Steps 1-6

Load the R packages we will use.

library(tidyverse) 
library(echarts4r) #install this package before using
library(hrbrthemes) #install this package before using

Read the data in the files, drug_cos.csv, health_cos.csv in to R and assign to the variables drug_cos and health_cos, respectively

drug_cos <- read_csv("https://estanny.com/static/week6/drug_cos.csv")
health_cos <- read_csv("https://estanny.com/static/week6/health_cos.csv")

use glimpse to get a glimpse of the data

drug_cos %>% glimpse()

Rows: 104
Columns: 9
$ ticker       <chr> "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "ZTS"~
$ name         <chr> "Zoetis Inc", "Zoetis Inc", "Zoetis Inc", "Zoet~
$ location     <chr> "New Jersey; U.S.A", "New Jersey; U.S.A", "New ~
$ ebitdamargin <dbl> 0.149, 0.217, 0.222, 0.238, 0.182, 0.335, 0.366~
$ grossmargin  <dbl> 0.610, 0.640, 0.634, 0.641, 0.635, 0.659, 0.666~
$ netmargin    <dbl> 0.058, 0.101, 0.111, 0.122, 0.071, 0.168, 0.163~
$ ros          <dbl> 0.101, 0.171, 0.176, 0.195, 0.140, 0.286, 0.321~
$ roe          <dbl> 0.069, 0.113, 0.612, 0.465, 0.285, 0.587, 0.488~
$ year         <dbl> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,~

health_cos %>% glimpse()

Rows: 464
Columns: 11
$ ticker      <chr> "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "ZTS",~
$ name        <chr> "Zoetis Inc", "Zoetis Inc", "Zoetis Inc", "Zoeti~
$ revenue     <dbl> 4233000000, 4336000000, 4561000000, 4785000000, ~
$ gp          <dbl> 2581000000, 2773000000, 2892000000, 3068000000, ~
$ rnd         <dbl> 427000000, 409000000, 399000000, 396000000, 3640~
$ netincome   <dbl> 245000000, 436000000, 504000000, 583000000, 3390~
$ assets      <dbl> 5711000000, 6262000000, 6558000000, 6588000000, ~
$ liabilities <dbl> 1975000000, 2221000000, 5596000000, 5251000000, ~
$ marketcap   <dbl> NA, NA, 16345223371, 21572007994, 23860348635, 2~
$ year        <dbl> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, ~
$ industry    <chr> "Drug Manufacturers - Specialty & Generic", "Dru~

Which variables are the same in both data sets

names_drug <- drug_cos %>% names()
names_health <- health_cos %>% names()
intersect(names_drug, names_health)

[1] "ticker" "name"   "year"

Select subset of variables to work with

For drug_cos select (in this order): ticker, year, grossmargin
Extract observations for 2018
Assign output to drug_subset
For health_cos select (in this order): ticker, year, revenue, gp, industry
- Extract observations for 2018
- Assign output to health_subset

drug_subset <- drug_cos %>% 
  select(ticker, year, grossmargin) %>% 
  filter(year == 2018)

health_subset <- health_cos %>% 
  select(ticker,year, revenue,gp, industry) %>% 
  filter(year == 2018)

keep all the rows and columns drug_subset join with columns in health_subset

drug_subset %>% left_join(health_subset)

# A tibble: 13 x 6
   ticker  year grossmargin     revenue          gp industry          
   <chr>  <dbl>       <dbl>       <dbl>       <dbl> <chr>             
 1 ZTS     2018       0.672  5825000000  3914000000 Drug Manufacturer~
 2 PRGO    2018       0.387  4731700000  1831500000 Drug Manufacturer~
 3 PFE     2018       0.79  53647000000 42399000000 Drug Manufacturer~
 4 MYL     2018       0.35  11433900000  4001600000 Drug Manufacturer~
 5 MRK     2018       0.681 42294000000 28785000000 Drug Manufacturer~
 6 LLY     2018       0.738 24555700000 18125700000 Drug Manufacturer~
 7 JNJ     2018       0.668 81581000000 54490000000 Drug Manufacturer~
 8 GILD    2018       0.781 22127000000 17274000000 Drug Manufacturer~
 9 BMY     2018       0.71  22561000000 16014000000 Drug Manufacturer~
10 BIIB    2018       0.865 13452900000 11636600000 Drug Manufacturer~
11 AMGN    2018       0.827 23747000000 19646000000 Drug Manufacturer~
12 AGN     2018       0.861 15787400000 13596000000 Drug Manufacturer~
13 ABBV    2018       0.764 32753000000 25035000000 Drug Manufacturer~

