library(tidyverse) library(glue) # similar to paste() but more powerful
All of the ideas in this talk come from other people, primarily the R for Data Science iteration chapter.
Load a couple libraries
R lets you create tools that work the way you want them to
For example you can define a new ifelse function that treats NA like any other value.
ifelse2 <- function(test, yes, no) ifelse(!is.na(test) & test, yes, no)
tibble(age = c(0, NA, 5, 15, 21, 35)) %>%
mutate(ifelse = ifelse(age < 18, "Minor", "Adult"), # Base R version
ifelse2 = ifelse2(age < 18, "Minor", "Adult"), # My custom version
if_else = if_else(age < 18, "Minor", "Adult")) # Tidyverse version
## # A tibble: 6 x 4 ## age ifelse ifelse2 if_else ## <dbl> <chr> <chr> <chr> ## 1 0 Minor Minor Minor ## 2 NA <NA> Adult <NA> ## 3 5 Minor Minor Minor ## 4 15 Minor Minor Minor ## 5 21 Adult Adult Adult ## 6 35 Adult Adult Adult
The tidyverse developers have re-implemented a lot of base R functionality
if_else has an argument for explicit NA handling
tibble(age = c(0, NA, 5, 15, 21, 35)) %>%
mutate(ifelse = ifelse(age < 18, "Minor", "Adult"),
ifelse2 = ifelse2(age < 18, "Minor", "Adult"),
if_else = if_else(age < 18, "Minor", "Adult", missing = "We don't know"))
## # A tibble: 6 x 4 ## age ifelse ifelse2 if_else ## <dbl> <chr> <chr> <chr> ## 1 0 Minor Minor Minor ## 2 NA <NA> Adult We don't know ## 3 5 Minor Minor Minor ## 4 15 Minor Minor Minor ## 5 21 Adult Adult Adult ## 6 35 Adult Adult Adult
Strangely, case_when treats NA like any other value
tibble(age = c(0, NA, 5, 15, 21, 35)) %>%
mutate(case_when = case_when(
age < 1 ~ "Baby",
age < 18 ~ "Minor",
T ~ "Adult"))
## # A tibble: 6 x 2 ## age case_when ## <dbl> <chr> ## 1 0 Baby ## 2 NA Adult ## 3 5 Minor ## 4 15 Minor ## 5 21 Adult ## 6 35 Adult
tibble(age = c(0, NA, 5, 15, 21, 35)) %>%
mutate(case_when = case_when(
age < 1 ~ "Baby",
age < 18 ~ "Minor",
is.na(age) ~ "We don't know",
T ~ "Adult"))
## # A tibble: 6 x 2 ## age case_when ## <dbl> <chr> ## 1 0 Baby ## 2 NA We don't know ## 3 5 Minor ## 4 15 Minor ## 5 21 Adult ## 6 35 Adult
The tidyverse functions tend to be more strict than base R
ifelse(1:6 > 3, ">3", c("less", "than 3"))
## [1] "less" "than 3" "less" ">3" ">3" ">3"
ifelse(1:6 > 3, ">3", 4)
## [1] "4" "4" "4" ">3" ">3" ">3"
if_else(1:6 > 3, ">3", c("less", "than 3"))
## Error: `false` must be length 6 (length of `condition`) or one, not 2
if_else(1:6 > 3, ">3", 4)
## Error: `false` must be a character vector, not a double vector
However this strictness has consequences
if_else(1:6 > 3, ">3", NA)
## Error: `false` must be a character vector, not a logical vector
typeof(NA)
## [1] "logical"
if_else(1:6 > 3, ">3", NA_character_)
## [1] NA NA NA ">3" ">3" ">3"
There are lots of convenience functions in the tidyverse
complete() makes implicit missing values explicit by filling in missing combinations.
