CODEWITHSUNDEEP
rdatetidyversedata-manipulation
mexicanseafood
mexicanseafood

Reputation: 97

Date Subtraction for Sweep in R

I have a datavectorthat looks like this:

enter image description here

And a dataframe with a columns of dates, that looks like this:

enter image description here

Where the number of rows in this dataframe is equal to the length of new_first_dates.

I want to subtract new_first_dates, from each row of the dataframe. I tried using sweep but it won't work.

Does anyone have any ideas?

Cheers

Attached is the data:

structure(list(`>50 gatherings` = structure(c(18346, 18342, 18339, 18341, 18346, 18339, 18339, 18355, 18340, 18346, 18339, 18338, 18346, 18349, 18343, 18347, 18345, 18340, 18337, 18351, 18338, 18345, 18338, 21915, 18347, 18343, 18340, 18346, 18339), class = "Date"),     `>500 gatherings` = structure(c(18346, 18335, 18339, 18341,     18335, 18339, 18339, 18355, 18340, 18335, 18339, 18335, 18346,     18349, 18343, 18347, 18334, 18340, 18337, 18351, 18338, 18345,     18338, 21915, 18347, 18336, 18334, 18346, 18339), class = "Date"),     `public schools` = structure(c(18341, 18338, 18338, 18341,     18345, 18338, 18340, 18355, 18339, 18341, 18340, 18338, 18338,     18340, 18341, 18342, 18338, 18338, 18339, 18339, 18338, 18338,     18338, 18338, 18342, 18345, 18338, 18338, 18340), class = "Date"),     `restaurant dine-in` = structure(c(18339, 18341, 18341, 18337,     18342, 18342, 18342, 18339, 18340, 18339, 18346, 18338, 18338,     18339, 18343, 18347, 18339, 18340, 18337, 18354, 18339, 18339,     18345, 18345, 18345, 18341, 18340, 18345, 18339), class = "Date"),     `entertainment/gym` = structure(c(18339, 18350, 18341, 18337,     18339, 18342, 18342, 18339, 18340, 18339, 18346, 18338, 18338,     18339, 18359, 18347, 18345, 18340, 18344, 18354, 18339, 18339,     18345, 21915, 18345, 18341, 18340, 18338, 18339), class = "Date"),     `federal guidelines` = structure(c(18338, 18338, 18338, 18338,     18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338,     18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338, 18338,     18338, 18338, 18338, 18338, 18338, 18338, 18338), class = "Date"),     `foreign travel ban` = structure(c(18333, 18333, 18333, 18333,     18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333,     18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333, 18333,     18333, 18333, 18333, 18333, 18333, 18333, 18333), class = "Date"),     `stay at home rollback` = structure(c(18377, 18383, 18398,     18392, 18379, 18432, 18383, 21915, 18413, 18387, 18398, 18415,     18380, 18398, 18387, 18379, 18403, 18391, 18424, 18419, 18392,     18387, 18409, 21915, 18398, 18419, 21915, 18396, 18396), class = "Date"),     `>50 gatherings rollback` = structure(c(18391, 21915, 18415,     21915, 21915, 21915, 21915, 21915, 18426, 18405, 18398, 21915,     21915, 18387, 18429, 18415, 21915, 21915, 18404, 21915, 21915,     21915, 21915, 18384, 21915, 21915, 21915, 21915, 21915), class = "Date"),     `>500 gatherings rollback` = structure(c(18405, 21915, 18415,     21915, 21915, 21915, 21915, 21915, 18426, 18405, 18398, 21915,     21915, 18387, 18429, 18415, 21915, 21915, 21915, 21915, 21915,     21915, 21915, 18384, 21915, 21915, 21915, 21915, 21915), class = "Date"),     `restaurant dine-in rollback` = structure(c(18405, 18394,     21915, 18392, 18391, 18432, 18380, 18394, 18391, 21915, 18384,     18390, 18380, 18424, 18429, 18384, 18387, 18411, 21915, 18391,     21915, 21915, 18409, 21915, 18387, 18398, 18384, 18394, 21915    ), class = "Date"), `entertainment/gym rollback` = structure(c(18405,     18394, 21915, 18392, 18391, 18432, 18377, 18394, 18391, 21915,     18384, 18390, 18380, 18424, 18429, 18384, 21915, 18415, 21915,     18391, 21915, 21915, 18409, 21915, 18387, 18398, 18384, 18394,     21915), class = "Date"), State = c("AK", "AL", "AZ", "CA",     "CO", "FL", "GA", "IA", "IL", "IN", "KS", "MD", "MI", "MN",     "MO", "MS", "NC", "NM", "OH", "OK", "OR", "PA", "RI", "SD",     "TN", "TX", "UT", "VA", "WI"), cluster = c(3, 1, 1, 1, 1,     1, 1, 1, 2, 3, 3, 2, 3, 1, 3, 1, 1, 1, 1, 3, 1, 2, 2, 3,     1, 1, 1, 1, 1)), row.names = c(NA, -29L), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), groups = structure(list(State = c("AK", "AL", "AZ", "CA", "CO", "FL", "GA", "IA", "IL", "IN", "KS", "MD", "MI", "MN", "MO", "MS", "NC", "NM", "OH", "OK", "OR", "PA", "RI", "SD", "TN", "TX", "UT", "VA", "WI"), .rows = list(1L, 2L, 3L,     4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,     17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,     29L)), row.names = c(NA, -29L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))

