Code for Quiz 6, more dplyr and our first interactive chart using echarts4r.
drug_cos.csv
, health_cos.csv
into R and assign to variables drug_cos
and health_cos
, respectivelyglimpse
to get a glimpse of the dataRows: 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,…
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…
names_drug <- drug_cos %>% names()
names_health <- health_cos %>% names()
intersect(names_drug, names_health)
[1] "ticker" "name" "year"
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
join with the columns in health_subset
# A tibble: 13 × 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…
Start with drug_cos
Extract observation for the ticker BIIB from drug_cos
Assign output to the variable drug_cos_subset
drug_cos_subset
drug_cos_subset
# A tibble: 8 × 9
ticker name location ebitdamargin grossmargin netmargin ros roe
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 BIIB Biog… Massach… 0.404 0.908 0.245 0.333 0.204
2 BIIB Biog… Massach… 0.402 0.901 0.25 0.335 0.211
3 BIIB Biog… Massach… 0.432 0.876 0.269 0.355 0.233
4 BIIB Biog… Massach… 0.475 0.879 0.302 0.404 0.294
5 BIIB Biog… Massach… 0.493 0.885 0.33 0.437 0.321
6 BIIB Biog… Massach… 0.491 0.871 0.323 0.431 0.322
7 BIIB Biog… Massach… 0.495 0.867 0.207 0.407 0.209
8 BIIB Biog… Massach… 0.511 0.865 0.329 0.435 0.334
# … 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
*Display combo_df
combo_df
# A tibble: 8 × 17
ticker name location ebitdamargin grossmargin netmargin ros roe
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 BIIB Biog… Massach… 0.404 0.908 0.245 0.333 0.204
2 BIIB Biog… Massach… 0.402 0.901 0.25 0.335 0.211
3 BIIB Biog… Massach… 0.432 0.876 0.269 0.355 0.233
4 BIIB Biog… Massach… 0.475 0.879 0.302 0.404 0.294
5 BIIB Biog… Massach… 0.493 0.885 0.33 0.437 0.321
6 BIIB Biog… Massach… 0.491 0.871 0.323 0.431 0.322
7 BIIB Biog… Massach… 0.495 0.867 0.207 0.407 0.209
8 BIIB Biog… Massach… 0.511 0.865 0.329 0.435 0.334
# … with 9 more variables: year <dbl>, revenue <dbl>, gp <dbl>,
# rnd <dbl>, netincome <dbl>, assets <dbl>, liabilities <dbl>,
# marketcap <dbl>, industry <chr>
ticker
, name
, location
and industry
are the same for all observationsco_name
co_location
co_industry
Put the r inline commands used in the blanks below. When you knit the document the result of the commands will be diplayed in your text.
The company Biogen Inc is located in Massachusetts; U.S.A and is a member of the Drug Manufacturers - General 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_subset
# A tibble: 8 × 6
year grossmargin netmargin revenue gp netincome
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2011 0.908 0.245 5048634000 4581854000 1234428000
2 2012 0.901 0.25 5516461000 4970967000 1380033000
3 2013 0.876 0.269 6932200000 6074500000 1862300000
4 2014 0.879 0.302 9703300000 8532300000 2934800000
5 2015 0.885 0.33 10763800000 9523400000 3547000000
6 2016 0.871 0.323 11448800000 9970100000 3702800000
7 2017 0.867 0.207 12273900000 10643900000 2539100000
8 2018 0.865 0.329 13452900000 11636600000 4430700000
grossmargin_check
to compare with the variable grossmargin
. They should be equal.
