group_by()
by_package <- group_by(cran,package)
by_package
# A tibble: 225,468 × 11
# Groups: package [6,023]
X date time size r_version r_arch r_os package version country ip_id
<int> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <int>
1 1 2014-07-08 00:54:41 80589 3.1.0 x86_64 mingw32 htmltools 0.2.4 US 1
2 2 2014-07-08 00:59:53 321767 3.1.0 x86_64 mingw32 tseries 0.10-32 US 2
3 3 2014-07-08 00:47:13 748063 3.1.0 x86_64 linux-gnu party 1.0-15 US 3
4 4 2014-07-08 00:48:05 606104 3.1.0 x86_64 linux-gnu Hmisc 3.14-4 US 3
5 5 2014-07-08 00:46:50 79825 3.0.2 x86_64 linux-gnu digest 0.6.4 CA 4
6 6 2014-07-08 00:48:04 77681 3.1.0 x86_64 linux-gnu randomForest 4.6-7 US 3
7 7 2014-07-08 00:48:35 393754 3.1.0 x86_64 linux-gnu plyr 1.8.1 US 3
8 8 2014-07-08 00:47:30 28216 3.0.2 x86_64 linux-gnu whisker 0.3-2 US 5
9 9 2014-07-08 00:54:58 5928 NA NA NA Rcpp 0.10.4 CN 6
10 10 2014-07-08 00:15:35 2206029 3.0.2 x86_64 linux-gnu hflights 0.1 US 7
# … with 225,458 more rows
summarize(by_package,mean(size))
# A tibble: 6,023 × 2
package `mean(size)`
<chr> <dbl>
1 A3 62195.
2 abc 4826665
3 abcdeFBA 455980.
4 ABCExtremes 22904.
5 ABCoptim 17807.
6 ABCp2 30473.
7 abctools 2589394
8 abd 453631.
9 abf2 35693.
10 abind 32939.
# … with 6,013 more rows
pack_sum <- summarize(by_package,
count = n(),
unique = n_distinct(ip_id),
countries = n_distinct(country),
avg_bytes = mean(size))
pack_sum
# A tibble: 6,023 × 5
package count unique countries avg_bytes
<chr> <int> <int> <int> <dbl>
1 A3 25 24 10 62195.
2 abc 29 25 16 4826665
3 abcdeFBA 15 15 9 455980.
4 ABCExtremes 18 17 9 22904.
5 ABCoptim 16 15 9 17807.
6 ABCp2 18 17 10 30473.
7 abctools 19 19 11 2589394
8 abd 17 16 10 453631.
9 abf2 13 13 9 35693.
10 abind 396 365 50 32939.
# … with 6,013 more rows
quantile()
quantile(pack_sum$count, probs = 0.99)
top_counts <- filter(pack_sum, count > 679)
# A tibble: 61 × 5
package count unique countries avg_bytes
<chr> <int> <int> <int> <dbl>
1 bitops 1549 1408 76 28715.
2 car 1008 837 64 1229122.
3 caTools 812 699 64 176589.
4 colorspace 1683 1433 80 357411.
5 data.table 680 564 59 1252721.
6 DBI 2599 492 48 206933.
7 devtools 769 560 55 212933.
8 dichromat 1486 1257 74 134732.
9 digest 2210 1894 83 120549.
10 doSNOW 740 75 24 8364.
# … with 51 more rows
top_counts_sorted <- arrange(top_counts,desc(count))
pip
ctrl+shift+M
GSFA : group_by -> summarize -> filter -> arrange
result3 <-
cran %>%
group_by(package) %>%
summarize(count = n(),
unique = n_distinct(ip_id),
countries = n_distinct(country),
avg_bytes = mean(size)
) %>%
filter(countries > 60) %>%
arrange(desc(countries), avg_bytes)
