I want to filter a data frame so that I only keep columns that have the values 1, 2, 3, 4, 5, or NA in them.
x = data.frame(col1 = c("a" , "b", "d", "e", "f", "g"),
col2 = c(12, 45, 235, 2134, NA, 1),
col3 = c(1, 2, 3, 1, 2, NA),
col4 = c(1, 2, 3, 4, 5, NA),
col5 = c(1, 2, 3, 4, 5, 6))
With this example data, I would like to return x with only col 3 and 4.
You can use the following solution:
library(dplyr)
x %>%
select(where(function(x) all(x %in% c(1:5, NA))))
col3 col4
1 1 1
2 2 2
3 3 3
4 1 4
5 2 5
6 NA NA
Or using a formula:
x %>%
select(where(~ all(.x %in% c(1:5, NA))))
Since the discussion just heated up on this, in case you would like to know how R interprets formulas created by ~ pronounced twiddle, just wrap it inside purrr::as_mapper. This is a function R calls behind the scene when you use this syntax for an anonymous function:
as_mapper(~ all(.x %in% c(1:5, NA)))
<lambda>
function (..., .x = ..1, .y = ..2, . = ..1)
all(.x %in% c(1:5, NA))
attr(,"class")
[1] "rlang_lambda_function" "function"
Here .x argument is equivalent to the first argument of our anonymous function.
Related
I want to summarise both factor and numerical variables using group_by and summarise. For example, if I have the following data frame:
group<- c(1, 1, 2, 2, 3, 3, 4, 4)
var1<- c(3, 6, 3, 2, 7, 5, 2, 5)
var2<- c("A", "B", "B", "B", "A", "A", "B", "A")
df<- data.frame(group, var1, var2)
I want to achieve the following output:
# A tibble: 4 x 3
group max_1 sum_A
<dbl> <dbl> <dbl>
1 6 1
2 3 0
3 7 2
4 5 1
I have tried various iterations of the following using "tally", and "n", and "sum", but none work
summary<- df %>% group_by (group) %>%
summarise(max_1 = max(var1)),
mutate (var2A = sum (var2 == "A"))
Thank you!
Let's say I test 3 drugs (A, B, C) at 3 conditions (0, 1, 2), and then I want to compare two of the conditions (1, 2) to a reference condition (0). This is the plot I would like to get:
First: I do get there, but my solution seems overly complex.
# The data I have
df <- data.frame(
drug = c("A", "A", "A", "B", "B", "B", "C", "C", "C"),
cond = c(0, 1, 2, 0, 1, 2, 0, 1, 2),
result = c(1, 2, 3, 2, 4, 6, 3, 6, 9),
)
# The data I want
df_wider0 <- data.frame(
drug = c("A", "A", "B", "B", "C", "C"),
result0 = c(1, 1, 2, 2, 3, 3),
cond = c(1, 2, 1, 2, 1, 2),
result = c(2, 3, 4, 6, 6, 9)
)
# This pivots also condition 1 and 2 ...
df_wider <- tidyr::pivot_wider(
df,
names_from = cond,
values_from = result
)
# ... so I pivot out these two again ...
colnames(df_wider)[colnames(df_wider) == "0"] <- "result0"
df_wider0 <- tidyr::pivot_longer(
df_wider,
cols = c("1", "2"),
names_to = "cond",
values_to = "result"
)
# ... so that I can use this ggplot command:
library(ggplot2)
ggplot(df_wider0, aes(x = result0, y = result, label = drug)) +
geom_label() +
facet_wrap("cond")
As you can see, I use a sequence of pivot_wider and pivot_longer to do a selective pivot_wider (by inverting some of its effects later). Is there an integrated command that I can use to achieve this more elegantly?
This can also be a strategy. (Will work even if there are unequal number of conditions per group)
df %>%
filter(cond != 0) %>%
right_join(df %>% filter(cond == 0), by = "drug", suffix = c("", "0")) %>%
select(-cond0)
Revised df adopted
df <- data.frame(
drug = c("A", "A", "A", "B", "B", "B", "C", "C", "C", "D"),
cond = c(0, 1, 2, 0, 1, 2, 0, 1, 2, 0),
result = c(1, 2, 3, 2, 4, 6, 3, 6, 9, 10)
)
Result of above syntax
drug cond result result0
1 A 1 2 1
2 A 2 3 1
3 B 1 4 2
4 B 2 6 2
5 C 1 6 3
6 C 2 9 3
7 D NA NA 10
You may also fill cond if desired so
You can do this without any pivot statement at all.
library(dplyr)
library(ggplot2)
df_wider0 <- df %>%
mutate(result0 = result[match(drug, unique(drug))]) %>%
filter(cond != 0)
df_wider0
# drug cond result result0
#1 A 1 2 1
#2 A 2 3 1
#3 B 1 4 2
#4 B 2 6 2
#5 C 1 6 3
#6 C 2 9 3
Plot the data :
ggplot(df_wider0, aes(x = result0, y = result, label = drug)) +
geom_label() +
facet_wrap("cond")
I have a data frame where I want to delete duplicates rows, but I want to delete them only if a value from another column is the same for all the rows. (To be more clear I want to delete the duplicates rows which have the same "Number" value for all rows)
There is a example of my data frame :
df <- data.frame("Name" = c("a", "a", "b", "b", "b", "c", "c", "c"),
"Number" = c(1, 1, 1, 2, 3, 4, 5, 5), stringsAsFactors = FALSE)
And the result I expect is :
result <- data.frame("Name" = c("b", "b", "b", "c", "c", "c"),
"Number" = c(1, 2, 3, 4, 5, 5), stringsAsFactors = FALSE)
We can group_by Name and remove groups which have more than 1 row and have only one distinct value.
