I am working with data with multiple row headers. I am willing to add these headers into data.
EX:
I want to change this
X
A 1
B 2
C 3
D 4
E 5
into this
Y X
A 1
B 2
C 3
D 4
E 5
I want to keep Y and X as headers but make A,B,C,D,E are column values.
We can use rownames_to_column from tibble
library(tibble)
library(dplyr)
df1 %>%
rownames_to_column("Y")
-output
# Y X
#1 A 1
#2 B 2
#3 C 3
#4 D 4
#5 E 5
Or with data.frame
data.frame(Y = row.names(df1), X = df1$X)
-output
# Y X
#1 A 1
#2 B 2
#3 C 3
#4 D 4
#5 E 5
NOTE: Both are single line codes
data
df1 <- structure(list(X = 1:5), class = "data.frame", row.names = c("A",
"B", "C", "D", "E"))
You can also try:
#Code
new <- as.data.frame(cbind(Y=rownames(df),df),row.names = NULL)
rownames(new)<-NULL
Output:
new
Y X
1 A 1
2 B 2
3 C 3
4 D 4
5 E 5
Some data used:
#Data
df <- structure(list(X = 1:5), class = "data.frame", row.names = c("A",
"B", "C", "D", "E"))
Related
I would like to know how to use row_number or anything else to transform a variable group into a integer
tibble_test <- tibble(A = letters[1:10], group = c("A", "A", "A", "B", "B", "C", "C", "C", "C", "D"))
# to get the enumeration inside each group of 'group'
tibble_test %>%
group_by(group) %>%
mutate(G1 = row_number())
But I would like to have this output:
# A tibble: 10 x 4
A group G1 G2
<chr> <chr> <dbl> <dbl>
1 a A 1 1
2 b A 2 1
3 c A 3 1
4 d B 1 2
5 e B 2 2
6 f C 1 3
7 g C 2 3
8 h C 3 3
9 i C 4 3
10 j D 1 4
My question is: how to get this column G2, I know i could transform the 'group' var into a factor then integer (after the tibble is arranged) but I would like to know if it can be done using a counting.
You just need one more step and include the group indices with group_indices(). Be aware that how your data is arranged/sorted will affect the index.
library(dplyr)
tibble_test <- tibble(A = letters[1:10], group = c("A", "A", "A", "B", "B", "C", "C", "C", "C", "D"))
# to get the enumeration inside each group of 'group'
tibble_test %>%
group_by(group) %>%
mutate(G1 = row_number(),
G2 = group_indices())
# A tibble: 10 x 4
# Groups: group [4]
A group G1 G2
<chr> <chr> <int> <int>
1 a A 1 1
2 b A 2 1
3 c A 3 1
4 d B 1 2
5 e B 2 2
6 f C 1 3
7 g C 2 3
8 h C 3 3
9 i C 4 3
10 j D 1 4
I have 2 different dataframes. I want add a column to my second dataframe and have it assigned a value 0 or 1. In the case where df1$code == df2$code & df1$date == df2$date I want a 0 for these rows. A visual and reproducible example maybe makes it more easy to understand.
df1 <- data.frame(code = c("A", "B", "C", "D"), date = c(1,2,3,4))
df2 <- data.frame(code = c("A", "B", "E", "R", "V", "F"), date = c(1,2,3,4,5,6))
df3 <- data.frame(code = c("A", "B", "E", "R", "V", "F"), date = c(1,2,3,4,5,6), value =c(1,1,0,0,0,0))
DF1
code date
1 A 1
2 B 2
3 C 3
4 D 4
DF2
code date
1 A 1
2 B 2
3 E 3
4 R 4
5 V 5
6 F 6
The resulting DF I want
code date value
1 A 1 1
2 B 2 1
3 E 3 0
4 R 4 0
5 V 5 0
6 F 6 0
We can use %in% to create a logical vector and then coerce it to binary with as.integer or +
df2$value <- +(df2$code %in% df1$code)
df2
# code date value
#1 A 1 1
#2 B 2 1
#3 E 3 0
#4 R 4 0
#5 V 5 0
#6 F 6 0
I would do it like this:
df2 %>% left_join(mutate(df1, value = 1)) %>%
mutate(value = coalesce(value, 0))
# Joining, by = c("code", "date")
# code date value
# 1 A 1 1
# 2 B 2 1
# 3 E 3 0
# 4 R 4 0
# 5 V 5 0
# 6 F 6 0
I've a dataframe which I want to add a row on the basis of the following conditions. The conditions are when column a is equal to C and column b is equal to 3 or 5.
Here is my dataframe
df <- data.frame(a = c("A", "B", "C", "D", "C", "A", "C", "E"),
b = c(seq(8)), stringsAsFactors = TRUE)
Whenever the condition is TRUE I want to add a row below where the condition is met add 3. I have tried the following
rbind(df, data.frame(a="add", b = "3"))
# a b
# 1 A 1
# 2 B 2
# 3 C 3
# 4 D 4
# 5 C 5
# 6 A 6
# 7 C 7
# 8 E 8
# 9 add 3
This is not the output I want. The output I want is
# a b
# 1 A 1
# 2 B 2
# 3 C 3
# 4 add 3
# 5 D 4
# 6 C 5
# 7 add 3
# 8 A 6
# 9 C 7
# 10 E 8
How can I do that? I am new to R and thank you for your help.
lens = ifelse(df$b %in% c(3, 5) & df$a == "C", 2, 1)
ind = rep(1:NROW(df), lens)
df2 = df[ind,]
df2$a = as.character(df2$a)
df2$a[cumsum(lens)[which(lens == 2)]] = "add"
df2$b[cumsum(lens)[which(lens == 2)]] = 3
df2
# a b
#1 A 1
#2 B 2
#3 C 3
#3.1 add 3
#4 D 4
#5 C 5
#5.1 add 3
#6 A 6
#7 C 7
#8 E 8
A solution using the tidyverse package.
