Below is the sample data and code. I have two issues. First, I need the indtotal column to be the sum by the twodigit code and have it stay constant as shown below. The reasons is so that I can do a simple calculation of one column divided by the other to arrive at the smbshare number. When I try the following,
second <- first %>%
group_by(twodigit,smb) %>%
summarize(indtotal = sum(employment))
it breaks it down by twodigit and smb.
Second issue is having it produce an 0 if the value does not exist. Best example is twodigit code of 51 and smb = 4. When there are not 4 distinct smb values for a given two digit, I am looking for it to produce a 0.
Note: smb is short for small business
naicstest <- c (512131,512141,521921,522654,512131,536978,541214,531214,621112,541213,551212,574121,569887,541211,523141,551122,512312,521114,522112)
employment <- c(11,130,315,17,190,21,22,231,15,121,19,21,350,110,515,165,12,110,111)
smb <- c(1,2,3,1,3,1,1,3,1,2,1,1,4,2,4,3,1,2,2)
first <- data.frame(naicstest,employment,smb)
first<-first %>% mutate(twodigit = substr(naicstest,1,2))
second <- first %>% group_by(twodigit) %>% summarize(indtotal = sum(employment))
Desired result is below
twodigit indtotal smb smbtotal smbshare
51 343 1 23 (11+12) 23/343
51 343 2 130 130/343
51 343 3 190 190/343
51 343 4 0 0/343
52 1068 1 17 23/1068
52 1068 2 221 (110+111) 221/1068
52 1068 3 315 315/1068
52 1068 4 515 515/1068
This gives you all the columns you need, but in a slightly different order. You could use select or relocate to get them in the order you want I suppose:
first %>%
group_by(twodigit, smb) %>%
summarize(smbtotal = sum(employment)) %>%
ungroup() %>%
complete(twodigit, smb, fill = list('smbtotal' = 0)) %>%
group_by(twodigit) %>%
mutate(
indtotal = sum(smbtotal),
smbshare = smbtotal / indtotal
)
`summarise()` has grouped output by 'twodigit'. You can override using the `.groups` argument.
# A tibble: 32 × 5
# Groups: twodigit [8]
twodigit smb smbtotal indtotal smbshare
<chr> <dbl> <dbl> <dbl> <dbl>
1 51 1 23 343 0.0671
2 51 2 130 343 0.379
3 51 3 190 343 0.554
4 51 4 0 343 0
5 52 1 17 1068 0.0159
6 52 2 221 1068 0.207
7 52 3 315 1068 0.295
8 52 4 515 1068 0.482
9 53 1 21 252 0.0833
10 53 2 0 252 0
# … with 22 more rows
Related
I have data that looks like this:
library(dplyr)
Data <- tibble(
ID = c("Code001", "Code001","Code001","Code002","Code002","Code002","Code002","Code002","Code003","Code003","Code003","Code003"),
Value = c(107,107,107,346,346,346,346,346,123,123,123,123))
I need to work out the average value per group per row. However, the value needs to be rounded (so no decimal places) and the group sum needs to equal the group sum of Value.
