Data Manipulation

ACode for Quiz 5. More practice with dplyr functions

1. Load the R packages we will use.

library(tidyverse)

2. 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")

3. 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", "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…

4. 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

5. 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>

7. 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>

8. Extract the tickers “PFE” and “MYL”

drug_cos  %>% 
  filter(ticker %in% c("PFE, MYL"))

# A tibble: 0 x 9
# … with 9 variables: ticker <chr>, name <chr>, location <chr>,
#   ebitdamargin <dbl>, grossmargin <dbl>, netmargin <dbl>,
#   ros <dbl>, roe <dbl>, year <dbl>

Use select() to select, rename and reorder columns.

9. 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

10. 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

11. 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

• start with drug_cos THEN
• extract information for the tickers MYL, LLY, JNJ THEN
• select the variables ticker, year and grossmargin

drug_cos  %>%
  filter(ticker %in% c("MYL", "LLY", "JNJ"))  %>% 
  select(ticker, year, grossmargin)

# A tibble: 24 x 3
   ticker  year grossmargin
   <chr>  <dbl>       <dbl>
 1 MYL     2011       0.418
 2 MYL     2012       0.428
 3 MYL     2013       0.44 
 4 MYL     2014       0.457
 5 MYL     2015       0.447
 6 MYL     2016       0.424
 7 MYL     2017       0.402
 8 MYL     2018       0.35 
 9 LLY     2011       0.791
10 LLY     2012       0.788
# … with 14 more rows

Question: rename

• start with drug_cos THEN
• extract information for the tickers AGN, and ZTS THEN
• select the variables ticker, netmargin and roe. Change the name of roe to return_on_equity

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

# A tibble: 16 x 2
   ticker netmargin
   <chr>      <dbl>
 1 ZTS        0.069
 2 ZTS        0.113
 3 ZTS        0.612
 4 ZTS        0.465
 5 ZTS        0.285
 6 ZTS        0.587
 7 ZTS        0.488
 8 ZTS        0.694
 9 AGN        0.075
10 AGN        0.026
11 AGN       -0.147
12 AGN       -0.085
13 AGN        0.05 
14 AGN        0.184
15 AGN       -0.06 
16 AGN       -0.074

12. 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 position

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

13. 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 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

14. 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>

15. Maximum roe for all companies

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

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

max_roe 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 the year

• find mean ros for each year and call the variable mean_ros.
• extract the mean for 2013.

drug_cos  %>%
  group_by(year) %>% 
  summarise(mean_ros = mean(ros))  %>%
  filter(year == 2013)

# A tibble: 1 x 2
   year mean_ros
  <dbl>    <dbl>
1  2013    0.227

The mean_ros for 2013 is .227 or 22.7%.

Median for year

• find the median ros for each year and call the variable median_ros.
• extract median for 2013.

drug_cos  %>%
  group_by(year) %>% 
  summarise(median_ros = median(ros)) %>% 
  filter(year == 2013)

# A tibble: 1 x 2
   year median_ros
  <dbl>      <dbl>
1  2013      0.224

The median ros for 2013 is .224 or 22.4%.

16. Pick a ratio and a year and compare the companies.

drug_cos  %>% 
  filter(year ==2013)  %>% 
  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 2013",
       x = NULL, y = NULL) +
  theme_classic()

17. 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", "2021-03-04-data-manipulation"))