Data Manipulation

Code for Quiz 5 More practice with dplyr functions.

  1. Load the R packages we will use.
library(tidyverse)
  1. Read the data in the file, drug_cos.csv in to R and assign it to drug_cos
drug_cos  <- read_csv("https://estanny.com/static/week5/drug_cos.csv")
  1. Use glimpse() to get a glimpse of your data.
glimpse(drug_cos)

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,…
  1. Use distinct() to subset distinct rows.
drug_cos %>%
  distinct(year)

# A tibble: 8 × 1
   year
  <dbl>
1  2011
2  2012
3  2013
4  2014
5  2015
6  2016
7  2017
8  2018
  1. Use count() to count observations by group.
drug_cos  %>% 
  count(year)

# A tibble: 8 × 2
   year     n
  <dbl> <int>
1  2011    13
2  2012    13
3  2013    13
4  2014    13
5  2015    13
6  2016    13
7  2017    13
8  2018    13
drug_cos  %>% 
  count(name)

# A tibble: 13 × 2
   name                        n
   <chr>                   <int>
 1 AbbVie Inc                  8
 2 Allergan plc                8
 3 Amgen Inc                   8
 4 Biogen Inc                  8
 5 Bristol Myers Squibb Co     8
 6 ELI LILLY & Co              8
 7 Gilead Sciences Inc         8
 8 Johnson & Johnson           8
 9 Merck & Co Inc              8
10 Mylan NV                    8
11 PERRIGO Co plc              8
12 Pfizer Inc                  8
13 Zoetis Inc                  8
drug_cos  %>% 
  count(ticker, name)

# A tibble: 13 × 3
   ticker name                        n
   <chr>  <chr>                   <int>
 1 ABBV   AbbVie Inc                  8
 2 AGN    Allergan plc                8
 3 AMGN   Amgen Inc                   8
 4 BIIB   Biogen Inc                  8
 5 BMY    Bristol Myers Squibb Co     8
 6 GILD   Gilead Sciences Inc         8
 7 JNJ    Johnson & Johnson           8
 8 LLY    ELI LILLY & Co              8
 9 MRK    Merck & Co Inc              8
10 MYL    Mylan NV                    8
11 PFE    Pfizer Inc                  8
12 PRGO   PERRIGO Co plc              8
13 ZTS    Zoetis Inc                  8
  1. Extract rows in non-consecutive years.
drug_cos  %>% 
  filter(year %in% c(2013, 2018))

# A tibble: 26 × 9
   ticker name     location   ebitdamargin grossmargin netmargin   ros
   <chr>  <chr>    <chr>             <dbl>       <dbl>     <dbl> <dbl>
 1 ZTS    Zoetis … New Jerse…        0.222       0.634     0.111 0.176
 2 ZTS    Zoetis … New Jerse…        0.379       0.672     0.245 0.326
 3 PRGO   PERRIGO… Ireland           0.236       0.362     0.125 0.19 
 4 PRGO   PERRIGO… Ireland           0.178       0.387     0.028 0.088
 5 PFE    Pfizer … New York;…        0.634       0.814     0.427 0.51 
 6 PFE    Pfizer … New York;…        0.34        0.79      0.208 0.221
 7 MYL    Mylan NV United Ki…        0.228       0.44      0.09  0.153
 8 MYL    Mylan NV United Ki…        0.258       0.35      0.031 0.074
 9 MRK    Merck &… New Jerse…        0.282       0.615     0.1   0.123
10 MRK    Merck &… New Jerse…        0.313       0.681     0.147 0.206
# … with 16 more rows, and 2 more variables: roe <dbl>, year <dbl>
  1. Extract every other year from 2012 to 2018.
drug_cos  %>% 
  filter(year %in% seq(2012, 2018, by = 2))