So solutions like this won't work:
Data %>%
add_count(ID) %>%
group_by(ID) %>%
mutate(Prop_Value_1 = Value/n,
Prop_Value_2 = round(Value/n))
Is there a solution that can produce an output like this:
Data %>%
mutate(Prop_Value = c(35,36,36,69,69,69,69,70,30,31,31,31))
Can use ceiling and then row_number to get there:
Data %>%
group_by(ID) %>%
mutate(count = n(),
ceil_avg = ceiling(Value/count)) %>%
mutate(sum_ceil_avg = sum(ceil_avg),
diff_sum = sum_ceil_avg - Value,
rn = row_number()) %>%
mutate(new_avg = ifelse(rn <= diff_sum,
ceil_avg - 1,
ceil_avg))
# A tibble: 12 × 8
# Groups: ID [3]
ID Value count ceil_avg sum_ceil_avg diff_sum rn new_avg
<chr> <dbl> <int> <dbl> <dbl> <dbl> <int> <dbl>
1 Code001 107 3 36 108 1 1 35
2 Code001 107 3 36 108 1 2 36
3 Code001 107 3 36 108 1 3 36
4 Code002 346 5 70 350 4 1 69
5 Code002 346 5 70 350 4 2 69
6 Code002 346 5 70 350 4 3 69
7 Code002 346 5 70 350 4 4 69
8 Code002 346 5 70 350 4 5 70
9 Code003 123 4 31 124 1 1 30
10 Code003 123 4 31 124 1 2 31
11 Code003 123 4 31 124 1 3 31
12 Code003 123 4 31 124 1 4 31
A first solution is to use integer division:
Data %>%
group_by(ID) %>%
mutate(Prop_Value = ifelse(row_number() <= Value %% n(), Value %/% n() + 1, Value %/% n()))
# A tibble: 12 × 3
# Groups: ID [3]
ID Value Prop_Value
<chr> <dbl> <dbl>
1 Code001 107 36
2 Code001 107 36
3 Code001 107 35
4 Code002 346 70
5 Code002 346 69
6 Code002 346 69
7 Code002 346 69
8 Code002 346 69
9 Code003 123 31
10 Code003 123 31
11 Code003 123 31
12 Code003 123 30
I have a dataset called PimaDiabetes.
PimaDiabetes <- read.csv("PimaDiabetes.csv")
PimaDiabetes[2:8][PimaDiabetes[2:8]==0] <- NA
mean_1 = 40.5
mean_0 = 30.7
p.tib <- PimaDiabetes %>%
as_tibble()
Here is a snapshot of the data:
And the dataset can be pulled from here.
I'm trying to navigate the columns in such a way that I can group the dataset by Outcomes (so to select for Outcome 0 and 1), and impute a different value (the median of the respected groups) into columns depending on the outcomes.
So for instance, in the fifth column, Insulin, there are some NA values down the line where the Outcome is 1, and some where the Outcome is 0. I would like to place a value (40.5) into it when the value in a row is NA, and the Outcome is 1. Then I'd like to put the mean_2 into it when the value is NA, and the Outcome is 0.
I've gotten advice prior to this and tried:
p.tib %>%
mutate(
p.tib$Insulin = case_when((p.tib$Outcome == 0) & (is.na(p.tib$Insulin)) ~ IN_0,
(p.tib$Outcome == 1) & (is.na(p.tib$Insulin) ~ IN_1,
TRUE ~ p.tib$Insulin))
However it constantly yields the following error:
Error: unexpected '=' in "p.tib %>% mutate(p.tib$Insulin ="
Can I know where things are going wrong, please?
Setup
It appears this dataset is also in the pdp package in R, called pima. The only major difference between the R package data and yours is that the pima dataset's Outcome variable is simply called "diabetes" instead and is labeled "pos" and "neg" instead of 0/1. I have loaded that package and the tidyverse to help.
#### Load Libraries ####
library(pdp)
library(tidyverse)
First I transformed the data into a tibble so it was easier for me to read.
#### Reformat Data ####
p.tib <- pima %>%
as_tibble()
Printing p.tib, we can see that the insulin variable has a lot of NA values in the first rows, which will be quicker to visualize later than some of the other variables that have missing data. Therefore, I used that instead of glucose, but the idea is the same.
