Tidy up all of the R builtin datasets.
library(tidyr)
library(dplyr)
knitr::opts_chunk$set(comment='#>')
format_strs <- function(strings, sep='.', ...) {
strings %>%
stringr::str_to_lower() %>%
stringr::str_replace_all(' ', sep)
}ability.cov_tidy <- ability.cov %>%
as.data.frame() %>%
select(-n.obs, -center) %>%
mutate(test = rownames(.)) %>%
gather(cov, value, -test) %>%
tbl_df()
ability.cov_tidy #> Source: local data frame [36 x 3]
#>
#> test cov value
#> (chr) (chr) (dbl)
#> 1 general cov.general 24.641
#> 2 picture cov.general 5.991
#> 3 blocks cov.general 33.520
#> 4 maze cov.general 6.023
#> 5 reading cov.general 20.755
#> 6 vocab cov.general 29.701
#> 7 general cov.picture 5.991
#> 8 picture cov.picture 6.700
#> 9 blocks cov.picture 18.137
#> 10 maze cov.picture 1.782
#> .. ... ... ...
airmiles_tidy <- airmiles %>%
as.data.frame() %>%
setNames('airmiles') %>%
mutate(year = time(airmiles)) %>%
tbl_df()
airmiles_tidy#> Source: local data frame [24 x 2]
#>
#> airmiles year
#> (dbl) (dbl)
#> 1 412 1937
#> 2 480 1938
#> 3 683 1939
#> 4 1052 1940
#> 5 1385 1941
#> 6 1418 1942
#> 7 1634 1943
#> 8 2178 1944
#> 9 3362 1945
#> 10 5948 1946
#> .. ... ...
# gawd time series are awful ...
# from http://stackoverflow.com/questions/5331901/transforming-a-ts-in-a-data-frame-and-back
dmn <- list(month.abb, unique(floor(time(AirPassengers))))
AirPassengers_df <- tbl_df(data.frame(matrix(AirPassengers, 12, dimnames = dmn)))
AirPassengers_tidy <- AirPassengers_df %>%
mutate(month = rownames(.)) %>%
gather(year, value, -month)
AirPassengers_tidy#> Source: local data frame [144 x 3]
#>
#> month year value
#> (chr) (chr) (dbl)
#> 1 Jan X1949 112
#> 2 Feb X1949 118
#> 3 Mar X1949 132
#> 4 Apr X1949 129
#> 5 May X1949 121
#> 6 Jun X1949 135
#> 7 Jul X1949 148
#> 8 Aug X1949 148
#> 9 Sep X1949 136
#> 10 Oct X1949 119
#> .. ... ... ...
# Figure out how to get the 'Qtr1' ... colnames# Combine the beaver1 and beaver2 datasets ...
beaver1_tidy <- beaver1 %>%
mutate(obs = seq_len(n())) %>%
gather(key, value, -obs) %>%
mutate(beaver = '1')
beaver2_tidy <- beaver2 %>%
mutate(obs = seq_len(n())) %>%
gather(key, value, -obs) %>%
mutate(beaver = '2')
beavers_tidy <- rbind_list(beaver1_tidy, beaver2_tidy)#> Warning: `rbind_list()` is deprecated. Please use `bind_rows()` instead.
beavers_tidy#> Source: local data frame [856 x 4]
#>
#> obs key value beaver
#> (int) (chr) (dbl) (chr)
#> 1 1 day 346 1
#> 2 2 day 346 1
#> 3 3 day 346 1
#> 4 4 day 346 1
#> 5 5 day 346 1
#> 6 6 day 346 1
#> 7 7 day 346 1
#> 8 8 day 346 1
#> 9 9 day 346 1
#> 10 10 day 346 1
#> .. ... ... ... ...
BOD_tidy <- BOD %>% tbl_df %>%
mutate(obs = seq_len(n())) %>%
gather(key, value, -obs)
BOD_tidy#> Source: local data frame [12 x 3]
#>
#> obs key value
#> (int) (chr) (dbl)
#> 1 1 Time 1.0
#> 2 2 Time 2.0
#> 3 3 Time 3.0
#> 4 4 Time 4.0
#> 5 5 Time 5.0
#> 6 6 Time 7.0
#> 7 1 demand 8.3
#> 8 2 demand 10.3
#> 9 3 demand 19.0
#> 10 4 demand 16.0
#> 11 5 demand 15.6
#> 12 6 demand 19.8
cars_tidy <- cars %>% tbl_df %>%
mutate(obs = seq_len(n())) %>%
gather(key, value, -obs)
cars_tidy#> Source: local data frame [100 x 3]
#>
#> obs key value
#> (int) (chr) (dbl)
#> 1 1 speed 4
#> 2 2 speed 4
#> 3 3 speed 7
#> 4 4 speed 7
#> 5 5 speed 8
#> 6 6 speed 9
#> 7 7 speed 10
#> 8 8 speed 10
#> 9 9 speed 10
#> 10 10 speed 11
#> .. ... ... ...
ChickWeight_tidy <- ChickWeight %>%
gather(key, value, -Chick, convert = TRUE) %>%
tbl_df
ChickWeight_tidy#> Source: local data frame [1,734 x 3]
#>
#> Chick key value
#> (fctr) (chr) (chr)
#> 1 1 weight 42
#> 2 1 weight 51
#> 3 1 weight 59
#> 4 1 weight 64
#> 5 1 weight 76
#> 6 1 weight 93
#> 7 1 weight 106
#> 8 1 weight 125
#> 9 1 weight 149
#> 10 1 weight 171
#> .. ... ... ...
# `chickwts` already tidy# `CO2` already tidy# `DNase` already tidy# `esoph` already tidy# `faithful` already tidy# `Formaldehyde` already tidymtcars_tidy <- mtcars %>%
mutate(car = format_strs(rownames(.))) %>%
gather(key, value, -car) %>% tbl_df
mtcars_tidy#> Source: local data frame [352 x 3]
#>
#> car key value
#> (chr) (chr) (dbl)
#> 1 mazda.rx4 mpg 21.0
#> 2 mazda.rx4.wag mpg 21.0
#> 3 datsun.710 mpg 22.8
#> 4 hornet.4.drive mpg 21.4
#> 5 hornet.sportabout mpg 18.7
#> 6 valiant mpg 18.1
#> 7 duster.360 mpg 14.3
#> 8 merc.240d mpg 24.4
#> 9 merc.230 mpg 22.8
#> 10 merc.280 mpg 19.2
#> .. ... ... ...
WorldPhones_tidy <- WorldPhones %>%
as.data.frame() %>%
mutate(year = rownames(.)) %>%
gather(continent, value, -year) %>% tbl_df
WorldPhones_tidy#> Source: local data frame [49 x 3]
#>
#> year continent value
#> (chr) (chr) (dbl)
#> 1 1951 N.Amer 45939
#> 2 1956 N.Amer 60423
#> 3 1957 N.Amer 64721
#> 4 1958 N.Amer 68484
#> 5 1959 N.Amer 71799
#> 6 1960 N.Amer 76036
#> 7 1961 N.Amer 79831
#> 8 1951 Europe 21574
#> 9 1956 Europe 29990
#> 10 1957 Europe 32510
#> .. ... ... ...