# A tibble: 52 × 9
   ticker name    location   ebitdamargin grossmargin netmargin    ros
   <chr>  <chr>   <chr>             <dbl>       <dbl>     <dbl>  <dbl>
 1 ZTS    Zoetis… New Jerse…        0.217       0.64      0.101  0.171
 2 ZTS    Zoetis… New Jerse…        0.238       0.641     0.122  0.195
 3 ZTS    Zoetis… New Jerse…        0.335       0.659     0.168  0.286
 4 ZTS    Zoetis… New Jerse…        0.379       0.672     0.245  0.326
 5 PRGO   PERRIG… Ireland           0.226       0.345     0.127  0.183
 6 PRGO   PERRIG… Ireland           0.157       0.371     0.059  0.104
 7 PRGO   PERRIG… Ireland          -0.791       0.389    -0.76  -0.877
 8 PRGO   PERRIG… Ireland           0.178       0.387     0.028  0.088
 9 PFE    Pfizer… New York;…        0.447       0.82      0.267  0.307
10 PFE    Pfizer… New York;…        0.359       0.807     0.184  0.247
# … with 42 more rows, and 2 more variables: roe <dbl>, year <dbl>
  1. Extract the tickers “PFE” and “MYL”.
drug_cos  %>% 
  filter(ticker %in% c("PFE", "MYL"))

# A tibble: 16 × 9
   ticker name       location ebitdamargin grossmargin netmargin   ros
   <chr>  <chr>      <chr>           <dbl>       <dbl>     <dbl> <dbl>
 1 PFE    Pfizer Inc New Yor…        0.371       0.795     0.164 0.223
 2 PFE    Pfizer Inc New Yor…        0.447       0.82      0.267 0.307
 3 PFE    Pfizer Inc New Yor…        0.634       0.814     0.427 0.51 
 4 PFE    Pfizer Inc New Yor…        0.359       0.807     0.184 0.247
 5 PFE    Pfizer Inc New Yor…        0.289       0.803     0.142 0.183
 6 PFE    Pfizer Inc New Yor…        0.267       0.767     0.137 0.158
 7 PFE    Pfizer Inc New Yor…        0.353       0.786     0.406 0.233
 8 PFE    Pfizer Inc New Yor…        0.34        0.79      0.208 0.221
 9 MYL    Mylan NV   United …        0.245       0.418     0.088 0.161
10 MYL    Mylan NV   United …        0.244       0.428     0.094 0.163
11 MYL    Mylan NV   United …        0.228       0.44      0.09  0.153
12 MYL    Mylan NV   United …        0.242       0.457     0.12  0.169
13 MYL    Mylan NV   United …        0.243       0.447     0.09  0.133
14 MYL    Mylan NV   United …        0.19        0.424     0.043 0.052
15 MYL    Mylan NV   United …        0.272       0.402     0.058 0.121
16 MYL    Mylan NV   United …        0.258       0.35      0.031 0.074
# … with 2 more variables: roe <dbl>, year <dbl>

Use select() to select, rename and reorder columns

  1. Select columns ticker,name and ros
drug_cos  %>% 
  select(ticker, name, ros)

# A tibble: 104 × 3
   ticker name             ros
   <chr>  <chr>          <dbl>
 1 ZTS    Zoetis Inc     0.101
 2 ZTS    Zoetis Inc     0.171
 3 ZTS    Zoetis Inc     0.176
 4 ZTS    Zoetis Inc     0.195
 5 ZTS    Zoetis Inc     0.14 
 6 ZTS    Zoetis Inc     0.286
 7 ZTS    Zoetis Inc     0.321
 8 ZTS    Zoetis Inc     0.326
 9 PRGO   PERRIGO Co plc 0.178
10 PRGO   PERRIGO Co plc 0.183
# … with 94 more rows
  1. Use select to exclude columnsticker,name and ros
drug_cos  %>% 
  select(-ticker, -name, -ros)

