(Builds on: Visualization basics, Manipulation basics)
(Leads to: Exploratory data analysis (2D), Function basics, Essentials of relational data, String basics, Tidy data)
Exploratory data analysis is partly a set of techniques, but is mostly a mindset: you want to remain open to what the data is telling you.
library(tidyverse)
library(nycflights13)
Whenever you start working with a new variable, it’s a really good idea to first take a look at the variable by itself, before you start combining it with other variables. As well as the visual techniques you’ll learn in the readings, another quick and dirty function is count().
df %>% count(grp) is shorthand for df %>% group_by(grp) %>% summarise(n = n()).
flights %>%
count(carrier)
#> # A tibble: 16 x 2
#> carrier n
#> <chr> <int>
#> 1 9E 18460
#> 2 AA 32729
#> 3 AS 714
#> 4 B6 54635
#> 5 DL 48110
#> 6 EV 54173
#> 7 F9 685
#> 8 FL 3260
#> 9 HA 342
#> 10 MQ 26397
#> 11 OO 32
#> 12 UA 58665
#> 13 US 20536
#> 14 VX 5162
#> 15 WN 12275
#> 16 YV 601
It has two convenient arguments:
sort = TRUE automatically arranges the result so the most common values are at the top
flights %>%
count(dest, sort = TRUE)
#> # A tibble: 105 x 2
#> dest n
#> <chr> <int>
#> 1 ORD 17283
#> 2 ATL 17215
#> 3 LAX 16174
#> 4 BOS 15508
#> 5 MCO 14082
#> 6 CLT 14064
#> 7 SFO 13331
#> 8 FLL 12055
#> 9 MIA 11728
#> 10 DCA 9705
#> # … with 95 more rows
wt = my_variable switches from a count to a weighted sum of my_variable. For example, the following code gives the total distance traveled by each carrier. It is particularly useful if you have data that has already been aggregated.
flights %>%
count(carrier, wt = distance)
#> # A tibble: 16 x 2
#> carrier n
#> <chr> <dbl>
#> 1 9E 9788152
#> 2 AA 43864584
#> 3 AS 1715028
#> 4 B6 58384137
#> 5 DL 59507317
#> 6 EV 30498951
#> 7 F9 1109700
#> 8 FL 2167344
#> 9 HA 1704186
#> 10 MQ 15033955
#> 11 OO 16026
#> 12 UA 89705524
#> 13 US 11365778
#> 14 VX 12902327
#> 15 WN 12229203
#> 16 YV 225395
You can also count() the value of expression. This is a useful technique to get a quick count of how many missing values there are:
flights %>%
count(is.na(dep_delay))
#> # A tibble: 2 x 2
#> `is.na(dep_delay)` n
#> <lgl> <int>
#> 1 FALSE 328521
#> 2 TRUE 8255
flights %>%
count(
dep_missing = is.na(dep_time),
arr_missing = is.na(arr_time)
)
#> # A tibble: 3 x 3
#> dep_missing arr_missing n
#> <lgl> <lgl> <int>
#> 1 FALSE FALSE 328063
#> 2 FALSE TRUE 458
#> 3 TRUE TRUE 8255
You can combine count() with the cut_* functions from ggplot2 to compute histograms “by hand”:
# five bins of equal widths
flights %>%
count(cut_interval(arr_delay, 5))
#> # A tibble: 6 x 2
#> `cut_interval(arr_delay, 5)` n
#> <fct> <int>
#> 1 [-86,186] 323807
#> 2 (186,457] 3465
#> 3 (457,729] 45
#> 4 (729,1e+03] 25
#> 5 (1e+03,1.27e+03] 4
#> 6 <NA> 9430
# five bins with approximately equal numbers of points
flights %>%
count(cut_number(arr_delay, 5))
#> # A tibble: 6 x 2
#> `cut_number(arr_delay, 5)` n
#> <fct> <int>
#> 1 [-86,-19] 70875
#> 2 (-19,-10] 61570
#> 3 (-10,1] 66972
#> 4 (1,21] 62970
#> 5 (21,1.27e+03] 64959
#> 6 <NA> 9430
# hourly bins
flights %>%
count(cut_width(arr_delay, 60, boundary = 0))
#> # A tibble: 22 x 2
#> `cut_width(arr_delay, 60, boundary = 0)` n
#> <fct> <int>
#> 1 [-120,-60] 240
#> 2 (-60,0] 194102
#> 3 (0,60] 105215
#> 4 (60,120] 17755
#> 5 (120,180] 6191
#> 6 (180,240] 2291
#> 7 (240,300] 941
#> 8 (300,360] 365
#> 9 (360,420] 144
#> 10 (420,480] 37
#> # … with 12 more rows
Introduction [r4ds-7.1]
Questions [r4ds-7.2]
Variation [r4ds-7.3]