Package 'Rrepest'

Group Averages

Description

Group Averages

Usage

average_groups(
  res,
  data = NULL,
  group,
  by = NULL,
  over = NULL,
  est = NULL,
  svy = NULL,
  user_na = FALSE,
  ...
)

Arguments

res	(dataframe) df of results with b. and se. to average
data	(dataframe) df from which to get replicated weights
group	(grp function) that takes arguments group.name, column, cases to create averages at the end of dataframe
by	(string vector) column in which we'll break down results
over	(vector string) columns over which to do analysis
est	(est function) that takes arguments what = estimate, tgt = target, rgr = regressor
svy	(string) name of possible projects to analyse TALISSCH and TALISTCH
user_na	(bool) TRUE: show nature of user defined missing values for by.var
...	Additional arguments such as na_to_zero : (Bool) TRUE → will take NA as zero for the simple average calculation

Value

Dataframe with avergas or weighted averages (totals) in last rows for the selected cases

---

Dagger or double dagger according to coverage level

Description

Transform coverage columns in Rrepest to dagger and double dagger according to the coverage (cvge) column. Default levels are 75 (dagger) and 50 (double dagger). If coverage is above both levels, no symbol is produced (empty). Used with the coverage o option in Rrepest set to TRUE to produce columns with coverage percentages.

Usage

coverage_daggers(res_df, one_dagger = 75, two_dagger = 50)

Arguments

res_df	(data frame) Rrepest output with columns for coverage (cvge)
one_dagger	(numeric) Level at which the coverage is transformed into a dagger. 75 by default.
two_dagger	(numeric) Level at which the coverage is transformed into a double dagger. 50 by default

Value

Dataframe with daggers or double daggers in coverage column

Examples

cvge_data <- Rrepest(df_talis18, est = est("freq", "tt3g23o"), 
svy = "TALISTCH", by = "cntry", coverage = TRUE)
coverage_daggers(cvge_data, one_dagger = 95, two_dagger = 90)

---

Coverage percentage (1 - mean(is.na)) * 100

Description

Cpmputes teh coverage percentage for the column/variable of interest.

Usage

coverage_pct(df, by, x, w = NULL, limit = NULL)

Arguments

df	(data frame) Data to analyse
by	(string vector) Variable(s) used for tabulating the variable of interest
x	(string) Variable of interest for which to compute the number of valid (i.e. non-missing) observations
w	(string) Vector of weights
limit	(numeric) Threshold at which, if lower, value will be TRUE

Value

Data frame containing the number of valid (i.e. non-missing) observations for the variable of interest

Examples

data(df_pisa18)
data(df_talis18) 

coverage_pct(df = df_pisa18, by = "cnt",x = "wb173q03ha")
coverage_pct(df = df_talis18, by = "cntry",x = "tt3g01", w = "TCHWGT")

---

Program for International Student Assessment (PISA) 2018 noisy data subset

Description

A subset of noisy data from the Program for International Student Assessment (PISA) 2018 cycle for Mexico, Italy, and France.

This dataset is a subset of the PISA 2018 database produced by the OECD for the countries of France, Italy, and Mexico.

Usage

df_pisa18

data(df_pisa18)

Format

df_pisa18

A data frame in tibble format with 1269 rows and 1106 columns from which only some relevant variables are listed below. For a detailed description of all variables go to https://www.oecd.org/en/data/datasets/pisa-2018-database.html#data

idlang

Country language

CNT

Country ISO 3166-1 alpha-3 codes.

st001d01t

Student International Grade

st004d01t

Gender

...

A data frame with 1269 rows/observations and 1120 columns/variables.

Source

https://www.oecd.org/en/data/datasets/pisa-2018-database.html#data

---

Teaching and Learning International Survey (TALIS) 2018 noisy data subset

Description

A subset of noisy data from the Teaching and Learning International Survey (TALIS) 2018 cycle for Mexico, Italy, and France.

This dataset is a subset of the TALIS 2018 database produced by the OECD for the countries of France, Italy, and Mexico.

