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
, respectively.glimpse
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
, revenue
, gp
, industry
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
# 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~
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_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
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 observationsco_name
co_location
co_industry
groupPut 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 Mylan NV 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_subject
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 grossmargin_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 than 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>
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 x 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 BMY from health_cos
and assign to the variable health_cos_subset
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 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>
?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.
BMY
is Bristol Myers Squibb CoIn following chunk
co_industry
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
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 chartdf
industry
is mapped to the x-axis reorder it based the value of med_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 title, subtitle 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_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 RND 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("chalk")