# A tibble: 768 × 9
pregnant glucose press…¹ triceps insulin mass pedig…² age diabe…³
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
1 6 148 72 35 NA 33.6 0.627 50 pos
2 1 85 66 29 NA 26.6 0.351 31 neg
3 8 183 64 NA NA 23.3 0.672 32 pos
4 1 89 66 23 94 28.1 0.167 21 neg
5 0 137 40 35 168 43.1 2.29 33 pos
6 5 116 74 NA NA 25.6 0.201 30 neg
7 3 78 50 32 88 31 0.248 26 pos
8 10 115 NA NA NA 35.3 0.134 29 neg
9 2 197 70 45 543 30.5 0.158 53 pos
10 8 125 96 NA NA NA 0.232 54 pos
# … with 758 more rows, and abbreviated variable names ¹pressure,
# ²pedigree, ³diabetes
# ℹ Use `print(n = ...)` to see more rows
Finding the Mean
After glimpsing the data, I checked the mean for each group who did and didn't have diabetes by first grouping by diabetes with group_by, then collapsing the data frame into a summary of each group's mean, thus creating the mean_insulin variable (which you can see removes NA values to derive the mean):
#### Check Mean by Group ####
p.tib %>%
group_by(diabetes) %>%
summarise(mean_insulin = mean(insulin,
na.rm=T))
The values we should be imputing seem to be below. Here the groups are labeled as "neg" or 0 in your data, and "pos", or 1 in your data. You can convert these groups into those numbers if you want, but I left it as is so it was easier to read:
# A tibble: 2 × 2
diabetes mean_insulin
<fct> <dbl>
1 neg 130.
2 pos 207.
Mean Imputation
From there, we will use case_when as a vectorized ifelse statement. First, we use mutate to transform insulin. Then we use case_when by setting up three tests. First, if the group is negative and the value is NA, we turn it into the mean value of 130. If the group is positive for the same condition, we use 207. For all other values (the TRUE part), we just use the normal value of insulin. The & operator here just says "this transformation can only take place if both of these tests are true". What follows the ~ is the transformation to take place.
#### Impute Mean ####
p.tib %>%
mutate(
insulin = case_when(
(diabetes == "neg") & (is.na(insulin)) ~ 130,
(diabetes == "pos") & (is.na(insulin)) ~ 207,
TRUE ~ insulin
)
)
You will now notice that the first rows of insulin data are replaced with the mutation and the rest are left alone:
# A tibble: 768 × 9
pregnant glucose press…¹ triceps insulin mass pedig…² age diabe…³
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
1 6 148 72 35 207 33.6 0.627 50 pos
2 1 85 66 29 130 26.6 0.351 31 neg
3 8 183 64 NA 207 23.3 0.672 32 pos
4 1 89 66 23 94 28.1 0.167 21 neg
5 0 137 40 35 168 43.1 2.29 33 pos
6 5 116 74 NA 130 25.6 0.201 30 neg
7 3 78 50 32 88 31 0.248 26 pos
8 10 115 NA NA 130 35.3 0.134 29 neg
9 2 197 70 45 543 30.5 0.158 53 pos
10 8 125 96 NA 207 NA 0.232 54 pos
# … with 758 more rows, and abbreviated variable names ¹pressure,
# ²pedigree, ³diabetes
# ℹ Use `print(n = ...)` to see more rows
I have 4 data frames that all look like this:
Product 2018
Number
Minimum
Maximum
1
56
1
5
2
42
12
16
3
6523
23
56
4
123
23
102
5
56
23
64
6
245623
56
87
7
546
25
540
8
54566
253
560
Product 2019
Number
Minimum
Maximum
1
56
32
53
2
642
423
620
3
56423
432
560
4
3
431
802
5
2
2
6
6
4523
43
68
7
555
23
54
8
55646
3
6
Product 2020
Number
Minimum
Maximum
1
23
2
5
2
342
4
16
3
223
3
5
4
13
4
12
5
2
4
7
6
223
7
8
7
5
34
50
8
46
3
6
Product 2021
Number
Minimum
Maximum
1
234
3
5
2
3242
4
16
3
2423
43
56
4
123
43
102
5
24
4
6
6
2423
4
18
7
565
234
540
8
5646
23
56
I want to join all the tables so I get a table that looks like this:
Products
Number 2021
Min-Max 2021
Number 2020
Min-Max 2020
Number 2019
Min-Max 2019
Number 2018
Min-Max 2018
1
234
3 to 5
23
2 to 5
...
...
...
...
2
3242
4 to 16
342
4 to 16
...
...
...
...
3
2423
43 to 56
223
3 to 5
...
...
...
...
