ID 543: Day 3

Introduction to R

Homework review

Today’s goals

Understand how to use summarize() to get summary statistics
Learn how to read in data from different file types
Understand how to use the here package to refer to files
Learn some tools for dealing with missing data
Learn how to join datasets using left_join(), right_join(), full_join(), and inner_join()

Summary statistics

We can get certain summary statistics about our data with summary(), which we can use either on an entire dataframe or on a single variable

nlsy_sleep <- nlsy |>
  select(id, contains("sleep"), age_bir, sex)
summary(nlsy_sleep$age_bir)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  13.00   19.00   22.00   23.45   27.00   52.00

summary(nlsy_sleep)

       id          sleep_wkdy       sleep_wknd        age_bir     
 Min.   :    3   Min.   : 0.000   Min.   : 0.000   Min.   :13.00  
 1st Qu.: 2317   1st Qu.: 6.000   1st Qu.: 6.000   1st Qu.:19.00  
 Median : 4744   Median : 7.000   Median : 7.000   Median :22.00  
 Mean   : 5229   Mean   : 6.643   Mean   : 7.267   Mean   :23.45  
 3rd Qu.: 7937   3rd Qu.: 8.000   3rd Qu.: 8.000   3rd Qu.:27.00  
 Max.   :12667   Max.   :13.000   Max.   :14.000   Max.   :52.00  
      sex       
 Min.   :1.000  
 1st Qu.:1.000  
 Median :2.000  
 Mean   :1.584  
 3rd Qu.:2.000  
 Max.   :2.000

Summary statistics

We can also apply certain functions to a variable(s) to get a single statistic: mean(), median(), var(), sd(), cov(), cor(), min(), max(), quantile(), etc.

median(nlsy$age_bir)

[1] 22

cor(nlsy$sleep_wkdy, nlsy$sleep_wknd)

[1] 0.7101579

quantile(nlsy$income, probs = c(0.1, 0.9))

    10%     90% 
 3177.2 33024.0

New function: `summarize()`

But what if we want a lot of summary statistics – just not those that come with the summary() function?

For example, it doesn’t give us a standard deviation!

We can use summarize()

summarize(nlsy,
          sd_age_bir = sd(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 × 4
  sd_age_bir cor_sleep ten_pctle_inc ninety_pctle_inc
       <dbl>     <dbl>         <dbl>            <dbl>
1       5.99     0.710         3177.            33024

`summarize()` specifics

Important to note:

Takes a dataframe as its first argument. That means we can use pipes!
Returns a tibble – helpful if you want to use those values in a figure or table.
Can give the summary statistics names.
Can ask for any type of function of the variables (including one you make up yourself).

Combining with other functions

Because we can pipe, we can also look at statistics of variables that we make using mutate(), in a dataset we’ve subsetted with filter().

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 × 1
  mean_men
     <dbl>
1    0.283

Note

Note that we’re standardizing the data before filtering. Or else the mean would be 0!

Exercise

What if we want both groups at once?

nlsy |>
  filter(sex_cat == "Male") |>
  summarize(age_bir_male = mean(age_bir))

# A tibble: 1 × 1
  age_bir_male
         <dbl>
1         25.1

nlsy |>
  filter(sex_cat == "Female") |>
  summarize(age_bir_female = mean(age_bir))

# A tibble: 1 × 1
  age_bir_female
           <dbl>
1           22.2

We can group our calculations using the `.by =` argument in `summarize()`

Instead of creating separate summaries, we can get statistics for all groups at once:

nlsy |>
  summarize(mean_age_bir = mean(age_bir),
            .by = sex_cat)

# A tibble: 2 × 2
  sex_cat mean_age_bir
  <fct>          <dbl>
1 Female          22.2
2 Male            25.1

This is cleaner and more efficient than filtering multiple times!

Why `.by =`?

You’ll see some optional arguments in the tidyverse start with a dot, like .by =.

Since you can name a variable whatever you want within summarize(), by = (with no dot) looks like it will be another variable
The dot is a convention to indicate that this is an optional argument, and it won’t conflict with any variable names in your data (just don’t start your variable names with dots!)

