Data Manipulation

Code for quiz 5. More pracrice with dplyr functions.

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

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

# A tibble: 8 x 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 x 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 x 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 x 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

Use ‘filter()’ to extract rows that meet criteria.

6.Extract rows in non-consecutive years.

drug_cos %>%
  filter(year %in% c(2013, 2018))

# A tibble: 26 x 9
   ticker name  location ebitdamargin grossmargin netmargin   ros
   <chr>  <chr> <chr>           <dbl>       <dbl>     <dbl> <dbl>
 1 ZTS    Zoet… New Jer…        0.222       0.634     0.111 0.176
 2 ZTS    Zoet… New Jer…        0.379       0.672     0.245 0.326
 3 PRGO   PERR… Ireland         0.236       0.362     0.125 0.19 
 4 PRGO   PERR… Ireland         0.178       0.387     0.028 0.088
 5 PFE    Pfiz… New Yor…        0.634       0.814     0.427 0.51 
 6 PFE    Pfiz… New Yor…        0.34        0.79      0.208 0.221
 7 MYL    Myla… United …        0.228       0.44      0.09  0.153
 8 MYL    Myla… United …        0.258       0.35      0.031 0.074
 9 MRK    Merc… New Jer…        0.282       0.615     0.1   0.123
10 MRK    Merc… 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 x 9
   ticker name  location ebitdamargin grossmargin netmargin    ros
   <chr>  <chr> <chr>           <dbl>       <dbl>     <dbl>  <dbl>
 1 ZTS    Zoet… New Jer…        0.217       0.64      0.101  0.171
 2 ZTS    Zoet… New Jer…        0.238       0.641     0.122  0.195
 3 ZTS    Zoet… New Jer…        0.335       0.659     0.168  0.286
 4 ZTS    Zoet… New Jer…        0.379       0.672     0.245  0.326
 5 PRGO   PERR… Ireland         0.226       0.345     0.127  0.183
 6 PRGO   PERR… Ireland         0.157       0.371     0.059  0.104
 7 PRGO   PERR… Ireland        -0.791       0.389    -0.76  -0.877
 8 PRGO   PERR… Ireland         0.178       0.387     0.028  0.088
 9 PFE    Pfiz… New Yor…        0.447       0.82      0.267  0.307
10 PFE    Pfiz… 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 x 9
   ticker name  location ebitdamargin grossmargin netmargin   ros
   <chr>  <chr> <chr>           <dbl>       <dbl>     <dbl> <dbl>
 1 PFE    Pfiz… New Yor…        0.371       0.795     0.164 0.223
 2 PFE    Pfiz… New Yor…        0.447       0.82      0.267 0.307
 3 PFE    Pfiz… New Yor…        0.634       0.814     0.427 0.51 
 4 PFE    Pfiz… New Yor…        0.359       0.807     0.184 0.247
 5 PFE    Pfiz… New Yor…        0.289       0.803     0.142 0.183
 6 PFE    Pfiz… New Yor…        0.267       0.767     0.137 0.158
 7 PFE    Pfiz… New Yor…        0.353       0.786     0.406 0.233
 8 PFE    Pfiz… New Yor…        0.34        0.79      0.208 0.221
 9 MYL    Myla… United …        0.245       0.418     0.088 0.161
10 MYL    Myla… United …        0.244       0.428     0.094 0.163
11 MYL    Myla… United …        0.228       0.44      0.09  0.153
12 MYL    Myla… United …        0.242       0.457     0.12  0.169
13 MYL    Myla… United …        0.243       0.447     0.09  0.133
14 MYL    Myla… United …        0.19        0.424     0.043 0.052
15 MYL    Myla… United …        0.272       0.402     0.058 0.121
16 MYL    Myla… 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 x 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 x 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 x 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

Question: filter and select

Use inputs from quiz question.

drug_cos %>%
  filter(ticker %in% c("ABBV", "ZTS", "AMGN")) %>%
  select(ticker, year, netmargin)

# A tibble: 24 x 3
   ticker  year netmargin
   <chr>  <dbl>     <dbl>
 1 ZTS     2011     0.058
 2 ZTS     2012     0.101
 3 ZTS     2013     0.111
 4 ZTS     2014     0.122
 5 ZTS     2015     0.071
 6 ZTS     2016     0.168
 7 ZTS     2017     0.163
 8 ZTS     2018     0.245
 9 AMGN    2011     0.236
10 AMGN    2012     0.252
# … with 14 more rows

Question: rename

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

# A tibble: 16 x 3
   ticker netmargin return_on_equity
   <chr>      <dbl>            <dbl>
 1 ZTS        0.058            0.069
 2 ZTS        0.101            0.113
 3 ZTS        0.111            0.612
 4 ZTS        0.122            0.465
 5 ZTS        0.071            0.285
 6 ZTS        0.168            0.587
 7 ZTS        0.163            0.488
 8 ZTS        0.245            0.694
 9 BMY        0.175            0.229
10 BMY        0.111            0.131
11 BMY        0.156            0.177
12 BMY        0.126            0.132
13 BMY        0.095            0.104
14 BMY        0.229            0.292
15 BMY        0.048            0.072
16 BMY        0.218            0.373
  1. ‘select’ ranges of columns
# A tibble: 104 x 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
# A tibble: 104 x 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
drug_cos %>%
  select(ticker, contains("locat"))

# A tibble: 104 x 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 x 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 x 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

Use ‘group_by’ to set up data for operations by group.

  1. ‘group_by’
drug_cos %>%
  group_by(ticker)

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

Use ‘summarize’ to calculate summary statistics

  1. Maximum ‘roe’ for all companies
drug_cos %>%
  summarize(max_roe = max(roe))

# A tibble: 1 x 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 x 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 x 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: summarize

Mean for year

drug_cos %>%
  group_by(year) %>%
  summarize(mean_ebitdamargin = mean(ros)) %>%
  filter(year == 2016)

# A tibble: 1 x 2
   year mean_ebitdamargin
  <dbl>             <dbl>
1  2016             0.253

Median for year

Find the median SEE QUIZ for each ‘year’ and call the variable median_SEE QUIZ***

Extract the median for SEE QUIZ*

drug_cos %>%
  group_by(year) %>%
  summarize(median_ebitdamargin = median(ros)) %>%
  filter(year == 2016)

# A tibble: 1 x 2
   year median_ebitdamargin
  <dbl>               <dbl>
1  2016               0.286
  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_x_continuous(labels = scales::percent) +
  labs(title = "Comparison of net margin",
       subtitle = "for Pfizer companies during 2011 to 2018",
       x = NULL, y = NULL) +
  theme_classic()

ggsave(filename = "preview.png",
       path = here::here("_posts", "2021-03-03-data-manipulation"))