Usage

df_talis18

data(df_talis18)

Format

df_talis18

A data frame in tibble format with 496 rows and 548 columns from which only some relevant variables are listed below. For a detailed description of all variables go to https://www.oecd.org/en/data/datasets/talis-2018-database.html#data

idlang

Country language

cntry

Country ISO 3166-1 alpha-3 codes.

tt3g01

Gender

tt3g02

Age

tt3g03

Highest level of formal education completed

...

A data frame with 548 rows/observations and 496 columns/variables.

Source

https://www.oecd.org/en/data/datasets/talis-2018-database.html#data

---

Estimate list

Description

Obtains a list with the arguments for the est() function used within the Rrepest() function.

Usage

est(statistic, target, regressor = NULL)

Arguments

statistic	(string vector) Statistics of interest that can include mean ("mean"), variance ("var"), standard deviation ("std"), quantile ("quant"), inter-quantile range ("iqr"), frequency count ("freq"), correlation ("corr"), linear regression ("lm"), covariance ("cov") and any other statistics that are not pre-programmed into Rrepest but take a data frame and weights as parameters ("gen")
target	(string vector) Variable(s) of interest of the estimation
regressor	(string vector) Independent variable(s) to be included in a linear regression

Value

List containing the arguments for the est() function used within Rrepest() function

Examples

est(c("mean","quant",.5,"corr"),c("pv1math","pv1read","pv1scie"))

---

Format categorical variables as factor for Rrepest

Description

Format categorical variables as factor for Rrepest

Usage

format_data_categ_vars(df, categ.vars, show_na = F)

Arguments

df	(data frame) Data to analyse.
categ.vars	(string vector) Categorical variables for analysis.
show_na	(bool) Keeps NAs as categories to get frequency from.

Value

Data frame with categorical variables as factors for frequencies or over variables.

Examples

format_data_categ_vars(df = mtcars, categ.vars = "cyl")

---

Format continuous variables as numeric for Rrepest

Description

Format continuous variables as numeric for Rrepest

Usage

format_data_cont_vars(df, cont.vars)

Arguments

df	(data frame) Data to be analysed.
cont.vars	(string vector) continuous variables for analysis

Value

Data frame with cantinuous variables converted to numeric for a continuous analysis (means, regression, etc.)

Examples

format_data_cont_vars(mtcars,"hp")

---

Formatting target, by, and over variables for Rrepest.

Description

Formatting target, by, and over variables for Rrepest.

Usage

format_data_repest(df, svy, x, by.over, user_na = F, ...)

Arguments

df	(data frame) Data to analyze.
svy	(string) Possible projects to analyse: PIAAC, PISA, TALISSCH, TALISTCH, etc.
x	(string vector) Target variables.
by.over	(string vector) Variables to break analysis by.
user_na	(bool) TRUE → show nature of user defined missing values
...	Optional arguments such as custom weights (cm.weights)

Value

Data frame with variables in numeric format for analysis.

Examples

df1 <- format_data_repest(df_pisa18, "PISA", "pv1math", "cnt")
df2 <- format_data_repest(df_pisa18, "PISA", "pv1math", c("cnt","st004d01t"))
df3 <- format_data_repest(df_pisa18, "PISA", "pv1math", c("cnt","st004d01t","iscedl"))
df4 <- format_data_repest(df_talis18, "TALISTCH", "tt3g02", "cntry", isced = 2)

---

Grouped frequency counts

Description

Computes a data frame with frequency counts.

Usage

grouped_sum_freqs(data, small.level, big.level, w = NULL)

grouped_sum_freqs(data, small.level, big.level, w = NULL)

Arguments

data	(data frame) Data to analyze.
small.level	(string vector) all variables to get grouped sum.
big.level	(string vector) Must be fully contained in variables from small.level
w	(string) Numeric variable from which to get weights (if NULL then 1).

Value

Data frame containing the frequency counts

Data frame with frequencies from the grouped sum of small.level and big.level used for getting percentages.

