R dplyr rowwise mean or min and other methods?

Question

How can I get with dplyr the minimum (or mean) value of each row on a data.frame? I mean the same result as

apply(mydataframe, 1, mean) 
apply(mydataframe, 1         


        

Answer 1

There seems to be talk that some dplyr functions like rowwise could be deprecated in the long term (such rumblings on display here). Instead, certain functions from the map family of functions -- such as the pmap function -- from purrr can be used to perform this sort of calculation:

library(tidyverse)

df %>% mutate(Min = pmap(df, min), Mean = rowMeans(.))

#              A          B           C        Min       Mean
# 1  -1.38507062  0.3183367 -1.10363778  -1.385071 -0.7234572
# 2   0.03832318 -1.4237989  0.44418506  -1.423799 -0.3137635
# 3  -0.76303016 -0.4050909 -0.20495061 -0.7630302 -0.4576905
# 4   0.21230614  0.9953866  1.67563243  0.2123061  0.9611084
# 5   1.42553797  0.9588178 -0.13132225 -0.1313222  0.7510112
# 6   0.74447982  0.9180879 -0.19988298  -0.199883  0.4875616
# 7   0.70022940 -0.1509696  0.05491242 -0.1509696  0.2013907
# 8  -0.22935461 -1.2230688 -0.68216549  -1.223069 -0.7115296
# 9   0.19709386 -0.8688243 -0.72770415 -0.8688243 -0.4664782
# 10  1.20715377 -1.0424854 -0.86190429  -1.042485 -0.2324120

Mean is a special case (hence the use of the base function rowMeans), since mean on data.frame objects was deprecated with R 3.0.

Answer 2

I suppose this is what you were trying to accomplish:

df <- data.frame(A=rnorm(10), B=rnorm(10), C=rnorm(10))

library(dplyr)
df %>% rowwise() %>% mutate(Min = min(A, B, C), Mean = mean(c(A, B, C)))

#             A          B           C        Min        Mean
# 1   1.3720142  0.2156418  0.61260582  0.2156418  0.73342060
# 2  -1.4265665 -0.2090585 -0.05978302 -1.4265665 -0.56513600
# 3   0.6801410  1.5695065 -2.70446924 -2.7044692 -0.15160724
# 4   0.0335067  0.8367425 -0.83621791 -0.8362179  0.01134377
# 5  -0.2068252 -0.2305140  0.23764322 -0.2305140 -0.06656532
# 6  -0.3571095 -0.8776854 -0.80199141 -0.8776854 -0.67892877
# 7   1.0667424 -0.6376245 -0.41189564 -0.6376245  0.00574078
# 8  -1.0003376 -1.5985281  0.90406055 -1.5985281 -0.56493504
# 9  -0.8218494  1.1100531 -1.12477401 -1.1247740 -0.27885677
# 10  0.7868666  0.6099156 -0.58994138 -0.5899414  0.26894694

Answer 3

One dplyr and purrr option where the select helpers could be used:

df %>%
 mutate(Min = select(., everything()) %>% reduce(pmin),
        Max = select(., everything()) %>% reduce(pmax))

             A          B           C        Min        Max
1  -1.38507062  0.3183367 -1.10363778 -1.3850706  0.3183367
2   0.03832318 -1.4237989  0.44418506 -1.4237989  0.4441851
3  -0.76303016 -0.4050909 -0.20495061 -0.7630302 -0.2049506
4   0.21230614  0.9953866  1.67563243  0.2123061  1.6756324
5   1.42553797  0.9588178 -0.13132225 -0.1313222  1.4255380
6   0.74447982  0.9180879 -0.19988298 -0.1998830  0.9180879
7   0.70022940 -0.1509696  0.05491242 -0.1509696  0.7002294
8  -0.22935461 -1.2230688 -0.68216549 -1.2230688 -0.2293546
9   0.19709386 -0.8688243 -0.72770415 -0.8688243  0.1970939
10  1.20715377 -1.0424854 -0.86190429 -1.0424854  1.2071538

Answer 4

Think found a solution - just transpose your data.frame:

x <- data_frame(x = rnorm(10), 
            y = rnorm(10))

# A tibble: 10 × 2
        x             y
    <dbl>         <dbl>
1  -1.1240392  0.9306028477
2  -0.8213379  0.2500495105
3  -0.8289104 -0.3693704483
4  -0.6486601 -1.1421141986
5   0.5098542 -0.3703368343
6  -0.3644690 -0.0003744377
7   0.7404057  0.1166905738
8  -0.2475214 -0.0802864865
9   0.2637841 -0.7717699521
10  1.4092874  0.2998021578

x %>% 
  t() %>% 
  data.frame() %>% 
  mutate_all(funs(min)) %>% 
  unique() %>% 
  t()

         1
X1  -1.1240392
X2  -0.8213379
X3  -0.8289104
X4  -1.1421142
X5  -0.3703368
X6  -0.3644690
X7   0.1166906
X8  -0.2475214
X9  -0.7717700
X10  0.2998022

Answer 5

How about this?

library(dplyr)
as.data.frame(t(mtcars)) %>%
  summarise_all(funs(mean))

For extra clarity, you could add another t() at the end:

as.data.frame(t(mtcars)) %>%
  summarise_all(funs(mean)) %>%
  t()