My Blog: Data Manipulation

Load the R packages we will use.

library(tidyverse)

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")

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,~

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

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

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    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>

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    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>

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    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

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

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

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 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 your quiz question filter and select and replace tickers with inputs from your quiz and replace the variables in the code

Start with ‘drug_cos’ THEN
Extract information for the tickers MYL, AGN, PFE THEN
Select the variables ‘ticker’, ‘year’ and ‘grossmargin’

drug_cos  %>% 
  filter(ticker %in% c("MYL", "AGN", "PFE"))  %>% 
  select(ticker, year, grossmargin)

# A tibble: 24 x 3
   ticker  year grossmargin
   <chr>  <dbl>       <dbl>
 1 PFE     2011       0.795
 2 PFE     2012       0.82 
 3 PFE     2013       0.814
 4 PFE     2014       0.807
 5 PFE     2015       0.803
 6 PFE     2016       0.767
 7 PFE     2017       0.786
 8 PFE     2018       0.79 
 9 MYL     2011       0.418
10 MYL     2012       0.428
# ... with 14 more rows

Question: rename

Start with ‘drug_cos’ THEN
Extract information for the tickers MRK, AMGN, THEN
Select the variables ‘ticker’, netmargin’ and ‘roe’. Change the name of ‘roe’ to ‘return_on_equity’

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

# A tibble: 16 x 3
   ticker netmargin return_on_equity
   <chr>      <dbl>            <dbl>
 1 MRK        0.131            0.114
 2 MRK        0.13             0.113
 3 MRK        0.1              0.089
 4 MRK        0.282            0.248
 5 MRK        0.112            0.096
 6 MRK        0.098            0.092
 7 MRK        0.06             0.063
 8 MRK        0.147            0.199
 9 AMGN       0.236            0.158
10 AMGN       0.252            0.225
11 AMGN       0.272            0.242
12 AMGN       0.257            0.21 
13 AMGN       0.32             0.252
14 AMGN       0.336            0.259
15 AMGN       0.087            0.066
16 AMGN       0.353            0.585

‘select’ ranges of columns

by name

drug_cos  %>%
  select(ebitdamargin:netmargin)

# 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

by postion

drug_cos  %>%
  select(4:6)

# 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

‘select’ helper functions

‘starts_with(“abc”)’ matches columns start with “abc”
‘ends_with(“abc”)’ matches columns ends with “abc”
‘contains(“abc”)’ matches columns contain “abc”

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

‘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    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 x 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>

Use ‘summarize’ to calculate summary statistics

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( median_netmargin = median(netmargin))

# A tibble: 8 x 2
   year median_netmargin
  <dbl>            <dbl>
1  2011            0.164
2  2012            0.161
3  2013            0.194
4  2014            0.126
5  2015            0.142
6  2016            0.229
7  2017            0.06 
8  2018            0.188

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

Find the mean netmargin for each ‘year’ and call the variable mean_netmargin
Extract the mean for 2016

drug_cos %>%
  group_by(year) %>% 
  summarize( mean_netmargin = mean(netmargin))

# A tibble: 8 x 2
   year mean_netmargin
  <dbl>          <dbl>
1  2011         0.164 
2  2012         0.173 
3  2013         0.166 
4  2014         0.155 
5  2015         0.200 
6  2016         0.201 
7  2017         0.0885
8  2018         0.152

The mean netmargin for 2016 is 0.201 or 20.1%

Median for year

Find the median netmargin for each ‘year’ and call the variable median_netmargin
Extract the median for 2016

drug_cos %>%
  group_by(year) %>% 
  summarize( median_netmargin = median(netmargin))

# A tibble: 8 x 2
   year median_netmargin
  <dbl>            <dbl>
1  2011            0.164
2  2012            0.161
3  2013            0.194
4  2014            0.126
5  2015            0.142
6  2016            0.229
7  2017            0.06 
8  2018            0.188

The median netmargin for 2016 is 0.229 or 22.9%

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",
        subititle = "for drug companies during 2018",
        x = NULL, y = NULL) +
  theme_classic()

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 from 2011 to 2018",
        x = NULL, y = NULL) +
  theme_classic()

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