4
123
43 to 102
13
4 to 12
...
...
...
...
5
24
4 to 6
2
4 to 7
...
...
...
...
6
2423
4 to 18
223
7 to 8
...
...
...
...
7
565
234 to 540
5
34 to 50
...
...
...
...
8
5646
23 to 56
46
3 to 6
...
...
...
...
The Product for all years are the same so I would like to have a data frame that contains the number for each year as a column and joins the column for minimum and maximum as one.
Any help is welcome!
How about something like this. You are trying to join several dataframes by a single column, which is relatively straight forward using full_join. The difficulty is that you are trying to extract information from the column names and combine several columns at the same time. I would map out everying you want to do and then reduce the list of dataframes at the end. Here is an example with two dataframes, but you could add as many as you want to the list at the begining.
library(tidyverse)
#test data
set.seed(23)
df1 <- tibble("Product 2018" = seq(1:8),
Number = sample(1:100, 8),
Minimum = sample(1:100, 8),
Maximum = map_dbl(Minimum, ~sample(.x:1000, 1)))
set.seed(46)
df2 <- tibble("Product 2019" = seq(1:8),
Number = sample(1:100, 8),
Minimum = sample(1:100, 8),
Maximum = map_dbl(Minimum, ~sample(.x:1000, 1)))
list(df1, df2) |>
map(\(x){
year <- str_extract(colnames(x)[1], "\\d+?$")
mutate(x, !!quo_name(paste0("Min-Max ", year)) := paste(Minimum, "to", Maximum))|>
rename(!!quo_name(paste0("Number ", year)) := Number)|>
rename_with(~gsub("\\s\\d+?$", "", .), 1) |>
select(-c(Minimum, Maximum))
}) |>
reduce(full_join, by = "Product")
#> # A tibble: 8 x 5
#> Product `Number 2018` `Min-Max 2018` `Number 2019` `Min-Max 2019`
#> <int> <int> <chr> <int> <chr>
#> 1 1 29 21 to 481 50 93 to 416
#> 2 2 28 17 to 314 78 7 to 313
#> 3 3 72 40 to 787 1 91 to 205
#> 4 4 43 36 to 557 47 55 to 542
#> 5 5 45 70 to 926 52 76 to 830
#> 6 6 34 96 to 645 70 20 to 922
#> 7 7 48 31 to 197 84 6 to 716
#> 8 8 17 86 to 951 99 75 to 768
This is a similar answer, but includes bind_rows to combine the data.frames, then pivot_wider to end in a wide format.
The first steps strip the year from the Product XXXX column name, as this carries relevant information on year for that data.frame. If that column is renamed as Product they are easily combined (with a separate column containing the Year). If this step can be taken earlier in the data collection or processing timeline, it is helpful.
library(tidyverse)
list(df1, df2, df3, df4) %>%
map(~.x %>%
mutate(Year = gsub("Product", "", names(.x)[1])) %>%
rename(Product = !!names(.[1]))) %>%
bind_rows() %>%
mutate(Min_Max = paste(Minimum, Maximum, sep = " to ")) %>%
pivot_wider(id_cols = Product, names_from = Year, values_from = c(Number, Min_Max), names_vary = "slowest")
Output
Product Number_2018 Min_Max_2018 Number_2019 Min_Max_2019 Number_2020 Min_Max_2020 Number_2021 Min_Max_2021
<int> <int> <chr> <int> <chr> <int> <chr> <int> <chr>
1 1 56 1 to 5 56 32 to 53 23 2 to 5 234 3 to 5
2 2 42 12 to 16 642 423 to 620 342 4 to 16 3242 4 to 16
3 3 6523 23 to 56 56423 432 to 560 223 3 to 5 2423 43 to 56
4 4 123 23 to 102 3 431 to 802 13 4 to 12 123 43 to 102
5 5 56 23 to 64 2 2 to 6 2 4 to 7 24 4 to 6
6 6 245623 56 to 87 4523 43 to 68 223 7 to 8 2423 4 to 18
7 7 546 25 to 540 555 23 to 54 5 34 to 50 565 234 to 540
8 8 54566 253 to 560 55646 3 to 6 46 3 to 6 5646 23 to 56
I am trying to group_by a variable and then do operations per row per group. I got lost when using ifelse vs case_when. There is something basic I am failing to understand between the usage of two. I was assuming both would give me same output but that is not the case here. Using ifelse didn't give the expected output but case_when did. And I am trying to understand why ifelse didn't give me the expected output.
