Financial Econometrics Practical

Transcription

1 Financial Econometrics Practical Practical 3: Plotting in R NF Katzke Table of Contents 1 Introduction Install ggplot Get data Tidy Plotting Tidy data Introduction The aim of this tutorial is to introduce you to tidy analysis in R - which is essential for making plots easily and nicely # Plotting in R While there are many and extremely diverse packages that can be used for plotting purposes in R - the one I use most is undoubtedly ggplot2. For dynamic plotting and financial series plots, there are also packages like dygraphs and plotly. Here follows a very basic, high level view of ggplot2 plotting functionality. I suggest supplementing this tutorial by reading this post with examples, and saving or printing out this ggplot cheatsheet. 1

2 1.0.1 Install ggplot2 First things first, let s ensure ggplot2 is correctly installed on our machines: if (!require("devtools")) install.packages("devtools") if (!require("rmsfuns")) devtools::install_github("nicktz/rmsfuns") library(rmsfuns) load_pkg("ggplot2") 1.1 Get data Tidy Very important: ggplot2 wants your data to be in a tidy format. Basically (and I suggest reading Hadley s Tidy data paper), tidy data is summarised by Hadley Wickham as follows: Each variable has a column Each observation has its own row Each type of observation then forms a table The following is a perfect example of tidy data: pkgstoload <- c("lubridate", "tidyverse") load_pkg(pkgstoload) # Create some real looking fake data: data <- bind_rows( bind_rows( data.frame( Financial Econometrics (NF KATZKE) Page 2

3 date = ymd( ), Universe = "JALSH", Tickers = rep(paste0(c("aaa", "BBB", "CCC", "DDD", "EEE", "FFF"), " SJ Equity"), 6), SubFactors = rep(c("roe", "ROA", "EBIT.EV", "FCF.EV", "Volat.D.60", "TRR6M1M"),each = 6), Sectors = rep(c("fin", "Ind", "Cons", "Fin", "Fin", "Ind"), 6), Currencies = "ZAR", Score = rnorm(36, 13, 4)) %>% tbl_df() %>% mutate(date = ymd(date)) %>% mutate_at(.vars = vars(-date, -Score),.funs = funs(as.character)), data.frame(date = ymd( ), Universe = "SPGLOB", Tickers = rep(paste0(c("ttt", "UUU", "VVV", "XXX", "YYY", "ZZZ"), " SPGLOB Equity"), 6), SubFactors = rep(c("roe", "ROA", "EBIT.EV", "FCF.EV", "Volat.D.60", "TRR6M1M"), each = 6), Sectors = rep(c("fin", "Ind", "Cons", "Fin", "Fin", "Ind"), 6), Currencies = "Dollar", Score = rnorm(36)) %>% tbl_df() %>% mutate(date = ymd(date)) %>% mutate_at(.vars = vars(-date, -Score),.funs = funs(./lag(.) - 1))), bind_rows( data.frame( date = ymd( ), Universe = "JALSH", Tickers = rep(paste0(c("aaa", "BBB", "CCC", "DDD", "EEE", "FFF"), " SJ Equity"), 6), SubFactors = rep(c("roe", "ROA", "EBIT.EV", "FCF.EV", "Volat.D.60", "TRR6M1M"),each = 6), Sectors = rep(c("fin", "Ind", "Cons", "Fin", "Fin", "Ind"), 6), Currencies = "ZAR", Score = rnorm(36, 10, 4)) %>% Financial Econometrics (NF KATZKE) Page 3

4 tbl_df() %>% mutate(date = ymd(date)) %>% mutate_at(.vars = vars(-date, -Score),.funs = funs(as.character)), data.frame(date = ymd( ), Universe = "SPGLOB", Tickers = rep(paste0(c("ttt", "UUU", "VVV", "XXX", "YYY", "ZZZ"), " SPGLOB Equity"), 6), SubFactors = rep(c("roe", "ROA", "EBIT.EV", "FCF.EV", "Volat.D.60", "TRR6M1M"), each = 6), Sectors = rep(c("fin", "Ind", "Cons", "Fin", "Fin", "Ind"), 6), Currencies = "Dollar", Score = rnorm(36)) %>% tbl_df() %>% mutate(date = ymd(date)) %>% mutate_at(.vars = vars(-date, -Score),.funs = funs(as.character)))) %>% filter(!is.na(universe)) # View(data) Notice that all the variables are in their own columns, and each observation has its own row. This is called a long format (many rows, few columns). We could also spread the data back to wide format (many columns). Let s do a little wrangling and look at what a wide format would look like, and then how to bring it back to long format: # First, let's drop sectors and currencies for this illustration, # else a whole lot of NA's are created when spreading.. (check yourself) datawide <- data %>% select(-sectors, -Currencies, -Universe) %>% spread(key = Tickers, value = Score) %>% mutate(universe = "JALSH", AnotherColumn = "Random") # Add some noise columns head(datawide) ## # A tibble: 6 x 16 Financial Econometrics (NF KATZKE) Page 4

5 ## date SubFactors `AAA SJ Equity` `BBB SJ Equity` `CCC SJ Equity` ## <date> <chr> <dbl> <dbl> <dbl> ## EBIT.EV ## FCF.EV ## ROA ## ROE ## TRR6M1M ## Volat.D ## #... with 11 more variables: `DDD SJ Equity` <dbl>, `EEE SJ ## # Equity` <dbl>, `FFF SJ Equity` <dbl>, `TTT SPGLOB Equity` <dbl>, `UUU ## # SPGLOB Equity` <dbl>, `VVV SPGLOB Equity` <dbl>, `XXX SPGLOB ## # Equity` <dbl>, `YYY SPGLOB Equity` <dbl>, `ZZZ SPGLOB Equity` <dbl>, ## # Universe <chr>, AnotherColumn <chr> Notice that in the wrangle above, all the Tickers have their own column. But as mentioned, by definition of tidyness, all columns must be a variable. As there are similarities between the wide columns (they are all Tickers) - they belong to the same column called Tickers! So we need to make the wide data tidy by gathering all the ticker columns into a single column. I specifically mutated two random columns to show you how to gather only the Ticker columns... datatidyagain <- datawide %>% gather(key = Tickers, value = Scores, contains(" Equity") ) head(datatidyagain) Note how easy that was if you know the three inputs above: key is the name column by which to distinguish the observations Financial Econometrics (NF KATZKE) Page 5

