I have a very simple case here in which I would like to subtract each column from its previous one. As a matter of fact I am looking for a sliding subtraction as the first column stays as is and then the first one subtracts the second one and second one subtracts the third one and so on till the last column.
here is my sample data set:
structure(list(x = c(1, 0, 0, 0), y = c(1, 0, 1, 1), z = c(0,
1, 1, 1)), class = "data.frame", row.names = c(NA, -4L))
and my desired output:
structure(list(x = c(1, 0, 0, 0), y = c(0, 0, 1, 1), z = c(-1,
1, 0, 0)), class = "data.frame", row.names = c(NA, -4L))
I am personally looking for a solution with purrr family of functions. I also thought about slider but I'm not quite familiar with the latter one. So I would appreciate any help and idea with these two packages in advance. Thank you very much.
A simple dplyr only solution-
cur_data() inside mutate/summarise just creates a whole copy. So
just substract cur_data()[-ncol(.)] from cur_data()[-1]
with pmap_df you can do similar things
df <- structure(list(x = c(1, 0, 0, 0), y = c(1, 0, 1, 1), z = c(0,
1, 1, 1)), class = "data.frame", row.names = c(NA, -4L))
library(dplyr)
df %>%
mutate(cur_data()[-1] - cur_data()[-ncol(.)])
#> x y z
#> 1 1 0 -1
#> 2 0 0 1
#> 3 0 1 0
#> 4 0 1 0
similarly
pmap_dfr(df, ~c(c(...)[1], c(...)[-1] - c(...)[-ncol(df)]))
I think you are looking for pmap_df with lag to subtract the previous value.
library(purrr)
library(dplyr)
pmap_df(df, ~{x <- c(...);x - lag(x, default = 0)})
# A tibble: 4 x 3
# x y z
# <dbl> <dbl> <dbl>
#1 1 0 -1
#2 0 0 1
#3 0 1 0
#4 0 1 0
Verbose, but simple:
df %>%
select(x) %>%
bind_cols(df %>%
select(-1) %>%
map2_dfc(df %>%
select(-ncol(df)), ~.x -.y))
# x y z
#1 1 0 -1
#2 0 0 1
#3 0 1 0
#4 0 1 0
We can just do (no need of any packages)
cbind(df1[1], df1[-1] - df1[-ncol(df1)])
-output
x y z
1 1 0 -1
2 0 0 1
3 0 1 0
4 0 1 0
Or using dplyr
library(dplyr)
df1 %>%
mutate(.[-1] - .[-ncol(.)])
Related
I want to count the number of occurrences that a specific factor level occurs across multiple factor varaibles per row.
Simplified, I want to know how many times each factor level is chosen across specific variables per row (memberID).
Example data:
results=data.frame(MemID=c('A','B','C','D','E','F','G','H'),
value_a = c(1,2,1,4,5,1,4,0),
value_b = c(1,5,2,3,4,1,0,3),
value_c = c(3,5,2,1,1,1,2,1)
)
In this example, I want to know the frequency of each factor level for value_a and value_b for each MemID. How many times does A respond 1? How many times does A respond 2? Etc...for each level and for each MemID but only for value_a and value_b.
I would like the output to look something like this:
counts_by_level = data.frame(MemID=c('A','B','C','D','E','F','G','H'),
count_1 = c(2, 0, 1, 0, 0, 2, 0, 0),
count_2 = c(0, 1, 1, 0, 0, 0, 0, 0),
count_3 = c(0, 0, 0, 1, 0, 0, 0, 1),
count_4 = c(0, 0, 0, 1, 1, 0, 1, 0),
count_5 = c(0, 1, 0, 0, 1, 0, 0, 0))
I have been trying to use add_count or add_tally as well as table and searching other ways to answer this question. However, I am struggling to identify specific factor levels across multiple variables and then output new columns for the counts of those levels for each row.
