Books | Little World

The Catcher in Rye

Sun, 17 Nov 2019 00:00:00 +0000

原来The Catcher in Rye并不是讲稻草人和乌鸦的故事，是中二少年失败的离家出走尝试。如果我中二期看的这书，应该会很喜欢吧。虽然现在也挺喜欢的。更奇妙的是这么多年了，竟然一点没被剧透。To Kill A Mockingbird也是如此，并不是一个讲猎人的故事，我到底是有多文盲啊！

Data Visualization Chapter 2-4

Thu, 26 Sep 2019 00:00:00 +0000

Chapter 2

geom_point

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point()

Chapter 3

geom_smooth

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point() + geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

scale_x_log10

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point() + geom_smooth(method = "gam") + scale_x_log10()

scales::dollar

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point() +
geom_smooth(method = "gam") +
scale_x_log10(labels = scales::dollar)

Wrong way to set color

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp,
color = "purple"))
p + geom_point() + geom_smooth(method = "loess") + scale_x_log10()

The aes() function is for mappings only. Do not use it to change properties to a particular value. If we want to set a property, we do it in the geom_ we are using, and outside the mapping =aes(...)step.

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(color = "purple") + geom_smooth(method = "loess") + scale_x_log10()

The various geom_ functions can take many other arguments that will affect how the plot looks but do not involve mapping variables to aesthetic elements. “alpha” is an aesthetic property that points (and some other plot elements) have, and to which variables can be mapped. It controls how transparent the object will appear when drawn. It’s measured on a scale of zero to one.

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(alpha = 0.3) + geom_smooth(color = "orange", se = FALSE,
                                          size = 8, method = "lm") + scale_x_log10()

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y=lifeExp))
p + geom_point(alpha = 0.3) +
  geom_smooth(method = "gam") +
  scale_x_log10(labels = scales::dollar) +
  labs(x = "GDP Per Capita", y = "Life Expectancy in Years",
       title = "Economic Growth and Life Expectancy",
       subtitle = "Data points are country-years",
       caption = "Source: Gapminder.")

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp,
                                            color = continent))
p + geom_point() + geom_smooth(method = "loess") + scale_x_log10()

The color of the standard error ribbon is controlled by the fill aesthetic.

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp,
                                            color = continent, fill = continent))
p + geom_point() + geom_smooth(method = "loess") + scale_x_log10()

Aesthetics Can Be Mapped per Geom

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(mapping = aes(color = factor(year))) + 
  geom_smooth(method = "loess") +
  scale_x_log10()

Order doesn’t matter!!! Besides scale_x_log10(), you can try scale_x_sqrt() and scale_x_reverse()

p <- ggplot(data = gapminder, mapping = aes(x = pop, y = lifeExp))
p + geom_smooth(method = "loess") + 
  geom_point(mapping = aes(color = continent)) + 
  scale_x_reverse(labels = scales::number)

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(mapping = aes(color = log(pop))) + scale_x_log10()

Save plots

p_out <-  p + geom_point() + geom_smooth(method = "loess") + scale_x_log10()
ggsave("my_figure.pdf", plot = p_out)

Chapter 4

Group data and the “Group” Aesthetic

p <- ggplot(data = gapminder, mapping = aes(x = year, y = gdpPercap))
p + geom_line()

use the group aesthetic to tell ggplot explicitly about this country-level structure

p <- ggplot(data = gapminder, mapping = aes(x = year, y = gdpPercap))
p + geom_line(aes(group = country))

Facet to make small multiples

use facet_wrap() to split our plot by continent.

p <- ggplot(data = gapminder, mapping = aes(x = year, y = gdpPercap))
p + geom_line(aes(group = country)) + facet_wrap(~continent)

Add another enhancements

p <- ggplot(data = gapminder, mapping = aes(x = year, y = gdpPercap))
p + geom_line(color="gray70", aes(group = country)) + 
  geom_smooth(size= 1.1, method = "loess", se = FALSE) +
  scale_y_log10(labels=scales::dollar) +
  facet_wrap(~continent , ncol = 5) +
  labs(x = "Year",
       y = "GDP per capita on Five Continents")

Use facet_grid

p <- ggplot(data = gss_sm, mapping = aes(x = age, y = childs))
p + geom_point(alpha = 0.2) +
  geom_smooth() + 
  facet_grid(sex ~ race)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Warning: Removed 18 rows containing non-finite values (stat_smooth).