Examples

grouped_sum_freqs(data = mtcars,small.level = c("cyl","am"),big.level = c("cyl"))

grouped_sum_freqs(data = mtcars,small.level = c("cyl","gear"),big.level = c("cyl"))

---

Group list

Description

Obtains a list with the arguments for the grp() function used within Rrepest() function's average() and group() options.

Usage

grp(group.name, column, cases, full_weight = FALSE)

Arguments

group.name	(string) Name of the group to be displayed
column	(string) Column/variable of interest to be grouped
cases	(string vector) Rows/values to be included in the group
full_weight	(bool) TRUE: average of the group will be weighted average

Value

List containing the arguments for the grp() function used within Rrepest() function's average() and group() options.

Examples

append(grp("OECD Average","CNTRY",c("HUN","MEX")), grp("Europe","CNTRY",c("ITA","FRA")))

---

Independent Differences of columns

Description

Get in a dataframe all bivariate combinations of differences and standard errors from columns starting with b. and se. assuming independence. For a time series the vector must be order from oldest to newest.

Usage

indep_diff(df, vec_series)

Arguments

df	(dataframe) Dataframe that contains the columns with b. and se. on their name to get the difference from
vec_series	(numeric vector) Column names to get difference from, not including "b." at the start. Must have a column in dataframe also including "se.".

Value

Dataframe with extra columns containing the difference of the columns along with their standard error.

Examples

indep_diff(rrepest_pisa_age_isced, paste0("mean.age..ISCED level ",c("1","3A","2")))

---

inv_test

Description

Invert test column from Rrepest test = TRUE by name on "b." and "se." in the column name and by sign (*-1) on "b."

Usage

inv_test(data, name_index)

Arguments

data	(dataframe) df to analyze
name_index	(string/numeric) name or index for the estimate (b.) columns containing the data for the test in Rrepest)

Value

Dataframe cointaining inverted test column names for "b." and "se." according to Rrepest structure and column multiplied by (-1) for "b."

Examples

inv_test(rrepest_pisa_age_gender, name_index = 6)

---

Check if a number is prime

Description

To check if a number n is prime, you only need to check for factors up to the square root of n. This is because if n has a factor greater than its square root, it must also have a smaller factor (since a factor is a number that divides n without leaving a remainder). This method significantly reduces the number of checks needed to determine if a number is prime.

Usage

is_prime(n)

Arguments

n	(numeric) Number to verify if it prime

Value

(bool) TRUE if the number is prime, FALSE if the number is not prime

Examples

is_prime(0)
is_prime(1)
is_prime(7)
is_prime(10)
is_prime(100011869)

---

Number of valid (i.e. non-missing) observations for column/variable x

Description

Computes the number of valid (i.e. non-missing) observations for the column/variable of interest.

Usage

n_obs_x(df, by, x, svy = NULL)

Arguments

df	(data frame) Data to analyse with lowercase column names.
by	(string vector) Variable(s) used for tabulating the variable of interest
x	(string) Variable of interest for which to compute the number of valid (i.e. non-missing) observations
svy	(string) Survey settings that must be equal to one of the following: ALL, IALS, ICCS, ICILS, IELS, PBTS, PIAAC, PIRLS, PISA, PISAOOS, PISA2015, SSES, SSES2023, SVY, TALISSCH, TALISTCH, TALISEC_LEADER, TALISEC_STAFF, TIMSS

Value

Data frame containing the number of valid (i.e. non-missing) observations for the variable of interest

Examples

n_obs_x(df = df_pisa18, by = "cnt",x = "wb173q03ha", svy = "PISA2015")
n_obs_x(df = df_talis18, by = "cntry",x = "tt3g01", svy = "TALISTCH")

---

Paired independent differences

Description

Get differences and standard errors from two columns starting with b. and se. assuming independence. The second column will be subtracted from the first.

Usage

paired_indep_diff(df, col1, col2)

Arguments

df	(dataframe) Dataframe that contains the columns with b. and se. on their name to get the difference from
col1	(numeric vector) Column name, not including "b.". Must have a column in dataframe also including "se.".
col2	(numeric vector) Column name, not including "b.". Must have a column in dataframe also including "se.".