Counting groups

Sometimes we just want to know how many observations are in a group. We already saw how to do that with count(), but we can also do it with summarize() and the n() function, which counts the number of rows:

nlsy |>
  count(sex_cat)

# A tibble: 2 × 2
  sex_cat     n
  <fct>   <int>
1 Male      501
2 Female    704

nlsy |>
  summarize(n = n(),
            .by = sex_cat)

# A tibble: 2 × 2
  sex_cat     n
  <fct>   <int>
1 Female    704
2 Male      501

Group by multiple variables

You can group by multiple variables by putting them in a vector:

nlsy |>
  summarize(mean_income = mean(income),
            .by = c(sex_cat, race_eth_cat))

# A tibble: 6 × 3
  sex_cat race_eth_cat            mean_income
  <fct>   <fct>                         <dbl>
1 Female  Non-Black, Non-Hispanic      17404.
2 Male    Non-Black, Non-Hispanic      20839.
3 Female  Black                         9457.
4 Female  Hispanic                     10884.
5 Male    Black                        11755.
6 Male    Hispanic                     10651.

Alternative `group_by()` approach

You will also see the traditional group_by() approach:

nlsy |>
  group_by(region) |>
  summarize(mean_inc = mean(income))

# A tibble: 4 × 2
  region mean_inc
   <dbl>    <dbl>
1      1   17771.
2      2   16698.
3      3   14101.
4      4   13360.

Tibbles are “group-ed” when using `group_by()`

We can tell it’s “grouped” and how many groups there are by printing out the data.

The data itself won’t look (much) different, but we’ll be able to perform grouped functions on it.

nlsy_by_region <- group_by(nlsy, region)
nlsy_by_region

# A tibble: 1,205 × 15
# Groups:   region [4]
      id glasses eyesight sleep_wkdy sleep_wknd nsibs race_eth   sex region
   <dbl>   <dbl>    <dbl>      <dbl>      <dbl> <dbl>    <dbl> <dbl>  <dbl>
 1     3       0        1          5          7     3        3     2      1
 2     6       1        2          6          7     1        3     1      1
 3     8       0        2          7          9     7        3     2      1
 4    16       1        3          6          7     3        3     2      1
 5    18       0        3         10         10     2        3     1      3
 6    20       1        2          7          8     2        3     2      1
 7    27       0        1          8          8     1        3     2      1
 8    49       1        1          8          8     6        3     2      1
 9    57       1        2          7          8     1        3     2      1
10    67       0        1          8          8     1        3     1      1
# ℹ 1,195 more rows
# ℹ 6 more variables: income <dbl>, age_bir <dbl>, eyesight_cat <fct>,
#   glasses_cat <fct>, race_eth_cat <fct>, sex_cat <fct>

`.by =` vs. `group_by()`

The .by = approach is generally simpler and doesn’t leave your data grouped (which can cause issues later)

Use group_by() when you need to perform multiple grouped operations in sequence or just create a new variable with mutate()

For example, you want to group your data by person id when you have multiple rows per person, e.g., to create a lagged variable

data |>
  group_by(person_id) |>
  mutate(last_visit = lag(visit)) |>
  ungroup()

Exercise

Getting other data into R

We have been reading in data as an .rds file:

nlsy_rds <- read_rds("https://github.com/louisahsmith/data/raw/main/nlsy/nlsy.rds")

We could also read it in as a .csv file:

nlsy_csv <- read_csv("https://github.com/louisahsmith/data/raw/main/nlsy/nlsy-cc.csv")

What do you notice about the differences?

nlsy_rds |>
  select(id, eyesight_cat)

# A tibble: 1,205 × 2
      id eyesight_cat
   <dbl> <fct>       
 1     3 Excellent   
 2     6 Very Good   
 3     8 Very Good   
 4    16 Good        
 5    18 Good        
 6    20 Very Good   
 7    27 Excellent   
 8    49 Excellent   
 9    57 Very Good   
10    67 Excellent   
# ℹ 1,195 more rows

nlsy_csv |>
  select(id, eyesight_cat)

