Publications by Name
FINApply11
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 ...
530 sym 4 tbl
CodeAlong10FIN
# Load packages # Core library(tidyverse) library(tidyquant) 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 = symbols, get = "stock.prices", from = "2012-12-31"...
387 sym R (4728 sym/19 pcs) 2 img
Apply10FIN
# Load packages # Core library(tidyverse) library(tidyquant) Goal Calculate and visualize your portfolio’s beta. Choose your stocks and the baseline market. from 2012-12-31 to present 1 Import stock prices symbols <- c("AAPL", "META", "MSFT", "TSLA", "AMZN") prices <- tq_get(x = symbols, get = "stock.prices", ...
906 sym R (4162 sym/18 pcs) 1 img
CodeAlong10DAT
Chapter 15 Factors Creating Factors General social survey gss_cat ## # A tibble: 21,483 × 9 ## year marital age race rincome partyid relig denom tvhours ## <int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int> ## 1 2000 Never married 26 White $8000 to 9999 Ind,near … Prot… Sout… ...
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Apply10DAT
Import your data myData <- read_excel("../00_data/Salaries.xlsx") myData ## # A tibble: 397 × 6 ## rank discipline yrs.since.phd yrs.service sex salary ## <chr> <chr> <dbl> <dbl> <chr> <dbl> ## 1 Prof B 19 18 Male 139750 ## 2 Prof B 20 ...
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CodeAlong9FIN
# Load packages # Core library(tidyverse) library(tidyquant) 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 = symbols, get = "stock.prices", from = "2012-12-31"...
370 sym R (4976 sym/18 pcs) 3 img
Apply9FIN
# Load packages # Core library(tidyverse) library(tidyquant) Goal Visualize and examine changes in the underlying trend in the performance of your portfolio in terms of Sharpe Ratio. Choose your stocks. from 2012-12-31 to present 1 Import stock prices symbols <- c("AAPL", "MSFT", "JNJ", "MMM", "AGG") prices <- tq_get(x = symbols, ...
1037 sym R (3533 sym/16 pcs) 1 img
Apply9DAT
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 ...
112 sym
CodeAlong9
Introduction String basics chac_data <- "I'm 'very' hungry." stringr::str_length("I am Hungry") ## [1] 11 stringr::str_c(c("I", " am"), collapse = "") ## [1] "I am" stringr::str_c("I ", "am", sep = ";") ## [1] "I ;am" str_sort(c("John", "Mary", "Aaron")) ## [1] "Aaron" "John" "Mary" Matching Patterns with regular expressions flights %>% glimps...
363 sym
CodeAlong8DAT
# Load packages # Core library(tidyverse) library(tidyquant) 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 ...
652 sym R (3734 sym/16 pcs) 3 img