Code for Quiz 6, more dplyr and our first interactive chart using echarts4r.
drug_cos.csv
, health_cos.csv
in to R and assign to the variables drug_cos
and health_cos
, respectivelyglimpse
to get a glimpse of the datadrug_cos %>% glimpse()
Rows: 104
Columns: 9
$ ticker <chr> "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "Z...
$ name <chr> "Zoetis Inc", "Zoetis Inc", "Zoetis Inc", "Z...
$ location <chr> "New Jersey; U.S.A", "New Jersey; U.S.A", "N...
$ ebitdamargin <dbl> 0.149, 0.217, 0.222, 0.238, 0.182, 0.335, 0....
$ grossmargin <dbl> 0.610, 0.640, 0.634, 0.641, 0.635, 0.659, 0....
$ netmargin <dbl> 0.058, 0.101, 0.111, 0.122, 0.071, 0.168, 0....
$ ros <dbl> 0.101, 0.171, 0.176, 0.195, 0.140, 0.286, 0....
$ roe <dbl> 0.069, 0.113, 0.612, 0.465, 0.285, 0.587, 0....
$ year <int> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 20...
health_cos %>% glimpse()
Rows: 464
Columns: 11
$ ticker <chr> "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "ZT...
$ name <chr> "Zoetis Inc", "Zoetis Inc", "Zoetis Inc", "Zo...
$ revenue <dbl> 4233000000, 4336000000, 4561000000, 478500000...
$ gp <dbl> 2581000000, 2773000000, 2892000000, 306800000...
$ rnd <dbl> 427000000, 409000000, 399000000, 396000000, 3...
$ netincome <dbl> 245000000, 436000000, 504000000, 583000000, 3...
$ assets <dbl> 5711000000, 6262000000, 6558000000, 658800000...
$ liabilities <dbl> 1975000000, 2221000000, 5596000000, 525100000...
$ marketcap <dbl> NA, NA, 16345223371, 21572007994, 23860348635...
$ year <int> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 201...
$ industry <chr> "Drug Manufacturers - Specialty & Generic", "...
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 columns in health_subset
drug_subset %>% left_join(health_subset)
ticker year grossmargin revenue gp
1 ZTS 2018 0.672 5825000000 3914000000
2 PRGO 2018 0.387 4731700000 1831500000
3 PFE 2018 0.790 53647000000 42399000000
4 MYL 2018 0.350 11433900000 4001600000
5 MRK 2018 0.681 42294000000 28785000000
6 LLY 2018 0.738 24555700000 18125700000
7 JNJ 2018 0.668 81581000000 54490000000
8 GILD 2018 0.781 22127000000 17274000000
9 BMY 2018 0.710 22561000000 16014000000
10 BIIB 2018 0.865 13452900000 11636600000
11 AMGN 2018 0.827 23747000000 19646000000
12 AGN 2018 0.861 15787400000 13596000000
13 ABBV 2018 0.764 32753000000 25035000000
industry
1 Drug Manufacturers - Specialty & Generic
2 Drug Manufacturers - Specialty & Generic
3 Drug Manufacturers - General
4 Drug Manufacturers - Specialty & Generic
5 Drug Manufacturers - General
6 Drug Manufacturers - General
7 Drug Manufacturers - General
8 Drug Manufacturers - General
9 Drug Manufacturers - General
10 Drug Manufacturers - General
11 Drug Manufacturers - General
12 Drug Manufacturers - General
13 Drug Manufacturers - General
start with drug_cos
Extract observations for the ticker BIIB from drug_cos
Assign output to the variable drug_cos_subset
drug_cos_subset <- drug_cos %>%
filter(ticker == "BIIB")
drug_cos_subset
drug_cos_subset
ticker name location ebitdamargin grossmargin
1 BIIB Biogen Inc Massachusetts; U.S.A 0.404 0.908
2 BIIB Biogen Inc Massachusetts; U.S.A 0.402 0.901