tibble(patkey = 1:3, visit_number = 1:3) %>% complete(patkey, visit_number)
## # A tibble: 9 x 2 ## patkey visit_number ## <int> <int> ## 1 1 1 ## 2 1 2 ## 3 1 3 ## 4 2 1 ## 5 2 2 ## 6 2 3 ## 7 3 1 ## 8 3 2 ## 9 3 3
Create some fake visit data
Creating fake data is really helpful for creating a reproducible example (aka reprex)
npats <- 100
nvisits <- 10
create_visit_df <- function(visit_number) {
tibble(
patid = 1:npats,
visitdate = lubridate::as_date(sample(0:1e4, 100)),
blooddraw = rbinom(npats, 1, prob = .7),
bloodrawwho_nurse = blooddraw*rbinom(npats, 1, prob = .7),
bloodrawwho_other = blooddraw*(1-bloodrawwho_nurse)
) %>%
rename_at(vars(-patid), ~paste0(., "_", visit_number))
}
visit_wide <- map(1:nvisits, create_visit_df) %>%
reduce(inner_join, by = "patid")
One row per patient with grouped columns
The goal: One row per visit
## # A tibble: 1,000 x 5 ## patid visitdate blooddraw bloodrawwho_nurse bloodrawwho_other ## <int> <date> <int> <int> <dbl> ## 1 1 1986-09-08 1 1 0 ## 2 1 1986-10-19 0 0 0 ## 3 1 1980-08-15 0 0 0 ## 4 1 1975-11-30 1 0 1 ## 5 1 1993-03-26 0 0 0 ## 6 1 1994-03-24 1 0 1 ## 7 1 1973-05-16 0 0 0 ## 8 1 1995-04-21 1 0 1 ## 9 1 1990-11-17 1 0 1 ## 10 1 1992-01-12 1 1 0 ## # … with 990 more rows
Visually…
Lists
In computer science, a list or sequence is an abstract data type that represents a countable number of ordered values, where the same value may occur more than once.
https://en.wikipedia.org/wiki/List_(abstract_data_type)
Basically it is just a container.
Lists can contain anything
random_stuff <- list(mtcars,
qplot(mpg, cyl, data = mtcars),
function(x) x^2,
list("a", "b", "c"))
str(random_stuff, max.level = 1)
## List of 4 ## $ :'data.frame': 32 obs. of 11 variables: ## $ :List of 9 ## ..- attr(*, "class")= chr [1:2] "gg" "ggplot" ## $ :function (x) ## ..- attr(*, "srcref")= 'srcref' int [1:8] 4 22 4 36 22 36 4 4 ## .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x8026b08> ## $ :List of 3
More complex objects in R are often just lists.
Lists that contain objects of one type are especially useful
list(1:3, 4:10, seq(0, 20, by = 2))
## [[1]] ## [1] 1 2 3 ## ## [[2]] ## [1] 4 5 6 7 8 9 10 ## ## [[3]] ## [1] 0 2 4 6 8 10 12 14 16 18 20
Apply a function to each element of a list
Put the output of the element in a new list
numeric_list <- list(1:3, 4:10, seq(0, 20, by = 2)) map(numeric_list, sum)
## [[1]] ## [1] 6 ## ## [[2]] ## [1] 49 ## ## [[3]] ## [1] 110
Apply a custom function
numeric_list <- list(1:3, 4:10, seq(0, 20, by = 2)) L2norm <- function(x) sqrt(sum(x^2)) map(numeric_list, L2norm)
## [[1]] ## [1] 3.741657 ## ## [[2]] ## [1] 19.26136 ## ## [[3]] ## [1] 39.24283
Create a custom function on the fly
Also called an anonymous function since it has no name
numeric_list <- list(1:3, 4:10, seq(0, 20, by = 2)) map(numeric_list, function(x) sqrt(sum(x^2)))
## [[1]] ## [1] 3.741657 ## ## [[2]] ## [1] 19.26136 ## ## [[3]] ## [1] 39.24283
Use the ~ . shortcut to create a custom function on the fly
This shortcut only works in the tidyverse.
numeric_list <- list(1:3, 4:10, seq(0, 20, by = 2)) map(numeric_list, ~sqrt(sum(.^2)))
## [[1]] ## [1] 3.741657 ## ## [[2]] ## [1] 19.26136 ## ## [[3]] ## [1] 39.24283
Reduce a list to a single value
Use a function that takes two arguments and returns one value to reduce a list.