library(dplyr)
df %>%
group_by(Name) %>%
filter(!(n_distinct(Number) == 1 & n() > 1))
# Name Number
# <chr> <dbl>
#1 b 2
#2 b 2
#3 b 3
and using base R ave, the same logic can be written as
df[with(df, !as.logical(ave(Number, Name, FUN = function(x)
length(unique(x)) == 1 & length(x) > 1))), ]
Here is a solution with data.table
library("data.table")
df <- data.table("Name" = c("a", "a", "b", "b", "b"),
"Number" = c(1, 1, 2, 2, 3))
df[, if (uniqueN(Number)!=1 || .N==1) .SD, Name]
and here is a solution with base R:
df <- data.frame("Name" = c("a", "a", "b", "b", "b"),
"Number" = c(1, 1, 2, 2, 3), stringsAsFactors = FALSE)
df[as.logical(ave(df$Number, df$Name, FUN=function(x) length(unique(x))!=1 || length(x)==1)),]
We can use data.table methods
library(data.table)
setDT(df)[, .SD[uniqueN(Number) > 1] , Name]
# Name Number
#1: b 1
#2: b 2
#3: b 3
#4: c 4
#5: c 5
#6: c 5
I am trying to calculate the median (but that could be substituted by similar metrics) by group for multiple columns based on subsets defined by other columns. This is direct follow-on question from this previous post of mine. I have attempted to incorporate calculating the median via aggregate into the Map(function(x,y) dosomething, x, y) solution kindly provided by #Frank, but that didn't work. Let me illustrate:
Calculate median for A and B by groups GRP1 and GRP2
df <- data.frame(GRP1 = c("A","A","A","A","A","A","B","B","B","B","B","B"), GRP2 = c("A","A","A","B","B","B","A","A","A","B","B","B"), A = c(0,4,6,7,0,1,9,0,0,8,3,4), B = c(6,0,4,8,6,7,0,9,9,7,3,0))
med <- aggregate(.~GRP1+GRP2,df,FUN=median)
Simple. Now add columns defining which rows to be used for calculating the median, i.e. rows with NAs should be dropped, column a defines which rows to be used for calculating the median in column A, same for columns b and B:
a <- c(1,4,7,3,NA,3,7,NA,NA,4,8,1)
b <- c(5,NA,7,9,5,6,NA,8,1,7,2,9)
df1 <- cbind(df,a,b)
As mentioned above, I have tried combining Map and aggregate, but that didn't work. I assume that Map doesn't know what to do with GRP1 and GRP2.
med1 <- Map(function(x,y) aggregate(.~GRP1+GRP2,df1[!is.na(y)],FUN=median), x=df1[,3:4], y=df1[, 5:6])
This is the result I'm looking for:
GRP1 GRP2 A B
1 A A 4 5
2 B A 9 9
3 A B 4 7
4 B B 4 3
Any help will be much appreciated!
Using data.table
library(data.table)
setDT(df1)
df1[, .(A = median(A[!is.na(a)]), B = median(B[!is.na(b)])), by = .(GRP1, GRP2)]
GRP1 GRP2 A B
1: A A 4 5
2: A B 4 7
3: B A 9 9
4: B B 4 3
Same logic in dplyr
library(dplyr)
df1 %>%
group_by(GRP1, GRP2) %>%
summarise(A = median(A[!is.na(a)]), B = median(B[!is.na(b)]))
The original df1:
df1 <- data.frame(
GRP1 = c("A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B"),
GRP2 = c("A", "A", "A", "B", "B", "B", "A", "A", "A", "B", "B", "B"),
A = c(0, 4, 6, 7, 0, 1, 9, 0, 0, 8, 3, 4),
B = c(6, 0, 4, 8, 6, 7, 0, 9, 9, 7, 3, 0),
a = c(1, 4, 7, 3, NA, 3, 7, NA, NA, 4, 8, 1),
b = c(5, NA, 7, 9, 5, 6, NA, 8, 1, 7, 2, 9)
)
With dplyr:
library(dplyr)
df1 %>%
mutate(A = ifelse(is.na(a), NA, A),
B = ifelse(is.na(b), NA, B)) %>%
# I use this to put as NA the values we don't want to include
group_by(GRP1, GRP2) %>%
summarise(A = median(A, na.rm = T),
B = median(B, na.rm = T))
# A tibble: 4 x 4
# Groups: GRP1 [?]
GRP1 GRP2 A B
<fct> <fct> <dbl> <dbl>
1 A A 4 5
2 A B 4 7
3 B A 9 9
4 B B 4 3
I have data.frame like this
test <- data.frame(plot = c(1, 1, 2, 2, 3, 3), sort = c(10, 20, 11, 12, 15, 20))
I want to create a new variable callled treat that will be "A" if any sort in the plot is 20. Otherwise it should be B.
The expected output is
data.frame(plot = c(1, 1, 2, 2, 3, 3), sort = c(10, 20, 11, 12, 15, 20), treat = c("A", "A", "B", "B", "A", "A"))
We can use ave and group by plot variable. Check if any sort variable has value as 20 in it and assign the group accordingly
test$treat<-ave(test$sort,test$plot,FUN =function(x) ifelse(any(x ==20),"A","B"))
test
# plot sort treat
#1 1 10 A
#2 1 20 A
#3 2 11 B
#4 2 12 B
#5 3 15 A
#6 3 20 A
Similary with dplyr
library(dplyr)
test %>%
group_by(plot) %>%
mutate(treat = ifelse(any(sort == 20), "A", "B"))