# A tibble: 104 × 6
   location          ebitdamargin grossmargin netmargin   roe  year
   <chr>                    <dbl>       <dbl>     <dbl> <dbl> <dbl>
 1 New Jersey; U.S.A        0.149       0.61      0.058 0.069  2011
 2 New Jersey; U.S.A        0.217       0.64      0.101 0.113  2012
 3 New Jersey; U.S.A        0.222       0.634     0.111 0.612  2013
 4 New Jersey; U.S.A        0.238       0.641     0.122 0.465  2014
 5 New Jersey; U.S.A        0.182       0.635     0.071 0.285  2015
 6 New Jersey; U.S.A        0.335       0.659     0.168 0.587  2016
 7 New Jersey; U.S.A        0.366       0.666     0.163 0.488  2017
 8 New Jersey; U.S.A        0.379       0.672     0.245 0.694  2018
 9 Ireland                  0.216       0.343     0.123 0.248  2011
10 Ireland                  0.226       0.345     0.127 0.236  2012
# … with 94 more rows
  1. Rename and reorder columns with select

start with drug_cos THEN

change the name of location to headquarter

put the columns in this order: year, ticker, headquarter, netmargin, roe

drug_cos  %>% 
  select(year, ticker, headquarter =location, netmargin, roe )

# A tibble: 104 × 5
    year ticker headquarter       netmargin   roe
   <dbl> <chr>  <chr>                 <dbl> <dbl>
 1  2011 ZTS    New Jersey; U.S.A     0.058 0.069
 2  2012 ZTS    New Jersey; U.S.A     0.101 0.113
 3  2013 ZTS    New Jersey; U.S.A     0.111 0.612
 4  2014 ZTS    New Jersey; U.S.A     0.122 0.465
 5  2015 ZTS    New Jersey; U.S.A     0.071 0.285
 6  2016 ZTS    New Jersey; U.S.A     0.168 0.587
 7  2017 ZTS    New Jersey; U.S.A     0.163 0.488
 8  2018 ZTS    New Jersey; U.S.A     0.245 0.694
 9  2011 PRGO   Ireland               0.123 0.248
10  2012 PRGO   Ireland               0.127 0.236
# … with 94 more rows

Questions:2

Start with drug_cos THEN extract information for the tickers MRK, MYL, PFE THEN select the variables ticker,year and ebitdamargin

drug_cos  %>%
   filter(ticker %in% c("MRK", "MYL", "PFE"))  %>% 
   select(ticker, year,ebitdamargin)

# A tibble: 24 × 3
   ticker  year ebitdamargin
   <chr>  <dbl>        <dbl>
 1 PFE     2011        0.371
 2 PFE     2012        0.447
 3 PFE     2013        0.634
 4 PFE     2014        0.359
 5 PFE     2015        0.289
 6 PFE     2016        0.267
 7 PFE     2017        0.353
 8 PFE     2018        0.34 
 9 MYL     2011        0.245
10 MYL     2012        0.244
# … with 14 more rows

###Question:3

start with drug_cos THEN extract information for the tickers AMGN, JNJ THEN select the variables ticker, ros and roe. Change the name of roe to return_on_equity

drug_cos  %>% 
   filter( ticker %in% c("AMGN", "JNJ"))  %>% 
   select(ticker,ros,roe = roe)

# A tibble: 16 × 3
   ticker   ros   roe
   <chr>  <dbl> <dbl>
 1 JNJ    0.199 0.161
 2 JNJ    0.218 0.173
 3 JNJ    0.224 0.197
 4 JNJ    0.284 0.217
 5 JNJ    0.282 0.219
 6 JNJ    0.286 0.229
 7 JNJ    0.243 0.019
 8 JNJ    0.233 0.244
 9 AMGN   0.305 0.158
10 AMGN   0.351 0.225
11 AMGN   0.337 0.242
12 AMGN   0.332 0.21 
13 AMGN   0.419 0.252
14 AMGN   0.453 0.259
15 AMGN   0.477 0.066
16 AMGN   0.461 0.585
  1. select ranges of columns

by name

drug_cos  %>% 
  select(ebitdamargin:netmargin)

# A tibble: 104 × 3
   ebitdamargin grossmargin netmargin
          <dbl>       <dbl>     <dbl>
 1        0.149       0.61      0.058
 2        0.217       0.64      0.101
 3        0.222       0.634     0.111
 4        0.238       0.641     0.122
 5        0.182       0.635     0.071
 6        0.335       0.659     0.168
 7        0.366       0.666     0.163
 8        0.379       0.672     0.245
 9        0.216       0.343     0.123
10        0.226       0.345     0.127
# … with 94 more rows

by position

drug_cos  %>% 
  select(4:6)

