Data Minipulation

Code for quiz 5 and more practice with dyplr functions

  1. Load the R packages
  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 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    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 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>
  1. Extract the tickers “PFE” and “MYL”
drug_cos %>%
  filter(ticker %in% c("LLY", "MYL", "JNJ"))
# A tibble: 24 x 9
   ticker name       location ebitdamargin grossmargin netmargin   ros
   <chr>  <chr>      <chr>           <dbl>       <dbl>     <dbl> <dbl>
 1 MYL    Mylan NV   United ~        0.245       0.418     0.088 0.161
 2 MYL    Mylan NV   United ~        0.244       0.428     0.094 0.163
 3 MYL    Mylan NV   United ~        0.228       0.44      0.09  0.153
 4 MYL    Mylan NV   United ~        0.242       0.457     0.12  0.169
 5 MYL    Mylan NV   United ~        0.243       0.447     0.09  0.133
 6 MYL    Mylan NV   United ~        0.19        0.424     0.043 0.052
 7 MYL    Mylan NV   United ~        0.272       0.402     0.058 0.121
 8 MYL    Mylan NV   United ~        0.258       0.35      0.031 0.074
 9 LLY    ELI LILLY~ Indiana~        0.277       0.791     0.179 0.22 
10 LLY    ELI LILLY~ Indiana~        0.304       0.788     0.181 0.239
# ... with 14 more rows, and 2 more variables: roe <dbl>, year <dbl>

Use select() to select, rename and reorder columns

  1. Select columns ticker , year and grossmargin
drug_cos %>%
  select(ticker, year, grossmargin)
# A tibble: 104 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 PRGO    2011       0.343
10 PRGO    2012       0.345
# ... 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 the 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
  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 function starts_with("abc") matches columns start with “abc” ends_with("adc") 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    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 calculation summary statistics

  1. Maximum roe for all companies
drug_cos %>%
  summarise(mean_roe = mean(roe))
# A tibble: 1 x 1
  mean_roe
     <dbl>
1    0.229

maximum roe for each year

drug_cos %>%
  group_by(ticker) %>%
  summarize( meanroe = mean(roe))
# A tibble: 13 x 2
   ticker  meanroe
   <chr>     <dbl>
 1 ABBV    0.478  
 2 AGN    -0.00387
 3 AMGN    0.250  
 4 BIIB    0.266  
 5 BMY     0.189  
 6 GILD    0.527  
 7 JNJ     0.182  
 8 LLY     0.200  
 9 MRK     0.127  
10 MYL     0.130  
11 PFE     0.177  
12 PRGO    0.035  
13 ZTS     0.414  
  1. pick a ration and a year and compare the companies
drug_cos %>%
  filter(year == 2016) %>%
  ggplot(aes(x = netmargin, y = reorder(name, netmargin)))
geom_col()
geom_col: width = NULL, na.rm = FALSE
stat_identity: na.rm = FALSE
position_stack 
scale_x_continuous(labels = scales:: percent)
<ScaleContinuousPosition>
 Range:  
 Limits:    0 --    1
labs(title = "Comparison of net margin", 
     subtitle = "for drug companies during 2018", 
     x = NULL, y = NULL)
$x
NULL

$y
NULL

$title
[1] "Comparison of net margin"

$subtitle
[1] "for drug companies during 2018"

attr(,"class")
[1] "labels"
ggsave(filename = "preview.png", 
       path = here::here("_posts", "2022-02-28-data-minipulation"))