Code for Session 1 Sup

Note that the directories used to store data are likely different on your computer, and such references will need to be changed before using any such code.

library(knitr)
library(kableExtra)

#' Pretty formatted HTML tables
#' 
#' @param df The dataframe to format as a table
#' @param names Names to use for columns if not using the df column names. Vector
#' @param highlight_cols Columns to highlight. Default is c(1) for highlighting the first row. Use c() to remove.
#' @param highlight_rows Rows to highlight. Default is c() to not highlight any rows.
#' @param color_rows Rows to highlight in color. Default is c() to not highlight any rows.
#' @param highlight_end_rows Rows to highlight. Uses a reverse indexing where 1 is the last row, 2 is second to last, etc.
#' @param full Use to span the full width of a slide. FALSE by default.
#' @param fixed_header Whether the first row should be treated as a header row. TRUE by default.
html_df <- function(df, names=NULL, highlight_cols=c(1), highlight_rows=c(), color_rows=c(), highlight_end_rows=c(), full=F, fixed_header=T) {
  if(!length(names)) {
    names=colnames(df)
  }
  kbl(df,"html", col.names = names, align = c("l",rep('c',length(cols)-1))) %>%
    kable_paper(c("striped","hover"), full_width=full, fixed_thead=F, html_font = "\"Source Sans Pro\", Helvetica, sans-serif") %>%
    {if(length(highlight_cols)) column_spec(., highlight_cols,bold=T, background = "#ffffff99") else .} %>%
    {if(length(highlight_rows)) row_spec(., highlight_rows,bold=T, background = "#ffffff99") else .} %>%
    {if(length(color_rows)) row_spec(., color_rows,bold=T, background = "#aaaaff99") else .} %>%
    {if(length(highlight_end_rows)) row_spec(., nrow(df) + 1 - highlight_end_rows,bold=T, background = "#ffffff99") else .}
}

library(tidyverse)

company <- c("Google", "Microsoft", "Goldman")
company

[1] "Google"    "Microsoft" "Goldman"  

tech_firm <- c(TRUE, TRUE, FALSE)
tech_firm

[1]  TRUE  TRUE FALSE

earnings <- c(12662, 21204, 4286)
earnings

[1] 12662 21204  4286

1:5

[1] 1 2 3 4 5

seq(from=0, to=100, by=5)

 [1]   0   5  10  15  20  25  30  35  40  45  50  55  60  65  70  75  80  85  90
[20]  95 100

rep(1,times=10)

 [1] 1 1 1 1 1 1 1 1 1 1

rep("hi",times=5)

[1] "hi" "hi" "hi" "hi" "hi"

earnings  # previously defined

[1] 12662 21204  4286

earnings + earnings  # Add element-wise

[1] 25324 42408  8572

earnings * earnings  # multiply element-wise

[1] 160326244 449609616  18369796

earnings + 10000  # Adding a scalar to a vector

[1] 22662 31204 14286

10000 + earnings  # Order doesn't matter

[1] 22662 31204 14286

earnings / 1000  # Dividing a vector by a scalar

[1] 12.662 21.204  4.286

# Dot product: sum of product of elements
earnings %*% earnings  # returns a matrix though...

          [,1]
[1,] 628305656

drop(earnings %*% earnings)  # Drop drops excess dimensions

[1] 628305656

length(earnings)  # returns the number of elements

[1] 3

sum(earnings)  # returns the sum of all elements

[1] 38152

earnings

[1] 12662 21204  4286

names(earnings) <- c("Google",
                     "Microsoft",
                     "Goldman")
earnings

   Google Microsoft   Goldman 
    12662     21204      4286 

# Equivalently:
names(earnings) <- company
earnings

   Google Microsoft   Goldman 
    12662     21204      4286 

earnings[1]

Google 
 12662 

earnings["Google"]

Google 
 12662 

# Each of the above 3 is equivalent
earnings[1:2]

   Google Microsoft 
    12662     21204 

c1 <- c(1,2,3)
c2 <- c(4,5,6)
c3 <- c(c1,c2)
c3

[1] 1 2 3 4 5 6

library(tidyverse)
df <- read.csv("../../Data/Session_1-2.csv")
# Filter to firms with at least $1M USD revenue, known net income, and fiscal year of 2017
clean_df <- df %>% filter(fyear==2017, !is.na(revt), !is.na(ni), revt > 1)
tech_df <- clean_df %>%
  filter(gsector==45) %>%
  mutate(revenue = revt,
         earnings = ni,
         margin = ni/revt)
earnings_2017 <- tech_df$ni
revenue_2017 <- tech_df$revt
names_2017 <- tech_df$conm
names(earnings_2017) <- names_2017
names(revenue_2017) <- names_2017