# A tibble: 1,205 × 2
      id eyesight_cat
   <dbl> <chr>       
 1     3 Excellent   
 2     6 Very Good   
 3     8 Very Good   
 4    16 Good        
 5    18 Good        
 6    20 Very Good   
 7    27 Excellent   
 8    49 Excellent   
 9    57 Very Good   
10    67 Excellent   
# ℹ 1,195 more rows

`.rds` is an R-specific file for a single object

It will be the exact same object when you read it back in.

write_rds(nlsy_rds, "nlsy.rds")

You can save any object, not just a dataframe:

x <- c(4, 5, 6)
write_rds(x, "numbers.rds")

What is y going to print?

y <- read_rds("numbers.rds")
y

`.csv` files are much more general but don’t maintain things like factors

`.csv` files might need a little more specification to read in

read_csv("https://github.com/louisahsmith/data/raw/main/nlsy/nlsy.csv") |> print(n = 2)

# A tibble: 12,686 × 14
  H0012400 H0012500 H0022300 H0022500 R0000100 R0009100 R0173600 R0214700
     <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
1       -4       -4       -4       -4        1        1        5        3
2        0        1        4        3        2        8        5        3
# ℹ 12,684 more rows
# ℹ 6 more variables: R0214800 <dbl>, R0216400 <dbl>, R0217900 <dbl>,
#   R0402800 <dbl>, R7090700 <dbl>, T4120500 <dbl>

nlsy_full <- read_csv(
  "https://github.com/louisahsmith/data/raw/main/nlsy/nlsy.csv", skip = 1,
  col_names = c("glasses", "eyesight", "sleep_wkdy", "sleep_wknd",
                "id", "nsibs", "samp", "race_eth", "sex", "region",
                "income", "res_1980", "res_2002", "age_bir"),
                na = c("-1", "-2", "-3", "-4", "-5", "-998"))
print(nlsy_full, n = 2)

# A tibble: 12,686 × 14
  glasses eyesight sleep_wkdy sleep_wknd    id nsibs  samp race_eth   sex region
    <dbl>    <dbl>      <dbl>      <dbl> <dbl> <dbl> <dbl>    <dbl> <dbl>  <dbl>
1      NA       NA         NA         NA     1     1     5        3     2      1
2       0        1          4          3     2     8     5        3     2      1
# ℹ 12,684 more rows
# ℹ 4 more variables: income <dbl>, res_1980 <dbl>, res_2002 <dbl>,
#   age_bir <dbl>

Corresponding `write_()` function

If you are sharing data with collaborators who don’t use R, or you want to look at it in Excel, you can save a dataframe as a .csv file:

nlsy_rds <- read_rds("https://github.com/louisahsmith/data/raw/main/nlsy/nlsy.rds")
write_csv(nlsy_rds, "nlsy.csv", na = "")

The data will be saved in your “working directory” (see the top of your console) unless you specify otherwise

Note

We’ll talk about directories in a little bit!

Other functions come from the `{haven}` package

library(haven)
medical_dta <- read_dta("http://www.principlesofeconometrics.com/stata/medical.dta")
medical_sas <- read_sas("http://www.principlesofeconometrics.com/sas/medical.sas7bdat")

glimpse(medical_dta)

Rows: 1,000
Columns: 6
$ id     <dbl> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, …
$ year   <dbl> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, …
$ medexp <dbl> 9, 9, 9, 10, 11, 6, 7, 7, 7, 7, 4, 3, 5, 4, 4, 5, 3, 6, 6, 3, 4…
$ inc    <dbl> 49, 51, 55, 58, 61, 48, 48, 58, 59, 63, 46, 51, 55, 58, 63, 68,…
$ age    <dbl> 51, 52, 53, 54, 55, 62, 63, 64, 65, 66, 57, 58, 59, 60, 61, 48,…
$ insur  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, …

glimpse(medical_sas)

