My Blog: Data Manipulation

Load the R packages we will use.

library(tidyverse)

Read the data in the file. drug_cos.csv into R and assign into 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", "Z...
$ name         <chr> "Zoetis Inc", "Zoetis Inc", "Zoetis Inc", "Z...
$ location     <chr> "New Jersey; U.S.A", "New Jersey; U.S.A", "N...
$ ebitdamargin <dbl> 0.149, 0.217, 0.222, 0.238, 0.182, 0.335, 0....
$ grossmargin  <dbl> 0.610, 0.640, 0.634, 0.641, 0.635, 0.659, 0....
$ netmargin    <dbl> 0.058, 0.101, 0.111, 0.122, 0.071, 0.168, 0....
$ ros          <dbl> 0.101, 0.171, 0.176, 0.195, 0.140, 0.286, 0....
$ roe          <dbl> 0.069, 0.113, 0.612, 0.465, 0.285, 0.587, 0....
$ year         <dbl> 2011, 2012, 2013, 2014, 2015, 2016, 2017, 20...

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() 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    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>

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>

Extract the tickers “AMGN”, “ABBV” and “JNJ”

drug_cos %>% 
  filter(ticker %in% c("AMGN", "ABBV","JNJ")) %>% 
  select(ticker,year,ros)

# A tibble: 24 x 3
   ticker  year   ros
   <chr>  <dbl> <dbl>
 1 JNJ     2011 0.199
 2 JNJ     2012 0.218
 3 JNJ     2013 0.224
 4 JNJ     2014 0.284
 5 JNJ     2015 0.282
 6 JNJ     2016 0.286
 7 JNJ     2017 0.243
 8 JNJ     2018 0.233
 9 AMGN    2011 0.305
10 AMGN    2012 0.351
# ... with 14 more rows

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 locations to headquarters
put the columns in this order yeah,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 SEE QUIZ with inputs from your quiz and replace the ??? in the code

-start with drug_cos THEN - extract information for the tickers SEE QUIZ THEN - select variables tickers.year`, and SEE QUIZ

drug_cos %>%  
  filter(ticker %in% c("AMGN", " ABBV", "JNJ")) %>% 
  select(ticker,year,ros)

# A tibble: 16 x 3
   ticker  year   ros
   <chr>  <dbl> <dbl>
 1 JNJ     2011 0.199
 2 JNJ     2012 0.218
 3 JNJ     2013 0.224
 4 JNJ     2014 0.284
 5 JNJ     2015 0.282
 6 JNJ     2016 0.286
 7 JNJ     2017 0.243
 8 JNJ     2018 0.233
 9 AMGN    2011 0.305
10 AMGN    2012 0.351
11 AMGN    2013 0.337
12 AMGN    2014 0.332
13 AMGN    2015 0.419
14 AMGN    2016 0.453
15 AMGN    2017 0.477
16 AMGN    2018 0.461

Question: rename

-start with drug_cos THEN -extract information for the tickers ZTS,BMY THEN -select the variables ticker,net and roe. Change the name of roe to reutn_on_equity

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

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

select helper functions

-start_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

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    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

Maximum roe for all companies

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

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

maximum roefor eachyear

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

mean roefor eachyear

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

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

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

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

Pick a ratio and a year and compare the companies in the year 2013

drug_cos %>% 
  filter(year == 2013) %>% 
  ggplot(aes(x = netmargin, y = reorder(name, netmargin)))+
geom_col()+
  scale_x_continuous(labels = scales::percent)+
  labs(title = "Comparision of net margin",
     subtitle = "for drug companies during ",
     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_y_continuous(labels = scales::percent)+
  labs(title = "Comparision of net margin",
     subtitle = "for Pfizer from 2016 to 2018",
     x = NULL, y = NULL)+
theme_classic()

ggsave(filename = "preview.png",
       path = here::here("_posts", "2021-03-04-data-manipulation"))

Data Manipulation

Use select() to select, rename and reorder columns

Question: filter and select

Question: rename

Use summarize to calculate summary statistics

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

Use `summarize` to calculate summary statistics