EDIT: input
very new to this.
I have a similar problem to this: group by and then count missing variables?
Taking the input data from that question:
df1 <- data.frame(
Z = sample(LETTERS[1:5], size = 10000, replace = T),
X1 = sample(c(1:10,NA), 10000, replace = T),
X2 = sample(c(1:25,NA), 10000, replace = T),
X3 = sample(c(1:5,NA), 10000, replace = T))
as one user proposed, it's possible to use summarise_each:
df1 %>%
group_by(Z) %>%
summarise_each(funs(sum(is.na(.))))
#Source: local data frame [5 x 4]
#
# Z X1 X2 X3
# (fctr) (int) (int) (int)
#1 A 169 77 334
#2 B 170 77 316
#3 C 159 78 348
#4 D 181 79 326
#5 E 174 69 341
However, I would like to get only the total number of missing values per group.
I've also tried this but it didn't work: R count NA by group
Ideally, it should give me something like:
# Z sumNA
# (fctr) (int)
#1 A 580
#2 B 493
#3 C 585
#4 D 586
#5 E 584
Thanks in advance.
You can use the tidyverse approach.
require(tidyverse)
#Sample data
dat <- data.frame(group = rep(c("a", "b", "c", "d", "g"), 3),
y = rep(c(1, NA, 2, NA, 3), 3))
dat %>%
group_by(group) %>%
summarise(sumNA = sum(is.na(y)))
Output:
group sumNA
<fct> <int>
1 a 0
2 b 3
3 c 0
4 d 3
5 g 0
However, if you have more than one column, you can use summarize_all (or summarize_at if you'd like to specify the columns; thank you @ bschneidr for the comment):
#Sample data
set.seed(123)
dat <- data.frame(group = sample(letters[1:4], 10, replace = T),
x = sample(c(1,NA), 10, replace = T),
y = sample(c(1,NA), 10, replace = T),
z = sample(c(1, NA), 10, replace = T))
dat %>%
group_by(group) %>%
summarize_all(.funs = funs('NA' = sum(is.na(.))))
# A tibble: 4 x 4
group x_NA y_NA z_NA
<fct> <int> <int> <int>
1 a 1 1 0
2 b 3 2 2
3 c 0 1 1
4 d 1 4 2
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