Data Manipulation

Code for Quiz 5. More practice with dplyr functions.

library(tidyverse)
  1. Read the data in the file
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 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 Inc New Jer…        0.222       0.634     0.111 0.176
 2 ZTS    Zoetis Inc New Jer…        0.379       0.672     0.245 0.326
 3 PRGO   PERRIGO C… Ireland         0.236       0.362     0.125 0.19 
 4 PRGO   PERRIGO C… Ireland         0.178       0.387     0.028 0.088
 5 PFE    Pfizer Inc New Yor…        0.634       0.814     0.427 0.51 
 6 PFE    Pfizer Inc New Yor…        0.34        0.79      0.208 0.221
 7 MYL    Mylan NV   United …        0.228       0.44      0.09  0.153
 8 MYL    Mylan NV   United …        0.258       0.35      0.031 0.074
 9 MRK    Merck & C… New Jer…        0.282       0.615     0.1   0.123
10 MRK    Merck & C… New Jer…        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 I… New Jer…        0.217       0.64      0.101  0.171
 2 ZTS    Zoetis I… New Jer…        0.238       0.641     0.122  0.195
 3 ZTS    Zoetis I… New Jer…        0.335       0.659     0.168  0.286
 4 ZTS    Zoetis I… New Jer…        0.379       0.672     0.245  0.326
 5 PRGO   PERRIGO … Ireland         0.226       0.345     0.127  0.183
 6 PRGO   PERRIGO … Ireland         0.157       0.371     0.059  0.104
 7 PRGO   PERRIGO … Ireland        -0.791       0.389    -0.76  -0.877
 8 PRGO   PERRIGO … Ireland         0.178       0.387     0.028  0.088
 9 PFE    Pfizer I… New Yor…        0.447       0.82      0.267  0.307
10 PFE    Pfizer I… New Yor…        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>
  1. Select ticker 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 columns ticker, 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.
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
drug_cos %>% 
  filter(ticker %in% c("MYL", "AGN", "PFE")) %>% 
  select(ticker, year, grossmargin)

drug_cos %>% 
  filter(ticker %in% c("ABBV", "BMY")) %>% 
  select(ticker, netmargin, return_on_equity = roe)

# A tibble: 16 × 3
   ticker netmargin return_on_equity
   <chr>      <dbl>            <dbl>
 1 BMY        0.175            0.229
 2 BMY        0.111            0.131
 3 BMY        0.156            0.177
 4 BMY        0.126            0.132
 5 BMY        0.095            0.104
 6 BMY        0.229            0.292
 7 BMY        0.048            0.072
 8 BMY        0.218            0.373
 9 ABBV       0.197            0.248
10 ABBV       0.287            0.69 
11 ABBV       0.22             1.13 
12 ABBV       0.089            0.435
13 ABBV       0.225            1.31 
14 ABBV       0.232            1.11 
15 ABBV       0.188            0.932
16 ABBV       0.174           -2.03 
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
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. 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 function starts_with, ends_with, and contains “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. Use group_by to setup data for operations by group
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 Inc New Jer…        0.149       0.61      0.058 0.101
 2 ZTS    Zoetis Inc New Jer…        0.217       0.64      0.101 0.171
 3 ZTS    Zoetis Inc New Jer…        0.222       0.634     0.111 0.176
 4 ZTS    Zoetis Inc New Jer…        0.238       0.641     0.122 0.195
 5 ZTS    Zoetis Inc New Jer…        0.182       0.635     0.071 0.14 
 6 ZTS    Zoetis Inc New Jer…        0.335       0.659     0.168 0.286
 7 ZTS    Zoetis Inc New Jer…        0.366       0.666     0.163 0.321
 8 ZTS    Zoetis Inc New Jer…        0.379       0.672     0.245 0.326
 9 PRGO   PERRIGO C… Ireland         0.216       0.343     0.123 0.178
10 PRGO   PERRIGO C… 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 Inc New Jer…        0.149       0.61      0.058 0.101
 2 ZTS    Zoetis Inc New Jer…        0.217       0.64      0.101 0.171
 3 ZTS    Zoetis Inc New Jer…        0.222       0.634     0.111 0.176
 4 ZTS    Zoetis Inc New Jer…        0.238       0.641     0.122 0.195
 5 ZTS    Zoetis Inc New Jer…        0.182       0.635     0.071 0.14 
 6 ZTS    Zoetis Inc New Jer…        0.335       0.659     0.168 0.286
 7 ZTS    Zoetis Inc New Jer…        0.366       0.666     0.163 0.321
 8 ZTS    Zoetis Inc New Jer…        0.379       0.672     0.245 0.326
 9 PRGO   PERRIGO C… Ireland         0.216       0.343     0.123 0.178
10 PRGO   PERRIGO C… Ireland         0.226       0.345     0.127 0.183
# … with 94 more rows, and 2 more variables: roe <dbl>, year <dbl>
  1. Maximize roe for all companies
drug_cos %>% 
  summarize(max_roe = max(roe))

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

maximize 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
  1. Pick a ratio and a year and compare the companies
drug_cos %>% 
  filter(year == 2018) %>% 
  ggplot(aes(x = netmargin, y = reorder(name, netmargin))) + geom_col() + scale_x_continuous(labels = scales::percent) + labs(title = "Comparison 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 = "Comparison of net margin",
     subtitle = "for Pfizer from 2011 to 2018", x = NULL, y = NULL) + 
theme_classic()

ggsave(filename = "preview.png", 
       path = here::here("_posts", "2022-03-01-data-manipulation"))