![[Stable]](figures/lifecycle-stable.svg)
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.mean_by()For computing arithmetic means.min_by()For compuing minimum values.n_by()For getting the length.sd_by()For computing sample standard deviation.var_by()For computing sample variance.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.databy default).av_dev()computes the average absolute deviation.ci_mean_t()computes the t-interval for the mean.ci_mean_z()computes the z-interval for the mean.cv()computes the coefficient of variation.freq_table()Computes a frequency table for either numeric and categorical/discrete data. For numeric data, it is possible to define the number of classes to be generated.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 (notNA) 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.ave_dev()computes the average of the absolute deviations.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
av_dev(.data, ..., na.rm = FALSE)
ci_mean_t(.data, ..., na.rm = FALSE, level = 0.95)
ci_mean_z(.data, ..., na.rm = FALSE, level = 0.95)
cv(.data, ..., na.rm = FALSE)
freq_table(.data, var, k = NULL, digits = 3)
freq_hist(
table,
xlab = NULL,
ylab = NULL,
fill = "gray",
color = "black",
ygrid = TRUE
)
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)
ave_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, ..., .vars = NULL, na.rm = FALSE)
max_by(.data, ..., .vars = NULL, na.rm = FALSE)
min_by(.data, ..., .vars = NULL, na.rm = FALSE)
means_by(.data, ..., .vars = NULL, na.rm = FALSE)
mean_by(.data, ..., .vars = NULL, na.rm = FALSE)
n_by(.data, ..., .vars = NULL, na.rm = FALSE)
sd_by(.data, ..., .vars = NULL, na.rm = FALSE)
var_by(.data, ..., .vars = NULL, na.rm = FALSE)
sem_by(.data, ..., .vars = NULL, na.rm = FALSE)
sum_by(.data, ..., .vars = NULL, na.rm = FALSE)Arguments
- .data
A data frame or a numeric vector.
- ...
The argument depends on the function used.
For
*_byfunctions,...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. IfTRUE, missing values are silently removed.- level
The confidence level for the confidence interval of the mean. Defaults to 0.95.
- var
The variable to compute the frequency table. See
Detailsfor more details.- k
The number of classes to be created. See
Detailsfor more details.- digits
The number of significant figures to show. Defaults to 2.
- table
A frequency table computed with
freq_table().- xlab, ylab
The
xandylabels.- fill, color
The color to fill the bars and color the border of the bar, respectively.
- ygrid
Shows a grid line on the
yaxis? Defaults toTRUE. freq_hist <- function(table,- .vars
Used to select variables in the
*_by()functions. One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions likex:ycan be used to select a range of variables. Defaults toNULL(all numeric variables are analyzed)..
Value
Functions
*_by()returns atbl_dfwith the computed statistics by each level of the factor(s) declared in....All other functions return a named integer if the input is a data frame or a numeric value if the input is a numeric vector.
freq_table()Returns a list with the frequency table and the breaks used for class definition. These breaks can be used to construct an histogram of the variable.
Details
The function freq_table() computes a frequency table for either
numerical or categorical variables. If a variable is categorical or
discrete (integer values), the number of classes will be the number of
levels that the variable contains.
If a variable (say, data) is continuous, the number of classes (k) is given by
the square root of the number of samples (n) if n =< 100 or 5 * log10(n)
if n > 100.
The amplitude (\(A\)) of the data is used to define the size of the class (\(c\)), given by
\[c = \frac{A}{n - 1}\]
The lower limit of the first class (LL1) is given by min(data) - c / 2. The upper limit is given by LL1 + c. The limits of the other classes are given in the same way. After the creation of the classes, the absolute and relative frequencies within each class are computed.
