4.1 Using pipes in Rstudio

There’s a keyboard shortcut for entering the pipe: shift + ctrl/cmd + m. Try adding one after nlsy below, and then keep typing to add some code. Might save you a split second!

nlsy

You can also create “snippets” of your own if you find there’s something you’re typing out a lot. We saw in an earlier lab that if you start typing lib, you can then press enter to finish typing library() and have your cursor ready to type in the package name.

Others you might find useful for the content in the next few weeks of the course are fun and for.

You can also use snippets in the text area of R Markdown documents, you just have to press shift + tab to make them appear. For example, r makes a code chunk.

One snippet that I saw online somewhere and liked, so I added to my own RStudio setup, is for typing something like this, when I want to make some changes to the dataset but don’t want to change its name:

nlsy <- nlsy %>%

I’ll demonstrate how to make a snippet!

• Tools -> Global Options -> Code -> Editing -> Edit Snippets
• read more about snippets here

4.2 Why pipes?

We compared several ways of making the same dataset:

nlsy2 <- mutate(nlsy, 
                only = case_when(
                  nsibs == 0 ~ "yes",
                  TRUE ~ "no")
                )
nlsy3 <- select(nlsy2, 
                id, contains("sleep"), only)
only_kids <- filter(nlsy3, 
                    only == "yes")

vs:

only_kids <- filter(
  select(
    mutate(nlsy,
      only = case_when(
        nsibs == 0 ~ "yes",
        TRUE ~ "no"
      )
    ),
    id, contains("sleep"), only
  ),
  only == "yes"
)

vs:

only_kids <- nlsy %>%
  mutate(only = case_when(
                nsibs == 0 ~ "yes",
                TRUE ~ "no"
                )
         ) %>%
  select(id, contains("sleep"), only) %>%
  filter(only == "yes")

Then I asked you to translate this code into a piped version:

nlsy2 <- mutate(nlsy, slp_cat_wkdy = case_when(
  sleep_wkdy < 5 ~ "little",
  sleep_wkdy < 7 ~ "some",
  sleep_wkdy < 9 ~ "ideal",
  sleep_wkdy < 12 ~ "lots",
  TRUE ~ NA_character_))
missing_sleep <- filter(nlsy2, is.na(slp_cat_wkdy))
missing_sleep <- select(missing_sleep, starts_with("slp"), contains("sleep"))
missing_sleep

#>  # A tibble: 3 x 3
#>    slp_cat_wkdy sleep_wkdy sleep_wknd
#>    <chr>             <dbl>      <dbl>
#>  1 <NA>                 12         12
#>  2 <NA>                 12         12
#>  3 <NA>                 13         13

missing_sleep <- nlsy %>% ...

Which functions could we move around?

4.3 Summary statistics

We also used the summarize() function to calculate summary statistics:

summarize(nlsy, 
          med_age_bir = median(age_bir),
          cor_sleep = cor(sleep_wkdy, sleep_wknd),
          ten_pctle_inc = quantile(income, probs = 0.1),
          ninety_pctle_inc = quantile(income, probs = 0.9))

#>  # A tibble: 1 x 4
#>    med_age_bir cor_sleep ten_pctle_inc ninety_pctle_inc
#>          <dbl>     <dbl>         <dbl>            <dbl>
#>  1          22     0.710         3177.           33024.

You can use summarize() at the end (or the beginning, or middle, depending on what you’re doing!) of a pipe:

nlsy %>%
  mutate(age_bir_stand = (age_bir - mean(age_bir)) / sd(age_bir)) %>%
  filter(sex == 1) %>%
  summarize(mean_men = mean(age_bir_stand))

#>  # A tibble: 1 x 1
#>    mean_men
#>       <dbl>
#>  1    0.283

nlsy %>%
  filter(nsibs == 1) %>%
  summarise(cor(sleep_wkdy, sleep_wknd))

#>  # A tibble: 1 x 1
#>    `cor(sleep_wkdy, sleep_wknd)`
#>                            <dbl>
#>  1                         0.581

nlsy %>%
  mutate(yes = between(income, 20000, 30000)) %>%
  summarise(prop = mean(yes))

#>  # A tibble: 1 x 1
#>     prop
#>    <dbl>
#>  1 0.166

nlsy %>%
  summarise(prop = mean(between(income, 20000, 30000)))

#>  # A tibble: 1 x 1
#>     prop
#>    <dbl>
#>  1 0.166

nlsy %>%
  count(between(income, 20000, 30000)) %>%
  mutate(prop = n / sum(n))