Value

Dataframe with extra columns containing the difference of the columns along with their standard error.

Examples

paired_indep_diff(rrepest_pisa_age_gender,"mean.age..Male","mean.age..Female")

---

Estimation using replicate weights

Description

Estimates statistics using replicate weights (Balanced Repeated Replication (BRR) weights, Jackknife replicate weights,...), thus accounting for complex survey designs in the estimation of sampling variances. It is designed specifically to be used with the data sets produced by the Organization for Economic Cooperation and Development (OECD), some of which include the Programme for the International Assessment of Adult Competencies (PIAAC), Programme for International Student Assessment (PISA) and Teaching and Learning International Survey (TALIS) data sets, but works for any educational large-scale assessment and survey that uses replicated weights. It also allows for analyses with multiply imputed variables (plausible values); where plausible values are used, average estimator across plausible values is reported and the imputation error is added to the variance estimator.

Usage

Rrepest(
  data,
  svy,
  est,
  by = NULL,
  over = NULL,
  test = FALSE,
  user_na = FALSE,
  show_na = FALSE,
  flag = FALSE,
  fast = FALSE,
  tabl = FALSE,
  average = NULL,
  total = NULL,
  coverage = FALSE,
  invert_tests = FALSE,
  save_arg = FALSE,
  cores = NULL,
  ...
)

Arguments

data	(data frame) Data to analyse
svy	(string) Declares the survey settings. It must be equal to one of the following: ALL, IALS, ICCS, ICILS, IELS, PBTS, PIAAC, PIRLS, PISA, PISAOOS, PISA2015, SSES, SSES2023, SVY, TALISSCH, TALISTCH, TALISEC_LEADER, TALISEC_STAFF, TIMSS.
est	(est function) Specifies the estimates of interest. It has three arguments: statistics type, target variable and an (optional) regressor list in case of a linear regression.
by	(string vector) Produces separate estimates by levels of the variable(s) specified by the string vector.
over	(string vector) Requests estimates to be obtained separately for each level of categorical variable(s) identified by the string vector.
test	(bool) if TRUE: Computes the difference between estimates obtained for the lowest and highest values of the 'over' variable(s). (See 'over' option above.) It is useful to test for differences between dependent samples (e.g. female-male).
user_na	(bool) if TRUE: Shows the nature of user defined missing values.
show_na	(bool) if TRUE: Includes missing values (i.e. NAs) when estimating frequencies for the variable of interest.
flag	(bool) if TRUE: Replaces estimation results that are based on fewer observations than required for reporting with NaN. When used with the PIAAC survey settings, it checks if each estimation result is based on at least 30 observations. When used with the PISA, PISAOOS, PISA2015 survey settings, it checks if each estimation result is based on at least 30 observations and 5 schools. When used with the TALISSCH survey settings, it checks if each estimation result is based on at least 10 schools. When used with the TALISTCH survey settings, it checks if each estimation result is based on at least 30 observations and 10 schools.
fast	(bool) if TRUE: Computes estimates by using only 6 replicated weights.
tabl	(bool) if TRUE: Creates customisable and transferable tables using the flextable R package.
average	(grp function) Computes an arithmetic average (or weighted average). It has three arguments: name of the average, column/variable used for computing the average, rows/observations included in the average. It has three arguments: name of the group, column/variable used for computing the group, rows/observations included in the group.
total	(grp function) Computes an average weighted by the estimated size of the target population covered.
coverage	(bool/numeric) TRUE: shows column next to se. Numeric: Shows NaN if bellow the set coverage.
invert_tests	(bool) Invert test columns from Rrepest test = TRUE by name on "b." and "se." in the column name and by sign (*-1) on "b."
save_arg	(bool) TRUE: returns a named list with the estimation data frame and all arguments used in Rrepest.
cores	(numeric) NULL: Will recruit max-1 cores when doing PVs. Else, will recruit the specified number of cores for PVs
...	Other optional parameters include: isced = Filters the data used for analysis by ISCED level (e.g. isced = 2), n.pvs = Customizes the number of plausible values used in the estimation (e.g. n.pvs = 5), cm.weights = Customizes the weights used in the estimation (e.g. cm.weights = c("finw",paste0("repw",1:22))), var.factor = Customizes the variance factor used in the estimation (e.g. var.factor = 1/(0.5^2)), z.score = qnorm(1-0.05/2)

Value

Data frame containing estimation "b." and standard error "se.".

