I have a dataset in which the number of pixels is counted for each value looking like this:
HISTO_2 HISTO_3 HISTO_4 HISTO_5 HISTO_6 HISTO_7 HISTO_10 HISTO_11 HISTO_14 HISTO_18 HISTO_19 HISTO_23
1 0 390 652 157 32 7 0 0 0 0 0 0
2 0 22 41 27 23 11 8 5 4 11 2 4
3 0 916 671 167 40 7 4 5 2 1 2 2
4 0 2600 810 172 38 0 0 0 0 0 0 0
5 0 110 987 791 248 59 11 5 0 1 0 0
6 0 778 808 182 43 5 0 0 0 0 0 0
7 0 1095 846 199 55 12 8 3 0 0 0 0
8 0 1045 545 60 0 0 0 0 0 0 0 0
9 0 868 422 92 2 0 0 0 0 0 0 0
10 0 1225 597 160 57 27 0 0 0 0 0 0
11 0 1092 1096 635 150 33 0 0 0 0 0 0
HISTO_2 caputres the number of pixels with value 2, HISTO_3 caputres the number of pixels with value 3 and so on. I need to find a way that enables me to efficiently count the total value of all pixels per row. This has to be done for ten datasets. As can be seen in the table, the value of the columns doesn't follow a balanced sequence and for each dataset, the sequence can be different.
Any efficient solutions to my problem?
PS: If you come up better title for my question, feel free to edit :)
CodePudding user response：
Is this what you are looking for:
apply(df,1,sum,na.rm=T)
1 2 3 4 5 6 7 8 9 10 11
1238 158 1817 3620 2212 1816 2218 1650 1384 2066 3006
CodePudding user response：
library(dplyr)
dataset1 <- data.frame(HISTO_2 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
HISTO_3 = c(390, 22, 916, 2600, 110, 778, 1095, 1045, 868, 1225, 1092),
HISTO_4 = c(652, 41, 671, 810, 987, 808, 846, 545, 422, 597, 1096),
HISTO_5 = c(157, 27, 167, 172, 791, 182, 199, 60, 92, 160, 635),
HISTO_6 = c(32, 23, 40, 38, 248, 43, 55, 0, 2, 57, 150),
HISTO_7 = c(7, 11, 7, 0, 59, 5, 12, 0, 0, 27, 33),
HISTO_10 = c(0, 8, 4, 0, 11, 0, 8, 0, 0, 0, 0),
HISTO_11 = c(0, 5, 5, 0, 5, 0, 3, 0, 0, 0, 0),
HISTO_14 = c(0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0),
HISTO_18 = c(0, 11, 1, 0, 1, 0, 0, 0, 0, 0, 0),
HISTO_19 = c(0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0),
HISTO_23 = c(0, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0))
dataset1 %>%
summarize(total_pixels = rowSums(.[1:11]))
CodePudding user response：
With dplyr
library(dplyr)
df %>%
rowwise() %>%
mutate(total = sum(c_across(everything()))) %>%
ungroup() %>%
print(Inf)
# A tibble: 11 × 13
HISTO_2 HISTO_3 HISTO_4 HISTO_5 HISTO_6 HISTO_7 HISTO_10 HISTO_11 HISTO_14
<int> <int> <int> <int> <int> <int> <int> <int> <int>
1 0 390 652 157 32 7 0 0 0
2 0 22 41 27 23 11 8 5 4
3 0 916 671 167 40 7 4 5 2
4 0 2600 810 172 38 0 0 0 0
5 0 110 987 791 248 59 11 5 0
6 0 778 808 182 43 5 0 0 0
7 0 1095 846 199 55 12 8 3 0
8 0 1045 545 60 0 0 0 0 0
9 0 868 422 92 2 0 0 0 0
10 0 1225 597 160 57 27 0 0 0
11 0 1092 1096 635 150 33 0 0 0
HISTO_18 HISTO_19 HISTO_23 total
<int> <int> <int> <int>
1 0 0 0 1238
2 11 2 4 158
3 1 2 2 1817
4 0 0 0 3620
5 1 0 0 2212
6 0 0 0 1816
7 0 0 0 2218
8 0 0 0 1650
9 0 0 0 1384
10 0 0 0 2066
11 0 0 0 3006
For all 10 data sets
df_list <- list(df1, df2, df3, df4, df5, df6, df7, df8, df9, df10)
lapply(df_list ,function(x)
x %>%
rowwise() %>%
mutate(total = sum(c_across(everything()))) %>%
ungroup())
}
Data
df <- structure(list(HISTO_2 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), HISTO_3 = c(390L, 22L, 916L, 2600L, 110L, 778L, 1095L,
1045L, 868L, 1225L, 1092L), HISTO_4 = c(652L, 41L, 671L, 810L,
987L, 808L, 846L, 545L, 422L, 597L, 1096L), HISTO_5 = c(157L,
27L, 167L, 172L, 791L, 182L, 199L, 60L, 92L, 160L, 635L), HISTO_6 = c(32L,
23L, 40L, 38L, 248L, 43L, 55L, 0L, 2L, 57L, 150L), HISTO_7 = c(7L,
11L, 7L, 0L, 59L, 5L, 12L, 0L, 0L, 27L, 33L), HISTO_10 = c(0L,
8L, 4L, 0L, 11L, 0L, 8L, 0L, 0L, 0L, 0L), HISTO_11 = c(0L, 5L,
5L, 0L, 5L, 0L, 3L, 0L, 0L, 0L, 0L), HISTO_14 = c(0L, 4L, 2L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), HISTO_18 = c(0L, 11L, 1L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L), HISTO_19 = c(0L, 2L, 2L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L), HISTO_23 = c(0L, 4L, 2L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11"))
CodePudding user response：
Since you are interested in the TOTAL VALUE of the row pixels, you could do:
Base R:
colSums(t(df) * as.numeric(gsub('\\D', '',names(df))))
1 2 3 4 5 6 7 8 9 10 11
4804 1099 6781 12128 10317 6769 8191 5615 4764 7394 11966
or even:
as.matrix(df)%*%as.numeric(gsub('\\D', '',names(df)))
[,1]
1 4804
2 1099
3 6781
4 12128
5 10317
6 6769
7 8191
8 5615
9 4764
10 7394
11 11966
if you are unfamiliar with gsub and \\D ie regex, then use
library(readr)
as.matrix(df) %*% parse_number(names(df))
[,1]
1 4804
2 1099
3 6781
4 12128
5 10317
6 6769
7 8191
8 5615
9 4764
10 7394
11 11966