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
glimpseto 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_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
drug_cosExtract 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,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 Mylan NV is located in United Kingdom and is a member of the Drug Manufacturers - Specialty & Generic 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 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 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 = netmargin / 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 1.44e-11
2 2012 0.428 0.094 6.80e 9 2.91e9 640900000 1.38e-11
3 2013 0.44 0.09 6.91e 9 3.04e9 623700000 1.30e-11
4 2014 0.457 0.12 7.72e 9 3.53e9 929400000 1.55e-11
5 2015 0.447 0.09 9.43e 9 4.22e9 847600000 9.54e-12
6 2016 0.424 0.043 1.11e10 4.70e9 480000000 3.88e-12
7 2017 0.402 0.058 1.19e10 4.78e9 696000000 4.87e-12
8 2018 0.35 0.031 1.14e10 4.00e9 352500000 2.71e-12
# … 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 70.78127%
-median_grossmargin_percent for the industry Medical Devices is 71.982771%
-min_grossmargin_percent for the industry Medical Devices is 53.206895%
-max_grossmargin_percent for the industry Medical Devices is 84.70033%
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(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 Zoetis Inc is a member of the Drug Manufacturers - Specialty & Generic 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
glimpseto 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", "2021-03-15-5joining-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("purple-passion")