-12-31 to present 1 Import stock prices symbols <- c("JNJ", "MMM", "AMZN", "IBM", "MSFT") prices <- tq_get(x = symbols, ...

574 sym R (3056 sym/14 pcs) 1 img

Appply8DAT

25.03.2023

Introduction nycflights13 Keys Mutating Joins Inner Join x <- tribble( ~key, ~val_x, 1, "x1", 2, "x2", 3, "x3" ) y <- tribble( ~key, ~val_y, 1, "y1", 2, "y2", 4, "y3" ) Outer Joins left_join(x, y, by = "key") ## # A tibble: 3 × 3 ## key val_x val_y ## <dbl> <chr> <chr> ## 1 1 x1 y1 ...

202 sym Python (5312 sym/40 pcs)

Apply8DAT

25.03.2023

Import your data myData <- read_excel("../00_data/myData_charts.xlsx") myData ## # A tibble: 10,879 × 8 ## team team_name year total home away week weekly_attendance ## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> ## 1 San Francisco 49ers 2000 1057954 541964 515990 1 54626 ...

325 sym

Apply7DAT

18.03.2023

Import your data myData <- read_excel("../00_data/myData_charts.xlsx") myData ## # A tibble: 10,879 × 8 ## team team_name year total home away week weekly_attendance ## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> ## 1 San Francisco 49ers 2000 1057954 541964 515990 1 54626 ...

167 sym Python (5930 sym/12 pcs)

CodeAlong7FIN

18.03.2023

Goal Measure portfolio risk using skewness. Skewness is the extent to which returns are asymmetric around the mean. It is important because a positively skewed distribution means large positive returns are more likely while a negatively skewed distribution implies large negative returns are more likely. five stocks: “SPY”, “EFA”, “IJS...

600 sym 3 img

Apply7FIN

18.03.2023

# Load packages # Core library(tidyverse) library(tidyquant) Goal Visualize and compare skewness of your portfolio and its assets. Choose your stocks. from 2012-12-31 to 2017-12-31 1 Import stock prices symbols <- c("JNJ", "UNH", "HD", "GOOG", "NVDA") prices <- tq_get(x = symbols, get = "stock.prices", ...

750 sym R (2736 sym/14 pcs) 1 img

CodeAlong7DAT

18.03.2023

Tidy data Pivoting Long to wide form table4_long <- table4a %>% pivot_longer(cols = c(`1999`, `2000`), names_to = "Year", values_to = "Cases") ###Wide to long form table4_long %>% pivot_wider(names_from = Year, values_from = Cases) ## # A tibble: 3 × 3 ## country `1999` `2000` ## <chr> <dbl> <dbl> ## 1 Afghanistan ...

168 sym Python (3535 sym/19 pcs)

CodeAlong6Ch7

06.03.2023

# Load packages # Core library(tidyverse) library(tidyquant) Goal Collect individual returns into a portfolio by assigning a weight to each stock five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG” from 2012-12-31 to 2017-12-31 1 Import stock prices symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG") prices <- tq_get(x = symbo...

360 sym R (4471 sym/21 pcs) 2 img

apply6fin

06.03.2023

# Load packages # Core library(tidyverse) library(tidyquant) Goal Visualize expected returns and risk to make it easier to compare the performance of multiple assets and portfolios. Choose your stocks. from 2012-12-31 to 2017-12-31 1 Import stock prices symbols <- c("AAPL", "TSLA", "AMZN", "EEM", "AGG") prices <- tq_get(x = symbols, ...

1126 sym R (4480 sym/21 pcs) 2 img

CodeAlong6DAT

03.03.2023

Introduction Questions Variation Visualizing Distributions diamonds %>% ggplot(aes(x = cut)) + geom_bar() diamonds %>% ggplot(mapping = aes(x = carat)) + geom_histogram(binwidth = 0.5) diamonds %>% filter(carat < 3) %>% ggplot(aes(x = carat)) + geom_histogram(binwidth = 0.5) diamonds %>% ggplot(aes(x ...

282 sym R (1769 sym/16 pcs) 14 img