Create Multiple Graphs from One Dataframe - R

Question

I am trying to create a workflow that collects information (stock ticker data, 30 different tickers, with three different prices associated with an individual ticker) from a website, cleans the data (adds a date column relative to the day the information was collected), pushes it into a master file tsibble dataframe that holds new data points everyday and then graphs the price ranges on individual plots compiled onto one page.

Example df for one day below to be pushed into master df to hold all the data:

df <- data.frame(ticker = c("XLU", "XLK", "XLF", "XLE", "XLP"),
             buy_price = c(62.00, 68.00, 37.00, 55.00, 41.00),
             sale_price = c(64.00, 71.00, 42.00, 60.00, 45.00),
             close_price = c(63.00, 70.00, 38.00, 56.00, 43.00),
             date = c("April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021", "April 29th, 2021"))

Second day of data:

df2 <- data.frame(ticker = c("XLU", "XLK", "XLF", "XLE", "XLP"),
                 buy_price = c(63.00, 69.00, 38.00, 53.00, 44.00),
                 sale_price = c(66.00, 77.00, 47.00, 63.00, 48.00),
                 close_price = c(65.00, 74.00, 39.00, 55.00, 45.00),
                 date = c("April 30th, 2021", "April 30th, 2021", "April 30th, 2021", "April 30th, 2021", "April 30th, 2021"))

DF Master file: rbind(df, df2)

       ticker buy_price sale_price close_price             date
1     XLU        62         64          63 April 29th, 2021
2     XLK        68         71          70 April 29th, 2021
3     XLF        37         42          38 April 29th, 2021
4     XLE        55         60          56 April 29th, 2021
5     XLP        41         45          43 April 29th, 2021
6     XLU        63         66          65 April 30th, 2021
7     XLK        69         77          74 April 30th, 2021
8     XLF        38         47          39 April 30th, 2021
9     XLE        53         63          55 April 30th, 2021
10    XLP        44         48          45 April 30th, 2021

I had used facet_wrap_paginate to facet by stock ticker name, and create multiple graphs. However, I do not have the fine control over the axes and individual plots that I need when using a facet, so I must use an approach of plotting each ticker individually and compiling onto the same pages. I had used the code below:

for(i in 1:4){
    rr_plot <- ggplot(rr_tsibble, aes(x = DATE, color = TREND)) +
    geom_point(aes(y = BUY.TRADE), size = 1.5) +
    geom_point(aes(y = SELL.TRADE), size = 1.5) +
    geom_point(aes(y = PREV.CLOSE), color = "black", size = 1, shape = 1) +
    ggforce::facet_wrap_paginate(~TICKER,
                                 nrow = 2,
                                 ncol = 4,
                                 scales = "free_y",
                                 page = i) +
    scale_y_continuous()
    print(rr_plot)

to achieve this. The original datafram has ~30 induvidual tickers with the same 30 added to the df the next day, and then 30 more. I have tried using dplyr to group_by and plot, although I haven't been to acheive desired results. I do not think that creating 30 plots manually with ggplot2 is very efficent, there must be a for loop that can allow for the selection of only certain tickers to then plot all of the data and use cowplot and extraGrid to compile all 30 generated plots. Any help or thoughts on how to accomplish this would be great! Thanks!

Answer 1

Generated some random data with some 30 random tickers across 4 days:

r <- function() {abs(c(rnorm(29,50,2),100000)*rnorm(1,10,1))}
tickers = sapply(1:30, function(x) toupper(paste0(sample(letters, 3), collapse = "")))
df <- data.frame(ticker = tickers,
                 buy_price = r(),
                 sale_price = r(),
                 close_price = r(),
                 date = rep("April 29th, 2021",30))
df2 <- data.frame(ticker = tickers,
                  buy_price = r(),
                  sale_price = r(),
                  close_price = r(),
                  date = rep("April 30th, 2021",30))
df3 <- data.frame(ticker = tickers,
                  buy_price = r(),
                  sale_price = r(),
                  close_price = r(),
                  date = rep("May 1st, 2021",30))
df4 <- data.frame(ticker = tickers,
                  buy_price = r(),
                  sale_price = r(),
                  close_price = r(),
                  date = rep("May 2nd, 2021",30))
rr_tsibble <- rbind(df, df2, df3, df4)

Converted date to date format:

rr_tsibble$date = as.Date(gsub("st|th|nd","",rr_tsibble$date), "%b %d, %Y")

Add the addUnits() function for formatting the large numbers:

addUnits <- function(n) {
  labels <- ifelse(n < 1000, n,  # less than thousands
                   ifelse(n < 1e6, paste0(round(n/1e3,3), 'k'),  # in thousands
                          ifelse(n < 1e9, paste0(round(n/1e6,3), 'M'),  # in millions
                                 ifelse(n < 1e12, paste0(round(n/1e9), 'B'), # in billions
                                        ifelse(n < 1e15, paste0(round(n/1e12), 'T'), # in trillions
                                               'too big!'
                                        )))))}

Make the list of plots:

plotlist <- list()
for (i in 1:ceiling(30/8))
{
  plotlist[[i]] <- ggplot(rr_tsibble, aes(x = date)) +
    geom_point(aes(y = buy_price), size = 1.5) +
    geom_point(aes(y = sale_price), size = 1.5) +
    geom_point(aes(y = close_price), color = "black", size = 1, shape = 1) +
    scale_y_continuous(breaks = pretty_breaks(), labels = addUnits) +
    ggforce::facet_wrap_paginate(~ticker,
                                 nrow = 2,
                                 ncol = 4,
                                 scales = "free_y",
                                 page = i)
}

There are 4 pages in total, each stored as an element of plotlist list. For example, the final page is the 4th element, and looks like this:

plotlist[[4]]