# Calculating proit margin for all public US tech firms
# 715 tech firms with >1M sales in 2017
summary(earnings_2017)  # Cleaned data from Compustat, in $M USD

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-4307.49   -15.98     1.84   296.84    91.36 48351.00 

summary(revenue_2017)  # Cleaned data from Compustat, in $M USD

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
     1.06    102.62    397.57   3023.78   1531.59 229234.00 

profit_margin <- earnings_2017 / revenue_2017
summary(profit_margin)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-13.97960  -0.10253   0.01353  -0.10967   0.09295   1.02655 

# These are the worst, midpoint, and best profit margin firms in 2017. Our names carried over :)
profit_margin[order(profit_margin)][c(1,length(profit_margin)/2,length(profit_margin))]

HELIOS AND MATHESON ANALYTIC                   NLIGHT INC 
                -13.97960161                   0.01325588 
           CCUR HOLDINGS INC 
                  1.02654899 

columns <- c("Google", "Microsoft", "Goldman")
rows <- c("Earnings","Revenue")

# equivalent: matrix(data=c(12662, 21204, 4286, 110855, 89950, 42254),ncol=3)
firm_data <- matrix(data=c(12662, 21204, 4286, 110855, 89950, 42254),nrow=2)
firm_data

      [,1]   [,2]  [,3]
[1,] 12662   4286 89950
[2,] 21204 110855 42254

firm_data + firm_data

      [,1]   [,2]   [,3]
[1,] 25324   8572 179900
[2,] 42408 221710  84508

firm_data / 1000

       [,1]    [,2]   [,3]
[1,] 12.662   4.286 89.950
[2,] 21.204 110.855 42.254

firm_data_T <- t(firm_data)
firm_data_T

      [,1]   [,2]
[1,] 12662  21204
[2,]  4286 110855
[3,] 89950  42254

firm_data %*% firm_data_T

           [,1]        [,2]
[1,] 8269698540  4544356878
[2,] 4544356878 14523841157

rownames(firm_data) <- rows
colnames(firm_data) <- columns
firm_data

         Google Microsoft Goldman
Earnings  12662      4286   89950
Revenue   21204    110855   42254

firm_data[2,3]

[1] 42254

firm_data[,c("Google","Microsoft")]

         Google Microsoft
Earnings  12662      4286
Revenue   21204    110855

firm_data[1,]

   Google Microsoft   Goldman 
    12662      4286     89950 

indcode <- c(45,45,40)
jpdata <- c(17370, 115475)
# Preloaded: industry codes as indcode (vector)

# Preloaded: industry codes as indcode (vector)
#     - GICS codes: 40=Financials, 45=Information Technology
#     - See: https://en.wikipedia.org/wiki/Global_Industry_Classification_Standard
# Preloaded: JPMorgan data as jpdata (vector)

mat <- rbind(firm_data,indcode)  # Add a row
rownames(mat)[3] <- "Industry"  # Name the new row
mat

         Google Microsoft Goldman
Earnings  12662      4286   89950
Revenue   21204    110855   42254
Industry     45        45      40

mat <- cbind(firm_data,jpdata)  # Add a column
colnames(mat)[4] <- "JPMorgan"  # Name the new column
mat

         Google Microsoft Goldman JPMorgan
Earnings  12662      4286   89950    17370
Revenue   21204    110855   42254   115475

# Ignore this code for now...
model <- summary(lm(earnings ~ revenue, data=tech_df))
#Note that this function is hiding something...
model

Call:
lm(formula = earnings ~ revenue, data = tech_df)

Residuals:
     Min       1Q   Median       3Q      Max 
-16045.0     20.0    141.6    177.1  12104.6 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.837e+02  4.491e+01  -4.091 4.79e-05 ***
revenue      1.589e-01  3.564e-03  44.585  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1166 on 713 degrees of freedom
Multiple R-squared:  0.736, Adjusted R-squared:  0.7356 
F-statistic:  1988 on 1 and 713 DF,  p-value: < 2.2e-16

model["r.squared"]