Rows: 1,000
Columns: 6
$ ID     <dbl> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, …
$ YEAR   <dbl> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, …
$ MEDEXP <dbl> 9, 9, 9, 10, 11, 6, 7, 7, 7, 7, 4, 3, 5, 4, 4, 5, 3, 6, 6, 3, 4…
$ INC    <dbl> 49, 51, 55, 58, 61, 48, 48, 58, 59, 63, 46, 51, 55, 58, 63, 68,…
$ AGE    <dbl> 51, 52, 53, 54, 55, 62, 63, 64, 65, 66, 57, 58, 59, 60, 61, 48,…
$ INSUR  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, …

Or `{readxl}`

library(readxl)
dat <- read_excel("excel_data.xlsx")

All these functions take arguments, but read_excel() takes a ton of arguments – which sheet, how many rows to read, whether there are column names, a specific range to read in, etc….

See help(read_excel) for details!

Exercise

Where are these files?

File paths tell R where the data is located

They can be absolute (from the root/home of your computer) or relative (from the current working directory)
Absolute paths are not portable (i.e., your computer and my computer have different absolute paths)
Relative paths are more portable, but you have to be careful about where you run the code

The problem with `setwd()`

Often you’ll see code that uses setwd() to set the working directory to a specific folder

setwd() changes the working directory, leading to potential issues in collaboration and reproducibility
It’s really annoying to change your working directory when you move around files and folders, even if it’s just you using them

Note

This is a great blog post explaining more: https://www.tidyverse.org/blog/2017/12/workflow-vs-script/

Do you think this code from 2015 still works?

Better alternative: R Projects

my-project/
├─ my-project.Rproj
├─ README
├─ data/
│   ├── raw/
│   └── processed/
├─ R/
├─ results/
│   ├── tables/
│   ├── figures/
│   └── output/
└─ docs/

An .Rproj file is mostly just a placeholder. It remembers various options, and makes it easy to open a new RStudio session that starts up in the correct working directory. You never need to edit it directly.
A README file can just be a text file that includes notes for yourself or future users.
I like to have a folder for raw data – which I never touch – and a folder(s) for datasets that I create along the way.

R Projects

Demo

Referring to files with the `here` package

source(here::here("R/functions.R"))

dat <- read_csv(here::here("data/raw/data.csv"))

p <- ggplot(dat) + geom_point(aes(x, y))

ggsave(plot = p,
       filename = here::here("results/figures/fig.pdf"))

The here package lets you refer to files without worrying too much about relative file paths.
Construct file paths with reference to the top directory holding your .Rproj file.
here::here("data/raw/data.csv") for me, here, becomes /Users/l.smith/Documents/Teaching/Harvard/ID543 2026/data/raw/data.csv
But if I send you my code to run, it will become whatever file path you need it to be, as long as you’re running it within the R Project.

Referring to the `here` package

here::here()

is equivalent to

library(here)
here()

I just prefer to write out the package name whenever I need it, but you can load the package for your entire session if you want.

Note

Note that you can refer to any function without loading the whole package this way, e.g. haven::read_dta()

Exercise

Missing values

R uses NA for missing values
Unlike some other statistical software, it will return NA to any logical statement
This makes it somewhat harder to deal with but also harder to make mistakes

3 < NA

[1] NA

mean(c(1, 2, NA))

[1] NA

mean(c(1, 2, NA), na.rm = TRUE)

[1] 1.5

Special `NA` functions

Certain functions deal with missing values explicitly

vals <- c(1, 2, NA)
is.na(vals)

[1] FALSE FALSE  TRUE

anyNA(vals)

[1] TRUE

na.omit(vals)

[1] 1 2

Induce missingness

You know some value is implausible, whether for everyone or for a specific observation

nlsy <- nlsy |>
  mutate(sleep_wknd = if_else(sleep_wknd > 24, NA, sleep_wknd),
        # OR
         sleep_wknd = case_when(
           sleep_wknd > 24 ~ NA,
           .default = sleep_wknd
         ),
         income = if_else(id == 283, NA, income),
         nsibs = na_if(nsibs, 99))

na_if(x, y) will replace values in x that are equal to y with NA

Read in NA’s directly

In NLSY, -1 = Refused, -2 = Don’t know, -3 = Invalid missing, -4 = Valid missing, -5 = Non-interview

