Dataset              Samples
1     WGS        nrow(WGS.ped)
2     WES    nrow(WES.ped.exp)
3    MIPS   nrow(MIPS.ped.exp)

ldply(list(WGS=WGS.ped, WES=WES.ped.exp, MIPS=mips.ped.exp),
      function(l)(Samples=nrow(l)))

   .id    V1
1  WGS  3908
2  WES 26367
3 MIPS 14193

ldply(list(WGS=WGS.ped, WES=WES.ped.exp, MIPS=mips.ped.exp), nrow)

   .id    V1
1  WGS  3908
2  WES 26367
3 MIPS 14193

lapply(list(WGS=WGS.ped, WES=WES.ped.exp, MIPS=mips.ped.exp), nrow) %>%
  as.data.frame

   WGS   WES  MIPS
1 3908 26367 14193

sapply(list(WGS=WGS.ped, WES=WES.ped.exp, MIPS=mips.ped.exp), nrow) %>%
  stack()

  values  ind
1   3908  WGS
2  26367  WES
3  14193 MIPS

map(list(WGS=WGS.ped, WES=WES.ped.exp, MIPS=mips.ped.exp), nrow) %>%
  as.data.frame()

 WGS   WES  MIPS
 3908 26367 14193

Dataset Samples
WGS     nrow(WGS.ped)
WES     nrow(WES.ped.exp)
MIPS    nrow(MIPS.ped.exp)

# Load some built-in dataframes to use as an example
df1 <- mtcars
df2 <- iris
df3 <- PlantGrowth

# Build list of dataframes; use "=" so each element will be named
df_list <- list(df1 = df1,
                df2 = df2,
                df3 = df3)

# Calculate number of rows in each dataframe
nrows_list <- map(df_list, nrow)

# Create summary dataframe
summary_df <- tibble(df = names(df_list),
                     nrows = unlist(nrows_list))

# Output
# A tibble: 3 x 2
  df    nrows
  <chr> <int>
1 df1      32
2 df2     150
3 df3      30