$r.squared
[1] 0.7360059

model[["r.squared"]]

[1] 0.7360059

model$r.squared

[1] 0.7360059

earnings["Google"]

Google 
 12662 

earnings[["Google"]]

[1] 12662

#Can't use $ with vectors

str(model)

List of 11
 $ call         : language lm(formula = earnings ~ revenue, data = tech_df)
 $ terms        :Classes 'terms', 'formula'  language earnings ~ revenue
  .. ..- attr(*, "variables")= language list(earnings, revenue)
  .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:2] "earnings" "revenue"
  .. .. .. ..$ : chr "revenue"
  .. ..- attr(*, "term.labels")= chr "revenue"
  .. ..- attr(*, "order")= int 1
  .. ..- attr(*, "intercept")= int 1
  .. ..- attr(*, "response")= int 1
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. ..- attr(*, "predvars")= language list(earnings, revenue)
  .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. ..- attr(*, "names")= chr [1:2] "earnings" "revenue"
 $ residuals    : Named num [1:715] -59.7 173.8 -620.2 586.7 613.6 ...
  ..- attr(*, "names")= chr [1:715] "1" "2" "3" "4" ...
 $ coefficients : num [1:2, 1:4] -1.84e+02 1.59e-01 4.49e+01 3.56e-03 -4.09 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "(Intercept)" "revenue"
  .. ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
 $ aliased      : Named logi [1:2] FALSE FALSE
  ..- attr(*, "names")= chr [1:2] "(Intercept)" "revenue"
 $ sigma        : num 1166
 $ df           : int [1:3] 2 713 2
 $ r.squared    : num 0.736
 $ adj.r.squared: num 0.736
 $ fstatistic   : Named num [1:3] 1988 1 713
  ..- attr(*, "names")= chr [1:3] "value" "numdf" "dendf"
 $ cov.unscaled : num [1:2, 1:2] 1.48e-03 -2.83e-08 -2.83e-08 9.35e-12
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "(Intercept)" "revenue"
  .. ..$ : chr [1:2] "(Intercept)" "revenue"
 - attr(*, "class")= chr "summary.lm"

library(DT) # This library is great for including larger collections of data in output
options(DT.options = list(scrollY="450px"))

# The DT library is great for including larger collections of data in output
DT::datatable(tech_df[1:20,c("conm","tic","margin")], rownames=FALSE)

df <- data.frame(companyName=company,
                 earnings=earnings,
                 tech_firm=tech_firm)
df

          companyName earnings tech_firm
Google         Google    12662      TRUE
Microsoft   Microsoft    21204      TRUE
Goldman       Goldman     4286     FALSE

df[,1]

[1] "Google"    "Microsoft" "Goldman"  

df$companyName

[1] "Google"    "Microsoft" "Goldman"  

df[[1]]

[1] "Google"    "Microsoft" "Goldman"  

df$all_zero <- 0
df$revenue <- c(110855, 89950, 42254)
df$margin <- df$earnings / df$revenue
# Custom function for small tables -- see last slide for code
html_df(df)

	companyName	earnings	tech_firm	all_zero	revenue	margin
Google	Google	12662	TRUE	0	110855	0.1142213
Microsoft	Microsoft	21204	TRUE	0	89950	0.2357310
Goldman	Goldman	4286	FALSE	0	42254	0.1014342

sort(df$earnings)

[1]  4286 12662 21204

ordering <- order(df$earnings)
ordering

[1] 3 1 2

df <- df[ordering,]
df

          companyName earnings tech_firm all_zero revenue    margin
Goldman       Goldman     4286     FALSE        0   42254 0.1014342
Google         Google    12662      TRUE        0  110855 0.1142213
Microsoft   Microsoft    21204      TRUE        0   89950 0.2357310

# Example of multicolumn sorting:
example <- data.frame(firm=c("Google","Microsoft","Google","Microsoft"),
                      year=c(2017,2017,2016,2016))
example

       firm year
1    Google 2017
2 Microsoft 2017
3    Google 2016
4 Microsoft 2016

# with() allows us to avoiding prepending each column with "example$"
ordering <- order(example$firm, example$year)
example <- example[ordering,]
example

       firm year
3    Google 2016
1    Google 2017
4 Microsoft 2016
2 Microsoft 2017

df[df$tech_firm,]  # Remember the comma!

