dplyr pipe multiple datasets to summarize() - r

Question

I am making a table using dplyr. I want to perform the same "summarize" command on multiple datasets. I know in ggplot2, you can just change out the dataset and rerun the plot, which is cool.
here's what I want to avoid:
table_1 <-
group_by(df_1, boro) %>%
summarize(n_units = n(),
mean_rent = mean(rent_numeric, na.rm = TRUE),
sd_rend = sd(rent_numeric,na.rm = TRUE),
median_rent = median(rent_numeric, na.rm = TRUE),
mean_bedrooms = mean(bedrooms_numeric, na.rm = TRUE),
sd_bedrooms = sd(bedrooms_numeric, na.rm = TRUE),
mean_sqft = mean(sqft, na.rm = TRUE),
sd_sqft = sd(sqft, na.rm = TRUE),
n_broker = sum(ob=="broker"),
pr_broker = n_broker/n_units)
table_2 <-
group_by(df_2, boro) %>%
summarize(n_units = n(),
mean_rent = mean(rent_numeric, na.rm = TRUE),
sd_rend = sd(rent_numeric,na.rm = TRUE),
median_rent = median(rent_numeric, na.rm = TRUE),
mean_bedrooms = mean(bedrooms_numeric, na.rm = TRUE),
sd_bedrooms = sd(bedrooms_numeric, na.rm = TRUE),
mean_sqft = mean(sqft, na.rm = TRUE),
sd_sqft = sd(sqft, na.rm = TRUE),
n_broker = sum(ob=="broker"),
pr_broker = n_broker/n_units)
Basically, is there a way to set up the summarize command as a function or something maybe so I can just pour in df_1 and df_2?

Answer 1

If you know all the variable names in advance and if they are the same in all the data sets you want to look at, you can just do something like:
myfunc <- function(df) {
df %>%
group_by(cyl) %>%
summarize(n = n(),
mean_hp = mean(hp))
}
myfunc(mtcars)
#Source: local data frame [3 x 3]
#
# cyl n mean_hp
#1 4 11 82.63636
#2 6 7 122.28571
#3 8 14 209.21429
And then use it with a different data set (that would have the same structure and variable names). If you need flexibility, i.e. you don't know all the variables in advance and what to be able to specify them as input in the function, look at the dplyr non standard evaluation vignette.
Here's just a tiny example of how you could implement "standard evaluation" into your function to allow for more flexibility. Consider if you wanted to allow the user of the function to specify by which column the data should be grouped, you could do:
myfunc <- function(df, grp) {
df %>%
group_by_(grp) %>% # notice that I use "group_by_" instead of "group_by"
summarize(n = n(),
mean_hp = mean(hp))
}
and then use it:
myfunc(mtcars, "gear")
#Source: local data frame [3 x 3]
#
# gear n mean_hp
#1 3 15 176.1333
#2 4 12 89.5000
#3 5 5 195.6000
myfunc(mtcars, "cyl")
#Source: local data frame [3 x 3]
#
# cyl n mean_hp
#1 4 11 82.63636
#2 6 7 122.28571
#3 8 14 209.21429

Answer 2

The %>% operator just passes on a tbl object as the first parameter to the next function. And summarize just expects a tbl. So you can define
mysummary <- function(.data) {
summarize(.data, n_units = n(),
mean_rent = mean(rent_numeric, na.rm = TRUE),
sd_rend = sd(rent_numeric,na.rm = TRUE),
median_rent = median(rent_numeric, na.rm = TRUE),
mean_bedrooms = mean(bedrooms_numeric, na.rm = TRUE),
sd_bedrooms = sd(bedrooms_numeric, na.rm = TRUE),
mean_sqft = mean(sqft, na.rm = TRUE),
sd_sqft = sd(sqft, na.rm = TRUE),
n_broker = sum(ob=="broker"),
pr_broker = n_broker/n_units)
}
And then call
table_1 <- group_by(df_1, boro) %>% mysummary
table_2 <- group_by(df_2, boro) %>% mysummary
With an actual working example
mysummary <- function(.data) {
summarize(.data,
ave.mpg=mean(mpg),
ave.hp=mean(hp)
)
}
mtcars %>% group_by(cyl) %>% mysummary
mtcars %>% group_by(gear) %>% mysummary