In a previous post, we explored the dividend history of stocks included in the SP500. Today we’ll extend that anlaysis to cover the Nasdaq because, well, because in the previous post I said I would do that. We’ll also explore a different source for dividend data, do some string cleaning and check out ways to customize a tooltip in
plotly. Bonus feature: we’ll get into some animation too. We have a lot to cover, so let’s get to it.
We need to load up our packages for the day.
library(tidyverse) library(tidyquant) library(janitor) library(plotly)
First we need all the companies listed on the Nasdaq. Not so long ago, it wasn’t easy to import that information into R. Now we can use
tq_exchange("NASDAQ") from the
nasdaq <- tq_exchange("NASDAQ") nasdaq %>% head()
# A tibble: 6 x 7 symbol company last.sale.price market.cap ipo.year sector industry <chr> <chr> <dbl> <chr> <dbl> <chr> <chr> 1 YI 111, Inc. 2.69 $219.66M 2018 Health… Medical/N… 2 PIH 1347 Prope… 4.85 $29.16M 2014 Finance Property-… 3 PIHPP 1347 Prope… 25.7 $17.96M NA Finance Property-… 4 TURN 180 Degree… 2.09 $65.04M NA Finance Finance/I… 5 FLWS 1-800 FLOW… 15.3 $983.42M 1999 Consum… Other Spe… 6 BCOW 1895 Banco… 9.33 $45.5M 2019 Finance Banks
Notice how the
market.cap column is of type character? Let’s coerce it to a
as.numeric and while we’re at it, let’s remove the periods in all the column numes with
clean_names from the
nasdaq %>% clean_names() %>% mutate(market_cap = as.numeric(market_cap)) %>% select(symbol, market_cap) %>% head()
# A tibble: 6 x 2 symbol market_cap <chr> <dbl> 1 YI NA 2 PIH NA 3 PIHPP NA 4 TURN NA 5 FLWS NA 6 BCOW NA
Not exactly what we had in mind. The presence of those
$ characters are causing
as.numeric() to coerce the column to NAs. If we want to do any sorting by market cap, we’ll need to clean that up and it’s a great chance to explore some
stringr. Let’s start with
str_remove_all and remove those non-numeric characters. The call is
market_cap %>% str_remove_all("\\$|M|B"), and then an
arrange(desc(market_cap)) so that the largest cap company is first.
nasdaq %>% clean_names() %>% mutate(market_cap = market_cap %>% str_remove_all("\\$|M|B") %>% as.numeric()) %>% arrange(desc(market_cap)) %>% head()
# A tibble: 6 x 7 symbol company last_sale_price market_cap ipo_year sector industry <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> 1 CIVBP Civista B… 66 667920 NA Finance Major Banks 2 ASRVP AmeriServ… 29.6 621600 NA Finance Major Banks 3 ESGRP Enstar Gr… 26.6 425920 NA Finance Property-C… 4 AGNCN AGNC Inve… 26.0 337870 NA Consum… Real Estat… 5 SBFGP SB Financ… 15.8 237221. NA Finance Major Banks 6 ESGRO Enstar Gr… 26.4 115984 NA Finance Property-C…
Well, that wasn’t too bad!
Wait, that looks weird, where’s AMZN and MSFT shouldn’t they be at the top of the market cap? Look closely at
market_cap and notice it’s been coerced to a numeric value as we intended but we didn’t account for the fact that those
B letters were abbreviating values and standing place for a whole bunch of zeroes. The first symbol above,
CIVBP, didn’t have an
B because it’s market cap is low, so it didn’t have any zeroes lopped off of it.
We need a way to remove the
M and the
B account for those zeroes that got removed. Here’s how I chose to tackle this.
- Find all the cells that do not have an
B, remove the
$sign, convert to numeric and divide by 1000. We do that with
if_else(str_detect(market_cap, "M|B", negate = TRUE), str_remove_all(market_cap, "\\$") %>% as.numeric() %>%/
- Find all the cells that have a
B, remove the
$sign, convert to numeric and multiply by 1000. We do that with
if_else(str_detect(market_cap, "B"), str_remove_all(market_cap, "\\$|B") %>% as.numeric() %>%*
- Find all the cells that have an
M, remove the
$sign, convert to numeric and don’t multiply or divide. We do that with
str_remove_all(market_cap, "\\$|M") %>% as.numeric())).
Stay tuned for the next installment, in which Jonathan will continue coding this tibble in R. To download the full R script, visit his blog http://www.reproduciblefinance.com/2019/08/25/tech-dividends/ .
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