utils_stats: Useful functions for computing descriptive statistics In metan: Multi Environment Trials Analysis

Description

• The following functions compute descriptive statistics by levels of a factor or combination of factors quickly.

• cv_by() For computing coefficient of variation.

• max_by() For computing maximum values.

• means_by() For computing arithmetic means.

• min_by() For compuing minimum values.

• n_by() For getting the length.

• sd_by() For computing sample standard deviation.

• sem_by() For computing standard error of the mean.

• Useful functions for descriptive statistics. All of them work naturally with \%>\%, handle grouped data and multiple variables (all numeric variables from .data by default).

• av_dev() computes the average absolute deviation.

• ci_mean() computes the confidence interval for the mean.

• cv() computes the coefficient of variation.

• freq_table() Computes frequency fable. Handles grouped data.

• hmean(), gmean() computes the harmonic and geometric means, respectively. The harmonic mean is the reciprocal of the arithmetic mean of the reciprocals. The geometric mean is the nth root of n products.

• kurt() computes the kurtosis like used in SAS and SPSS.

• range_data() Computes the range of the values.

• n_valid() The valid (not NA) length of a data.

• n_unique() Number of unique values.

• n_missing() Number of missing values.

• row_col_mean(), row_col_sum() Adds a row with the mean/sum of each variable and a column with the the mean/sum for each row of the data.

• sd_amo(), sd_pop() Computes sample and populational standard deviation, respectively.

• sem() computes the standard error of the mean.

• skew() computes the skewness like used in SAS and SPSS.

• sum_dev() computes the sum of the absolute deviations.

• sum_sq() computes the sum of the squared values.

• sum_sq_dev() computes the sum of the squared deviations.

• var_amo(), var_pop() computes sample and populational variance.

desc_stat() is wrapper function around the above ones and can be used to compute quickly all these statistics at once.

Usage

 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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 av_dev(.data, ..., na.rm = FALSE) ci_mean(.data, ..., na.rm = FALSE, level = 0.95) cv(.data, ..., na.rm = FALSE) freq_table(.data, ...) hmean(.data, ..., na.rm = FALSE) gmean(.data, ..., na.rm = FALSE) kurt(.data, ..., na.rm = FALSE) n_missing(.data, ..., na.rm = FALSE) n_unique(.data, ..., na.rm = FALSE) n_valid(.data, ..., na.rm = FALSE) pseudo_sigma(.data, ..., na.rm = FALSE) range_data(.data, ..., na.rm = FALSE) row_col_mean(.data, na.rm = FALSE) row_col_sum(.data, na.rm = FALSE) sd_amo(.data, ..., na.rm = FALSE) sd_pop(.data, ..., na.rm = FALSE) sem(.data, ..., na.rm = FALSE) skew(.data, ..., na.rm = FALSE) sum_dev(.data, ..., na.rm = FALSE) sum_sq_dev(.data, ..., na.rm = FALSE) sum_sq(.data, ..., na.rm = FALSE) var_pop(.data, ..., na.rm = FALSE) var_amo(.data, ..., na.rm = FALSE) cv_by(.data, ..., na.rm = FALSE) max_by(.data, ..., na.rm = FALSE) means_by(.data, ..., na.rm = FALSE) min_by(.data, ..., na.rm = FALSE) n_by(.data, ..., na.rm = FALSE) sd_by(.data, ..., na.rm = FALSE) sem_by(.data, ..., na.rm = FALSE) sum_by(.data, ..., na.rm = FALSE)

Arguments

 .data A data frame or a numeric vector. ... The argument depends on the function used. For *_by functions, ... is one or more categorical variables for grouping the data. Then the statistic required will be computed for all numeric variables in the data. If no variables are informed in ..., the statistic will be computed ignoring all non-numeric variables in .data. For the other statistics, ... is a comma-separated of unquoted variable names to compute the statistics. If no variables are informed in n ..., the statistic will be computed for all numeric variables in .data. na.rm If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed. level The confidence level for the confidence interval of the mean. Defaults to 0.95.

Value

• Functions *_by() returns a tbl_df with the computed statistics by each level of the factor(s) declared in ....

• All other functions return a nammed integer if the input is a data frame or a numeric value if the input is a numeric vector.

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com

Examples

 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 library(metan) # means of all numeric variables by ENV means_by(data_ge2, GEN, ENV) # Coefficient of variation for all numeric variables # by GEN and ENV cv_by(data_ge2, GEN, ENV) # Skewness of a numeric vector set.seed(1) nvec <- rnorm(200, 10, 1) skew(nvec) # Confidence interval 0.95 for the mean # All numeric variables # Grouped by levels of ENV data_ge2 %>% group_by(ENV) %>% ci_mean() # standard error of the mean # Variable PH and EH sem(data_ge2, PH, EH) # Frequency table for variable NR data_ge2 %>% freq_table(NR)

metan documentation built on Nov. 10, 2021, 9:11 a.m.