Examples

data(df_pisa18)

Rrepest(data = df_pisa18,
svy = "PISA2015",
est = est("mean","AGE"),
by = c("CNT"))

---

Rrepest table of results for PISA 2018 showing age and gender

Description

A table of results from Rrepest of the mean age of students broken down by gender for PISA 2018.

Usage

rrepest_pisa_age_gender

Format

rrepest_pisa_age_gender

A data frame in tibble format with 3 rows and 7 columns:

cnt

Country ISO 3166-1 alpha-3 codes.

b.mean.age..Female

Mean age for female students.

se.mean.age..Female

Standar error of the mean age for female students.

b.mean.age..Male

Mean age for male students.

se.mean.age..Male

Standar error of the mean age for male students.

b.mean.age..(Female-Male)

Difference of the mean age from female to male students.

se.mean.age..(Female-Male)

Standard error of the difference of the mean age from female to male students.

Source

https://www.oecd.org/en/data/datasets/pisa-2018-database.html#data

---

Rrepest table of results for PISA 2018 showing the age and completed schooling level of students' mothers

Description

A table of results from Rrepest of the mean age of students broken down by completed schooling level of students' mothers for PISA 2018.

Usage

rrepest_pisa_age_isced

Format

rrepest_pisa_age_isced

A data frame in tibble format with 3 rows and 7 columns:

cnt

Country ISO 3166-1 alpha-3 codes.

b.mean.age..ISCED level 1

Mean age of students whose mothers completed ISCED 1.

se.mean.age..ISCED level 1

Standard error of the mean age of students whose mothers completed ISCED 1.

b.mean.age..ISCED level 2

Mean age of students whose mothers completed ISCED 2.

se.mean.age..ISCED level 2

Standard error of the mean age of students whose mothers completed ISCED 2.

b.mean.age..ISCED level 3A

Mean age of students whose mothers completed ISCED 3A.

se.mean.age..ISCED level 3A

Standard error of the mean age of students whose mothers completed ISCED 3A.

b.mean.age..ISCED level 3B, 3C

Mean age of students whose mothers completed ISCED 3B/3C.

se.mean.age..ISCED level 3B, 3C

Standard error of the mean age of students whose mothers completed ISCED 3B/3C.

b.mean.age..She did not complete ISCED level 1

Mean age of students whose mothers did not complete ISCED 1.

se.mean.age..She did not complete ISCED level 1

Standard error of the mean age of students whose mothers did not complete ISCED 1.

b.mean.age..(ISCED level 1-She did not complete ISCED level 1)

Mean age difference between ISCED 1 completed vs. non-ISCED 1 completed mothers.

se.mean.age..(ISCED level 1-She did not complete ISCED level 1)

Standard error of the mean age difference between ISCED 1 completed vs. non-ISCED 1 completed mothers.

...

Source

https://www.oecd.org/en/data/datasets/pisa-2018-database.html#data

---

Weighted bivariate correlation

Description

Computes the weighted Pearson correlation coefficient of two numeric vectors.

Usage

weighted.corr(x, y, w, na.rm = TRUE)

Arguments

x	(numeric vector) Variable of interest x for computing the correlation
y	(numeric vector) Variable of interest y for computing the correlation
w	(numeric vector) Vector with the weights
na.rm	(bool) if TRUE: Excludes missing values before computing the correlation

Value

Scalar containing the Pearson correlation coefficient

Examples

data(df_talis18) 

weighted.corr(x = df_talis18$t3stake, y = df_talis18$t3team, w = df_talis18$tchwgt)

---

Multivariate correlation and covariance

Description

Computes multivariate correlation and covariance for the variables of interest.