#>  # A tibble: 2 x 3
#>    `between(income, 20000, 30000)`     n  prop
#>    <lgl>                           <int> <dbl>
#>  1 FALSE                            1005 0.834
#>  2 TRUE                              200 0.166

4.4 Stratified summary statistics

When we group data, functions within summarize() will be applied within the groups:

nlsy %>%
  mutate(income_stand = (income - mean(income))/sd(income)) %>%
  group_by(region) %>%
  summarize(mean_inc = mean(income_stand),
            sd_inc = sd(income_stand), 
            .groups = "drop")

#>  # A tibble: 4 x 3
#>    region mean_inc sd_inc
#>     <dbl>    <dbl>  <dbl>
#>  1      1   0.186   1.17 
#>  2      2   0.106   0.958
#>  3      3  -0.0891  1.03 
#>  4      4  -0.145   0.810

You can group by multiple variables, but notice what happens to the grouping if you don’t specify what to do with the grouping structure:

nlsy %>%
  group_by(region, sex) %>%
  summarize(mean_inc = mean(income),
            sd_inc = sd(income))

#>  `summarise()` regrouping output by 'region' (override with `.groups` argument)

#>  # A tibble: 8 x 4
#>  # Groups:   region [4]
#>    region   sex mean_inc sd_inc
#>     <dbl> <dbl>    <dbl>  <dbl>
#>  1      1     1   20393. 16929.
#>  2      1     2   15928. 14442.
#>  3      2     1   19488. 14885.
#>  4      2     2   14699. 10613.
#>  5      3     1   15137. 14592.
#>  6      3     2   13293. 12879.
#>  7      4     1   12315.  9244.
#>  8      4     2   14001. 11621.

When you group by multiple variables and then summarize, the default is to “peel off” the last grouping layer and leave the rest, as the message describes. This can be confusing/annoying if you don’t realize it, so you might want to use .groups = "drop".

nlsy %>%
  mutate(region = fct_recode(factor(region),
                             "Northeast" = "1", "North Central" = "2", 
                             "South" = "3", "West" = "4")) %>%
  group_by(region) %>%
  summarise(med_inc = median(income))

#>  `summarise()` ungrouping output (override with `.groups` argument)

#>  # A tibble: 4 x 2
#>    region        med_inc
#>    <fct>           <dbl>
#>  1 Northeast       13000
#>  2 North Central   13460
#>  3 South           10000
#>  4 West            10000

4.5 Tables with the tableone package

We saw how we could use the tableone package to create easy tables:

library(tableone) # in general, put at the top of your script

tab1 <- CreateTableOne(
  data = nlsy,
  vars = c("eyesight", "nsibs", "race_eth",
           "sex", "region", "income", "age_bir"),
  strata = "glasses",
  factorVars = c("eyesight", "race_eth", "sex", "region")
)

tab1

Not only are there many possible arguments to the CreateTableOne() function, but also for the print() function when applied to a TableOne object. You can look up the documentation for both:

print(tab1, catDigits = 2, contDigits = 2, test = FALSE, smd = TRUE)

help("CreateTableOne")
help("print.TableOne")

You can use this code to help get your table 1 in an easy-to-share format:

table1_to_share <- tab1 %>% 
  print() %>% 
  as_tibble(rownames = "id")
write_csv(table1_to_share, "table1.csv")

Check out some other packages for tables as well: https://github.com/kaz-yos/tableone#similar-or-complementary-projects.

analysis_dat <- nlsy %>%
  filter(region %in% c(1, 4), nsibs >= 2, glasses == 0)

analysis_dat <- analysis_dat %>%
  mutate(income_cat = 
           case_when(
             income < quantile(income, probs = .25) ~ "lowest quartile",
             income < quantile(income, probs = .5) ~ "second quartile",
             income < quantile(income, probs = .75) ~ "third quartile",
             TRUE ~ "highest quartile"
             ),
         income_cat = fct_relevel(income_cat, 
                                  "lowest quartile", "second quartile", 
                                  "third quartile", "highest quartile"))

tab1 <- CreateTableOne(
  data = analysis_dat,
  vars = c("eyesight", "nsibs", "race_eth",
           "sex", "region", "income", "age_bir"),
  strata = "income_cat",
  factorVars = c("eyesight", "race_eth", "sex", "region")
)
print(tab1, exact = "region", pDigits = 2)