I have a dataset the contains a large number of columns every column has a name of date in the form of x2019.10.10
what I want is to remove the x letter and change the type of the date to be 2019-10-10
How this could be done in the R environment?
One solution would be:
Get rid of x
Replace . with -.
Here I create a dataframe that has similar columns to yours:
df = data.frame(x2019.10.10 = c(1, 2, 3),
x2020.10.10 = c(4, 5, 6))
df
x2019.10.10 x2020.10.10
1 1 4
2 2 5
3 3 6
And then, using dplyr (looks much tidier):
library(dplyr)
names(df) = names(df) %>%
gsub("x", "", .) %>% # Get rid of x and then (%>%):
gsub("\\.", "-", .) # replace "." with "-"
df
2019-10-10 2020-10-10
1 1 4
2 2 5
3 3 6
If you do not want to use dplyr, here is how you would do the same thing in base R:
names(df) = gsub("x", "", names(df))
names(df) = gsub("\\.", "-", names(df))
df
2019-10-10 2020-10-10
1 1 4
2 2 5
3 3 6
Related
I have below data frame
library(dplyr)
data = data.frame('A' = 1:3, 'CC' = 1:3, 'DD' = 1:3, 'M' = 1:3)
Now let define a vectors of strings which represents a subset of column names of above data frame
Target_Col = c('CC', 'M')
Now I want to find the column names in data that match with Target_Col and then replace them with
paste0('Prefix_', Target_Col)
I prefer to do it using dplyr chain rule.
Is there any direct function available to perform this?
Other solutions can be found here!
clickhere
vars<-cbind.data.frame(Target_Col,paste0('Prefix_', Target_Col))
data <- data %>%
rename_at(vars$Target_Col, ~ vars$`paste0("Prefix_", Target_Col)`)
or
data %>% rename_with(~ paste0('Prefix_', Target_Col), all_of(Target_Col))
We may use
library(stringr)
library(dplyr)
data %>%
rename_with(~ str_c('Prefix_', .x), all_of(Target_Col))
A Prefix_CC DD Prefix_M
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
With dplyrs rename_with
library(dplyr)
rename_with(data, function(x) ifelse(x %in% Target_Col, paste0("Prefix_", x), x))
A Prefix_CC DD Prefix_M
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
How would I combine any row with a single column entry into a single combined input in a new column? e.g. when column A has value, but B-C are empty, I would like to merge the row entries into a single input in column D.
original file is a txt file that looks like this:
A|B|C
1|2|3
1
text
2
[end]
4|5|6
2
1
[end]
df <-read.delim("file.txt", header=TRUE, sep="|", blank.lines.skip = TRUE)
A B C
1 2 3
1
text
2
[end]
4 5 6
2
1
[end]
desired out data table with newly added column D:
A B C D
1 2 3 1 text 2 [end]
4 5 6 2 1 [end]
I imagine this would be combination of is.na and mutate functions but have been unable to find a solution. The code could also include ends_with("[end]") since each row that I want to combine ends with this text. Any thoughts on this?
Not sure if this is what you need given that the questions about your data structure are unanswered:
library(tidyverse)
df %>%
# change empty cells to NA:
mutate(across(everything(), ~na_if(., ""))) %>%
# filter rows with NA:
filter(if_any(everything(), is.na)) %>%
# contract rows in new column `D`:
summarise(D = str_c(A, collapse = " ")) %>%
# bind original `df` (after mutations) to result:
bind_cols(df %>%
mutate(across(everything(), ~na_if(., ""))) %>%
filter(!if_any(everything(), is.na)), .) %>%
# remove duplicated values in `D`:
mutate(D = ifelse(duplicated(D), NA, D))
A B C D
1 1 2 3 1 text 2 [end]
2 4 5 6 <NA>
Data:
df <- data.frame(
A = c(1,1, "text", 2, "[end]", 4),
B = c(2, "", "", "", "", 5),
C = c(3, "", "", "", "", 6)
)
This is my dataframe:
df <- tibble(col1 = c("1. word","2. word","3. word","4. word","5. N. word","6. word","7. word","8. word"))
I need to split in two columns using separate function and rename them as Numbers and other called Words. Ive doing this but its not working:
df %>% separate(col = col1 , into = c('Number','Words'), sep = "^. ")
The problem is that the fifth has 2 dots. And I dont know how to handle with this regarding the regex.
Any help?
Here is an alternative using readrs parse_number and a regex:
library(dplyr)
library(readr)
df %>%
mutate(Numbers = parse_number(col1), .before=1) %>%
mutate(col1 = gsub('\\d+\\. ','',col1))
Numbers col1
<dbl> <chr>
1 1 word
2 2 word
3 3 word
4 4 word
5 5 N. word
6 6 word
7 7 word
A tidyverse approach would be to first clean the data then separate.
df %>%
mutate(col1 = gsub("\\s.*(?=word)", "", col1, perl=TRUE)) %>%
tidyr::separate(col1, into = c("Number", "Words"), sep="\\.")
Result:
# A tibble: 8 x 2
Number Words
<chr> <chr>
1 1 word
2 2 word
3 3 word
4 4 word
5 5 word
6 6 word
7 7 word
8 8 word
I'm assuming that you would like to keep the cumbersome "N." in the result. For that, my advice is to use extract instead of separate:
df %>%
extract(
col = col1 ,
into = c('Number','Words'),
regex = "([0-9]+)\\. (.*)")
The regular expression ([0-9]+)\\. (.*) means that you are looking first for a number, that you want to put in a first column, followed by a dot and a space (\\. ) that should be discarded, and the rest should go in a second column.