Usage

weighted.corr.cov.n(
  data,
  x,
  w = rep(1, length(data[x[1]])),
  corr = TRUE,
  na.rm = TRUE
)

Arguments

data	(data frame) Data to analyse
x	(string vector) Variables of interest for which to compute the correlation/covariance
w	(string) Name of the numeric variable representing the weights
corr	(bool) if TRUE: Computes correlation; if FALSE: Computes covariance
na.rm	(bool) if TRUE: Excludes missing values before computing the correlation/covariance.

Value

Data frame containing each pairwise bivariate correlation/covariance

Examples

data(df_talis18)

weighted.corr.cov.n(df_talis18,c("t3stake","t3team","t3stud"),"tchwgt")

---

Weighted bivariate covariance

Description

Computes the weighted covariance coefficient of two numeric vectors.

Usage

weighted.cov(x, y, w, na.rm = TRUE)

Arguments

x	(numeric vector) Variable of interest x for computing the covariance
y	(numeric vector) Variable of interest y for computing the covariance
w	(numeric vector) Vector with the weights
na.rm	(bool) if TRUE: Excludes missing values before computing the covariance

Value

Scalar containing the covariance

Examples

data(df_talis18) 

weighted.cov(x = df_talis18$t3stake, y = df_talis18$t3team, w = df_talis18$tchwgt)

---

Weighted inter-quantile range

Description

Computes the weighted inter-quantile range of a numeric vector.

Usage

weighted.iqr(x, w = rep(1, length(x)), rang = c(0.25, 0.75))

Arguments

x	(numeric vector) Variable of interest for which to compute the inter-quantile range
w	(numeric vector) Vector with the weights
rang	(numeric vector) Two numbers between 0 and 1 indicating the desired inter-quantile range

Value

Scalar containing the inter-quantile range

Examples

weighted.iqr(x = mtcars$mpg, w = mtcars$wt,  rang = c(.5,.9))

---

Mode

Description

Calculate the arithmetic mode of a vector. If multiple elements have the same frequency, all of them will be displayed.

Usage

weighted.mode(x, w = rep(1, length(x)))

Arguments

x	(numeric vector) vector from which we'll obtain the mode
w	(numeric vector) vector of weights. If not provided, it defaults to non-weighted mode.

Value

(numeric vector) one or multiple elements that will be the arithmetic mode

Examples

weighted.mode(c(1,2,3,4,5,4,3,4,5,3))
weighted.mode(c(NA,1,3,NA))

---

Weighted quantile

Description

Computes weighted quantiles of a numeric vector.

Usage

weighted.quant(x, w = rep(1, length(x)), q = 0.5)

Arguments

x	(numeric vector) Variable of interest for which to compute the quantile
w	(numeric vector) Vector with the weights
q	(numeric vector) A number between 0 and less than 1 indicating the desired quantile

Value

Scalar containing the quantile

Examples

weighted.quant(x = mtcars$mpg, w = mtcars$wt,  q = seq(.1,.9,.1))

---

Weighted standard deviation

Description

Computes the weighted standard deviation of a numeric vector.

Usage

weighted.std(x, w, na.rm = TRUE)

Arguments

x	(numeric vector) Variable of interest for which to compute the standard deviation.
w	(numeric vector) Vector with the weights.
na.rm	(bool) if TRUE: Excludes missing values before computing the standard deviation

Value

Scalar containing the standard deviation

Examples

data(df_talis18)

weighted.std(df_talis18$TT3G02, df_talis18$TRWGT1)

---

Weighted variance

Description

Computes the weighted variance of a numeric vector.

Usage

weighted.var(x, w, na.rm = TRUE)

Arguments

x	(numeric vector) Variable of interest for which to compute the variance
w	(numeric vector) Vector with weights
na.rm	(bool) if TRUE: Excludes missing values before computing the variance

Value

Scalar containing the variance

Examples

data(df_talis18) 

weighted.var(df_talis18$TT3G02, df_talis18$TRWGT1)

---