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", "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…- 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_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 MYL from drug_cos
Assign output to the variable drug_cos_subset
drug_cos_subset <- drug_cos %>%
filter(ticker == "MYL")
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 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)
- 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 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>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(name) %>%
pull()
*Assign the industry to co_industry group
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 Mylan NV is located in Mylan NV and is a member of the Mylan NV 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 0.418 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 352500000Create 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 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 = 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>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_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
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>- Mean_grossmargin_percent for the industry Medical Devices is 70.78%
- median_grossmargin_percent for the industry Medical Devices is 71.98%
- min_grossmargin_percent for the industry Medical Devices is 53.20%
- max_grossmargin_percent for the industry Medical Devices is 84.70%
Question: inline_ticker
Fill in the blanks
Use the health_cos data
Extract observations for the ticker AMGN from health_cos and assign to the variable health_cos_subset
health_cos_subset <- health_cos %>%
filter(ticker == "AMGN")
- 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 AMGN Amge… 1.56e10 1.29e10 3.17e9 3.68e9 4.89e10 29842000000
2 AMGN Amge… 1.73e10 1.41e10 3.38e9 4.34e9 5.43e10 35238000000
3 AMGN Amge… 1.87e10 1.53e10 4.08e9 5.08e9 6.61e10 44029000000
4 AMGN Amge… 2.01e10 1.56e10 4.30e9 5.16e9 6.90e10 43231000000
5 AMGN Amge… 2.17e10 1.74e10 4.07e9 6.94e9 7.14e10 43366000000
6 AMGN Amge… 2.30e10 1.88e10 3.84e9 7.72e9 7.76e10 47751000000
7 AMGN Amge… 2.28e10 1.88e10 3.56e9 1.98e9 8.00e10 54713000000
8 AMGN Amge… 2.37e10 1.96e10 3.74e9 8.39e9 6.64e10 53916000000
# … 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] "Amgen 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 is AMGN 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 Amgen Inc is a member of the Drug Manufacturers - General group.
Steps 7-11
- 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", "Dru…
$ med_rnd_rev <dbl> 0.48317287, 0.05620271, 0.17451442, 0.06851879,…- 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","2021-03-16-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")