Here is the example df
structure(list(Pos = c(73L, 146L, 146L, 150L, 150L, 151L, 151L,
152L, 182L, 182L), Percentage = c(81.2, 13.5, 86.4, 66.1, 33.9,
48.1, 51.9, 86.1, 48, 52)), row.names = c(NA, -10L), class = c("tbl_df",
"tbl", "data.frame")) -> foo
I am grouping by Pos and I want to round Percentage if their sum is 100. The following is using ifelse:
library(tidyverse)
foo %>%
group_by(Pos) %>%
mutate(sumn = n()) %>%
mutate(Val = ifelse(sumn == 1,100,
ifelse(sum(Percentage) == 100, unlist(map(Percentage,round)), 0)
# case_when(sum(Percentage) == 100 ~ unlist(map(Percentage,round)),
# TRUE ~ 0
# )
))
the output is
# A tibble: 10 x 4
# Groups: Pos [6]
Pos Percentage sumn Val
<int> <dbl> <int> <dbl>
1 73 81.2 1 100
2 146 13.5 2 0
3 146 86.4 2 0
4 150 66.1 2 66
5 150 33.9 2 66
6 151 48.1 2 48
7 151 51.9 2 48
8 152 86.1 1 100
9 182 48 2 48
10 182 52 2 48
I don't want this, rather I want the following which I get using case_when
foo %>%
group_by(Pos) %>%
mutate(sumn = n()) %>%
mutate(Val = ifelse(sumn == 1,100,
#ifelse(sum(Percentage) == 100, unlist(map(Percentage,round)), 0)
case_when(sum(Percentage) == 100 ~ unlist(map(Percentage,round)),
TRUE ~ 0
)
))
# A tibble: 10 x 4
# Groups: Pos [6]
Pos Percentage sumn Val
<int> <dbl> <int> <dbl>
1 73 81.2 1 100
2 146 13.5 2 0
3 146 86.4 2 0
4 150 66.1 2 66
5 150 33.9 2 34
6 151 48.1 2 48
7 151 51.9 2 52
8 152 86.1 1 100
9 182 48 2 48
10 182 52 2 52
What is ifelse doing different?
According to ?ifelse
A vector of the same length and attributes (including dimensions and "class") as test and data values from the values of yes or no.
If we replicate to make the lengths same, then it should work
foo %>%
group_by(Pos) %>%
mutate(sumn = n()) %>%
mutate(Val = ifelse(sumn == 1,100,
ifelse(rep(sum(Percentage) == 100,
n()), unlist(map(Percentage,round)), 0)
))
# A tibble: 10 x 4
# Groups: Pos [6]
Pos Percentage sumn Val
<int> <dbl> <int> <dbl>
1 73 81.2 1 100
2 146 13.5 2 0
3 146 86.4 2 0
4 150 66.1 2 66
5 150 33.9 2 34
6 151 48.1 2 48
7 151 51.9 2 52
8 152 86.1 1 100
9 182 48 2 48
10 182 52 2 52
I'm currently on R trying to create for a DF multiple columns with the sum of previous one. Imagine I got a DF like this:
df=
sep-2016 oct-2016 nov-2016 dec-2016 jan-2017
1 70 153 NA 28 19
2 57 68 73 118 16
3 29 NA 19 32 36
4 177 36 3 54 53
and I want to add at the end the sum of the rows previous of the month that I'm reporting so for October you end up with the sum of sep and oct, and for November you end up with the sum of sep, oct and november and end up with something like this:
df=
sep-2016 oct-2016 nov-2016 dec-2016 jan-2017 status-Oct2016 status-Nov 2016
1 70 153 NA 28 19 223 223
2 57 68 73 118 16 105 198
3 29 NA 19 32 36 29 48
4 177 36 3 54 53 213 93
I want to know a efficient way insted of writing a lots of lines of rowSums() and even if I can get the label on the iteration for each month would be amazing!
