Is there a neater way to carry out several one-sample T-tests in R

Question

I'm trying to see if the class 'means' of four separate groups (pKa recorded at pH 6.1, 6.7, 7.3 and 8.1) are significantly different to the individual data points I obtained for each group (pKa recorded at pH 6.1, 6.7, 7.3 and 8.1)

Here is a sample of my data set

#Sample of my data (called Exp2)
structure(list(
pKa = c(6.946, 7.1, 6.625, 7.528, 7.102, 6.743,6.936, 6.579, 6.672, 7.27), 

pH = c("pH_6.1", "pH_6.7", "pH_7.3", "pH_8.1", "pH_6.1", "pH_6.7", "pH_7.3", "pH_8.1", "pH_6.1", "pH_6.7"), 

id = c("XAU", "XAU", "XAU", "XAU", "MyData", "MyData", "MyData","MyData", "PQ", "PQ")),
 row.names = c(NA, 10L), class = "data.frame")

Here I have transformed the data frame in order to run several one sample t-tests of the class means for each group (different buffer pHs of 6.1, 6.7, 7.3 and 8.1) respectively. However I wondering if there is a more effective/ better way to carry out my analysis.

#Obtaining values to compare class means to
tapply(Exp2MyData$pKa, Exp2MyData$pH, mean)

#pH_6.1 pH_6.7 pH_7.3 pH_8.1 
#7.102  6.743  6.936  6.579 

#pH = 8.1
Buff_8.1 <- subset(Exp2,pH=="pH_8.1")
m8.1 <- Buff_8.1[,1]
#pH = 7.3
Buff_7.3 <- subset(Exp2,pH=="pH_7.3")
m7.3 <- Buff_7.3[,1]
#pH =
Buff_6.7 <- subset(Exp2,pH=="pH_6.7")
m6.7 <- Buff_6.7[,1]
#pH = 6.1
Buff_6.1 <- subset(Exp2,pH=="pH_6.1")
m6.1 <- Buff_6.1[,1]

###### Checking normality of individual data sets

shapiro.test(m6.1)
shapiro.test(m6.7)
shapiro.test(m7.3)
shapiro.test(m8.1)
# All not significantly different from normal distribution 

####### Applying t.test

#t.test
t.test(m8.1, mu= 6.579) # p-value = 5.025e-11
t.test(m7.3, mu= 6.936) #p-value = 0.00564
t.test(m6.7, mu= 6.743) # p-value = 0.0005285
t.test(m6.1, mu= 7.102) # p-value = 3.85e-06
# All significantly different 

Answer 1

Special thanks to Mr. Yuriy Saraykin that helped me fix the bug in my codes. This is not at all an alternative solution I just wanted to find out what went wrong.

Exp2 <- structure(list(
  pKa = c(6.946, 7.1, 6.625, 7.528, 7.102, 6.743,6.936, 6.579, 6.672, 7.27), 
  
  pH = c("pH_6.1", "pH_6.7", "pH_7.3", "pH_8.1", "pH_6.1", "pH_6.7", "pH_7.3", "pH_8.1", "pH_6.1", "pH_6.7"), 
  
  id = c("XAU", "XAU", "XAU", "XAU", "MyData", "MyData", "MyData","MyData", "PQ", "PQ")),
  row.names = c(NA, 10L), class = "data.frame")

library(dplyr)
library(tidyr)
library(broom)

Exp2 %>%
  select(-id) %>%
  group_by(pH) %>%
  nest(data = c(pKa)) %>%
  ungroup() %>%
  mutate(mu = c(6.579, 6.936, 6.743, 7.102), 
         ttest = map2(data, mu, ~t.test(.x, mu = .y)),
         tidied = map(ttest, ~ tidy(.x))) %>%
  unnest(tidied)


# A tibble: 4 x 12
  pH     data        mu ttest estimate statistic p.value parameter conf.low conf.high method   
  <chr>  <list>   <dbl> <lis>    <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>    
1 pH_6.1 <tibble~  6.58 <hte~     6.91     2.61    0.121         2     6.37      7.45 One Samp~
2 pH_6.7 <tibble~  6.94 <hte~     7.04     0.655   0.580         2     6.37      7.71 One Samp~
3 pH_7.3 <tibble~  6.74 <hte~     6.78     0.241   0.849         1     4.80      8.76 One Samp~
4 pH_8.1 <tibble~  7.10 <hte~     7.05    -0.102   0.935         1     1.02     13.1  One Samp~
# ... with 1 more variable: alternative <chr>