Question: join_ticker

Start with drug_cos data
Extract observations for the ticker MYL from drug_cos
Assign output to the variable drug_cos_subset

drug_cos_subset <- drug_cos %>%
  filter(ticker == "MYL")

display drug_cos_subset

drug_cos_subset

# A tibble: 8 x 9
  ticker name  location ebitdamargin grossmargin netmargin   ros   roe
  <chr>  <chr> <chr>           <dbl>       <dbl>     <dbl> <dbl> <dbl>
1 MYL    Myla~ United ~        0.245       0.418     0.088 0.161 0.146
2 MYL    Myla~ United ~        0.244       0.428     0.094 0.163 0.184
3 MYL    Myla~ United ~        0.228       0.44      0.09  0.153 0.209
4 MYL    Myla~ United ~        0.242       0.457     0.12  0.169 0.283
5 MYL    Myla~ United ~        0.243       0.447     0.09  0.133 0.089
6 MYL    Myla~ United ~        0.19        0.424     0.043 0.052 0.044
7 MYL    Myla~ United ~        0.272       0.402     0.058 0.121 0.054
8 MYL    Myla~ United ~        0.258       0.35      0.031 0.074 0.028
# ... with 1 more variable: year <dbl>

Use left_join to combine the rows and columns of drug_cos_subset with the columns of health_cos
Assign the output to combo_df

combo_df <- drug_cos_subset %>% 
  left_join(health_cos)

Display combo_df

combo_df

# A tibble: 8 x 17
  ticker name  location ebitdamargin grossmargin netmargin   ros   roe
  <chr>  <chr> <chr>           <dbl>       <dbl>     <dbl> <dbl> <dbl>
1 MYL    Myla~ United ~        0.245       0.418     0.088 0.161 0.146
2 MYL    Myla~ United ~        0.244       0.428     0.094 0.163 0.184
3 MYL    Myla~ United ~        0.228       0.44      0.09  0.153 0.209
4 MYL    Myla~ United ~        0.242       0.457     0.12  0.169 0.283
5 MYL    Myla~ United ~        0.243       0.447     0.09  0.133 0.089
6 MYL    Myla~ United ~        0.19        0.424     0.043 0.052 0.044
7 MYL    Myla~ United ~        0.272       0.402     0.058 0.121 0.054
8 MYL    Myla~ United ~        0.258       0.35      0.031 0.074 0.028
# ... with 9 more variables: year <dbl>, revenue <dbl>, gp <dbl>,
#   rnd <dbl>, netincome <dbl>, assets <dbl>, liabilities <dbl>,
#   marketcap <dbl>, industry <chr>

Note: the variables ticker, name, location, and industry are the same for all the observations

Assign the company name to co_name

co_name <- combo_df %>% 
  distinct(name) %>% 
  pull()

*Assign the company location to co_location

co_location <- combo_df %>%
  distinct(location) %>% 
  pull()

Assign the industry to co_industry group

co_industry <- combo_df %>% 
  distinct(industry) %>% 
  pull()

Put the r inline commands used in the blanks below. When you knit the document the results of the commands will be displayed in your text.

The company MYL is located in United Kingdom and is a member of the Drug Manufacturers - Specialty & Generic industry group.

Start with combo_df
Select variables (in this order): year, grossmargin, netmargin, revenue, gp, netincome
Assign the output to combo_df_subset

combo_df_subset <- combo_df %>% 
  select(year, grossmargin, netmargin, revenue, gp, netincome)

Display combo_df_subset

combo_df_subset

# A tibble: 8 x 6
   year grossmargin netmargin     revenue         gp netincome
  <dbl>       <dbl>     <dbl>       <dbl>      <dbl>     <dbl>
1  2011       0.418     0.088  6129825000 2563364000 536810000
2  2012       0.428     0.094  6796100000 2908300000 640900000
3  2013       0.44      0.09   6909100000 3040300000 623700000
4  2014       0.457     0.12   7719600000 3528000000 929400000
5  2015       0.447     0.09   9429300000 4216100000 847600000
6  2016       0.424     0.043 11076900000 4697000000 480000000
7  2017       0.402     0.058 11907700000 4783100000 696000000
8  2018       0.35      0.031 11433900000 4001600000 352500000

Create the variable frossmargin_check to compare with the variable grossmargin. They should be equal.
- grossmargin_check = gp/revenue
Create the variable close_enough to check that the absolute value of the difference between grossmargin_check and grossmargin is less that 0.001

combo_df_subset %>% 
  mutate(grossmargin_check = gp/revenue, close_enough = abs(grossmargin_check - grossmargin)< 0.001)