## Warning: Removed 18 rows containing missing values (geom_point).

p <- ggplot(data = gss_sm, mapping = aes(x = age, y = childs))
p + geom_point(alpha = 0.2) +
  geom_smooth() + 
  facet_grid(sex ~ race + degree)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 18 rows containing non-finite values (stat_smooth).

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 62.87

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.13

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 582.26

## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : span too small.
## fewer data values than degrees of freedom.

## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 62.87

## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.13

## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 0

## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 582.26

## Warning: Removed 18 rows containing missing values (geom_point).

Geoms can transform data

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion))
p + geom_bar()

geom_bar called the default stat_ function associated with it,stat_count().

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion))
p + geom_bar(mapping = aes(y = ..prop..))

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion))
p + geom_bar(mapping = aes(y = ..prop.., group = 1))

table(gss_sm$religion)

## 
## Protestant   Catholic     Jewish       None      Other 
##       1371        649         51        619        159

p <- ggplot(data = gss_sm, mapping = aes(x = religion, color = religion))
p + geom_bar()

p <- ggplot(data = gss_sm, mapping = aes(x = religion, fill = religion))
p + geom_bar() + guides(fill = FALSE)

p + geom_bar()

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion, fill = religion))
p + geom_bar()

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion, fill = religion))
p + geom_bar(position = "fill")

if you want separate bars

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion, fill = religion))
p + geom_bar(position = "dodge", mapping = aes(y = ..prop..,
                                               group = religion))

However, they don’t sum to one within each region. They sum to one across regions.

p <- ggplot(data = gss_sm, mapping = aes(x = religion))
p + geom_bar(position = "dodge", mapping = aes(y = ..prop..,
                                               group = bigregion)) +
  facet_wrap(~bigregion, ncol=1)

Histgrams and Density Plots

p <- ggplot(data = midwest, mapping = aes( x = area))
p + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p <- ggplot(data = midwest, mapping = aes( x = area))
p + geom_histogram(bins = 10)

oh_wi <- c("OH", "WI")
p <- ggplot(data = subset(midwest, subset = state %in% oh_wi),
            mapping = aes(x = percollege, fill = state))
p + geom_histogram(alpha = 0.4, bins = 20)

p <- ggplot(data = midwest, mapping = aes( x = area))
p + geom_density()

p <- ggplot(data = midwest, mapping = aes( x = area, fill = state,
                                           color = state))
p + geom_density(alpha = 0.3)

Avoid Transformations When Necessary

p <- ggplot(data = titanic, mapping = aes(x = fate, y = percent,
                                          fill = sex))
p + geom_bar(position = "dodge", stat = "identity") + theme(legend.position = "top")

p <- ggplot(data = oecd_sum,
            mapping = aes(x = year, y = diff, fill = hi_lo))
p + geom_col() + guides(fill = FALSE) + 
  labs(x = NULL, y = "Difference in Years",
       title = "The US Life Expectancy Gap",
       subtitle = "Difference between US and OECD 
       average life expectancies, 1960-2015",
       caption = "Data: OECD. After a chart by Christopher Ingraham,
       Washington Post, December 27th 2017.")

