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

Use filter() to extract rows that meet criteria

  1. Extract rows in in 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 ticker “PFE”, “MRK” and “BMY”
drug_cos %>% 
  filter(year %in% c("PFE", "MRK","BMY"))

# A tibble: 0 × 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

  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

10.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

-start with drug_cos THEN

-change the name of location to headquarter

-put the columns in the this year 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

Question: Filter and select

Use inputs from your quiz questions filter and select and replace SEE QUIZ with inputs from your quiz and replace the ??? in the code

drug_cos %>% 
  filter(ticker %in% c("PFE", "MRK", "BMY"))  %>% 
  select(ticker, year, ros)

# A tibble: 24 × 3
   ticker  year   ros
   <chr>  <dbl> <dbl>
 1 PFE     2011 0.223
 2 PFE     2012 0.307
 3 PFE     2013 0.51 
 4 PFE     2014 0.247
 5 PFE     2015 0.183
 6 PFE     2016 0.158
 7 PFE     2017 0.233
 8 PFE     2018 0.221
 9 MRK     2011 0.15 
10 MRK     2012 0.182
# … with 14 more rows

###Question: rename

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

# A tibble: 16 × 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
  1. select ranges of columns
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
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

Use group_by to set up data for operations by group

14.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 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>

Use summarize to calculate summary statistics

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

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

Mean for year

drug_cos %>% 
  group_by(year) %>% 
  summarize(mean_grossmargin = mean(grossmargin)) %>% 
  filter(year == 2015)

# A tibble: 1 × 2
   year mean_grossmargin
  <dbl>            <dbl>
1  2015            0.712

Median for year

drug_cos  %>% 
   group_by(year)  %>% 
   summarize( median_grossmargin = median(grossmargin))  %>% 
  filter(year == 2015)

# A tibble: 1 × 2
   year median_grossmargin
  <dbl>              <dbl>
1  2015              0.764
  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 = "Comparision pf 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", "2022-02-28-data-manipulation"))