You could do something like this. Note that you didn't include a zero count, but there are some zero selections.
library(tidyverse)
results |>
select(-value_c) |>
pivot_longer(cols = starts_with("value"),
names_pattern = "(value)") |>
mutate(count = 1) |>
select(-name) |>
pivot_wider(names_from = value,
values_from = count,
names_prefix = "count_",
values_fill = 0,
values_fn = sum)
#> # A tibble: 8 x 7
#> MemID count_1 count_2 count_5 count_4 count_3 count_0
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A 2 0 0 0 0 0
#> 2 B 0 1 1 0 0 0
#> 3 C 1 1 0 0 0 0
#> 4 D 0 0 0 1 1 0
#> 5 E 0 0 1 1 0 0
#> 6 F 2 0 0 0 0 0
#> 7 G 0 0 0 1 0 1
#> 8 H 0 0 0 0 1 1
Another solution:
results %>%
group_by(MemID, value_a, value_b) %>%
summarise(n=n()) %>%
pivot_longer(c(value_a,value_b)) %>%
group_by(MemID, value) %>%
summarise(n=sum(n)) %>%
pivot_wider(MemID,
names_from = value, names_sort = T, names_prefix = "count_",
values_from=n, values_fn=sum, values_fill = 0)
I have a data set that looks like this
ID Group 1 Group 2 Group 3 Group 4
1 1 0 1 0
2 0 1 1 1
3 1 1 0 0
.
.
.
100 0 1 0 1
I want to make another column lets say Group 5 where if the condition of Group 1 is 1 then Group 5 would be 1. If Group 2 = 1, then Group 5 = 2. If Group 3 = 1, then Group 5 = 3, and if Group 4 = 1, then Group 5 = 4. How do I do this?
I tried these lines of code, but I seem to be missing something.
Group5 <- data.frame(Group1, Group2, Group3, Group4, stringsAsFactors=FALSE)
df$Group5 <- with(finalmerge, ifelse(Group1 %in% c("1", "0"),
"1", ""))
Any advice would be helpful, thanks in advance.
You could use which.max(), and apply this to each row.
df["Group_5"] <- apply(df[, -1], 1, which.max)
Output:
ID Group_1 Group_2 Group_3 Group_4 Group_5
1 1 0 0 0 1 4
2 2 0 1 0 0 2
3 3 0 0 1 0 3
4 4 1 0 0 0 1
Input:
df = structure(list(ID = c(1, 2, 3, 4), Group_1 = c(0, 0, 0, 1), Group_2 = c(0,
1, 0, 0), Group_3 = c(0, 0, 1, 0), Group_4 = c(1, 0, 0, 0)), class = "data.frame", row.names = c(NA,
-4L))
I have a df that looks like this:
It can be build using codes:
structure(list(ID = c(1, 2, 3, 4, 5), Pass = c(0, 1, 1, 1, 1),
Math = c(0, 0, 1, 1, 1), ELA = c(0, 1, 0, 1, 0), PE = c(0,
0, 1, 1, 1)), row.names = c(NA, -5L), class = c("tbl_df",
"tbl", "data.frame"))
Where pass stand for a student pass any test or not. Now I want to build a new var Result to capture a student's test results like following, what should I do?
Try the base R code below
q <- with(data.frame(which(df[-(1:2)] == 1, arr.ind = TRUE)),
tapply(names(df[-(1:2)])[col], factor(row, levels = 1:nrow(df)), toString))
df$Result <- ifelse(is.na(q), "Not Pass", paste0("Pass: ", q))
which gives
> df
# A tibble: 5 x 6
ID Pass Math ELA PE Result
<dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 1 0 0 0 0 Not Pass
2 2 1 0 1 0 Pass: ELA
3 3 1 1 0 1 Pass: Math, PE
4 4 1 1 1 1 Pass: Math, ELA, PE
5 5 1 1 0 1 Pass: Math, PE
Using dplyr with rowwise
library(dplyr)
library(stringr)
df1 %>%
rowwise %>%
mutate(Result = if(as.logical(Pass))
str_c('Pass: ', toString(names(select(., Math:PE))[as.logical(c_across(Math:PE))])) else 'Not pass' ) %>%
ungroup
# A tibble: 5 x 6
# ID Pass Math ELA PE Result
# <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#1 1 0 0 0 0 Not pass
#2 2 1 0 1 0 Pass: ELA
#3 3 1 1 0 1 Pass: Math, PE
#4 4 1 1 1 1 Pass: Math, ELA, PE
#5 5 1 1 0 1 Pass: Math, PE
data
df1 <- structure(list(ID = c(1, 2, 3, 4, 5), Pass = c(0, 1, 1, 1, 1),