## Warning: Removed 1 rows containing missing values (position_stack).

Data Visualization Chapter 5

Thu, 26 Sep 2019 00:00:00 +0000

Chapter 5

Use Pipes to Summerize Data

rel_by_region <- gss_sm %>%
  group_by(bigregion, religion) %>%
  summarize(N = n()) %>%
  mutate(freq = N / sum(N),
         pct = round((freq*100), 0))

## Warning: Factor `religion` contains implicit NA, consider using
## `forcats::fct_explicit_na`

rel_by_region

## # A tibble: 24 x 5
## # Groups:   bigregion [4]
##    bigregion religion       N    freq   pct
##    <fct>     <fct>      <int>   <dbl> <dbl>
##  1 Northeast Protestant   158 0.324      32
##  2 Northeast Catholic     162 0.332      33
##  3 Northeast Jewish        27 0.0553      6
##  4 Northeast None         112 0.230      23
##  5 Northeast Other         28 0.0574      6
##  6 Northeast <NA>           1 0.00205     0
##  7 Midwest   Protestant   325 0.468      47
##  8 Midwest   Catholic     172 0.247      25
##  9 Midwest   Jewish         3 0.00432     0
## 10 Midwest   None         157 0.226      23
## # … with 14 more rows

rel_by_region %>% group_by(bigregion) %>% summarize(total = sum(pct))

## # A tibble: 4 x 2
##   bigregion total
##   <fct>     <dbl>
## 1 Northeast   100
## 2 Midwest     101
## 3 South       100
## 4 West        101

p <- ggplot(rel_by_region, aes(x = bigregion, y = pct, fill = religion))
p + geom_col(position = "dodge2") +
  labs(x = "Region",y = "Percent", fill = "Religion") +
  theme(legend.position = "top")

Use coord_flip()

p <- ggplot(rel_by_region, aes(x = bigregion, y = pct, fill = religion))
p + geom_col(position = "dodge2") +
  labs(x = "Region",y = "Percent", fill = "Religion") +
  guides(fill = FALSE) + 
  coord_flip() + 
  facet_grid(~ bigregion)

p <- ggplot(rel_by_region, aes(x = religion, y = pct, fill = religion))
p + geom_col(position = "dodge2") +
  labs(x = NULL,y = "Percent", fill = "Religion") +
  guides(fill = FALSE) + 
  coord_flip() + 
  facet_grid(~ bigregion)

Continuous Variables by Group or Category

p <- ggplot(data = organdata, mapping = aes(x = year, y = donors))
p + geom_line(aes(group = country)) + facet_wrap(~country)

## Warning: Removed 34 rows containing missing values (geom_path).

p <- ggplot(data = organdata, mapping = aes(x = country, y = donors))
p + geom_boxplot() + coord_flip()

## Warning: Removed 34 rows containing non-finite values (stat_boxplot).

p <- ggplot(data = organdata, mapping = aes(x = reorder(country,
                                                        donors, na.rm = TRUE), y = donors))
p + geom_boxplot() + labs(x = NULL) + coord_flip()

## Warning: Removed 34 rows containing non-finite values (stat_boxplot).

p <- ggplot(data = organdata, mapping = aes(x = reorder(country, donors, na.rm = TRUE), 
                                            y = donors, fill = world))
p + geom_boxplot() + labs(x = NULL) + 
  coord_flip() + theme(legend.position = "top")

## Warning: Removed 34 rows containing non-finite values (stat_boxplot).

p <- ggplot(data = organdata, mapping = aes(x = reorder(country, donors, na.rm = TRUE), 
                                            y = donors, color = world))
p + geom_point() + labs(x = NULL) + 
  coord_flip() + theme(legend.position = "top")

## Warning: Removed 34 rows containing missing values (geom_point).

p <-
  ggplot(data = organdata,
         mapping = aes(
           x = reorder(country, donors, na.rm = TRUE),
           y = donors,
           color = world
         ))
p + geom_jitter() + labs(x = NULL) +
  coord_flip() + theme(legend.position = "top")

## Warning: Removed 34 rows containing missing values (geom_point).

p <-
  ggplot(data = organdata,
         mapping = aes(
           x = reorder(country, donors, na.rm = TRUE),
           y = donors,
           color = world
         ))
p + geom_jitter(position = position_jitter(width = 0.15)) + labs(x = NULL) +
  coord_flip() + theme(legend.position = "top")