+ is such a function
numeric_list <- list(1:3, 4:10, seq(0, 20, by = 2)) map(numeric_list, ~sqrt(sum(.^2))) %>% reduce(`+`)
## [1] 62.24585
Another example using glue for string concatenation
Anything inside {} is evaluated as code
map(1:3, ~glue("_{.}")) %>%
reduce(c)
## [1] "_1" "_2" "_3"
Variants of map will do the reduce step for us
map_chr returns a character vector.
There are many other variants of the map function.
map_chr(1:3, ~glue("_{.}"))
## [1] "_1" "_2" "_3"
And just for the fun of it…
Back to the problem at hand
We can easily select all variables for one visit
select(visit_wide, patid, ends_with("_1"))
## # A tibble: 100 x 5 ## patid visitdate_1 blooddraw_1 bloodrawwho_nurse_1 bloodrawwho_other_1 ## <int> <date> <int> <int> <dbl> ## 1 1 1986-09-08 1 1 0 ## 2 2 1973-07-28 0 0 0 ## 3 3 1980-10-20 1 1 0 ## 4 4 1997-03-23 1 1 0 ## 5 5 1970-03-08 1 1 0 ## 6 6 1991-11-23 0 0 0 ## 7 7 1973-02-05 0 0 0 ## 8 8 1983-04-28 1 0 1 ## 9 9 1981-03-06 1 1 0 ## 10 10 1982-12-08 1 1 0 ## # … with 90 more rows
Split the dataframe into a list of dataframes
df_list <- map(1:nvisits, ~select(visit_wide, patid, ends_with(glue("_{.}"))))
str(df_list, max.level = 1)
## List of 10 ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables: ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables: ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables: ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables: ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables: ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables: ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables: ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables: ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables: ## $ :Classes 'tbl_df', 'tbl' and 'data.frame': 100 obs. of 5 variables:
The first visit
df_list[[1]]
## # A tibble: 100 x 5 ## patid visitdate_1 blooddraw_1 bloodrawwho_nurse_1 bloodrawwho_other_1 ## <int> <date> <int> <int> <dbl> ## 1 1 1986-09-08 1 1 0 ## 2 2 1973-07-28 0 0 0 ## 3 3 1980-10-20 1 1 0 ## 4 4 1997-03-23 1 1 0 ## 5 5 1970-03-08 1 1 0 ## 6 6 1991-11-23 0 0 0 ## 7 7 1973-02-05 0 0 0 ## 8 8 1983-04-28 1 0 1 ## 9 9 1981-03-06 1 1 0 ## 10 10 1982-12-08 1 1 0 ## # … with 90 more rows
The second visit
df_list[[2]]
## # A tibble: 100 x 5 ## patid visitdate_2 blooddraw_2 bloodrawwho_nurse_2 bloodrawwho_other_2 ## <int> <date> <int> <int> <dbl> ## 1 1 1986-10-19 0 0 0 ## 2 2 1985-01-26 1 0 1 ## 3 3 1984-04-29 0 0 0 ## 4 4 1991-07-18 0 0 0 ## 5 5 1972-08-25 1 0 1 ## 6 6 1978-08-27 0 0 0 ## 7 7 1975-12-11 1 1 0 ## 8 8 1981-11-08 1 0 1 ## 9 9 1975-08-18 1 0 1 ## 10 10 1974-08-14 0 0 0 ## # … with 90 more rows
We need to rename the columns
df_list2 <- map(df_list, ~rename_all(., ~str_remove(., "_[:digit:]+$"))) map(df_list2, names)