grossmargin_check
= gp
/ revenue
close_enough
to check that the absolute value of the difference between grossmargin_check
and grossmargin
is less than 0.001combo_df_subset %>%
mutate(grossmargin_check = gp / revenue,
close_enough = abs(grossmargin_check - grossmargin) < 0.001)
# A tibble: 8 × 8
year grossmargin netmargin revenue gp netincome
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2011 0.908 0.245 5048634000 4581854000 1234428000
2 2012 0.901 0.25 5516461000 4970967000 1380033000
3 2013 0.876 0.269 6932200000 6074500000 1862300000
4 2014 0.879 0.302 9703300000 8532300000 2934800000
5 2015 0.885 0.33 10763800000 9523400000 3547000000
6 2016 0.871 0.323 11448800000 9970100000 3702800000
7 2017 0.867 0.207 12273900000 10643900000 2539100000
8 2018 0.865 0.329 13452900000 11636600000 4430700000
# … 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 that 0.001
combo_df_subset %>%
mutate(netmargin_check = gp / revenue,
close_enough = abs(netmargin_check - netmargin) < 0.001)
# A tibble: 8 × 8
year grossmargin netmargin revenue gp netincome
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2011 0.908 0.245 5048634000 4581854000 1234428000
2 2012 0.901 0.25 5516461000 4970967000 1380033000
3 2013 0.876 0.269 6932200000 6074500000 1862300000
4 2014 0.879 0.302 9703300000 8532300000 2934800000
5 2015 0.885 0.33 10763800000 9523400000 3547000000
6 2016 0.871 0.323 11448800000 9970100000 3702800000
7 2017 0.867 0.207 12273900000 10643900000 2539100000
8 2018 0.865 0.329 13452900000 11636600000 4430700000
# … with 2 more variables: netmargin_check <dbl>, close_enough <lgl>
Fill in the blanks
Put the command you use in the Rchunks in the Rmd file for this quiz
Use the health_cos
data
For each industry calculate
health_cos %>%
group_by(industry) %>%
summarize(mean_netmargin_percent = mean(netincome/ revenue) * 100,
median_netmargin_percent = median(netincome / revenue) * 100,
min_netmargin_percent = min(netincome / revenue) * 100,
max_netmargin_percent = max(netincome / revenue) * 100)
# A tibble: 9 × 5
industry mean_netmargin_… median_netmargi… min_netmargin_p…
<chr> <dbl> <dbl> <dbl>
1 Biotechnology -4.66 7.62 -197.
2 Diagnostics & Re… 13.1 12.3 0.399
3 Drug Manufacture… 19.4 19.5 -34.9
4 Drug Manufacture… 5.88 9.01 -76.0
5 Healthcare Plans 3.28 3.37 -0.305
6 Medical Care Fac… 6.10 6.46 1.40
7 Medical Devices 12.4 14.3 -56.1
8 Medical Distribu… 1.70 1.03 -0.102
9 Medical Instrume… 12.3 14.0 -47.1
# … with 1 more variable: max_netmargin_percent <dbl>
Fill in the blanks
Use the health_cos
data
Extract observations for the ticker from health_cos
and assign to the variable health_cos_subset
health_cos_subset
health_cos_subset
# A tibble: 8 × 11
ticker name revenue gp rnd netincome assets liabilities
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 BMY Bristol… 2.12e10 1.56e10 3.84e9 3.71e9 3.30e10 17103000000
2 BMY Bristol… 1.76e10 1.30e10 3.90e9 1.96e9 3.59e10 22259000000
3 BMY Bristol… 1.64e10 1.18e10 3.73e9 2.56e9 3.86e10 23356000000
4 BMY Bristol… 1.59e10 1.19e10 4.53e9 2.00e9 3.37e10 18766000000
5 BMY Bristol… 1.66e10 1.27e10 5.92e9 1.56e9 3.17e10 17324000000
6 BMY Bristol… 1.94e10 1.45e10 5.01e9 4.46e9 3.37e10 17360000000
7 BMY Bristol… 2.08e10 1.47e10 6.48e9 1.01e9 3.36e10 21704000000
8 BMY Bristol… 2.26e10 1.60e10 6.34e9 4.92e9 3.50e10 20859000000
# … with 3 more variables: marketcap <dbl>, year <dbl>,
# industry <chr>
In the console, type ?distinct. Go to the help 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
co_name
You can take output from your code and include it in your test.
health_ticker
is co_name
In the following chuck
co_industry
This outside the Rchunk. Put the r inline comands used in the blanks below. When you knit the document the results of the comands will be displayed in your text.
The company Bristol Myers Squibb Co is a member of the Drug Manufacturers - General group.
df
glimpse
to glimpse the data for the plotsRows: 9
Columns: 2
$ industry <chr> "Biotechnology", "Diagnostics & Research", "Drug…
$ med_rnd_rev <dbl> 0.48317287, 0.05620271, 0.17451442, 0.06851879, …
ggplot
to initialize the chartindustry
is mapped to the x-axis
med_rnd_rev
med_rnd.rev
is mapped to the y-axisgeom_col
scale_y_continous
to label the y-axiscoord_flip()
to flip the coordinateslabs
to add title, subtitile and remove x and y-axestheme_ipsum()
from the hrbrthemes package to improve the themeggplot(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 expenditures",
subtitle = "by industry as a percent of revenue from 2011 to 2018",
x = NULL, y = NULL) +
theme_ipsum()