Modified version of the code so that column id is included:

Exp2 %>%
  group_by(pH) %>%
  nest(data = c(id, pKa)) %>%
  ungroup() %>%
  mutate(mu = c(6.579, 6.936, 6.743, 7.102), 
         ttest = map2(data, mu, ~t.test(.x$pKa, mu = .y)),   #Just pay attention that we use $ to subset .x variable because pKa is not the only varibale nested in data list column and we also have id variable, but in the previous code I did not use $.
         tidied = map(ttest, ~ tidy(.x))) %>%
  unnest(c(data, tidied)) %>%
  select(-ttest)

# A tibble: 10 x 12
   pH     id      pKa    mu estimate statistic p.value parameter conf.low conf.high method     
   <chr>  <chr> <dbl> <dbl>    <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>      
 1 pH_6.1 XAU    6.95  6.58     6.91     2.61    0.121         2     6.37      7.45 One Sample~
 2 pH_6.1 MyDa~  7.10  6.58     6.91     2.61    0.121         2     6.37      7.45 One Sample~
 3 pH_6.1 PQ     6.67  6.58     6.91     2.61    0.121         2     6.37      7.45 One Sample~
 4 pH_6.7 XAU    7.1   6.94     7.04     0.655   0.580         2     6.37      7.71 One Sample~
 5 pH_6.7 MyDa~  6.74  6.94     7.04     0.655   0.580         2     6.37      7.71 One Sample~
 6 pH_6.7 PQ     7.27  6.94     7.04     0.655   0.580         2     6.37      7.71 One Sample~
 7 pH_7.3 XAU    6.62  6.74     6.78     0.241   0.849         1     4.80      8.76 One Sample~
 8 pH_7.3 MyDa~  6.94  6.74     6.78     0.241   0.849         1     4.80      8.76 One Sample~
 9 pH_8.1 XAU    7.53  7.10     7.05    -0.102   0.935         1     1.02     13.1  One Sample~
10 pH_8.1 MyDa~  6.58  7.10     7.05    -0.102   0.935         1     1.02     13.1  One Sample~
# ... with 1 more variable: alternative <chr>

Answer 2

possible solution

df <- structure(list(
  pKa = c(6.946, 7.1, 6.625, 7.528, 7.102, 6.743,6.936, 6.579, 6.672, 7.27), 
  
  pH = c("pH_6.1", "pH_6.7", "pH_7.3", "pH_8.1", "pH_6.1", "pH_6.7", "pH_7.3", "pH_8.1", "pH_6.1", "pH_6.7"), 
  
  id = c("XAU", "XAU", "XAU", "XAU", "MyData", "MyData", "MyData","MyData", "PQ", "PQ")),
  row.names = c(NA, 10L), class = "data.frame")

library(tidyverse)
library(broom)
df %>% 
  group_nest(pH) %>% 
  transmute(
    pH,
    mu = c(6.579, 6.936, 6.743, 7.102),
    pValue = map2_dbl(data, mu, ~t.test(x = .x$pKa, mu = .y)$p.value),
    Mean = map_dbl(data, ~mean(.x$pKa)))
#> # A tibble: 4 x 4
#>   pH        mu pValue  Mean
#>   <chr>  <dbl>  <dbl> <dbl>
#> 1 pH_6.1  6.58  0.121  6.91
#> 2 pH_6.7  6.94  0.580  7.04
#> 3 pH_7.3  6.74  0.849  6.78
#> 4 pH_8.1  7.10  0.935  7.05

df %>% 
  group_nest(pH) %>% 
  transmute(
    pH,
    mu = c(6.579, 6.936, 6.743, 7.102),
    pValue = map2(data, mu, ~t.test(x = .x$pKa, mu = .y) %>% glance(.))
) %>% 
  unnest(pValue)
#> # A tibble: 4 x 10
#>   pH        mu estimate statistic p.value parameter conf.low conf.high method   
#>   <chr>  <dbl>    <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>    
#> 1 pH_6.1  6.58     6.91     2.61    0.121         2     6.37      7.45 One Samp~
#> 2 pH_6.7  6.94     7.04     0.655   0.580         2     6.37      7.71 One Samp~
#> 3 pH_7.3  6.74     6.78     0.241   0.849         1     4.80      8.76 One Samp~
#> 4 pH_8.1  7.10     7.05    -0.102   0.935         1     1.02     13.1  One Samp~
#> # ... with 1 more variable: alternative <chr>

^{Created on 2021-04-05 by the reprex package (v2.0.0)}