Print Tibble with Column Breaks as in V1.3.0

Print tibble with column breaks as in v1.3.0

Try out this function:

print_width_inf <- function(df, n = 6) {
  df %>%
    head(n = n) %>%
    as.data.frame() %>%
    tibble:::shrink_mat(width = Inf, rows = NA, n = n, star = FALSE) %>%
    `[[`("table") %>%
    print()
}

Better way to add tibble column to df in iterative fashion

tibbles square bracket subsetting doesn't drop by default, so it always stays a tibble.

Just change

df$output = df_tibble[, i]

to

df$output = df_tibble[, i, drop = TRUE]

How to change column data type of a tibble (with least typing)

This solution is probably the shortest:

mtcars$qsec %<>% as.integer

The trick is to perform the cast operation directly on the column > no need for mutate() any more.

add a tibble to a second one with an additional modification

We may first rbind the matching columns and then add the other one with recycling:

cbind(rbind(tb1[-3], tb2), tb1["tarima"])
#    symbol open tarima
# 1       A 25.3   59.5
# 2       A 27.1   61.7
# 11      B 60.8   59.5
# 21      B 60.3   61.7
# 3      CD 32.4   59.5
# 4      CD 33.6   61.7

Create a tibble with tribble using defaults that don't cause an error, e.g. like tibble

If your use case is easily reading a table with unknown structure into a data frame, you can do it like this.

data <- list("Header1_A", "Header2_A",
             0.19,          0.20,
             "Header1_B",  "Header2_B",
             0.19,          0.20)

df <- as.data.frame(matrix(unlist(data), ncol = 2, byrow = TRUE), stringsAsFactors = FALSE)

df 
#>          V1        V2
#> 1 Header1_A Header2_A
#> 2      0.19       0.2
#> 3 Header1_B Header2_B
#> 4      0.19       0.2

^{Created on 2020-05-24 by the reprex package (v0.3.0)}

To preserve column types (if possible) you could send the data list to this

dribble <- function(data, ncol){
  df <- list()
  for (i in 1:ncol){
    x <- unlist(data[seq(i, length(data), ncol)])
    df[[paste0("X", i)]] <- x
  }
  as.data.frame(df, stringsAsFactors = FALSE)
}

I need to copy numbers between two text strings in column, transpose it to columns next to the first text string

Split on a regex-based logical:

(I defined the break points based on the presence of capital letters (i.e. [A-Z]); you may want to modify the pattern based on your expected break points in df1$column1.)

a <- split(df1$column1, cumsum(grepl('[A-Z]', df1$column1)))
a
$`1`
[1] "STOUT"  "18"     "9341"   "4"      "0,2005"

$`2`
[1] "STOUT"  "1"      "9341"   "25"     "0,2004"

$`3`
[1] "STIN"   "7"      "9341"   "0,2003"

$`4`
[1] "OFF" "7"  

$`5`
[1] "L(1)"   "9342"   "0,2005"

Then rbind() and fill with NA:

(plyr::rbind.fill() expects a dataframe, so I'm using lapply() to call as.data.frame() to each list element.)

library(plyr)

plyr::rbind.fill(lapply(a,function(y){as.data.frame(t(y),stringsAsFactors=FALSE)}))
     V1   V2     V3     V4     V5
1 STOUT   18   9341      4 0,2005
2 STOUT    1   9341     25 0,2004
3  STIN    7   9341 0,2003   <NA>
4   OFF    7   <NA>   <NA>   <NA>
5  L(1) 9342 0,2005   <NA>   <NA>

Fit model using each predictor columns indiviually store results in dataframe

1) This gives the glance and tidy output for the model fits:

library(broom)

make_model <- function(nm) lm(iris[c("Sepal.Length", nm)])
fits <- Map(make_model, iris_vars)

glance_tidy <- function(x) c(unlist(glance(x)), unlist(tidy(x)[, -1]))
out <- sapply(fits, glance_tidy)

1a) or as a magrittr pipeline:

library(magrittr)
out <- iris_vars %>% Map(f = make_model) %>% sapply(glance_tidy)

Either one gives the following matrix:

> out
                Sepal.Width   Petal.Length    Petal.Width
r.squared      1.382265e-02   7.599546e-01   6.690277e-01
adj.r.squared  7.159294e-03   7.583327e-01   6.667914e-01
sigma          8.250966e-01   4.070745e-01   4.779948e-01
statistic      2.074427e+00   4.685502e+02   2.991673e+02
p.value        1.518983e-01   1.038667e-47   2.325498e-37
df             2.000000e+00   2.000000e+00   2.000000e+00
logLik        -1.829958e+02  -7.702021e+01  -1.011107e+02
AIC            3.719917e+02   1.600404e+02   2.082215e+02
BIC            3.810236e+02   1.690723e+02   2.172534e+02
deviance       1.007561e+02   2.452503e+01   3.381489e+01
df.residual    1.480000e+02   1.480000e+02   1.480000e+02
estimate1      6.526223e+00   4.306603e+00   4.777629e+00
estimate2     -2.233611e-01   4.089223e-01   8.885803e-01
std.error1     4.788963e-01   7.838896e-02   7.293476e-02
std.error2     1.550809e-01   1.889134e-02   5.137355e-02
statistic1     1.362763e+01   5.493890e+01   6.550552e+01
statistic2    -1.440287e+00   2.164602e+01   1.729645e+01
p.value1       6.469702e-28  2.426713e-100  3.340431e-111
p.value2       1.518983e-01   1.038667e-47   2.325498e-37

or transposed:

> t(out)
              r.squared adj.r.squared     sigma  statistic      p.value df
Sepal.Width  0.01382265   0.007159294 0.8250966   2.074427 1.518983e-01  2
Petal.Length 0.75995465   0.758332718 0.4070745 468.550154 1.038667e-47  2
Petal.Width  0.66902769   0.666791387 0.4779948 299.167312 2.325498e-37  2
                 logLik      AIC      BIC  deviance df.residual estimate1
Sepal.Width  -182.99584 371.9917 381.0236 100.75610         148  6.526223
Petal.Length  -77.02021 160.0404 169.0723  24.52503         148  4.306603
Petal.Width  -101.11073 208.2215 217.2534  33.81489         148  4.777629
              estimate2 std.error1 std.error2 statistic1 statistic2
Sepal.Width  -0.2233611 0.47889634 0.15508093   13.62763  -1.440287
Petal.Length  0.4089223 0.07838896 0.01889134   54.93890  21.646019
Petal.Width   0.8885803 0.07293476 0.05137355   65.50552  17.296454
                  p.value1     p.value2
Sepal.Width   6.469702e-28 1.518983e-01
Petal.Length 2.426713e-100 1.038667e-47
Petal.Width  3.340431e-111 2.325498e-37

2) If we remove the first unlist from the glance_tidy function definition then we get a 2d list (rather than a 2d numeric matrix):

glance_tidy_l <- function(x) c(glance(x), unlist(tidy(x)[, -1]))
iris_vars %>% Map(f = make_model) %>% sapply(glance_tidy_l)

              Sepal.Width  Petal.Length  Petal.Width  
r.squared     0.01382265   0.7599546     0.6690277    
adj.r.squared 0.007159294  0.7583327     0.6667914    
sigma         0.8250966    0.4070745     0.4779948    
statistic     2.074427     468.5502      299.1673     
p.value       0.1518983    1.038667e-47  2.325498e-37 
df            2            2             2            
logLik        -182.9958    -77.02021     -101.1107    
AIC           371.9917     160.0404      208.2215     
BIC           381.0236     169.0723      217.2534     
deviance      100.7561     24.52503      33.81489     
df.residual   148          148           148          
estimate1     6.526223     4.306603      4.777629     
estimate2     -0.2233611   0.4089223     0.8885803    
std.error1    0.4788963    0.07838896    0.07293476   
std.error2    0.1550809    0.01889134    0.05137355   
statistic1    13.62763     54.9389       65.50552     
statistic2    -1.440287    21.64602      17.29645     
p.value1      6.469702e-28 2.426713e-100 3.340431e-111
p.value2      0.1518983    1.038667e-47  2.325498e-37 

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