Other files might have . for missing, or 999.

nlsy_cols <- c("glasses", "eyesight", "sleep_wkdy", "sleep_wknd",
               "id", "nsibs", "samp", "race_eth", "sex", "region",
               "income", "res_1980", "res_2002", "age_bir")
nlsy <- read_csv("https://github.com/louisahsmith/data/raw/main/nlsy/nlsy.csv",
    na = c("-1", "-2", "-3", "-4", "-5", "-998"),
                 skip = 1, col_names = nlsy_cols)

You have to write the values as strings, even if they’re numbers

Complete cases

Sometimes you may just want to get rid of all the rows with missing values.

nrow(nlsy)

[1] 12686

nlsy_cc <- nlsy |> filter(complete.cases(nlsy))
nrow(nlsy_cc)

[1] 1205

nlsy2 <- nlsy |> na.omit() # same

Caution

Don’t do this without good reason! It will exlude rows with any missing values, even in variables you’re not using.

Exercise

Joins

There are multiple functions in the tidyverse (specifically, the {dplyr} package) for joining/merging data

Mutating joins merge two datasets based on matching variable(s), adding together the new columns from the joined dataframe

left_join(x, y, by = join_by(xcol == ycol))

Note

We will also refer to the x dataframe as the left-hand side (LHS) and the y dataframe as the right-hand side (RHS)

Why join?

I may have people and dates in one dataset, and want to merge with a separate dataset on prescriptions:

id	start_date	end_date
1	01/01/2021	01/01/2024
2	03/01/2023	01/01/2025
3	09/01/2020	10/01/2021

id	Rx_date	Rx_name
1	02/21/2020	Tylenol
1	04/06/2022	Aspirin
2	06/09/2023	Claritin

Goal: person 1 was taking Aspirin at some point during that interval, person 2 was taking Claritin, and person 3 was not taking any medications

Merging with kids

The NLSY also included the kids of the moms in the NLSY79 survey that we’re using.

nlsy_kids

# A tibble: 11,551 × 6
   id_kid id_mom sex_kid dob_kid agebir_mom bwt_kid
    <dbl>  <dbl>   <dbl>   <dbl>      <dbl>   <dbl>
 1    201      2       2    1993         34     139
 2    202      2       2    1994         35      NA
 3    301      3       2    1981         19     162
 4    302      3       2    1983         22     144
 5    303      3       2    1986         24     112
 6    401      4       1    1980         18     107
 7    403      4       2    1997         34      NA
 8    801      8       2    1976         17     119
 9    802      8       1    1979         20     107
10    803      8       2    1982         24     146
# ℹ 11,541 more rows

Note

Birthweight is in ounces

Left join

left_join(nlsy_sleep, nlsy_kids)

Error in `left_join()`:
! `by` must be supplied when `x` and `y` have no common variables.
ℹ Use `cross_join()` to perform a cross-join.

It will automatically look for matching columns (can be dangerous!) but if none, need to specify:

left_join(nlsy_sleep, nlsy_kids,
          by = join_by(id == id_mom))

# A tibble: 2,284 × 10
      id sleep_wkdy sleep_wknd age_bir   sex id_kid sex_kid dob_kid agebir_mom
   <dbl>      <dbl>      <dbl>   <dbl> <dbl>  <dbl>   <dbl>   <dbl>      <dbl>
 1     3          5          7      19     2    301       2    1981         19
 2     3          5          7      19     2    302       2    1983         22
 3     3          5          7      19     2    303       2    1986         24
 4     6          6          7      30     1     NA      NA      NA         NA
 5     8          7          9      17     2    801       2    1976         17
 6     8          7          9      17     2    802       1    1979         20
 7     8          7          9      17     2    803       2    1982         24
 8    16          6          7      31     2   1601       1    1990         31
 9    16          6          7      31     2   1602       1    1993         34
10    16          6          7      31     2   1603       2    1996         37
# ℹ 2,274 more rows
# ℹ 1 more variable: bwt_kid <dbl>

