Data Minipulation

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", "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...
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
  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
  1. 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>
  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 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 SEE QUIZ with inputs from your quiz and replace the ??? in the code

start with drug_cos THEN extract information for the tickers AGN,ZTS,BIIB select the variablesticker , year grossmargin

drug_cos %>% 
  filter(ticker %in% c("AGN", "ZTS", "BIIB")) %>% 
  select(ticker, year, grossmargin)

# A tibble: 24 x 3
   ticker  year grossmargin
   <chr>  <dbl>       <dbl>
 1 ZTS     2011       0.61 
 2 ZTS     2012       0.64 
 3 ZTS     2013       0.634
 4 ZTS     2014       0.641
 5 ZTS     2015       0.635
 6 ZTS     2016       0.659
 7 ZTS     2017       0.666
 8 ZTS     2018       0.672
 9 BIIB    2011       0.908
10 BIIB    2012       0.901
# ... with 14 more rows

Question: rename start with drug_cos THEN extract information for the tickers ** PFE,BMY** select the variables ticker,ebitdamargin and roe. Change the name of roe to return_on_equity

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

# A tibble: 16 x 3
   ticker ebitdamargin return_on_equity
   <chr>         <dbl>            <dbl>
 1 PFE           0.371            0.114
 2 PFE           0.447            0.179
 3 PFE           0.634            0.279
 4 PFE           0.359            0.12 
 5 PFE           0.289            0.105
 6 PFE           0.267            0.116
 7 PFE           0.353            0.342
 8 PFE           0.34             0.162
 9 BMY           0.285            0.229
10 BMY           0.141            0.131
11 BMY           0.222            0.177
12 BMY           0.178            0.132
13 BMY           0.144            0.104
14 BMY           0.322            0.292
15 BMY           0.286            0.072
16 BMY           0.292            0.373
  1. 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
  1. 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

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 Find the mean netmargin for each year and call the variable mean_netmargin Extract the mean for 2018

drug_cos  %>%
  group_by(year)  %>% 
  summarize(mean_netmargin = mean(netmargin))  %>% 
  filter( year == 2018)

# A tibble: 1 x 2
   year mean_netmargin
  <dbl>          <dbl>
1  2018          0.152

The mean netmargin for 2018 is 0.152 or 15.2%

Median for year Find the median netmargin for each year and call the variable median_netmargin Extract the median for 2018

drug_cos  %>% 
  group_by(year)  %>% 
  summarize(median_netmargin = median(netmargin))  %>% 
  filter(year == 2018)

# A tibble: 1 x 2
   year median_netmargin
  <dbl>            <dbl>
1  2018            0.188

he median netmargin for 2018 is 0.188 or 18.8%