## [[1]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other" ## ## [[2]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other" ## ## [[3]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other" ## ## [[4]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other" ## ## [[5]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other" ## ## [[6]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other" ## ## [[7]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other" ## ## [[8]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other" ## ## [[9]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other" ## ## [[10]] ## [1] "patid" "visitdate" "blooddraw" ## [4] "bloodrawwho_nurse" "bloodrawwho_other"
Finally we reduce using bind_rows
bind_rows takes two dataframes and rowbinds them reducing the list with bindrows will iteratively apply bind_rows so we end up with one dataframe
reduce(df_list2, bind_rows)
## # A tibble: 1,000 x 5 ## patid visitdate blooddraw bloodrawwho_nurse bloodrawwho_other ## <int> <date> <int> <int> <dbl> ## 1 1 1986-09-08 1 1 0 ## 2 2 1973-07-28 0 0 0 ## 3 3 1980-10-20 1 1 0 ## 4 4 1997-03-23 1 1 0 ## 5 5 1970-03-08 1 1 0 ## 6 6 1991-11-23 0 0 0 ## 7 7 1973-02-05 0 0 0 ## 8 8 1983-04-28 1 0 1 ## 9 9 1981-03-06 1 1 0 ## 10 10 1982-12-08 1 1 0 ## # … with 990 more rows
Using the pipe %>% we can do all this three lines
map(1:nvisits, ~select(visit_wide, patid, ends_with(glue("_{.}")))) %>%
map(~rename_all(., ~str_remove(., "_[:digit:]+$"))) %>%
reduce(bind_rows)
## # A tibble: 1,000 x 5 ## patid visitdate blooddraw bloodrawwho_nurse bloodrawwho_other ## <int> <date> <int> <int> <dbl> ## 1 1 1986-09-08 1 1 0 ## 2 2 1973-07-28 0 0 0 ## 3 3 1980-10-20 1 1 0 ## 4 4 1997-03-23 1 1 0 ## 5 5 1970-03-08 1 1 0 ## 6 6 1991-11-23 0 0 0 ## 7 7 1973-02-05 0 0 0 ## 8 8 1983-04-28 1 0 1 ## 9 9 1981-03-06 1 1 0 ## 10 10 1982-12-08 1 1 0 ## # … with 990 more rows
And since bind_rows will accept a list we don’t even need reduce in this example
map(1:nvisits, ~select(visit_wide, patid, ends_with(glue("_{.}")))) %>%
map(~rename_all(., ~str_remove(., "_[:digit:]+$"))) %>%
bind_rows()
## # A tibble: 1,000 x 5 ## patid visitdate blooddraw bloodrawwho_nurse bloodrawwho_other ## <int> <date> <int> <int> <dbl> ## 1 1 1986-09-08 1 1 0 ## 2 2 1973-07-28 0 0 0 ## 3 3 1980-10-20 1 1 0 ## 4 4 1997-03-23 1 1 0 ## 5 5 1970-03-08 1 1 0 ## 6 6 1991-11-23 0 0 0 ## 7 7 1973-02-05 0 0 0 ## 8 8 1983-04-28 1 0 1 ## 9 9 1981-03-06 1 1 0 ## 10 10 1982-12-08 1 1 0 ## # … with 990 more rows
Using the map_dfr variant we can do it in two lines
map(1:nvisits, ~select(visit_wide, patid, ends_with(glue("_{.}")))) %>%
map_dfr(~rename_all(., ~str_remove(., "_[:digit:]+$")))
## # A tibble: 1,000 x 5 ## patid visitdate blooddraw bloodrawwho_nurse bloodrawwho_other ## <int> <date> <int> <int> <dbl> ## 1 1 1986-09-08 1 1 0 ## 2 2 1973-07-28 0 0 0 ## 3 3 1980-10-20 1 1 0 ## 4 4 1997-03-23 1 1 0 ## 5 5 1970-03-08 1 1 0 ## 6 6 1991-11-23 0 0 0 ## 7 7 1973-02-05 0 0 0 ## 8 8 1983-04-28 1 0 1 ## 9 9 1981-03-06 1 1 0 ## 10 10 1982-12-08 1 1 0 ## # … with 990 more rows
Add a visit number column
map2 is similar to map but loops over two arguments .x and .y in parallel.