`by = join_by()` syntax

The most flexible way to specify the join is with by = join_by()

This can accommodate not only variables you want to match exactly, but also variables that are related in other ways (e.g., within or overlapping intervals)
You’ll also see by = c("id" = "id_mom") syntax, which is more limited but works for simple cases

Left join

LHS rows are duplicated if we have multiple matches, but we lose any rows in the RHS dataset that don’t have a match

n_distinct(nlsy_kids$id_kid)

[1] 11551

nlsy_left <- left_join(nlsy_sleep, nlsy_kids,
                       by = join_by(id == id_mom))

n_distinct(nlsy_left$id_kid)

[1] 1784

In this case, the moms of some of the kids aren’t in the nlsy_sleep dataset, so kids without moms are lost

Right join

right_join(nlsy_sleep, nlsy_kids, by = join_by(id == id_mom))

# A tibble: 11,551 × 10
      id sleep_wkdy sleep_wknd age_bir   sex id_kid sex_kid dob_kid agebir_mom
   <dbl>      <dbl>      <dbl>   <dbl> <dbl>  <dbl>   <dbl>   <dbl>      <dbl>
 1     3          5          7      19     2    301       2    1981         19
 2     3          5          7      19     2    302       2    1983         22
 3     3          5          7      19     2    303       2    1986         24
 4     8          7          9      17     2    801       2    1976         17
 5     8          7          9      17     2    802       1    1979         20
 6     8          7          9      17     2    803       2    1982         24
 7    16          6          7      31     2   1601       1    1990         31
 8    16          6          7      31     2   1602       1    1993         34
 9    16          6          7      31     2   1603       2    1996         37
10    20          7          8      30     2   2001       2    1990         30
# ℹ 11,541 more rows
# ℹ 1 more variable: bwt_kid <dbl>

Now we don’t have the dads, because there are no matching ids in the RHS dataset
But we do keep all the kids, even those without moms in the LHS

Full join: we want everything!

full_join(nlsy_sleep, nlsy_kids, by = join_by(id == id_mom))

# A tibble: 12,052 × 10
      id sleep_wkdy sleep_wknd age_bir   sex id_kid sex_kid dob_kid agebir_mom
   <dbl>      <dbl>      <dbl>   <dbl> <dbl>  <dbl>   <dbl>   <dbl>      <dbl>
 1     3          5          7      19     2    301       2    1981         19
 2     3          5          7      19     2    302       2    1983         22
 3     3          5          7      19     2    303       2    1986         24
 4     6          6          7      30     1     NA      NA      NA         NA
 5     8          7          9      17     2    801       2    1976         17
 6     8          7          9      17     2    802       1    1979         20
 7     8          7          9      17     2    803       2    1982         24
 8    16          6          7      31     2   1601       1    1990         31
 9    16          6          7      31     2   1602       1    1993         34
10    16          6          7      31     2   1603       2    1996         37
# ℹ 12,042 more rows
# ℹ 1 more variable: bwt_kid <dbl>

This dataset is larger than either of the initial datasets alone: it has the dads without kids and the kids without moms

Inner join: we only want matches

inner_join(nlsy_sleep, nlsy_kids, by = join_by(id == id_mom))

# A tibble: 1,783 × 10
      id sleep_wkdy sleep_wknd age_bir   sex id_kid sex_kid dob_kid agebir_mom
   <dbl>      <dbl>      <dbl>   <dbl> <dbl>  <dbl>   <dbl>   <dbl>      <dbl>
 1     3          5          7      19     2    301       2    1981         19
 2     3          5          7      19     2    302       2    1983         22
 3     3          5          7      19     2    303       2    1986         24
 4     8          7          9      17     2    801       2    1976         17
 5     8          7          9      17     2    802       1    1979         20
 6     8          7          9      17     2    803       2    1982         24
 7    16          6          7      31     2   1601       1    1990         31
 8    16          6          7      31     2   1602       1    1993         34
 9    16          6          7      31     2   1603       2    1996         37
10    20          7          8      30     2   2001       2    1990         30
# ℹ 1,773 more rows
# ℹ 1 more variable: bwt_kid <dbl>

