#Steps 1-6
- Load the R packages we will use.
- Read the data in the files, ‘drug_cos.csv’, ‘health_cos.csv’ in to R and assign to the variables ‘drug_cos’ and ‘health_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", "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 <dbl> 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 <dbl> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 201...
$ industry <chr> "Drug Manufacturers - Specialty & Generic", "...4.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" 5.Select subset of variables to work with -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
- Keep all the rows and columns ‘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 - ~#Question: join_ticker
*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")
*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 BIIB Biog~ Massach~ 0.404 0.908 0.245 0.333 0.204
2 BIIB Biog~ Massach~ 0.402 0.901 0.25 0.335 0.211
3 BIIB Biog~ Massach~ 0.432 0.876 0.269 0.355 0.233
4 BIIB Biog~ Massach~ 0.475 0.879 0.302 0.404 0.294
5 BIIB Biog~ Massach~ 0.493 0.885 0.33 0.437 0.321
6 BIIB Biog~ Massach~ 0.491 0.871 0.323 0.431 0.322
7 BIIB Biog~ Massach~ 0.495 0.867 0.207 0.407 0.209
8 BIIB Biog~ Massach~ 0.511 0.865 0.329 0.435 0.334
# ... 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)
*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 BIIB Biog~ Massach~ 0.404 0.908 0.245 0.333 0.204
2 BIIB Biog~ Massach~ 0.402 0.901 0.25 0.335 0.211
3 BIIB Biog~ Massach~ 0.432 0.876 0.269 0.355 0.233
4 BIIB Biog~ Massach~ 0.475 0.879 0.302 0.404 0.294
5 BIIB Biog~ Massach~ 0.493 0.885 0.33 0.437 0.321
6 BIIB Biog~ Massach~ 0.491 0.871 0.323 0.431 0.322
7 BIIB Biog~ Massach~ 0.495 0.867 0.207 0.407 0.209
8 BIIB Biog~ Massach~ 0.511 0.865 0.329 0.435 0.334
# ... 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’, ‘location’ and industry are 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 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)
*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 0.908 0.245 5048634000 4581854000 1234428000
2 2012 0.901 0.25 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 0.33 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*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 0.908 0.245 5.05e 9 4.58e 9 1.23e9
2 2012 0.901 0.25 5.52e 9 4.97e 9 1.38e9
3 2013 0.876 0.269 6.93e 9 6.07e 9 1.86e9
4 2014 0.879 0.302 9.70e 9 8.53e 9 2.93e9
5 2015 0.885 0.33 1.08e10 9.52e 9 3.55e9
6 2016 0.871 0.323 1.14e10 9.97e 9 3.70e9
7 2017 0.867 0.207 1.23e10 1.06e10 2.54e9
8 2018 0.865 0.329 1.35e10 1.16e10 4.43e9
# ... 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
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2011 0.908 0.245 5.05e 9 4.58e 9 1.23e9
2 2012 0.901 0.25 5.52e 9 4.97e 9 1.38e9
3 2013 0.876 0.269 6.93e 9 6.07e 9 1.86e9
4 2014 0.879 0.302 9.70e 9 8.53e 9 2.93e9
5 2015 0.885 0.33 1.08e10 9.52e 9 3.55e9
6 2016 0.871 0.323 1.14e10 9.97e 9 3.70e9
7 2017 0.867 0.207 1.23e10 1.06e10 2.54e9
8 2018 0.865 0.329 1.35e10 1.16e10 4.43e9
# ... with 2 more variables: netmargin_check <dbl>,
# close_enough <lgl>Question: summarize_industry
*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 -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_~ median_netmargi~ min_netmargin_p~
* <chr> <dbl> <dbl> <dbl>
1 Biotech~ -4.66 7.62 -197.
2 Diagnos~ 13.1 12.3 0.399
3 Drug Ma~ 19.4 19.5 -34.9
4 Drug Ma~ 5.88 9.01 -76.0
5 Healthc~ 3.28 3.37 -0.305
6 Medical~ 6.10 6.46 1.40
7 Medical~ 12.4 14.3 -56.1
8 Medical~ 1.70 1.03 -0.102
9 Medical~ 12.3 14.0 -47.1
# ... with 1 more variable: max_netmargin_percent <dbl>mean_netmargin_percent for the industry Medical Distribution is 1.70%
median_netmargin_percent for the industry Medical Distribution is 1.03%
min_netmargin_percent for the industry Medical Distribution is -0.102%
max_netmargin_percent for the industry Medical Distribution is 4.51%
Question:inline_ticker
*Fill in the blanks
*Use the health_cos data
*Extract observations for the ticker ZTS from ‘health_cos’ and assign to the variable ‘health_cos_subset’
health_cos_subset <- health_cos %>%
filter(ticker == "ZTS")
*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 ZTS Zoet~ 4.23e9 2.58e9 4.27e8 2.45e8 5.71e 9 1975000000
2 ZTS Zoet~ 4.34e9 2.77e9 4.09e8 4.36e8 6.26e 9 2221000000
3 ZTS Zoet~ 4.56e9 2.89e9 3.99e8 5.04e8 6.56e 9 5596000000
4 ZTS Zoet~ 4.78e9 3.07e9 3.96e8 5.83e8 6.59e 9 5251000000
5 ZTS Zoet~ 4.76e9 3.03e9 3.64e8 3.39e8 7.91e 9 6822000000
6 ZTS Zoet~ 4.89e9 3.22e9 3.76e8 8.21e8 7.65e 9 6150000000
7 ZTS Zoet~ 5.31e9 3.53e9 3.82e8 8.64e8 8.59e 9 6800000000
8 ZTS Zoet~ 5.82e9 3.91e9 4.32e8 1.43e9 1.08e10 8592000000
# ... 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] "Zoetis Inc"*Assign the output to co_name
co_name <- health_cos_subset %>%
distinct(name) %>%
pull(name)
You can take output from your code and include it in your text.
*The name of the company with ticker ZTS is Zoetis Inc
In following chuck
*Assign the company’s industry group to the variable ‘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 ‘Zoetis Inc’ is a member of the ‘Drug Manufacturers - Specialty & Generic’ group.
Steps 7-11
7.Prepare the data for the plots *start with health_cos THEN
*group_by industry THEN
*calculate the median research and development expenditure as a percent of revenue by industry
*assign the output to df
- Use glimpse to glimpse the data for the plots
df %>% glimpse()
Rows: 9
Columns: 2
$ industry <chr> "Biotechnology", "Diagnostics & Research", "D...
$ med_rnd_rev <dbl> 0.48317287, 0.05620271, 0.17451442, 0.0685187...- Create a static bar chart
*use ggplot to initialize the chart
*data is df
the variable industry is mapped to the x-axis reorder it based the value of med_rnd_rev
*the variable med_rnd_rev is mapped to the y-axis
*add a bar chart using geom_col
*use scale_y_continuous to label the y-axis with percent
*use coord_flip() to flip the coordinates
*use labs to 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 previous plot to preview.png and add to the yaml chunk at the top
ggsave(filename = "preview.png",
path = here::here("_posts", "2021-03-15-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")