joining data

Code for Quiz 6, more dplyr and our first interactive chart using echarts4r.

Steps 1-6

Load the R packages we will use.

library(tidyverse)
library(echarts4r)  #install this package before using
library(hrbrthemes) #install this package before using

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

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 the 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

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_subset join with columns in health_subset

drug_subset %>% left_join(health_subset)

# A tibble: 13 × 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…

Question: join_ticker

Start with drug_cos
Extract observations for the ticker JNJ from drug_cos
Assign output to the variable drug_cos_subset

drug_cos_subset <- drug_cos %>% 
  filter(ticker == "JNJ")

Display drug_cos_subset

drug_cos_subset

# A tibble: 8 × 9
  ticker name  location ebitdamargin grossmargin netmargin   ros   roe
  <chr>  <chr> <chr>           <dbl>       <dbl>     <dbl> <dbl> <dbl>
1 JNJ    John… New Jer…        0.247       0.687     0.149 0.199 0.161
2 JNJ    John… New Jer…        0.272       0.678     0.161 0.218 0.173
3 JNJ    John… New Jer…        0.281       0.687     0.194 0.224 0.197
4 JNJ    John… New Jer…        0.336       0.694     0.22  0.284 0.217
5 JNJ    John… New Jer…        0.335       0.693     0.22  0.282 0.219
6 JNJ    John… New Jer…        0.338       0.697     0.23  0.286 0.229
7 JNJ    John… New Jer…        0.317       0.667     0.017 0.243 0.019
8 JNJ    John… New Jer…        0.318       0.668     0.188 0.233 0.244
# … 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 outputs to combo_df

combo_df <- drug_cos_subset %>% 
  left_join(health_cos)

Display combo_df

combo_df

# A tibble: 8 × 17
  ticker name  location ebitdamargin grossmargin netmargin   ros   roe
  <chr>  <chr> <chr>           <dbl>       <dbl>     <dbl> <dbl> <dbl>
1 JNJ    John… New Jer…        0.247       0.687     0.149 0.199 0.161
2 JNJ    John… New Jer…        0.272       0.678     0.161 0.218 0.173
3 JNJ    John… New Jer…        0.281       0.687     0.194 0.224 0.197
4 JNJ    John… New Jer…        0.336       0.694     0.22  0.284 0.217
5 JNJ    John… New Jer…        0.335       0.693     0.22  0.282 0.219
6 JNJ    John… New Jer…        0.338       0.697     0.23  0.286 0.229
7 JNJ    John… New Jer…        0.317       0.667     0.017 0.243 0.019
8 JNJ    John… New Jer…        0.318       0.668     0.188 0.233 0.244
# … 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 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 command will be displayed in your text.

The company Johnson & Johnson is located in Johnson & Johnson and is a member of the Johnson & Johnson 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 × 6
   year grossmargin netmargin     revenue          gp   netincome
  <dbl>       <dbl>     <dbl>       <dbl>       <dbl>       <dbl>
1  2011       0.687     0.149 65030000000 44670000000  9672000000
2  2012       0.678     0.161 67224000000 45566000000 10853000000
3  2013       0.687     0.194 71312000000 48970000000 13831000000
4  2014       0.694     0.22  74331000000 51585000000 16323000000
5  2015       0.693     0.22  70074000000 48538000000 15409000000
6  2016       0.697     0.23  71890000000 50101000000 16540000000
7  2017       0.667     0.017 76450000000 51011000000  1300000000
8  2018       0.668     0.188 81581000000 54490000000 15297000000

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 × 8
   year grossmargin netmargin     revenue          gp   netincome
  <dbl>       <dbl>     <dbl>       <dbl>       <dbl>       <dbl>
1  2011       0.687     0.149 65030000000 44670000000  9672000000
2  2012       0.678     0.161 67224000000 45566000000 10853000000
3  2013       0.687     0.194 71312000000 48970000000 13831000000
4  2014       0.694     0.22  74331000000 51585000000 16323000000
5  2015       0.693     0.22  70074000000 48538000000 15409000000
6  2016       0.697     0.23  71890000000 50101000000 16540000000
7  2017       0.667     0.017 76450000000 51011000000  1300000000
8  2018       0.668     0.188 81581000000 54490000000 15297000000
# … 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 × 8
   year grossmargin netmargin     revenue          gp   netincome
  <dbl>       <dbl>     <dbl>       <dbl>       <dbl>       <dbl>
1  2011       0.687     0.149 65030000000 44670000000  9672000000
2  2012       0.678     0.161 67224000000 45566000000 10853000000
3  2013       0.687     0.194 71312000000 48970000000 13831000000
4  2014       0.694     0.22  74331000000 51585000000 16323000000
5  2015       0.693     0.22  70074000000 48538000000 15409000000
6  2016       0.697     0.23  71890000000 50101000000 16540000000
7  2017       0.667     0.017 76450000000 51011000000  1300000000
8  2018       0.668     0.188 81581000000 54490000000 15297000000
# … 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 × 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>

Mean_netmargin_percent for the industry diagnostics & Research is 13.1% Median_netmargin_percent for the industry diagnostics & Research is 12.3% Min_netmargin_percent for the industry diagnostics & Research is 0.399% Max_netmargin_percent for the industry diagnostics & Research is 3.23%

joining data

Authors

Affiliations

Published

DOI

Steps 1-6

Question: join_ticker

Footnotes