library(tidyverse)
df2 <- df %>%
mutate(Group = lag(cumsum(a == "C" & b %in% c(3, 5)), default = FALSE)) %>%
group_split(Group) %>%
map_dfr(~ .x %>% bind_rows(tibble(a = "add", b = 3))) %>%
slice(-n()) %>%
select(-Group)
df2
# # A tibble: 10 x 2
# a b
# <chr> <dbl>
# 1 A 1
# 2 B 2
# 3 C 3
# 4 add 3
# 5 D 4
# 6 C 5
# 7 add 3
# 8 A 6
# 9 C 7
# 10 E 8
In base R, we can find out position where a = "c" and b is 3 or 5. Repeat those rows in the dataframe and replace them with required values.
pos <- which(df$a == "C" & df$b %in% c(3, 5))
df <- df[sort(c(seq(nrow(df)), pos)), ]
df[seq_along(pos) + pos, ] <- list("add", 3)
row.names(df) <- NULL
df
# a b
#1 A 1
#2 B 2
#3 C 3
#4 add 3
#5 D 4
#6 C 5
#7 add 3
#8 A 6
#9 C 7
#10 E 8
data
df <- data.frame(a = c("A", "B", "C", "D", "C", "A", "C", "E"),
b = c(seq(8)), stringsAsFactors = FALSE)
For example you have this data frame :
dd <- data.frame(b = c("cpg1", "cpg2", "cpg3", "cpg4"),
x = c("A", "D", "A", "C"), y = c(8, 3, 9, 9),
z = c(1, 1, 1, 2))
dd
b x y z
1 cpg1 A 8 1
2 cpg2 D 3 1
3 cpg3 A 9 1
4 cpg4 C 9 2
I want to order the column names (b,x,y,z) by a row in another data frame which is:
d <- data.frame(pos = c("x", "z", "b"),
g = c("A", "D", "A"), h = c(8, 3, 9))
d
pos g h
1 x A 8
2 z D 3
3 b A 9
So I want to order the column name of dd with the row d$pos and dd also needs to have the same number in the row d$pos.
I tried with order and match but it did not give me the need result. My dataset is quite large, so something automtic would be ideal.
Thanks a lot for your help!
We can do a match and then order the columns
i1 <- match(d$pos, names(dd), nomatch = 0)
dd[i1]
# x z b
#1 A 1 cpg1
#2 D 1 cpg2
#3 A 1 cpg3
#4 C 2 cpg4
Or if we want only the columns based on the 'd$pos'
dd[as.character(d$pos)]
# x z b
#1 A 1 cpg1
#2 D 1 cpg2
#3 A 1 cpg3
#4 C 2 cpg4
I have several csv files all named with dates and for all of them I want to create a new column in each file that contains data from two other columns placed together. Then, I want to combine them into one big dataframe and choose only two of those columns to keep. Here's an example:
Say I have two dataframes:
a b c a b c
x 1 2 3 x 3 2 1
y 2 3 1 y 2 1 3
Then I want to create a new column d in each of them:
a b c d a b c d
x 1 2 3 13 x 3 2 1 31
y 2 3 1 21 y 2 1 3 23
Then I want to combine them like this:
a b c d
x 1 2 3 13
y 2 3 1 21
x 3 2 1 31
y 2 1 3 23
Then keep two of the columns a and d and delete the other two columns b and c:
a d
x 1 13
y 2 21
x 3 31
y 2 23
Here is my current code (It doesn't work when I try to combine two of the columns or when I try to only keep two of the columns):
f <- list.files(pattern="201\\d{5}\\.csv") # reading in all the files
mydata <- sapply(f, read.csv, simplify=FALSE) # assigning them to a dataframe
do.call(rbind,mydata) # combining all of those dataframes into one
mydata$Data <- paste(mydata$LAST_UPDATE_DT,mydata$px_last) # combining two of the columns into a new column named "Data"
c('X','Data') %in% names(mydata) # keeping two of the columns while deleting the rest
The object mydata is a list of data frames. You can change the data frames in the list with lapply:
lapply(mydata, function(x) "[<-"(x, "c", value = paste0(x$a, x$b)))
file1 <- "a b
x 2 3"
file2 <- "a b
x 3 1"
mydata <- lapply(c(file1, file2), function(x) read.table(text = x, header =TRUE))
lapply(mydata, function(x) "[<-"(x, "c", value = paste0(x$a, x$b)))
# [[1]]
# a b c
# x 2 3 23
#
# [[2]]
# a b c
# x 3 1 31
You can use rbind (data1,data2)[,c(1,3)] for that. I assume that you can create col d in each dataframe which is a basic thing.
data1<-structure(list(a = 1:2, b = 2:3, c = c(3L, 1L), d = c(13L, 21L
)), .Names = c("a", "b", "c", "d"), row.names = c("x", "y"), class = "data.frame")
> data1
a b c d
x 1 2 3 13
y 2 3 1 21
data2<-structure(list(a = c(3L, 2L), b = c(2L, 1L), c = c(1L, 3L), d = c(31L,
23L)), .Names = c("a", "b", "c", "d"), row.names = c("x", "y"
), class = "data.frame")
> data2
a b c d
x 3 2 1 31
y 2 1 3 23
data3<-rbind(data1,data2)
> data3
a b c d
x 1 2 3 13
y 2 3 1 21
x1 3 2 1 31
y1 2 1 3 23
finaldata<-data3[,c("a","d")]
> finaldata
a d
x 1 13
y 2 21
x1 3 31
y1 2 23