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
# means of all numeric variables by ENV
mean_by(data_ge2, GEN, ENV)
#> # A tibble: 52 × 17
#> GEN ENV PH EH EP EL ED CL CD CW KW NR NKR
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 H1 A1 2.72 1.68 0.626 15.4 51.1 28.1 15.7 23.5 203. 16.3 33.3
#> 2 H1 A2 2.93 1.80 0.612 15.0 51.9 31.5 15.5 30.2 188. 17.1 31.4
#> 3 H1 A3 2.20 1.10 0.497 14.8 50.6 30.9 15.8 26.8 157. 15.9 28.4
#> 4 H1 A4 2.64 1.44 0.547 15.2 51.2 29.8 15.8 26.4 187. 17.2 35.7
#> 5 H10 A1 2.78 1.62 0.584 16.1 53.2 31.4 16.8 24.6 192. 16.7 31.2
#> 6 H10 A2 2.05 0.987 0.494 15.5 46.7 26.8 16.3 26.3 160. 14 33.5
#> 7 H10 A3 2.04 1.01 0.503 14.0 43.9 24.8 15.2 12.3 121. 15.3 33.3
#> 8 H10 A4 2.39 1.43 0.600 14.9 50.0 30.7 15.3 28.0 183. 16.4 31.6
#> 9 H11 A1 2.75 1.58 0.574 16.6 48.9 29.0 17.2 23.6 188. 15.2 34.6
#> 10 H11 A2 2.15 1.02 0.475 15.1 47.3 27.2 15.7 24.3 164. 13.7 35
#> # … with 42 more rows, and 4 more variables: CDED <dbl>, PERK <dbl>, TKW <dbl>,
#> # NKE <dbl>
# Coefficient of variation for all numeric variables
# by GEN and ENV
cv_by(data_ge2, GEN, ENV)
#> # A tibble: 52 × 17
#> GEN ENV PH EH EP EL ED CL CD CW KW NR
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 H1 A1 4.93 5.36 5.34 7.15 1.98 1.48 6.91 7.93 8.31 5.12
#> 2 H1 A2 3.46 5.98 2.71 2.04 1.92 1.92 1.98 3.75 5.44 3.58
#> 3 H1 A3 4.14 4.98 1.01 7.00 3.58 2.25 6.43 14.6 15.2 2.91
#> 4 H1 A4 8.66 9.84 1.89 9.88 3.72 5.51 11.4 19.1 17.9 4.65
#> 5 H10 A1 1.97 6.11 5.33 6.46 1.43 2.72 6.78 13.6 7.99 6.04
#> 6 H10 A2 5.31 6.83 12.8 5.29 0.800 1.38 4.89 8.81 9.34 7.56
#> 7 H10 A3 11.9 11.3 2.24 3.23 0.436 5.56 5.40 5.80 5.23 3.98
#> 8 H10 A4 4.38 3.53 8.82 3.11 3.42 3.89 3.09 5.02 6.07 2.44
#> 9 H11 A1 0.988 5.01 4.44 4.94 4.95 5.27 4.72 12.3 13.9 10.5
#> 10 H11 A2 1.43 3.00 4.00 4.84 0.762 2.48 5.02 15.8 5.37 3.36
#> # … with 42 more rows, and 5 more variables: NKR <dbl>, CDED <dbl>, PERK <dbl>,
#> # TKW <dbl>, NKE <dbl>
# Skewness of a numeric vector
set.seed(1)
nvec <- rnorm(200, 10, 1)
skew(nvec)
#> [1] 0.1977769
# Confidence interval 0.95 for the mean
# All numeric variables
# Grouped by levels of ENV
data_ge2 %>%
group_by(ENV) %>%
ci_mean_t()
#> # A tibble: 4 × 16
#> ENV PH EH EP EL ED CL CD CW KW NR NKR
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A1 0.0401 0.0415 0.0142 0.382 0.598 0.725 0.352 1.83 6.31 0.638 0.948
#> 2 A2 0.129 0.113 0.0206 0.442 0.882 0.819 0.436 2.24 11.9 0.467 1.06
#> 3 A3 0.0724 0.0625 0.0153 0.326 0.897 0.781 0.307 1.66 7.03 0.498 1.01
#> 4 A4 0.0539 0.0483 0.0128 0.423 0.700 0.579 0.394 1.48 8.70 0.433 1.20
#> # … with 4 more variables: CDED <dbl>, PERK <dbl>, TKW <dbl>, NKE <dbl>
# standard error of the mean
# Variable PH and EH
sem(data_ge2, PH, EH)
#> # A tibble: 1 × 2
#> PH EH
#> <dbl> <dbl>
#> 1 0.0267 0.0228
# Frequency table for variable NR
data_ge2 %>%
freq_table(NR)
#> $freqs
#> class abs_freq abs_freq_ac rel_freq rel_freq_ac
#> 1 11.96 |--- 12.84 1 1 0.006 0.006
#> 2 12.84 |--- 13.72 10 11 0.064 0.071
#> 3 13.72 |--- 14.6 18 29 0.115 0.186
#> 4 14.6 |--- 15.48 23 52 0.147 0.333
#> 5 15.48 |--- 16.36 31 83 0.199 0.532
#> 6 16.36 |--- 17.24 40 123 0.256 0.788
#> 7 17.24 |--- 18.12 21 144 0.135 0.923
#> 8 18.12 |--- 19 5 149 0.032 0.955
#> 9 19 |--- 19.88 1 150 0.006 0.962
#> 10 19.88 |--- 20.76 5 155 0.032 0.994
#> 11 20.76 |---| 21.64 1 156 0.006 1.000
#> 12 Total 156 156 1.000 1.000
#>
#> $LL
#> [1] 11.96 12.84 13.72 14.60 15.48 16.36 17.24 18.12 19.00 19.88 20.76
#>
#> $UL
#> [1] 12.84 13.72 14.60 15.48 16.36 17.24 18.12 19.00 19.88 20.76 21.64
#>
#> $vartype
#> [1] "continuous"
#>
#> attr(,"class")
#> [1] "freq_table"
# }