Description Usage Arguments Details Value Examples

`row_sums()`

and `row_means()`

compute row sums or means
for at least `n`

valid values per row. The functions are designed
to work nicely within a pipe-workflow and allow select-helpers
for selecting variables.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
row_sums(x, ...)
## Default S3 method:
row_sums(x, ..., n, var = "rowsums", append = TRUE)
## S3 method for class 'mids'
row_sums(x, ..., var = "rowsums", append = TRUE)
row_means(x, ...)
total_mean(x, ...)
## Default S3 method:
row_means(x, ..., n, var = "rowmeans", append = TRUE)
## S3 method for class 'mids'
row_means(x, ..., var = "rowmeans", append = TRUE)
``` |

`x` |
A vector or data frame. |

`...` |
Optional, unquoted names of variables that should be selected for
further processing. Required, if |

`n` |
May either be a numeric value that indicates the amount of valid values per row to calculate the row mean or sum; a value between 0 and 1, indicating a proportion of valid values per row to calculate the row mean or sum (see 'Details'). or `Inf` . If`n = Inf` , all values per row must be non-missing to compute row mean or sum.
If a row's sum of valid (i.e. non- |

`var` |
Name of new the variable with the row sums or means. |

`append` |
Logical, if |

For `n`

, must be a numeric value from `0`

to `ncol(x)`

. If
a *row* in `x`

has at least `n`

non-missing values, the
row mean or sum is returned. If `n`

is a non-integer value from 0 to 1,
`n`

is considered to indicate the proportion of necessary non-missing
values per row. E.g., if `n = .75`

, a row must have at least `ncol(x) * n`

non-missing values for the row mean or sum to be calculated. See 'Examples'.

For `row_sums()`

, a data frame with a new variable: the row sums from
`x`

; for `row_means()`

, a data frame with a new variable: the row
means from `x`

. If `append = FALSE`

, only the new variable
with row sums resp. row means is returned. `total_mean()`

returns
the mean of all values from all specified columns in a data frame.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
data(efc)
efc %>% row_sums(c82cop1:c90cop9, n = 3, append = FALSE)
library(dplyr)
row_sums(efc, contains("cop"), n = 2, append = FALSE)
dat <- data.frame(
c1 = c(1,2,NA,4),
c2 = c(NA,2,NA,5),
c3 = c(NA,4,NA,NA),
c4 = c(2,3,7,8),
c5 = c(1,7,5,3)
)
dat
row_means(dat, n = 4)
row_sums(dat, n = 4)
row_means(dat, c1:c4, n = 4)
# at least 40% non-missing
row_means(dat, c1:c4, n = .4)
row_sums(dat, c1:c4, n = .4)
# total mean of all values in the data frame
total_mean(dat)
# create sum-score of COPE-Index, and append to data
efc %>%
select(c82cop1:c90cop9) %>%
row_sums(n = 1)
``` |

strengejacke/sjmisc documentation built on Aug. 18, 2018, 1:22 p.m.

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