# A tibble: 8 x 8
   year grossmargin netmargin     revenue         gp netincome
  <dbl>       <dbl>     <dbl>       <dbl>      <dbl>     <dbl>
1  2011       0.418     0.088  6129825000 2563364000 536810000
2  2012       0.428     0.094  6796100000 2908300000 640900000
3  2013       0.44      0.09   6909100000 3040300000 623700000
4  2014       0.457     0.12   7719600000 3528000000 929400000
5  2015       0.447     0.09   9429300000 4216100000 847600000
6  2016       0.424     0.043 11076900000 4697000000 480000000
7  2017       0.402     0.058 11907700000 4783100000 696000000
8  2018       0.35      0.031 11433900000 4001600000 352500000
# ... with 2 more variables: grossmargin_check <dbl>,
#   close_enough <lgl>

Create the variable netmargin_check to compare with the variable netmargin. They should be equal.
create the variable close_enough to check that the absolute value of the difference between netmargin_check and netmargin is less than 0.001

combo_df_subset %>% 
  mutate(netmargin_check = netincome / revenue,
         close_enough = abs(netmargin_check - netmargin) < 0.001)

# A tibble: 8 x 8
   year grossmargin netmargin revenue     gp netincome netmargin_check
  <dbl>       <dbl>     <dbl>   <dbl>  <dbl>     <dbl>           <dbl>
1  2011       0.418     0.088 6.13e 9 2.56e9 536810000          0.0876
2  2012       0.428     0.094 6.80e 9 2.91e9 640900000          0.0943
3  2013       0.44      0.09  6.91e 9 3.04e9 623700000          0.0903
4  2014       0.457     0.12  7.72e 9 3.53e9 929400000          0.120 
5  2015       0.447     0.09  9.43e 9 4.22e9 847600000          0.0899
6  2016       0.424     0.043 1.11e10 4.70e9 480000000          0.0433
7  2017       0.402     0.058 1.19e10 4.78e9 696000000          0.0584
8  2018       0.35      0.031 1.14e10 4.00e9 352500000          0.0308
# ... with 1 more variable: close_enough <lgl>

Question: summarize_industry

Fill in the blancs
Put the command you use in the Rchunks in the Rmd file for your quiz
Use the health_cos data
For each industry calculate
- mean_grossmargin_percent = mean(gp / revenue) * 100
- median_grossmargin_percent = median(gp / revenue) * 100
- min_grossmargin_percent = min(gp / revenue) * 100
- max_grossmargin_percent = max(gp / revenue) * 100

health_cos %>% 
  group_by(industry) %>%
  summarize(mean_grossmargin_percent = mean(gp / revenue) * 100,
            median_grossmargin_percent = median(gp / revenue) * 100,
            min_grossmargin_percent = min(gp / revenue) * 100,
            max_grossmargin_percent = max(gp / revenue) * 100)

# A tibble: 9 x 5
  industry          mean_grossmargi~ median_grossmar~ min_grossmargin~
  <chr>                        <dbl>            <dbl>            <dbl>
1 Biotechnology                 92.5            92.7             81.7 
2 Diagnostics & Re~             50.5            52.7             28.0 
3 Drug Manufacture~             75.4            76.4             36.8 
4 Drug Manufacture~             47.9            42.6             34.3 
5 Healthcare Plans              20.5            19.6             10.0 
6 Medical Care Fac~             55.9            37.4             28.1 
7 Medical Devices               70.8            72.0             53.2 
8 Medical Distribu~             10.4             5.38             2.49
9 Medical Instrume~             53.9            52.8             40.5 
# ... with 1 more variable: max_grossmargin_percent <dbl>

mean_grossmargin_percent for the industry Medical Devices is 70.8%
median_grossmargin_percent for the industry Medical Devices is 72.0%
min_grossmargin_percent for the industry Medical Devices is 53.2%
max_grossmargin_percent for the industry medical Devices is 84.7%

Question inline_ticker

Fill in the blanks
Use the health_cos data
Extract observations for the ticker ZTS from health_cos and assign to the variable health_cos_subset

health_cos_subset <- health_cos %>% 
  filter(ticker =="ZTS")