## Warning: Removed 34 rows containing missing values (geom_point).

by_country <-
  organdata %>% group_by(consent_law, country) %>% summarize(
    donors_mean = mean(donors, na.rm = TRUE),
    donors_sd = sd(donors, na.rm = TRUE),
    gdp_mean = mean(gdp, na.rm = TRUE),
    health_mean = mean(health, na.rm = TRUE),
    roads_mean = mean(roads, na.rm = TRUE),
    cerebvas_mean = mean(cerebvas, na.rm = TRUE)
  )

by_country

## # A tibble: 17 x 8
## # Groups:   consent_law [2]
##    consent_law country donors_mean donors_sd gdp_mean health_mean
##    <chr>       <chr>         <dbl>     <dbl>    <dbl>       <dbl>
##  1 Informed    Austra…        10.6     1.14    22179.       1958.
##  2 Informed    Canada         14.0     0.751   23711.       2272.
##  3 Informed    Denmark        13.1     1.47    23722.       2054.
##  4 Informed    Germany        13.0     0.611   22163.       2349.
##  5 Informed    Ireland        19.8     2.48    20824.       1480.
##  6 Informed    Nether…        13.7     1.55    23013.       1993.
##  7 Informed    United…        13.5     0.775   21359.       1561.
##  8 Informed    United…        20.0     1.33    29212.       3988.
##  9 Presumed    Austria        23.5     2.42    23876.       1875.
## 10 Presumed    Belgium        21.9     1.94    22500.       1958.
## 11 Presumed    Finland        18.4     1.53    21019.       1615.
## 12 Presumed    France         16.8     1.60    22603.       2160.
## 13 Presumed    Italy          11.1     4.28    21554.       1757 
## 14 Presumed    Norway         15.4     1.11    26448.       2217.
## 15 Presumed    Spain          28.1     4.96    16933        1289.
## 16 Presumed    Sweden         13.1     1.75    22415.       1951.
## 17 Presumed    Switze…        14.2     1.71    27233        2776.
## # … with 2 more variables: roads_mean <dbl>, cerebvas_mean <dbl>

by_country <- organdata %>% group_by(consent_law, country) %>%
  summarize_if(is.numeric, lst(mean, sd), na.rm = TRUE) %>%
  ungroup()
by_country

## # A tibble: 17 x 28
##    consent_law country donors_mean pop_mean pop_dens_mean gdp_mean
##    <chr>       <chr>         <dbl>    <dbl>         <dbl>    <dbl>
##  1 Informed    Austra…        10.6   18318.         0.237   22179.
##  2 Informed    Canada         14.0   29608.         0.297   23711.
##  3 Informed    Denmark        13.1    5257.        12.2     23722.
##  4 Informed    Germany        13.0   80255.        22.5     22163.
##  5 Informed    Ireland        19.8    3674.         5.23    20824.
##  6 Informed    Nether…        13.7   15548.        37.4     23013.
##  7 Informed    United…        13.5   58187.        24.0     21359.
##  8 Informed    United…        20.0  269330.         2.80    29212.
##  9 Presumed    Austria        23.5    7927.         9.45    23876.
## 10 Presumed    Belgium        21.9   10153.        30.7     22500.
## 11 Presumed    Finland        18.4    5112.         1.51    21019.
## 12 Presumed    France         16.8   58056.        10.5     22603.
## 13 Presumed    Italy          11.1   57360.        19.0     21554.
## 14 Presumed    Norway         15.4    4386.         1.35    26448.
## 15 Presumed    Spain          28.1   39666.         7.84    16933 
## 16 Presumed    Sweden         13.1    8789.         1.95    22415.
## 17 Presumed    Switze…        14.2    7037.        17.0     27233 
## # … with 22 more variables: gdp_lag_mean <dbl>, health_mean <dbl>,
## #   health_lag_mean <dbl>, pubhealth_mean <dbl>, roads_mean <dbl>,
## #   cerebvas_mean <dbl>, assault_mean <dbl>, external_mean <dbl>,
## #   txp_pop_mean <dbl>, donors_sd <dbl>, pop_sd <dbl>, pop_dens_sd <dbl>,
## #   gdp_sd <dbl>, gdp_lag_sd <dbl>, health_sd <dbl>, health_lag_sd <dbl>,
## #   pubhealth_sd <dbl>, roads_sd <dbl>, cerebvas_sd <dbl>,
## #   assault_sd <dbl>, external_sd <dbl>, txp_pop_sd <dbl>