6 Observation values are given by Scores third, the columns to gather. Note I used contains(" Equity ), as all the Tickers end with Equity. Calling columns this way is useful, with other commands including: ends_with, one_of(... ), etc (see cheatsheet!). You can also pass in vectors to gather or select by - this would likely require using the package lazyeval though... (I will help with this if required as it can be complex). If we now want to focus only on, e.g., the factor ROE in order to calculate the mean ROE, we simply pipe it in dplyr (don t forget the dplyr cheatsheet): data %>% group_by(date, Universe, SubFactors) %>% filter(!is.na(score)) %>% # Filter only valid scores mutate( ZScore = (Score - mean(score, na.rm = TRUE)) / sd(score, na.rm = TRUE)) %>% ungroup() # Zscore column created ## # A tibble: 108 x 8 ## date Universe Tickers SubFactors Sectors Currencies ## <date> <chr> <chr> <chr> <chr> <chr> ## JALSH AAA SJ Equity ROE Fin ZAR ## JALSH BBB SJ Equity ROE Ind ZAR ## JALSH CCC SJ Equity ROE Cons ZAR ## JALSH DDD SJ Equity ROE Fin ZAR ## JALSH EEE SJ Equity ROE Fin ZAR ## JALSH FFF SJ Equity ROE Ind ZAR ## JALSH AAA SJ Equity ROA Fin ZAR ## JALSH BBB SJ Equity ROA Ind ZAR Financial Econometrics (NF KATZKE) Page 6

7 ## JALSH CCC SJ Equity ROA Cons ZAR ## JALSH DDD SJ Equity ROA Fin ZAR ## #... with 98 more rows, and 2 more variables: Score <dbl>, ZScore <dbl> Notice that a ZScore column has now been added to our dataframe with minimal effort. What you need to get right though (and focus with) is correctly grouping and accurately mutating. Tip: use ViewXL to check your calculation in excel if you are uncertain Plotting Tidy data To plot from a tidy format - I suggest using the powerful plotting platform ggplot2. It thinks as follows: tidy data as input aesthetic properties (is it a boxplot, lineplot, scatterplot, etc) faceting (repeating a plot type in a grid, e.g.) To plot the last tut s BRICS returns data in a line plot, e.g., do the following: # Get data: retdata <- read_csv(" load_pkg("ggthemes") # Gives you nice themes to play with... # Make data tidy: retdata <- retdata %>% gather(key = Countries, value = TRI, -Date) Financial Econometrics (NF KATZKE) Page 7

8 # Plot each country's TRI on the same plot: ggplot(data = retdata) + geom_line(aes(x = Date, y = TRI, colour = Countries)) TRI Countries brz chn ind rus zar Date # Plot each country's TRI on different plots: g1 <- ggplot(data = retdata) + geom_line(aes(x = Date, y = TRI, colour = Countries)) + facet_wrap(~countries, scales = "free") + theme_hc() # Type theme and you should see options pop up in Rstudio... Financial Econometrics (NF KATZKE) Page 8

9 print(g1) brz chn ind TRI rus zar Date Countries brz chn ind rus zar # Remove scales = free to make plots have similar scales... # To keep the plot specifications, but only plot a subset o/t data: g1 %+% subset(retdata, Date > as.date(' ')) + ggtitle("post-crisis TRI") Financial Econometrics (NF KATZKE) Page 9

10 TRI Post Crisis TRI brz chn ind rus zar Date Countries brz chn ind rus zar g1 %+% subset(retdata, Date <= as.date(' ')) + ggtitle("pre-crisis TRI") Financial Econometrics (NF KATZKE) Page 10

11 Pre Crisis TRI brz chn ind TRI rus zar Date Countries brz chn ind rus zar # How amazing was that?! Financial Econometrics (NF KATZKE) Page 11

12 Figure 1.1: Saving the plot is simple too: # Scatterplot of our created dataset earlier: g <- ggplot(data = data) + geom_boxplot(aes(x = SubFactors, y = Score, fill = SubFactors) ) print(g) # printed in Rstudio Financial Econometrics (NF KATZKE) Page 12

13 25 20 Score SubFactors EBIT.EV FCF.EV ROA ROE TRR6M1M Volat.D.60 0 EBIT.EV FCF.EV ROA ROE TRR6M1M Volat.D.60 SubFactors # Specify folder to save plot in: pathloc <- file.path("c:","practical3plot") # Specify your own location # On a mac - specify this path by hand... build_path(pathloc, Silent = F) ## [1] "C:/Practical3Plot" ggsave(filename = file.path(pathloc,"plot.png"), plot = g, width = 6, height = 6, device = "png") Financial Econometrics (NF KATZKE) Page 13

14 Play around with documentation and examples here, keep the cheatsheet close by and, of course, stackoverflow is your friend. As the course progresses, I will add plots using ggplot2. For now, take note and play with examples. You should also be able to now understand your Texevier template s graphing command (note how I added the figure s details in a function and sourced it in text). Financial Econometrics (NF KATZKE) Page 14