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
, respectivelydrug_cos <- read_csv("https://estanny.com/static/week6/drug_cos.csv")
health_cos <- read_csv("https://estanny.com/static/week6/health_cos.csv")
drug_cos %>% glimpse()
Rows: 104
Columns: 9
$ ticker <chr> "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "ZTS", "ZTS…
$ name <chr> "Zoetis Inc", "Zoetis Inc", "Zoetis Inc", "Zoe…
$ 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, 0.36…
$ grossmargin <dbl> 0.610, 0.640, 0.634, 0.641, 0.635, 0.659, 0.66…
$ netmargin <dbl> 0.058, 0.101, 0.111, 0.122, 0.071, 0.168, 0.16…
$ ros <dbl> 0.101, 0.171, 0.176, 0.195, 0.140, 0.286, 0.32…
$ roe <dbl> 0.069, 0.113, 0.612, 0.465, 0.285, 0.587, 0.48…
$ 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", "Zoet…
$ revenue <dbl> 4233000000, 4336000000, 4561000000, 4785000000,…
$ gp <dbl> 2581000000, 2773000000, 2892000000, 3068000000,…
$ rnd <dbl> 427000000, 409000000, 399000000, 396000000, 364…
$ netincome <dbl> 245000000, 436000000, 504000000, 583000000, 339…
$ assets <dbl> 5711000000, 6262000000, 6558000000, 6588000000,…
$ liabilities <dbl> 1975000000, 2221000000, 5596000000, 5251000000,…
$ marketcap <dbl> NA, NA, 16345223371, 21572007994, 23860348635, …
$ year <dbl> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,…
$ industry <chr> "Drug Manufacturers - Specialty & Generic", "Dr…
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)
# A tibble: 13 x 6
ticker year grossmargin revenue gp industry
<chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 ZTS 2018 0.672 5.82e 9 3.91e 9 Drug Manufacturers - …
2 PRGO 2018 0.387 4.73e 9 1.83e 9 Drug Manufacturers - …
3 PFE 2018 0.79 5.36e10 4.24e10 Drug Manufacturers - …
4 MYL 2018 0.35 1.14e10 4.00e 9 Drug Manufacturers - …
5 MRK 2018 0.681 4.23e10 2.88e10 Drug Manufacturers - …
6 LLY 2018 0.738 2.46e10 1.81e10 Drug Manufacturers - …
7 JNJ 2018 0.668 8.16e10 5.45e10 Drug Manufacturers - …
8 GILD 2018 0.781 2.21e10 1.73e10 Drug Manufacturers - …
9 BMY 2018 0.71 2.26e10 1.60e10 Drug Manufacturers - …
10 BIIB 2018 0.865 1.35e10 1.16e10 Drug Manufacturers - …
11 AMGN 2018 0.827 2.37e10 1.96e10 Drug Manufacturers - …
12 AGN 2018 0.861 1.58e10 1.36e10 Drug Manufacturers - …
13 ABBV 2018 0.764 3.28e10 2.50e10 Drug Manufacturers - …
Start with drug_cos
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")
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)
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>
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(name) %>%
pull()
co_industry <- combo_df %>%
distinct(name) %>%
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 co_name
is located in co_location
and is a member of the co_industry
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
# 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
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 x 8
year grossmargin netmargin revenue gp netincome
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2011 0.418 0.088 6.13e 9 2.56e9 536810000
2 2012 0.428 0.094 6.80e 9 2.91e9 640900000
3 2013 0.44 0.09 6.91e 9 3.04e9 623700000
4 2014 0.457 0.12 7.72e 9 3.53e9 929400000
5 2015 0.447 0.09 9.43e 9 4.22e9 847600000
6 2016 0.424 0.043 1.11e10 4.70e9 480000000
7 2017 0.402 0.058 1.19e10 4.78e9 696000000
8 2018 0.35 0.031 1.14e10 4.00e9 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 = gp / 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 0.418
2 2012 0.428 0.094 6.80e 9 2.91e9 640900000 0.428
3 2013 0.44 0.09 6.91e 9 3.04e9 623700000 0.440
4 2014 0.457 0.12 7.72e 9 3.53e9 929400000 0.457
5 2015 0.447 0.09 9.43e 9 4.22e9 847600000 0.447
6 2016 0.424 0.043 1.11e10 4.70e9 480000000 0.424
7 2017 0.402 0.058 1.19e10 4.78e9 696000000 0.402
8 2018 0.35 0.031 1.14e10 4.00e9 352500000 0.350
# … with 1 more variable: 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_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 observations for the ticker BMY from `health_cos
and assign to the variable health_cos_subset
health_cos_subset <- health_cos %>%
filter(ticker == "BMY")
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 BMY Bris… 2.12e10 1.56e10 3.84e9 3.71e9 3.30e10 17103000000
2 BMY Bris… 1.76e10 1.30e10 3.90e9 1.96e9 3.59e10 22259000000
3 BMY Bris… 1.64e10 1.18e10 3.73e9 2.56e9 3.86e10 23356000000
4 BMY Bris… 1.59e10 1.19e10 4.53e9 2.00e9 3.37e10 18766000000
5 BMY Bris… 1.66e10 1.27e10 5.92e9 1.56e9 3.17e10 17324000000
6 BMY Bris… 1.94e10 1.45e10 5.01e9 4.46e9 3.37e10 17360000000
7 BMY Bris… 2.08e10 1.47e10 6.48e9 1.01e9 3.36e10 21704000000
8 BMY Bris… 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
health_cos_subset %>%
distinct(name) %>%
pull(name)
[1] "Bristol Myers Squibb Co"
co_name <- health_cos_subset %>%
distinct(name) %>%
pull(name)
You can take output from your code and include it in your text.
In following chuck
co_industry
co_industry <- health_cos_subset %>%
distinct(industry) %>%
pull()
This is outside the R chunk. 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 Bristol Myers Squibb Co is a member of the Drug Manufacturers - General group.
df
df %>% glimpse()
Rows: 9
Columns: 2
$ industry <chr> "Biotechnology", "Diagnostics & Research", "Dru…
$ med_rnd_rev <dbl> 0.48317287, 0.05620271, 0.17451442, 0.06851879,…
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 expenditures",
subtitle = "by industry as a percent of revenue from 2011 to 2018",
x = NULL, y = NULL) +
theme_ipsum()
ggsave(filename = "preview.png",
path = here::here("_posts", "2021-03-01-joining-data"))
df
arrange
to reorder med_rnd_rev
e_charts
to initialize a chart the variable 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")