Display health_cos_subset

health_cos_subset

# A tibble: 8 x 11
  ticker name      revenue     gp    rnd netincome  assets liabilities
  <chr>  <chr>       <dbl>  <dbl>  <dbl>     <dbl>   <dbl>       <dbl>
1 ZTS    Zoetis I~  4.23e9 2.58e9 4.27e8    2.45e8 5.71e 9  1975000000
2 ZTS    Zoetis I~  4.34e9 2.77e9 4.09e8    4.36e8 6.26e 9  2221000000
3 ZTS    Zoetis I~  4.56e9 2.89e9 3.99e8    5.04e8 6.56e 9  5596000000
4 ZTS    Zoetis I~  4.78e9 3.07e9 3.96e8    5.83e8 6.59e 9  5251000000
5 ZTS    Zoetis I~  4.76e9 3.03e9 3.64e8    3.39e8 7.91e 9  6822000000
6 ZTS    Zoetis I~  4.89e9 3.22e9 3.76e8    8.21e8 7.65e 9  6150000000
7 ZTS    Zoetis I~  5.31e9 3.53e9 3.82e8    8.64e8 8.59e 9  6800000000
8 ZTS    Zoetis I~  5.82e9 3.91e9 4.32e8    1.43e9 1.08e10  8592000000
# ... with 3 more variables: marketcap <dbl>, year <dbl>,
#   industry <chr>

In the console, type ?distinct. Go to the pane to see what distinct does
In the console, type ?pull. Go to the help pane to see what pull does

Run the code below

health_cos_subset %>% 
  distinct(name) %>% 
  pull(name)

[1] "Zoetis Inc"

Assign the output to co_name

co_name <- health_cos_subset %>% 
  distinct(name) %>% 
  pull(name)

You can take output from your code and include it in your text.

The name of the company wih ticker ZTS is Zoetis Inc

In following chunk

Assign the company’s industry group to the variable co_industry

co_industry <- health_cos_subset %>% 
  distinct(industry) %>% 
  pull(industry)

This is outside Rchunk. Put the r inline commands used in the blanks below. When you knit the document the results of the commands will be displayed in your text.

The company Zoetis Inc is a member of Drug Manufacturers - Specialty & Generic group.

steps 7-11

Prepare the data for the plots

start with health_cos THEN
group_by(industry) THEN
Calculate the median research and development expenditure by industry
assign the output to df

df <- health_cos %>% 
  group_by(industry) %>% 
  summarize(med_rnd_rev = median(rnd/revenue))

Use glimpse to glimpse the data for the plots

df %>% glimpse()

Rows: 9
Columns: 2
$ industry    <chr> "Biotechnology", "Diagnostics & Research", "Drug~
$ med_rnd_rev <dbl> 0.48317287, 0.05620271, 0.17451442, 0.06851879, ~

Create a static bar chart

use ggplot to initialize the chart
data is df
the variable industry is mapped to the x-axis
- reorder it based the value of med_rnd-rev
the variable med_rnd_rev is mapped to the y-axis
add a bar chart using geom_col
use scale_y_continuous to label the y-axis with percent
use coord_flip() to flip the coordinates
use labs to add title, subtitle and remove x and y-axes
use theme_ipsum() from the hrbrthemes package to improve the theme

ggplot(data = df,
       mapping = aes(
          x = reorder(industry, med_rnd_rev),
          y = med_rnd_rev
          )) +
  geom_col() +
  scale_y_continuous(labels = scales::percent) +
  coord_flip() +
  labs( 
    title = "Median R&D espenditures",
    subtitle = "by industry as a percent of revenue from 2011 to 2018",
    x = NULL, y = NULL) +
  theme_ipsum()

Save the last plot to preview.png and add to the yaml chunk at the top

ggsave(filename = "preview.png",path = here::here("_posts", "2022-03-07-joining-data"))

Create an interactive bar chart using the package echarts4r

start with the data df
use arrange to reorder med_rnd_rev
use e_charts to initialize a chart
- the variable indusry is mapped to the x-axis
add a bar chart using e_bar with the values of med_rnd_rev
use `e_flip_coords() to flip the coordinates
use e_title to add the title and the subtitle
use e_legend to remove the legends
use e_x_acis to change format of labels on x-axis to percent
use e_y_axis to remove labels on y-axisi
use e_theme to change the theme. find more themes here

df %>%
  arrange(med_rnd_rev) %>% 
  e_charts(
    x = industry,
    ) %>% 
  e_bar(
    serie = med_rnd_rev,
    name = "median"
  ) %>% 
  e_flip_coords() %>% 
  e_tooltip() %>% 
  e_title(
    text = "Median industry R&D expenditures",
    subtext = "by industry as a percent of revenue form 2021 to 2018",
    left = "center") %>% 
  e_legend(FALSE) %>% 
  e_x_axis(
    formatter = e_axis_formatter("percent", digits = 0)
    ) %>% 
  e_y_axis(
    show = FALSE 
  ) %>% 
  e_theme("infographic")

Joining data

Steps 1-6

Question: join_ticker

Question: summarize_industry

Question inline_ticker

steps 7-11