Code for Quiz 6, more dplyr and our first interactive chart using echarts4r.
drug_cos.csv
, health_cos.csv
into R and assign to the 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
-Assign output to health_subset
drug_subset
join with 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…
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 AMGN from health_cosand 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 AMGN Amgen I… 1.56e10 1.29e10 3.17e9 3.68e9 4.89e10 29842000000
2 AMGN Amgen I… 1.73e10 1.41e10 3.38e9 4.34e9 5.43e10 35238000000
3 AMGN Amgen I… 1.87e10 1.53e10 4.08e9 5.08e9 6.61e10 44029000000
4 AMGN Amgen I… 2.01e10 1.56e10 4.30e9 5.16e9 6.90e10 43231000000
5 AMGN Amgen I… 2.17e10 1.74e10 4.07e9 6.94e9 7.14e10 43366000000
6 AMGN Amgen I… 2.30e10 1.88e10 3.84e9 7.72e9 7.76e10 47751000000
7 AMGN Amgen I… 2.28e10 1.88e10 3.56e9 1.98e9 8.00e10 54713000000
8 AMGN Amgen I… 2.37e10 1.96e10 3.74e9 8.39e9 6.64e10 53916000000
# … with 3 more variables: marketcap <dbl>, year <dbl>,
# industry <chr>
?distinct
. Go to the help pane to see what distinct
does?pull
. Go to the help pane to see what pull
doesRun the code below
co_name
You can take output from your code and include it in your text.
In the following chunk
co_industry
This is outside the 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 Amgen Inc is a member of the Drug Manufacturers - General group.
Start with drug_cos
Extract observations for the ticker MRK 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 MRK Merc… New Jer… 0.305 0.649 0.131 0.15 0.114
2 MRK Merc… New Jer… 0.33 0.652 0.13 0.182 0.113
3 MRK Merc… New Jer… 0.282 0.615 0.1 0.123 0.089
4 MRK Merc… New Jer… 0.567 0.603 0.282 0.409 0.248
5 MRK Merc… New Jer… 0.298 0.622 0.112 0.136 0.096
6 MRK Merc… New Jer… 0.254 0.648 0.098 0.117 0.092
7 MRK Merc… New Jer… 0.278 0.678 0.06 0.162 0.063
8 MRK Merc… New Jer… 0.313 0.681 0.147 0.206 0.199
# … 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
combo_df
# A tibble: 8 × 17
ticker name location ebitdamargin grossmargin netmargin ros roe
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 MRK Merc… New Jer… 0.305 0.649 0.131 0.15 0.114
2 MRK Merc… New Jer… 0.33 0.652 0.13 0.182 0.113
3 MRK Merc… New Jer… 0.282 0.615 0.1 0.123 0.089
4 MRK Merc… New Jer… 0.567 0.603 0.282 0.409 0.248
5 MRK Merc… New Jer… 0.298 0.622 0.112 0.136 0.096
6 MRK Merc… New Jer… 0.254 0.648 0.098 0.117 0.092
7 MRK Merc… New Jer… 0.278 0.678 0.06 0.162 0.063
8 MRK Merc… New Jer… 0.313 0.681 0.147 0.206 0.199
# … 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 the observationsco_name
co_location
co_industry
groupThe company Merck & Co Inc is located in New Jersey; 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 0.649 0.131 48047000000 31176000000 6272000000
2 2012 0.652 0.13 47267000000 30821000000 6168000000
3 2013 0.615 0.1 44033000000 27079000000 4404000000
4 2014 0.603 0.282 42237000000 25469000000 11920000000
5 2015 0.622 0.112 39498000000 24564000000 4442000000
6 2016 0.648 0.098 39807000000 25777000000 3920000000
7 2017 0.678 0.06 40122000000 27210000000 2394000000
8 2018 0.681 0.147 42294000000 28785000000 6220000000
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 0.649 0.131 48047000000 31176000000 6272000000
2 2012 0.652 0.13 47267000000 30821000000 6168000000
3 2013 0.615 0.1 44033000000 27079000000 4404000000
4 2014 0.603 0.282 42237000000 25469000000 11920000000
5 2015 0.622 0.112 39498000000 24564000000 4442000000
6 2016 0.648 0.098 39807000000 25777000000 3920000000
7 2017 0.678 0.06 40122000000 27210000000 2394000000
8 2018 0.681 0.147 42294000000 28785000000 6220000000
# … 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 netsmargin_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 × 8
year grossmargin netmargin revenue gp netincome
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2011 0.649 0.131 48047000000 31176000000 6272000000
2 2012 0.652 0.13 47267000000 30821000000 6168000000
3 2013 0.615 0.1 44033000000 27079000000 4404000000
4 2014 0.603 0.282 42237000000 25469000000 11920000000
5 2015 0.622 0.112 39498000000 24564000000 4442000000
6 2016 0.648 0.098 39807000000 25777000000 3920000000
7 2017 0.678 0.06 40122000000 27210000000 2394000000
8 2018 0.681 0.147 42294000000 28785000000 6220000000
# … with 2 more variables: netmargin_check <dbl>, close_enough <lgl>
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 a chartdf
industry
is mapped to the x-axismed_rnd_rev
med_rnd_rev
is mapped to the y-axisgeom_col
scale_y_continuous
to label the y axis with percentcoord_flip()
to flip the coordinateslabs
to add titlem subtitle and remove x and y axistheme_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_classic()
df
arrange
to reorder med_rnd_rev
e_charts
to initialize a chart
industry
is mapped to the x-axise_bar
with the values of med_rnd_rev
e_flip_coords()
to flip the coordinatese_title
to add the title and the subtitlee_legend
to remove the legendse_x_axis
to change format of labels on x-axis to percente_y_axis
to remove labels on y-axis-e_theme
to change the theme. Find more themes heredf %>%
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 from 2011 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")