Math = c(0, 0, 1, 1, 1), ELA = c(0, 1, 0, 1, 0), PE = c(0,
0, 1, 1, 1)), row.names = c(NA, -5L), class = c("tbl_df",
"tbl", "data.frame"))
Here's one solution:
library(dplyr)
library(magrittr)
library(stringr)
df <- structure(list(ID = c(1, 2, 3, 4, 5), Pass = c(0, 1, 1, 1, 1),
Math = c(0, 0, 1, 1, 1), ELA = c(0, 1, 0, 1, 0), PE = c(0,
0, 1, 1, 1)), row.names = c(NA, -5L), class = c("tbl_df",
"tbl", "data.frame"))
df %<>% pivot_longer(cols = -c(ID, Pass), names_to = "sub", values_to = "done")
df %<>% group_by(ID) %>% mutate(Result = paste0(ifelse(done == 1, sub, NA), collapse = ", ")) %>% ungroup()
df %<>% pivot_wider(names_from = sub, values_from = done)
df %<>% mutate(Result = paste0("Pass: ", str_replace_all(Result, "NA[, ]*", "")))
df %<>% mutate(Result = ifelse(str_detect(Result, "Pass: $"), "Not pass", str_replace_all(Result, ",[\\s]*$", "")))
df
# # A tibble: 5 x 6
# ID Pass Result Math ELA PE
# <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
# 1 1 0 Not pass 0 0 0
# 2 2 1 Pass: ELA 0 1 0
# 3 3 1 Pass: Math, PE 1 0 1
# 4 4 1 Pass: Math, ELA, PE 1 1 1
# 5 5 1 Pass: Math, PE 1 0 1
I can provide an explanation of what the code is doing if necessary.
I am using case_when to summarise a data frame using rowwise in dplyr. I have a sample data frame as shown below
structure(list(A = c(NA, 1, 0, 0, 0, 0, 0), B = c(NA, 0, 0, 1,
0, 0, 0), C = c(NA, 1, 0, 0, 0, 0, 0), D = c(NA, 1, 0, 1, 0,
0, 1), E = c(NA, 1, 0, 1, 0, 0, 1)), row.names = c(NA, -7L), class = "data.frame")
The code works when I mention all the names
df %>%
rowwise() %>%
mutate(New = case_when(any(c(A,B,C,D,E) == 1) ~ 1,
all(c(A,B,C,D,E) == 0 ) ~ 0
))
Can I pass the names in a vector, e.g cols <- colnames(df), and then that in case_when
To answer your question you can use cur_data() in dplyr 1.0.0 or c_across()
library(dplyr)
df %>%
rowwise() %>%
mutate(New = case_when(any(cur_data() == 1) ~ 1,
all(cur_data() == 0 ) ~ 0))
# A B C D E New
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 NA NA NA NA NA NA
#2 1 0 1 1 1 1
#3 0 0 0 0 0 0
#4 0 1 0 1 1 1
#5 0 0 0 0 0 0
#6 0 0 0 0 0 0
#7 0 0 0 1 1 1
With c_across() :
df %>%
rowwise() %>%
mutate(New = case_when(any(c_across()== 1) ~ 1,
all(c_across()== 0 ) ~ 0))
But you can also solve this using rowSums :
df %>%
mutate(New = case_when(rowSums(. == 1, na.rm = TRUE) > 0 ~ 1,
rowSums(. == 0, na.rm = TRUE) == ncol(.) ~ 0))
If you only have 0's and 1's in your dataset you could use this
df$New <- ifelse(rowSums(df) > 0, 1, 0)
If the rowsum > 0 it means that at least one '1' is present. Output
A B C D E New
1 NA NA NA NA NA NA
2 1 0 1 1 1 1
3 0 0 0 0 0 0
4 0 1 0 1 1 1
5 0 0 0 0 0 0
6 0 0 0 0 0 0
7 0 0 0 1 1 1
In base R, we can do this with
df$New <- +( rowSums(df) > 0)
What would be a good visualization to use in R to show the association of 2 binary variables?
I understand that phi coefficient would be the best statistic to use, but how can I show it graphically? Considering that if I use a scatterplot, it would be very condensed since there are only 4 possible values.
One idea would be to create a mosaicplot from the contigency table of the two binary variables.
Let's assume our data looks like this:
var1 var2
1 1 1
2 0 0
3 1 1
4 0 0
5 1 1
6 1 1
7 0 0
8 0 1
9 0 1
10 1 0
We could visualize it in the following way:
mosaicplot(table(df))
Data
df <- structure(list(var1 = c(1, 0, 1, 0, 1, 1, 0, 0, 0, 1), var2 = c(1,
0, 1, 0, 1, 1, 0, 1, 1, 0)), .Names = c("var1", "var2"), row.names = c(NA,
-10L), class = "data.frame")