descriptive statistics in r

Question

I was trying to get descriptive stat for my data. I went through many suggestions. However I just want to know if there is any package(s) to perform descriptive stats on the data format provided below.

head(mydata)
   X  A1  A2  A3  M1  M2  M3  U1  U2  U3
1      A   A   A   M   M   M   U   U   U
2 X1 100 200 250 200 230 400 400 100 200
3 X2 600 300 400 300 550 750 800 900 540
4 X3 500 300 200 200 200 100 500 400 600

The data has samples on the column and variables on rows. First row is samples name, second row is groups (A, M, U). I want to get descriptive statistics for each group. For example mean, sd.... for group A (A1, A2, A3). Could anyone please let me know how can I do this. I have seen most of the answers on descriptive stats and they are for columns. Please let me know if the question is not clear. Thank you for your help.

Higgs

Answer 1

@Phil is dead right with his recommendation.

One of the key principles you'll learn in Hadley's book is the tidy data principle (very basically: variables in columns, individual observations in rows). If you want a quick introduction to tidy data, try this vignette.

There are multiple ways to go about fixing and analysing your data, but here is an example using tools from the 'tidyverse'.

# Load useful 'tidy data' packages
library(tidyverse)

# Make 'mydata'
mydata <- data_frame(X = c('', 'X1', 'X2', 'X3'),
                     A1 = c('A', 100, 600, 500),
                     A2 = c('A', 200, 300, 300),
                     A3 = c('A', 250, 400, 200),
                     M1 = c('M', 200, 300, 200),
                     M2 = c('M', 230, 550, 200),
                     M3 = c('M', 400, 750, 100),
                     U1 = c('U', 400, 800, 500),
                     U2 = c('U', 100, 900, 400),
                     U3 = c('U', 200, 540, 600))

# View 'mydata'
mydata

#> # A tibble: 4 x 10
#>   X     A1    A2    A3    M1    M2    M3    U1    U2    U3   
#>   <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ""    A     A     A     M     M     M     U     U     U    
#> 2 X1    100   200   250   200   230   400   400   100   200  
#> 3 X2    600   300   400   300   550   750   800   900   540  
#> 4 X3    500   300   200   200   200   100   500   400   600

Convert to a tidy dataframe

# Transpose rows and columns and convert resulting matrix back into a dataframe
mydata_new <- as_data_frame(t(mydata))

# View 'mydata_new'
mydata_new

#> # A tibble: 10 x 4
#>    V1    V2    V3    V4   
#>    <chr> <chr> <chr> <chr>
#>  1 ""    X1    X2    X3   
#>  2 A     100   600   500  
#>  3 A     200   300   300  
#>  4 A     250   400   200  
#>  5 M     200   300   200  
#>  6 M     230   550   200  
#>  7 M     400   750   100  
#>  8 U     400   800   500  
#>  9 U     100   900   400  
#> 10 U     200   540   600

# Clean 'mydata_new'
## Add column names
colnames(mydata_new) <- c('Group', 'X1', 'X2', 'X3')
## Remove first row
mydata_new <- mydata_new[-1, ]

# View cleaned 'mydata_new'
mydata_new

#> # A tibble: 9 x 4
#>   Group X1    X2    X3   
#>   <chr> <chr> <chr> <chr>
#> 1 A     100   600   500  
#> 2 A     200   300   300  
#> 3 A     250   400   200  
#> 4 M     200   300   200  
#> 5 M     230   550   200  
#> 6 M     400   750   100  
#> 7 U     400   800   500  
#> 8 U     100   900   400  
#> 9 U     200   540   600

Now summarise the data.

# Summarise numeric data
mydata_new %>% 
    # Convert all data columns from 'character' to 'numeric'
    mutate_at(vars(starts_with('X')), 
              as.numeric) %>%
    # Group data by the grouping variable before summarising
    group_by(Group) %>% 
    # Calculate MEAN and SD for each data column
    summarise_at(vars(starts_with('X')), 
                 funs(MEAN = mean, SD = sd))

#> # A tibble: 3 x 7
#>   Group X1_MEAN X2_MEAN X3_MEAN X1_SD X2_SD X3_SD
#>   <chr>   <dbl>   <dbl>   <dbl> <dbl> <dbl> <dbl>
#> 1 A        183.    433.    333.  76.4  153. 153. 
#> 2 M        277.    533.    167. 108.   225.  57.7
#> 3 U        233.    747.    500  153.   186. 100

---

Update: 10 May 2018 following query about adding the coefficient of variation.

Coefficient of variation is not a base R function, so create a user-defined function.

# Define function: (cv = sd / mean)
coef_var = function(x) {
    sd(x, na.rm = TRUE) / mean(x, na.rm = TRUE)
}

Re-execute the summary with added summary functions

# Execute summary 
mydata_new %>% 
    # Convert all data columns from 'character' to 'numeric'
    mutate_at(vars(starts_with('X')), 
              as.numeric) %>%
    # Group data by the grouping variable before summarising
    group_by(Group) %>% 
    # Calculate summaries each data column 
    ## Call the summary functions with a dummy "." argument so that 
    ## Additional arguments can be added to the called functions 
    ## (e.g., adding na.rm = TRUE to cope with missing data)
    ## See ?dplyr::funs for details
    summarise_at(vars(starts_with('X')), 
                 funs(MEAN = mean(., na.rm = TRUE), # Mean
                      SD = sd(., na.rm = TRUE), # SD
                      CV = coef_var, # Coefficient of variation
                      # Add other summary stats as needed
                      MEDIAN = median(., na.rm = TRUE), # Median
                      Q25 = quantile(., prob = 0.25, na.rm = TRUE), # 25th percentile
                      Q75 = quantile(., prob = 0.75, na.rm = TRUE), # 75th percentile
                      min = min(., na.rm = TRUE), # Minimum
                      max = max(., na.rm = TRUE))) # Maximum

#> # A tibble: 3 x 25
#>   Group X1_MEAN X2_MEAN X3_MEAN X1_SD X2_SD X3_SD X1_CV X2_CV X3_CV
#>   <chr>   <dbl>   <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A        183.    433.    333.  76.4  153. 153.  0.417 0.353 0.458
#> 2 M        277.    533.    167. 108.   225.  57.7 0.390 0.423 0.346
#> 3 U        233.    747.    500  153.   186. 100   0.655 0.249 0.2  
#> # ... with 15 more variables: X1_MEDIAN <dbl>, X2_MEDIAN <dbl>,
#> #   X3_MEDIAN <dbl>, X1_Q25 <dbl>, X2_Q25 <dbl>, X3_Q25 <dbl>,
#> #   X1_Q75 <dbl>, X2_Q75 <dbl>, X3_Q75 <dbl>, X1_min <dbl>, X2_min <dbl>,
#> #   X3_min <dbl>, X1_max <dbl>, X2_max <dbl>, X3_max <dbl>

Created on 2018-05-10 by the reprex package (v0.2.0).