          companyName earnings tech_firm all_zero revenue    margin
Google         Google    12662      TRUE        0  110855 0.1142213
Microsoft   Microsoft    21204      TRUE        0   89950 0.2357310

subset(df,earnings < 20000)

        companyName earnings tech_firm all_zero revenue    margin
Goldman     Goldman     4286     FALSE        0   42254 0.1014342
Google       Google    12662      TRUE        0  110855 0.1142213

df$earnings

[1]  4286 12662 21204

df$earnings < 20000

[1]  TRUE  TRUE FALSE

sum(tech_df$revenue > 10000)

[1] 46

sum(tech_df$revenue > 10000 & tech_df$earnings < 0)

[1] 4

columns <- c("conm","tic","earnings","revenue")
tech_df[tech_df$revenue > 10000 & tech_df$earnings < 0, columns]

                            conm   tic  earnings  revenue
35                   CORNING INC   GLW  -497.000 10116.00
45  TELEFONAKTIEBOLAGET LM ERICS  ERIC -4307.493 24629.64
120        DELL TECHNOLOGIES INC 7732B -3728.000 78660.00
214                   NOKIA CORP   NOK -1796.087 27917.49

# Outputs odd for odd numbers and even for even numbers
even <- rep("even",5)
odd <- rep("odd",5)
numbers <- 1:5
ifelse(numbers %% 2, odd, even)

[1] "odd"  "even" "odd"  "even" "odd" 

i = 0
while(i < 5) {
  print(i)
  i = i + 2
}

[1] 0
[1] 2
[1] 4

for(i in c(0,2,4)) {
  print(i)
}

[1] 0
[1] 2
[1] 4

# Profit margin, all US tech firms
start <- Sys.time()
margin_1 <- rep(0,length(tech_df$ni))
for(i in seq_along(tech_df$ni)) {
  margin_1[i] <- tech_df$earnings[i] /
                 tech_df$revenue[i]
}
end <- Sys.time()
time_1 <- end - start
time_1

Time difference of 0.01099896 secs

# Profit margin, all US tech firms
start <- Sys.time()
margin_2 <- tech_df$earnings /
            tech_df$revenue
end <- Sys.time()
time_2 <- end - start
time_2

Time difference of 0.001000881 secs

identical(margin_1, margin_2)  # Are these calculations identical?  Yes they are.

[1] TRUE

paste(as.numeric(time_1) / as.numeric(time_2), "times") # How much slower is the loop?

[1] "10.9892806098142 times"

args(data.frame)

function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, 
    fix.empty.names = TRUE, stringsAsFactors = FALSE) 
NULL

args(data.frame)

function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, 
    fix.empty.names = TRUE, stringsAsFactors = FALSE) 
NULL

library(tidyverse)
library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

plot <- tech_df %>%
  subset(revenue > 10000) %>%
  ggplot(aes(x=revenue,y=earnings)) + # ggplot comes from ggplot2, part of tidyverse
  geom_point(shape=1, aes(text=sprintf("Ticker: %s", tic)))  # Adds point, and ticker

Warning: Ignoring unknown aesthetics: text

ggplotly(plot)  # Makes the plot interactive

library(tidyverse)
library(plotly)

plot <- ggplot(subset(tech_df, revenue > 10000), aes(x=revenue,y=earnings)) +
  geom_point(shape=1, aes(text=sprintf("Ticker: %s", tic)))

Warning: Ignoring unknown aesthetics: text

ggplotly(plot)  # Makes the plot interactive

vector = c(-2,-1,0,1,2)
sum(vector)

[1] 0

abs(vector)

[1] 2 1 0 1 2

sign(vector)

[1] -1 -1  0  1  1

quantile(tech_df$earnings)

        0%        25%        50%        75%       100% 
-4307.4930   -15.9765     1.8370    91.3550 48351.0000 

range(tech_df$earnings)

[1] -4307.493 48351.000

add_two <- function(n) {
  n + 2
}

add_two(500)

[1] 502

mult_together <- function(n1, n2=0, square=FALSE) {
  if (!square) {
    n1 * n2
  } else {
    n1 * n1
  }
}

mult_together(5,6)

[1] 30

mult_together(5,6,square=TRUE)

[1] 25

mult_together(5,square=TRUE)

[1] 25