3 BIIB Biogen Inc Massachusetts; U.S.A 0.432 0.876
4 BIIB Biogen Inc Massachusetts; U.S.A 0.475 0.879
5 BIIB Biogen Inc Massachusetts; U.S.A 0.493 0.885
6 BIIB Biogen Inc Massachusetts; U.S.A 0.491 0.871
7 BIIB Biogen Inc Massachusetts; U.S.A 0.495 0.867
8 BIIB Biogen Inc Massachusetts; U.S.A 0.511 0.865
netmargin ros roe year
1 0.245 0.333 0.204 2011
2 0.250 0.335 0.211 2012
3 0.269 0.355 0.233 2013
4 0.302 0.404 0.294 2014
5 0.330 0.437 0.321 2015
6 0.323 0.431 0.322 2016
7 0.207 0.407 0.209 2017
8 0.329 0.435 0.334 2018
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)
combo_df
combo_df
ticker name location ebitdamargin grossmargin
1 BIIB Biogen Inc Massachusetts; U.S.A 0.404 0.908
2 BIIB Biogen Inc Massachusetts; U.S.A 0.402 0.901
3 BIIB Biogen Inc Massachusetts; U.S.A 0.432 0.876
4 BIIB Biogen Inc Massachusetts; U.S.A 0.475 0.879
5 BIIB Biogen Inc Massachusetts; U.S.A 0.493 0.885
6 BIIB Biogen Inc Massachusetts; U.S.A 0.491 0.871
7 BIIB Biogen Inc Massachusetts; U.S.A 0.495 0.867
8 BIIB Biogen Inc Massachusetts; U.S.A 0.511 0.865
netmargin ros roe year revenue gp rnd
1 0.245 0.333 0.204 2011 5048634000 4581854000 1219602000
2 0.250 0.335 0.211 2012 5516461000 4970967000 1334919000
3 0.269 0.355 0.233 2013 6932200000 6074500000 1444100000
4 0.302 0.404 0.294 2014 9703300000 8532300000 1893400000
5 0.330 0.437 0.321 2015 10763800000 9523400000 2012800000
6 0.323 0.431 0.322 2016 11448800000 9970100000 1973300000
7 0.207 0.407 0.209 2017 12273900000 10643900000 2253600000
8 0.329 0.435 0.334 2018 13452900000 11636600000 2597200000
netincome assets liabilities marketcap
1 1234428000 9049604000 2622617000 26733054258
2 1380033000 10130118000 3166323000 34630691473
3 1862300000 11863335000 3242497000 66038521266
4 2934800000 14314700000 3500700000 80162952906
5 3547000000 19504800000 10129900000 68286367442
6 3702800000 22876800000 10748200000 61699770755
7 2539100000 23652600000 11054500000 67370207502
8 4430700000 25288900000 12257300000 60630142487
industry
1 Drug Manufacturers - General
2 Drug Manufacturers - General
3 Drug Manufacturers - General
4 Drug Manufacturers - General
5 Drug Manufacturers - General
6 Drug Manufacturers - General
7 Drug Manufacturers - General
8 Drug Manufacturers - General
ticker
, name
, location
and industry
are the same for all the observationsco_name
co_name <- combo_df %>%
distinct(name) %>%
pull()
co_location
co_location <- combo_df %>%
distinct(location) %>%
pull()
co_industry
groupco_industry <- combo_df %>%
distinct(industry) %>%
pull()
Put the r inline commands used in the bricks blanks below. When you knit the document the results of the commands will be displayed in your test
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 %>%
select(year, grossmargin, netmargin, revenue, gp, netincome)
combo_df_subset
combo_df_subset
year grossmargin netmargin revenue gp netincome
1 2011 0.908 0.245 5048634000 4581854000 1234428000
2 2012 0.901 0.250 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 0.330 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)
year grossmargin netmargin revenue gp netincome