p <- ggplot(data = by_country,
            mapping = aes(
              x = donors_mean,
              y = reorder(country, donors_mean),
              color = consent_law
            ))
p + geom_point(size = 3) +
  labs(x = "Donor Procurement Rate",
       y = "", color = "Consent Law") +
  theme(legend.position = "top")

p <- ggplot(data = by_country,
            mapping = aes(x = donors_mean,
                          y = reorder(country, donors_mean)))

p + geom_point(size = 3) +
  facet_wrap( ~ consent_law, scales = "free_y", ncol = 1) +
  labs(x = "Donor Procurement Rate",
       y = "")

p <- ggplot(data = by_country,
            mapping = aes(x = reorder(country,
                                      donors_mean), y = donors_mean))

p + geom_pointrange(mapping = aes(ymin = donors_mean - donors_sd,
                                  ymax = donors_mean + donors_sd)) +
  labs(x = "", y = "Donor Procurement Rate") + coord_flip()

### Plot Text Directly

p <- ggplot(data = by_country,
            mapping = aes(x = roads_mean,
                          y = donors_mean))
p + geom_point() + geom_text(mapping = aes(label = country))

p <- ggplot(data = by_country,
            mapping = aes(x = roads_mean,
                          y = donors_mean))
p + geom_point() + geom_text(mapping = aes(label = country), hjust = 0)

ggrepel is better than geom_text()

library(ggrepel)

p_title <-
  "Presidential Elections: Popular & Electoral College Margins"
p_subtitle <- "1824-2016"
p_caption <- "Data for 2016 are provisional."
x_label <- "Winner's share of Popular Vote"
y_label <- "Winner's share of Electoral College Votes"

p <- ggplot(elections_historic,
            aes(x = popular_pct, y = ec_pct,
                label = winner_label))

p + geom_hline(yintercept = 0.5,
               size = 1.4,
               color = "gray80") +
  geom_vline(xintercept = 0.5,
             size = 1.4,
             color = "gray80") +
  geom_point() +
  geom_text_repel() +
  scale_x_continuous(labels = scales::percent) +
  scale_y_continuous(labels = scales::percent) +
  labs(
    x = x_label,
    y = y_label,
    title = p_title,
    subtitle = p_subtitle,
    caption = p_caption
  )

### Label Outliers

p <- ggplot(data = by_country,
            mapping = aes(x = gdp_mean, y = health_mean))

p + geom_point() +
  geom_text_repel(data = subset(by_country, gdp_mean > 25000),
                  mapping = aes(label = country))

p <- ggplot(data = by_country,
            mapping = aes(x = gdp_mean, y = health_mean))

p + geom_point() +
  geom_text_repel(
    data = subset(
      by_country,
      gdp_mean > 25000 | health_mean < 1500 |
        country %in% "Belgium"
    ),
    mapping = aes(label = country)
  )

organdata$ind <- organdata$ccode %in% c("Ita", "Spa") &
  organdata$year > 1998

p <- ggplot(data = organdata,
            mapping = aes(x = roads,
                          y = donors, color = ind))
p + geom_point() +
  geom_text_repel(data = subset(organdata, ind),
                  mapping = aes(label = ccode)) +
  guides(label = FALSE, color = FALSE)

## Warning: Removed 34 rows containing missing values (geom_point).

Write and Draw in the Plot Area

p <- ggplot(data = organdata, mapping = aes(x = roads, y = donors))
p + geom_point() + annotate(
  geom = "text",
  x = 91,
  y = 33,
  label = "A surprisingly high \n recovery rate.",
  hjust = 0
)

## Warning: Removed 34 rows containing missing values (geom_point).

p <- ggplot(data = organdata,
            mapping = aes(x = roads, y = donors))
p + geom_point() +
  annotate(
    geom = "rect",
    xmin = 125,
    xmax = 155,
    ymin = 30,
    ymax = 35,
    fill = "red",
    alpha = 0.2
  ) +
  annotate(
    geom = "text",
    x = 157,
    y = 33,
    label = "A surprisingly high \n recovery rate.",
    hjust = 0
  )