structure(c(18338, 18345, 18340, 18325, 18332, 18334, 18333, 18333, 18339, 18339, 18340, 18337, 18335, 18337, 18339, 18340, 18340, 18336, 18337, 18340, 18335, 18334, 18339, 18346, 18335, 18334, 18338, 18335, 18337), class = "Date")

Upvotes: 1

Views: 154

Answers (2)

DonLi
DonLi

Reputation: 78

If your dataset is large, you may want to look into using the data.table package for the modify-in-place semantics.

library( data.table )
dates_dataset = data.table( dates_dataset )

# Find the columns that are date class or otherwise select the columns you want to subtract
date_columns = names(dates_dataset)[ sapply( dates_dataset, class ) == "Date" ]

# Use the := in-place assignment operator to update the columns
dates_dataset[ , eval(date_columns) := {
    lapply( date_columns, function(x){
        get( x ) - new_first_dates
    } )
} ]

You have the option to change eval(date_columns) to something else if you want to create new columns.

The output is:

get_vars = c(">50 gatherings", ">500 gatherings")
head( dates_dataset[,mget(get_vars) ] )

   >50 gatherings >500 gatherings
1:         8 days          8 days
2:        -3 days        -10 days
3:        -1 days         -1 days
4:        16 days         16 days
5:        14 days          3 days
6:         5 days          5 days

Upvotes: 1

akrun
akrun

Reputation: 887541

Some of the columns are not Date class. So, we need to exclude those before doing the subtraction. Also, based on the structure of OP's post, there are some attributes in the dataset, which can be removed with ungrouping

library(dplyr) # version >= 1.0.0
df1 %>%
  ungroup %>%
  mutate(across(-c(State, cluster),  ~ . - as.Date(v1)))

-output

# A tibble: 29 x 14
#   `>50 gatherings` `>500 gathering… `public schools` `restaurant din… `entertainment/… `federal guidel… `foreign travel… `stay at home r…
#   <drtn>           <drtn>           <drtn>           <drtn>           <drtn>           <drtn>           <drtn>           <drtn>          
# 1  8 days            8 days          3 days           1 days           1 days           0 days           -5 days           39 days       
# 2 -3 days          -10 days         -7 days          -4 days           5 days          -7 days          -12 days           38 days       
# 3 -1 days           -1 days         -2 days           1 days           1 days          -2 days           -7 days           58 days       
# 4 16 days           16 days         16 days          12 days          12 days          13 days            8 days           67 days       
# 5 14 days            3 days         13 days          10 days           7 days           6 days            1 days           47 days       
# 6  5 days            5 days          4 days           8 days           8 days           4 days           -1 days           98 days       
# 7  6 days            6 days          7 days           9 days           9 days           5 days            0 days           50 days       
# 8 22 days           22 days         22 days           6 days           6 days           5 days            0 days         3582 days       
# 9  1 days            1 days          0 days           1 days           1 days          -1 days           -6 days           74 days       
#10  7 days           -4 days          2 days           0 days           0 days          -1 days           -6 days           48 days       
# … with 19 more rows, and 6 more variables: `>50 gatherings rollback` <drtn>, `>500 gatherings rollback` <drtn>, `restaurant dine-in
#   rollback` <drtn>, `entertainment/gym rollback` <drtn>, State <chr>, cluster <dbl>

Or if the OP prefer sweep

df1[1:12] <- sweep(as.data.frame(df1[1:12]), 1, as.Date(v1),  '-')

Or using lapply from base R

df1[1:12] <- lapply(df1[1:12], `-`, as.Date(v1))

Upvotes: 1

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