# A tibble: 104 × 3
   ebitdamargin grossmargin netmargin
          <dbl>       <dbl>     <dbl>
 1        0.149       0.61      0.058
 2        0.217       0.64      0.101
 3        0.222       0.634     0.111
 4        0.238       0.641     0.122
 5        0.182       0.635     0.071
 6        0.335       0.659     0.168
 7        0.366       0.666     0.163
 8        0.379       0.672     0.245
 9        0.216       0.343     0.123
10        0.226       0.345     0.127
# … with 94 more rows
  1. select helper functions

starts_with("abc") matches columns start with “abc”

ends_with("abc") matches columns end with “abc”

contains("abc") matches columns contain “abc”

drug_cos  %>% 
  select(ticker, contains("locat"))

# A tibble: 104 × 2
   ticker location         
   <chr>  <chr>            
 1 ZTS    New Jersey; U.S.A
 2 ZTS    New Jersey; U.S.A
 3 ZTS    New Jersey; U.S.A
 4 ZTS    New Jersey; U.S.A
 5 ZTS    New Jersey; U.S.A
 6 ZTS    New Jersey; U.S.A
 7 ZTS    New Jersey; U.S.A
 8 ZTS    New Jersey; U.S.A
 9 PRGO   Ireland          
10 PRGO   Ireland          
# … with 94 more rows
drug_cos  %>% 
  select(ticker, starts_with("r"))

# A tibble: 104 × 3
   ticker   ros   roe
   <chr>  <dbl> <dbl>
 1 ZTS    0.101 0.069
 2 ZTS    0.171 0.113
 3 ZTS    0.176 0.612
 4 ZTS    0.195 0.465
 5 ZTS    0.14  0.285
 6 ZTS    0.286 0.587
 7 ZTS    0.321 0.488
 8 ZTS    0.326 0.694
 9 PRGO   0.178 0.248
10 PRGO   0.183 0.236
# … with 94 more rows
drug_cos  %>% 
  select(year, ends_with("margin"))

# A tibble: 104 × 4
    year ebitdamargin grossmargin netmargin
   <dbl>        <dbl>       <dbl>     <dbl>
 1  2011        0.149       0.61      0.058
 2  2012        0.217       0.64      0.101
 3  2013        0.222       0.634     0.111
 4  2014        0.238       0.641     0.122
 5  2015        0.182       0.635     0.071
 6  2016        0.335       0.659     0.168
 7  2017        0.366       0.666     0.163
 8  2018        0.379       0.672     0.245
 9  2011        0.216       0.343     0.123
10  2012        0.226       0.345     0.127
# … with 94 more rows
  1. group_by
drug_cos  %>% 
  group_by(ticker)

# A tibble: 104 × 9
# Groups:   ticker [13]
   ticker name     location   ebitdamargin grossmargin netmargin   ros
   <chr>  <chr>    <chr>             <dbl>       <dbl>     <dbl> <dbl>
 1 ZTS    Zoetis … New Jerse…        0.149       0.61      0.058 0.101
 2 ZTS    Zoetis … New Jerse…        0.217       0.64      0.101 0.171
 3 ZTS    Zoetis … New Jerse…        0.222       0.634     0.111 0.176
 4 ZTS    Zoetis … New Jerse…        0.238       0.641     0.122 0.195
 5 ZTS    Zoetis … New Jerse…        0.182       0.635     0.071 0.14 
 6 ZTS    Zoetis … New Jerse…        0.335       0.659     0.168 0.286
 7 ZTS    Zoetis … New Jerse…        0.366       0.666     0.163 0.321
 8 ZTS    Zoetis … New Jerse…        0.379       0.672     0.245 0.326
 9 PRGO   PERRIGO… Ireland           0.216       0.343     0.123 0.178
10 PRGO   PERRIGO… Ireland           0.226       0.345     0.127 0.183
# … with 94 more rows, and 2 more variables: roe <dbl>, year <dbl>
drug_cos  %>% 
  group_by(year)