1 2011 0.908 0.245 5048634000 4581854000 1234428000
2 2012 0.901 0.250 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 0.330 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 close_enough
1 0.9075433 TRUE
2 0.9011152 TRUE
3 0.8762730 TRUE
4 0.8793194 TRUE
5 0.8847619 TRUE
6 0.8708424 TRUE
7 0.8671979 TRUE
8 0.8649882 TRUE
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)
year grossmargin netmargin revenue gp netincome
1 2011 0.908 0.245 5048634000 4581854000 1234428000
2 2012 0.901 0.250 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 0.330 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
netmargin_check close_enough
1 0.2445073 TRUE
2 0.2501664 TRUE
3 0.2686449 TRUE
4 0.3024538 TRUE
5 0.3295305 TRUE
6 0.3234225 TRUE
7 0.2068699 TRUE
8 0.3293491 TRUE
*Put the command you use in the Rchunks in the Rmd file for this quiz
Use the health_cos
data
For each indusrty caluculate 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 Biotech~ 92.5 92.7 81.7
2 Diagnos~ 50.5 52.7 28.0
3 Drug Ma~ 75.4 76.4 36.8
4 Drug Ma~ 47.9 42.6 34.3
5 Healthc~ 20.5 19.6 10.0
6 Medical~ 55.9 37.4 28.1
7 Medical~ 70.8 72.0 53.2
8 Medical~ 10.4 5.38 2.49
9 Medical~ 53.9 52.8 40.5
# ... with 1 more variable: max_grossmargin_percent <dbl>
Fill in the blanks
Use the health_cos
data
Extract the observations for the ticker ZTS from health_cos
and assign to the variable health_cos_subset
health_cos_subset <- health_cos %>%
filter(ticker == "ZTS")
health_cos_subset
health_cos_subset
ticker name revenue gp rnd netincome assets
1 ZTS Zoetis Inc 4.233e+09 2.581e+09 4.27e+08 2.450e+08 5.7110e+09
2 ZTS Zoetis Inc 4.336e+09 2.773e+09 4.09e+08 4.360e+08 6.2620e+09
3 ZTS Zoetis Inc 4.561e+09 2.892e+09 3.99e+08 5.040e+08 6.5580e+09
4 ZTS Zoetis Inc 4.785e+09 3.068e+09 3.96e+08 5.830e+08 6.5880e+09
5 ZTS Zoetis Inc 4.765e+09 3.027e+09 3.64e+08 3.390e+08 7.9130e+09
6 ZTS Zoetis Inc 4.888e+09 3.222e+09 3.76e+08 8.210e+08 7.6490e+09
7 ZTS Zoetis Inc 5.307e+09 3.532e+09 3.82e+08 8.640e+08 8.5860e+09
8 ZTS Zoetis Inc 5.825e+09 3.914e+09 4.32e+08 1.428e+09 1.0777e+10
liabilities marketcap year
1 1.975e+09 NA 2011
2 2.221e+09 NA 2012
3 5.596e+09 16345223371 2013
4 5.251e+09 21572007994 2014
5 6.822e+09 23860348635 2015
6 6.150e+09 26434855920 2016
7 6.800e+09 35104245170 2017
8 8.592e+09 41097768446 2018
industry
1 Drug Manufacturers - Specialty & Generic
2 Drug Manufacturers - Specialty & Generic
3 Drug Manufacturers - Specialty & Generic
4 Drug Manufacturers - Specialty & Generic
5 Drug Manufacturers - Specialty & Generic
6 Drug Manufacturers - Specialty & Generic
7 Drug Manufacturers - Specialty & Generic
8 Drug Manufacturers - Specialty & Generic
?distinct
. Go to the help pane to see what it does?pull
. Go to the help pane to see what it doesRun the code below
health_cos_subset %>%
distinct(name) %>%
pull(name)
[1] "Zoetis Inc"
co_name
co_name <- health_cos_subset %>%
distinct(name) %>%
pull(name)
health_cos_subset %>%
distinct(industry) %>%
pull()
[1] "Drug Manufacturers - Specialty & Generic"
The name of the company Zoetis Inc is in the Drug Manufacturers - General