Steps 1-6
Load the R packages we will use.
- Read the data in the files,
drug_cos.csv,health_cos.csvin to R and assign to the variablesdrug_cosandhealth_cos, respectively
drug_cos <- read_csv("https://estanny.com/static/week6/drug_cos.csv")
health_cos <- read_csv("https://estanny.com/static/week6/health_cos.csv")
- Use glimpse to get a glimpse of the data
drug_cos %>% glimpse()
Rows: 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,~health_cos %>% glimpse()
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~- Which variables are the same in both data sets
names_drug <- drug_cos %>% names()
names_health <- health_cos %>% names()
intersect(names_drug, names_health)
[1] "ticker" "name" "year" - Select subset of variables to work with
For
drug_cosselect (in this order):ticker,year.grossmarginExtract observations for 2018
assign output to
drug_subset
For
health_cosselect in order:ticker,year,revenue,gp,industryextract observations for 2018
Assign output to
health_subset
drug_subset <- drug_cos %>%
select(ticker, year, grossmargin) %>%
filter(year == 2018)
health_subset <- health_cos %>%
select(ticker, year, revenue, gp, industry) %>%
filter(year == 2018)
- Keep all the rows and columns
drug_subsetjoin with columns inhealth_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 - ~Question: join_ticker
. Start with the drug_cos data
. Extract observations for the ticker MRK from drug_cos . Assign output to the variables drug_cos_subset
drug_cos_subset <- drug_cos %>%
filter(ticker == "MRK")
. Display 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 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 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)
. Display 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 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>- Note: the variables
ticker,name,locationandindustryare the same for all the observations
. Assign the company name to co_name
co_name <- combo_df %>%
distinct(name) %>%
pull()
. Assign the company location to co_location
co_location <- combo_df %>%
distinct(location) %>%
pull()
. Assign the industry to co_industry group
co_industry <- combo_df %>%
distinct(industry) %>%
pull()
The 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 %>%
select(year, grossmargin, netmargin, revenue, gp, netincome)
. Display 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 0.649 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. Create the variables grossmargin_check to compare with the variables grossmargin. They should be equal - grossmargin_check = gp / revenue
. Create the variable close_enough to check the absolute value of the difference between grossmargin_check and grossmargin is less than 0.00
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 0.649 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 differences 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
<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>Question summarize_industry
- for each industry calculate
- 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
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_pe~ median_netmargin~ min_netmargin_p~
<chr> <dbl> <dbl> <dbl>
1 Biotechnology -4.66 7.62 -197.
2 Diagnostics &~ 13.1 12.3 0.399
3 Drug Manufact~ 19.4 19.5 -34.9
4 Drug Manufact~ 5.88 9.01 -76.0
5 Healthcare Pl~ 3.28 3.37 -0.305
6 Medical Care ~ 6.10 6.46 1.40
7 Medical Devic~ 12.4 14.3 -56.1
8 Medical Distr~ 1.70 1.03 -0.102
9 Medical Instr~ 12.3 14.0 -47.1
# ... with 1 more variable: max_netmargin_percent <dbl>Question: inline_ticker
- Extract observations for the ticker BMY from
health_cosand assign to the variablehealth_cos_subset
health_cos_subset <- health_cos %>%
filter(ticker == "BMY")
- Display
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 Bristo~ 2.12e10 1.56e10 3.84e9 3.71e9 3.30e10 17103000000
2 BMY Bristo~ 1.76e10 1.30e10 3.90e9 1.96e9 3.59e10 22259000000
3 BMY Bristo~ 1.64e10 1.18e10 3.73e9 2.56e9 3.86e10 23356000000
4 BMY Bristo~ 1.59e10 1.19e10 4.53e9 2.00e9 3.37e10 18766000000
5 BMY Bristo~ 1.66e10 1.27e10 5.92e9 1.56e9 3.17e10 17324000000
6 BMY Bristo~ 1.94e10 1.45e10 5.01e9 4.46e9 3.37e10 17360000000
7 BMY Bristo~ 2.08e10 1.47e10 6.48e9 1.01e9 3.36e10 21704000000
8 BMY Bristo~ 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 wahtdisticntdoesIne the console, type
?pull. Go to the help pane to see what it does.Run the code below
health_cos_subset %>%
distinct(name) %>%
pull(name)
[1] "Bristol Myers Squibb Co"- Assign the output to
co_name
co_name <- health_cos_subset %>%
distinct(name) %>%
pull(name)
- The name of the company ticker BMY is Bristol Myers Squibb Co
In following chunk
- Assign the comapnys industry group to the variable
co_industry
co_industry <- health_cos_subset %>%
distinct(industry) %>%
pull()
The company Bristol Myers Squibb Co is a member of Drug Manufacturers - General
Steps 7-11
- Prepare the data for the plots
- starts with health_cos THEN
- group_by industry THEN
- calculate the median research and development expenditure by industry
- assign the output to
df
- Use
glimpseto glimpse the data for the plots
df %>% glimpse()
Rows: 9
Columns: 2
$ industry <chr> "Biotechnology", "Diagnostics & Research", "Drug~
$ med_rnd_rev <dbl> 0.48317287, 0.05620271, 0.17451442, 0.06851879, ~- Create a static bar chart
- use
ggplotto initailize the chart - data is
df - the variable
industryis mapped to the x-axis -reorder it based the value ofmed_rnd_rev - the variable
med_rnd_revis mapped on the y axis - add a bar chart using
geom_col - use
scale_y_contnuousto label the y-axis with percent - use
cord_flip()to flip the coordiantes - use
labsto add title, subtitle and remove x and y axes - use
theme_ipsum()from the hrbrthemes package to improve the theme
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()
- Save the last plot to preview.png and add the yaml chunk at the top
ggsave(filename = "preview.png", path = here::here("_posts", "2021-03-16-joining-data"))
Create an interactive bar chart using the package echarts4r -start with the data df -use arrange to reorder med_rnd_rev -use e_charts to initialize a chart -the variable industry is mapped to the x-axis -add a bar chart using e_bar with the values of med_rnd_rev -use e_flip_coords() to flip the coordinates -use e_title to add the title and the subtitle -use e_legend to remove the legends -use e_x_axis to change format of labels on x-axis to percent -use e_y_axis to remove labels on y-axis- -use e_theme to change the theme. Find more themes here
df %>%
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")