In this case we are looping over our datafame list and the numbers 1 to 10.
map(1:nvisits, ~select(visit_wide, patid, ends_with(glue("_{.}")))) %>%
map2(1:nvisits, ~mutate(.x, visit_number = .y)) %>%
map_dfr(~rename_all(., ~str_remove(., "_[:digit:]+$")))
## # A tibble: 1,000 x 6 ## patid visitdate blooddraw bloodrawwho_nur… bloodrawwho_oth… ## <int> <date> <int> <int> <dbl> ## 1 1 1986-09-08 1 1 0 ## 2 2 1973-07-28 0 0 0 ## 3 3 1980-10-20 1 1 0 ## 4 4 1997-03-23 1 1 0 ## 5 5 1970-03-08 1 1 0 ## 6 6 1991-11-23 0 0 0 ## 7 7 1973-02-05 0 0 0 ## 8 8 1983-04-28 1 0 1 ## 9 9 1981-03-06 1 1 0 ## 10 10 1982-12-08 1 1 0 ## # … with 990 more rows, and 1 more variable: visit_number <int>
It even works with a database
Create a SQLite database in memory and load our visit data into it
library(DBI) con <- dbConnect(RSQLite::SQLite(), ":memory:") dbWriteTable(con, "visit_wide", visit_wide) visit_db <- tbl(con, "visit_wide")
R writes the SQL for us and sends it to the database
We just change bind_rows to union_all
map(1:nvisits, ~select(visit_db, patid, ends_with(glue("_{.}")))) %>%
map(~rename_all(., ~str_remove(., "_[:digit:]+$"))) %>%
reduce(union_all)
## # Source: lazy query [?? x 5] ## # Database: sqlite 3.22.0 [:memory:] ## patid visitdate blooddraw bloodrawwho_nurse bloodrawwho_other ## <int> <dbl> <int> <int> <dbl> ## 1 1 6094 1 1 0 ## 2 2 1304 0 0 0 ## 3 3 3945 1 1 0 ## 4 4 9943 1 1 0 ## 5 5 66 1 1 0 ## 6 6 7996 0 0 0 ## 7 7 1131 0 0 0 ## 8 8 4865 1 0 1 ## 9 9 4082 1 1 0 ## 10 10 4724 1 1 0 ## # … with more rows
map saved us from having to write a lot of SQL
map(1:nvisits, ~select(visit_db, patid, ends_with(glue("_{.}")))) %>%
map(~rename_all(., ~str_remove(., "_[:digit:]+$"))) %>%
reduce(union_all) %>%
show_query()
## <SQL> ## SELECT `patid`, `visitdate_1` AS `visitdate`, `blooddraw_1` AS `blooddraw`, `bloodrawwho_nurse_1` AS `bloodrawwho_nurse`, `bloodrawwho_other_1` AS `bloodrawwho_other` ## FROM `visit_wide` ## UNION ALL ## SELECT `patid`, `visitdate_2` AS `visitdate`, `blooddraw_2` AS `blooddraw`, `bloodrawwho_nurse_2` AS `bloodrawwho_nurse`, `bloodrawwho_other_2` AS `bloodrawwho_other` ## FROM `visit_wide` ## UNION ALL ## SELECT `patid`, `visitdate_3` AS `visitdate`, `blooddraw_3` AS `blooddraw`, `bloodrawwho_nurse_3` AS `bloodrawwho_nurse`, `bloodrawwho_other_3` AS `bloodrawwho_other` ## FROM `visit_wide` ## UNION ALL ## SELECT `patid`, `visitdate_4` AS `visitdate`, `blooddraw_4` AS `blooddraw`, `bloodrawwho_nurse_4` AS `bloodrawwho_nurse`, `bloodrawwho_other_4` AS `bloodrawwho_other` ## FROM `visit_wide` ## UNION ALL ## SELECT `patid`, `visitdate_5` AS `visitdate`, `blooddraw_5` AS `blooddraw`, `bloodrawwho_nurse_5` AS `bloodrawwho_nurse`, `bloodrawwho_other_5` AS `bloodrawwho_other` ## FROM `visit_wide` ## UNION ALL ## SELECT `patid`, `visitdate_6` AS `visitdate`, `blooddraw_6` AS `blooddraw`, `bloodrawwho_nurse_6` AS `bloodrawwho_nurse`, `bloodrawwho_other_6` AS `bloodrawwho_other` ## FROM `visit_wide` ## UNION ALL ## SELECT `patid`, `visitdate_7` AS `visitdate`, `blooddraw_7` AS `blooddraw`, `bloodrawwho_nurse_7` AS `bloodrawwho_nurse`, `bloodrawwho_other_7` AS `bloodrawwho_other` ## FROM `visit_wide` ## UNION ALL ## SELECT `patid`, `visitdate_8` AS `visitdate`, `blooddraw_8` AS `blooddraw`, `bloodrawwho_nurse_8` AS `bloodrawwho_nurse`, `bloodrawwho_other_8` AS `bloodrawwho_other` ## FROM `visit_wide` ## UNION ALL ## SELECT `patid`, `visitdate_9` AS `visitdate`, `blooddraw_9` AS `blooddraw`, `bloodrawwho_nurse_9` AS `bloodrawwho_nurse`, `bloodrawwho_other_9` AS `bloodrawwho_other` ## FROM `visit_wide` ## UNION ALL ## SELECT `patid`, `visitdate_10` AS `visitdate`, `blooddraw_10` AS `blooddraw`, `bloodrawwho_nurse_10` AS `bloodrawwho_nurse`, `bloodrawwho_other_10` AS `bloodrawwho_other` ## FROM `visit_wide`
The main takeaway
The map and reduce functions allow us to program on a higher level of abstraction. As the number of visits increases SQL requires more code while the R code does not need to change. map and reduce basically just write for loops for you.
We can think of the scoped variants of dplyr in much the same way.
mutate_if(); mutate_at(); mutate_all() select_if(); select_at(); select_all() filter_if(); filter_at(); filter_all() rename_if(); rename_at(); rename_all() summarise_if(); summarise_at(); summarise_all()
These functions map over a dataframe which is a list of columns.
Extensions: Functions as data
lst(min, mean, median, max, sd) %>% map_dfr(~map(mtcars, .), .id = "summary")
## # A tibble: 5 x 12 ## summary mpg cyl disp hp drat wt qsec vs am gear carb ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 min 10.4 4 71.1 52 2.76 1.51 14.5 0 0 3 1 ## 2 mean 20.1 6.19 231. 147. 3.60 3.22 17.8 0.438 0.406 3.69 2.81 ## 3 median 19.2 6 196. 123 3.70 3.32 17.7 0 0 4 2 ## 4 max 33.9 8 472 335 4.93 5.42 22.9 1 1 5 8 ## 5 sd 6.03 1.79 124. 68.6 0.535 0.978 1.79 0.504 0.499 0.738 1.62
This works because a dataframe is a list. It is a list of columns.
is.list(mtcars)
## [1] TRUE
Extensions: List-columns in a dataframe
Since list can contain other lists we can put anything inside a dataframe as a list column.
Here we split the mtcars dataframe by the cyl variable and fit a linear model to each subset. Then we pull out the \(R^2\) value from each model.
model_df <- mtcars %>% nest(data = -cyl) %>% mutate(model = map(data, ~lm(mpg ~ ., data = .))) %>% mutate(r_squared = map_dbl(model, ~summary(.)$r.squared)) model_df
## # A tibble: 3 x 4 ## cyl data model r_squared ## <dbl> <list<df[,10]>> <list> <dbl> ## 1 6 [7 × 10] <lm> 1 ## 2 4 [11 × 10] <lm> 0.928 ## 3 8 [14 × 10] <lm> 0.675
Extensions: Many models idea from R4ds
We can permanently save all our models and data in a single object.
write_rds(model_df, "model_df.rds")
The dataframe then becomes a more general data structure to store like objects together on a single row. Not every problem or analysis should be fit into this framework but it is a powerful concept that applies to many problems analysts face.
Could this transposition have been done with pivot_longer() or gather()?
I don’t think so since pivot_longer and gather take many columns and collapse then into one column. Here we have columns of different types that can’t easily be put into a single column. I could be wrong though!
visit_wide %>% pivot_longer(cols = -patid)
## Error: No common type for `visitdate_1` <date> and `blooddraw_1` <integer>.