This dataset has only the moms with kids (no dads) and the kids with moms
It still has multiple rows per mom – one for each kid

Join by multiple variables

Kids dataset has maternal age at birth (age_bir_mom)
I only want the kid that was the mom’s first
I’m going to match on the age at first birth (age_bir)in the moms dataset

first_births <- inner_join(nlsy_sleep, nlsy_kids,
                           by = join_by(id == id_mom,
                                        age_bir == agebir_mom))

first_births

# A tibble: 708 × 9
      id sleep_wkdy sleep_wknd age_bir   sex id_kid sex_kid dob_kid bwt_kid
   <dbl>      <dbl>      <dbl>   <dbl> <dbl>  <dbl>   <dbl>   <dbl>   <dbl>
 1     3          5          7      19     2    301       2    1981     162
 2     8          7          9      17     2    801       2    1976     119
 3    16          6          7      31     2   1601       1    1990     109
 4    20          7          8      30     2   2001       2    1990     129
 5    27          8          8      27     2   2701       2    1988     117
 6    49          8          8      24     2   4901       1    1982     139
 7    57          7          8      21     2   5701       1    1979     148
 8    86          8          8      17     2   8601       2    1977      97
 9    96          7          7      19     2   9601       2    1980     124
10    97          7          8      29     2   9701       1    1987      48
# ℹ 698 more rows

Exercise

Today’s summary

We can use summarize() to get summary statistics of our data
We can use group_by() to group our data and then get summary statistics within those groups
Missing values in R are NA
R projects are a good way to keep your files organized
We can use the here package to refer to files in a project
We can use left_join(), right_join(), full_join(), and inner_join() to merge datasets

Today’s functions

summarize(): calculate summary statistics (with .by = argument for stratified statistics)
group_by(): group data
here::here(): refer to files in a project
read_rds(), read_csv(), read_dta(), read_sas(), read_excel(): read in data
complete.cases(), na.omit(): remove missing values
is.na(), anyNA(): check for missing values
na_if(): replace values with NA
left_join(), right_join(), full_join(), inner_join(): merge datasets

ID 543: Day 3

Homework review

Today’s goals

Summary statistics

Summary statistics

New function: summarize()

summarize() specifics

Combining with other functions

Exercise

What if we want both groups at once?

We can group our calculations using the .by = argument in summarize()

Why .by =?

Counting groups

Group by multiple variables

Alternative group_by() approach

Tibbles are “group-ed” when using group_by()

.by = vs. group_by()

Exercise

Getting other data into R

What do you notice about the differences?

.rds is an R-specific file for a single object

.csv files are much more general but don’t maintain things like factors

.csv files might need a little more specification to read in

Corresponding write_() function

Other functions come from the {haven} package

Or {readxl}

Exercise

Where are these files?

The problem with setwd()

Do you think this code from 2015 still works?

Better alternative: R Projects

R Projects

Referring to files with the here package

Referring to the here package

Exercise

Missing values

Special NA functions

Induce missingness

Read in NA’s directly

Complete cases

Exercise

Joins

Why join?

Merging with kids

Left join

by = join_by() syntax

Left join

Right join

Full join: we want everything!

Inner join: we only want matches

Join by multiple variables

Exercise

Today’s summary

Today’s functions

New function: `summarize()`

`summarize()` specifics

We can group our calculations using the `.by =` argument in `summarize()`

Why `.by =`?

Alternative `group_by()` approach

Tibbles are “group-ed” when using `group_by()`

`.by =` vs. `group_by()`

`.rds` is an R-specific file for a single object

`.csv` files are much more general but don’t maintain things like factors

`.csv` files might need a little more specification to read in

Corresponding `write_()` function

Other functions come from the `{haven}` package

Or `{readxl}`

The problem with `setwd()`

Referring to files with the `here` package

Referring to the `here` package

Special `NA` functions

`by = join_by()` syntax