## Warning: Removed 34 rows containing missing values (geom_point).

Scales, Guides, and Themes

p <- ggplot(data = organdata,
            mapping = aes(x = roads,
                          y = donors,
                          color = world))
p + geom_point()

## Warning: Removed 34 rows containing missing values (geom_point).

p <- ggplot(data = organdata,
            mapping = aes(x = roads,
                          y = donors,
                          color = world))
p + geom_point() + scale_x_log10() + scale_y_continuous(breaks = c(5,
                                                                   15, 25),
                                                        labels = c("Five", "Fifteen", "Twenty Five"))

## Warning: Removed 34 rows containing missing values (geom_point).

p <- ggplot(data = organdata,
            mapping = aes(x = roads, y = donors,
                          color = world))
p + geom_point() + scale_color_discrete(labels = c("Corporatist",
                                                   "Liberal", "Social Democratic", "Unclassified")) + 
  labs(x = "Road Deaths",
       y = "Donor Procurement", color = "Welfare State")

## Warning: Removed 34 rows containing missing values (geom_point).

p <- ggplot(data = organdata,
            mapping = aes(x = roads, y = donors,
                          color = world))
p + geom_point() + labs(x = "Road Deaths", y = "Donor Procurement") +
  guides(color = FALSE)

## Warning: Removed 34 rows containing missing values (geom_point).

Meta-Analysis Note 1

Wed, 25 Sep 2019 00:00:00 +0000

本书第一章主要对一些术语进行了界定，把元分析同其它种文献综述的方式进行了区分。元分析同其它定性的总结以及定量的（Informal vote counting-一般采用多数原则来总结结论与formal vote counting-在前者基础之上采用了一些统计分析以期得到统计上显著的结论）一些分析的不同之处在于：元分析的关注点除了关注效果是否存在之外，主要关注效果的大小(effect size)。

元分析的工作步骤分为五个阶段：

确定问题(formulate a problem)

在确定问题开始综述工作时，要把关注的重点厘清。比如希望是一个更概括的结论或样本，还是一个适用范围有限定的结论和样本，这将决定第二三阶段对文献的取舍。

取得相关文献

要从尽可能多的样本里采样，确保文献是有代表性的(representive)和无偏的(unbiased)。后者因为学术刊物对于结果显著的论文的发表偏好而很难实现，因此别忘了未发表的工作论文或毕业论文等。

对文献进行评估精选

本阶段对上一阶段取得的文献进行相关性评估。在此阶段会对第一阶段确定下来的研究问题进行进一步的提炼。

对文献进行分析和解释

最花时间和最难的阶段，在此阶段中需要将文献的数据整理和输入。

文献综述的写作

有几点需要注意的：

对于整个文献综述的工作过程要完整描述，记录在此过程中所做的各项取舍
关键在与要回答感兴趣的问题，假如不能回答，也要解释为什么以及未来需要做什么来回答这个问题
要避免文献列表的堆砌

作者的几点建议

因为文献的收集、评估、整理直到分析很花时间和精力，所以一定要做到有规划。一些具体的建议如下：

在文献收集过程中做记录
系统的存放文献，假如同别人合作，确保你们用同一套系统来存放，处理文献

我对于工具的建议

文献收集可以用Mendeley的private group功能，这样加入同一组的成员可以直接在客户端上打开PDF和加标注。虽然mendeley对免费用户private group数有限制，但可以通过在group底下再加子目录的方式来绕过限制。

We have no idea

Wed, 26 Jun 2019 00:00:00 +0000

Bosons make up one of the two classes of particles, the other being fermions.

So far, we have some hints and some ideas about what the smallest distance in the universe might be (the Planck length). We have a pretty good catalog of twelve matter particles that so far we haven’t been able to break further apart (the Standard Model). And we have a list of three possible ways that these particles can interact (the electroweak and strong forces and gravity).