# A tibble: 104 × 9
# Groups:   year [8]
   ticker name     location   ebitdamargin grossmargin netmargin   ros
   <chr>  <chr>    <chr>             <dbl>       <dbl>     <dbl> <dbl>
 1 ZTS    Zoetis … New Jerse…        0.149       0.61      0.058 0.101
 2 ZTS    Zoetis … New Jerse…        0.217       0.64      0.101 0.171
 3 ZTS    Zoetis … New Jerse…        0.222       0.634     0.111 0.176
 4 ZTS    Zoetis … New Jerse…        0.238       0.641     0.122 0.195
 5 ZTS    Zoetis … New Jerse…        0.182       0.635     0.071 0.14 
 6 ZTS    Zoetis … New Jerse…        0.335       0.659     0.168 0.286
 7 ZTS    Zoetis … New Jerse…        0.366       0.666     0.163 0.321
 8 ZTS    Zoetis … New Jerse…        0.379       0.672     0.245 0.326
 9 PRGO   PERRIGO… Ireland           0.216       0.343     0.123 0.178
10 PRGO   PERRIGO… Ireland           0.226       0.345     0.127 0.183
# … with 94 more rows, and 2 more variables: roe <dbl>, year <dbl>
  1. Maximum roe for all companies
drug_cos  %>% 
    summarize( max_roe = max(roe))

# A tibble: 1 × 1
  max_roe
    <dbl>
1    1.31

maximum roe for each year

drug_cos  %>% 
  group_by(year)  %>% 
  summarize( max_roe = max(roe))

# A tibble: 8 × 2
   year max_roe
  <dbl>   <dbl>
1  2011   0.451
2  2012   0.69 
3  2013   1.13 
4  2014   0.828
5  2015   1.31 
6  2016   1.11 
7  2017   0.932
8  2018   0.694

maximum roe for each ticker

drug_cos  %>% 
  group_by(ticker)  %>% 
  summarize( max_roe = max(roe)) 

# A tibble: 13 × 2
   ticker max_roe
   <chr>    <dbl>
 1 ABBV     1.31 
 2 AGN      0.184
 3 AMGN     0.585
 4 BIIB     0.334
 5 BMY      0.373
 6 GILD     1.04 
 7 JNJ      0.244
 8 LLY      0.306
 9 MRK      0.248
10 MYL      0.283
11 PFE      0.342
12 PRGO     0.248
13 ZTS      0.694

Question 4:summarize

Mean for year

Find the mean ebitdamargin for each year and call the variable mean_ebitdamargin

Extract the mean for 2011

drug_cos  %>% 
   group_by( year)  %>% 
   summarize( mean_ebitdamargin =  mean( ebitdamargin))  %>% 
  filter( year ==  2011)

# A tibble: 1 × 2
   year mean_ebitdamargin
  <dbl>             <dbl>
1  2011             0.297

Find the median ebitdamargin for each year and call the variable median_ebitdamargin

Extract the median for 2011

drug_cos  %>% 
   group_by( year)  %>% 
   summarize( median_ebitdamargin =  median(ebitdamargin))  %>% 
  filter(year ==  2011)

# A tibble: 1 × 2
   year median_ebitdamargin
  <dbl>               <dbl>
1  2011               0.282
  1. Pick a ratio and a year and compare the companies.
drug_cos  %>% 
 filter(year == 2011)  %>% 
 ggplot(aes(x = netmargin, y = reorder(name, netmargin))) +
 geom_col() +
  scale_x_continuous(labels = scales::percent) +
  labs(title = "Comparision of net margin",
       subtitle = "for drug companies during 2018", 
       x = NULL, y = NULL) +
  theme_classic()

  1. Pick a company and a ratio and compare the ratio over time.
drug_cos  %>% 
 filter(ticker == "PFE")  %>% 
 ggplot(aes(x = year, y = netmargin)) +
 geom_col() +
  scale_y_continuous(labels = scales::percent) +
  labs(title = "Comparision of net margin",
       subtitle = "for Pfizer from 2011 to 2018", 
       x = NULL, y = NULL) +
  theme_classic()

ggsave(filename = "preview.png", 
       path = here::here("_posts", "2022-02-21-reading-and-writing-data"))