The result:
# A tibble: 8 × 2
Number Words
<chr> <chr>
1 1 word
2 2 word
3 3 word
4 4 word
5 5 N. word
6 6 word
7 7 word
8 8 word
Try read.table + sub
> read.table(text = sub("\\.", ",", df$col1), sep = ",")
V1 V2
1 1 word
2 2 word
3 3 word
4 4 word
5 5 N. word
6 6 word
7 7 word
8 8 word
I am not sure how to do this with tidyr, but the following should work with base R.
df$col1 <- gsub('N. ', '', df$col1)
df$Numbers <- as.numeric(sapply(strsplit(df$col1, ' '), '[', 1))
df$Words <- sapply(strsplit(df$col1, ' '), '[', 2)
df$col1 <- NULL
Result
> head(df)
Numbers Words
1 1 word
2 2 word
3 3 word
4 4 word
5 5 word
6 6 word
I have a data frame that looks like this:
head(df)
shotchart
1 BMMMBMMBMMBM
2 MMMBBMMBBMMB
3 BBBBMMBMMMBB
4 MMMMBBMMBBMM
Different patterns of the letter 'M' are worth certain values such as the following:
MM = 1
MMM = 2
MMMM = 3
I want to create an extra column to this data frame that calculates the total value of the different patterns of 'M' in each row individually.
For example:
head(df)
shotchart score
1 BMMMBMMBMMBM 4
2 MMMBBMMBBMMB 4
3 BBBBMMBMMMBB 3
4 MMMMBBMMBBMM 5
I can't seem to figure out how to assign the values to the different 'M' patterns.
I tried using the following code but it didn't work:
df$score <- revalue(df$scorechart, c("MM"="1", "MMM"="2", "MMMM"="3"))
We create a named vector ('nm1'), split the 'shotchart' to extract only 'M' and then use the named vector to change the values to get the sum
nm1 <- setNames(1:3, strrep("M", 2:4))
sapply(strsplit(gsub("[^M]+", ",", df$shotchart), ","),
function(x) sum(nm1[x[nzchar(x)]], na.rm = TRUE))
Or using tidyverse
library(tidyverse)
df %>%
mutate(score = str_extract_all(shotchart, "M+") %>%
map_dbl(~ nm1[.x] %>%
sum(., na.rm = TRUE)))
# shotchart score
#1 BMMMBMMBMMBM 4
#2 MMMBBMMBBMMB 4
#3 BBBBMMBMMMBB 3
#4 MMMMBBMMBBMM 5
You can also split on "B" and base the result on the count of "M" characters -1 as follows:
df <- data.frame(shotchart = c("BMMMBMMBMMBM", "MMMBBMMBBMMB", "BBBBMMBMMMBB", "MMMMBBMMBBMM"),
score = NA_integer_,
stringsAsFactors = F)
df$score <- lapply(strsplit(df$shotchart, "B"), function(i) sum((nchar(i)-1)[(nchar(i)-1)>0]))
# shotchart score
#1 BMMMBMMBMMBM 4
#2 MMMBBMMBBMMB 4
#3 BBBBMMBMMMBB 3
#4 MMMMBBMMBBMM 5
I have data looking somewhat similar to this:
number type results
1 5 x, y, z
2 6 a
3 8 x
1 5 x, y
Basically, I have data in Excel that has commas in a couple of individual cells and I need to count each value that is separated by a comma, after a certain requirement is met by subsetting.
Question: How do I go about receiving the sum of 5 when subsetting the data with number == 1 and type == 5, in R?
If we need the total count, then another option is str_count after subsetting
library(stringr)
with(df, sum(str_count(results[number==1 & type==5], "[a-z]"), na.rm = TRUE))
#[1] 5
Or with gregexpr from base R
with(df, sum(lengths(gregexpr("[a-z]", results[number==1 & type==5])), na.rm = TRUE))
#[1] 5
If there are no matching pattern for an element, use
with(df, sum(unlist(lapply(gregexpr("[a-z]",
results[number==1 & type==5]), `>`, 0)), na.rm = TRUE))
Here is an option using dplyr and tidyr. filter function can filter the rows based on conditions. separate_rows can separate the comma. group_by is to group the data. tally can count the numbers.
dt2 <- dt %>%
filter(number == 1, type == 5) %>%
separate_rows(results) %>%
group_by(results) %>%
tally()
# # A tibble: 3 x 2
# results n
# <chr> <int>
# 1 x 2
# 2 y 2
# 3 z 1
Or you can use count(results) only as the following code shows.
dt2 <- dt %>%
filter(number == 1, type == 5) %>%
separate_rows(results) %>%
count(results)
DATA
dt <- read.table(text = "number type results
1 5 'x, y, z'
2 6 a
3 8 x
1 5 'x, y'",
header = TRUE, stringsAsFactors = FALSE)
Here is a method using base R. You split results on the commas and get the length of each list, then add these up grouping by number.
aggregate(sapply(strsplit(df$results, ","), length), list(df$number), sum)
Group.1 x
1 1 5
2 2 1
3 3 1
Your data:
df = read.table(text="number type results
1 5 'x, y, z'
2 6 'a'
3 8 'x'
1 5 'x, y'",
header=TRUE, stringsAsFactors=FALSE)