Thanks!
We can use lapply to loop through the columns to apply the rowSums.
dat2 <- as.data.frame(lapply(2:ncol(dat), function(i){
rowSums(dat[, 1:i], na.rm = TRUE)
}))
names(dat2) <- paste0("status-", names(dat[, -1]))
dat3 <- cbind(dat, dat2)
dat3
# sep-2016 oct-2016 nov-2016 dec-2016 jan-2017 status-oct-2016 status-nov-2016 status-dec-2016 status-jan-2017
# 1 70 153 NA 28 19 223 223 251 270
# 2 57 68 73 118 16 125 198 316 332
# 3 29 NA 19 32 36 29 48 80 116
# 4 177 36 3 54 53 213 216 270 323
DATA
dat <- read.table(text = " 'sep-2016' 'oct-2016' 'nov-2016' 'dec-2016' 'jan-2017'
1 70 153 NA 28 19
2 57 68 73 118 16
3 29 NA 19 32 36
4 177 36 3 54 53",
header = TRUE, stringsAsFactors = FALSE)
names(dat) <- c("sep-2016", "oct-2016", "nov-2016", "dec-2016", "jan-2017")
Honestly I have no idea why you would want your data in this format, but here is a tidyverse method of accomplishing it. It involves transforming the data to a tidy format before spreading it back out into your wide format. The key thing to note is that in a tidy format, where month is a variable in a single column instead of spread across multiple columns, you can simply use group_by(rowid) and cumsum to calculate all the values you want. The last few lines are constructing the status- column names and spreading the data back out into a wide format.
library(tidyverse)
df <- read_table2(
"sep-2016 oct-2016 nov-2016 dec-2016 jan-2017
70 153 NA 28 19
57 68 73 118 16
29 NA 19 32 36
177 36 3 54 53"
)
df %>%
rowid_to_column() %>%
gather("month", "value", -rowid) %>%
arrange(rowid) %>%
group_by(rowid) %>%
mutate(
value = replace_na(value, 0),
status = cumsum(value)
) %>%
gather("vartype", "number", value, status) %>%
mutate(colname = ifelse(vartype == "value", month, str_c("status-", month))) %>%
select(rowid, number, colname) %>%
spread(colname, number)
#> # A tibble: 4 x 11
#> # Groups: rowid [4]
#> rowid `dec-2016` `jan-2017` `nov-2016` `oct-2016` `sep-2016`
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 28.0 19.0 0 153 70.0
#> 2 2 118 16.0 73.0 68.0 57.0
#> 3 3 32.0 36.0 19.0 0 29.0
#> 4 4 54.0 53.0 3.00 36.0 177
#> # ... with 5 more variables: `status-dec-2016` <dbl>,
#> # `status-jan-2017` <dbl>, `status-nov-2016` <dbl>,
#> # `status-oct-2016` <dbl>, `status-sep-2016` <dbl>
Created on 2018-02-16 by the reprex package (v0.2.0).
A clean way to do it is by convert your data in a long format.
library(tibble)
library(tidyr)
library(dplyr)
your_data <- tribble(~"sep_2016", ~"oct_2016", ~"nov_2016", ~"dec_2016", ~"jan_2017",
70, 153, NA, 28, 19,
57, 68, 73, 118, 16,
29, NA, 19, 32, 36,
177, 36, 3, 54, 53)
You can change the format of your data.frame with gather from the tidyr package.
your_data_long <- your_data %>%
rowid_to_column() %>%
gather(key = month_year, value = the_value, -rowid)
head(your_data_long)
#> # A tibble: 6 x 3
#> rowid month_year the_value
#> <int> <chr> <dbl>
#> 1 1 sep_2016 70
#> 2 2 sep_2016 57
#> 3 3 sep_2016 29
#> 4 4 sep_2016 177
#> 5 1 oct_2016 153
#> 6 2 oct_2016 68
Once your data.frame is in a long format. You can compute cumulative sum with cumsumand dplyrfunctions mutate and group_by.
result <- your_data_long %>%
group_by(rowid) %>%
mutate(cumulative_value = cumsum(the_value))
result
#> # A tibble: 20 x 4
#> # Groups: rowid [4]
#> rowid month_year the_value cumulative_value
#> <int> <chr> <dbl> <dbl>
#> 1 1 sep_2016 70 70
#> 2 2 sep_2016 57 57
#> 3 3 sep_2016 29 29
#> 4 4 sep_2016 177 177
#> 5 1 oct_2016 153 223
#> 6 2 oct_2016 68 125
#> 7 3 oct_2016 NA NA
#> 8 4 oct_2016 36 213
#> 9 1 nov_2016 NA NA
#> 10 2 nov_2016 73 198
#> 11 3 nov_2016 19 NA
#> 12 4 nov_2016 3 216
#> 13 1 dec_2016 28 NA
#> 14 2 dec_2016 118 316
#> 15 3 dec_2016 32 NA
#> 16 4 dec_2016 54 270
#> 17 1 jan_2017 19 NA
#> 18 2 jan_2017 16 332
#> 19 3 jan_2017 36 NA
#> 20 4 jan_2017 53 323
If you want to retrieve the starting form, you can do it with spread.
My preferred solution would be:
# library(matrixStats)
DF <- as.matrix(df)
DF[is.na(DF)] <- 0
RES <- matrixStats::rowCumsums(DF)
colnames(RES) <- paste0("status-", colnames(DF))
cbind.data.frame(df, RES)
This is closest to what you are looking for with the rowSums.
One option could be using spread and gather function from tidyverse.
Note: The status column has been added even for the 1st month. And the status columns are not in order but values are correct.
The approach is:
# Data
df <- read.table(text = "sep-2016 oct-2016 nov-2016 dec-2016 jan-2017
70 153 NA 28 19
57 68 73 118 16
29 NA 19 32 36
177 36 3 54 53", header = T, stringsAsFactors = F)
library(tidyverse)
# Just add an row number as sl
df <- df %>% mutate(sl = row_number())
#Calculate the cumulative sum after gathering and arranging by date
mod_df <- df %>%
gather(key, value, -sl) %>%
mutate(key = as.Date(paste("01",key, sep="."), format="%d.%b.%Y")) %>%
arrange(sl, key) %>%
group_by(sl) %>%
mutate(status = cumsum(ifelse(is.na(value),0L,value) )) %>%
select(-value) %>%
mutate(key = paste("status",as.character(key, format="%b.%Y"))) %>%
spread(key, status)
# Finally join cumulative calculated sum columns with original df and then
# remove sl column
inner_join(df, mod_df, by = "sl") %>% select(-sl)
# sep.2016 oct.2016 nov.2016 dec.2016 jan.2017 status Dec.2016 status Jan.2017 status Nov.2016 status Oct.2016 status Sep.2016
#1 70 153 NA 28 19 251 270 223 223 70
#2 57 68 73 118 16 316 332 198 125 57
#3 29 NA 19 32 36 80 116 48 29 29
#4 177 36 3 54 53 270 323 216 213 177
Another base solution where we build a matrix accumulating the row sums :
status <- setNames(
as.data.frame(t(apply(dat,1,function(x) Reduce(sum,'[<-'(x,is.na(x),0),accumulate = TRUE)))),
paste0("status-",names(dat)))
status
# status-sep-2016 status-oct-2016 status-nov-2016 status-dec-2016 status-jan-2017
# 1 70 223 223 251 270
# 2 57 125 198 316 332
# 3 29 29 48 80 116
# 4 177 213 216 270 323
Then bind it to your original data if needed :
cbind(dat,status[-1])